Foreign Competition and Domestic Innovation:
Evidence from U.S. Patents∗
David Autor
MIT and NBERDavid Dorn
University of Zurich and CEPR

Gary Pisano
Harvard University

Gordon H. Hanson
UC San Diego and NBER

Pian Shu
Georgia Institute of Technology
January 2019

Abstract
Manufacturing accounts for more than three-quarters of U.S. corporate patents. The competitive shock to this sector emanating from China’s economic ascent could in theory either
augment or stifle U.S. innovation. Using three decades of U.S. patents matched to corporate
owners, we quantify how foreign competition affects domestic innovation. Rising import exposure intensifies competitive pressure, reducing sales, profitability, and R&D expenditure at U.S.
firms. Accounting for confounding sectoral patenting trends, we find that U.S. patent production
declines in sectors facing greater import competition. This adverse effect is larger among initiallyless profitable and less capital-intensive firms.

∗

We are grateful to Rohan Thavarajah, Alex He, and Ante Malenica for excellent research assistance. Autorand Hanson acknowledge funding from the National Science Foundation (SES-1227334) and Autor also acknowledges
funding from the Alfred P. Sloan Foundation (#2011-10-120). Dorn acknowledges funding from the Swiss National
Science Foundation (BSSGI0-155804 and CRSII1-154446). Pisano and Shu acknowledge funding from the Division ofFaculty Research and Development at Harvard Business School.

Introduction
Despite accounting for less than one-tenth of U.S. private non-farm employment, U.S. manufacturing
still generates more than two-thirds of U.S. R&D spending and corporate patents.1 In light ofChina’s spectacular growth in manufacturing exports, the potential impact of import competition
on innovation by domestic firms has gained relevance.2 In this paper, we study the effect of rising
import competition on U.S. innovation at the firm level.
How product-market competition affects innovation is of longstanding theoretical interest.3 Schumpeterian models posit that a more competitive product market reduces firm profit margins, resulting
in lower investments in innovative activity (e.g., Dasgupta and Stiglitz, 1980). But as Arrow (1962)
noted, when competitive pressure is low and pre-innovation rents are high, firms may have little
incentive to invest in innovation. Aghion et al. (2005) formalize these competing insights, showing
how differences between pre-innovation and post-innovation rents determine a firm’s response tocompetition. This response depends on the dispersion in technological advancement across firms.
When dispersion is low, intensified competition encourages firms to innovate to “escape competition.”
When dispersion is high, more competition may stifle innovation among laggard enterprises.4
Following a large literature (Cohen, 2010), we measure innovation using firm patenting and R&D
expenditure, which we supplement with firm-level data on sales, investment, and profitability.5 We
match the assignees of all U.S. patents granted between 1975 and 2013 to publicly held firms listed in
Compustat through 2014 using a search-engine-based algorithm that disambiguates inconsistently
spelled and abbreviated renditions of firm names on patent applications. Following Autor et al.
(2014), we isolate the industry-specific component of U.S. import growth that is driven by exportsupply growth in China by instrumenting rising U.S. import competition with contemporaneouschanges in industry-level import penetration in other high-income countries. Using alternative identification strategies (Bloom et al., 2016; Pierce and Schott, 2016), we obtain comparable results.
We first show that increased imports from China ramped up competitive pressure on publicly
listed U.S. firms: reducing U.S. and global sales, diminishing book and stock values, curtailingpurchases of labor and capital inputs, and proportionally reducing R&D investment. We next showthat this increase in competitive pressure led U.S. firms to decrease their output of innovations
as measured by patent grants. These negative effects are confirmed when using two alternative
measures of trade exposure based on changes in trade policy, neither of which is likely to be driven
by technology trends: the U.S. grant of Permanent Normal Trade Relations to China in 2000;
1

Helper et al. (2012) compute a manufacturing share in U.S. R&D spending of 68%, based on data from the
National Science Foundation’s Business R&D Survey. In our data, manufacturing accounts for 71% of all corporate
patents with U.S.-based inventors and an application year of 2007.
2
A substantial literature shows that import competition from China has contributed to job loss in U.S. manufacturing (e.g., Bernard et al., 2006; Autor et al., 2013, 2014; Pierce and Schott, 2016; Acemoglu et al., 2015).
3
See Gilbert (2006) and Cohen (2010).
4
The prediction is unclear for firms that are technological leaders. Additional causal mechanisms raise further
ambiguity. See Hart (1983), Chen and Steinwender (2017), and Bloom et al. (2018), as well as Shu and Steinwender(2018) for a synthesis of this literature.
5
On using patents to measure innovation, see Jaffe and Trajtenberg (2002) and Moser (2016). On the economic
value of patents, see Hall et al. (2005) and Kogan et al. (2017).

2and the scheduled elimination of the Multi-Fiber Agreement tariffs in 2005. Notably, the induced
fall in patents is roughly proportional to the contraction of sales, employment, capital, and R&D
expenditure at import-competing firms.
To overcome the fact that our firm-level analysis necessarily excludes patents granted to firmsthat are not listed in Compustat, we exploit the fact that all patents are assigned a technology
class regardless of corporate ownership, which allows us to estimate the effect of import competition
on patenting at the technology class level. Consistent with the firm-level results, we find thattechnology classes with greater growth in import penetration saw a substantial relative decline incorporate patents during our analytic window of 1991-2007. We find no similar decline in patenting
by non-corporate entities (e.g., governments and universities), indicating that our key findings are
specific to private-sector innovation and do not reflect underlying correlations between contracting
innovation opportunities and rising trade exposure.
Our results stand in contrast to those of Bloom et al. (2016) for European firms and highlightthe underlying theoretical ambiguity in the relationships between competition and innovation. We
speculate that the differential impacts of foreign competition on domestic innovation in the U.S.
versus Europe may be reconciled through the lens of Aghion et al. (2005): U.S. industries havebeen found to display larger gaps in the technological capabilities of leading and lagging firms
when compared to firms in Europe (Bartelsman et al., 2013; Hashmi, 2013). We provide suggestive
evidence in support of the Aghion et al. (2005) model by showing that within the U.S., the negative
effects of import competition concentrate on firms that are initially less productive and less profitable.
Our work is contemporaneous to emerging evidence on the impacts of import competition on
innovation-related outcomes in North America.6 Xu and Gong (2017) and Hombert and Matray(2018) also use Compustat data to show that import competition has a negative impact on firms’
R&D expenditure and financial outcomes. Xu and Gong (2017) further show that R&D is reallocatedtoward more productive firms, and Hombert and Matray (2018) find that firms with large R&D
stocks mitigate the negative impact of import competition through product differentiation. Kueng
et al. (2016) find that more trade-exposed Canadian firms experience strong declines in self-reported
innovation outcomes. We contribute to this literature by providing a comprehensive analysis of the
impact of Chinese import competition on both output (patenting) and input (R&D expenditure) offirm innovation in the U.S. Our analysis accounts for industry pre-trends and establishes robustness
to a wide range of identification strategies. Consistent with a competitive market response, we findthat Chinese import competition reduces innovation at both the firm and technology-class levels in
the U.S. private sector.

1

Measuring Trade Exposure

To measure import penetration, we match trade data to U.S. manufacturing industries. In a second
step, we match industry-level trade exposure to firm-level data, which we then match to patent
6

An extended set of results are available in our earlier working paper, Autor et al. (2016).

3

records. The Online Appendix provides further details.
To create measures of changing import penetration, we match trade data to four-digit SIC U.S.
manufacturing industries using the UN Comtrade Database and the crosswalk in Pierce and Schott(2012). Our baseline measure of trade exposure is the change in the import penetration ratio for a
U.S. manufacturing industry over the period 1991 to 2007, defined as∆IP jτ =

UC
∆Mj,τ
,
Yj,91 + Mj,91 − Ej,91(1)

U C is the change in imports from China over two sub-periods, 1991
where for U.S. industry j, ∆Mjτ

to 1999 and 1999 to 2007, and Yj,91 + Mj,91 − Ej,91 is initial absorption (industry shipments, Yj,91 ,plus industry imports, Mj,91 , minus industry exports, Ej,91 ) at the start of the period. We choose
1991 as the starting year for the analysis as it is the earliest period for which we have disaggregatedbilateral trade data to match to U.S. manufacturing industries.7
Observed changes in import penetration may in part reflect domestic shocks to U.S. industries
that determine both U.S. import demand and innovative activity. Even if the dominant factors
driving China’s export growth are internal supply shocks, U.S. industry import demand shocks may
still contaminate bilateral trade flows. To capture this supply-driven component in U.S. imports
from China, we follow Autor et al. (2014) and instrument for trade exposure in (1) with the variable,OC∆Mj,τ
∆IP Ojτ =Yj,88 + Mj,88 − Xj,88(2)

OC is the growth in imports from China in industry j during the period τ for a groupwhere ∆Mj,τ

of eight industrialized countries that does not include the U.S.8 The denominator in (2) is initialabsorption in the industry in 1988. The motivation for the instrument in (2) is that high-income
economies are similarly exposed to growth in imports from China that is driven by supply shocks
such as expanding product variety, falling prices, rising quality, and diminishing trade and tariffcosts in China’s surging sectors. The identifying assumption is that industry import demand shocks
are uncorrelated across high-income economies. Autor et al. (2013) and Autor et al. (2014) provide
evidence on the robustness of this instrumentation approach in studying the China trade shock. We
complement this strategy by also using the identification approaches of Pierce and Schott (2016)
and Bloom et al. (2016), as discussed in Section 3.3.

2

Evidence of Increased Competitive Pressure from Imports

Prior to assessing the impact of rising Chinese import competition on realized innovation outcomes,
we verify that this force exerts significant competitive pressure on import-competing firms, mea7

Our empirical approach requires trade data reported under Harmonized System (HS) product codes in order tomatch with U.S. SIC industries. The year 1991 is the earliest in which many countries began using the HS classification.
8
These countries are Australia, Denmark, Finland, Germany, Japan, New Zealand, Spain, and Switzerland, which
represent the high-income countries for which we can obtain disaggregated bilateral HS trade data back to 1991.

4

sured by sales, profitability, and R&D investment.9 For our analysis, we match industry-level trade
exposure to Compustat North America, which provides industry affiliation and financial statement
data on companies whose shares are traded at a North American stock exchange.10 We estimate
the following regression model using these matched data:∆Yijτ = ατ + β1 ∆IP jτ + γXij0 + eijτ,(3)

where ∆Yijτ is the change in a firm outcome, for firm i in industry j over time period τ , defined as
100 × (Yij,t1 − Yij,t0 )/(0.5Yij,t1 + 0.5Yij,t0 ), and ∆IPjτ is growth of import exposure (in percentage
points) for industry j over period τ , as defined in equation (1) and instrumented by ∆IP Ojτ , asdefined in equation (2)).11 The control vector Xij0 comprises time trends for eleven U.S. manufacturing sectors. For consistency with the analysis of firms’ patenting activity, observations areweighted by the number of firm patents, averaged over the start and end year of period τ , though
we obtain similar results when observations are weighted by firms’ U.S. sales.
Table 1 reports results. The first two columns of panel I indicate that firms facing an exogenous
rise in Chinese import competition exhibit a relative decline in both U.S. and global sales between
1991 and 2007. The former effect is imprecisely measured, while the latter estimate implies that
global sales contract by eight-tenths of a percent for each percentage point increase in Chinese importpenetration. Columns 3 and 4 report a corresponding decline in firm utilization of labor and capital,suggesting an overall decrease in firms’ scale of operation. Consistent with these contractions, thebook and stock market value of exposed firms falls by approximately 1.5 percent for every percentage
point rise in import competition (columns 5 and 6). Column 7 indicates that R&D investment atimport-competing firms drops roughly one-for-one with rising import exposure, indicating a fall in
inputs used in innovation.
Could these patterns merely reflect firm-level trends that pre-date the “China Shock?” Panel IIof Table 1 indicates otherwise. When we regress firm outcomes during the pre-period of 1975-1991
on future change in Chinese imports occurring between 1991 and 2007, we find that in seven of eightcases, the point estimates are insignificant, and in all cases the coefficients are opposite signed tothose in Panel I.
9These results corroborate literature on the consequences of the China trade shock. See footnote 2, Xu and Gong
(2017), and Hombert and Matray (2018).
10
Compustat covers companies that have at least one regularly, actively, and publicly traded issue listed on a US
or Canadian exchange and that provide a regular filing of financial reports. The data includes foreign companies that
use American Depository Receipts (ADRs). The coverage of our data ends in 2014.
11
We use the proportional change scaling of the outcome variable rather than the log change because some valuesof Y are equal to zero.

5

TableEffectof ChineseCompetition
onEmployment
Firm Outcomes,
1991-2007 and1975-1991(for
Table 1:
1. Effectof Chinese
ImportImport
Competition
on Firm Sales,
and R&D Expenditures,
1991-2007
and 19751991(for FalsificationTest).
DependentChange
Variable: in
Relativein Sales,Value,
R&D.(in % pts).
FalsificationTest).
Dependent
Variable:
Sales,Change
Inputs,FirmInputs,
Value,
andandR&D
Firm Sales
U.S.
Global(1)(2)

Firm Global Inputs
Workers Capital(3)(4)

Firm ValueBook
Market(5)(6)

R&D
Investment(7)

I. Exposure Period 1991-2007
Δ U.S. Industry Exposure
to Chinese Imports-1.29(1.07)-0.79 ~
(0.41)-0.75 ~
(0.41)-1.28 *
(0.55)-1.42 *
(0.71)-1.65 *
(0.76)-0.95 *
(0.39)

Mean Outcome Variable
No. Observations

32.94
2200

54.42
3098

19.92
2803

55.55
3104

-14.80
2965

41.38
2679

50.77
2279

II. Pre Period 1975-1991
Δ U.S. Industry Exposure
to Chinese Imports

0.47(0.31)

0.48(0.40)

0.21(0.27)

0.80(0.49)

0.54(0.58)

0.28(0.50)

0.79 ~(0.41)

Mean Outcome Variable
No. Observations

45.44
1,971

60.63
2,188

3.55
2,084

58.19
2,178

-4.84
2,162

58.73
1,872

72.87
1,453

Notes: Every regression in Panel I comprises two stacked first differences 1991-1999 and 1999-2007, and includes a period dummy and eleven indicator variables forbroad sectors of manufacturing. The data includes all U.S. firms for whom the indicated outcome variable is reported in Compustat at the start and end of a given
period. The relative change of an outcome variable is defined as the first difference in the outcome over a period t,t+1, divided by the average of the outcome
across the two periods t and t+1. Panel II provides falsification tests that regress the change in outcomes during the periods 1975-1983 and 1983-1991 on the future
increase in Chinese import penetration, which is averaged over the 1991-1999 and 1999-2007 periods. All models are weighted by a firm's U.S.-inventor patents,
averaged over the start and end of a period. Standard errors are clustered on 4-digit SIC industries. ~ p ≤ 0.10, * p ≤ 0.05, ** p ≤ 0.01.

3

The Effect of Industry-Level Import Competition on Firm-Level
Patenting

3.1

Firm-Level Data and Patent Matching

We combine Compustat data with utility patents from the U.S. Patent and Inventor Database,which covers all patents granted by the U.S. Patent and Trademark Office (USPTO) between 1975
and March 2013.12 A longstanding limitation of patent records is that the firm names entered on
patents frequently contain unusual abbreviations or misspellings, which inhibit machine matching.
We overcome this problem with an algorithm that leverages the fact that internet search algorithms
function as repositories of information on common spelling variations of company names.13 If “International Bussiness Machines” is a common misspelling of IBM, an internet search engine will
suggest IBM.com or IBM’s Wikipedia page as its top search results. Our algorithm enters the firmnames appearing on patents and Compustat records into the Bing.com search engine and harvests
the URLs of the search results. This procedure allows us to match patents to firms based on shared
web search URLs in cases where the name strings on patents and firm records do not match exactly.14 Compared to the traditional string matching and manual inspection used in the NBER
Patent Data Project, our procedure significantly improves efficiency without sacrificing accuracy.
12

The data are available at https://github.com/funginstitute/downloads. See Li et al. (2014) for a description.
On supervised approaches to matching patents to diambiguating patent assignees, see also Ventura et al. (2015),
Morrison et al. (2017), and Balsmeier et al. (2018).
14
In case of IBM, we identify 147 name variations on its patents (Online Appendix, Table A1).
13

6

Since our search-engine-based algorithm minimizes the need for manual intervention, it is readily
scalable. Our approach is generalizable to the matching between any two firm-level datasets, and to
many other applications in which string matching is complicated by spelling variations. Section Aof the Online Appendix discusses our matching procedure in detail.
Our analysis uses utility patents applied for in the years 1975, 1983, 1991, 1999, and 2007. The
1991-1999 and 1999-2007 periods coincide with the surge in Chinese import competition; the 19751983 and 1983-1991 periods enable us to account for industry pre-trends. Since the mean difference
between the patent application and grant dates is 2.5 years in our data (standard deviation 1.5
years), right censoring due to not observing patents granted after March 2013 is unlikely to pose aserious problem. We use inventors’ addresses listed on patents to determine the location of invention,and restrict our patent sample to corporate patents with a U.S.-based primary inventor. We match
72% of these corporate patents to firms that appear in Compustat.15 Our patent sample consists ofmore than 170k patents that originate at 6,081 firms, which jointly accounted for 95% of all R&D
expenditure that Compustat records in 1991.
Panel A of Figure 1 plots by year of patent application the total number of patents with U.S.based primary inventors, corporate patents with U.S. inventors, and corporate patents with U.S.
inventors matched to Compustat. All three series show a sharp rise between 1983 and 1999 and amodest decline between 1999 and 2007.16 The fourth series in the figure shows that the decline ofpatenting in the early 2000s coincides with a strong acceleration in U.S. imports from China.
The aggregate trends in patenting shown in Panel A mask important heterogeneity in the shifting
concentration of patenting across sectors. Between 1975 and 2007, patenting in computers andelectronics expanded rapidly while patenting in chemicals and petroleum was stagnant. As a result,computers overtook chemicals as the sector that produces most patents in the early 1990s (Online
Appendix, Table A4).17 Panel B of Figure 1 plots the change in log patents for 1991-2007 against
the contemporaneous change in import penetration for computers and electronics, chemicals andpetroleum, and all other manufacturing industries. Since the growth in Chinese imports was muchstronger in computers than in chemicals, the raw correlation between patenting and trade exposure
at the broad sectoral level is positive. However, Panel B of Figure 1 also indicates a positive raw
correlation between sectoral patent growth in the pre-period of 1975-1991 and the subsequent growthin Chinese import competition between 1991 and 2007. The acceleration of patenting in the computer
sector and the stagnation in the chemical thus each predate the rise in Chinese imports. Accounting
for these secular (pre-)trends will be critical to our identification.
15

Unmatched corporate patents were likely granted to firms that were never public and thus lack a Compustat
record.
16
The literature offers several possible explanations for the slowdown in patenting in the early 2000s. See Jorgenson
et al. (2008), Gordon (2012), Boldrin and Levine (2013), and Arora et al. (2018).
17
On the growth of information technology and software patents, see Jorgenson (2001) and Bessen and Hunt (2007).

7

Figure 1: Trends in Patenting

300
200
100
0

Total US Imports from China(in billions of 2007 USD)

75
50
0

25

Number of Patents(in thousands)

100

400

A. Number of Patents by Application Year

1975

1983

1991

1999

2007

Year of Patent Application
All Patents with US Inventors
Corporate Patents with US Inventors
Corporate Patents with US Inventors & Matched to Compustat
Total US Imports from China

1.3
.3

.8

Computers-.2

All Other Mfg

Chemicals-.7

Log Change in Patents 1975-1991 & 1991-2007

B. Growth in Sectoral Patenting and Import Penetration from China

0

20

40

60

Growth of Import Penetration 1991-2007

Corporate Patents with US Inventors
Growth 1975-1991

3.2

Corporate Patents with US Inventors
Growth 1991-2007

Import Competition and Patent Production: Baseline Estimates

Following equation (3), we estimate the impact of rising Chinese import competition on patentproduction at the firm level. Panel I of Table 2 applies a bare-bones specification that includes nocovariates beyond the change in import penetration and a year-specific constant.18 The conditional18

The sample in Table 2 includes all firms that applied for at least one patent at the start or end of a given period.
Continuous coverage in Compustat during the period is not required. Table A5 in the Online Appendix alternatively

8correlation between the change in firm patents and the change in industry import penetration ispositive for 1991-1999 (column 1), negative for 1999-2007 (column 2), and negative for the stacked
first-difference model that include both periods with a separate time effect for each period (column
3). The difference between the OLS and IV estimates reported in rows (a) and (b) is modest.
Panel II of the Table confronts the pre-trends in patenting in the computers and chemical sectors
documented in Figure 1 Panel B. Merely including indicator variables for these two trending sectors(computers and chemicals) reveals a robust negative relationship between changes in industry importpenetration and changes in firm patenting. Specifically, both OLS and IV models (rows c and d)
detect a statistically significant negative effect of Chinese import competition on firm-level domestic
patent production in both sub-periods and in the stacked first-difference specification. IV estimates
are uniformly larger (more negative) than their OLS counterparts, suggesting that the IV models
purge simultaneity or measurement error (or both).
We document the importance of pre-trends in columns 4 to 6 of Table 2, where we regresschanges in firm-level patenting in the preceding 16-year (pre-China Shock) period of 1975 to 1991
(in two eight-year intervals) on the change in industry-level import penetration over the subsequent
1991-2007 period. In both eight-year time intervals as well as in the stacked model, and for both the
OLS (row a) and 2SLS (row b) specifications, we find a positive, statistically significant relationshipbetween changes in firm-level patenting and China import growth 16 years later. Notably, simply
adding sector dummies for computers and chemicals reduces this correlation to near zero and renders
it statistically insignificant across all columns (rows c and d). Column 7 shows that we can also
eliminate the influence of these pre-trends by double-differencing the post and pre-China shockoutcomes to estimate the effect of China import competition on the change in firm-level patenting
in 1991-2007 relative to 1975-1991. This approach requires no sectoral level dummies (rows a andb) and is little affected by their inclusion (rows c and d).
Panel III of Table 2 introduces additional controls that might mitigate the relationships detected
in Panel II, including: (row e) dummy variables for 11 manufacturing sectors; (row f) measuresof industry factor- and technology-intensity at the start of period (share of production workers inindustry employment, log capital over value added, log average industry wage, computer investment
as a share of overall investment, and high-tech equipment as a share of total investment);19 (row g)
firm start-of-period characteristics (a dummy for U.S. headquartered firms, log U.S. sales, and firm
global R&D spending as a share of firm global sales); (row h) the technology mix of a firm’s patents(the fractions of a firm’s patents that fall into each of six major technology fields); and (row i) thefirm’s prior patent growth using the 8-year and 16-year lags of the outcome variable.20 Results fromthese specifications are comparable in magnitude to the parsimonious 2SLS model in row (d) thatconsiders a balanced panel of Compustat-covered firms, which is also used for the analysis in Table 1.
19
The first three variables are based on the NBER productivity database and the latter two on Feenstra and Hanson(1999).
20
Following Hall, Jaffe, and Trajtenberg (2001), we group patents in six technology fields based on their primary
technology class: chemical; computers and communications; drugs and medical; electrical and electronics; mechanical;
and others. To maintain a constant sample size across specifications, missing values for the firm or industry controls
are replaced with a value of zero, and an indicator variable for each missing control is added to the regression.

9

includes only sectoral dummies to absorb patenting trends in computers and chemicals.

Table 2: Effects of Chinese Import Competition on Firm-Level Patenting, 1991-2007 and 19751991 (for Falsification Test): OLS and Instrumental Variables Models. Dependent Variable:
Change in Corporate Patents by US-Based Inventors (% pts), Relative to Mid-Period Number
of Patents.
Exposure Period: 1991-2007
1991-1999 1999-2007 1991-2007
(1)
(2)
(3)

Pre-Period: 1975-1991
1975-1983 1983-1991 1975-1991
(4)
(5)
(6)

D 1991-07-1975-91(7)

I. Models without Controlsa. OLS, no controls

1.37(1.14)-0.45(0.22)

b. 2SLS, no controls

0.40(1.39)-0.29(0.40)*-0.32(0.26)

0.91(0.33)**

1.09(0.61)~

1.02(0.45)*-1.34(0.57)*-0.26(0.40)

1.06(0.43)*

1.70(0.68)*

1.44(0.54)**-1.70(0.67)*

II. Controlling for Computer and Chemical Sector Main Effectsc. OLS, 2 mfg sector dummies(computers, chemicals)-0.87(1.02)

d. 2SLS, 2 mfg sector dummies(computers, chemicals)-2.36(1.40)~-0.63(0.12)**-0.91(0.15)**

0.36(0.25)-0.30(0.64)

0.00(0.38)-0.92(0.38)*-0.57(0.31)~-1.25(0.53)*

0.36(0.33)

0.17(0.61)

0.27(0.40)-1.53(0.56)**

III. Adding Detailed Controlse. 2SLS, 11 mfg sector dummies-1.77(1.16)-0.46(0.34)-1.10(0.51)*

0.54(0.41)

0.47(0.68)

0.52(0.49)-1.62(0.62)**

f. 2SLS, 11 mfg sector dummies +industry controls-1.10(1.26)-0.50(0.34)-1.11(0.48)*

0.60(0.44)

0.38(0.55)

0.50(0.43)-1.61(0.55)**

g. 2SLS, 11 mfg d. + industry/firm
controls-1.16(1.06)-0.52(0.34)-1.17(0.48)*

0.62(0.42)

0.33(0.58)

0.48(0.43)-1.65(0.54)**

h. 2SLS, 11 mfg d. + industry/firm
controls + technology mix-1.13(1.31)-0.72(0.35)*-1.35(0.50)**

0.31(0.34)

0.27(0.62)

0.27(0.39)-1.63(0.55)**

i. 2SLS, 11 mfg d. + industry/firm
controls + technology mix + 2 lags

-1.29
(1.27)-0.80(0.39)*-1.39(0.47)

**

n/a

n/a

n/a

n/a

Mean Outcome Variable
No. Observations

65.37
4,157

-7.61
4,114

-18.65
2,437

36.46
3,035

10.33
5,472

19.88
13,743

24.42
8,271

Notes: Each coefficient is derived from a separate firm-level regression of the relative change in patents on the change of Chinese import penetration. The relative change inpatents is defined as the first difference in patents over a period t,t+1, divided by the average number of patents across the two periods t and t+1. Import penetration
increased by a mean of 2.07 (s.e. 4.37) percentage points in 1991-1999, by 6.46 (s.e. 14.34) in 1999-2007, and by 4.54 (s.e. 11.34) pooled over both periods. Columns 4-6
provide falsification tests that regress the change in patents on the future increase in Chinese import penetration, averaged over the 1991-1999 and 1999-2007 periods.
Columns 3 and 6 present stacked first differences models for the periods 75-83/83-91 and 91-99/99-07 and include a period dummy, while column 7 indicates the difference
between the import exposure coefficients of the column 3 and 6 models. Models (c) and (d) include dummies for the computer/communication and chemical/petroleum
industries. Model (e) includes a full set of dummies for 11 manufacturing sectors. Model (f) additionally includes 5 industry-level controls for production characteristics(production workers as a share of total employment, log of average wage, and the ratio of capital to value added, all measured at the start of each period; as well as computer
investment and investment in high-tech equipment, both expressed as a share of total investment and measured in 1990 for the models of columns 1-3 and in 1972 for themodels of columns 4-6). Model (g) additionally includes a dummy variable for US-based firms, and controls for the log US sales of a firm and for its global R&D expenditure
expressed as a share of global sales. It also includes two dummy variables indicating firms for which the two latter controls are not available in the Compustat data. Model (h)additionally controls for the fraction of a firm's patents that fall into each of the six major patent technology categories defined by Hall, Jaffe and Trajtenberg (2011), averaged
over start-of-period and end-of-period patents. Model (i) additionally controls for two 8-year lags of the outcome variable. All models are weighted by a firm's U.S.-inventor
patents, averaged over the start and end of a period. Standard errors are clustered on 4-digit SIC industries. ~ p ≤ 0.10, * p ≤ 0.05, ** p ≤ 0.01.

10

3.3

Alternative Identification Strategies

We obtain causal identification by exploiting the external, supply-driven component of rising Chinese exports to the U.S, identified by the industry-level covariance between rising Chinese importpenetration to the U.S. and other high income countries. This interpretation would be threatened
if an exhaustion of technological opportunities in specific industries occurring worldwide renderedthese ‘mined out’ industries vulnerable to Chinese competition. To address this concern, we implement Pierce and Schott’s (2016) strategy of leveraging changes in trade-policy uncertainty related
to China’s attainment of Permanent Normal Trade Relations (PNTR) with the U.S. in 2000 to instrument for the acceleration of the growth in U.S. industry import competition in the 2000s versus
the 1990s.21 As a second strategy, we exploit the phase-out of import quotas from the Multi-Fiber
Agreement (MFA) between 1999 and 2005 as an additional source of within-industry changes in import exposure, following the approach of Bloom et al. (2016). These MFA quotas applied primarily
to imports from the textile, apparel and leather sectors.
Table 3: Response of Patenting to Alternative Measures of Trade Exposure, 1991-2007. DeTable 3. Responseof Patenting
to Alternative
Instruments
for Chinese
Import Competition,
1991-2007.
Dependent Variable:
Relative
pendent
Variable:
Relative
Changeof Numberof Corporate
Patentsby US-Based
Inventors:
Changeof
NumberofPatentsby
US-Based
Inventors:
OLS
and
OLS
ReducedForm
Estimates.
OLS and OLS Reduced Form Estimates.

Exposure Variable

US Imports from China
(OLS)
(1)
(2)

Exposure Variable
Third Country Imports
Normalized Tradefrom China
Relations Tariff Gap(OLS Reduced Form)(OLS Reduced Form)
(3)
(4)
(5)
(6)

Multi-Fiber Agreement
Quotas(OLS Reduced Form)(7)(8)-1.17 **
(0.16)-1.81 **
(0.69)-3.28(2.22)-0.71 *
(0.33)

Exposure Variable x
1999-2007 Period
Full Controls
Industry Fixed Effects

yes

yes
yes

yes

1.95 **(0.43)-1.63 *
(0.74)-1.85 **
(0.59)-0.92 ~
(0.56)-2.70 ~
(1.39)-2.27(2.11)

yes
yes

yes

yes
yes

yes

yes
yes

Descriptives for Exposure Variable in 1999-2007 Period
Mean
6.46
Std Dev(14.34)

5.62(9.17)

30.00(14.31)

1.47(4.49)

Impact of 1s Exposure-14.91-13.11-10.21100.0%

4.9%-10.15

Share of Manufacturing Patents in 4-Digit Industries with Non-Zero Exposure, Year 1999
100.0%
100.0%

Notes: N=8,271 except N=495 in columns 7-8 which include only the 3-digit sectors that comprise at least one MFA-affected 4-digit industry. Every regression comprises two
stacked first differences 1991-1999 and 1999-2007, and includes the full set of controls from model 3h in table 2. Data on average fill rates of MFA quotas in 1999 and on NTR
tariff gaps are based on Pierce and Schott (2016), and are measured in percentage points. All models are weighted by a firm's U.S.-inventor patents, averaged over the start andend of a period. Standard errors are clustered on 4-digit SIC industries. ~ p ≤ 0.10, * p ≤ 0.05, ** p ≤ 0.01.

Table 3 reports side-by-side estimates of the impact of import competition on firm-level patenting,estimated first by OLS and then with each of our three instrumentation strategies. Column 1 presents
an OLS regression of the change in firm-level patenting on the change in import penetration and21
Had China’s Most Favored Nation (MFN) trading status not been reauthorized in any year after 1980, its U.S.
tariff rates would have risen to the far-higher Smoot-Hawley rates set in the 1930s. Pierce and Schott (2016) andHandley and Limao (2017) show that U.S. industries with higher gaps between non-MFN and MFN tariffs experienced
more rapid acceleration in Chinese imports after 2000.

11

the full vector of control variables from Table 2. This estimate detects a highly significant negative
relationship between import competition and innovation that is comparable in magnitude to thecorresponding 2SLS estimate in Table 2 (column 3, row h). Column 2 adds a full set of 4-digit
industry fixed effects, which absorb any secular trend in patenting at the detailed industry level.
The coefficient on the interaction term between import exposure and the the 1999-2007 indicatorvariable in column 2 is now identified by within-industry, over-time changes in import exposure andpatenting. The significant negative point estimate of −0.71 means that industries that saw a greater
increase in import penetration in 1999-2007 relative to 1991-1999 saw a larger fall in the patentproduction in the latter relative to the former period.
Columns 3 and 4 apply our baseline China exposure instrument to this exercise.22 These estimates are negative and highly significant, as expected. This reduced-form IV inference is also
robust to the inclusion of four-digit industry main effects (column 4), so that identification comesfrom within-industry changes in import exposure during 1999-2007 versus 1991-1999.
We next implement the Pierce and Schott (2016) identification strategy by exploiting the gap
between non-MFN and MFN tariffs as a shock to industry-level China import competition facing U.S.
firms after 2000. Since the elimination of the non-MFN, Smoot-Hawley tariff threat improved thecompetitive position of Chinese exporters to the U.S. market, this gap measure should have a negative
impact on domestic U.S. patenting following China’s attainment of PNTR in 2000. Because we haveno strong prior on the relationship between the tariff gap and U.S. domestic innovation during thepre-2000 period, we include both a main effect for the tariff-gap instrument and an interaction of that
instrument with the 1999-2007 dummy.23 Column 5 shows that the tariff gap is positively correlatedwith industry-level patenting growth during the 1990s, prior to China’s WTO accession. Relative tothis trend, however, industries with higher tariff gaps experienced a statistically significant slowdown
in patenting in the 2000s (second row, column 5). Paralleling earlier estimates, column 6 introduces
industry fixed effects into the model. The coefficient of interest in this demanding specification(tariff-gap × post) remains negative and is marginally statistically significant.
In columns 7 and 8, we employ the instruments of Bloom et al. (2016), which correspond to theshare of industry imports subject to MFA quotas prior to the 2005 MFA termination.24 As with thetariff gap instrument, we focus on the contrast between the pre-removal period of 1991-1999, and theremoval period of 1999-2007. Following Bloom et al. (2016), we analyze the impact of the removalof MFA quotas exclusively for the subsample of firms operating in MFA-affected industries. This
reduces our sample from 8,271 to 495 observations, and the affected sectors account for just 4.9%of manufacturing patenting in 1999 (bottom row of Table 3). Nevertheless, the column 7 estimate
finds a negative association between MFA quotas and U.S. patent growth during 19991-1999, whichbecomes (marginally) significantly more negative in the 1999-2007 period when import quotas are22

For comparability with subsequent columns, we report these models as reduced-form rather than 2SLS regressions.
By contrast, the expected impact of rising predicted Chinese import competition using our primary strategy does
not differ across periods. When an interaction term with the 1999-2007 period is added to the regression in column
3 of Table 3, its coefficient is very small and imprecisely estimated (coefficient 0.04, s.e. 1.75), suggesting that thecausal effect is comparable across the two periods.
24
The MFA quota fill rate data come from Pierce and Schott (2016).
23

12

phased out. When four-digit industry fixed effects are added in column 8, the point estimate remains
comparable but it is less precisely estimated.
The second to last row of Table 3 shows that for all three sets of instruments employed, a one
standard deviation increase in the trade exposure instrument is estimated to reduce patenting by
10 to 15 log points. Thus, whether we exploit the observed growth in U.S. imports from China, thesupply-driven surge in China’s exports to the U.S. and other high income countries, the reduction in
China-U.S. trade-policy uncertainty following the grant of PNTR, or the removal of import quotas
in MFA-affected industries, we find qualitatively and quantitatively similar deterrent effects on
patenting by import-competing U.S. firms.
As detailed in the Online Appendix Tables A5 and A6, our key finding that rising importcompetition reduces patenting by U.S. firms is robust to variations of our basic approach, including:restricting the analysis to only patents granted within six years of the application date; excluding
patents from the computer or chemical technology classes; using alternative firm weights (patentcitations, R&D expenditure, sales, no weights); and assigning Compustat firms to corresponding
industries based on historical industry codes or using fractional industry assignments according tofirms’ sales shares across the industries in which they operate.25
A limitation of our firm-level analysis is that the analytic sample excludes patents granted tofirms that are not listed in Compustat. We overcome this limitation by estimating the effect ofimport competition on patenting at the technology class level. Exploiting the fact that every patent
is assigned to a specific technology class, we calculate the import exposure facing each technology
class by using the implicit mapping of patent classes to industries provided by our Compustat-patentmatched data. Fitting (3) to patent counts at the technology class level, we find that technology
classes with greater growth in import penetration during our window of 1991-2007 experienced asubstantial relative decline in total corporate patents (see Appendix Table A7). Yet, no such negative
effect is detected for patenting by non-corporate entities (e.g., government and universities). Ourfindings are thus specific to private-sector innovation and are unlikely to reflect a spurious correlation
between declining innovation opportunities and rising trade exposure.

3.4

Which Firms are Most Affected?

Our findings contrast with Bloom et al. (2016), who report a positive effect of exposure to theremoval of MFA quotas on patenting by European firms. These opposite-signed effects for the U.S.
and Europe are not intrinsically at odds: as Aghion et al. (2005) emphasize, a marginal increasein competition may either spur or hinder innovation depending on the initial competitiveness ofa market.26 Our findings could imply that U.S. manufacturing industries largely locate on the
downward sloping leg of the Aghion et al. (2005) inverted U-shaped locus, where greater competition25

We also find that import competition has little impact on firm industry representation: conditional on survival,
Compustat firms do not appear to change industry in response to rising China competition.
26
It is further possible that the shock from Chinese trade competition may be less pronounced in Europe due toa more balanced trade relationship with China. Dauth et al. (2014) estimate that the labor market effects of China
trade for Germany, for instance, have been much less severe.

13

diminishes innovation.

Table 4: Effect of Chinese Import Competition on Patenting 1991-2007: Splitting Sample According toTable
InitialSales/Worker,
Capital/Worker,
Returnon Investment,
andDebt/Equity.
Dependent
4. Effectof Chinese Import
Competition on Patenting
1991-2007:
Sample Splits
by Initial
Firm Sales/Worker,
Capital/Worker,
Returnon
Investment
andDebt/Equity.
Dependent
Variable:
Relative
ChangeinPatents.
Variable: Change in Corporate Patents by US-Based Inventors (% pts).
Firm Labor Productivity and Capital Intensity

Δ U.S. Industry Exposure
to Chinese Imports

Sales/Worker
>Avg
≤Avg
(1)
(2)

Capital/Worker
>Avg
≤Avg
(3)
(4)

Profit/Capital (ROI)
>Avg
≤Avg
(5)
(6)

-1.11
(0.79)-0.01(1.01)-0.86(0.57)

Test for Equal Coeff.
Mean Outcome Variable
No. Observations

Profitability and Leverage-2.32 ~
(1.38)
p=0.274

27.27
1,348

3.29
2,738

-2.41 **
(0.55)
p=0.001

29.80
1,529

7.83
2,560

Debt/Equity
≤Avg
>Avg
(7)
(8)

-2.06 ** -0.86 *(0.72)(0.40)
p=0.036

20.03
1,887

7.58
2,493

-2.92 *
(1.47)

p=0.426
19.18
3,154

14.23
766

Notes: Every regression comprises two stacked first differences 1991-1999 and 1999-2007, and includes the full set of controls from model 3h in table 2. Columns 1-2, 3-4, 56 and 7-8 split the firm sample into firms whose sales per employee, capital per employee, return on investment, or debt to equity ratio is above/below the patentweighted industry average in the start-of-period year. All models are weighted by a firm's U.S.-inventor patents, averaged over the start and end of a period. Standard
errors are clustered on 4-digit SIC industries. ~ p ≤ 0.10, * p ≤ 0.05, ** p ≤ 0.01.

To explore heterogeneous effects of competition on firm patenting, Table 4 splits U.S. firms
within detailed industries into groups according to four metrics: sales per worker, capital per work,
profit over capital (ROI), and debt to equity. The odd-numbered columns estimate the effect of import competition on patenting for firms that outperform their respective industry’s mean in termsof higher productivity, capital intensity, profitability, and lower indebtedness. The even-numberedcolumns present analogous regressions for the complementary samples. All four sample splits qualitatively confirm that firms with an initially weaker competitive position are more likely to be hurt byforeign competition, and this heterogeneity in treatment effects is statistically significant for the two
sample splits by capital intensity and profitability. In Table A9 of the Online Appendix, we extend
the results in Table 4 by redefining the dependent variable to be the change in patents relative tothe change in firm size (measured as sales or employment, either for the U.S. or globally). We findthat for the initially weaker firms—when measured by capital intensity or profitability—the tradeinduced contraction in patenting is not simply a byproduct of a reduction in firm scale. Instead,these firms experience a significant decline in patenting relative to sales and relative to employment.
Our analysis of firm heterogeneity suggests that rising import competition could, by culling theranks of weaker incumbents, catalyze the reallocation of sales and profits to the stronger firms thatremain. While it does not definitively explain why results differ between the U.S. and Europe, itdoes support the notion that the effect of increased competition on innovation depends on initialconditions.27
27
In complementary analyses, Gutierrez and Philippon (2017) find that the sensitivity of capital investment tochanges in industry import competition is greater in smaller relative to larger U.S. manufacturing enterprises, andAghion et al. (2018) find that patenting by French manufacturing firms responds more positively to export demand
shocks in more-productive relative to less-productive establishments.

14

4Discussion

We provide a comprehensive analysis of how the surge in import competition from China affects boththe inputs and outputs of U.S. innovative activities. Accounting for the important confounding effects of industry pre-trends, we demonstrate that the negative impact of Chinese import competition
on innovation at the firm and technology-class level is robust to a wide range of regression specifications and identification strategies; that it is evident on both output (patenting) and input (R&D
expenditure) margins; and that it applies to private-sector patent production but not to patenting
by universities or government, consistent with a competitive market response. These findings do
not constitute a welfare analysis of the impact of competition on domestic innovation since they
do not capture, for example, dynamic responses that may ultimately include the culling of weakerfirms and the entry of robust new innovators. Nevertheless, they demonstrate that the innovation
response of U.S. firms more exposed to rising market competition from China has been substantiallyand unambiguously negative.

15

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