Online Appendix to 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

A

Matching Patent Assignees to Firms

A key challenge in matching patents to firm-level data is that inconsistencies in the spelling of firmnames on patents generate many false negative matches. Because patent applications leave it to
the applicant to state the name of the assignee, there is little uniformity in how company names
appear. This non-uniformity of assignee names, combined with the lack of a unique firm identifierin the patent data, makes it challenging to group patents belonging to the same firm. IBM, forinstance, has over 140 different name spellings on its patents, comprising both abbreviations andmisspellings of the official firm name (see Table A1). Traditional methods of patent-to-firm matching
employed by prior work, most notably the NBER Patent Data Project (NBER-PDP), accommodate
some of these name variations by standardizing commonly used words in firm names, e.g., changing“Corp” to “Corporation” and “Ltd” to “Limited” (Bessen, 2009). This simple string standardization,
however, does not account for customized abbreviations of firm names, and thus fails to link “IBM”
to “International Business Machines”. Moreover, the data contain dozens of entries for assignees
such as “International Business Machine” and “International Business Machiens”, which are very
likely misspellings of the IBM name. Here, standardization is intractable as none of these names
is an officially recognized spelling of IBM. The researcher is then faced with the unpalatable choiceof either throwing away observations for unmatched patents or making manual corrections to firmnames for hundreds of thousands of records. The NBER Patent Data Project employs extensive
manual inspection in addition to string standardization to match between the patent data andCompustat, but its coverage of patents ends with patents granted by 2006.1
We develop a four-step procedure to match the U.S. Patent and Inventor Database (which
covers patents granted by March 2013) to Compustat.2 First, following NBER-PDP, we capitalize
all letters, remove punctuation and accent marks, and standardize commonly used words (e.g.,
Corp./Corporation) in firm names both in the patent and Compustat data. This allows us toperform an initial matching based on cleaned name strings.3 Next, we conduct an internet searchfor each firm name that we observe either in the patent or Compustat data (entered in quotation
marks and clean of punctuation and accents) using the Bing Web Search API. Based on the top
five URLs collected from Bing.com in March 2016, we consider a patent assignee and a Compustat
firm to be a match if the top search results for the patent assignee contain the company website
listed in Compustat (e.g., ibm.com). We also consider them a match if the top five search results
for the patent assignee and those for the Compustat firm share at least two URLs in common.
Since internet search engines function as repositories of information on common spelling variationsof company names, matching based on search results substantially reduces the need for manual
1More recent studies have developed supervised approaches to disambiguate patent assignees (Ventura et al., 2015;
Morrison et al., 2017; Balsmeier et al., 2018). Their algorithms group different spellings of assignee names based on
assignee locations and/or similarity scores from fuzzy string match, but they do not match patent data to Compustat
or other firm-level data.
2
The patent data are available at https://github.com/funginstitute/downloads and described in Li et al. (2014).
3
In rare cases, the same patent assignee can be matched to multiple Compustat firm records, which is usually due
to the same firm having multiple listings in Compustat. We apply tiebreakers based on the availability of segment
sales data, historical industry affiliation, and R&D spending data.

2

inspection and corrections. In the final two steps of our matching procedure, we append to our data
the matching from NBER-PDP between assignees and Compustat firms that our method has failed
to capture, and then manually match ourselves the few large assignees that remained unmatched
after the previous procedures.
Although our empirical analysis focuses on patents with application years 1975, 1983, 1991,
1999, and 2007, we have executed our readily scalable matching algorithm for all patents granted
from January 1975 to March 2013. Table A1 illustrates the outcome of our matching procedureusing IBM as a case study. It shows 71 firm names from patents that we identified as variations ofIBM. Each of these names appears on at least two patents in our data (column 1), while another76 variations of the IBM name that are not listed in the table appear on one patent each. Name
matching alone successfully links only the two most frequent name variations of IBM, but misses
the many alternative (mis-)spellings, which are detected only by our web matching algorithm or the
manual matching provided by NBER-PDP (column 2). For patents granted through 2006, our fully
automated procedure achieves a similar success as the much more laborious matching by NBERPDP. The web algorithm discovers 57 additional variations of the IBM name that appear on at least
two patents (indicated by ’web’ in column 2 of Table A1). While it misses four name variations thatwere matched to IBM by NBER-PDP (’nber-pdp’ of Table A1), it detects five name variations thatwere overlooked there (’X’ in column 3 of Table A1). Web matching additionally catches eight newname variations that appear on multiple patents which were granted after the coverage of NBERPDP ends in 2006. A comparison between our IBM patent sample and the number of patents inthe company’s annual reports shows that our sample corresponds to between 99.5% and 100% ofIBM’s self-reported patent output in each year between 1994 and 2012. Our matching algorithm
thus produces very few false negatives or false positives in the case of IBM.

3

Table A1: PatentsTable
matchedto IBM
Autor,
Dorn,Pisano
Hanson,A1. Patents matchedto IBMby
by Autor,
Dorn, Hanson,
and Shu Pisano and Shu
Number of
Patents(1)

Assignee Name

Source of Match Not Matched byin ADHPSNBER-PDP(2)(3)

Name variations that appear before 2006
INTERNATIONAL BUSINESS MACHINES CORPORATIONINTERNATIONAL BUSINESS MACHINES CORP
INTERNATIONAL BUSINESS MACHINE CORPORATIONIBM CORPORATIONINTERNATIONAL BUSINESS MACHINESINTERNATIONAL BUISNESS MACHINES CORPORATIONINTERNATIONAL BUSINESS MACHINE CORP
INTERNATIONAL BUSINESS MACHINES COPORATIONINTERNATIONAL BUSINES MACHINES CORPORATIONINTERNATIONAL BUSINESS MACHINES CORPORAITONINTERNATIONAL BUSINESS MACHIENS CORPORATIONINTERNATIONAL BUSINESS MACHINES CORPORTION
INTERNATIONAL BUSINESS MACHINES CORPROATIONINTERNATIONAL BUSINESS MACHINES CORPORATIONSINTERNATIONAL BUSINESSS MACHINES CORPORATIONINTERNATIONAL BUSINESS MACHINES INCORPORATEDINTERNATINAL BUSINESS MACHINES CORPORATIONINTERNATIONAL BUSINESS MACHINES INC
INTERNATIONAL BUSNIESS MACHINES CORPORATIONINTENATIONAL BUSINESS MACHINES CORPORATIONINTERNATIOANL BUSINESS MACHINES CORPORATIONINTERNATION BUSINESS MACHINES CORPORATIONINTERNATIONL BUSINESS MACHINES CORPORATIONINTERNATIONAL BUSINESS MACHINES COMPANY
INTERNATIONAL BUSINESS MACHINESS CORPORATIONINERNATIONAL BUSINESS MACHINES CORPORATIONINTERNATIONAL BUSINESS MACINES CORPORATIONINTERNATIONAL MACHINES CORPORATIONINTERNAITONAL BUSINESS MACHINES CORPORATIONINTERNATIONAL BUSINESS MACHINES CORPORATONINTERNATIONAL BUSSINESS MACHINES CORPORATIONINTERNTIONAL BUSINESS MACHINES CORPORATIONINTERNATIOAL BUSINESS MACHINES CORPORATIONINTERNATIONAL BUINESS MACHINES CORPORATIONINTERNATIONAL BUSINESS MACHINES COPRORATIONINTERNATIONAL BUSINESS MACHINES INCORPORATIONIBM JAPAN LTD
INTERNATIONAL BUSINESS MACHINCES CORPORATIONINTERNATIONAL BUSINESS MACHINES CORPORAIONINTERNATIONAL BUSINESS MACHINES CORPORARTION
INTERNATIONAL BUSINESS MAHINES CORPORATIONINTERNATONAL BUSINESS MACHINES CORPORATIONIBMINTERANTIONAL BUSINESS MACHINES CORPORATIONINTERNATIOANAL BUSINESS MACHINES CORPORATIONINTERNATIONAL BUSIENSS MACHINES CORPORATIONINTERNATIONAL BUSINESS MACHINES CORPORATIIONINTERNATIONAL BUSINESS MACHINES CORPORATIOIN
INTERNATIONAL BUSINESS MACHINES CORPORATOIN
INTERNATIONAL BUSINESS MACHNIES CORPORATIONINTERNATIONAL BUSINESS MAHCINES CORPORATIONINTERNATIONAL BUSINSS MACHINES CORPORATIONINTERNATIIONAL BUSINESS MACHINES CORPORATIONINTERNATIONAL BUISINESS MACHINES CORPORATIONINTERNATIONAL BUSINESS MACHINES COPROATIONINTERNATIONAL BUSINESS MACHINES COROPORATIONINTERNATIONAL BUSINESS MACHINES CORPOATIONINTERNATIONAL BUSINESS MACHINES CORPORATIN
INTERNATIONAL BUSINESS MACHNINES CORPORATIONINTERNATIONAL BUSNINESS MACHINES CORPORATIONLNTERNATIONAL BUSINESS MACHINES CORPORATIONINFORMATION BUSINESS MACHINES CORPORATIONINTELLECTUAL BUSINESS MACHINES CORPORATION

74488
766
90
85
71
29
27
26
19
18
15
15
14
12
12
12
11
11
11
10
9
9
9
8
8
7
7
7
6
6
6
6
5
5
5
5
4
4
4
4
4
4
3
3
3
3
3
3
3
3
3
3
2
2
2
2
2
2
2
2
2
2
2

name
name
web
web
web
web
web
web
web
web
web
web
web
web
web
nber-pdp
web
web
web
web
web
web
web
web
web
web
web
web
web
web
web
web
web
web
web
nber-pdp
web
web
web
web
web
web
web
web
web
web
web
web
web
web
web
web
web
web
web
web
web
web
web
web
web
nber-pdp
nber-pdp

X

X
X

X

X

Name variations that appear only after 2006
INTERNATIONAL BSUINESS MACHINES CORPORATION
4web
X
INTERNATIONANL BUSINESS MACHINES CORPORATION
3
web
X
INNTERNATIONAL BUSINESS MACHINES CORPORATION
2web
X
INTERNATIONA BUSINESS MACHINES CORPORATION
2web
X
INTERNATIONAL BUSINESS MACHINES CORPOARTION
2
web
X
INTERNATIONAL BUSINESS MACHINES CORPORATIONAL
2
web
X
INTERNATIONAL BUSINESS MACHINES CORPRATION
2web
X
INTRANATIONAL BUSINESS MACHINES CORPORATION
2
web
X
Notes: The table comprises all primary assignees with at least two corporate patents granted during 1975-2013 which we match to IBM
using the method indicated in column 2. 2006 marks the end of NBER-PDP coverage. IBM has 76 additional name variations that are notshown in this table, each appearing on just one patent in our data. NBER-PDP matches 50 of these assignees, while ADHPS web searchalgorithm matches 67. The listed assignee names have been subject to minimal cleaning, including standardizing cases, removing of accents,
and cleaning of non-alphabetic and non-numeric characters.

4
Note: In each Year 1994-2012, the number of patents that we attribute to IBM corresponds to 99.5%-100% of the patent total that IBM reports in its company statements.

Table A2 shows the construction of our patent analysis sample, which begins with patents withcorporate assignees, US-based primary inventors, and application years 1975, 1983, 1991, 1999, and2007. We consider corporate assignees to be those that categorize themselves as corporations on
patents and whose names indicate that they are not universities, institutions, hospitals, or government agencies.4 Across all five application years, our procedure matches 171,838 of the 239,110
patents in the starting sample and thus achieves a matching rate of 72% (columns 1-3). It exceeds
the matching rate of NBER-PDP by five percentage points in the three earliest years, and by ten
percentage points in 1999 (columns 4-5). The success of web matching alone is quite stable overtime, and links nearly two thirds of each year’s corporate patents to Compustat firms (columns 6-7).
The final four columns of Table A2 indicate the composition of matching methods that we use forthe construction of the matched sample reported in column 2. In any given application year, around
90% of the matched patents come from name matching and web matching (columns 8 and 9). Web
matching becomes increasingly useful (and name matching increasingly limited) over time as more
firms patent and more spelling variations occur.5 Among patents matched by both name matching
and web matching, 92% are matched to the same Compustat listing, suggesting that the automatedalgorithm significantly improves efficiency without substantially sacrificing accuracy.6
Table A2: Matching Patent Data to Compustat Data
Alternative Assignment of Patents to Compustat Firms% Matched
NBER-PDP % Matched
ADHPS Web % Matched
(3)
(4)
(5)
(6)
(7)

Source of Match in ADHPSWeb
NBER-PDP(9)(10)

All Corporate
Patents
(1)

ADHPS
(2)

Application Year 1975
Application Year 1983
Application Year 1991
Application Year 1999
Application Year 2007

29,93024,91838,091
74,496
71,67522,531
18,69627,09453,617
49,900

75%
75%
71%
72%
70%

20,804
17,48324,98446,553
n/a

70%
70%
66%
62%
n/a

19,61916,30623,71448,03344,773

66%
65%
62%
64%
62%

78%
79%
75%
68%
63%

12%
11%
14%
23%
27%

9%
10%
10%
7%
3%

1%
0%
1%
2%
7%

All Five Years

239,110

171,83872%

109,824

46%

152,445

64%

70%

20%

7%

3%

Name
(8)

Manual(11)

Notes: Column 1 reports the number of patents with corporate assignees and US-based primary inventors by application year. Columns 2 and 3 indicate the number and percentage of these patents that we match toCompustat firms in our final sample. Columns 4 and 5 indicate the number and percentage of patents that NBER-PDP matches. Columns 6 and 7 indicate the number and percentage of patents that can be matched toCompustat by the ADHPS web match algorithm (regardless of whether a name match exists). Columns 8-11 report the share of patents from the ADHPS sample in column 2 that are matched to Compustat in each of ourfour sequential steps: matching based on name strings (column 8), matching based on the ADHPS web search algorithm (column 9), matching based on assignee name-firm links in NBER-PDP (column 10), and matchingbased on our own manual searches (column 11).

Our Compustat data cover public firms that were listed on the North American stock markets
between 1950 and 2014. To match a firm to its patents, we do not require it to be included in
Compustat in the year of patent application. If a private company applies for a patent before going
public, we are able to determine an industry affiliation for the firm using the industry assignation in
Compustat after its listing. To this end, our baseline analysis assigns firms to industries using the4

We identify universities, institutions, hospitals and government agencies using key words in assignee names following the NBER Patent Data Project.
5
Our final sample of patents with application year 1975 includes 75% of all corporate patents (column 3 of Table
A2), of which 78% were matched through an exact correspondence of the company name used on patents and in
Compustat records (column 8). We are thus able to link 59% (=75%*78%) of all corporate patents from 1975 via
name matching. This percentage of corporate patents with successful name matching falls to 44% (=70%*63%) by
the application year 2007.
6
Among patents that have no simple name matches but are matched by both NBER-PDP’s manual corrections
and our web search algorithm, 84% are matched to the same listing. The remaining 16% are mismatched either by
the web algorithm or by NBER-PDP.

5

last available industry code that Compustat recorded for a given firm.7 We retain all firms to which
Compustat has assigned a valid industry code. These firms account for 170,788 patents across thefive sample years, corresponding to 99.4% of the matched patents from Table A2.
Table A3. Firm Characteristics
Table A3: Firm Characteristics

In-Sample Firms(1)

All Firms(2)

Contribution of
In-Sample Firms toOverall Volume(3)

I. Firms in All Sectors
Number of Firms
Number of Patenting Firms in 1975
Number of Patenting Firms in 1983
Number of Patenting Firms in 1991
Number of Patenting Firms in 1999
Number of Patenting Firms in 2007

6,081
1,682
1,671
2,270
3,153
2,389

36,273
1,682
1,671
2,270
3,153
2,389

16.8%
100.0%
100.0%
100.0%
100.0%
100.0%

Number of Firms Reporting in 1991
Avg US Sales in 1991 (millions)
Avg Global Sales in 1991 (millions)
Avg Global Employment in 1991
Avg Global Capital in 1991 (millions)
Avg Global R&D in 1991 (millions)

2,361$1,189.2$2,093.3
11,404.6$1,400.5
$74.2

8,030
$617.4$985.96,104.5$759.1
$36.3

29.4%
62.8%
63.1%
59.4%
54.2%
95.0%

II. Firms in Manufacturing Sector
Number of Firms
Number of Patenting Firms in 1975
Number of Patenting Firms in 1983
Number of Patenting Firms in 1991
Number of Patenting Firms in 1999
Number of Patenting Firms in 2007

4,413
1,393
1,364
1,827
2,399
1,758

11,556
1,393
1,364
1,827
2,399
1,758

38.2%
100.0%
100.0%
100.0%
100.0%
100.0%

Number of Firms Reporting in 1991
Avg US Sales in 1991 (millions)
Avg Global Sales in 1991 (millions)
Avg Global Employment in 1991
Avg Global Capital in 1991 (millions)
Avg Global R&D in 1991 (millions)

1,734$945.8$1,908.4
9,944.8$1,091.9
$73.3

3,178
$599.6$1,204.8
6,510.1$699.5
$49.7

54.6%
89.7%
86.6%
86.5%
85.1%
97.0%

Notes: "In-Sample firms" consist of Compustat firms with valid industry affiliations that have at least one patent included in
our analysis (i.e., patents with corporate assignees, US-based primary inventors, and application years 1975, 1983, 1991, 1999,
and 2007). The sample in column 2 consists of all Compustat firms with valid industry affiliations. Firms are assigned to
sectors based on the time-invariant main Compustat industry code. Average firm characteristics are calculated for firms thatreported a given accounting item to Compustat in 1991, regardless of whether they applied for patents that year. Column 3
measures the contribution of in-sample firms to the overall volume for an accounting item among all firms that reported that
item in 1991.
7

A challenge to this approach is that a firm’s industry may change over time, or a firm may be active in multiple
industries. For a subset of firms, Compustat also provides historical industry codes and information on the distributionof sales across multiple industries. We use these historical data to assign firms to their past industry, and to construct
a firm-specific measure of trade exposure using as weights the share of the firm’s sales in each industry in which it
operates. Table A6 shows that our results are robust to these various schemes for assigning industry codes to firms.

6

Table A3 provides descriptive statistics that characterize the firms in our sample. Column 1
indicates that the matched patents from our five sample years originate from 6,081 firms, nearly
three quarters of which operate in manufacturing. A comparison with the full sample of Compustat
firms in column 2 of A3 indicates that 17% of all firms that were covered by Compustat at any
time between 1950 and 2014—and 38% of the manufacturing firms—had at least one patent witha US-based primary inventor in one of the five sample years. Column 3 of Table A3 shows thatpatenting firms are responsible for a large fraction of the overall economic activity that is recordedin Compustat. Firms in our analysis sample account for 95% of all R&D expenditure that Compustat
records in the year 1991, and for 97% of R&D expenditure in manufacturing.8 They are larger than
the average Compustat firm in terms of sales, employment, and capital, and they comprise between
85% and 90% of Compustat-recorded sales, employment and capital in manufacturing in 1991.9
Table A4 complements Figure 1, Panel B, and reports the fraction of patents in 1975, 1983,
1991, 1999, and 2007 accounted for by 11 major manufacturing sectors, sorted by their share of
total manufacturing patents in 1991. In that year, just two sectors, chemicals and petroleum andcomputers and electronics, comprised 55.2% of patents by manufacturing companies.10 This sectoral
concentration of innovation is both persistent and accelerating. In 1975, the two sectors alreadyaccounted for 45.8% of manufacturing patents and by 2007, their collective share of patents had
reached 63.2%. However, there has been a dramatic reordering among these top two sectors interms of which is the locus of innovation. The share of the chemicals and petroleum sector in total
manufacturing patents declined from 33.4% in 1975 to 29.1% in 1991 and then fell to 13.4% in
2007. Computers and electronics, buoyed by the IT revolution, have displaced chemicals as themost prolific sector for the creation of new patents. The sector’s share in manufacturing patents
expanded from 12.4% in 1975 to 26.1% in 1991; in 2007, the computers sector was responsible forhalf (49.8%) of all patents by manufacturing firms in Compustat.
8

Conversely, firms without matched patents contribute very little to overall R&D. This pattern suggests that it isunusual for firms to spend on R&D while not patenting the resulting innovation. It also confirms that our strategy
of matching patents to firms successfully avoids false negative matches that would result in a frequent observation offirms that have large R&D expenditures but no matched patents.
9
Using reporting years other than 1991 yields very similar results to Table A3.
10
Chemicals and petroleum include the two-digit SIC industries 28 and 29. Computers and electronics track
the NAICS three-digit industry 334, which comprises the following three and four-digit SIC industries: computerand office equipment (SIC 357, except 3579), calculating and accounting equipment (SIC 3578), household audio
and video equipment (SIC 365), communication equipment (SIC 366), electronic components and accessories (SIC367), magnetic and optical recording media (SIC 3695), search and navigation equipment (SIC 381), measuring andcontrolling devices (SIC 382, except 3821, 3827, 3829), x-ray apparatus and tubes and electromedical equipment (SIC
3844, 3845), and watches and parts (SIC 387).

7

A4. Distributionof Patents
across Manufacturing
Sectors
Table Table
A4: Patentsfrom US-Based
Inventors
by Manufacturing
SectorPatent Application Year
1975
1983
1991
1999
Chem., Petrol., Rubber
Computers, Electronics
Machinery, Equipment
Transportation
Paper, Print
Metal, Metal Products
Food, Tobacco
Clay, Stone, Glass
Wood, Furniture
Other Manufacturing
Textile, Apparel, Leather

33.4%
12.4%
26.3%
10.8%
3.2%
5.8%
1.8%
4.2%
0.6%
0.8%
0.5%

33.3%
16.7%
26.2%
9.7%
3.2%
4.3%
2.0%
2.9%
0.6%
0.6%
0.6%

29.1%
26.1%
23.5%
9.7%
3.6%
3.1%
1.7%
1.6%
0.8%
0.5%
0.3%

18.3%
45.3%
20.0%
8.0%
2.7%
1.8%
0.8%
1.5%
0.7%
0.6%
0.2%

2007
13.4%
49.8%
18.3%
11.3%
1.9%
1.5%
0.4%
1.4%
0.7%
1.1%
0.2%

Notes: The sample consists of Compustat-matched patents with corporate assignees from the manufacturing sector and US-based
primary inventors. Manufacturing firms account for 80.7%/ 81.3%/82.2%/77.3%/71.0% of all corporate patents in
1975/1983/1991/1999/2007. The Computer and Electronics sector comprises the SIC industries that correspond to NAICS sector 334,
while the Machinery and Equipment sector comprises all other industries belonging to the 2-digit SIC codes 35, 36 and 38.

The number of patents in other manufacturing sectors, and their contribution to total patents,changed more modestly over time. Table A4 indicates that the third and fourth largest sectors interms of patenting during the sample period, machinery and equipment and transportation, saw their
combined share in manufacturing patents decline modestly over time, from 37.1% in 1975 to 33.2%
in 1991 and 29.6% in 2007.11 Other industries that figure prominently in overall manufacturing
activity hardly register when it comes to patenting. Furniture and wood products (SIC 24, 25) andapparel, textiles, and leather (SIC 22, 23, 31) are large labor-intensive sectors that historically havebeen important sources of manufacturing jobs. However, these industries together accounted for
only 1.1% of patents by manufacturing firms in 1991 and a paltry 0.9% in 2007. Two other major
sectors, stone, clay, and glass (SIC 32) and paper products and printed matter (SIC 26, 27), accountfor only slightly higher shares of patents.

B

Robustness of Baseline Estimates

B.1

Alternative Samples and Weights

Table A5 checks robustness of our primary results and compares them to the estimates from ourbaseline specification, which yielded a coefficient estimate −1.35 in column 3, row h of Table 2. We
first address the concern that the implicit maximum permissible time to patent approval varies over11

Machinery and equipment comprise the two-digit SIC industries 35, 36 and 38, except for computers and electronics, while transportation corresponds to SIC industry 37.

8the sample period (i.e., we observe patents with application dates between 1991 and 2007, whichwere granted by 2013). Whereas for the first year in the sample, we observe patents granted within
22 years of the application date, for the last year in the sample, we see only patents granted within
six years of the application date. In column 1 of Table A5, we examine the robustness of our results
to imposing a uniform time to approval for all patents considered in the analysis. We restrict the
sample to patents granted within six years of the time of application. Because the vast majority ofpatents are granted within a few years after an application is submitted, the impact of this restriction
on the sample size is small. The number of firm-years included in the analysis falls from 8, 271 in
our baseline specification in column 1 to 8, 167 in column 1, and the number of patents used forthe analysis declines from 129,585 to 127,654. The coefficient estimate on import penetration withthe six-year patent approval restriction (−1.37) is nearly identical to that in the baseline (−1.35),
suggesting that right censoring in patent approval times is of little consequence for the results.
Given the importance of innovations in computer applications and in chemical processes forpatenting by manufacturing firms, it is natural to wonder whether our results are sensitive to including patents in these technology fields in the analysis. In Table 2, we have already explored suchsensitivity by incorporating controls for the technology mix of patenting by the firm, as measured by
the average shares of firm patents that fall into the six primary technology classes that are indicatedon patents (chemical; electrical and electronic; computers and communication; mechanical; drugs
and medical; other). The results in Table 2 reveal that after adding controls for the firm’s broad
sector of activity, controlling for the technology mix of the firm’s patents has little extra effect. In
columns 2 and 3 of Table A5, we take the further step of dropping all patents with the primary
technology class in computers and communications or in chemicals, drugs and medical. Under either
restriction, the change in firm patents is thus calculated over new innovations in the remaining technology classes. These exclusions result in larger point estimates for the negative impact of greater
trade exposure on the firm-level change in patenting, with coefficient values rising from −1.35 in
the baseline specification to −1.83 when computer and communication patents are excluded and to−1.52 when chemical and pharmaceutical patents are excluded, with little effect on precision. The
responsiveness of patenting to import competition thus appears to be slightly greater, rather thansmaller, outside of the dominant technological areas for manufacturing innovation.

9

Table A5: Effect of Chinese Import Competition on Firm-Level Patenting, 1991-2007: Robustness to Alternative Samples and Weights. Dependent Variable: Change in Patents by US-Based
Inventors (% pts)

Δ U.S. Industry
Exposure to Chinese

Reduced Patent Samples
No Grant No CompNo
No Manual Compustat
Lag >6/Comm No Chem/ Manual or NBER Balanced
Years
Tech
Drug Tech Matches
Matches
Panel
(1)
(2)
(3)
(4)
(5)
(6)

PatentCitations(7)

GlobalR&D(8)-1.37 ** -1.83 ** -1.52 ** -1.32 ** -1.09 *(0.50)(0.54)(0.54)(0.44)(0.45)-1.60 **
(0.58)-1.30 *
(0.59)-2.70 ** -1.32 ** -0.52 *(0.36)(0.38)(0.25)

27.12
3,262
104,510

34.49
7,150
126,855

21.19
3,591
98,156

Mean Outcome Variable 25.77
No. Observations
8,167
No. Patents Used
127,654

18.82
6,837
83,69026.24
6,566
99,44019.98
8,257
125,533

22.77
7,795
117,847

Alternative Firm Weights
No
Weights(10)

US Sales
(9)

13.37
5,523
87,375

1.70
11,927
104,510Notes: 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. The dependent variable in column 1 omits
patents that were granted more than six years after patent application. Column 2 excludes patents in the computer and communications technology category, and column 3 excludes patents in thechemical or drug technology category. Column 4 excludes patents from firms that we manually matched to Compustat, while column 5 additionally excludes patents matched via NBER-PDP, thus
retaining only the result of fully automated matching based on firm names and our web search algorithm. Column 6 retains only firms that are observed in Compustat both at the start and end of aperiod. Column 7 weights firms by the share of their patents among all patent citations, averaged over the start and end of the period. Columns 8 and 9 weight firms by R&D expenditures or U.S.
sales, averaged over the start and end of the period. Column 10 uses an unweighted balanced panel of Compustat firms. Standard errors are clustered on 4-digit SIC industries. ~ p ≤ 0.10, * p ≤ 0.05,** p ≤ 0.01.

Our sample of patents matched to Compustat firms includes patents matched on standardized
firm names, using our web-based search procedure, or matched manually by NBER-PDP or byourselves. These two latter sets of manual matches may arguably introduce researcher subjectivity
into the construction of the data. We investigate whether our results are affected by dropping patents
that were manually matched. In column 4 of Table A5 we drop patents we matched manually (which
excludes 14 firm-years from the sample) and in column 5 we drop patents matched manually in theconstruction of the NBER data (which excludes 276 firm-years from the sample). In the first case,the resulting coefficient estimate is close to identical to our baseline estimate; in the second casewhich retains only patents that were matched using our automated algorithm, the coefficient ismodestly smaller in magnitude but still negative and precisely estimated. We take these results tomean that including manually matched patents in our data has little impact on our results.
In order to match a patent to a Compustat firm, it is required that the firm appears in Compustat
data at least once during the 1950 to 2014 period. It is not necessary, however, that Compustat lists
the firm in the year of the patent application, as we can for instance match a firm’s patents prior to
its listing in the stock market to the Compustat record that was created after the firm went public.
As a further robustness check, column 6 of Table A5 restricts the sample to a balanced panel of thosefirms that are covered by Compustat both at the start and at the end of an outcome period. This
sample corresponds closely to the ones used in Panel I of Table 1, which analyzes the change overtime in outcomes like firm sales and employment that are based on Compustat records. The firms
in this balanced panel account for 104,510 out of the 129,585 patents that we use in the baseline
specification, and the coefficient estimate from the corresponding regression is slightly larger.
In Table 2, we weight observations by firm patents averaged over the start and end of period.
Our motivation for doing so is to capture the impact of trade exposure on the overall scale ofinnovative activity in manufacturing. However, economists have long recognized that patent counts
may provide an imperfect indication of the magnitude of innovations by a firm (Trajtenberg, 1990).
Only a small share of patents lead to major innovations, with the rest mattering relatively little for10

firm profitability. Citations of a patent in subsequent patent applicants is a commonly used metricof the importance of an innovation (Jaffe and Trajtenberg, 2002).
With this reasoning in mind, column 7 of Table A5 reports estimates where we weight observations by the total number of subsequent citations to each firm’s start-of-period and end-of-period
patents. Relative to the baseline result, citation weighting produces a very similar estimated impactof trade exposure on firm patenting (−1.30). An alternative measure of a firm’s innovative heft
is its total spending on R&D. Because R&D is an input to innovation rather than an output, it
may imperfectly reflect a firm’s contribution to technological progress. Still, it offers an intuitive
measure of a firm’s attempts to advance technology frontier. Moreover, weighting by firm global
R&D spending extends the sample to include firms for which we observe positive R&D spending
but no patents in the sample period, although we loose a larger number of firms for which we observe patents but not R&D. The resulting regression estimate in column 8 of Table A5 indicates alarger and highly significant negative impact of trade exposure on firm patenting (−2.70). These
weighting schemes based on patents, patent citations or R&D expenditure allocate greater weight
to firms whose contribution to U.S. innovation is larger. In column 9 of Table A5, we instead weightfirm observations by their sales in the U.S. market, which yields an estimate (−1.32), which is very
close to the baseline effect in the patent-weighted sample. Finally, column 10 of Table A5 considers
an unweighted balanced panel of Compustat firms which also comprises non-innovative firms thatneither patent nor report R&D spending in a given time period, and that would hence have a zeroweight in the previous specifications that weight firms according to their innovative activity. The
estimated impact of import penetration on patenting in the unweighted firm sample is smaller inmagnitude (−0.52) than in the baseline specification, but remains significantly negative (t-statistic
2.1).

B.2

Alternative Industry Classifications

In the sample used for the estimation results in Table 2, we classify firms according to their mainindustry code, as reported in Compustat. This code generally corresponds to industry affiliation
during the most recent period. It is however possible that firms change their primary industries
in response to trade shocks. Bernard et al. (2006) find evidence of such movements at the level of
U.S. manufacturing plants during the 1980s and early 1990s. Among plants that survive from one
period to the next, those that are exposed to larger increases in import competition are more likely
to change their initial industry of affiliation. Our sample, however, is comprised of firms, not plants,
where any one firm may own dozens of manufacturing establishments. Inducing changes in primaryindustry affiliation at the firm level is likely to require a much stronger impetus than at the plantlevel. We proceed to examine whether our results are sensitive to changes in how we define a firm’s
primary industry.

11

Table A6: Effect of Chinese Import Competition on Firm-Level Patenting, and on Probabilityof Industry and Segment Change, 1991-2007. Dependent Variable: Change in Patents by USBased Inventors (% pts), Probability of Industry or Segment Change (% pts).
(1)

Source of Industry CodeMain

Δ U.S. Industry or Firm
Exposure to Chinese Imports-1.35(0.50)

Mean Outcome Variable
No. Observations
No. Patents Used

24.42
8,271
129,585**(5)

Pr(IndChange)(6)

Pr(Entered
Segment)(7)

Pr(Exited
Segment)(8)

Exact
Segment

Exact
Historical

Exact
Segment

Exact
Segment

0.17(0.24)-0.58(0.63)

0.37(0.42)

16.24
3,160
n/a

51.16
2,704
n/a

56.90
2,704
n/a

Relative Change in Patenting
(2)
(3)
(4)
Segment/
Historical / Historical/
Exact
Main
Main
Historical-1.33(0.49)-1.57(0.58)

24.42
8,271
129,585**-1.46(0.54)

**

24.42
8,271
129,585-1.34(0.50)
27.86
3,160
102,431

**

27.05
2,704
94,010**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. Column 1 assigns each firm to
its main, time-invariant industry code as reported in Compustat, and corresponds to model 3h in table 2. Column 2 assigns each firm-period observation to the historical Compustat
industry code at the start of the respective period if available, or else to the earliest available subsequent historical industry code, or else to the main industry code. Column 3 defines
firm-level trade exposure by weighting industry-level import shocks with a firm's start-of-period distribution of sales across industries. If sales by industry segment are unavailable at thestart of the period, then they are replaced by sales by industry in the earliest subsequent year. If a firm's sales are never disaggregated across industries, then trade exposure is defined asin column 2. Columns 4 and 5 only retain firms for which a historical industry code or historical segment sales data is available both for the start-of-period and end-of-period year.
The column 6 model uses the same sample and industry definition as column 4, and estimates the probability that a firm will have a different industry code at the end of a period thanat the start. Columns 7 and 8 use the same sample definition as column 5, and estimate the probability that a firm has positive sales in an industry segment only at the end of a period(entry into new industry segment, column 7) or only at the start of the period (exit from industry segment, column 8). 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.

In Table A6, we compare our baseline results in column 1, taken from column 3 and row (h) of
Table 2, to those obtained from alternative definitions of a firm’s industry affiliation. In column 2, we
designate a firm’s primary industry to be that at the start of the respective period, when available,
or else from the earliest available subsequent period. Historical industry codes are available for asubset of the firms in our sample as of the late 1980s. For firms where Compustat provides nohistorical industry information, we retain the main industry code that was used in the baseline
estimation. Therefore, the sample size is unchanged. The coefficient estimate on trade exposure
declines minimally from −1.35 in column 1 to −1.33 in column 2 and retains its statistical significancewhen using this modification. In column 3, we incorporate information on historical firm sales acrossindustries, again available for a subset of firms since the late 1980s. Where such data are available, weconstruct a firm-level measure of trade exposure, defined as the average import penetration across allindustries in which the firm was active in a given year, weighted by firm sales across these industries.
Again, a firm’s main historical or its most recent industry code is used when such segment sales
data are unavailable. The resulting coefficient estimate on trade exposure rises modestly in absolutevalue when compared to column 2. In column 4, we retain just those firms for which a historicalindustry code is available both at the start and end of the respective period, meaning we retain
only firms that had full Compustat coverage in the years for which we measure patent applications.
The resulting estimate for the impact of trade exposure on patenting is nearly identical to that incolumn 2, although it is computed based on a substantially smaller set of firms. Finally, in column
5 we retain only firms that have historical sales data by industry segment at the start and end of aperiod. This regression model, which just includes firms for which we can define a firm-specific trade
shock as opposed to an industry-level shock, produces a modestly larger impact coefficient for trade
exposure. Overall, adjusting for changes in firm industry of affiliation or the industry composition12

of firm sales leaves our coefficient estimate on import penetration materially unchanged.
These estimation results suggest that changes in import competition may have little impact onfirm industry representation. In columns 6 to 8 of Table A6, we test this proposition formally. The
column 6 specification has as the dependent variable an indicator for whether a firm changes its
primary industry of affiliation between the start of the period and the end of the period.12 The
impact of import penetration on industry switching is positive but small and quite imprecisely
estimated (t = 0.7). A one-standard-deviation increase in import penetration produces only a 1.6
percentage-point increase in the likelihood of changing the primary industry, relative to a mean
period likelihood of change of 16.8 percentage points. In columns 7 and 8, we examine the related
possibility that changes in import competition affect firm entry into an industry segment, as indicatedby zero segment sales at the start of period and positive segment sales at the end of period, or exitfrom an industry segment, as indicated by sales moving from positive to zero over the relevant timeinterval. There is a modest negative impact of import competition on a firm entering a new sales
segment and a modest positive impact of import exposure on a firm exiting an existing segment,
though neither result is close to statistical significance. At the level of corporate entities represented
in Compustat, greater import penetration suppresses patenting but appears to have little impact ona firm’s major industry orientation.

B.3

Analysis at the Technology Class Level

One limitation of using the Compustat firm data is that we do not observe smaller firms thatnever cross the threshold into being publicly listed. These firms likely account for the bulk of the
28% of corporate patents that our algorithm did not match to firms that have ever been covered
by Compustat. However, while we do not know the industry of these unmatched firms, we doobserve detailed technology classes for all patents. Using the sample of patents that are matched toCompustat firms, we impute the trade shock to which a technology class is exposed as the averageindustry trade shock of Compustat firms in that technology class, weighted by firms’ shares of patents
in the class. This allows us to examine how these imputed trade shocks at the technology-class level
affect patenting by corporate entities, whether or not they appear in Compustat and thereby expand
our analysis to include both public and non-public companies. Similarly, we also estimate the impactof import competition on patents by non-corporate entities—which include universities, hospitals,
other non-profit institutions, and private individuals. Table A7 presents these results.
The unit of analysis in Table A7 is a detailed patent technology class, rather than the firm.
Columns 1 to 3 show results for the change in patenting by all corporate entities, where acrossthe columns we expand the set of controls included in the analysis. As in our earlier results, theimpact of exposure to import competition on patenting is negative and precisely estimated. The
impact changes little, while retaining statistical significance, as we move from controls for the shareof Compustat firms in the class that are active in the computer or chemical sectors (column 1) tocontrolling for the 11 major industry sectors, six major technology fields (column 2), and two lags
12

The firm sample for this analysis corresponds to the one used in column 4.

13

on the change in patenting (column 3). Since the imputed import shock for a technology class is aweighted average of the original industry-level trade shocks, the import exposure measure in Table
A7 has a notably smaller standard deviation (4.38) than the import shock used in column 3 of Table
2 (11.34). The absolute size of the estimated regression coefficients in the two tables is inversely
proportional to that dispersion of the exposure variable. If we take the coefficient estimate fromcolumn 3 in Table A7, a one standard deviation increase in trade exposure over the 1991 to 2007
period would lead to a 14.2 percentage-point decrease in patenting in a technology class, whereas incolumn 3i of Table 2, we had found a 15.3 percentage-point reduction in firm-level patents associatedwith a one standard deviation in import exposure. The firm- and technology-class level regressions
thus find comparable sizes of effects of import competition on U.S. corporate patenting.
Table A7: Effect of Chinese Import Competition on Patenting 1991-2007: Technology Class-Level
Analysis. Dependent Variable: Change in Patents within Technology Class (% pts).
All Corporate Patents
(1)
(2)
(3)

Compustat-Matched
Corporate Only
(4)
(5)
(6)

Δ U.S. Technology Class
Exposure to Chinese Imports-3.33 * -3.25 * -2.64 *(1.33)(1.33)(1.15)

Two Sectors (Comp, Chem)
11 Sectors, Six Tech
2 Lags of Outcome

yes

yes
yes

yes
yes
yes

All Non-Corporate
Patents(7)(8)

Corporate + NonCorporate Patents
(9)
(10)

-3.06 * -3.35 * -2.89 *(1.49)(1.34)(1.17)

0.63(1.29)

2.10(1.41)-3.11 * -2.12 ~(1.25)(1.13)

yes

yes

yes
yes
yes

yes

yes
yes

yes
yes
yes

yes
yes
yes

Notes: Every regression comprises two stacked first differences 1991-1999 and 1999-2007. N=819, based on 184,262/130,611/52,084/236,346 patents in columns 1-4/5-6/7-8/9-10. The
mean of the outcome variable is 29.57/21.88/9.04/25.93 in columns 1-4/5-6/7-8/9-10. Column 1 includes a period dummy and controls for the fraction of Compustat-matched patents in atechnology class that have an assignee in either the computer or chemical sector, averaged over the start and end of a period. Column 2 controls for the distribution of Compustatmatched patents across 11 manufacturing sectors, and includes dummies for 6 major technology categories. Column 3 additionally controls for two 8-year lags of the outcome variable. All
models are weighted by the number of Compustat-matched U.S.-inventor patents in a technology class, averaged over the start and end of a period. Standard errors are clustered on 4digit SIC industries. ~ p ≤ 0.10, * p ≤ 0.05, ** p ≤ 0.01.

In columns 4 to 6, we limit the patents included in the analysis to those that can be matched toCompustat firms, such that the patents represented are the same as in Table 2 but now aggregatedto the technology class level. The coefficient estimates are similar to those for all corporate patents,
showing a modestly smaller negative effect in the specification with minimal controls (columns 4
versus 1) and a larger negative effect in the specification with full controls (columns 6 versus 3).
In columns 7 and 8, we find that the negative impact of trade shocks on patenting disappears
when we use patents by non-corporate entities; the impact coefficients of import competition on
non-corporate patenting are now positive but imprecisely estimated. Since non-corporate entities
such as universities and hospitals are not directly subject to manufacturing-industry market forces,
we would expect their patenting activities to reflect underlying availabilities of technological opportunities—which presumably apply to all types of invention—more so than responses to importcompetition. That import competition does not inhibit patenting by non-corporate entities suggeststhat the trade-exposed industries do not suffer from an exhaustion of technological opportunities.
Finally, in columns 9 and 10, we include in the analysis both corporate and non-corporate patents,
which constitutes the universe of patenting by U.S.-based inventors. For this combined sample, theimpact of trade shocks on patenting is negative, though smaller than for the sample of corporate
patents (i.e., when comparing columns 1 and 4 with columns 9 and 10). We conclude that adverse
14

trade shocks reduce in patenting for all types of corporate entities, whether or not these firms are
publicly listed, but has no such effect on non-corporate entities.

B.4

Heterogeneity by Inventor and Firm Locations

Many of the companies listed in Compustat are multinational enterprises with subsidiaries locatedaround the world. Most are owned by parent companies headquartered in the U.S., though some are
owned by parent companies located abroad.13 Through offshoring, multinational companies haverelocated a substantial share of their U.S. manufacturing employment to their subsidiaries or toarms-length contractors located in other countries (Harrison and McMillan, 2011). In Table A8,
we examine whether greater import competition may have had differential effects on innovation athome versus innovation abroad in a manner analogous to the impacts of trade on the global locationof employment engaged in production.
The data allow us to track the location of innovation via the address of the lead inventor listed
in the patent application. In its worldwide operations, IBM, for instance, has 12 R&D labs locatedin 10 different countries.14 Presumably, patents created in one of IBM’s three U.S.-based labs would
list the lead inventor as being located domestically, whereas patents created in one of IBM’s labs inAustralia, Israel, or Switzerland would list the lead inventor as being located abroad. To review thesample definitions used in the analysis so far, our baseline specification includes all Compustat firms,
whether or not the firm’s parent company is U.S. owned. It also restricts patents to those whose
lead inventor has a U.S. address, and thus captures innovation within the U.S. In what follows,
we differentiate between firms that are owned by a U.S. parent company versus a foreign parent
company and expand the sample to include patents created by inventors located abroad.
Column 1 of Table A8 repeats our main specification from model 3h in Table 2, which comprises
all firms with patents by U.S.-based primary inventors. We next split that sample into firms whose
headquarters are in the U.S. and firms that are based abroad. U.S. firms account for the a large
majority of corporate patents by U.S. inventors, and the impact of import competition on theirpatent output is similar to the baseline estimate (−1.17, column 2). The innovation by foreigncompanies is covered in our data only to a limited extent, namely for foreign firms that both patent
their innovations in the U.S. and have a listing at a U.S. stock market. For this select sample offoreign firms, there is again a negative impact of Chinese import competition on the patent output
by their U.S.-based inventors (−2.23, column 3).
13

All firms in Compustat are publicly listed in the U.S., whereas some have parent firms located in the U.S. andothers have parent firms located in other countries.
14
See https://www.research.ibm.com/labs/.

15

TableA8:
A8. Effectof ofChineseImportCompetition
on Firm-Level
1991-2007:
Alternative
Firm and Inventor
Table
EffectChineseImportCompetition
on Patenting,Firm-Level
Patenting,
1991-2007:
AlternaSamples.
Dependent
Variable:
Relative
Changeof
NumberofPatents.
tive Firm and Inventor Samples. Dependent Variable: Relative Change of Number of Patents.
US Inventors
All(1)

US Firms(2)

Foreign
Firms
(3)

All
(4)

US Firms
US
Inventors(5)

Foreign
Inventors(6)

All Firms
All
Inventors(7)

Δ U.S. Industry Exposure
to Chinese Imports-1.35 ** -1.17 ~(0.50)(0.64)-2.23 **
(0.47)-1.15 *
(0.57)-1.17 ~
(0.62)-1.05 ~
(0.59)-1.30 **
(0.41)

Mean Outcome Variable
No. Observations
No. Patents Used

24.42
8,271
129,58541.81
675
12,39524.87
7,996
133,151

21.92
7,596
117,190

61.98
2,003
15,961

26.70
9,381
217,489

22.50
7,596
117,190

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 1-3 use only patents whose main inventor is based in the U.S. (as observed in the patent), while columns 4-6 use only firms that are headquartered in
the U.S. (as observed in the most recent Compustat data). Column 7 includes all corporate patents from the U.S. Patent Office that we matched to Compustat
firms. Models in columns 1-3/4-6/7 are weighted by a firm's U.S.-inventor/U.S.-firm/overall 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.

Our main specification focuses on import competition and innovation that occur on U.S. soil.
We find a similar effect of import competition on patenting when we consider all patents by U.S.based firms rather than patents by U.S. inventors (−1.15, column 4). Innovation in import-exposedindustries does not appear to shift from the U.S. to other countries, as we observe similar declines
in U.S. firms’ patent output by domestic and foreign inventors (columns 5 and 6). Not observing
patents filed outside of the U.S. is unlikely to be an issue for the interpretation of our analysis as it
relates to U.S. firms. Given the importance of the U.S. market and its well-developed intellectual
property protection system, it is highly unlikely U.S. firms would patent elsewhere without patenting
in the U.S., even when inventions originate from firm’s foreign operations. Finally, we expand theset of firm sample patents to be based on all corporate patents that we matched to Compustat firms,
irrespective of inventor and firm location. The impact of import competition on patenting by all
inventors and firms (−1.30, column 7) is negative and precisely estimated, and almost equal to thethe baseline specification for U.S. inventors (−1.35, column 2). Overall, we find a uniformly negative
effect of Chinese import competition on firm patenting, regardless of firm and inventor locations.

B.5

Relative Change in Patenting vs Firm SizeFirms that are exposed to import competition from China reduce not only their patent output,
but, as the results in Table 1 indicate, also contract along multiple other margins, including global
sales (column 2), global employment (column 3), global capital stock (column 4), total valuation(columns 5 and 6), and R&D investment (column 7). In Table A9, we explore whether the reduction
in patenting in import-competing firms is proportional to the decline of firm scale of operation, or
whether patenting contracts disproportionately relative to sales. The outcome variable in column 1of panel I is the first difference between the outcome variables of column 1 in Table 1 and column
3 in Table 2, i.e., the relative change of a firm’s U.S.-inventor patents minus the relative change
in its U.S. sales. The subsequent panels of Table A9 similarly investigate the change in a firm’s

16

global patents relative to either the change in its global sales (panel II) or the change in its global
employment (panel III). Across all three panels, the results in column 1 suggest that patenting inimport-competing firms falls slightly more than sales or employment, although only one of the threeregression estimates is statistically significant.
Table 4 suggests that the adverse impacts of exposure to import competition on patenting are felt
disproportionately by weaker and less technologically advanced firms, as indicated by lower initialvalues of sales per worker, capital per worker, and ROI. In columns 2 to 9 of Table A9, we explore
whether these weaker firms experience a differential contraction in patenting relative to firm size.
Table A9: Effect of Chinese Import Competition on Change in Firm-Level Patenting vs Change
in Firm Size, 1991-2007. Dependent Variable: Relative Change of Number of Patents minus
Relative Change in Sales or Employment..
Splits by Firm Labor Productivity and Capital Intensity
All
Firms
(1)

Sales/Worker
>Avg
≤Avg
(2)
(3)

-0.40
(0.43)-0.01(0.66)

Capital/Worker
>Avg
≤Avg
(4)
(5)

Splits by Profitability and Leverage
Profit/Capital (ROI)
>Avg
≤Avg
(6)
(7)

Debt/Equity
≤Avg
>Avg
(8)
(9)

I. Change in US Inventor Patents vs Change in US SalesΔ U.S. Industry Exposure
to Chinese Imports
Test for Equal Coeff.
Mean Outcome Variable
No. Observations-1.04(0.87)

0.61(0.70)

p=0.328
-5.67
2,200

3.68
760

-1.27
(0.61)*

p=0.042-16.17
1,325

-6.44
771

0.54
(0.91)-1.67(0.56)**

p=0.013

-3.73
1,313

-6.45
1,039

-0.51
(0.34)-0.82(1.38)

p=0.883

-4.23
1,128

-12.29
1,595

0.62
405

-0.49
(0.32)-1.90(0.99)

II. Change in Global Patents vs Change in Global SalesΔ U.S. Industry Exposure
to Chinese Imports-0.66(0.38)

Test for Equal Coeff.

~

-0.31
(0.70)-1.51(1.00)

0.71(0.64)

p=0.283-1.66(0.51)**

p=0.002

Mean Outcome Variable
No. Observations-24.31
3,098-14.01968-35.29
1,871

Δ U.S. Industry Exposure
to Chinese Imports-0.85(0.53)

0.13(0.64)-2.44(1.38)-25.481,073

0.02(0.47)-1.62(0.47)**

p=0.003-22.591,765-24.481,388~

p=0.409-24.11
1,650-33.482,193-14.90584-0.41(0.49)-2.44(1.26)

III. Change in Global Patents vs Change in Global Employment

Test for Equal Coeff.
Mean Outcome Variable
No. Observations

p=0.107
9.16
2,803

11.63
951

6.19
1,842

~

0.69
(0.79)-1.82(0.79)

p=0.016
5.32
1,053

11.52
1,743

*

-0.30
(0.54)-1.75(0.76)

p=0.040
4.48
1,276

16.54
1,483

*

~

p=0.254
0.43
2,032

17.75
549

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. The outcome variable is the first difference
between the relative change of a firm's U.S.-iventor or global patents, and the firm's U.S. sales, global sales or global employment. Columns 2-3, 4-5, 6-7 and 8-9 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 patent-weighted industry average in the start-of-period year. All models are weighted by afirm'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.

The differential impacts of trade on firm patenting versus firm size indeed become much sharper
when we separate firms according to the initial characteristics used in Table 4. For all four initialperformance metrics (sales per worker, capital per worker, profit per unit of capital (ROI), anddebt/equity) and all three metrics of firm patenting relative to size (U.S. patents versus U.S. sales,
global patents versus global sales, global patents versus global employment), we detect more-negativeand more precisely estimated impacts on patents versus size for below-average firms and less-negativeand imprecisely estimated impacts of import competition on patents versus size for above-averagefirms. When separating firms according to sales per worker, the trade impact on patents versus size17

is negative for below-average firms under all three patent-size metrics but is marginally significant
only for global patents versus global employment in column 3, panel III (β = −2.44, t = −1.8).
However, when examine below-average firms based on capital per worker (column 5) or ROI (column
7), the impact of import competition is negative and statistically significant for all three measuresof patents versus size. The patent-size relative impacts tend to be a bit larger in absolute value for
global patents versus global employment—as seen in column 5, panel III (β = −1.82, t = −2.3) andcolumn 7, panel III (β = −1.75, t = −2.3)—and a bit smaller in absolute value for U.S. patents
versus U.S. sales—as seen in column 5, panel I (β = −1.27, t = −2.1) and column 7, panel I(β = −1.67, t = −3.0). Overall, we interpret these results as strong evidence that for initially lesscapital-intensive and lower-profit firms, import competition reduces patenting above and beyond its
depressive effects on firm scale.

References
Balsmeier, Benjamin, Mohamad Assaf, Tyler Chesebro, Gabe Fierro, Kevin Johnson,
Scott Johnson, Guan-Cheng Li, Sonja Lück, Doug O’Reagan, Bill Yeh et al., “Machinelearning and natural language processing on the patent corpus: Data, tools, and new measures,”
Journal of Economics & Management Strategy, 2018, 27 (3), 535–553.
Bernard, Andrew B., J. Bradford Jensen, and Peter K. Schott, “Survival of the best fit:
Exposure to low-wage countries and the (uneven) growth of U.S. manufacturing plants,” Journalof International Economics, January 2006, 68 (1), 219–237.
Bessen, James, “NBER PDP Project User Documentation: Matching Patent Data to Compustat
Firms,” 2009.
Harrison, Ann and Margaret McMillan, “Offshoring jobs? Multinationals and US manufacturing employment,” Review of Economics and Statistics, 2011, 93 (3), 857–875.
Jaffe, Adam B. and Manuel Trajtenberg, Patents, citations, and innovations: A window on
the knowledge economy, MIT press, 2002.
Li, Guan-Cheng, Ronald Lai, Alexander D’Amour, David M. Doolin, Ye Sun, Vetle I.
Torvik, Amy Z. Yu, and Lee Fleming, “Disambiguation and co-authorship networks of the
U.S. patent inventor database (1975-2010),” Research Policy, July 2014, 43 (6), 941–955.
Morrison, Greg, Massimo Riccaboni, and Fabio Pammolli, “Disambiguation of patent inventors and assignees using high-resolution geolocation data,” Scientific data, 2017, 4, 170064.
Trajtenberg, Manuel, “A penny for your quotes: patent citations and the value of innovations,”
The Rand Journal of Economics, 1990, pp. 172–187.

18

Ventura, Samuel L, Rebecca Nugent, and Erica RH Fuchs, “Seeing the non-stars:(some)sources of bias in past disambiguation approaches and a new public tool leveraging labeled
records,” Research Policy, 2015, 44 (9), 1672–1701.

19