the innovation consequences of mandatory patent disclosures

The Innovation Consequences of Mandatory Patent Disclosures*

Jinhwan Kim Stanford Graduate School of Business

Stanford University [email protected]

Kristen Valentine

Terry College of Business The University of Georgia [email protected]

October 2019

Abstract We investigate the effect of patent disclosures on corporate innovation. Using the American Inventor’s Protection Act (AIPA) as a shock that increased patent disclosures, we find an increase in innovation for firms whose rivals reveal more information after the AIPA and a decrease in innovation for firms whose own disclosures are divulged to competitors as a result of the law. These findings suggest patent disclosures generate both spillover benefits and proprietary costs. Further, we find that firms use strategic disclosure choices allowed by patent law in an attempt to mitigate proprietary costs. Our findings provide justification for patent disclosure requirements by demonstrating positive externalities: rivals’ disclosures facilitate a firm’s innovation. However, we also highlight that mandatory patent disclosure can impose proprietary costs on firms that are not fully mitigated by strategic disclosure responses.

Key words: Patent disclosure, innovation, spillovers, proprietary costs, corporate disclosure

JEL classification: D23, G38, O30, O31, O32, O34, O38

* This paper is a product of merging our two job market papers – Mandatory Corporate Patent Disclosures and Innovation by Jinhwan Kim and Can Disclosure Regulation Impede Innovation? by Kristen Valentine. We sincerely thank our respective dissertation committee members – for Jinhwan Kim: Rodrigo Verdi (co-Chair), Eric So (co-Chair), S.P. Kothari, and Andrew Sutherland; and for Kristen Valentine: Shuping Chen (Chair), Dain Donelson, Cesare Fracassi, Ross Jennings, and Yong Yu – for their invaluable guidance and feedback. We thank Brian Baik, Jeremy Bentley, Natalie Berfeld, Jason Choi, Ki-Soon Choi, Ted Christensen, John Core, Jacquelyn Gillette, Stephen Glaeser, Nick Guest, Nick Hallman, Michelle Hanlon, Deepak Hegde, Joohee Jung, Steve Kachelmeier, Chongho Kim, Lisa Koonce, Ingon Lee, John McInnis, Lil Mills, Chris Noe, Suzie Noh, Jihwon Park, Georg Rickmann, Delphine Samuels, Haresh Sapra, Jaime Schmidt, Mike Schuster, Nemit Shroff, Sara Toynbee, Joe Weber, Brady Williams, Heidi Williams, and Yuan Zou for helpful comments. We also thank workshop participants at NYU, Harvard, Stanford, University of North Carolina at Chapel Hill, Columbia, Northwestern, Duke, UCLA, Boston College, BYU, University of Texas at Austin, University of Georgia, University of Chicago, University of Illinois at Chicago, Berkeley, Texas A&M, Yale, University of Florida, MIT, University of Washington, and University of Kansas for constructive feedback. We are grateful to KB Do of Harvard Law School, Ephraim Park of Facebook Inc., Steven Yoon, and two anonymous industry researchers for their industry insights on patents and innovation. Discussions with the U.S. Patent and Trademark Office (USPTO), David Schell (a patent preparer at Thermo Fisher Scientific) and data providers at IFI Claims have also been helpful. We gratefully acknowledge financial support from Stanford University, the University of Georgia, and the Deloitte Foundation. Send correspondence to: Jinhwan Kim, 655 Knight Way, Stanford, CA 94305; 650-721-4741; [email protected]. All errors are our own.

Electronic copy available at: https://ssrn.com/abstract=3469400

mailto:[email protected]

http://jinhwankim.mit.edu/sites/default/files/documents/JinhwanKim_JMP_Jan2019.pdf

https://repositories.lib.utexas.edu/handle/2152/74974

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1. Introduction Additions to the general store of knowledge [from patent disclosures] are of such importance to the public wealth that the Federal Government is willing to pay the high price of 17 years of exclusive use for its disclosure, which disclosure, it is assumed, will stimulate ideas and the eventual development of further significant advances in the art. – The U.S. Supreme Court (Kewanee Oil Co. v. Bicron Corp., 416 U.S. 470).

This study examines the effects of patent disclosures on corporate innovation. A hallmark of

the corporate innovation environment is the existence of intellectual property protection, including

patents. Firms in industries with significant intellectual property rights are a powerful force in the

economy, supporting 30% of employment and accounting for 38% of U.S. GDP in 2014 (USPTO

2016). Fundamentally, the patent system grants firms short-term monopoly rights and in return,

the firm provides detailed, public disclosures of its inventions. As the Supreme Court quote above

suggests, the disclosure requirement aims to promote the dissemination of knowledge and

stimulate innovation. However, revealing detailed technical information through patents can

impose proprietary costs on firms, which could work against any disclosure benefits. Despite the

economic importance of patents and the crucial role of patent disclosure, evidence on whether or

how patent disclosures affect innovation is scarce (Williams (2017)).

We use the American Inventor’s Protection Act (AIPA) as a setting. The AIPA was enacted

on November 29, 2000 and represents an expansion of firms’ patent disclosure requirements by

increasing both the timeliness and scope of patent disclosures (Hegde and Luo (2018)). Before the

AIPA, only U.S. granted patents were disclosed at the time of grant, which happened, on average,

36 months after filing. Since the enactment of the AIPA, firms must disclose U.S. patent

applications 18 months after filing, regardless of whether the application is eventually granted

patent rights. Thus, the AIPA accelerated disclosure by more than a year and required the

disclosure of granted as well as pending applications.1

1 While the AIPA did not change the required content of patent disclosures, we take a broad definition of disclosure and refer to the increased timeliness and expanded scope of patent disclosures as increases or improvements in patent disclosure.


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We predict the AIPA affects corporate innovation through two countervailing forces. On the

one hand, similar to the literature on knowledge spillovers (e.g., Foster (1981); Furman and Stern

(2011); Badertscher, Shroff, and White (2013)), greater availability of knowledge from the

disclosure of others’ patents (which we term disclosure “spill-in”) is likely to facilitate a firm’s

innovation. On the other hand, the literature on proprietary costs (e.g., Verrecchia (1983); Anton

and Yao (1994); Verrecchia and Weber (2006); Berger and Hann (2007); Glaeser (2018)) would

suggest that the costs of disclosing proprietary patent information to others (which we term

disclosure “spill-out”) likely harms a firm’s innovative activities. Thus, the effect of the mandate

depends on which spillover effect – spill-ins vs. spill-outs – dominates (Minnis and Shroff (2017)).

We proxy for the amount of disclosure spill-out that the AIPA imposes on a firm by measuring

the increase in timeliness of patent publication due to the 18-month disclosure rule. The longer a

firm’s patents took to publish in the pre-event period, the more the AIPA accelerated a firm’s

patent disclosures. To measure increase in timeliness, we use the firm’s average historical filing-

to-publication lag, defined as the number of months between a patent’s filing date and publication

date. For example, Viacom Inc.’s patents had an average historical publication lag of 27 months

before the AIPA. Thus, the 18-month disclosure rule forced Viacom’s patents to be disclosed 9

months sooner, on average, and hence disclose more information to competitors. We expect firms

with historically longer (shorter) publication lags to disclose more (less) as a result of the AIPA (a

disclosure spill-out).2 Conversely, the AIPA also accelerates patent disclosures of a firm’s industry

peers, which represent a spill-in to the focal firm. We proxy for the amount of disclosure spill-in

2 We acknowledge that historical publication lags could be endogenously determined, which would reduce our ability to identify a causal effect of the AIPA on corporate innovation. To address this issue, we i) explore the determinants of firms’ historical publication lags, ii) include firm fixed effects to account for unmodelled heterogeneity across firms, iii) include relevant control variables, and iv) conduct various cross-sectional and robustness tests. These implementations collectively corroborate that our documented link between the changes in innovation patterns after the AIPA is unlikely to be solely driven by endogeneity in the publication lag as we discuss more below.


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induced by the AIPA using historical publication lags of a firm’s industry peers. For example,

Viacom’s peers had an average publication lag of 39 months. Thus, the 18-month disclosure rule

forced Viacom’s peers’ patents to be disclosed 21 months sooner, on average, and hence provide

valuable peer firm information to Viacom. We expect firms with peers that have historically longer

(shorter) publication lags to generate more (less) disclosure spill-in as a result of the AIPA.

Applying this intuition, we implement two related approaches.

First, we compute the ratio of our proxy for spill-in (industry peers’ historical publication lag)

to our proxy for spill-out (the firm’s own historical publication lag) to measure the relative

intensity of the two spillover effects for a given firm. For example, Viacom’s ratio would be 1.44

(= 39 months/27 months = potential spill-in/potential spill-out), suggesting that the spill-in effect

likely dominates the spill-out effect as a result of the AIPA for Viacom. By comparison, a firm

with a ratio less than 1 receives relatively less disclosure spill-in from industry peers than it reveals

through disclosure spill-outs. Intuitively, higher (lower) values of this ratio imply that the spill-in

(spill-out) effects are likely to dominate for a firm as a result of the AIPA.

Second, to better highlight the opposing predictions of disclosure spill-ins and spill-outs on

innovation, we partition firms into three groups: (i) firms likely to benefit the most from spill-ins

(spill-in firms), (ii) firms likely to lose the most from spill-outs (spill-out firms), and (iii) firms

likely to neither benefit nor lose (benchmark firms). We then test whether innovation increases

(decreases) as a result of the spill-in (spill-out) effects induced by the AIPA.

We employ a generalized difference-in-differences design using a sample of 621,579 firm-

patents over the period 1996-2005. Our main tests compare a firm’s innovation before vs. after the

AIPA, conditional on the likelihood that the spill-in or spill-out effect dominates. We use patent

citations as our main proxy for innovation following prior research and corroborate our findings


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with other proxies of innovation in further tests described below.3 We include firm and year fixed

effects to control for time-invariant firm characteristics (e.g., some firms are intrinsically more

innovative) and secular trends (e.g., introduction of high-speed internet). Following Lerner and

Seru (2017), we also include patent-technology-class fixed effects in some specifications to

account for heterogeneity across patents’ technology classes (e.g., patents in certain sectors tend

to be more innovative).

We find evidence consistent with the AIPA inducing both spill-in benefits and spill-out costs

on a firm’s innovation. Specifically, the portfolio of patents filed over the five years after the AIPA

held by spill-in (spill-out) firms receive 68 additional (48 fewer) patent portfolio citations, relative

to our benchmark group. Economically, this translates into about a $17.51 million ($12.36 million)

– or 3.9% (2.7%) market capitalization – effect over the five-year post AIPA period. Thus, while

increased patent disclosure fosters innovation for firms who benefit more from rival disclosures,

the mandate harms innovation for firms who experience significant disclosure spill-outs.

The observed change in patent citations could be interpreted as either a change in a firm’s

innovative activities or a change in patenting choice. For example, the decrease in patent citations

for the spill-out firms may be a result of these firms not patenting their most valuable inventions

after the AIPA, as opposed to a result of reducing innovation. We find that neither our spill-in nor

spill-out firms significantly change the number of patents they file after the AIPA, alleviating

concerns related to patenting choice driving our main results.4

We corroborate and extend these results by also examining real decisions – R&D spending and

inventor-level productivity. We find that spill-in (spill-out) firms invest more (less) in R&D, retain

3 A patent that receives more citations over its lifetime is assumed to be more innovative (Lerner and Seru (2017)). 4 We interpret these results with caution, however, because our sample includes firms that rely heavily on patenting to protect their intellectual property. For instance, 83% of our sample patents are owned by firms who patented in all five of the pre-AIPA years. Hence, these results may not extend to other settings where firms rely less on patents to protect their intellectual property and instead rely on trade secrecy (Glaser (2018)).


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more (fewer) educated scientists, and incumbent scientists become more (less) productive after the

AIPA. In addition, we show that these effects begin with patents filed two years after the AIPA,

consistent with the slow-moving nature of innovation (Galasso and Schankerman (2015)). These

results collectively corroborate the inference that the documented change in patent citations is

driven by fundamental shifts in firms’ innovative activity.

We conduct two additional tests to explore the mechanism underlying our findings. Our first

test builds on the idea that strong U.S. patent laws prevent scientific knowledge from being

expropriated and ensure the original inventor receives fair payouts (Galasso and Schankerman

(2015)). For example, the patent system will ensure a firm receives licensing fees for its patented

technique on building efficient car engines. Thus, when rivals use patent disclosures to glean

scientific knowledge, the disclosure will likely induce lower levels of proprietary costs as the

disclosing firm can be compensated through licensing its discoveries to technologically related

peers (Hegde and Luo (2018)). Conversely, when competitors use patent disclosures to obtain

information on exploitable business strategies, such as signals on the investment opportunities for

car engines, the proprietary costs to the disclosing firm are likely higher since the law does not

protect against rivals exploiting such disclosures. Consistent with these arguments, we find the

spill-in benefits (spill-out costs) manifest among firms that have peers closer in technology

(product market) spaces where more scientific (business-related) disclosures are likely to be

exchanged between firms.5 This result is consistent with the AIPA increasing innovation by

facilitating access to the scientific information contained in other firms’ patent disclosures, while

5 We use Bloom, Schankerman, and Van Reenen (2013)’s classification that distinguishes a firm’s position in the technology and product market spaces. This approach uses information on the distribution of the firm’s patenting across technology fields and its sales across different industries. These measures capture the likelihood that a firm exchanges useful scientific information or business-related information with other firms, conditional on a disclosure shock, such as the AIPA.


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reducing innovation by increasing the risk of leaking business-related strategies through its own

patent disclosures.

In our second additional test, we find that our main results are concentrated in sectors where

patent disclosures play an important role as a source of information. Specifically, we use survey

evidence in Ouellette (2017) that researchers in biotechnology, chemistry, and electronics pay

closer attention to patent disclosures – and find that both the spill-in benefits and spill-out costs

are concentrated among firms in industries where researchers read patents more frequently. This

result is consistent with our findings being driven by the increased disclosure of the AIPA and is

inconsistent with alternative explanations unrelated to disclosure.

The results discussed so far provide evidence that the effects of early patent disclosures impose

costs on spill-out firms. If so, we expect these firms to respond strategically within the flexibility

that patent law affords. Specifically, we explore two strategic patent disclosure choices –

obfuscating patents’ linguistic content and exercising an option to delay patent disclosures – and

find evidence consistent with spill-out firms choosing disclosure strategies that reduce the

proprietary costs associated with the AIPA. Specifically, after the AIPA, spill-out firms include

more vague expressions while reducing the use of figures and diagrams when drafting their patents

compared to our benchmark group. This is consistent with research in accounting that suggests a

firm can strategically obfuscate the linguistic content of its disclosures to conceal information from

rivals (Li (2010); Loughran and McDonald (2016); Bushee, Gow, and Taylor (2018)). Moreover,

we find that spill-out firms are more likely to exercise the option to delay disclosures relative to

our benchmark group when given the option to do so.6 By contrast, we find that spill-in firms do

6 While disclosure strategies likely mitigate the proprietary costs of the AIPA, there are also significant costs associated with strategic disclosure. Specifically, patent examiners can reject a patent application based on insufficient disclosure and firms can only choose to delay patent disclosure if they forgo foreign patent protection. Due to these reasons, we expect to see – and our cumulative findings suggest – spill-out firms experience proprietary costs despite efforts to mitigate them.


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not engage in these strategic disclosure behaviors after the AIPA. This validates there are costs

associated with engaging in strategic disclosure and that only spill-out firms with high proprietary

costs are incentivized to use strategic disclosure to mitigate the effects of the AIPA.

This paper contributes to the literature in several ways. First, it extends the broad and growing

literature on the real effects of disclosure that is central to accounting research. In particular, our

study’s focus on corporate innovation (a real outcome), corporate patent disclosures (a form of

disclosure)7, and the spill-in benefits and proprietary costs associated with mandatory disclosure

arrangements (a channel) are three important features of this study that expand our understanding

of the real effects of disclosure. By doing so we answer the call for research by Leuz and Wysocki

(2016) to examine the impact of disclosure on innovation and to explore nontraditional forms of

disclosure (e.g., Sutherland (2018)). We also contribute to the sparse literature on whether or how

spillovers and proprietary costs of disclosure have real effects (Roychowdhury, Shroff, and Verdi

(2019)). Furthermore, in contrast to prior studies that typically investigate the spill-in benefits and

the proprietary costs of disclosure in isolation, our research shows that the two effects are two

sides of the same coin: forcing firms to share proprietary patent information can be privately costly

but socially beneficial.

Second, our paper also relates to recent studies on disclosure and innovation that find

innovative benefits of transparency in a financial reporting setting (Zhong (2018), Breuer, Leuz,

and Vanhaverbeke (2019)). Our study is distinct from these studies by focusing on a relatively

underexplored form of disclosure – patent disclosures – as opposed to financial reporting. As

patent disclosures include detailed technical information on emerging inventions, patents provide

a more direct and precise setting in which to examine knowledge spillovers relative to aggregated,

7 Consistent with Glaeser, Michels and Verrecchia (2019), we consider patents as a form of disclosure.


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historical financial information, especially in the context of innovation. Furthermore, our focus on

spillover benefits and proprietary costs of disclosure contrasts with concurrent work using the

AIPA as a setting that examines mechanisms such as insider trading and reduction in duplicate

R&D efforts (Hussinger, Keusch, and Moers (2018), and Hegde, Herkenhoff, and Zhu (2019)).

Moreover, we validate our mechanism through analyzing the textual content of our sample patents

and find evidence of firms strategically responding through obfuscation, which to our knowledge

is also new to the literature.

Third, this paper contributes to the literature studying the patent system (e.g., Williams (2013);

Galasso and Schankerman (2015); Cockburn, Lanjouw, and Schankerman (2016)). In her recent

survey, Williams (2017) argues that estimating the economic impact of patent disclosures is central

to specifying an optimal design for the patent system. Despite the importance of patent disclosures,

very little empirical evidence is available on whether or how patent disclosures affect innovation.

This paper fills this void by contributing to the limited empirical evidence on how and to what

extent disclosures required by the patent system affect corporate innovation.


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2. Background and Hypotheses Development

2.1 Patents and its Disclosure Function

In the theoretical model of Nordhaus (1969), patents create incentives for innovation, ex-ante,

by promising inventors the right to extract monopoly profits from the invention ex-post (i.e., by

granting patent protection). In this sense, the patent system poses a tradeoff between the social

gains from increased incentives to innovate and the social deadweight losses from monopolistic

patent protection.8

To combat the social losses, the USPTO makes public applicants’ disclosure of inventions as

a condition of receiving a U.S. patent. This arrangement promotes the dissemination of knowledge

through patent disclosures and stimulates the creation of new innovations by other inventors

(Ouellette (2012), Fromer (2016)). Consistent with the motive to efficiently disseminate

knowledge, patentees in the U.S. must satisfy the following disclosure requirements: 1) written

description, 2) enablement, and 3) best mode. These collectively promote the efficient use of patent

information by other inventors. Specifically, the first paragraph of 35 U.S.C. section 112 says:

The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention. [Emphasis added] Patentees must communicate their discoveries not only in a clear fashion (written description)

but also in a way that someone “skilled in the art” can replicate and use the knowledge to innovate

further (enablement). Moreover, patentees must provide the details, if possible, on the most

8 The Nordhaus (1969) model is part of an extensive literature that examines patent protection and innovation (Hall and Harhoff (2012), Moser (2013), Williams (2017) provide excellent reviews). The theoretical relation, however, between patent protection and innovation is more nuanced. For example, Edmund (1977) argues that monopoly rights are needed to encourage innovation after the patent is granted. Others have theorized that patents may hinder innovation (Heller and Eisenberg (1998)). However, given our paper’s focus on patent disclosures, we abstract away from these nuanced relations and assume that patents are ex post costly, which is consistent with recent empirical evidence from Galasso and Schankerman (2015).


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efficient way to carry out the invention (best mode). Appendix C discusses a patent on coffee

delivering drones filed by IBM as an example. The institutional motive of patent disclosures is

consistent with prior research that views knowledge as a vital input for technological progress

(Scotchmer (1991); Furman and Stern (2011)).

2.2 American Inventor’s Protection Act (AIPA)

The AIPA required all patent applications filed on or after November 29, 2000, to be disclosed

18 months after the application date.9 Prior to the law’s enactment, patent applications were

disclosed only upon grant, which happened on average 36 months after the application date in our

sample. Thus, the AIPA not only significantly accelerated public access to patent information, it

also increased the scope of publicly available patent information, as it gave access to pending

applications. The policy change is summarized in Figure 1. Policymakers contend that the AIPA

is one of the most significant changes in patent law history, especially in the context of the

disclosure function of patents (Ergenzinger (2007)). Hence the AIPA’s effect on corporate

innovation is likely to be significant.

In addition to its economic significance, the AIPA’s passage was difficult for firms to

anticipate, due to strong disagreement among policymakers leading up to the final signing of the

bill in November 1999. For example, 26 Nobel laureates, including economist Franco Modigliani,

believed that the disclosure rule would impose significant proprietary costs and called for the law

not to be enacted. This opposition led to many rounds of debate and amendments, which caused

considerable uncertainty as to whether the mandate would eventually pass. Hence firms were

unlikely to take significant actions to adjust their innovation decisions, which often requires high

9 A key motivation for the enactment of the AIPA was to harmonize U.S. patent law with the patent laws of other countries, many of which had already required patents to be disclosed after 18 months of filing in their respective native languages. The effect of the AIPA will therefore be mitigated to the extent that similar foreign patents subsume the information in U.S. patents. We revisit this issue in section 3 when discussing our approach to defining firms that are likely most affected by the AIPA.


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adjustment costs (e.g., hiring a new team of scientists), prior to the confirmed enactment. Instead,

we expect firms to alter their innovation decisions after the enactment of the AIPA.

2.3 Hypothesis Development

2.3.1 Real Effects Hypothesis: Mandatory Patent Disclosures and Innovation

Mandatory disclosure regulation, such as the AIPA, can be characterized as inducing a forced

exchange of information among regulated firms, beyond what these firms would be willing to

disclose voluntarily. A firm receives more information from the disclosure of others while

simultaneously revealing more about itself. This framework is at the core of the theoretical work

on the relation between innovation and disclosures, such as patent applications.

Specifically, mandating patent disclosures can stimulate a firm to innovate by facilitating

knowledge spillovers from other firms’ disclosures, which we refer to as the knowledge spill-in

effect (e.g., Scotchmer (1991); Aghion, Dewatripont, and Stein (2008)). Or it can discourage the

firm from innovating due to the proprietary costs of having to disclose its patented innovations to

other firms, which we refer to as knowledge spill-outs (e.g., Arrow (1962); Bhattacharya and Ritter

(1983); Anton and Yao (1994); Bessen (2005); Aoki and Spiegel (2009)). This tension is perhaps

best described by Scotchmer (1991): “patent law requires disclosure for the same reason that

innovators dislike it: it is the vehicle by which technical knowledge is passed from the patenting

firm to its competitors.”

Knowledge spill-ins from mandated patent disclosure potentially influence corporate

innovation by 1) helping firms develop new inventions and 2) improving firms’ project selection

and continuation decisions by facilitating access to useful information. First, Ouellette (2012) finds

in her survey of scientific researchers that 70% of respondents who read patents do so to look for

technical information, including how to solve a technical problem, or to browse information on


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cutting-edge technologies. This suggests scientists engaged in innovation at the firm can use

competitor patent filings to improve their own projects. Second, firms could make better project

choices by reducing wasteful, duplicative innovative efforts. Concurrent research has found

evidence that the AIPA reduced duplicative research (Hegde, Herkenhoff, and Zhu (2019); Luck,

Balsmeier, Seliger and Fleming (2019)).10

For the same reason spill-ins are useful, knowledge spill-outs generate proprietary costs by

enabling rivals to make more competitive decisions. Levin, Klevorick, Nelson, and Winter (1987)

survey public firms with R&D activity and find lead time and moving quickly down the learning

curve are two of the top three most effective methods of protecting a firm’s competitive advantage.

Before the AIPA, a firm had from the time of a breakthrough to the date a patent was granted

(approximately three years on average) to develop inventions in secrecy. The AIPA reduces a

firm’s competitive advantage by giving rivals the opportunity to appropriate benefits a disclosing

firm would have had in the absence of the disclosure requirement.

In sum, the net effect of the AIPA on a firm’s innovation depends on the relative impact of 1)

the disclosure of other firms (the spill-in effect) and 2) a firm’s own disclosure (the spill-out effect),

as stated by our first hypothesis:

H1: After the AIPA, firms more affected by knowledge spill-ins (spill-outs) increase (decrease) innovation.

The above discussion notwithstanding, we may not observe our predicted outcome for at least

two reasons. First, while recent evidence suggests that patents contain useful information for

innovation (Ouellette (2017)), there is considerable debate about how much useful technical

information patents contain (Williams (2017)). For example, academic journals may subsume

10 Rivals’ patent disclosures can also be useful because they open the opportunity to engage in “patent invalidity searches,” where an attorney performs a search to identify prior inventions with the aim of invalidating a competitor’s patent. Relatedly, firms often engage in practices known as “patent landscaping.” Patent landscaping allows firms to examine the overall landscape of competitors’ patents to identify potential investment opportunities.


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patents’ usefulness as a source of technical information. Second, disclosing firms could

strategically disclose as little as possible in response to the AIPA. For example, firms can

deliberately obfuscate patents’ textual content to minimize disclosures to potential competitors

(Roin (2005); Devlin (2010)).11

2.3.2 Strategic Disclosure Hypothesis

If the AIPA imposes costs to spill-out firms’ innovation, we expect firms to respond

strategically within the flexibility that patent law affords. Specifically, we explore two strategic

disclosure choices that could help firms potentially reduce the impact of the AIPA.

First, we conjecture that spill-out firms, relative to spill-in firms, will use more obfuscating

language when drafting their patents to mitigate the costs of disclosing proprietary information to

competitors after the AIPA. Prior research in accounting suggests that a firm can strategically

obfuscate the linguistic content of its disclosures to conceal information from rivals (Li (2010);

Loughran and McDonald (2016); Bushee, Gow, and Taylor (2018)). Relatedly, research in law

and linguistics argues that using “vague expressions” in legal documents like patents can be an

effective way to achieve strategic obfuscation (Myers (1996); Choi and Triantis (2010); Hall and

Harhoff (2012)). Similarly, Bird and Karolyi (2016) and Abramova, Core, and Sutherland (2018)

show that the inclusion of figures and diagrams can be an effective way to communicate

information. To the extent that this is also true in patents, we expect spill-out firms, relative to

spill-in firms, to strategically exclude figures and diagrams from their patents after the AIPA.

Second, we expect that spill-out firms will be more likely to delay their disclosure when given

the option to do so relative to spill-in firms. For patents not filed in foreign jurisdictions, the AIPA

11 Another way in which firms can mitigate the costs of disclosure is to stop patenting and instead pursue trade secrecy to protect their intellectual property (Glaeser (2018)). We examine this possibility, but do not find this to be the case in our setting (Table 4), likely because our sample is composed of firms that rely heavily on patenting to protect their intellectual property. This suggests that even spill-out firms in our sample expect the benefits of patenting to be sufficiently high even after the AIPA.


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allows firms to opt out of the pre-grant disclosure requirement and delay the disclosure until a

patent is granted. This provides an opportunity for firms to mitigate the costs of the AIPA for a

subset of spill-out firms’ patent portfolios (i.e., patents that do not seek foreign protection).

In sum, we expect that firms with higher levels of proprietary costs (i.e., spill-out firms) likely

have stronger incentives to engage in strategic disclosure choices relative to firms with lower levels

of proprietary costs (i.e., spill-in firms) induced by the AIPA.12 Accordingly, our second hypothesis

is as follows:

H2: After the AIPA, spill-out firms are more likely to engage in strategic disclosure behavior – through obfuscation or delay – relative to spill-in firms.

3 Sample and Research Design

3.1 Sample Selection and Data

Our sample includes all patents filed by publicly traded companies with the U.S. Patent and

Trademark Office from 1996 to 2005. We use financial statement data from Compustat and market

data from CRSP. We use patent data obtained from Kogan, Papanikolaou, Seru, and Stoffman

(2017) and supplement with patent family filing dates available via Google Patents.

We retain only patents filed by firms that exist in both the pre-period (1996-2000) and post-

period (2001-2005). From this sample, we create a firm-patent filing date (or “firm-patent”) dataset

and a firm-fiscal year (or “firm-year”) dataset that we employ based on the dependent variable

used in our regressions. We use the firm-patent dataset when dependent variables can be measured

at the patent level (e.g., number of citations) and employ the firm-year dataset when the dependent

variable is measured at the firm level (e.g., R&D intensity). We also require firms to have relevant

CRSP/Compustat variables, such as market capitalization, book assets, and returns. This yields a

12 However, we do not expect these strategies to fully mitigate the proprietary costs of patent disclosures because of the limits on strategic disclosure. Firms need to find a balance between using vague language and having their patents rejected as a result of using too much vague language (Ouellette (2012)). Similarly, firms may not find it optimal to forgo foreign patent protection to be able to delay their patent disclosures.


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final sample of 621,579 firm-patent observations. Our sample represents a significant portion –

87% (621,579/710,712) – of all patented innovations by publicly traded firms over our sample

period. Our sample selection procedures are outlined in Appendix A.

3.2 Variable Measurement

3.2.1 Key Dependent Variable: Innovative Activity

Characteristics of patents can proxy for multiple elements of a firm’s innovative activities

(Hall, Jaffe, and Trajtenberg (2001); Lerner and Seru (2017)). The literature has frequently used

the number of citations a firm’s patents receive over the patents’ lifetime, referred to as forward

citations, as a proxy for a firm’s innovation (e.g., Aghion, Van Reenen, and Zingales (2013); Fang,

Tian, and Tice (2014); Bernstein (2015); Zhong (2018)). Following this literature, we use forward

citations as our main measure of innovation.13

Forward citations can vary over time and across technologies. Variation may stem from the

complexity of technologies or from changes in patent law (Lerner and Seru (2017)). Therefore, a

simple comparison of raw forward citations is only partially informative. Accordingly, we follow

Bernstein (2015) and construct Forward Citations by scaling a patent’s forward citation count by

the average number of citations among patents in the same technology class and filing year. We

then take the logarithm of weighted patent citations to correct for skewness.

3.2.2 Key Independent Variables: Disclosure Spillovers

Measurement

Our analysis requires a measure of the extent to which the spill-in effect dominates (or is

dominated by) the spill-out effect for each sample firm. To this end, we exploit the fact that the

impact of the 18-month disclosure requirement of the AIPA will depend on a firm’s historical

13 We also explore other proxies for innovation that exploits certain distributional properties of forward citations such as Originality, Generality, and KPSS Value (see, for example, Kogan et al. (2017)) as well as input-based proxies of innovation such as R&D.


16

filing-to-publication lag, defined as the months between the filing date and publication date prior

to the enactment of the AIPA. That is, we expect the 18-month disclosure requirement to have a

stronger (weaker) effect on a firm with longer (shorter) historical publication lags because the

requirement will imply more (less) disclosure.

Applying this intuition in the context of spill-ins and spill-outs, we compute for each firm,

Relative Spillover, defined as the log of the ratio between the industry average publication lag and

the firm’s own average publication lag measured over the 20 years prior to the enactment of the

AIPA.14 Mathematically, Relative Spillover is defined as follows for any sample firm i:

𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 𝑆𝑆𝑆𝑆𝑅𝑅𝑅𝑅𝑅𝑅𝑆𝑆𝑅𝑅𝑅𝑅𝑟𝑟𝑖𝑖 = log 1

𝑛𝑛−𝑖𝑖∑ 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑔𝑔𝑝𝑝−𝑖𝑖𝑛𝑛−𝑖𝑖𝑝𝑝−𝑖𝑖=1

1𝑛𝑛𝑖𝑖∑ 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑔𝑔𝑝𝑝𝑖𝑖𝑛𝑛𝑖𝑖𝑝𝑝𝑖𝑖=1

(1)

where -i is the set of i’s peer firms in the same NAICS 4-digit industry, 𝑃𝑃𝑃𝑃𝑃𝑃𝑅𝑅𝑅𝑅𝑔𝑔𝑝𝑝 is the filing to

publication lag for patents 𝑆𝑆𝑖𝑖 and 𝑆𝑆−𝑖𝑖 owned by firm i and peer firms -i, respectively, and 𝑛𝑛𝑖𝑖 and

𝑛𝑛−𝑖𝑖 are the total number of patents owned by firm i and peer firms -i, respectively over the 20-year

period leading up to the enactment of the AIPA.

To separately estimate the effects of spill-in and spill-out, we then construct two related

variables, Spill-in and Spill-out, which take on the value of one if a firm is in the top three or

bottom three deciles of Relative Spillover, respectively. We use the firms in the middle four deciles

as a benchmark group.

To aid in the economic interpretation of our independent variables, in Table 1 Panel A we

tabulate the untransformed (i.e., not logged) versions of both the numerator and denominator of

Relative Spillover for firms in the spill-in, spill-out, and benchmark groups. Spill-in firms are in

14 We use 4-digit NAICS codes to define a firm’s industry. In robustness tests further described below, we find our results hold when using 4-digit SIC codes as an alternative industry classification. Our results are also robust to excluding the 2 and 5 years prior to the AIPA over the 20-year measurement period and using a 10-year measurement period instead (see Table 9 Panel B).


17

industries with an average historical worldwide (U.S.) filing-to-publication lag of approximately

23 (30) months while their own publication lag is only 16 (23) months.15 Thus, the ratio of the

industry publication lag to a firm’s own publication lag (Relative Spillovers) is 1.40 for firms in

the spill-in group, indicating that spill-in firms benefit more from rivals’ timely patent disclosure

than they give up when their own patents are disclosed. In contrast, spill-out firms have Relative

Spillovers of 0.59 while Benchmark firms have a ratio close to 1. Figure 2 presents the distribution

of the untransformed Relative Spillover by USPTO’s technology section (Panel A) and NAICS 2-

digit industries (Panel B). The figures show that patents filed in mechanical engineering have the

highest median Relative Spillover, as well as patents filed by firms in the utilities industry. There

is significant variation both within technology sections and industries, which our technology class

and industry fixed effects exploit for identification, respectively.16

Discussion

Before we proceed, we discuss two important points related to our spillover measures. First,

for a single invention, a firm may file multiple patent documents in different jurisdictions in order

to obtain intellectual property protection in each of those countries. Because most foreign

jurisdictions implemented an 18-month patent disclosure rule long before the AIPA in the United

States, we use the “worldwide” filing date to measure filing-to-publication lags. That is, if a firm

seeks both U.S. and foreign patent protection for an invention, we use 1) the earliest filing date

and 2) the earliest publication date anywhere in the world. This approach assumes that competitors

could access a firm’s patent filings regardless of the jurisdiction in which a patent was filed,

without frictions such as language barriers and search costs. Alternatively, one could assume that

15 While we use the Relative Spillover variable based on worldwide filing dates in our main tests, we find our results are robust to employing U.S. dates instead (Table 9 Panel B). 16 For descriptive purposes, we use a patent’s technology section (defined as the first character of the patent’s International Patent Classification (IPC)). For fixed effects, we use a patent’s technology class, which is the first three digits of a patent’s IPC.


18

public firms listed in the U.S. only access patent filings in the U.S. The true flow of patent

information likely lies between these two extremes of no geographic frictions and complete

geographic frictions. While we use worldwide publication lags in our main tests, we note that using

U.S. filing dates to compute publication lags instead does not affect the main inferences of our

paper (see Table 9 Panel B).

Second, the filing-to-publication lag depends both on processing time at the patenting office

as well as a firm’s technology area. We explore the association between a firm’s average

publication lag and observable firm characteristics in Table 1 Panel B. If delays at the patent office

and patent technology area are the primary drivers of the publication lag, we would not expect to

see significant associations with firm characteristics. Indeed, Panel B of Table 1 shows that the

average, annual publication lag is not significantly associated with many of the typical firm-level

characteristics we use as control variables during our sample period (Column 1). However, since

our Relative Spillover variable is a function of both a firm’s own publication lag as well as the

industry average publication lag, we also use the annual ratio of the industry publication lag to a

firm’s own publication lag as an alternative dependent variable. We find the relative publication

lag is only significantly associated with Book-to-Market over our sample period (Column 2).

Finally, we also consider the association between the publication lag and firm characteristics over

the 20 years leading up to the AIPA, which coincides with our measurement window of Relative

Spillover.17 Columns 3 and 4 of Table 1 Panel B show no significant association between

publication lags and firm characteristics. These findings corroborate the notion that firm

characteristics are unlikely to be the primary driver of our Relative Spillover sorting variable.

17 We do not include institutional ownership as an explanatory variable when using our 20-year measurement window because institutional ownership is not widely available until 1989. Including institutional ownership, however, doesn’t affect our inferences.


19

Nonetheless, we recognize that publication lags are not randomly assigned and as such mitigate

this endogeneity issue by experimenting with various fixed effect structures, employing a long

historical measurement window similar in spirit to Bloom et al. (2013), calculating Relative

Spillover excluding the 2 or 5 years prior to the AIPA from our measurement window when an

endogeneity issue is likely most prevalent, testing whether the parallel trends assumption holds,

matching, adding controls, and conducting cross-sectional tests that support the conclusion that

our results are driven by patent disclosures.

3.2.3 Control Variables

Knowledge spill-ins and proprietary costs may not be the only channels through which patent

disclosures affect corporate innovation. Patent disclosures also can reduce information asymmetry

between firms and capital providers. For example, Saidi and Zaldokas (2019) show that patent

disclosures can help firms switch lenders, thereby reducing the cost of debt. Moreover, a recent

study by Hussinger, Keusch and Moers (2018) shows that patent disclosures can curb managers’

insider trading, reducing their incentive to take on risky, innovative projects. Accordingly, we

include in our models a set of control variables that represent a firm’s level of financing frictions,

agency conflict, and investment opportunity: Age, Size, Cash Holdings, ROA, Book-to-Market,

Leverage, Industry Concentration, and Institutional Ownership.

Concurrent research using the AIPA setting has also found the law induced changes in firms’

cost of debt and institutional ownership (Blanco, Garcia, Wehrheim (2018); Saidi and Zaldokas

(2019)). To remove the effect that changes in these variables could have on innovation outside of

the information channel we propose, we include Leverage*Post and Institutional Ownership*Post

as controls. Finally, given that the AIPA was implemented around the time the internet bubble

burst, it is feasible that firms’ investment opportunities or industry competition were also changing


20

during this time period. Consequently, we include Book-to-Market*Post and Industry

Concentration*Post to address this possibility.18

3.3 Research Design

Our primary empirical analyses employ a generalized difference-in-differences design around

implementation of the AIPA. The general model we use is as follows:

Innovation Proxyi,p,t = β1 Relative Spilloveri x Posti,p,t + 𝛾𝛾1𝑋𝑋𝑅𝑅,𝑅𝑅 + 𝛿𝛿𝑅𝑅 + 𝜋𝜋𝑅𝑅 + 𝜙𝜙𝑆𝑆 + 𝑃𝑃𝑅𝑅,𝑅𝑅 (2)

The unit of observation is at the firm 𝑅𝑅, patent 𝑆𝑆, and patent filing date t level. Innovation Proxy

is one of our proxies for innovation as discussed in section 3.2.1. The coefficient on the interaction

term Relative Spilloveri x Posti,p,t (i.e., β1) captures the impact of the AIPA conditional on the

extent to which the spill-in effect from peer firm disclosures dominates the spill-out effect from

own firm disclosures. That is, higher values of Relative Spillover imply that the spill-in effect from

peer firm disclosures dominate as discussed in section 3.2.2. Thus, we predict β1 to be positive

since we expect spill-ins from greater peer firm disclosures to have a positive impact on a firm’s

innovative activity. 𝑋𝑋𝑅𝑅 represents a vector of time-varying firm-level controls and 𝛿𝛿𝑅𝑅, 𝜋𝜋𝑅𝑅, 𝜙𝜙𝑆𝑆 are

firm, year, and patents’ technology-class fixed effects, respectively. We cluster standard errors at

the firm and year-month levels.

To further highlight the opposing predictions of spill-ins and spill-outs, we modify Equation

(2) to separately display the effects for the respective groups. First, we partition our sample into

three groups: (i) firms in the top three deciles of Relative Spillover (i.e., Spill-in firms), (ii) firms

in the bottom three deciles of Relative Spillover (i.e., Spill-out firms), and (iii) firms in the middle

four deciles (benchmark). We then estimate the following difference-in-differences regression:

Innovation Proxyi,p,t = β2 Spill-ini x Posti,p,t + β3 Spill-outi x Posti,p,t +𝛾𝛾1𝑋𝑋𝑅𝑅,𝑅𝑅 + 𝛿𝛿𝑅𝑅 + 𝜋𝜋𝑅𝑅 + 𝜙𝜙𝑆𝑆 + 𝑃𝑃𝑅𝑅,𝑅𝑅 (3)

18 Including control variables that are themselves changing as a result of the treatment can lead to a type of selection bias known as the “bad control” problem. To address this concern, we show in Table 3 Column 1 that our results hold without any controls.


21

The inclusion of both Spill-in and Spill-out firms is motivated by our prediction that the effect

of mandatory patent disclosure should have a positive spill-in effect from peer firm disclosures

and a negative spill-out effect from own firm disclosures. Note that this empirical design compares

the innovation behavior of firms in the “extreme” Spillover Intensity groups against firms that are

neither benefiting from peer firm disclosures nor losing from own firm disclosures, which we refer

to as benchmark firms. The coefficient on the interaction term Spill-ini x Posti,p,t (i.e., β2) captures

the effect of the AIPA for spill-in firms relative to the benchmark firms. Similarly, the coefficient

on Spill-outi x Posti,p,t (i.e., β3) represents the effect of the AIPA for spill-out firms relative to the

benchmark firms.

Overall, the inclusion of firm, year, and technology class fixed effects in Equations (2) and (3)

alleviates much of the concern that omitted firm, year, and technology characteristics are driving

a firm’s innovation, and isolates the effect of spillovers as a result of the AIPA. Another advantage

of this design in terms of identification is that we make completely opposite predictions for the

two spillover groups (i.e., β2 > 0 and β3 < 0). In other words, for potential endogenous factors to

pose identification issues (e.g., economy-wide shocks), they would have to affect the two spillover

groups in opposite directions. This nuanced prediction helps to reduce certain endogeneity

concerns. Nonetheless, to further corroborate the inferences of our main findings, we perform a

series of additional tests that help rule out other plausible interpretations of our results. We discuss

these results in more detail in section 4.

4 Empirical Results

4.1 Descriptive Statistics

Table 1 Panel C presents descriptive statistics for the main dependent variables of interest and

control variables. We find on average that patents in our sample receive 0.70 technology class-


22

weighted forward citations (e(0.53) – 1), which is about 8.36 citations on average on an unweighted

basis. Descriptive statistics indicate the average firm invests about 9% of assets on R&D

expenditures and files about 23 patents a year. Trade secrecy is mentioned in about half of firms’

10-K filings. Taken together, the statistics confirm that our sample firms engage in significant

activities related to intellectual property. Firms in our sample are a little over 17 years old (208

months) and have a market cap of $454 million.

4.2 Evidence of the AIPA’s effect on Innovative Activity

Our first hypothesis predicts that firms affected by knowledge spill-ins from peer firm

disclosures (spill-outs from own firm disclosures) to have a positive (negative) effect on innovation

(H1). Table 2 provides descriptive evidence consistent with our predictions regarding innovation.

Table 2 displays the average value of six different innovation proxies by each spillover group

during the pre- and post-AIPA periods. The definitions for the innovation proxies are in Appendix

B. The diff-in-diff estimates in Column ‘(1)-(2)’ show that the spill-in group innovates more

relative to the benchmark group after the AIPA, whereas estimates in Column ‘(3)-(2)’

demonstrate that the spill-out group reduces innovation relative to the benchmark group after the

AIPA. The effects for R&D intensity and number of patents, however, are statistically weaker.

Overall, the univariate diff-in-diff estimates in innovative activity are consistent with spill-in firms

benefitting from peer firm patent disclosures, while spill-out firms experience costs.19

In Table 3, we extend the descriptive findings of Table 2 by regressing Forward Citations on

Relative Spillover*Post. The significantly positive coefficients on Relative Spillover*Post across

Columns 1 through 3 indicate that the AIPA has a positive effect on firm innovation as the spill-

19 While Table 2 reveals pre-period differences in observables between groups, we note that the identifying assumption in a difference-in-differences design requires parallel trends. We document evidence consistent with the parallel trend assumption being valid in Figure 3. A potential issue with treatment and comparison firms having different pre-period dependent variable values is that difference-in-differences estimates are potentially sensitive to the functional form of the dependent variable (Roberts and Whited (2013)). In Table 9 Panel A we show that our results are robust to various alternative specifications.


23

in effect from peer firm disclosures dominate the spill-out effect from own firm disclosures.

Column 1 contains results without control variables but with firm and year fixed effects. Column

2 adds a host of control variables discussed in section 3.2.3 to the model. In Column 3, we further

include patent class fixed effects, to mitigate the potential that systematic differences in forward

citations across technology areas are influencing our result.

In Column 4, we include indicators for Spill-in and Spill-out in our model to investigate the

opposing predictions of disclosure spill-ins and spill-outs. Consistent with our first hypothesis, we

find that the AIPA has a positive innovation effect on spill-in firms, as shown by the positive

coefficient on Spill-in*Post (coef.=0.029; t-stat=3.41) while a negative impact on spill-out firms,

as demonstrated by the negative coefficient on Spill-out*Post (coef.=-0.020; t-stat=-2.09). This

implies that after the AIPA spill-in (spill-out) firms file patents that receive on average 6.9% more

(4.7% less) Forward Citations relative to the pre-AIPA average value of 0.42. In terms of raw

citations, this translates into spill-in (spill-out) firms’ portfolio of patents receiving 68 more (48

less) raw citations over the five years after AIPA.20 Taken together, the results from Tables 2 and

3 support H1.21 Kogan et al. (2017) estimate that one additional patent citation around the median

number of citations is approximately worth $15,000 to $500,000. Assuming a patent citation is

worth $257,500, a five-year 68 (48) citation effect is about $17.51 million ($12.36 million) – or

about 3.9% (2.7%) of the average sample market capitalization, of $454 million over the five-year

post AIPA period, suggesting the effect is material.

A key identifying assumption for our main tests is parallel trends. Though there may be

differences between firms of interest, the parallel trend assumption requires that those differences

20 In our sample, the pre-AIPA average number of patents filed by a firm is 20.48, each of which received 9.71 citations on average. Hence, the pre-AIPA average portfolio citations over a 5-year period is 994.31 = 20.48*9.71*5. Thus, a 6.9% (4.7%) estimated effect translates into a 68 = 994.31*6.9% (48 = 994.31*3.8%) raw portfolio citation effect over the 5-year post-period. 21 For the remainder of the paper we include the same set of control variables for our analyses unless otherwise stated, but do not tabulate them to conserve space.


24

are constant in the pre-event period and would have continued to be constant absent the treatment.

To test this assumption, Figure 3 plots the coefficient estimates of spill-in and spill-out for the

years leading up to the AIPA, leaving out the two years prior to AIPA (i.e., years -1 and -2).22 The

figure shows the Forward Citation effects of both spill-in and spill-out are absent in the pre-AIPA

years, whereas significant effects begin to appear in the years after the law.

Figure 3 also illustrates that the Forward Citation effects are gradual and begin only two years

after the AIPA. This is consistent with firms altering their research investments and the

corresponding innovative output changing slowly. The pattern, however, is inconsistent with

endogenous patenting, in which the AIPA simply affects firms’ decisions on which innovations to

patent, which would likely yield a more immediate effect (Galasso and Schankerman (2015)).

In Figure 4, we plot the distribution of the estimated coefficients and t-statistics of Forward

Citations on Spill-in*Post and Spill-out*Post, based on 500 placebo regressions using randomized

placebo dates during the period from 1996 to 2005. The distributions for the coefficients and t-

statistics, based on the placebo regressions, are centered around zero, which contrasts starkly with

the estimated effects of Table 3, shown by the red lines in Figure 4. We conclude that the

documented effects of the AIPA on corporate innovation are unlikely to be spurious.

4.3 Corroborating the Interpretation of the Innovation Results

Although patent citation-based measures are commonly used in the literature as proxies for

innovation, they have their shortcomings. A central issue with Forward Citations is that they are

affected by a firm’s patenting choices. For example, if a firm chooses to be more selective on

which inventions to patent, we may observe increases in Forward Citations even though the firm

did not change its innovative inputs (Bernstein (2015), Lerner and Seru (2017)). To investigate

22 The figure’s estimates and confidence intervals are taken from a regression that runs Forward Citations on a series of year dummy variables based on a patent’s filing year, relative to the AIPA’s passage on November 29, 2000.


25

this possibility, we examine the effect of the AIPA on firms’ patenting choice and trade secrecy

usage to see whether a firm’s propensity to patent significantly changed after the AIPA. We also

examine R&D expenditures as an input-based proxy for innovation that is less sensitive to

patenting choice.

In Table 4, we use a firm-year unit of observation to analyze the effect of the AIPA on R&D

Intensity, Number of Patents, and Trade Secrecy, as defined in Appendix B. In Column 1 of Table

4, we find a significant positive link between R&D Intensity and Relative Spillover*Post (coef. =

0.027; t-stat=3.63). In Column 2, we continue to find a positive (negative) effect when we

separately examine spill-in and spill-out firms, though the positive spill-in effect is statistically

weaker (coef.=0.004; t-stat=1.26 and coef.=-0.013; t-stat=-3.26 for Spill-in*Post and Spill-

out*Post, respectively).23 Columns 3 through 6 of Table 4 show that neither the number of patent

filings nor trade secrecy usage significantly vary with Relative Spillover, Spill-in, and Spill-out

after the AIPA.24 Taken together, it seems unlikely that the change in patent citations is

mechanically driven by the changes in firms’ patenting decisions.25

Hall and Lerner (2010) state that 50% or more of firms’ R&D expenditures relate to salary and

wages paid to inventors. To further corroborate that the documented innovation changes are linked

to shifts in real decisions, we examine inventor movements and productivity effects using inventor-

level data. Specifically, we examine the citations of inventors that continue with a firm after the

23 We replace missing R&D values with zero in our main tests. Following Koh and Reeb (2015), in unablated tests, we find that our results are robust to i) replacing missing R&D values with zero and including an indicator variable for missing R&D, ii) dropping observations with missing R&D, and iii) replacing missing values of R&D with industry averages. 24 Guo (2018) finds that firms in manufacturing industries substitute away from patenting into trade secrecy after the AIPA. Hussinger, Keusch, and Moers (2018) find that public firms file fewer patents and receive fewer citations after the AIPA relative to private firms. The differences in our findings are likely a result of using different comparison groups and sample. As discussed before, our sample comprises firms who rely heavily on patenting to protecting intellectual property. Thus, our findings may not generalize to other samples, especially where firms do not rely as much on patenting as an intellectual property protection strategy. 25 There are at least two reasons for the weak effect on Number of Patents. First, patent count-based proxies may be noisy proxies for innovation. Prior research argues that patent counts cannot distinguish between breakthroughs from marginal innovations and are less correlated with indicators of economic importance, such as firm’s market value, compared to citations-based proxies (Griliches (1990); Kogan et al. (2017)). Second, to the extent that patent counts represent the number of projects undertaken, firms may prefer to innovate by investing more (or less) on existing projects, rather than initiating (or terminating) projects.


26

AIPA, the likelihood of inventors staying with a firm (“stayers”), and the likelihood of high

productivity inventors staying with a firm. The inventor-level data does not have a time dimension

because an inventor is given a time-invariant characteristic as either a “stayer” or “leaver” relative

to the AIPA. Accordingly, we drop the post dummy variable in our regressions and use industry

fixed effects. The specifics of the inventor-level data and key variables calculations are outlined

in Appendix D. Table 5 Columns 1 and 2 demonstrate that stayers’ change in citations before vs.

after the AIPA increases in the extent of Relative Spillover, and these changes correspond to

increases for spill-in firms and (statistically weaker) decreases for spill-out firms (coef.=0.024; t-

stat=4.01, coef.=0.069; t-stat=4.93, coef.=-0.005; t-stat=-0.30 for Relative Spillover, Spill-in, and

Spill-out, respectively). Columns 3 and 4 show that firms receiving more disclosure spill-in

benefits are more likely to retain incumbent inventors and firms experiencing more disclosure

spill-out costs are less likely to retain their inventors (coef.=0.008; t-stat=4.45, coef.=0.010; t-

stat=2.23, coef.=-0.027; t-stat=-5.31 for Relative Spillover, Spill-in, and Spill-out, respectively).

Finally, Columns 5 and 6 show that inventor retention is concentrated among high productivity

inventors where high (low) productivity inventors are defined as those that are above (below) the

median total patent citations an inventor received for patents filed in the pre-AIPA period. An

untabulated Chi-squared test reveals that the coefficients in Columns 5 and 6 (coef.=0.004 vs.

coef.=0.014) are significantly different (p-value = 0.02).

4.4 Mechanism and Cross-sectional Tests

In this section, we conduct two additional tests to examine the mechanism through which our

results manifest and provide evidence consistent with patent disclosures driving our results. First,

we build on the idea that strong U.S. patent laws prevent scientific knowledge from being

expropriated and ensure the original inventor receives fair payouts (Galasso and Schankerman


27

(2015)). When rivals use patent disclosures to glean scientific knowledge, such as techniques on

building efficient engines, the disclosures will likely induce lower proprietary costs as the

disclosing firm can be compensated by licensing its discovery to technologically related peers

(Hegde and Luo (2017)). Conversely, when competitors use patent disclosures to obtain

information on business strategies, such as signals on the investment opportunities of car engines,

the proprietary costs to the disclosing firm are likely higher since the law does not protect such

disclosures.26 Consistent with these arguments, we find the spill-in benefits (spill-out costs)

manifest among firms with peers closer in technology (product market) spaces.27

Specifically, in Table 6 Panel A, we replicate our main findings, but split the sample on the

extent to which a firm is more closely positioned relative to its rivals in the technology space than

the product market space. To proxy for a firm’s scientific (business) position in the industry, which

we refer to as TECH (PROD), we employ Bloom et al. (2013)’s measure that uses the weighted

cosine similarity between a firm’s own distribution of patent filings (industry segment sales) and

other firms in its NAICS 4-digit industry. We then compute the ratio TECH/PROD and assign

firms above (below) the median of this ratio to represent a higher likelihood of exchanging

scientific (product market) information with their peers. We find that the benefits from disclosure

spill-ins are concentrated in the firms closest to its industry competitors in the technology space

(Spill-in*Post in Column 1 has coef.=0.041; t-stat=4.52, whereas Spill-out*Post has coef.=-0.006;

t-stat=-0.58). Conversely, decreases in innovative output for spill-out firms manifest in the subset

of firms that are closest to rivals in the product market space (Spill-out*Post in Column 2 has

26 A discussion on the scientific vs. business-related information contained in patents is in Appendix E. 27 We recognize that firms could use scientific information in patents to the disclosing firm’s detriment (Glaeser and Landsman (2019)). In this case, the cost associated with spill-outs may be concentrated when disclosing scientific information. While we view this perspective to be an interesting tension to our predictions, we find our expectation reasonable in our setting given our focus on mandatory disclosure where patent laws are likely to play a more significant role in ensuring the disclosing firms receive fair payouts for sharing their scientific knowledge.


28

coef.=-0.08; t-stat=-2.21, whereas Spill-in*Post has coef.=0.004; t-stat=-0.29). This evidence is

consistent with the interpretation that patent disclosure improves innovations when patents are

used to glean scientific information, but that there are proprietary costs to disclosing firms whose

competitors gain new information about the product market space as a result of patent disclosure.

In our second additional test, we find that both the spill-in benefits and spill-out costs are

concentrated among firms in industries where industry researchers read more patents. This is

consistent with our results being due to the increased disclosure of the AIPA and less consistent

with alternative explanations unrelated to disclosure. Specifically, building on Ouellette (2017)’s

findings that researchers in biotechnology, chemistry, and electronics pay close attention to patent

disclosures, we code each NAICS-4 to be above or below the median value based on the percentage

of patents filed in these research areas over the five years leading up to the AIPA. Panel B of Table

6 shows that both the spill-in benefits and spill-out costs are concentrated among firms in industries

where patents are a relatively more important source of information (Spill-in*Post and Spill-

out*Post in Column 1 has coef. = 0.026; t-stat=2.51 and coef.=-0.028; t-stat=-2.62, respectively,

whereas Spill-in*Post and Spill-out*Post in Column 2 are statistically insignificant).

4.5 Strategic Disclosure Response

The results discussed so far provide evidence that the effects of increased patent disclosures

have real costs on spill-out firms relative to spill-in firms. If so, we expect these firms to respond

strategically within the flexibility that patent law affords. We explore two patent disclosure choices

allowed by patent law and find evidence consistent with strategic disclosure.

First, research in accounting suggests that a firm can strategically obfuscate the linguistic

content of its disclosures to conceal information from rivals (Li (2010); Loughran and McDonald

(2016); Bushee et al. (2018)). In the context of patents, Hall and Harhoff (2012) argue that the


29

“benefits of [patent disclosures] may be limited by careful drafting of the patent or by the omission

of essential (tacit) know-how.” But using too much vagueness can be costly since examiners can

reject patents on grounds of incoherent writing (Ouellette (2012)).

Thus, firms with the highest level of proprietary costs (i.e., spill-out firms) likely have stronger

incentives to engage in the strategic use of vague expressions when drafting their patents relative

to spill-in firms after the AIPA. To identify vague expressions empirically, we rely on Arinas

(2012), who studies a sample of 350 randomly selected U.S. patents and compiles a list of vague

expressions most prevalent in the patent setting. Appendix F and Appendix G contain the list and

categories of vague expressions from Arinas (2012) used in our study, respectively. Using textual

analysis, we count the number of vague expressions in each of our sample patents and divide it by

the total number of words to create % Vague Exps. Column 1 of Table 7 Panel A shows that %

Vague Exps. has a negative link to Relative Spillover*Post (coef.=-0.156; t-stat=-2.20) consistent

with the lower likelihood of using vague expressions when the spill-in effect dominates (or

equivalently, vague expressions are used more when the spill-out effect dominates). In Column 2

of Table 7 Panel A we find that the documented effect in Column 1 is driven primarily by spill-

out firms increasing the use of vague expressions and the effect is absent among spill-in firms

(coef.=-0.027; t-stat=-1.37, coef.=0.062; t-stat=3.41 for Spill-in*Post and Spill-out*Post,

respectively). This suggests that using vague language in patents is costly and firms only employ

this strategy when the proprietary costs associated with disclosure are sufficiently high.

Moreover, similar in spirit to Bird and Karolyi (2016) and Abramova, Core, and Sutherland

(2018), we investigate whether firms strategically change the number of figures that they include

in their patents after the AIPA. To the extent that figures effectively communicate information in

patents, firms that are exposed to higher proprietary costs may choose to exclude them from their


30

patents after the AIPA. In Column 3, we document a positive link between Number of Figures and

Relative Spillover*Post, which is consistent with a lower likelihood of firms using figures when

the spill-out effect dominates. Similar findings are shown in Column 4 where the spill-out firms

significantly reduce the use of figures after the AIPA, whereas spill-in firms do not (coef.=0.016;

t-stat=0.53, coef.=-0.046; t-stat=-2.11 for Spill-in*Post and Spill-out*Post, respectively).

Second, in Panel B of Table 7 we examine firms’ opt-out choices. As previously discussed, the

AIPA allows firms to opt out of pre-grant disclosure if patent protection is only sought in the U.S.

(i.e., the firm does not file a patent for the same invention in any foreign jurisdiction). Graham and

Hegde (2015) provide data on firms’ opt-out choices for our post-period. In the subsample of

patents that are only filed in the U.S. during the post-period, we examine whether the likelihood

of delaying patent disclosure is related to a firm’s relative spillovers. The negative coefficient on

Relative Spillover in Column 1 (coef.=-0.282; t-stat=-6.96) is consistent with spill-out firms having

a higher likelihood of delaying disclosure relative to spill-in firms when given the option to do so

(i.e., spill-in firms are less likely to delay). This is also similarly shown in Column 2 where the

coefficient on Spill-out is significantly positive, whereas Spill-in is not (coef.=-0.007; t-stat=-1.26,

coef.=0.027; t-stat=2.90 for Spill-in*Post and Spill-out*Post, respectively).

4.6 Robustness Tests

4.6.1 Unmodeled heterogeneity across Spillover Groups

Although our difference-in-differences design with firm fixed effects mitigates the impact of

any unobservable, time-invariant firm characteristics, we acknowledge that unmodeled time-

varying characteristics across spillover groups may still impact the inferences of our main findings.

To further alleviate these concerns, we perform two types of matching procedures and find our

results to be qualitatively unaffected.


31

First, we utilize entropy balancing, which applies regression weights to observations such that

treatment and control observations are balanced at the mean of all control variables used in our

regressions (Hainmuller (2012)). Because matching techniques require a clear set of treatment and

control firms, we first rerun our main analyses separately comparing spill-in firms to benchmark

firms and spill-out firms to benchmark firms. Columns 1 and 2 of Table 8 demonstrate that our

inferences are robust to this alternative specification. In Columns 3 and 4 of Table 8, we replicate

these regressions after applying entropy balancing. Our inferences remain unchanged. Second, we

match firms on NAICS four-digit industry classification and size quartiles using coarsened exact

matching (Iacus, King, and Porro (2012)). The results in Table 8 Columns 5 and 6 are consistent

with our main analyses. Taken together, evidence from our matching procedures suggests our

results are unlikely to be driven solely by differences in observable, time-varying characteristics.

4.6.2. Alterative Model Specifications

We also explore the robustness of our results to both alternative dependent and independent

variable measurements. Table 9 Panel A presents results using alternative dependent variables.

Columns 1 and 2 show that our results continue to hold using raw number of citations and log-

transformed citations not scaled by technology class and year as we do for Forward Citations in

our main analyses.

In Columns 3 – 5 of Table 9 Panel A, we include conceptually distinct measures of innovation

as dependent variables and find our inferences are unchanged. Kogan et al. (2017) provide

estimates of a patent’s worth to a firm’s shareholders, KPSS Value. This measure is based on the

stock price responses around the date of patent grant and captures the private value of a patent,

more so than its scientific contribution. Moreover, we follow Trajtenberg, Henderson, and Jaffe

(1997), to construct measures of Originality and Generality, which use the distribution of


32

citations.28 In particular, a patent that cites a wider array of technology classes is viewed as more

original. Similarly, a patent cited by a wider array of technology classes is viewed as being more

general. We continue to find results that support our conclusion using these proxies.

We also consider alternative fixed effect structures and measurement of our independent

variables. In Table 9 Panel B Column 1, we include firm and industry by year fixed effects.

Industry by year fixed effects allow firms in different industries to be affected differently by

macroeconomic events, such as the internet bubble. Thus, this fixed effect structure better controls

for concurrent macroeconomic or regulatory events, and exploits variation of Relative Spillover

across firms in the same industry and year. Furthermore, in Column 2 of Table 9 Panel B, we drop

high-tech firms, who were arguably hardest hit when the dot com bubble burst, from our sample

and find our inferences are unchanged.29

Lastly, in Columns 3 – 7, we show that our results are not specific to the way we measure

Relative Spillover in our main tests. Specifically, we find similar results using 10 instead of 20

years as the historical period over which to measure a firm’s filing-to-publication lag when

creating Relative Spillover (Column 3; Relative Spillover_10yrs), using a firm’s four-digit SIC

code opposed to NAICS codes (Column 4; Relative Spillover_SIC), using U.S. filing dates as

opposed to worldwide dates to determine publication lags (Column 5; Relative Spillover_US date),

and excluding the two and five years prior to the AIPA when measuring Relative Spillover

(Column 6 and Column 7; Relative Spillover_Exclude2yrs; Relative Spillover_Exclude5yrs).

28 Originality (Generality) is the Herfindahl index of the cited (citing) patents that capture dispersion across technology classes. 29 We follow Efendi et al. (2012) and identify high-tech firms as those in SIC 4-digit codes between 7370 and 7379.


33

5 Conclusion

We find that the AIPA induces both spill-in benefits that improve innovation and proprietary

costs that harm innovation. We use patent citations as our main proxy for innovation. We also

examine firms’ real decisions, specifically R&D and labor market decisions. After the AIPA, firms

that are more likely to benefit (experience costs) from spill-in (spill-out) effects invest more (less)

in R&D and retain more (fewer) scientists that are more (less) productive. We find evidence that

the positive innovation effect is driven by the knowledge exchange between technologically

similar firms, whereas the negative innovation effect is driven by revealing exploitable business-

related signals to product market competitors. Moreover, we show that both the increase (decrease)

in patent citations, due to greater spill-in (spill-out) effects, begins two years after implementation

of the AIPA, consistent with the slow-moving nature of innovation.

The paper contributes to the literature by estimating the economic consequences of patent

disclosures, but there are still fruitful avenues left to future research. Future work might examine

the full interactive effects of patent disclosures with other more traditionally studied forms of

corporate disclosure, such as financial statements, press releases, and analyst reports (e.g., Koh

and Reeb (2015)). For example, Kanodia, Sapra, and Venugopalan (2004) show that intangible

investments should be separated from operating expenses only when intangibles can be measured

with sufficient precision. To the extent that patent disclosures provide useful information that helps

other firms reliably measure intangibles, these disclosures may inform related policies.

Future research might also delve further into the market-wide effects of the AIPA. This paper

provides significant heterogeneity in the innovation effect across firms. But to fully assess its

welfare implications, future research could extend our findings to other related outcomes, such as

economic growth, consumer welfare, and innovations of non-corporate entities.


34

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Appendix A. Sample Selection Criteria Criteria Firm-patent

observations Firm-year

observations Public firm observations from 1996-2005 710,712 79,578 Require firms to exist in both the pre- (1996-2000) and post-period (2001-2005)

694,376 61,964

Require firms to have non-missing Relative Spillover 685,564 23,141 Require firms to have non-missing primary dependent variables, control variables, and drop singleton observations

621,579 19,968

Notes: This table presents the sample selection criteria for our main analyses at the firm-patent (e.g., Table 3) and firm-year levels (e.g., Table 4). Since we do not explicitly require firms to have non-missing observations for alternative dependent variables, the number of observations used in certain analyses may be lower than the above depending on data availability (e.g., Table 9).


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Appendix B. Variables Definitions

Variable Definition Dependent Variables Annual Firm’s Own Publication Lag

The average publication lag across a firm’s patents filed in a year. Publication lag is defined as the difference in months between the earliest worldwide publication date and earliest worldwide filing date for a single invention.

Annual Relative Publication Lag

The ratio of the average publication lag for patents filed in a year by firms in the NAICS 4-digit industry to a firm’s own publication lag. We exclude the firm’s own contribution to its NAICS 4-digit industry when calculating this ratio.

Forward Citations The log of one plus the weighted number of forward citations a patent received up to 2012. We weight forward citations by the average number of citations among patents in the same technology class and grant year.

KPSS Value A patent’s economic value estimated based on the patent’s grant day announcement returns. Specifics are provided by Kogan et al. (2017). We log transform KPSS Value.

Invention Generality One minus the Herfindahl index for the technological classes of other patents that cite the focal patent. We transform Invention Generality by taking the log.

Invention Originality One minus the Herfindahl index for the technological classes of other patents that the focal patent cites. We transform Invention Originality by taking the log.

R&D Intensity The logarithm of one plus the ratio of R&D expense to total assets (XRD/ATt−1). Missing values are set to zero. Obtained from Compustat.

Number of Patents The log of one plus the weighted number of patents a firm files in year t. We weight the number of patents by the average number of patents filed in the same technology class and grant year.

Trade Secrecy Takes on the value of one for firm-years that mention the phrase “trade secrecy,” “trade secret,” or “trade secrets” in the 10-K filing and zero otherwise.

Stayer An inventor with at least one patent prior to and at least one patent after the AIPA at the same sample firm.

Leaver An inventor with at least one patent prior to the AIPA at a sample firm and at least one patent afterward at a different firm.

Change in Citation of Stayers Before vs. After the AIPA

Log of post-AIPA average patent citations divided by pre-AIPA average citations of Stayers.

High Productivity Inventor An inventor above the median total patent citations an inventor received for patents filed in the pre-AIPA period.

Low Productivity Inventor An inventor below the median total patent citations an inventor received for patents filed in the pre-AIPA period.

%Vague Exps. The total number of vague expressions based on the list of expressions in Appendix F divided by the total number of words in each patent times 100.

Number of Figures The number of figures and diagrams in each patent. Delayed Disclosure Takes on the value of one if a firm chooses to opt out of early patent disclosure and zero

otherwise. Obtained from Graham and Hegde (2015).


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Independent Variables Relative Spillover The ratio of the industry-level (NAICS 4 digit) average filing-to-publication lag over the

20 years prior to implementation of the AIPA to the firm-level average filing-to-publication lag over the same time period. We transform Relative Spillover by taking the log. See Equation (1). Filing-to-publication lag is defined as the months between a patent’s filing date and disclosure date. We use the “worldwide” filing date to measure filing-to-publication lags. That is, if a patent is filed both in the U.S. and abroad, we use 1) the earliest filing date and 2) the earliest publication date anywhere in the world.

Spill-in Equals one for firms in the top three deciles of Relative Spillover, zero otherwise. Spill-out Equals one for firms in the bottom three deciles of Relative Spillover, zero otherwise. Post For our firm-patent sample, takes on the value of one for patents filed on or after

November 29, 2000, and zero otherwise. For our firm-year sample, takes on the value of one for filing years 2001-2005 and zero for filing years 1996-2000.

Relative Spillover_10yrs Same as Relative Spillover but uses 10 years, instead of 20 years, as the historical period over which to measure a firm’s average filing-to-publication lag.

Relative Spillover_SIC Same as Relative Spillover but uses SIC 4-digit codes as opposed to NAICS to measure the historical industry average filing-to-publication lag.

Relative Spillover_US date Same as Relative Spillover but uses the earliest U.S. filing and publication dates to compute a firm’s average historical filing-to-publication lag.

Relative Spillover_Excl. 2yrs Same as Relative Spillover but excludes the 2 years leading up to the AIPA to measure a firm’s historical average filing-to-publication lag.

Relative Spillover_Excl. 5yrs Same as Relative Spillover but excludes the 5 years leading up to the AIPA to measure a firm’s historical average filing-to-publication lag.

TECH The Jaffe technological distance between any given two firms i and j based on the uncentered correlations of patenting activity from Bloom et al. (2013).

PROD The product market distance between any given two firms i and j based on the uncentered correlations of disaggregated sales activity from Bloom et al. (2013).

Patent is Important Disclosure Patents filed in biotech, chemistry, and electronics identified by Ouellette (2017) as sectors that have higher readership of patents among industry researchers.

Control Variables Age The number of months since IPO per CRSP. Firm Size Log(1+Market Capitalization). Cash Holdings Cash holdings divided by total assets (CHE/AT). Return-on-Assets Operating income before extraordinary items divided by total assets (IB/AT). Book-to-Market Book value of equity divided by total assets (CEQ+TXDB)/(PRCC_F*CSHO). Leverage Total liabilities divided by total assets (DLTT+DLC)/AT from Compustat. Inst. Ownership The percentage of institutional ownership.

Ind. Concentration The natural log of the sum of the squared market share of each firm in a four-digit SIC code in a year. Market share is a firm’s sales divided by the total sales of the SIC code.

All continuous variables are winsorized at 1%.


40

Appendix C. Patent Examples Inside an IBM patent

A typical patent starts with a cover page, which contains key patent information, such as the owner, application date, disclosure date, grant date, inventors, number of claims, the examiner, technology field, and an abstract. Exhibit 1 below shows the cover page of the IBM patent.

Exhibit 1. Cover Page


41

The body of the patent following the cover page generally consists of five sections, comprising four sections with textual content—the background and summary, description of drawings, detailed description, and claims and conclusion—and a section that contains figures, drawings, and charts. The IBM patent has a total of about 12,000 words with four sheets of drawings (or eight figures). To illustrate, Exhibit 2 shows the background and summary section of the IBM patent. This section provides a summary of what the innovation does. The detailed description section, shown in Exhibit 3, gives a more detailed account of how to practically implement the invention. For example, on page 13 of the patent, the inventors provide the technical details on how to analyze sleep data to predict human cognitive performance and predict consumer demand for coffee.

Exhibit 2. Background & Summary Exhibit 3. Page 13 of Detailed Description

…

…


42

Finally, Exhibits 4 and 5 show Figures 5 and 8 in the IBM patent, respectively. Figure 5 depicts the idea of drones delivering drinks when a potential consumer waves at the drone. Figure 8 of the patent depicts a mobile computing system that can be used as part of the drone.

Exhibit 4. “Fig. 5” of the IBM patent Exhibit 5. “Fig. 8” of the IBM patent


43

Appendix D. Inventor-level Sample and Variables The patent database provides an interesting opportunity to conduct inventor-level analyses, since

patents identify the name of the inventor in addition to its legal assignee (most often the inventor’s

employing firm). Inventor-level analyses, however, are challenging for two reasons. First, the inventors’

names are often unreliable, because the same inventor may use different abbreviations of his or her first

name across different patents, or different inventors may have identical names. Second, while it is possible

to infer whether an inventor switched firms (for example, an inventor filing a patent with Intel in 1998 and

with IBM in 2004), exactly when the inventor moved is unobservable. Moreover, if an inventor does not

file a patent at the new firm, we cannot observe the move.

To circumvent the name-matching issue, we use Harvard Business School’s patenting database, which

provides unique inventor identifiers. The unique identifiers are based on disambiguation techniques that

mitigate the name-matching problem (Ronald, Alexander, and Lee (2013)). When a patent has multiple

inventors, we divide the number of patent citations by the number of inventors to construct inventor-level

citations, although our inferences are not sensitive to this requirement.

To reliably measure inventor mobility, we restrict our attention to the subsample of inventors who file

a patent at least once before the AIPA and at least once afterward. Therefore, the analysis is likely to be

about inventors that are more inventive and frequent patent filers. Following Bernstein (2015), we classify

inventors into two types.

1. Stayer: An inventor with at least one patent prior to and at least one patent after the AIPA at the

same sample firm.

2. Leaver: An inventor with at least one patent prior to the AIPA at a sample firm and at least one

patent afterward at a different firm.

These restrictions yield a total 95,736 inventors, comprising 62,982 stayers and 32,754 leavers. Moreover,

for each of these inventors, we compute Change in Citations, defined as the percentage change in an

inventor’s average patent citations before and after AIPA. We also define high (low) productivity stayers

based on whether an inventor’s pre-AIPA total patent citations is above (below) the median.


44

Appendix E. Scientific and Business-related Signals in Patents30 Scientific Information in Patents In practice, there is considerable debate about how much useful technical information patents contain (Williams (2017)). On the one hand, critics argue they contain little valuable scientific information because patentees may deliberately obfuscate the technical content to disclose as little as possible to potential competitors. Moreover, other technical literature (e.g., academic journals) may subsume patents’ usefulness as a source for scientific information (Roin (2005); Devlin (2010)). On the other hand, recent survey evidence suggests that about 60% of all patent readers and 72% of those reading for scientific reasons found useful technical information in patents (Ouellette (2017)). Ouellette (2017) concludes that the usefulness of patents as a source of technical information, while heterogeneous, holds across a wide range of research fields and disciplines. For example, scientists responded that “patents can be useful in providing technical details that are often omitted from research publications” and that “most of the information in the journal literature is deliberately not published in a timely manner so it is absolutely essential to follow the patent literature.” While the survey responses provide important anecdotal evidence that patents contain useful scientific information, systematic/archival evidence, especially in the context of whether patent disclosures foster innovation is scarce (Williams (2017)). Hence our paper contributes to this discussion by examining whether patent disclosures influence firms’ innovation decisions. Business Information in Patents In addition to technical information, corporate patents can reveal information about the disclosing firm’s strategic business decisions (Horstmann et al. (1985); Oppenheim (1998)). For example, if a firm files an abnormally high rate of patents related to virtual reality technology, the patenting pattern can provide useful insights to its product market rivals (e.g., whether to enter or exit the market, whether to cut prices to better compete, etc.). Consistent with this view, a Science article by Boulakia (2001) provides an anecdotal account of AT&T, IBM, and Lucent allocating considerable amounts of resources to parse through, organize, and extract valuable information from the millions of publicly disclosed patents of their product market competitors. This business practice, known as “patent mapping” or “patent landscaping,” allows firms to examine the overall landscape of competitors’ patents without having to understand all of the intricate scientific details behind each one. By doing so, corporations can identify business entry points, litigation risk, potential customers, future competitive threats, and acquisition targets (see Entis (2014) for a recent discussion). Consistent with these arguments, economists at the European Commission also argue that “the best predictor of what [firms] will be doing 10 years into the future is the current portfolio of the patents they have now,” citing Dow Chemical and DuPont as prominent examples that track competitors’ patents for these reasons (Schechter (2017)). Whether these practices are part of a broader empirical regularity is again an empirical question.

30 Our own interviews with industry researchers suggest that tracking other firms’ patents for both scientific and business-related information is common. For example, one of our interviewees, who works in the chemical industry, says that the technical information in other firms’ patents helped with their firm’s development of chemical mechanical planarization pads. He also says that tracking rivals’ patents can affect business decisions, such as market entry and M&A.


45

Appendix F. Vague Expressions Used for Textual Analysis

Notes: This table is the list of vague expressions used in this study and originally compiled by Arinas (2012). We use this to count the number of vague expressions in our sample patents. A vague expression is defined as a word or set of words from any one of the following three categories: ‘Vague category identifiers,’ ‘Vague quantities,’ or ‘Lack of interpretation standard.’ ‘Vague category identifiers’ are expressions combining a word or expression in each of the three columns within a row. For example, in the first row, “according to + another + aspect of the present invention” would count as a vague expression. Similarly, in the second row, “this + invention is not limited + by” would count as a vague expression. ‘Vague quantities’ and ‘Lack of interpretation standard” are a list of words, bigrams, or trigrams that would count as a vague expression. See Appendix G for further discussion on the three categories of vague expressions.

Vague category identifiers According to + In accordance with + In + It is +

an/the alternate + an/the alternative + an/the + another + one + the above described + a (still) further exemplary + a further + an illustrative + a predetermined + a preferred + still/yet another + a broad +

embodiment of the present invention aspect of the present invention

This + The present + The +

invention is not limited + by in this respect thereto

The present disclosure relates + The present invention relates + This invention is related +

to generally to in general to

Vague quantities between, at least, ranging from, preferably, preferred, a plurality of, a ratio of, a set of, a subset of, a member of, a section of, a mixture of, a segment of, portions of, components of, embodiments of

Lack of interpretation standard

may be, may also be, can be, can also be, if, substantially, selectively


46

Appendix G. Vague Expressions as a Metric for Patent Disclosure Quality We discuss the three vague expression categories identified by prior studies (e.g., Ivanic (1991); Channell (1994)) that Arinas (2012) uses to construct the list of vague expressions used in U.S. patents as shown in Appendix F. 1) Vague category identifiers. Vague category identifiers are expressions that are vague enough to refer to a set of more specific concepts and, at the same time, unclear, as they can be used to refer to a concept introduced previously in each text. Consider the following example from a U.S. patent. Example 1. “Thus, the present invention is not limited by the above description but is defined by the appended claims.” (U.S. Patent 7,557,072) The expression in bold is vague because, first, it is unclear what “the present invention” is precisely referring to. It can refer to either an aspect of the described invention or the invention in its entirety. Second, the expression “is not limited by” serves the purpose of warning readers that the description covers other potential claims not explicitly described in the patent disclosure. 2) Vague quantities. Vague quantities can either refer to expressions that designate intervals of concrete numbers/quantities or whose truth can be interpreted within a scale in relation to a context. Example 2. “via a clearance so as to cover a region ranging from the vicinity of the upper end to the lower end vicinity of the internal door 22” (U.S. Patent 6,764,234) Example 3. “the engine 22 includes a lubricating system for providing lubricant to the various portions of the engine.” (U.S. Patent 6,763,795) The bold expressions above are vague because they refer to a range of possible quantities or values that make the statements true. 3) Lack of Interpretation Standard. The lack of interpretation standard refers to a set of expressions that open the interpretation of the patent disclosure to nondescribed aspects. Example 4. “Ball 503 may be a neodymium magnet, as described above, or may be any other permanent magnet …” (U.S. Patent 7,557,727) This example demonstrates that the vague expression “may be” opens the interpretation of the referenced object (i.e., Ball 503) to various types of magnets.


47

Figure 1. The AIPA 18-month Disclosure Rule

Notes: The figure compares the patent disclosure rule before and after the AIPA. Before the AIPA, U.S. patents were disclosed when granted patent rights, which on average happened 36 months after the U.S. filing date during our 10-year sample period. Afterward, all patent applications – including those that are rejected – are required to be disclosed 18 months after the filing date. We note that after the AIPA, firms are granted monopoly rights at the decision date, but those rights are retroactively applied from the date of disclosure. Thus, both before and after the AIPA, firms effectively are granted monopoly rights from the date of disclosure (conditional on a patent being ultimately granted).


48

Figure 2. Distribution of Untransformed Relative Spillover by Technology and Industry Panel A. Untransformed Relative Spillover by Patent’s Technology Section

Panel B. Untransformed Relative Spillover by NAICS 2-digit Codes

Notes: This figure presents box plots of the untransformed (i.e., not logged) Relative Spillover, as defined in Appendix B, by the first letter of the patent’s technology class “section symbol” according to the Cooperative Patent Classification (Panel A) and 2-digit NAICS industries (Panel B) in parentheses. The short vertical line in each of the interquartile box represents the median value of the untransformed Relative Spillover. The figures are sorted in descending median values.


49

Figure 3. Parallel Trends in Scaled Patent Citations

Notes: This figure shows changes in innovation quality, as measured by Scaled Citations, in the years around the AIPA. The estimates 𝛽𝛽𝑘𝑘 (blue circles) and 𝛾𝛾𝑘𝑘 (red squares) and their 90% confidence intervals are from the following model:

𝑌𝑌𝑖𝑖,𝑝𝑝,𝑡𝑡 = ∑ [𝛽𝛽𝑘𝑘𝑆𝑆𝑆𝑆𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑛𝑛 × 𝑅𝑅𝑅𝑅𝑅𝑅𝑌𝑌𝑅𝑅𝑅𝑅𝑟𝑟𝑖𝑖,𝑝𝑝,𝑡𝑡 𝑘𝑘=5𝑘𝑘=−5

𝑘𝑘≠−2,−1+ 𝛾𝛾𝑘𝑘𝑆𝑆𝑆𝑆𝑅𝑅𝑅𝑅𝑅𝑅𝑆𝑆𝑃𝑃𝑅𝑅 × 𝑅𝑅𝑅𝑅𝑅𝑅𝑌𝑌𝑅𝑅𝑅𝑅𝑟𝑟𝑖𝑖,𝑝𝑝,𝑡𝑡] + 𝛾𝛾1𝑋𝑋𝑖𝑖,𝑡𝑡 + 𝛾𝛾2𝑍𝑍𝑝𝑝,𝑡𝑡 + 𝛿𝛿𝑖𝑖 + 𝜋𝜋𝑡𝑡 + 𝑃𝑃𝑖𝑖,𝑡𝑡.

The unit of observation is at the patent level p, and the dependent variable is Forward Citations. 𝑅𝑅𝑅𝑅𝑅𝑅𝑌𝑌𝑅𝑅𝑅𝑅𝑟𝑟𝑖𝑖,𝑝𝑝,𝑡𝑡 is a dummy variable, indicating the relative year around AIPA (Nov. 29, 2000) in which a patent application was submitted (years -2 and -1 of the AIPA is the omitted category for comparison). For example, patents submitted in year “-1” are those submitted between Nov. 29, 1999 to Nov. 28, 2000; in year “1” are those submitted between Nov. 29, 2000 to Nov. 28, 2001, and so on. 𝑋𝑋𝑖𝑖,𝑡𝑡 represents a vector of time-varying firm-level controls.𝑍𝑍𝑝𝑝,𝑡𝑡 is a vector of time-varying patent-level controls. 𝛿𝛿𝑖𝑖, 𝜋𝜋𝑡𝑡 are firm and year fixed effects, respectively. Standard errors are clustered at firm and year-month. Detailed definitions of all variables are provided in Appendix B.

-0.08

-0.06

-0.04

-0.02

0

0.02

0.04

0.06

0.08

-5 -4 -3 [-2,-1] 0 1 1.5 2 3 4 5

Years Relative to AIPA

Spill-in Spill-out

Coefficient Estimates by Year


50

Figure 4. Coefficient Estimates from Placebo Regressions

Panel A. Spill-in*Post Coefficient (%) Panel B. Spill-out*Post Coefficient (%)

Panel C. Spill-in*Post t-statistic (%) Panel D. Spill-out*Post t-statistic (%)

Notes: Panels A and B plot 𝛽𝛽1 and 𝛽𝛽2 from estimating equation (3) – corresponding to the specification of column (1) of Table 3 – across 500 placebo experiments. In each experiment, we randomly generate a different patent application date within our sample period, which spans the years 1996 to 2005. We then re-estimate equation (3) based on these placebo application dates. The red solid lines represent the estimated values of 𝛽𝛽1 and 𝛽𝛽2, using the real data – Column (1) of Table 3. Panels C and D plot the t-statistics of the 500 placebo experiments.

0

5

10

15

20

25

30

-0.04 -0.02 0 0.020

5

10

15

20

25

30

-0.02 0 0.02 0.04

0

2

4

6

8

10

12

-4

-2

0 2 40

2

4

6

8

10

12

14

-4

-2

0 2 4


51

Table 1. Descriptive Statistics

Notes: Panel A presents descriptive statistics on the untransformed (i.e., not logged) numerator and denominator of Relative Spillover separately for Spill-in, Benchmark and Spill-out firms. Panel B presents the regression of the firm’s own publication lag (Column 1) or the relative publication lag (Column 2) on control variables, both during the sample period from 1996-2005 (Columns 1 and 2) and over the 20-year measurement window of Relative Spillover (Columns 3 and 4). Panel C presents descriptive statistics for the main dependent variables used in regressions and control variables. Detailed definitions of all variables are provided in Appendix B.

Panel A: Publication Lags by Spillover Group prior to the AIPA's 18-month disclosure rule

Worldwide filing date U.S. filing date

Industry Peers Own Firm Untransformed Relative Spillovers Industry Peers Own Firm Untransformed Relative Spillovers (= Industry Peers/Own Firm) (= Industry Peers/Own Firm)

Spill-in Group 22.94 16.38 1.40 29.62 22.96 1.29 Benchmark Group 19.96 21.23 0.94 27.24 30.35 0.90 Spill-out Group 20.46 34.48 0.59 28.28 50.45 0.56

Panel C: Summary Statistics of Key Variables

N Mean Std. Dev. 25% Median 75% Forward Citations 621,579 0.53 0.50 0.12 0.42 0.80 KPSS Value 621,579 1.70 1.37 0.34 1.60 2.65 Invention Generality 621,579 0.14 0.21 0.00 0.00 0.37 Invention Originality 621,579 0.27 0.23 0.00 0.34 0.48 R&D Intensity 19,968 0.09 0.12 0.00 0.04 0.13 Number of Patents 19,968 0.10 0.27 0.00 0.00 0.05 Trade Secrecy 16,354 0.51 0.50 0.00 1.00 1.00 Age (months) 19,968 207.70 201.93 64.26 135.37 300.76 Firm Size 19,968 6.12 2.24 4.44 5.94 7.64 Cash Holdings 19,968 0.25 0.25 0.04 0.15 0.40 Return-on-Assets 19,968 -0.08 0.32 -0.08 0.03 0.07 Book-to-Market 19,968 0.54 0.54 0.23 0.42 0.72 Leverage 19,968 0.19 0.20 0.01 0.14 0.30 Inst. Ownership 19,968 0.35 0.31 0.01 0.32 0.63 Ind. Concentration 19,968 0.20 0.14 0.09 0.17 0.27

Panel B: Determinants of Publication Lag (1) (2) (3) (4)

Annual Firm's Own

Publication Lag Annual Relative Publication Lag

Annual Firm's Own Publication Lag

Annual Relative Publication Lag

Age 0.261 -0.002 0.135 0.010 (1.04) (-0.13) (1.15) (1.02) Firm Size 0.052 -0.029** 0.109 -0.013 (0.28) (-2.54) (0.68) (-1.34) Cash Holdings 0.659 -0.072 0.078 -0.031 (0.79) (-1.27) (0.09) (-0.61) Return-on-Assets 0.319 -0.012 -0.605 0.036 (0.58) (-0.34) (-0.81) (0.83) Book-to-Market -0.176 -0.039** -0.287 -0.013 (-0.57) (-2.05) (-1.15) (-0.80) Leverage -1.758** -0.023 0.109 -0.071 (-2.18) (-0.37) (0.14) (-1.28) Inst. Ownership -2.757*** 0.086 - - (-3.23) (1.49) - - Ind. Concentration 2.518* -0.047 -0.587 -0.010 (1.88) (-0.44) (-0.65) (-0.17) Obs. (Firm-Year) 12,303 11,976 14,305 13,867 Adjusted R-squared 0.460 0.357 0.385 0.337 Sample Period 1996-2005 1996-2005 1981-2000 1981-2000 Year FE YES YES YES YES Firm FE YES YES YES YES


52

Table 2. The Effect of the AIPA on Corporate Innovation: Diff-in-Diff Tests

Notes: This table presents the means and differences in means of the innovation proxies for the pre- and post-AIPA periods for Spill-in, Benchmark and Spill-out firms in Columns (1), (2), and (3), respectively. The difference-in-difference estimates are in in the last three columns. ***, ***, * indicates statistical significance at 1%, 5%, and 10%, respectively. Detailed definitions of all variables are provided in Appendix B.

Spill-in Group Benchmark Group Spill-out Group (1) (2) (3) (1)-(3) (1)-(2) (3)-(2)

Innovation Proxies Pre Post Diff Pre Post Diff Pre Post Diff Diff-in-Diff

Forward Citations 0.51 0.46 -0.04 *** 0.59 0.50 -0.09 *** 0.62 0.53 -0.09 *** 0.04 *** 0.04 *** -0.002

KPSS Value 1.20 0.96 -0.25 *** 2.19 1.57 -0.62 *** 2.61 1.87 -0.73 *** 0.49 *** 0.37 *** -0.12 ***

Invention Generality 0.19 0.06 -0.14 *** 0.23 0.07 -0.16 *** 0.28 0.09 -0.19 *** 0.06 *** 0.02 *** -0.04 ***

Invention Originality 0.22 0.23 0.01 *** 0.28 0.27 -0.002 ** 0.30 0.29 -0.01 *** 0.02 *** 0.01 *** -0.01 ***

R&D Intensity 0.09 0.08 -0.01 ** 0.09 0.08 -0.01 *** 0.12 0.10 -0.02 *** 0.01 * 0.001 -0.01 **

Number of Patents 0.08 0.09 0.01 0.15 0.14 -0.003 0.05 0.05 0.003 0.003 0.01 0.01


53

Table 3. The Effect of the AIPA on Corporate Innovation: Regression Results

(1) (2) (3) (4)

Forward Citations Forward Citations Forward Citations Forward Citations

Relative Spillover*Post 0.108*** 0.089*** 0.092***

(3.51) (3.02) (3.14)

Spill-in*Post 0.029***

(3.41)

Spill-out*Post -0.020**

(-2.09)

Age -0.001*** -0.001*** -0.001***

(-2.99) (-3.51) (-3.37)

Firm Size -0.015*** -0.015*** -0.016***

(-2.88) (-2.72) (-3.04)

Cash Holdings -0.015 -0.014 -0.015

(-0.61) (-0.54) (-0.61)

Return-on-Assets 0.018 0.017 0.016

(1.38) (1.29) (1.19)

Book-to-Market -0.056*** -0.054*** -0.058***

(-4.28) (-4.18) (-4.33)

Leverage -0.107*** -0.111*** -0.105***

(-3.35) (-3.55) (-3.73)

Inst. Ownership -0.035 -0.037 -0.041

(-1.34) (-1.42) (-1.56)

Ind. Concentration 0.004 0.003 -0.004

(0.11) (0.09) (-0.12)

Book-to-Market*Post 0.048*** 0.045*** 0.049***

(3.90) (3.69) (3.73)

Leverage*Post 0.109*** 0.111*** 0.108***

(3.80) (4.01) (4.01)

Inst. Ownership*Post 0.005 0.002 0.010

(0.38) (0.16) (0.73)

Ind. Concentration*Post -0.019 -0.016 -0.012

(-0.69) (-0.56) (-0.43)

Obs. (Firm-Patent) 621,579 621,579 621,579 621,579

Adjusted R-squared 0.065 0.065 0.074 0.074

Year FE YES YES YES YES

Firm FE YES YES YES YES

Class FE NO NO YES YES Notes: This table presents generalized DID regression results from the regression of Forward Citations on Relative Spillover*Post (Columns 1-3). Column 1 includes the baseline regression with firm and year fixed effects. Column 2 adds a vector of control variables. Column 3 adds a patent class fixed effect. Column 4 presents regression results from regressing Forward Citations on Spill-in*Post and Spill-out*Post. The unit of observation is at patent-firm level. The t-statistics reported below the coefficient estimates in parentheses are computed based on standard errors clustered by firm and year-month. ***, ***, * indicates statistical significance at 1%, 5%, and 10%, respectively (two-tailed). Detailed definitions of all variables are provided in Appendix B.


54

Table 4. Validating the Forward Citations Results (1) (2) (3) (4) (5) (6)

R&D

Intensity R&D

Intensity # of

Patents # of

Patents Trade

Secrecy Trade

Secrecy Relative Spillover*Post 0.027*** -0.011 0.058

(3.63) (-0.93) (1.49) Spill-in*Post 0.004 0.009 0.017

(1.26) (1.33) (1.05) Spill-out*Post -0.013*** 0.008 -0.016 (-3.26) (1.29) (-0.99) Obs. (Firm-Year) 19,968 19,968 19,968 19,968 16,337 16,337 Adjusted R-squared 0.773 0.773 0.853 0.853 0.812 0.812 Controls YES YES YES YES YES YES Year FE YES YES YES YES YES YES Firm FE YES YES YES YES YES YES

Notes: This table presents generalized DID regression results from the regression of R&D Intensity, # of Patents and Trade Secrecy on Relative Spillover*Post (Columns 1, 3 and 5) and Spill-in*Post and Spill-out*Post (Columns 2, 4 and 6). The unit of observation is at the firm-year level. We include firm and year fixed effects. The t-statistics reported below the coefficient estimates in parentheses are computed based on standard errors clustered by firm. ***, ***, * indicates statistical significance at 1%, 5%, and 10%, respectively (two-tailed). Detailed definitions of all variables are provided in Appendix B.


55

Table 5. The AIPA's Effect on Labor Mobility and Productivity

Notes: This table presents regression results from the regression of Change in Citation of Stayers and the Likelihood of Staying on Relative Spillover (Columns 1, 3 and 5) and Spill-in and Spill-out (Columns 2, 4 and 6). The unit of observation is at the inventor level. A detailed discussion of the sample is discussed in Appendix D. Regressions in Columns (3) through (6) include both Stayers and Leavers defined in Appendix B. We include industry fixed effects. Robust standard errors are reported in parentheses. ***, ***, * indicates statistical significance at 1%, 5%, and 10%, respectively (two-tailed).

(1) (2) (3) (4) (5) (6) Change in Citation of Stayers Likelihood of Staying Likelihood of Staying Likelihood of Staying

Before vs. After the AIPA (Low Productivity Inventor) (High Productivity Inventor) Relative Spillover 0.024*** 0.008*** 0.004 0.014*** (4.01) (4.45) (1.50) (5.22) Spill-in 0.069*** 0.010** (4.93) (2.23) Spill-out -0.005 -0.027*** (-0.30) (-5.31) Obs. (Inventor-Patent) 62,982 62,982 95,736 95,736 47,922 47,798 Adjusted R-squared 0.020 0.021 0.066 0.066 0.071 0.065 Industry FE YES YES YES YES YES YES


56

Table 6. Heterogeneity Panel A: Bloom et al. (2013)'s Science and Business information Exchange (1) (2)

Forward Citations Forward Citations (TECH/PROD = High) (TECH/PROD = Low) Spill-in*Post 0.041*** 0.004

(4.52) (0.29) Spill-out*Post -0.006 -0.080** (-0.58) (-2.21) Obs. (Firm-Patent) 330,396 146,433 Adjusted R-squared 0.058 0.081 Controls YES YES Year FE YES YES Firm FE YES YES

Panel B: Ouellette (2017)’s The Importance of Patents as a Source of Information (1) (2)

Forward Citations Forward Citations (Patent is Important Disclosure = High) (Patent is Important Disclosure = Low) Spill-in*Post 0.026** 0.013

(2.51) (0.77) Spill-out*Post -0.028*** 0.009 (-2.62) (0.45) Obs. (Firm-Patent) 495,045 126,475 Adj. R-squared 0.052 0.108 Controls YES YES Year FE YES YES Firm FE YES YES

Notes: Panel A presents generalized DID regression results from the regression of Forward Citations on Spill-in*Post and Spill-out*Post separately for firms closer to competitors in the technology space than those in the product market space (high-TECH/PROD) and firms closer to product market competitors than technological rivals (low- TECH/PROD). Panel B presents DID regression results from the regression of Forward Citations on Spill-in*Post and Spill-out*Post separately by industries with a high proportion of patents filed in biotechnology, chemistry, and electronics identified by Ouellette (2017) as sectors that have higher readership of patents among industry researchers. We sort NAICS 4-digit industries based on the median value of the proportion of patents filed in these sectors where above (below) is assigned to the high (low) subsample of “Patent is Important Disclosure”. The unit of observation is at the firm-patent level for both panels. We include firm, year, and patent class fixed effects. The t-statistics reported below the coefficient estimates in parentheses are computed based on standard errors clustered by firm and year-month. ***, ***, * indicates statistical significance at 1%, 5%, and 10%, respectively (two-tailed). Detailed definitions of all variables are provided in Appendix B.


57

Table 7. Strategic Disclosure Responses of Spill-out Firms Panel A: Evidence of Strategic Patent Drafting (1) (2) (3) (4) %Vague Exps. %Vague Exps. Number of Figures Number of Figures Relative Spillover*Post -0.156** 0.109* (-2.20) (1.83) Spill-in*Post -0.027 0.016

(-1.37) (0.53) Spill-out*Post 0.062*** -0.046** (3.41) (-2.11) Obs. (Firm-Patent) 621,579 621,579 621,579 621,579 Adjusted R-sq. 0.168 0.168 0.333 0.333 Controls YES YES YES YES Year FE YES YES YES YES Firm FE YES YES YES YES

Panel B: Evidence of Strategic Delaying (1) (2)

Likelihood of Delayed

Disclosure Likelihood of Delayed

Disclosure | Filing only in the US | Filing only in the US

(Post-AIPA) (Post-AIPA) Relative Spillover -0.282*** (-6.96) Spill-in -0.007 (-1.26) Spill-out 0.027*** (2.90) Obs. (Firm-Patent) 188,460 188,460 Adjusted R-squared 0.151 0.143 Controls YES YES Class x Year FE YES YES

Notes: Panel A presents generalized DID regression results from the regression of %Vague Exps. on Relative Spillover*Post and Spill-in*Post and Spill-out*Post. Panel B presents regression results from the regression of Likelihood of Delayed Disclosure on Relative Spillover*Post and Spill-in*Post and Spill-out*Post in the sample of patents eligible to delay disclosure (patents filed only in the U.S. in the post-period). The unit of observation is at the firm-patent level. The t-statistics reported below the coefficient estimates in parentheses are computed based on standard errors clustered by firm and year-month. ***, ***, * indicates statistical significance at 1%, 5%, and 10%, respectively (two-tailed). Detailed definitions of all variables are provided in Appendix B.


58

Table 8. Matched Sample Analyses

Notes: This table presents generalized DID regression results from the regression of Forward Citations separately on Spill-in*Post and Spill-out*Post with Benchmark firms as the comparison group. Columns 1 and 2 present results for an unmatched sample of firms. Columns 3 and 4 present results using an entropy balanced sample and Columns 5 and 6 using a coarsened exact matching sample. The unit of observation is at the firm-patent level. We include firm, year and patent class fixed effects. The t-statistics reported below the coefficient estimates in parentheses are computed based on standard errors clustered by firm and year-month. ***, ***, * indicates statistical significance at 1%, 5%, and 10%, respectively (two-tailed). Detailed definitions of all variables are provided in Appendix B.

(1) (2) (3) (4) (5) (6)

Spill-in vs. Benchmark

Spill-out vs. Benchmark

Entropy Balanced

Spill-in vs. Benchmark

Entropy Balanced

Spill-out vs. Benchmark

CEM Spill-in vs. Benchmark

CEM Spill-out vs. Benchmark

Forward Citations

Forward Citations

Forward Citations

Forward Citations

Forward Citations

Forward Citations

Spill-in*Post 0.027*** 0.029*** 0.026*** (3.08) (3.36) (3.58)

Spill-out*Post -0.021** -0.020* -0.027** (-2.17) (-1.97) (-2.57) Obs. (Firm-Patent) 498,426 461,703 498,426 461,703 498,238 445,039 Adjusted R-sq. 0.072 0.070 0.081 0.071 0.077 0.077 Controls YES YES YES YES YES YES Year FE YES YES YES YES YES YES Firm FE YES YES YES YES YES YES Class FE YES YES YES YES YES YES


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Table 9. Sensitivity Analyses

Notes: This table presents generalized DID regression results from the regression of innovation proxies on Relative Spillover*Post. Panel A uses various alternative measures of innovation as dependent variables. Panel B employs alternative methods of calculating Relative Spillover. The unit of observation is at the firm-patent level. The t-statistics reported below the coefficient estimates in parentheses are computed based on standard errors clustered by firm and year-month. ***, ***, * indicates statistical significance at 1%, 5%, and 10%, respectively (two-tailed). Detailed definitions of all variables are provided in Appendix B.

Panel B: Alternative Implementation, Spillover Proxies, FE structures, and subsamples (1) (2) (3) (4) (5) (6) (7) Forward Citations Forward Citations Forward Citations Forward Citations Forward Citations Forward Citations Forward Citations (Industry by Year FE) (No High-Tech Firms) Relative Spillover*Post 0.063*** 0.075*** - - - - -

(2.68) (2.68) - - - - - Relative Spillover_10yrs*Post - - 0.082*** - - - -

- - (4.66) - - - - Relative Spillover_SIC*Post - - - 0.066*** - - -

- - - (3.28) - - - Relative Spillover_US date*Post - - - - 0.112*** - -

- - - - (3.17) - - Relative Spillover_Excl.2yrs*Post - - - - - 0.017*** -

- - - - - (3.11) - Relative Spillover_Excl.5yrs*Post - - - - - - 0.027** - - - - - (2.49) Obs. (Firm-Patent) 621,406 566,892 600,772 621,063 621,579 615,764 591,077 Adjusted R-squared 0.075 0.072 0.075 0.074 0.065 0.064 0.058 Controls YES YES YES YES YES YES YES Year FE NO YES YES YES YES YES YES Firm FE YES YES YES YES YES YES YES Class FE YES YES YES YES YES YES YES Industry x Year FE YES NO NO NO NO NO NO

Panel A: Alternative Innovation Proxies (1) (2) (3) (4) (5) Raw Forward Citations Forward Citations (not scaled) KPSS Value Invention Generality Invention Originality Relative Spillover*Post 11.272*** 0.477*** 0.650** 0.102*** 0.019 (4.06) (2.82) (2.13) (2.72) (0.87) Obs. (Firm-Patent) 621,579 621,579 621,579 621,579 621,579 Adjusted R-squared 0.240 0.352 0.852 0.246 0.108 Controls YES YES YES YES YES Year FE YES YES YES YES YES Firm FE YES YES YES YES YES


the innovation consequences of mandatory patent disclosures

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