Does the media spotlight burn or spur innovation?

Lili Dai, Rui Shen, and Bohui Zhang☆Current Version: October 2017☆

Dai (lili.dai@unsw.edu.au) is from the School of Accounting, Australian School of Business, University of New South
Wales, Sydney, NSW 2052, Australia. Shen (shenrui@ntu.edu.sg) is from the Nanyang Business School, Nanyang
Technological University, Singapore 639798. Zhang (bohui.zhang@unsw.edu.au) is from the School of Banking and
Finance, Australian School of Business, University of New South Wales, Sydney, NSW 2052, Australia. We are gratefulfor helpful comments and suggestions from David Reeb, Yuanto Kusnadi, Kai Li, Laura Xiaolei Liu, Gustavo Manso, Ana
Marques, Vikram Nanda, Krishnamurthy Subramanian, Lewis Tam, Xuan Tian, Steven Xiao, Qianqian Yu, Alminas
Zaldokas, and conference and seminar participants at the 2015 Accounting and Finance Association of Australia and New
Zealand in Hobart, the 2015 Asian Finance Association Annual Meeting in Changsha, the 2015 Auckland Finance
Meeting in Auckland, the 2015 China International Conference in Finance in Chengdu, the 2016 ADBI Finance andInnovation Conference in Tokyo, the 2016 Financial Research Network Conference in Barossa Valley, the 2017 European
Accounting Association Annual Congress in Valencia, Australian National University, Fudan University, Massey
University, Queensland University of Technology, Shanghai Jiao Tong University, University of Technology Sydney, andZhongnan University of Economics and Law. We are grateful to Ning Jia for providing us the USPTO Bulk Downloads
patent data. Rui Shen acknowledges the financial support from Singapore Ministry of Education Academic Research Fund
Tier 1 (Official Number: 2014-T1-001-167). Bohui Zhang acknowledges the research grants from the ARC discovery
grant (DP 120104755) and ARC linkage grant (LP130101050) from the Australian Research Council and the CIFR
research grants (E026 and E028) from the Centre for International Finance and Regulation.

Does the media spotlight burn or spur innovation?
ABSTRACT
We examine the effect of media coverage on firm innovation. Using a comprehensive sample ofcorporate news coverage and patenting activities over the period from 2000 to 2012, we find anegative relation between media coverage and firm innovation. Multiple identification strategies
alleviate the endogeneity concern regarding the attenuation effect of media coverage on innovation.
We also find supports for two economic mechanisms underlying this impact: excessive pressure on
managers and the mitigation of financial constraints. Our findings provide new insights into the effectof news coverage on firms’ long-term growth.

Keywords: Media coverage; Firm innovation; Managerial myopia; Patents; Citations
JEL classification: G14, G32, O31

Harvard University Professor Michael Porter, the world's leading academic strategist, noted recently,“Capital markets can be toxic to strategy.” …
The Aspen Institute echoes this sentiment. It recently issued a clarion call for "Overcoming Shorttermism" that was endorsed by 28 national leaders.
– The Wall Street Journal, October 30, 2009

1. Introduction
The business media is perhaps the broadest information intermediary in capital markets.
How the media affects firm value is of central interest to financial economists. According to
the Gordon growth model, firm value is equal to a firm’s future cash flows (c) divided by thedifference between the cost of capital (k) and the long-term growth rate (g). By disclosing
and disseminating information to the public, the media is known to reduce the cost of capital(k) and to protect firms’ cash flows (c) against expropriation.1 However, little is known about
the media’s role in firms’ long-term growth (g). Given that innovation is a key determinant oflong-term growth,2 this study examines the effect of media coverage on corporate innovation.
Media coverage can impede firm innovation by imposing excessive pressure on managers
or by inducing knowledge leakage to rivals. We call this statement the spotlight-burning
hypothesis. First, market pressure leads managers to forgo long-term interests in order toboost short-term profits (Stein, 1988). 3 This view is echoed by Graham, Harvey, andRajgopal (2005), who survey 401 chief financial officers (CFOs) in the U.S. and find that the
majority of CFOs are willing to sacrifice long-term value for short-term performance because
they are pressured to meet short-term earnings targets. This managerial myopia can beexacerbated by the media. The media’s news dissemination function is thus a double-edged
sword that increases public attention on not only the bright side but also the dark side byexposing managers to market pressure. Moreover, driven by profit-seeking incentives, themedia may publish sensational articles to cater to readers (Core, Guay, and Larcker, 2008;
1

See, for example, Klibanoff, Lamont, and Wizman (1998), Chan (2003), Tetlock (2007), Tetlock, SaarTsechansky, and Macskassy (2008), Fang and Peress (2009), Bushee, Core, Guay, and Hamm (2010),
Blankespoor, Miller, and White (2014), and Roger, Skinner, and Zechman (2016) for the media’s impact on
information asymmetry and therefore on the cost of capital; see, for example, Miller (2006), Core, Guay, andLarcker (2008), Dyck, Volchkova, and Zingales (2008), Joe, Louis, and Robinson (2009), Dyck, Morse, andZingales (2010), Liu and McConnell (2013), Dai, Parwada, and Zhang (2015), and You, Zhang, and Zhang(2017) for the media’s impact on corporate governance.
2
Firm innovation has long been established by economists as one of the most important drivers of firms’ longterm economic growth and competitive advantage (Solow, 1957; Romer, 1987; Hall, Jaffe, and Trajtenberg,2005).
3
Porter (1992) also notes: “[t]he U.S. system first and foremost advances the goals of shareholders interested innear-term appreciation of their shares – even at the expense of the long-term performance of Americancompanies.”

1Gentzkow and Shapiro, 2010; Ahern and Sosyura, 2015). Compared with the typically vague
description of firms’ long-run growth, a news release announcing the current quarter or
year’s earnings attracts greater investor attention. Therefore, the media’s sensational articles
may primarily focus on firms’ short-term performance instead of their long-term growth.
Given that innovation is an output of long-term investment, the threat of media coverage can
impede firm innovation. We regard this effect of media on innovation as the market pressurechannel.
Second, the economics literature has documented that knowledge spillovers occur in thecontext of innovation (e.g., Jaffe, Trajtenberg, and Henderson, 1993; Jaffe, Trajtenberg, andFogarty, 2000; Bloom, Schankerman, and Van Reenen, 2013). Specifically, one firm’s
investments in technology creation can engender external benefits for other firms, including
competitors. Given that innovative projects are both risky and costly, the fear of knowledge
leakage to rivals can discourage firms from innovating, especially in competitive industries.
The media draws competitor attention to the existence of the innovation and encourages themto acquire knowledge from either public or private information channels. Therefore, media
coverage can reduce firms’ innovation incentives and outputs via the knowledge spillover
channel.
Media coverage may also enhance firm innovation by allowing firms to overcome
financial constraints or providing external governance. We refer to this view as the spotlightspurring hypothesis. First, according to World Bank Enterprise Surveys (2006-2010), almost
40% of firms cite insufficient access to finance as the foremost obstacle to their operations
and growth. Innovation is a long-term process that tends to exhaust internal capital and thatentails uncertainty, which hinders effective communication with outside investors(Bhattacharya and Ritter, 1983). Innovative firms thus suffer more severely from limited
external financing. The media provides a potential solution to this financial difficulty by
conveying firms’ inside information to the public (Tetlock, Saar-Tsechansky, and Macskassy,2008) and increasing managers’ visibility and credibility (Milbourn, 2003). Both outcomes
provide firms with increased access to capital and with reduced financing costs (Fang andPeress, 2009; Bushee, Core, Guay, and Hamm, 2010; Bushman, Williams, and WittenbergMoerman, 2017). Therefore, we expect media coverage to increase firms’ innovation outputs
via this financial constraint channel.
Second, when the market cannot observe the full spectrum of managerial actions, moral
hazard will induce managers to shirk and avoid investment in innovative projects that arerisky and effortful (Bertrand and Mullainathan, 2003). Even worse, managers will divert
2

firms’ resources for their private benefits and retain less capital for investment in innovative
projects. The prior literature suggests that the business media can play an important role inaligning interests between managers and shareholders by making appropriate capital
allocation decisions (e.g., Liu and McConnell, 2013). This is also supported by anecdotal
evidence. For example, Sears Roebuck’s board was shamed as “[t]he non-performing assets
of Sears” in The Wall Street Journal in April 1992, because of the poor performance of Sears’
stock. As a response, these directors adopted many proposals advocated by a shareholder
activist, which improved the firm’s value by 37% in the next year (Dyck and Zingales, 2002).
Thus, we expect that the monitoring role of the media can help resolve the agency problem of“lazy” or “greedy” managers and thus increase firms’ investment in innovation. We view thismechanism as the external governance channel.
To test these two hypotheses, we use both a comprehensive database of firm-level patents
and citations from Kogan, Papanikolaou, Seru, and Stoffman (2017) and United States Patent
and Trademark Office Bulk Downloads, along with corporate news coverage data from
RavenPack that provides us with full coverage of Dow Jones news releases. Specifically, wemeasure a firm’s innovation outputs by using the number of citations per patent, the market
value of ultimately successful patents filed by the firm in a year, as well as the number ofpatents. Our media coverage variable is constructed based on the number of news articles forthe firm in a given year. Consistent with our spotlight-burning hypothesis, our baseline
regression shows a negative relation between media coverage and corporate innovation
outputs. The effect is not only statistically significant but also economically relevant. For
example, a one-standard-deviation increase in media coverage is associated with a 7.0%
decrease in citations per patent, a 14.5% decrease in patent values, and a 3.6% decrease inpatent counts relative to the sample means.
Endogeneity is an important consideration in our empirical tests because the media cancater to public demand and because the extent of news coverage may be driven by the degreeof sensationalism. For example, Miller (2006) finds that media outlets with a larger group of
potential readers are more likely to report accounting fraud by firms. Moreover, Core, Guay,
and Larcker (2008) show that negative press coverage is more severe among CEOs who haveexercised more options. Similarly, in our context, the media may be more likely to coverlarge-sized and mature firms, which tend to have lower long-term growth and fewer
innovation outputs. To substantiate our main findings, we implement a multipronged
approach to mitigate the issue of endogeneity.

3

We first employ an instrumental variable approach. Our instrumental variable is themedian travel time between a firm’s headquarters and Dow Jones’ eight offices. This
instrumental variable is negatively associated with the extent of the firm’s news coverage,
because longer travel time increases the costs of collecting information and reduces media
attention (Hirshleifer, Lim, and Teoh, 2009; Dai, Parwada, and Zhang, 2015). Importantly,
our instrument is not directly linked to firm investment strategies or innovation outputs(Engelberg and Parsons, 2011; Gurun and Butler, 2012; You, Zhang, and Zhang 2017). The
results based on the instrumental variable approach support our baseline findings.
Then, we employ the 2003 change in the Dow Jones News Services as an exogenous
shock to the impact of media coverage. In March 2003, Dow Jones launched NewsPlus,
which provides Dow Jones News subscribers with simple web-navigation techniques andmakes it easier for subscribers to access and explore useful information. The new features of
NewsPlus include: 1) more intuitive layouts and powerful search tools, 2) customizable filters
to isolate content, 3) news updates on mobile devices, and 4) popup notifications for saved
news searches. We document the greater effect of media coverage on innovation in the postNewsPlus period, especially for firms that are highly exposed to the spotlight.
Moreover, we address the concern that the endogeneity problem can arise because poor
firm fundamentals can lead to both negative news coverage and fewer investments ininnovation. To address this concern, we decompose news articles into positive news coverageand negative news coverage, and we find that both positive news coverage and negative news
coverage have a significantly negative impact on corporate innovation. Finally, to control forthe lifecycle of a firm and mean reversion in innovation outputs, we include either the current
level of innovation outputs or the change in innovation outputs in regressions. We also
employ the change in change test. All these alternative specifications do not alter ourconclusions.
Although the main findings suggest that news coverage has a negative effect oninnovation activities in general, the observed negative effect of media may be a net outcome.
In other words, the spotlight-burning hypothesis may dominate the spotlight-spurring
hypothesis in the baseline analysis. To further disentangle the two hypotheses, we perform
additional tests to examine the mechanisms through which media coverage affects firm
innovation. First, we decompose our news coverage measure into several news subcomponents based on news categories, namely, earnings-related news coverage, product andinnovation-related news coverage, financing-related news coverage, and governance-related
news coverage. Consistent with the market pressure channel and the financial constraint4channel, innovation outputs decrease with earnings-related news coverage while they
increase with financing-related news coverage.
Second, we conduct further analyses by examining the interaction terms between news
coverage sub-components and several channel-related factors. For example, we use theweighted average of the churn rates of institutional investors holding the firm (Gaspar, Massa,
and Matos, 2005) as the proxy for market pressure, and calculate the Whited and Wu’s (2006)
index to measure a firm’s external finance constraints. Consistent with the two channels, wefind that the negative effect of earnings-related news coverage is stronger for firms with alarger proportion of short-term institutional investors, while the positive effect of financingrelated news coverage is more pronounced for firms with greater financial constraints.
In the final part of our study, we implement a series of additional tests to further enrich
our main findings. First, we construct a measure of the productivity of innovators and findthat the main impeding effect of media coverage is not driven by its effect on lowproductivity innovators. Second, we find that not only patenting activities, but also a firm’s
general growth prospects, are attenuated by media coverage. Finally, we show that our main
findings are robust to the use of various measures of innovation outputs and news coverage,
different sample selections, alternative clustering techniques, and other news data source.
Our study contributes to two strands of literature. First, we add to the literature on the realeffects of the media. In their seminal papers, Zingales (2000) and Dyck and Zingales (2002)
propose that the media plays a significant role in affecting corporate policies and guiding
firms in resource allocation decisions. This role can be either positive or negative. For
example, the literature recognizes the business media’s positive role in detecting accounting
fraud (Miller, 2006; Dyck, Morse, and Zingales, 2010), reversing governance violations(Dyck, Volchkova, and Zingales, 2008), exposing board ineffectiveness (Joe, Louis, andRobinson, 2009), monitoring executive compensation (Kuhnen and Niessen, 2012), limiting
the use of dual class shares (Braggion and Giannetti, 2013), influencing managers’ capital
allocation decisions (Liu and McConnell, 2013), disciplining insiders’ transactions (Dai,Parwada, and Zhang, 2015), and increasing the chance of forced CEO turnover (You, Zhang,
and Zhang, 2017). Only a few papers provide evidence of the dark side of media coverage.
For example, Core, Guay, and Larcker (2008) show that the media engages in sensationalism
and that firms do not respond to the negative tones of media coverage by reducing excess
CEO compensation or increasing CEO turnover. Moreover, Gurun and Butler (2012) findthat a positive media slate is associated with firms’ local media advertising expenditures. By

5linking media coverage with firm innovation, our paper is among the first to show thenegative effects of media coverage on firms’ long-term growth.4
Second, we contribute to the growing literature on finance and innovation. Specifically,
empirical evidence shows that laws (Acharya and Subramanian, 2009; Acharya, Baghai, andSubramanian, 2014), financial market liberalization (Moshirian, Tian, Zhang, and Zhang,2015), foreign institutional ownership (Luong, Moshirian, Nguyen, Tian, and Zhang, 2017),
firm boundaries (Seru, 2014), stock liquidity (Fang, Tian, and Tice, 2014), financial analysts(He and Tian, 2013), managerial contracts (Ederer and Manso, 2013), product market
competition (Aghion, Bloom, Blundell, Griffith, and Howitt, 2005), and corporate venture
capital (Chemmanur, Loutskina, and Tian, 2014) all significantly affect innovation. However,there is little insight into the role of media coverage in encouraging or impeding innovation.
We fill this gap by showing that the media can be the key factor that determines managers’
innovation decisions.
The remainder of the paper proceeds as follows. We develop our hypotheses in Section 2.
Section 3 describes the data sources and variable construction. We present the main findings
in Section 4 and the results of additional tests in Section 5. Section 6 concludes the paper.

2. Hypothesis development
Serving as a powerful market force, the media can pressure managers to forgo long-term
investments in exchange for short-term performance (Stein, 1988). Top U.S. executives admitthat when they are under pressure to meet or beat earnings targets, they are willing tosacrifice long-term firm value (Graham, Harvey, and Rajgopal, 2005), especially when there
is a lack of commitment to long-term contract of managerial compensation (Manso, 2011).
Moreover, the media may publish sensational articles to cater to readers’ interests (Core,
Guay, and Larcker, 2008; Gentzkow and Shapiro, 2010; Ahern and Sosyura, 2015). Given
that coverage of firms’ short-term performance is usually more eye-catching than that offirms’ long-term growth, the media’s sensational articles may primarily focus on firms’ shortterm performance instead of their long-term growth prospects. The threat of biased media
coverage of earnings and other short-term performance indicators thus exacerbates

4

Previous studies argue both that firms choose to disclose less information to the market because of proprietary
costs (e.g., Verrecchia, 1983; Li, 2010) and that enforcing more corporate disclosure may have a negative effecton firms’ information environments (e.g., Skinner, 2003). Our study adds to this literature by showing that theoverwheling information reported by the media can indeed affect firms’ real decisions and negatively affectfirms’ long-term growth prospects.

6managerial short-termism, leading to reduced long-term corporate investment. Thus, we positthat the media can impede firms’ innovation activities through this market pressure channel.
Moreover, the prior economics literature provides evidence of knowledge leakage in thecontext of patenting activities. For example, Jaffe, Trajtenberg, and Henderson (1993) findthat the costs of acquiring knowledge, proxied by the geographic distance between two
innovators, influence the likelihood of technology spillover. In a survey study, Jaffe,
Trajtenberg, and Fogarty (2000) suggest that knowledge leakage can occur from innovators
to other parties, such as competitors. Bloom, Schankerman, and Van Reenen (2013) further
show that knowledge leakage among rivals can negatively affect firm value. As informationintermediaries, the media might draw attention to the existence of innovation and encourage
competitors to acquire the information some other way. We regard this mechanism as theknowledge spillover channel. Through this channel, managers can be discouraged frominnovating when firms are highly exposed in the media spotlight because knowledge leakage,
especially in competitive industries, may induce external benefits for competitive firms.
Considering the above discussion, we develop our first hypothesis (H1), the spotlightburning hypothesis, through both the market pressure channel and the knowledge spillover
channel and make the following prediction:
H1: Media coverage is negatively associated with firms’ innovation outputs.
Conversely, media coverage may enhance firm innovation by alleviating financial
constraints and imposing external governance. Innovation is a long-term, uncertain processwith a substantial likelihood of failure (Holmstrom, 1989). Firms that invest heavily ininnovative projects are subject to substantial information asymmetry (Bhattacharya and Ritter,1983), and they are more likely to encounter severe financing constraint problems. The
difficulties in conveying the promising prospects of long-term projects to the market allow
bad firms to mimic the investment decisions of good firms, thus creating a lemon problem(Myers and Majluf, 1984; Trueman, 1986): good firms either overinvest as a signal (Bebchuk
and Stole, 1993) or underinvest completely, depending on the preference of the market(Brandenburger and Polak, 1996).
Effective communication between managers and outside investors through informationintermediaries can reduce information asymmetry, increase visibility, and thus resolve
financial constraint problems. On the one hand, prior studies have documented the media’s
impact on stock price by conveying inside information to the public (Klibanoff, Lamont, andWizman, 1998; Chan 2003; Tetlock, 2007; Tetlock, Saar-Tsechansky, and Macskassy, 2008).
7

On the other hand, the media could help attract more investors or partners due to increased
managers’ visibility, and lend credibility to their investment decisions and viability(Milbourn, 2003; Falato, Li, Milbourn, 2015). Following these studies, recent research has
shown that media coverage reduces financing and transaction costs (Fang and Peress, 2009;
Bushee, Core, Guay, and Hamm, 2010; Blankespoor, Miller, and White, 2014; Bushman,
Williams, and Wittenberg-Moerman, 2017). Therefore, we conjecture that media coveragecan encourage investment in innovative projects by reducing financial constraints. We callthis mechanism underlying the positive impact of media on innovation the financial
constraint channel.
Finally, given that outside investors cannot observe firms’ full set of potential projects,
managers tend to steer their investment decisions toward projects that are less risky andeffortful (Bertrand and Mullainathan, 2003). Under severe moral hazard, managers can evenconsume firms’ resources for private benefits at the cost of long-term growth (Narayanan,
1985; Stein, 1988; Benmelech, Kandel, and Veronesi, 2010). The existing literature has
documented that in general the business media plays an important role in disciplining
managers to mitigate agency costs (Miller, 2006; Dyck, Volchkova, and Zingales, 2008; Joe,Louis, and Robinson, 2009; Dyck, Morse, and Zingales, 2010; Kuhnen and Niessen, 2012;
Braggion and Giannetti, 2013; Dai, Parwada, and Zhang, 2015; You, Zhang, and Zhang,2017). In particular, Liu and McConnell (2013) find that the media encourages managers tomake appropriate investment decisions in takeover markets. Therefore, we expect the media
to exert a positive effect on innovation investment through this external governance channel,
given managers’ tendency to select less risky and effortful projects.
In sum, we develop our hypothesis (H2), the spotlight-spurring hypothesis, based on boththe financial constraint channel and the external governance channel and make the following
prediction:
H2: Media coverage is positively associated with firms’ innovation outputs.
3. Research design
3.1. Data sample and sources
We estimate news coverage by using RavenPack, a comprehensive news database thathas been widely used in the literature (e.g., Kolasinski, Reed, and Ringgenberg, 2013; Dang,
Moshirian, and Zhang, 2014; Shroff, Verdi, and Yu, 2014; Dai, Parwada, Zhang, 2015;
Rogers, Skinner, and Zechman, 2016; Wang, Zhang, and Zhu, 2017). RavenPack consists of
two news sources, namely, the Dow Jones Edition (available from January 1, 2000) and the8Web Edition (available from January 1, 2007). The Dow Jones Edition covers real-time,
firm-level news articles from leading news providers such as Dow Jones Newswires, The
Wall Street Journal, Barron's, and MarketWatch while the Web Edition collects news articles
from other major publishers and web aggregators, including industry and businesspublications, regional and local newspapers, blog sites, government and regulatory updates,
and trustworthy financial websites.
We measure corporate innovation based on the U.S. firm patent data provided by Kogan,
Papanikolaou, Seru, and Stoffman (2017, hereafter KPSS) ending in 2009. KPSS construct
the dataset by downloading a history of U.S. patent documents from Google Patents(https://patents.google.com). This patent search engine includes patent applications andgrants from the United States Patent and Trademark Office (USPTO). Other studies using
Google Patents include Moser and Voena (2012) and Moser, Voena, and Waldinger (2014).
After identifying the assignee name for each patent in Google Patents, KPSS match thesepatents to firms from the CRSP and construct a sample with firm coverage similar to the
NBER Patent Citation database.5 To further extend our sample period, we complement the
KPSS data with the patent data from 2010 to 2012 collected from USPTO Bulk Downloads(https://pairbulkdata.uspto.gov).
Our initial sample starts with a comprehensive dataset of corporate news coverage andpatenting activities over the period from calendar year 2000 to 2012 for U.S. stocks listed on
the NYSE, Amex, and Nasdaq. This sample is a matching outcome between data from
RavenPack available from 2000 to 2010 and combined KPSS and USPTO Patent data from2002 to 2012. We relate the media coverage variables in the current year t to innovation
outcome variables two years ahead (t+2) in order to account for the long-term nature ofinnovation processes. In robustness tests reported in the Internet Appendix, we also use athree-year lead period to match the innovation outcome variables with news coverage metrics
and find that our conclusions remain the same.
Next, we merge Compustat and the initial RavenPack-Patent sample and require a firm to
be covered by the media for at least once during our sample period. This results in a
combined sample that comprises 52,955 firm-year observations. Further, as illustrated in

5

NBER Patent Citation database covers the period 1976-2006. By utilizing information after 2006, KPSS
provides more accurate data of patents and citations, especially after year 2000. For example, in the KPSS
database, there are 1,808 unique listed firms successfully filing 80,967 patents in year 2003. In contrast, there
are only 1,338 unique listed firms successfully filing 42,315 patents in the NBER Patent Citation database inyear 2003. The online appendix of KPSS (2017) provides more details regarding the two databases.

9

Table 1, we take the following steps to filter the sample: (1) drop regulated industries with
SIC Codes either between 4900 and 4999 or between 6000 and 6999; 6 (2) remove
observations with missing values for the control variables; and (3) exclude firms without
information about bid-ask spreads from the CRSP. The final sample for main analyses
comprises 36,782 firm-year observations with the media coverage variables calculated inyear t from 2000 to 2010 and with the innovation outcome variables estimated in year t+2
from 2002 to 2012.
Moreover, we obtain the accounting information from Compustat, institutional ownershipdata from Thomson Reuters Institutional Holdings, and analyst data from I/B/E/S. To
construct our instrumental variables, we use location information for firm headquarters from
Compustat, location information for Dow Jones’ U.S. offices from the Dow Jones website,
and the detailed information on flights between firm headquarters and Dow Jones’ offices
from the U.S. Department of Transportation T-100 Segment Data. Due to the missing
information for firm headquarters, the sample for instrumental variable analyses consists of
34,642 firm-year observations. In analyses of the economic channels through which media
coverage affects corporate innovation, we construct channel variables by using additional
data sources such as product market competition data from Hoberg, Philips, and Prabhala(2014) and corporate governance information from BoardEx.

[Insert Table 1 Here]3.2. Variable construction
We measure firm’s overall news coverage (Newst) as the number of news articles of firm iin calendar year t, and scale Newst by 100 in the regression analysis. First, we exclude firminitiated press releases that are initiated by firms in the estimation of Newst. Then, we require
the Relevance Score provided by RavenPack to be 100 for each news article, which indicatesthat a firm linked to the article is prominent and plays a key role in the news story as
identified by RavenPack. Furthermore, because RavenPack classifies news articles into
various types by using proprietary text and part-of-speech tagging or labeling, the news

6

Consistent with the majority in the literature, we include all firms except for those in utilities or financial
industries in our main analysis (e.g., He and Tian, 2013; Fang, Tian and Tice, 2014; Cornaggia, Mao, Tian, and
Wolfe, 2015; Mukherjee, Singh, and Žaldokas, 2017). In Panel B of Table 3, we provide consistent results withdifferent sampling filters. These filters include firms with positive R&D expenditure, firms with non-missing
R&D expenditure, manufacturing firms only, and patenting firms only.

10

category information allows us to trace news articles related to our four economic channels
through which the media effect operates. Accordingly, we decompose the overall news
coverage into four sub-components, namely, earnings-related news coverage (News Earnings,t),
product and innovation-related (News Product,t), governance-related news coverage (NewsGovernance,t),

and financing-related news coverage (News Financing,t). 7 Figure 1 presents the

distribution of news categories adopted in our study from RavenPack.
We define three metrics of innovation productivity in calendar year t+2. CitaPatt+2 is theaverage number of citations per patent for ultimately successful patents filed by a firm in year
t+2.8 PValuet+2 is the sum market value of successful patents filed in year t+2 scaled by themarket value of equity for following the approach of KPSS. 9 Patentt+2 is the number ofultimately successful patents filed by a firm in year t+2. These measures have been widelyused in the innovation literature as observable innovation outputs (e.g., Aghion, Bloom,
Blundell, Griffith, and Howitt, 2005; Nanda and Rhodes-Kropf, 2013; KPSS, 2017), and theysuccessfully capture three dimensions of innovation outputs: quality, value, and quantity. The
logarithm values of these innovation metrics are used in regression analyses. In robustness
tests, we perform our analyses by using alternative innovation metrics for which the variable
definitions and results are reported in Tables IA1 to IA3 of the Internet Appendix.
Following the literature, we include as control variables firm and industry characteristics
estimated in year t that affect corporate innovation outcomes (e.g., He and Tian, 2013; Fang,
Tian, and Tice, 2014): Assetst, the logarithm value of the book value of total assets; R&Dt,
research and development expenses scaled by assets; Aget, the logarithm value of firm age inyears; ROAt, net income scaled by assets; PPEt, the net value of property, plant andequipment scaled by assets; Leveraget, the sum of debt in current liabilities and long-term
debt scaled by assets; Capext, capital expenditures scaled by assets; TobinQt, the ratio of themarket value to the book value of assets; KZIndext, the Kaplan and Zingales (1997) financial
Earnings-related news articles include, for example, those related to “earnings-above-expectations”, “earningsbelow-expectations”, and “earnings-meet-expectations”, which highlight the media’s focus on short-term
performance. Product and innovation-related news articles cover both “product” news and “patent” news.
Governance-related news articles include articles related to “insider trading”, “fraud”, “board-meeting”,“executive appointment”, “executive compensation”, and so on. Finally, financing-related news articles are
related to “debt”, “debt restructuring”, and “credit rating”.
8
Number of citations per patent is adjusted for truncation, where the raw value is divided by the sample annual
mean (Hall, Jaffe, and Trajtenberg, 2001).
9
KPSS estimate the patent value based on market adjusted return in a window [0, 2] following patent grant day
zero, taking into account the number of multiple patents filed on the same day, the firm market capitalization,and the volatility of firm returns. See more details regarding the estimation of patent value in Section 2.4 of
KPSS (2017).
7

11

constraint index; Hindext and Hindex2t, the Herfindahl index of two-digit SIC industries using
sales data and its square term; InstOwnt, shares owned by institutions scaled by total shares
outstanding (Aghion, Van Reenen, Zingales, 2013); Analystt, the natural logarithm of one
plus the number of analysts issuing annual earnings per share (EPS) forecasts; and Spreadt,
the average daily bid-ask spread estimated based on the CRSP according to Corwin and
Schultz (2012). See Appendix for detailed variable definitions.

[Insert Figure 1 Here]3.3. Summary statistics
Table 2 reports the descriptive statistics of the main variables in our baseline model forthe sample of 36,782 firm-year observations. All of the continuous variables are winsorizedat the 1 and 99 percent levels. The mean, median, and 90th percentile values of CitaPatt+2 are
1.42, zero, and 4.00, respectively. Consistent with the innovation literature (e.g., He and Tian,2013), these results indicate that the distribution of this measure is right skewed. Similarly,
the distributions of PValuet+2 and Patentt+2 are also right skewed as shown in Table 2, where
PValuet+2 is multiplied by 100 for the sake of exposition. That is, both have mean values
equal to 1.38 and 7.67, median values equal to zero, and 90th percentile values equal to 4.51
and 7.00, respectively.
Regarding Newst, an average firm is reported for 66.26 times per year, and the medianvalue is 39.00. Figure 2 shows the distribution of news coverage across sub-groups of firms
split into quintiles by firm size (Assetst). The mean value of Newst for firms in the largest sizegroup (Quintile 5) is 159.68, and that for firms in the smallest size group (Quintile 1) is 23.09,
indicating that even for smaller firms they still have fairly good exposure in the media
spotlight in our sample. Similarly, the average value of Patentt+2 is 29.13 (0.35) for firms in
Quintile 5 (Quintile 1). These results also suggest that both news coverage and the number ofpatents increase with firm size, for which we should control in our analyses together withother fundamentals that can affect media reports and innovation outputs. Moreover, we findthat the control variables reported in Table 2 have distributions that are consistent with theliterature. For example, on average, R&D expenses account for 6.10 percent of total assets,
firm age is approximately 18.81 years, Tobin’s Q is 2.10, and a firm is covered by 5.43
analysts.

[Insert Table 2 Here]12

4. Results
4.1. Baseline findings
In this section, we examine the effect of media coverage on corporate innovation.
Following the two strands of the literature on the effects of media coverage and thedeterminants of innovation outputs, we specify our baseline model as follows: 10
Innovationt+2 = α + βNews Newst + βCV Control Variablest + βFE Fixed Effects + ε,(1)where Newst is our main variable of interest estimated in year t and Innovationt+2 denotes thelogarithm-transformed innovation measures, i.e., Log (CitaPatt+2 + 1), Log (PValuet+2 + 1),
and Log (Patentt+2 + 1), estimated in year t+2. We expect the coefficient on Newst to besignificantly negative (positive) based on our spotlight-burning hypothesis (spotlightspurring hypothesis). In Equation (1), Control Variablest represents the vector of control
variables of firm and industry factors as described in Section 3.2. We also include firm andyear fixed effects to control for cross-sectional and time-series omitted factors and cluster
standard errors at the firm level.
We present the results of our main analysis in Table 3. In Model (1), the coefficient on
Newst for CitaPatt+2 is significantly negative (-0.062, t-stat = -4.71), suggesting that thequality of corporate innovation is attenuated by media coverage. This result is consistent withthe spotlight-burning hypothesis. The coefficient is also economically significant. For
example, a one-standard-deviation increase in Newst is associated with a 7.0% decline in thecitation per patent at the sample mean.11
Similar results are reported in Models (2) and (3), in which we regress Log (PValuet+2+1)
and Log (Patentt+2+1) on Newst (coefficients = -0.127 and -0.078, t-stat = -8.11 and -3.35,
respectively). These findings indicate that not only the quality, but also the value of patents,
measured by the total market value of granted patents, and the quantity of patents, measured
by the total number of patents filed and granted, are negatively associated with media

10

See, for example, the papers related to the impact of media on executive compensation (Core, Guay andLarcker, 2008; Kuhnen and Niessen, 2012) and on limited voting shares (Braggion and Giannetti, 2013).
11
Based on the summary statistics in Table 2, the economic impact of Newst on Log (Patentt+2+1) = (0.063×0.990) / log (1.424+1) = -7.0%, where the standard deviation of Newst is 0.990 (divided by 100) and themean value of Patentt+2 is 1.424.

13

coverage. 12 In terms of economic magnitude, a one-standard-deviation increase in Newst
leads to 14.5% (3.6%) decrease in PValuet+2 (Patentt+2) at the mean value.13
Following the majority in the literature (e.g. He and Tian, 2013; Fang, Tian and Tice,
2014; Cornaggia, Mao, Tian, and Wolfe, 2015; Mukherjee, Singh, and Žaldokas, 2017), we
include both patenting and non-patenting firms in our main analyses. We repeat our analyses
using alternative sample selection criteria to sustain the robustness of our results. Specifically,
we exclude firms that are possibly non-innovators because of the fundamentals of theirbusiness, and those for which patent generation may not play a key role in innovation andgrowth.
The results are reported in Panel B of Table 3. We conduct the analyses for firms withpositive R&D expenses in year t in Models (1) to (3), for firms with R&D expenses
recognized for at least once during the sample period in Models (4) to (6), for firms
incorporated in manufacture industries (SIC Codes 2000-3999) in Models (7) to (9), and forfirms with non-missing patenting information in at least one year throughout the sample
period in Models (10) to (12). Consistently, we find that the negative and significant effect ofnews coverage on innovation outcomes holds across all these sub-samples.
Overall, we find a negative effect of media coverage on corporate innovation, whichsuggests that the spotlight-burning hypothesis dominates the spotlight-spurring hypothesis inpredicting the effect of media coverage on firms’ innovation activities.

[Insert Table 3 Here]

4.2. Endogeneity tests
Our media coverage variable Newst is unlikely to occur randomly (Engelberg and Parsons,2011). If media coverage and innovation outputs are jointly determined by unobservable firm
characteristics, our prior results may be subject to an endogeneity concern. In this section, we
adopt multiple approaches to alleviate this endogeneity concern.

12

Although we find a strong relation between Newst and Innovationt+2, the incremental R2s due to news
coverage are not large (0.24%, 1.21%, and 0.11% increases in Adjusted R2 for CitaPatt+2, PValuet+2, andPatentt+2, respectively). This suggests that news coverage does not explain the majority of innovation variation,and provides comfort that our model does not omit a critical variable.
13
The economic impact of Newst on Log (PValuet+2+1) = (-0.127×0.990) / log (1.375+1) = -14.5%, where thestandard deviation of Newst is 0.990 (divided by 100) and the mean value of PValuet+2 is 1.375. Similarly, theeconomic impact of Newst on Log (Patentt+2+1) = (-0.078×0.990) / log (7.674+1) = -3.6%, where the standard
deviation of Newst is 0.990 (divided by 100) and the mean value of Patentt+2 is 7.674.

14

4.2.1. Instrumental variable analysis
We first conduct a two-stage instrumental variable analysis, as set forth below, to address
the endogeneity concern:
Newst = α + βIV IVt + βCV Control Variablest + βFE Fixed Effects + ε,

(2)

and
Innovationt+2 = α + β News Predicted News Predicted t + βCV Control Variablest+ βFE Fixed Effects + ε,(3)where we include the same set of control variables as in Equation (1) as well as industry andyear fixed effects. Standard errors are again clustered at the firm level. We regress Newst on
our instrumental variable (IVt) in the first-stage regression and then use the predicted value ofNewst in the second-stage regression.
Our instrument is TravelTimet, which is the median value of the number of minutes fortravel between a firm’s headquarters and Dow Jones’ eight offices in year t, scaled by 100 inregression analysis.14 We expect the travel time between the firm’s headquarters and Dow
Jones’ eight offices to have a negative impact on media coverage for two reasons. First,
Gurun and Butler (2012) and You, Zhang, and Zhang (2017) find that a firm’s media
coverage and the content of such coverage are dependent on the distance between the firm
and news outlets. To the extent that journalists incur higher costs by collecting and analyzing
information from distant firms, longer travel time lowers the likelihood of news coverage.
Second, a long travel time between news outlets and the firm can reduce the media’s
attention and interest in following the firm. More importantly, there is no theory or evidence
that the travel time between the firm’s headquarters and Dow Jones’ eight offices affects thefirm’s innovation outputs. Therefore, TravelTimet meets both relevance and exclusion
conditions, and it can serve as a valid instrument for Newst.
Following Giroud (2013), the number of minutes for travel between the firm’s
headquarters and one of the eight Dow Jones offices is computed under the assumption thattravelers (e.g., news reporters) optimally choose the route and means (e.g., car, or plane) oftransportation. We use five-digit ZIP codes to identify the location of firms’ headquarters,
Dow Jones offices, and airports. Following Huber and Rust (2016), we first use the Open14

The eight Dow Jones offices are at Boston, Chicago, Minneapolis, New York, Princeton, San Francisco,
Waltham and Washington. See http://www.dowjones.com/contact.

15

Source Routing Machine (OSRM) and Open-Sourced Maps (http://www.openstreetmap.org)
to calculate the travel time by car between the firm’ headquarters and Dow Jones’ eight
offices. Second, we identify the largest nearby airport for each Dow Jones office and the
airport nearby each firm’s headquarters. 15 Third, we calculate the fastest airline route
between the firm’ headquarters and Dow Jones’ eight offices by summing up threecomponents: (1) the travel time by car from the firm’ headquarters and its nearby airport, (2)the duration of the flight and layover time, and (3) the travel time by car from the Dow Jones
offices’ nearby to the Dow Jones’ offices.16 Finally, by comparing the travel time by car withthat by airplane, we use the shorter one to construct our TravelTime variable.
We use the following example to illustrate the construction of our instrument variable.
ADC Telecommunications is a communication firm located in Eden Prairie, Minnesota (ZIPcode 55343). We have two options to estimate the travel time between this company and
Dow Jones’ Boston office in 2006. The first choice is that we can drive from Eden Prairie tothe Boston office directly. As shown in the upper half of Figure 3, this choice will take us
about 24 hours according to OSRM. Alternatively, we can first drive from Eden Prairie to
Minneapolis airport (driving time about 19 minutes), take a flight operated by Northwest
Airlines (ramp-to-ramp time about 2 hour 45 minutes), and then drive from Boston airport to
Dow Jones’ Boston office (driving time about 8 minutes). As shown in the bottom half of
Figure 3, this choice will take us about 3 hour 12 minutes. Comparing the two choices, we
use the second and faster route, so the value that we use to estimate our instrument variable,
TravelTime, is 3 hour 12 minutes in this example.

[Insert Figure 3 Here]In Panel A of Table 4, we provide information regarding nearby airports for each Dow
Jones office, the average travel time (in minutes) from the firm’s headquarters to each Dow
Jones office and the percentage of means of transportation. On average, it takes around fourhours to travel from the firm’s headquarters to the Dow Jones office, varying from 201
minutes for the Dow Jones office in Chicago to 313 minutes for that in San Francisco. In
general, more than 90% of the transportation involves airplane, and more than 80% of thetransportation relies on at least one transfer flight (i.e., at least taking two flights). The
15

It always results in an increase in travel time by using alternative airports for the Dow Jones offices.
Following Giroud (2013), we use ramp-to-ramp time from the U.S. Department of Transportation T-100
segment data to measure flight duration and assume a 60-minute layover time for transfer flight.
16

16

Princeton office has the largest proportion of merely car driving (15.55%), while theMinneapolis office mostly involves the most transportation by flight (97.00%).
We conduct the instrumental variable analyses in Panel B of Table 4. Specifically, in thefirst stage, we regress Newst on TravelTimet in year t to estimate the predicted value of news
coverage, News Predicted t. In the second stage, we then use News Predicted t as our variable ofinterest in determining Innovation t+2. Model (1) presents the result of the first-stage
regression. As predicted, TravelTimet is negatively and significantly associated with Newst(coefficient = -0.030, t-stat = -2.96), and a one-standard-deviation increase in TravelTimetleads to a 5.0% decrease in news coverage relative to the mean value.17 The Cragg-Donald F
statistic is 64.44, which suggests that TravelTimet is not a weak instrument (Cragg and
Donald, 1993).18 Throughout Models (2) to (4), we present the results of the second-stage
regression for the three innovation variables. We find that the coefficients on News Predicted t
are negative and significant, which supports the baseline finding that media coverage
impedes corporate innovation.

[Insert Table 4 Here]

4.2.2. Natural experiment
To further mitigate the endogeneity concern on the relation between the media andinnovation, we employ the introduction of NewsPlus as an exogenous shock to the impact ofmedia coverage. In 2003, the Dow Jones News Services launched a new product, NewsPlus.
This is a platform designed for quick navigation and seamless news streaming. The newfeatures of NewsPlus include: 1) more intuitive layouts and powerful search tools to navigate
news stories, 2) customizable filters to isolate and sort the content which interests subscribers,3) news updates on mobile devices based on customization of filters ported from desktops,
and 4) popup notifications for updates to saved news searches.19 See a vivid illustration for
NewsPlus (https://www.dowjones.com/products/newswires/newsplus) in Dow Jones website
and Figure 4 for the snapshots from this illustration regarding the key features of NewsPlus.

17

The economic impact of TravelTimet on Newst = (-0.030×1.143) / 0.686 = -5.0%, where the standard
deviation of TravelTimet is 1.143 and the mean value of Newst is 0.686 in the sample for instrumental variable
analyses (both scaled by 100).
18
The critical value of the Stock-Yogo test is 16.38 for 10% maximal IV size (Stock and Yogo, 2005).
19
Other product features of NewsPlus include: 1) quick reference for synopses of stocks, bonds, currencies andcommodities, and 2) market overview for financial markets summary including futures and currencies.

17All these enhancements enable the subscribers of Dow Jones News Services to easily
access not only relevant, but critical information, beyond thousands of daily news articles.
More importantly, the enhancement is provided to the subscribers of Dow Jones News
Services at no additional cost. The team of Dow Jones Newswires introduced NewsPlus in an
extremely positive tone: “Our mutual subscriber base of financial advisors, wealth managers,
brokers and other investment professionals will find that Dow Jones NewsPlus is a valuable
tool that makes it easier to access and use the news that's critical to their practices andcustomers.” Taken together, we expect the effect of news coverage on innovation to be more
pronounced after the launch of NewsPlus, which facilitates the news dissemination process,especially for firms which are more highly exposed in the spotlight.

[Insert Figure 4 Here]

We conduct the analyses for this quasi-experiment in a subsample period from 2000 to
2005. We first define Post NewsPlus as a dummy variable equal to one if year t is between 2003
and 2005, otherwise zero for years 2000 to 2002. Then, we add the interaction between News t
and Post NewsPlus in our baseline models and expect the coefficients on News t × Post NewsPlus tobe negative and significant. Furthermore, we focus on firms with high media coverage andexpect the quasi-experiment effect to be stronger for these highly-media-exposed firms, i.e.,significantly negative coefficients on News t × Post NewsPlus × News High, where News High is adummy variable equal to one if a firm has news coverage above the sample median in year t.
The results are presented in Table 5. Consistent with our prediction, the negative impact
of media coverage on innovation becomes stronger after the implementation of NewsPlus in
Models (1) to (3) (coefficients on News t × Post NewsPlus = -0.034, -0.042, and -0.070, t-stats = 1.72, -2.94, and -3.19, respectively). We further show that after the implementation of
NewsPlus, the impact of media coverage becomes even stronger for firms with news
coverage above the sample median in Models (4) and (6) (coefficients on News t × Post
NewsPlus × News High = -0.390 and -0.646, t-stats = -3.23 and -3.76 for CitaPatt+2 and Patentt+2).

Therefore, the evidence from the above analyses further confirms that the negative impact ofmedia on innovation outcomes is intuitively meaningful.

[Insert Table 5 Here]

4.2.3. Other endogeneity tests18

In this section, we conduct a series of additional tests to further address the endogeneity
concern. These tests involve separating positive news from negative news, controlling forpast innovation outcomes, and adopting a change-in-change specification.
First, our main findings regarding the negative effect of media on innovation may besubject to an alternative interpretation. That is, firms with poor fundamentals are associatedwith more negative news coverage, and those firms in turn make fewer investments ininnovative projects. To address this concern, we decompose Newst into NewsPositive,t and
NewsNegative,t, which are the numbers of positive and negative news articles for firm i in year t.
Following Bushman, Williams, and Wittenberg-Moerman (2017), we code the tone of news
articles based on RavenPack’s Composite Sentiment Scores (CSS).20
If the alternative interpretation holds, we would expect the coefficient on News Negative t tobe significantly negative and that on News Positive t to be insignificant. In contrast, we find that
not only negative news coverage but also positive news coverage has a negative impact oninnovation. For example, the coefficients on News Positive,t and News Negative,t for CitaPatt+2 are 0.081 and -0.187 (t-stats equal to -2.49 and -6.22) in Model (1) of Panel A, Table 6. Similar
results are found for PValuet+2 and Patentt+2 in Models (2) and (3). These findings indeed
indicate that news coverage with a positive tone exerts a significant impeding effect on
corporate innovation, thus alleviating the concern that our results are driven by firms withpoor fundamentals.

[Insert Table 6 Here]

Second, there are two potential concerns regarding our main specification that does notcontrol for current innovation or a trend in innovation. First, our main results might bedriven by the lifecycle effect, i.e., mature firms tend to gain greater media coverage and at themean time have a lower level of innovation investments, which leads to less innovation
outputs in the future. Moreover, the mean reversion effect of innovation activities predicts
that firms with more current innovative project tend to attract a higher level of attention fromthe media, and these firms may have mean reversion to a lower level of innovation outputs.

RavenPack’s CSS range between 0 and 100, representing the news sentiment of a given story. CSS above(below) 50 are defined as positive (negative) news. The direction of the score is determined based on various
sentiment analysis techniques (e.g., by looking at emotionally charged words and phrases and by matchingstories typically rated by experts as having a short-term positive or negative impact on share prices).
20

19

Both concerns have been alleviated in our main analyses to some extent, because in ourbaseline models we control for firm age (Age t) and current research and development
expenses (R&D t), to take the lifecycle effect and the current level of long-term investment
into account. To further mitigate this concern, in Panel B of Table 6, we control for either thecurrent level of innovation or the change in innovation to illustrate the robustness of our main
findings. Across all the Models from (1) to (6), we find that the coefficients on innovation
outputs in year t (CitaPatt, PValuet, and Patentt) and the changes in innovation outputs fromyear t-1 to year t (∆ CitaPat, ∆ PValuet, and ∆ Patentt) are positive and significant, suggesting
the stickiness of innovation investment policy rather than a pattern of mean reversion. More
importantly, the coefficients on News t are all negative and significant at the one percent level,
indicating that the attenuation effect of news coverage on corporate innovation still holds
when the potential lifecycle effect and mean reversion effects are controlled.
Finally, we use the change-in-change specification to revisit our main analyses, as analternative approach to control for unobserved time invariant factors that influence both news
coverage and innovation outputs, instead of controlling for firm fixed effects. Specifically,
we regress the changes in innovation measures from year t+1 to year t+2 on the change innews coverage from year t-1 to year t, and include the changes of control variables specified
in Equation (1), as well as industry and year fixed effects. Results are reported in Panel C of
Table 6. Consistent with our main findings, we find a negative effect of the change in news
coverage (∆ News t) on the changes in patenting activities (∆ CitaPatt+2, ∆ PValuet+2, and ∆Patentt+2). This suggests that any time invariant factors missed in our model are less likely todrive our headline findings regarding the impact of news coverage on innovation outputs, andthus mitigates the endogeneity concern linked to these unobserved factors.

4.3. Economic channels
Although the results so far show a negative effect of news coverage on innovation
activities, this attenuation effect may be a net outcome in that the spotlight-burning
hypothesis offsets the spotlight-spurring hypothesis. To further examine the two hypotheses,
in this section, we investigate the underlying economic channels through which media
coverage affects innovation.

4.3.1. Tests on economic channels based on news content

20

First, we decompose our overall news coverage measure (Newst) into several news subcomponents based on news categories as defined in Section 3.2, which capture various media
effects on innovation regarding the four economics channels.
According to the spotlight-burning hypothesis, we expect corporate innovation to be
impeded by earnings-related news coverage (NewsEarnings,t) because the media can placepressure on managers to meet short-term earnings target and forego long-term innovation
projects (market pressure channel). We also expect the product and innovation related news
coverage (NewsProduct,t) to exert a negative impact on innovation, due to the media effects ofknowledge leakage and attentions attracted to competitors, which lead to large competitive
threats and thus deter firms from making innovation investments (innovation spillover
channel).
Based on the spotlight-spurring hypothesis, we predict that innovation outputs would
increase with financing-related news coverage (NewsFinancing,t) because the media can help
overcome financial constraints by improving firms’ information environments and enhancing
firms’ credibility to investors (financial constraint channel). Moreover, we conjecture thatgovernance-related news coverage (NewsGovernance,t) would be positively associated with
innovation, because the monitoring role of the media can help resolve the agency problem of“lazy” or “greedy” managers and thus increase firms’ investment in innovative projects(external governance channel).
Table 7 reports the results of the analyses on the sub-components of news coverage. We
first regress innovation metrics on each individual news component separately, and theninclude all the four news sub-metrics to jointly test their impacts on innovation outcomes. In
addition, across all the models, we include the measure of other news coverage (NewsOther,t)to control for the potential confounding effects from other unspecified economic channels.
Specifically, NewsOther,t is defined as the number of news articles that do not fall into any of
the above four news categories.
Consistent with our expectation, the coefficients on NewsEarnings,t are all negative andsignificant at the one percent level from Models (1) to (3), which provides support for the
economic channel in terms of placing short-term pressure on managers. In contrast, we findlimited evidence on the economic channel regarding the knowledge leakage and attention
attraction effects. For example, the coefficient on News Product,t is only significantly negativein Model (4) for CitaPat t+2, while the coefficients are insignificant although negative for
PValuet+2 and Patentt+2 in Models (5) and (6).

21

Regarding the financial constraint channel, Models (7) to (9) report the positive andsignificant impact of News Financing t on corporate innovation. This result is consistent with ourprior that the media may alleviate firms’ financial constraints by reducing information
asymmetry, increasing managers’ visibility and credibility, and enhancing investment ininnovation. However, inconsistent with the external governance channel, we find that thecoefficients on News Governance,t are significantly negative in Models (10) to (12). This implies
that the governance role of media may discourage managers from making investments ininnovative projects, possibly implying an excessive monitoring effect of the media.21 Lastly,
we find that the above results on news sub-components are qualitatively similar in Models(13) to (15) when they are included together in the regression analyses.
Overall, the results in Table 7 provide strong support for both the market pressurechannel and the financial constraint channel.

[Insert Table 7 Here]

4.3.2. Tests on economic channels based on interaction variables
Second, to further investigate our economic channels, we conduct analyses by including
the interaction terms between news coverage components and several channel-related factors
in the following model:
Innovationt+2 = α + βNews News Component, t + βNewsCF News Component, t × Channel Factort+ βCF Channel Factort + βCV Control Variablest + βFE Fixed Effects + ε,

(4)where News Component,t represents a sub-component of overall news coverage for eacheconomic channel (i.e., NewsEarnings,t, NewsProduct,t, NewsFinancing,t, and NewsGovernance,t),
Channel Factort is a list of variables associated with the economic channels. Control
Variablest is the same set of control variables as in Equation (1), and Fixed Effects refers tofirm and year fixed effects.
To test the market pressure channel, we follow Gaspar, Massa, and Matos (2005) toconstruct the channel factor for investor horizon, which is the weighted average of the total
portfolio churn rates of institutional investors of firm i in the last quarter of year t (Short
21

Prior literature on corporate governance suggests the potential excessive monitoring effects, for example, by
shareholders (e.g., Burkart, Gromb, and Panunzi, 1997; Pagano and Röell, 1998) and by the board of directors(e.g., Almazan and Suarez, 2003; Adams and Ferreira, 2007).

22

Horizont). The higher the value of Short Horizont, the larger the proportion of short-term
institutional investors who can pressure managers to forgo long-term investments in
exchange for short-term performance. The results are reported in Models (1) to (3) of Table 8.
Consistent with our expectation, we find that the negative effect of News Earnings,t on corporate
innovation is stronger when a firm has a larger proportion of short-term institutional investors,i.e., negative and significant coefficients on News Earnings t × Short Horizont. This finding
supports the market pressure channel.
Next, to test the innovation spillover channel, we use the product competition metric,
Fluidityt, developed by Hoberg, Phillips, and Prabhala (2014) to proxy for the potential
competitive threat that can be amplified by media coverage. Fluidityt captures the changes inrival firms’ products relative to a firm’s own products, which are estimated based on thebusiness descriptions from 10-K annual filings for firm i in year t. 22 Using this measure,
Hoberg, Phillips, and Prabhala (2014) find that firms associated with a higher level of
Fluidity make fewer payouts and hold more cash because they face greater product market
threats. From Models (4) to (6), we find the interactions between News Product t and Fluidityt
are insignificant, even for CitaPat t+2, of which the main News Product t effect is significantly
negative. These results are not supportive in terms of the innovation spillover channel.
We adopt a popular proxy for firm-level financial constraints, WWindext, to test thefinancial constraint channel. Following Whited and Wu (2006), WWindext is estimated as anindex of the external finance constraints of firm i in year t. More specifically, this index isequal to -0.091×CF – 0.062×DIVPOS + 0.021×TLTD – 0.044×LNTA + 0.102×ISG –0.035×SG, where CF is cash flows scaled by total assets, DIVPOS is an indicator equal to one
if cash dividends are paid, TLTD is the long-term debt scaled by total assets, LNTA is the log
value of total assets, ISG is industry sales growth based on the 3-digit SIC code, and SG isfirm sales growth. A higher value of WWindext suggests greater financial constraints faced byfirm i. Models (7) to (9) show that the interaction terms between News Financing t and WWindext
are positive and significant for CitaPatt+2 and PValuet+2. This finding is consistent with our
hypothesis relating to the financial constraint channel and indicates that the negative impact
of media coverage on innovation is weakened for firms with financial constraints.
With regard to the external governance channel, we use a firm’s internal governancesystem, BIndependencet, as the channel factor. BIndependencet is defined as the proportion of
22

Firms are required to describe and update information about their significant products by the U.S. Securities
and Exchange Commission (SEC) in Item 101 of Regulation S-K. The data for Fluidity is obtained from
Professor Gerard Hoberg’s website.

23

independent directors on the board for firm i in year t based on BoardEx data. We regard thisestimate as a proxy for firm’s strong internal governance, because the prior literature
documents that independent directors play a monitoring role in restricting management
behavior (e.g., Weisbach, 1988; Hermalin and Weisbach, 1998). However, the negative main
effect of News Governance t reported in Table 7 suggests the possible over-monitoring role ofmedia in dampening innovation, in line with the literature on excessive monitoring (Burkart,
Gromb, and Panunzi, 1997; Pagano and Röell, 1998; Almazan and Suarez, 2003; Adams andFerreira, 2007). Thus, we may expect this excessive monitoring effect of media to be more
pronounced for firms with stronger governance mechanisms in place. Accordingly, we report
the results for the external governance channel in Models (10) to (12). The coefficients on
the interactions between News Governance t and BIndependencet are negative and significant for
CitaPat t+2 and PValuet+2. This implies that the potential excessive governance effect ofmedia is stronger for firms with a larger proportion of independent directors, consistent withthe findings in Table 7.
In summary, the results in Section 4.3 provide supportive evidence for the underlying
economic channels through which media coverage affects innovation, and suggest that themain attenuation effect of news coverage on innovation is a net outcome when the market
pressure channel dominates the financial constraint channel, and the external governancechannel possibly exerts an over-monitoring effect.

[Insert Table 8 Here]

5. Additional tests
5.1. Does the quality of the innovator matter?
One could argue that the negative effect of media coverage on innovation is driven by thedecrease in the innovation outputs of low-quality corporate innovators. In other words, themedia may help the market effectively allocate resources from low-quality innovators tohigh-quality innovators.23
To test this possibility, following Clarke, Dass, and Patel (2015), we construct two
measures of observed innovator quality in year t (IQuality Citation,t and IQuality PValue,t).
IQuality Citation,t is estimated based on the annual citation-to-patent ratio, and IQuality PValue,t is23

Clarke, Dass, and Patel (2015) show that the impeding effect of financial analysts on innovation, documented
by He and Tian (2013), is subject to innovator quality.

24the sum of patent value scaled by the market value of equity, both averaged for all the
successful patents filed by a firm from year t-3 to t-1. The patent value is estimated based on
the abnormal stock return after the issuance of a patent following KPSS (2017).
In Models (1) to (3) of Table 9, we include the interaction Newst × IQualityCitation,t in the
regressions. If the main impeding effect of news coverage is more pronounced for lowquality innovators, we would expect the coefficients on Newst × IQualityCitation,t to be positive.
However, we find that the coefficients are significantly negative, which suggests that themedia’s impeding effect is stronger for innovators with higher citation-to-patent ratios inprior years. For Models (4) through (6), we conduct the interaction analysis by testing Newst
× IQuality PValue t in regressions, and we find that the impeding effect of media coverage isalso more pronounced for high-quality innovators who generate patents with greater values.
The evidence from the above analyses indicates that the main impeding effect of news
coverage is not driven by a reduction in innovation from corporate innovators with poor
productivity.

[Insert Table 9 Here]5.2. News coverage and alternative growth measures
Our main findings are based on patenting activities, which may not well capture growthprospects for certain types of firms, especially for those with limited demand for patenting. In
this section, we examine the impact of news coverage on several alternative growth metrics
to further understand the real effects of the media within the framework of the Gordon
growth model.
To identify the candidates for alternative growth proxies, we conjecture three steps in aloose timeline through which the media can shape firms’ growth prospects: 1) altering
investment decisions such as research and development expenses (R&Ds) in year t, 2)
influencing innovation outputs in year t+2, and 3) changing the growth of cash flows in year
t+3. Given that the second stage has been examined in our main analyses, we close the loopby estimating the alternative growth measures according to the first and third steps. To do so,
we choose three firm growth metrics, GrowthCFO,t+3, GrowthSG&A,t, and GrowthR&D,t toconstruct a common factor for a firm’s growth prospect, Growth Overall (Guay, 1999).
Specifically, we estimate Growth CFO t+3 as the growth rate of cash flows from operation
adjusted by the industry median in year t+3, which is less subject to managerial accounting
choices. Following Roychowdhury (2006), we define GrowthSG&A,t as the discretionary total
25

selling, general, and administrative expenses scaled by total assets based on each two-digit
SIC industry-year group in year t. Similarly, we calculate the discretionary research anddevelopment expenses scaled by total assets, GrowthR&D,t, as the proxy for the inputs ofinnovative projects in year t.
We regress these growth metrics on news coverage and present the results in Table 10. In
Panel A, Model (1) shows that the coefficients on News t are negative and significant for thecompounded growth measure, Growth Overall. Models (2) to (4) indicate that the attenuation
news effect on growth prospect holds for each of the three individual growth proxies,
GrowthCFO,t+3, GrowthSG&A,t, and GrowthR&D,t. In Panel B, we use the same instrument
variable, TravelTime t, to conduct the two-stage instrument variable analysis for thesealternative growth metrics. Consistent with the OLS regression, we find that the predicted
news coverage, NewsPredict,t, has negative and significant impacts on GrowthOverall,
GrowthCFO,t+3, and Growth R&D,t, with the only exception that the coefficient on NewsPredict,t isinsignificant although negative for GrowthSG&A,t.
In summary, we confirm that not only patenting activities but also a firm’s general growthprospects are attenuated by media coverage. This evidence is in support of the generality of
our main findings.

[Insert Table 10 Here]5.3. Robustness tests
In this section, we analyze whether the impact of media coverage on corporate innovation
activities holds against different measures of innovation outputs and news coverage,
alternative sample selections, and various clustering techniques. We also examine how themedia effect varies according to news article characteristics. Last, we conduct our analysis
using the alternative data source of news coverage. All these results are reported in theInternet Appendix (IA). The definitions of new variables constructed for these tests aredetailed in Table IA 1.

5.3.1. Alternative measures of innovation outcome and news coverage
In this sub-section, we test the robustness of our main results using different measures ofcorporate innovation and news coverage. First, we examine whether our results hold in theanalysis when predicting three-year ahead innovation outputs (Innovation t+3). In Models (1)26to (3) of Table IA 2, we find that the use of a two-year window versus a three-year window
between news coverage and innovation outcomes does not drive our baseline findings.
Second, we use the count of citations for patents granted in year t+2 (Citation t+2), thesum of patent value for patents granted in year t+2 (PValueRaw, t+2), and the average patentvalue for patents granted in year t+2 (PValueAdj, t+2) as the alternative innovation metrics. The
patent value is again estimated based on the stock return following the patent grant date
(KPSS, 2017). Accordingly, the results presented in Models (4) to (6) of Table IA 2 areconsistent with our main findings that news coverage exerts a negative and significant impact
on innovation outcomes.
Third, in Table IA 3, we deflate news coverage and innovation outputs using different
firm size proxies. In particular, corporate innovation and news coverage measures are scaledby the market value of equity in Models (1) to (3), by the book value of total assets in Models(4) to (6), and by the number of employees in Models (6) to (9). The results in Table IA 3
show negative and significant coefficients on news coverage regardless of which scalar offirm size is used, indicating that the attenuation effect of media coverage on innovation holds
after the scaling effect is considered.

5.3.2. Alternative sample selections and clustering techniques
We further address the sample selection concern in Table IA 4. We drop observations
with Newst = 0 in Models (1) to (3) to check whether the inclusion of zero-news-coveragefirm-years drives our main findings. In Models (4) to (6), we focus on the sub-sample period
from 2000 to 2007 to further alleviate the concern on the truncation bias of patent
information. We find similar results based on these selection criteria. Furthermore, in Table
IA 5, we cluster standard errors by industry (state) instead of by firm in Models (1) to (3)(Models 4 to 6) to control for cross-sectional industry-level (state-level) dependence. We
again find that the negative impact of news coverage on corporate innovation is statisticallysignificant.

5.3.3. Additional tests on news characteristics
In this section, we explore whether the media effect on innovation varies according to thecharacteristics of news articles.
The media can play two important roles as information intermediaries in capital markets.
On the one hand, in terms of the information exploration function, the media can influence
corporate decisions by providing new information to the market based on original27

investigations and analyses (Miller, 2006; Dyck, Volchkova, and Zingales, 2008). On theother hand, in the news dissemination function, the media affects corporate policies bydisseminating news about corporate events to the public (Liu and McConnell, 2013; Dai,
Parwada, and Zhang, 2015). We investigate how these two functions affect managers’
decisions regarding innovation activities.
Specifically, we decompose Newst into two news coverage metrics. NewsRepeated,t (NewsOriginal,t) is the number of repeated (original) news articles for firm i in year t. News articles

are grouped into repeated or original news articles based on RavenPack’s Event Novelty
Scores (ENS). For example, the first news article reporting an event receives a novelty scoreof 100. Subsequent news articles reporting the same event receive scores of less than 100. We
classify news articles with ENS=100 as original news articles and those with ENS<100 as
repeated news articles. We present the results in Models (1) to (3) of Table IA 6. In general,the coefficients on both News Repeated,t and News Original,t are negative and significant, which
provide evidence for both the news dissemination and information exploration functions of
the media.
Next, we examine whether corporate initiated press releases matter for the media effect,
which are excluded from the estimation of our main news coverage measure, Newst. From
Models (4) to (6), we include the number of press releases initiated by firms themselves(News Press Release,t) in the regression analyses. We find that the coefficients on News t after
controlling for NewsPress Release,t are significantly negative, while NewsPressRelease,t does notconsistently exert an impeding effect. This finding suggests that the news coverage effectconducted by external reporters on managers’ innovation decisions is not driven or
dominated by managers’ voluntary information disclosures through press releases.

5.3.4. Alternative data source of news coverage
One might be concerned about the breadth of news coverage measured based on
RavenPack, because RavenPack is sourced mainly from Dow Jones Newswires, The WallStreet Journal, and Barron’s prior to 2007 (i.e., based on the Dow Jones Edition). If young
and small firms are largely covered by local and regional papers, RavenPack may not
sufficiently capture news articles on these firms potentially with large growth opportunities.
This is less of a concern for the years from 2007 to 2010, because in 2007 RavenPack started
to be sourced from the Web Edition data that covers a broader set of information sources
including, for example, local news articles.

28

To address this concern, we use an alternative news data, the Thomson Reuters News
Archive (TRNA) database (Li, Ramesh, Shen, and Wu, 2015). TRNA collects news articles
from Reuters News based on the sources such as Business Wire and PR Newswire. We obtainthe TRNA data from Thomson Reuters in a limited sample period between 2003 and 2010.
News TRNA, t is estimated as the number of news articles from TRNA for firm i in year t. The
correlation coefficient between News TRNA t and News t (based on RavenPack) is 66.49% in theoverlapping period 2003-2010. This suggests that the two news data sources have a similar
but not identical coverage of firms. In Table IA 7, we regress Innovation t+2 on News TRNA, tfrom Models (1) to (3) and find similar results to those based on RavenPack. The coefficientson News TRNA, t are negative and significant for CitaPat t+2 and PValue t+2, though insignificant
for Patent t+2.
Given that RavenPack and TRNA are sourced from different newswires, we also merge
RavenPack with TRNA to construct the two combined measures of news coverage. First, we
define News TRNA+RavenPack, 1, t as News t in year t over the period from 2003 to 2010, which is
replaced by News TRNA t if News t is zero. The results reported in Models (4) to (6) show thatthe coefficients on News TRNA+RavenPack, 1, t are both negative and significant at the one percent
level. Second, we estimate News TRNA+RavenPack, 2, t as the sum of News t and News TRNA, t forfirm i in year t from 2000 to 2010. For the years between 2000 and 2002, we denote the valueof News TRNA+RavenPack, 2, t the same as News t. Across Models (7) to (9), we find that thecoefficients on News TRNA+RavenPack,2, t remain significantly negative. Taken together, we showthat our prior main findings hold using this alternative data source of media coverage,
alleviating the concern regarding the breadth of RavenPack data.

6. Conclusion
Theoretically, a firm’s ultimate goal is to maximize shareholders’ wealth, which isdetermined by both the risk and the growth prospect of future cash flows. Therefore, it isimportant to understand how media coverage affects firms’ long-term growth throughinnovation. In this study, we examine the effect of media coverage on corporate innovation
based on two hypotheses: the spotlight-burning hypothesis and the spotlight-spurring
hypothesis.
We use a comprehensive dataset of corporate news coverage and innovation outputs forthe period from 2000 to 2012 to perform our analyses. Our baseline finding is consistent withthe spotlight-burning hypothesis that media coverage exerts a negative effect on firm
innovation. Moreover, we find support for two economic mechanisms that underlie the effect29

of news coverage on innovation, namely, news coverage imposes excessive market pressure
on managers and overcomes financial constraints. Our work thus sheds some light on how themedia affects corporate long-term policies and provides avenues for further research on thereal effects of news coverage.

30Appendix
Variable definitions and data sources.
This appendix presents variable definitions and data sources.
Variable

Definition

Innovation outcome variables
CitaPatAverage number of citations per patent for patents granted in one year based on the data provided by KPSS (Kogan, Papanikolaou, Seru, and Stoffman, 2017)from Google Patents, which is also complemented by the data collected from Google United States Patent and Trademark Office (USPTO) Bulk Downloads. The
number of citations per patent is adjusted for truncation, where the raw value is divided by the sample annual mean (Hall, Jaffe, and Trajtenberg, 2001). Log
value of CitaPat is taken in the regression analysis.

PValueSum of patent values scaled by market value of equity for patents granted in one year based on the data provided by KPSS (2017) and the data collected from
Google USPTO Bulk Downloads, as well as Compustat Annual. The patent value is estimated based on the stock return following patent grant date using theapproach from KPSS (2017). Log value of PValue is taken in the regression analysis, and PValue reported in Table 2 is multiplied by 100 for exposition purpose.

PatentNumber of patents filed and eventually granted in one year based on the data extracted by KPSS (2017) and the data collected from Google USPTO Bulk
Downloads. Log value of Patent is taken in the regression analysis.

News coverage variables
News
NewsPredicted
NewsHigh

Number of news articles in one year based on RavenPack and divided by 100 in regression analysis.
Predicted number of news articles released in one year, which is estimated in an instrumental variable approach based on RavenPack.
Indicator equal to one if the number of news articles is above the sample median in one year based on RavenPack.

NewsPositive
NewsNegative
NewsEarnings
NewsProduct
NewsFinancing
NewsGovernance
NewsOther

Number of positive news articles in one year based on RavenPack.
Number of negative news articles in one year based on RavenPack.
Number of earnings related news articles in one year based on RavenPack.
Number of product related news articles in one year based on RavenPack.
Number of financing related news articles in one year based on RavenPack.
Number of governance related news articles in one year based on RavenPack.
Number of all other news articles in one year based on RavenPack.

Firm-level control variables
Assets
R&D
Age
ROA
PPE
Leverage
Capex
TobinQ
KZIndexBook value of total assets in billions (US dollars) based on Compustat Annual. Log value of (1 + Assets × 1000) is taken in the regression analysis.
Research and development expenses / Assets in one year based on Compustat Annual.
Firm age in years based on Compustat Annual. Log value of Age is taken in the regression analysis.
Net income / Assets in one year based on Compustat Annual.
Property, plant and equipment / Assets based on Compustat Annual.
(Debt in current liabilities + Long-term debt) / Assets based on Compustat Annual.
Capital expenditures / Assets in one year based on Compustat Annual.
(Assets – Book value of equity + Number of common shares × Year-end share price) / Assets based on Compustat Annual.
Kaplan and Zingales index divided by 100 based on Compustat Annual. See Kaplan and Zingales (1997) for details.

31Appendix - Continued
Variable

Definition

HIndex

Herfindahl index of four-digit standard industrial classification (SIC) using Sales information based on Compustat Annual.

InstOwn

Shares owned by institutions scaled by total shares outstanding based on Thomson Reuters Institutional (13f) Holdings.

AnalystNumber of analysts issuing annual EPS forecasts based on I/B/E/S Summary Statistics. Log value of (1 + Analyst) is taken in the regression analysis.

SpreadAverage daily bid-ask spread estimated in one year based on CRSP. See Corwin and Schultz (2012) for details.

Other variables
R&D Existence

Indicator equal to one if the R&D expense is non-missing for at least one year throughout the sample period and equal to zero otherwise, based on Compustat Annual.

Industry Manufacture

Indicator equal to one if a firm is incorporated in the manufacture industry and equal to zero otherwise, based on Compustat Annual.

Patent Existence

Indicator equal to one if the patenting information is non-missing for at least one year throughout the sample period and equal to zero otherwise, based on the data
provided by KPSS (2017) and also the data collected from Google USPTO Bulk Downloads.

TravelTime

Median value of the number of minutes for trips between headquarters of a firm and Dow Jones offices in one year, estimated based on the flight information from the
U.S. Department of Transportation T-100 Segment data.

PostNewsPlus

Indicator equal to one if the calendar year is between 2003 and 2005 and equal to zero if it is between 2000 and 2002. These are the event windows around the launch of
Dow Jones NewsPlus service provided by Dow Jones, which provides the subscribers of Dow Jones News Service with simple web navigation techniques.

ShortHorizon

Weighted average churn rate of all the institutional investors of a firm in the fourth quarter of one year estimated based on Thomson Reuters Institutional (13f) Holdings.
See Gaspar, Massa and Matos (2005) for details.

Fluidity

Text-based measure of competitive threats faced by a firm in the product market that captures changes in rival firms’ products relative to the firm estimated using 10-Ks in
one year provided by Hoberg, Phillips, and Prabhala (2014).

BIndependence

Proportion of independent directors on the board of a firm in one year based on BoardEx.

WWIndexIndex of external finance constraints of a firm estimated in one year based on Compustat Annual, which is transformed to decile-ranking metric. See Whited and Wu(2006) for details.

IQualityCitation

Annual truncation adjusted citation-to-patent ratio averaged over previous three years as a proxy for innovator quality, estimated based on the data provided by KPSS
(2017) and the data collected from Google USPTO Bulk Downloads. Number of citations per patent is adjusted for truncation, where the raw value is divided by the
sample annual mean (Hall, Jaffe, and Trajtenberg, 2001). Log value of IQualityCitation is taken in the regression analysis.

IQualityPValue

Sum of patent value scaled by market value of equity averaged over previous three years as a proxy for innovator quality, estimated based on the data provided by KPSS
(2017) and the data collected from Google USPTO Bulk Downloads. The patent value is estimated based on the stock return following patent grant date using the approachfrom KPSS (2017). Log value of IQualityPValue is taken in the regression analysis.

GrowthOverall

Principal component of Growth CFO t+3, Growth SG&A t, and Growth R&D t based on Compustat Annual.

32

Appendix - Continued
Variable

Definition

GrowthCFO

Growth rate of cash flow from operation adjusted by industry median in one year based on Compustat Annual.

GrowthSG&A

Discretionary selling, general and administrative expenses scaled by total assets in one year, estimated based on Compustat Annual using Roychowdhury's (2006)
approach which estimates discretionary total expenses.

GrowthR&D

Discretionary research and development expenses scaled by total assets in one year, estimated based on Compustat Annual using Roychowdhury's (2006) approach which
estimates discretionary total expenses.

33

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38

Figure 1
Distribution of news categories
This figure presents the distribution of news categories for News Earnings, News Product, News Governance, News Financing, and othertypes of news, News Other in RavenPack database.

Distribution of News Categories
News Earnings

News ProductNews Financing

News GovernanceNews Other

News Product, 1.30%
News Earnings,
16.22%

News Other, 52.92%

39News Governance,
28.41%

News Financing,
1.16%

Figure 2
Distributions of news coverage and patent grant by firm size
This figure presents the mean values of two metrics, news coverage in year t (Newst) and number of patents granted inyear t+2 (Patentt+2) in sub-samples divided by firm size in terms of total assets (Assetst).

Newst
200

150

100

50

0
Quintile 1

Quintile 2

Quintile 3

Quintile 4

Quintile 5

Patentt+2

40

30

20

10

0
Quintile 1

Quintile 2

Quintile 3

40

Quintile 4

Quintile 5

Figure 3
Illustration of the calculation of the instrumental variable: TravelTime.
This figure illustrates the calculation of our instrumental variable TravelTime by using ADC Telecommunication Inc. and Dow Jones’ Boston office as an example.

Route one: Driving directly for 24 hours 4 minutes

ADC
Eden Prairie

Dow Jones
BostonRoute two: Driving and flight for 3 hour 12 minutes
Driving for 19 minutes
from ADC Eden Prairie
headquarters to
Minneapolis Airport

Driving for 8 minutes from
Boston Airport to Dow
Jones’ Boston Office
Minneapolis
AirportBoston Airport

Flight for 2 hour 45 minutes by Northwest Airlines

41

Figure 4
Snapshots from the illustration of NewsPlus on Dow Jones website.
This figure presents the snapshots regarding the key features of NewsPlus from the illustration from Dow Jones website(https://www.dowjones.com/products/newswires/newsplus).

42

Table 1
Sample selection
This table presents the sample selection procedure. We start with a comprehensive dataset of corporate news coverageand innovation outputs for the period from 2000 to 2012 based on Compustat and RavenPack. The sample in our main
analysis comprises 34,642 firm-year observations with news coverage variables estimated in year t from 2000 to 2010,
and with innovation outcome variables estimated in year t+2 from 2002 to 2012. In supplementary analyses, thesample period varies across different specifications. Variable definitions are detailed in the Appendix.

Step

Selection Criteria

Observations

1.

Firm-year observations from news-release in a combined sample based on Compustat
and RavenPack.

52,9552.

- Less observations with two-digit SIC codes between 4900 and 4999, or between 6000
and 6999.
39,4393.

- Less observations with missing values of control variables estimated based on
Compustat, such as Assets, PPE, Leverage, Capex, and KZindex.
37,0644.

- Less observations with missing value of Spread estimated based on CRSP.
36,7825.

- Less observations with missing value of Travel Time estimated based on theinformation of firm’s headquarters location from Compustat.
34,64243

Table 2
Summary statistics
This table presents the summary statistics of the variables in our main analysis for the mean, median, standard deviation(STD), and decile (90% and 10%) distributions of the variables. The full panel sample comprises 36,782 firm-year
observations for news coverage variable from 2000 to 2010 and innovation outcome variables from 2002 to 2012.
Variable definitions are detailed in the Appendix.
Mean

STD

10%

Median90%

CitaPatt+2

1.424

4.712

0.000

0.000

PValuet+2

1.375

3.759

0.000

0.000

Patentt+2

7.674

35.879

0.000

0.000

4.005
5.000
4.511
0.821
7.000

Newst

66.25899.0106.000

39.000Assetst

2.632

9.671

0.025

0.307

R&Dt

0.061

0.117

0.000

0.005

Aget

18.80614.0206.000

14.000ROAt-0.0540.269-0.332

0.029

PPEt

0.249

0.232

0.031

0.168

Leveraget

0.181

0.186

0.000

0.136

Capext

0.053

0.061

0.007

0.032

TobinQt

2.103

1.693

0.899

1.545

KZIndext-0.1250.508-0.234-0.014HIndext

0.227

0.177

0.067

0.173

InstOwnt

0.429

0.357

0.000

0.417

Analystt

5.429

6.271

0.000

3.000

Spreadt

0.021

0.012

0.009

0.018

140.000
16.000
4.713
3.986
0.179
42.0000.187
0.122
42.000
0.629
0.452
0.119
0.124
0.569
3.875
0.019
0.439
0.466
0.111
0.928
14.000
3.948
0.037
1.881
0.444
0.912
15.0000.035

44

Table 3
News coverage and innovation outcomesThis table presents regressions of corporate innovation outcome variables on news coverage, including other control
variables and unreported firm- and year-fixed effects (FY). The corporate innovation outcome variables are the log valuesof the average number of citations per patent (CitaPat), the sum of patent values scaled by market value of equity(PValue), and the number of patents (Patent) measured in year t+2. News coverage (News) is the number of news articles
estimated in year t. The full panel sample comprises 36,782 firm-year observations for news coverage variable from 2000
to 2010 and innovation outcome variables from 2002 to 2012. Panel A examines the full sample, while Panel B
investigates the sub-samples of firms with more patenting activities. Variable definitions are detailed in the Appendix.
Key results are highlighted in bold. The t-statistics shown in parentheses are based on standard errors adjusted forheteroskedasticity and firm-level clustering.
Panel A: Full Sample
CitaPatt+2

PValuet+2

Patentt+2

Variable

M1

M2

M3

Newst

-0.062(-4.71)
0.085(4.88)-0.181(-1.37)0.036(0.59)-0.060(-1.69)0.128(1.61)-0.185(-3.13)0.038(0.35)
0.042(7.94)-0.034(-2.60)0.740(2.40)-0.670(-2.29)-0.068(-1.66)0.010(0.78)
6.266(8.14)-0.127(-8.11)
0.113(7.70)-0.276(-2.62)0.175(3.06)-0.059(-2.40)0.108(1.53)-0.146(-2.73)0.121(1.45)
0.042(10.17)-0.022(-2.25)1.022(3.56)-0.940(-3.21)-0.037(-1.00)0.022(1.89)
5.909(9.61)-0.078(-3.35)0.134(7.81)-0.186(-1.69)-0.270(-4.47)-0.040(-1.50)0.096(1.20)-0.163(-2.69)0.146(1.70)
0.027(5.93)-0.012(-1.32)0.817(2.77)-0.900(-2.96)-0.033(-0.77)-0.005(-0.39)0.220(0.34)

FY
36,78245.37%

FY
36,78255.91%

FY
36,78274.83%

Assetst
R&Dt
Aget
ROAt
PPEt
Leveraget
CapextTobinQt
KZIndext
HIndext
HIndex2t
InstOwnt
Analystt
Spreadt
Fixed Effects
Observations
R2Adj

45

Panel B: Sub-Sample

Variable
Newst
Assetst
R&Dt
Aget
ROAt
PPEt
Leveraget
CapextTobinQt
KZIndext
HIndext
HIndex2t
InstOwnt
Analystt
Spreadt
Fixed
Observations
Effects
R2Adj

CitaPatt+2
M1

R&D t > 0
PValuet+2
M2

Patentt+2
M3

CitaPatt+2
M4-0.038(-2.34)-0.012(-0.44)-0.074(-0.52)-0.019(-0.20)0.007(0.14)-0.288(-1.72)-0.154(-1.70)0.305(1.11)
0.019(2.81)-0.037(-2.16)0.399(0.75)-0.492(-0.98)-0.113(-1.61)0.039(1.88)
3.549(2.80)
FY
19,61949.38%-0.152(-7.85)0.072(2.97)-0.082(-0.73)0.301(3.27)-0.006(-0.19)-0.361(-2.48)-0.141(-1.72)0.552(2.73)
0.025(5.07)-0.025(-2.00)1.216(2.38)-1.195(-2.24)-0.073(-1.14)0.056(3.11)
3.273(3.23)
FY
19,61961.33%-0.089(-2.91)
0.168(5.17)-0.008(-0.06)-0.441(-4.08)0.002(0.04)
0.062(0.35)-0.157(-1.48)-0.061(-0.26)0.021(3.63)-0.012(-0.92)
1.312(2.22)-1.482(-2.48)-0.072(-0.89)0.005(0.22)-2.302(-2.04)FY
19,61974.53%-0.049(-3.06)0.010(0.38)-0.081(-0.58)0.030(0.33)
0.008(0.19)-0.238(-1.54)-0.181(-2.11)
0.322(1.25)
0.024(3.82)-0.036(-2.22)0.413(0.84)-0.469(-1.04)-0.120(-1.84)0.035(1.80)
3.995(3.40)
FY
21,13148.62%

R&D Existence = 1
PValuet+2
Patentt+2
M5
M6-0.156(-8.09)0.078(3.53)-0.116(-1.07)0.309(3.58)-0.016(-0.51)-0.310(-2.33)-0.163(-2.11)0.523(2.79)
0.029(6.20)-0.024(-1.98)1.129(2.44)-1.055(-2.26)-0.074(-1.24)0.050(2.94)
3.716(3.96)
FY
21,13160.35%-0.102(-3.21)
0.162(5.70)-0.045(-0.38)-0.385(-3.88)-0.006(-0.18)0.044(0.27)-0.173(-1.78)0.016(0.08)
0.024(4.31)-0.011(-0.89)1.027(1.96)-1.170(-2.27)-0.076(-1.04)0.001(0.04)-2.060(-2.02)
FY
21,13174.48%

46

Industry Manufacture = 1
CitaPatt+2
PValuet+2
Patentt+2
M7
M8
M9

CitaPatt+2
M10-0.078(-4.81)0.023(0.81)-0.205(-1.34)-0.146(-1.49)-0.056(-1.10)-0.183(-1.18)-0.227(-2.58)0.479(1.90)
0.030(4.38)-0.022(-1.24)1.208(2.60)-0.884(-2.10)-0.094(-1.40)0.030(1.40)
5.485(4.58)
FY
18,90450.01%-0.035(-2.38)-0.004(-0.13)-0.165(-1.07)-0.336(-3.65)0.012(0.22)-0.060(-0.38)-0.173(-1.94)0.191(0.73)
0.024(3.50)-0.040(-2.01)0.238(0.49)-0.302(-0.66)-0.058(-0.91)0.032(1.59)
6.251(4.84)
FY
20,65547.82%-0.188(-8.17)0.104(4.07)-0.231(-1.90)-0.008(-0.09)-0.093(-2.32)-0.202(-1.50)-0.247(-2.98)0.661(3.41)
0.039(7.07)-0.013(-0.91)
1.569(3.43)-1.149(-2.58)-0.117(-1.77)0.054(2.78)
7.260(7.17)
FY
18,90460.18%-0.101(-3.09)0.171(5.24)-0.168(-1.25)-0.654(-5.98)-0.085(-1.87)0.106(0.69)-0.278(-2.80)0.082(0.39)
0.026(4.30)-0.006(-0.42)
1.114(2.23)-1.074(-2.25)-0.042(-0.51)
0.003(0.12)-0.165(-0.15)FY
18,90474.55%

Patent Existence = 1
PValuet+2
Patentt+2
M11
M12-0.123(-7.00)0.068(2.98)-0.200(-1.66)-0.044(-0.52)0.014(0.40)-0.103(-0.76)-0.156(-1.96)0.420(2.20)
0.032(6.16)-0.026(-1.86)1.059(2.32)-1.066(-2.30)-0.040(-0.68)0.040(2.31)
6.137(5.96)
FY
20,65559.70%-0.085(-2.90)0.173(5.72)-0.074(-0.54)-0.580(-5.39)-0.007(-0.16)0.213(1.27)-0.172(-1.70)0.100(0.47)
0.024(3.77)-0.011(-0.74)0.950(1.81)-1.177(-2.24)-0.061(-0.81)-0.003(-0.14)-1.862(-1.59)FY
20,65573.33%

Table 4
Endogeneity tests using the instrumental variable analysis
This table presents endogeneity tests using instrumental variable analysis. In the first stage, News, the number of news articles is regressed on the instrumental variable Travel
Time in year t, including other control variables and unreported firm- and industry-fixed effects (IY). Travel Time is the median value of the number of minutes for trips taken
between the headquarters of the firm and Dow Jones offices in year t. In the second stage, corporate innovation outcome variables estimated in year t+2 are regressed on thepredicted news coverage (News Predicted) estimated in year t from the first stage. The corporate innovation outcome variables are the log values of the average number ofcitations per patent (CitaPat), the sum of patent values scaled by market value of equity (PValue), and the number of patents (Patent) measured in year t+2. News coverage(News) is the number of news articles estimated in year t. Panel A describes the cities of Dow Jones offices, the airport cities closest to Dow Jones offices, the average travel
time in minutes between headquarters and Dow Jones offices, and the distribution of travel method in percentage with regard to Dow Jones office. Panel B presents the twostage regression results. The panel sample comprises 34,642 firm-year observations for news coverage variables from 2000 to 2010 and innovation outcome variables from2002 to 2012, with the available information of firm headquarters location from Compustat. Variable definitions are detailed in the Appendix. Key results are highlighted inbold. The t-statistics shown in parentheses are based on standard errors adjusted for heteroskedasticity and firm-level clustering.
Panel A: Travel Time and Travel Method between Firm’s Headquarters and Dow Jones Offices
Dow Jones Office
BostonChicagoMinneapolis
New York
Princeton
San Francisco
Waltham
Washington

AirportTravel Time

By CarBy Direct Flight

By Transfer Flight

By Flight

BostonChicagoMinneapolis
New York
Philadelphia
San Francisco
BostonWashington

233.58201.40217.69229.19270.49313.14249.86218.35

6.39%
4.32%
3.00%
13.08%
15.55%
7.13%
6.75%
3.96%

6.65%
11.14%
11.99%
5.83%
4.50%
7.39%
6.58%
8.55%

86.96%
84.54%
85.01%
81.09%
79.95%
85.48%
86.67%
87.50%

93.61%
95.68%
97.00%
86.92%
84.45%
92.87%
93.25%
96.04%

47

Table 4 - Continued
Panel B: Two-Stage Instrumental Variable Regression
Stage 1
Stage 2
CitaPatt+2
News tVariable

M1

News Predicted t
TravelTime t-0.030(-2.96)

Assets t

0.307(15.11)
0.255(3.36)
0.185(9.88)-0.085(-3.62)-0.187(-3.03)-0.337(-5.36)0.434(3.22)
0.070(9.89)-0.004(-0.35)-0.138(-0.71)0.270(1.24)-0.319(-6.94)0.140(10.22)
11.400(16.24)IY
34,64243.18%

R&D t
Age t
ROAt
PPE t
Leverage t
Capex tTobinQ t
KZIndex t
HIndex t
HIndex2 t
InstOwn t
Analystt
Spread t
Fixed Effects
Observations
R2Adj

Stage 2
PValuet+2

Stage 2
Patentt+2

M2

M3

M4

-1.159(-5.97)-0.825(-4.63)-1.339(-3.54)0.443(7.44)
0.844(9.49)
0.219(5.66)
0.032(0.95)-0.328(-5.64)-0.597(-8.08)0.666(4.97)
0.123(8.75)-0.004(-0.44)-0.268(-1.85)0.417(2.49)-0.324(-4.95)0.219(7.29)
18.147(7.82)
IY
34,64225.11%

0.360(6.59)
0.874(10.77)0.185(5.17)
0.058(2.19)-0.267(-4.82)-0.421(-6.20)0.603(5.11)
0.093(7.16)-0.003(-0.36)-0.189(-1.46)0.325(2.16)-0.302(-5.05)0.164(6.07)
15.662(7.35)
IY
34,64229.77%

0.702(5.98)
1.292(8.50)
0.449(6.06)
0.061(1.30)-0.615(-5.29)-0.833(-5.60)1.397(6.02)
0.163(5.93)
0.012(1.06)-0.673(-2.32)1.000(2.83)-0.555(-4.42)0.281(5.05)
25.026(5.64)
IY
34,64234.38%

48

Table 5
Endogeneity tests using a natural experimentThis table presents the endogeneity tests using a natural experiment. We regress corporate innovation outcome variables
measured in year t+2 on news coverage measures in year t, including other control variables and unreported firm- andyear-fixed effects (FY). The corporate innovation outcome variables are the log values of the average number of citations
per patent (CitaPat), the sum of patent values scaled by market value of equity (PValue), and the number of patents(Patent) measured in year t+2. News coverage (News) is the number of news articles estimated in year t. We focus on thesample of 20,157 firm-year observations for news coverage variable from 2000 to 2005 and innovation outcome variablesfrom 2002 to 2007. During this period, the Dow Jones NewsPlus was launch in March 2003. Post NewsPlus is a dummy
variable equal to one if year t is between 2003 and 2005. News High is dummy equal to one if firms have news coverage
above sample median in year t. Variable definitions are detailed in the Appendix. Key results are highlighted in bold. The
t-statistics shown in parentheses are based on standard errors adjusted for heteroskedasticity.
Variable
News t × Post NewsPlus

CitaPatt+2
M1-0.034(-1.72)

PValuet+2
M2-0.042(-2.94)

Patentt+2
M3-0.070(-3.19)(0.29)
0.069(4.18)-0.040(-0.37)0.048(0.79)-0.009(-0.33)0.090(1.13)-0.189(-3.33)-0.111(-1.07)0.020(4.76)-0.032(-2.71)0.122(0.51)-0.152(-0.59)-0.010(-0.27)-0.008(-0.69)1.158(2.07)-0.320(-10.07)
0.021(1.74)CitaPatt+2M4
0.370(2.89)-0.390(-3.23)0.142
0.011(0.44)
0.080(2.88)
0.003(0.01)-0.188(-1.95)-0.029(-0.50)0.100(0.78)-0.252(-2.92)-0.056(-0.29)0.030(3.94)-0.054(-2.28)0.033(0.08)-0.079(-0.19)-0.048(-0.79)-0.028(-1.48)3.498(3.27)-0.548(-11.04)
0.033(1.38)

PValuet+2M5
0.074(0.87)-0.113(-1.41)
0.554-0.007(-0.37)0.059(3.70)-0.034(-0.27)-0.108(-1.77)0.008(0.28)-0.007(-0.09)-0.157(-2.82)0.067(0.60)
0.020(4.47)-0.031(-2.10)0.138(0.60)-0.165(-0.68)-0.013(-0.35)-0.022(-1.78)1.497(2.44)-0.245(-7.81)0.022(1.51)

Patentt+2M6
0.554(2.93)-0.646(-3.76)0.370
0.026(0.56)
0.145(3.72)-0.046(-0.17)-0.187(-1.31)-0.015(-0.20)0.127(0.68)-0.405(-3.23)-0.098(-0.38)0.050(4.89)-0.077(-2.53)0.289(0.49)-0.410(-0.64)-0.007(-0.08)-0.029(-1.08)
5.198(3.71)-0.819(-10.90)0.077(2.22)-0.0730.020-0.117-0.004-0.0330.008(0.78)
0.077(2.78)-0.006(-0.03)-0.190(-1.97)-0.031(-0.54)0.095(0.75)-0.257(-2.98)-0.048(-0.25)0.030(4.00)-0.055(-2.29)0.045(0.11)-0.082(-0.20)-0.055(-0.91)-0.028(-1.45)3.448(3.22)-0.506(-10.72)-0.003(-0.18)(-0.23)0.058(3.65)-0.037(-0.29)-0.108(-1.79)0.008(0.25)-0.008(-0.10)-0.158(-2.85)0.069(0.62)
0.020(4.50)-0.032(-2.11)0.141(0.61)-0.165(-0.69)-0.015(-0.40)-0.022(-1.77)1.483(2.42)-0.233(-7.91)
0.012(0.98)

FY
20,15758.18%

FY
20,15780.00%

FY
20,15789.98%

FY
20,15758.21%

FY
20,15780.00%

FY
20,15778.68%

News t × Post NewsPlus × News HighNews tAssets t
R&D t
Age t
ROAt
PPE t
Leverage t
Capex tTobinQ t
KZIndex t
HIndex t
HIndex2 t
InstOwn t
Analystt
Spread t
Post NewsPlus
News High
Fixed Effects
Observations
R2Adj

49

Table 6
Additional endogeneity tests
This table presents the additional endogeneity tests based on news sentiment, controlling for past innovation, and usingchange-in-change specification. We regress corporate innovation outcome variables measured in year t+2 on news
coverage measures in year t. The corporate innovation outcome variables are the log values of the average number ofcitations per patent (CitaPat), the sum of patent values scaled by market value of equity (PValue), and the number ofpatents (Patent) measured in year t+2. News coverage (News) is the number of news articles estimated in year t. The
models include other control variables and unreported firm- and year-fixed effects (FY), or industry- and year-fixed effects(IY). Panel A presents the tests on positive news (News Positive) versus negative news (News Negative); Panel B presents thetests controlling for innovation outcome variables in prior year; and Panel C presents the tests based on the change-inchange specification. The full panel sample comprises 36,782 firm-year observations for news coverage variables from2000 to 2010 and innovation outcome variables from 2002 to 2012, and sample size is restricted to 28,780 in change-inchange tests. Variable definitions are detailed in the Appendix. Key results are highlighted in bold. The t-statistics shownin parentheses are based on standard errors adjusted for heteroskedasticity.
Panel A: Endogeneity Tests on News Sentiment
CitaPatt+2
M1
Variable
News Positive tNews Negative t
Assets t
R&D t
Age t
ROAt
PPE t
Leverage t
Capex tTobinQ t
KZIndex t
HIndex t
HIndex2 t
InstOwn t
Analystt
Spread t
Fixed Effects
Observations
R2Adj

PValuet+2
M2

Patentt+2
M3-0.081(-2.49)-0.187(-6.22)-0.135(-3.31)-0.295(-9.01)-0.114(-2.04)-0.073(-1.75)0.091(5.22)-0.181(-1.37)0.037(0.61)-0.064(-1.80)0.133(1.68)-0.182(-3.09)0.036(0.32)
0.042(7.99)-0.033(-2.52)0.747(2.44)-0.679(-2.33)-0.064(-1.57)0.010(0.75)
6.303(8.22)

0.117(7.95)-0.280(-2.67)0.188(3.27)-0.064(-2.58)0.117(1.68)-0.139(-2.61)0.113(1.36)
0.041(10.16)-0.021(-2.15)1.042(3.64)-0.958(-3.28)-0.032(-0.87)0.020(1.75)
5.909(9.67)

0.132(7.67)-0.187(-1.70)-0.255(-4.26)-0.037(-1.39)0.098(1.23)-0.159(-2.63)0.141(1.65)
0.026(5.80)-0.013(-1.35)0.828(2.80)-0.908(-2.97)-0.035(-0.82)-0.006(-0.47)0.188(0.29)

FY
36,78245.48%

FY
36,78256.02%

FY
36,78274.79%

50

Table 6 - Continued
Panel B: Endogeneity Tests Controlling for Past Innovation Outcomes
Variable
News tCitaPatt

CitaPatt+2
M1

PValuet+2
M2

Patentt+2
M3

CitaPatt+2
M4

PValuet+2
M5

Patentt+2
M6-0.048(-4.55)0.237(23.72)-0.098(-8.60)-0.068(-3.05)-0.063(-4.74)-0.122(-8.05)-0.074(-3.24)

PValuet0.440(41.91)Patentt

0.186(9.60)∆ CitaPatt

0.014(2.21)∆ PValuet

0.176(19.68)∆ Patentt
Assets t
R&D t
Age t
ROAt
PPE t
Leverage t
Capex tTobinQ t
KZIndex t
HIndex t
HIndex2 t
InstOwn t
Analystt
Spread t
Fixed Effects
Observations
R2Adj

0.144
(10.75)0.049(3.45)-0.230(-1.91)0.060(1.24)-0.047(-1.48)0.091(1.38)-0.123(-2.46)0.008(0.08)
0.029(6.13)-0.027(-2.13)0.601(2.38)-0.573(-2.37)-0.051(-1.49)0.005(0.41)
4.195(6.32)

0.046(4.31)-0.327(-3.73)0.131(3.27)-0.030(-1.43)0.026(0.49)-0.061(-1.49)0.110(1.54)
0.027(7.88)-0.018(-2.05)0.725(3.49)-0.713(-3.40)-0.015(-0.56)0.007(0.74)
3.571(7.47)

0.107(6.74)-0.222(-2.18)-0.262(-4.74)-0.031(-1.21)
0.062(0.85)-0.134(-2.38)0.138(1.67)
0.023(5.41)-0.013(-1.41)0.717(2.68)-0.809(-2.99)-0.022(-0.55)-0.014(-1.12)-0.528(-0.88)0.085(4.92)-0.181(-1.37)0.038(0.63)-0.061(-1.73)0.129(1.62)-0.185(-3.13)0.039(0.35)
0.042(7.97)-0.034(-2.59)0.749(2.43)-0.679(-2.32)-0.068(-1.66)0.011(0.80)
6.277(8.16)

0.112(7.92)-0.263(-2.59)0.179(3.23)-0.066(-2.71)0.115(1.71)-0.136(-2.63)0.100(1.24)
0.040(10.10)-0.020(-2.19)1.014(3.65)-0.928(-3.28)-0.038(-1.04)0.021(1.88)
5.725(9.61)

0.131(7.97)-0.182(-1.71)-0.225(-3.95)-0.047(-1.79)0.108(1.41)-0.158(-2.72)0.119(1.43)
0.025(5.86)-0.012(-1.33)0.802(2.82)-0.874(-2.97)-0.030(-0.74)-0.006(-0.46)0.013(0.02)

FY
36,78250.05%

FY
36,78265.03%

FY
36,78275.45%

FY
36,78245.40%

FY
36,78257.26%

FY
36,78275.32%

51

Table 6 - Continued
Panel C: Endogeneity Tests Using Change-in-Change Specification∆CitaPatt+2
M1∆PValuet+2
M2∆Patentt+2
M3∆ News t-0.056(-4.50)-0.076(-7.47)-0.209(-6.39)∆ Assets t-0.040(-1.83)-0.218(-1.67)0.046(0.37)-0.023(-0.69)-0.141(-1.81)-0.081(-1.44)0.113(1.00)
0.018(2.90)-0.034(-2.24)0.482(1.56)-0.329(-1.09)-0.071(-1.94)-0.019(-1.46)0.955(1.41)-0.025(-1.69)-0.077(-0.79)0.173(1.97)
0.000(0.00)-0.061(-1.07)0.039(0.86)-0.044(-0.59)0.009(2.18)-0.017(-1.63)0.101(0.47)-0.102(-0.50)-0.012(-0.39)0.008(0.81)
0.008(0.02)-0.058(-1.97)-0.214(-2.02)
1.172(5.35)
0.006(0.16)-0.060(-0.71)
0.053(0.77)-0.063(-0.64)0.011(2.03)-0.026(-2.22)0.700(1.76)-0.589(-1.58)0.090(1.83)
0.003(0.17)-3.659(-4.78)IY
28,7804.58%

IY28,7803.74%

IY
28,780
5.61%

Variable∆ R&D t∆ Aget∆ ROAt∆ PPE t∆ Leverage t∆ Capex t∆ TobinQ t∆ KZIndex t∆ HIndex t∆ HIndex2 t∆ InstOwn t∆ Analyst t∆ Spread t
Fixed Effects
Observations
R2Adj

52

Table 7
Tests on economic channels based on news content
This table presents the tests based on news content on the economic channels through which news coverage affects corporate innovation outcomes. The corporate innovation
outcome variables are the log values of the average number of citations per patent (CitaPat), the sum of patent values scaled by market value of equity (PValue), and the number ofpatents (Patent) measured in year t+2. The main variables of interest are the news coverage variables, which are the numbers of news articles related to earnings (News Earnings),
product (News Product), financing (News Financing), and governance (News Governance) news estimated in year t. The models also include the news coverage for other news content (NewsOther), as well as other control variables and unreported firm- and year-fixed effects (FY). The coefficients on control variables are omitted for brevity. The full panel sample
comprises 36,782 firm-year observations for news coverage variables from 2000 to 2010 and innovation outcome variables from 2002 to 2012. Variable definitions are detailed in
the Appendix. Key results are highlighted in bold. The t-statistics shown in parentheses are based on standard errors adjusted for heteroskedasticity.
Variable
News Earnings t

CitaPatt+2
M1

PValuet+2
M2

Patentt+2
M3-0.378(-5.36)-0.183(-2.50)-0.521(-5.18)News Product t

CitaPatt+2
M4-0.281(-2.27)

PValuet+2
M5-0.038(-0.17)

Patentt+2
M6

CitaPatt+2
M7

PValuet+2
M8

Patentt+2
M9

CitaPatt+2
M10

PValuet+2
M11

Patentt+2
M12

CitaPatt+2
M13

PValuet+2
M14

Patentt+2
M15-0.132(-1.79)-0.039(-0.17)0.350(1.17)-0.078(-4.19)-0.172(-6.06)-0.482(-4.77)-0.046(-0.12)0.981(2.55)-0.056(-1.59)-0.060(-1.51)-0.083(-4.41)-0.177(-6.71)-0.073(-2.08)-0.080(-2.09)-0.344(-4.76)-0.262(-2.09)0.352(1.71)-0.044(-1.96)-0.038(-1.72)-0.071(-0.18)News Financing t

0.372(1.77)

0.373(1.25)

1.013(2.65)

News Governance t
News Other t-0.041(-1.94)-0.171(-6.21)-0.046(-1.15)-0.063(-3.20)-0.185(-7.13)-0.086(-2.27)-0.078(-3.67)-0.193(-7.00)-0.107(-2.77)-0.055(-2.43)-0.065(-3.19)Control Variables

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

FY
36,78245.39%

FY
36,78255.88%

FY
36,78274.85%

FY
36,78245.33%

FY
36,78255.86%

FY
36,78274.79%

FY
36,78245.33%

FY
36,78255.87%

FY
36,78274.81%

FY
36,78245.35%

FY
36,78255.95%

FY
36,78274.81%

FY
36,78245.42%

FY
36,78255.96%

FY
36,78274.88%

Fixed Effects
Observations
R2Adj

53

Table 8
Tests on economic channels based on interaction variables
This table presents the tests based on interaction variables on the economic channels through which news coverage affects corporate innovation outcomes. The corporate innovation
outcome variables are the log values of the average number of citations per patent (CitaPat), the sum of patent values scaled by market value of equity (PValue), and the number ofpatents (Patent) measured in year t+2. The main variables of interest are the news coverage variables, the numbers of news articles related to earnings (News Earnings), product (News
Product), financing (News Financing), and governance (News Governance) news estimated in year t, which are interacted with the factors associated with the relevant economic channels. The
models also include the news coverage of other news content (News Other), other control variables and unreported firm- and year-fixed effects (FY). The coefficients on other control
variables are omitted for brevity. The full panel sample comprises 36,782 firm-year observations for news coverage variables from 2000 to 2010 and innovation outcome variables from2002 to 2012, which varies according to the availability of the factors associated with the economic channels. Variable definitions are detailed in the Appendix. Key results arehighlighted in bold. The t-statistics shown in parentheses are based on standard errors adjusted for heteroskedasticity.
Variable
News Earnings t × Short Horizon t
Short Horizon t

CitaPatt+2
M1

PValuet+2
M2

Patentt+2
M3-0.351(-2.17)0.062(2.57)-0.549(-3.40)0.053(2.84)-0.657(-3.21)-0.009(-0.44)

News Product t × Fluidity t
Fluidity t

CitaPatt+2
M4

PValuet+2
M5

Patentt+2
M6

0.020(0.56)
0.008(2.58)

0.038(0.90)
0.004(1.55)

0.010(0.17)
0.001(0.25)

News Financing t × WWIndex t
WWIndex t

CitaPatt+2
M7

PValuet+2
M8

Patentt+2
M9

3.162(2.48)-0.175(-3.29)2.771(1.93)-0.115(-2.62)-0.630(-0.37)-0.071(-1.60)News Governance t × BIndependence t

CitaPatt+2
M10

PValuet+2
M11

Patentt+2
M12-0.495(-3.34)0.358(3.04)-0.062(-0.66)-0.275(-1.15)0.331(2.92)
0.161(0.44)-0.130(-4.20)-0.050(-0.19)0.142(1.02)-0.402(-3.07)-0.206(-0.51)-0.058(-0.27)1.068(2.18)-0.024(-0.55)

Yes

Yes-0.195(-2.03)-0.259(-2.07)-0.045(-1.99)0.346(1.69)-0.038(-1.69)0.107(1.01)-0.035(-0.16)-0.079(-4.16)0.346(1.19)-0.171(-6.03)-0.183(-1.32)-0.043(-0.11)-0.055(-1.57)0.984(2.52)-0.059(-1.48)-0.344(-4.59)-0.417(-1.39)-0.043(-1.87)0.348(1.66)-0.040(-1.72)-0.143(-1.88)-0.326(-0.88)-0.076(-4.11)
0.262(0.85)-0.176(-6.10)-0.450(-4.35)-0.071(-0.13)-0.062(-1.74)0.892(2.32)-0.055(-1.31)-0.349(-4.83)-0.270(-2.14)-0.045(-2.01)
0.093(0.36)-0.034(-1.54)-0.137(-1.85)-0.044(-0.19)-0.079(-4.20)0.132(0.34)-0.169(-5.88)-0.478(-4.71)-0.057(-0.15)-0.057(-1.60)1.033(2.20)-0.064(-1.59)-0.341(-2.41)0.299(2.43)-0.253(-3.30)-0.288(-2.36)0.253(2.28)
0.091(0.36)-0.007(-0.32)Control Variables

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Fixed Effects
Observations
R2Adj

FY
36,782FY
36,782FY
36,78245.44%

55.99%

74.91%

BIndependence t
News Earnings t
News Product tNews Governance t
News Financing t
News Other t

FY

FY

FY

FY

FY

FY

FY

FY

FY

34,40045.98%

34,40056.27%

34,40075.16%

36,65345.63%

36,65356.15%

36,65374.91%

21,19248.81%

21,19256.57%

21,19274.83%

54

Table 9News coverage and innovator quality
This table presents the tests on whether the impact of media on innovation outcomes is associated with the quality of innovators. We
regress corporate innovation outcome variables measured in year t+2 on news coverage measures in year t. The corporate
innovation outcome variables are the log values of the average number of citations per patent (CitaPat), the summed values ofpatents scaled by market value of equity (PValue), and the number of patents (Patent) measured in year t+2. News coverage (News)is the number of news articles estimated in year t. The proxies of innovator quality are the annual truncation adjusted citation-topatent ratio averaged from year t-3 to t-1 (IQuality Citation), and the sum of patent values scaled by market value of equity averaged
from year t-3 to t-1 (IQuality PValue). The models include other control variables and unreported firm- and year-fixed effects (FY).
The full panel sample comprises 36,782 firm-year observations for news coverage variable from 2000 to 2010 and innovation
outcome variables from 2002 to 2012. Variable definitions are detailed in the Appendix. Key results are highlighted in bold. The tstatistics shown in parentheses are based on standard errors adjusted for heteroskedasticity.
Variable
News t × IQuality Citation t
IQuality Citation t

CitaPatt+2
M1

PValuet+2
M2

Patentt+2
M3-0.187(-4.91)0.070(2.06)-0.153(-4.69)0.175(6.55)-0.112(-2.88)0.065(2.34)

News t × IQuality PValue t
IQuality PValue t
News tAssets t
R&D t
Age t
ROAt
PPE t
Leverage t
Capex tTobinQ t
KZIndex t
HIndex t
HIndex2 t
InstOwn t
Analystt
Spread t
Fixed Effects
Observations
R2Adj

0.019
(1.32)
0.078(4.54)-0.187(-1.42)0.044(0.73)-0.061(-1.73)0.120(1.53)-0.182(-3.13)0.032(0.30)
0.041(7.69)-0.033(-2.52)0.677(2.22)-0.600(-2.07)-0.070(-1.73)0.012(0.94)
6.186(8.11)
FY
36,78245.87%-0.061(-4.01)
0.104(7.14)-0.298(-2.85)0.187(3.31)-0.058(-2.37)0.099(1.43)-0.138(-2.62)0.121(1.48)
0.039(9.66)-0.021(-2.21)
0.954(3.37)-0.869(-3.01)-0.038(-1.03)0.022(1.95)
5.647(9.32)
FY
36,78256.42%-0.030(-1.36)0.129(7.46)-0.193(-1.76)-0.264(-4.38)-0.040(-1.52)0.090(1.14)-0.160(-2.66)0.143(1.68)
0.026(5.69)-0.012(-1.26)0.776(2.63)-0.856(-2.81)-0.034(-0.80)-0.004(-0.32)0.134(0.21)
FY
36,78274.90%

55

CitaPatt+2
M4

PValuet+2
M5

Patentt+2
M6-1.145(-3.99)3.125(6.34)-0.007(-0.26)0.107(6.40)-0.228(-2.14)-0.250(-4.25)-0.024(-0.93)0.069(0.89)-0.153(-2.57)0.155(1.85)
0.024(5.33)-0.016(-1.76)0.703(2.47)-0.793(-2.73)-0.029(-0.70)-0.010(-0.75)-0.506(-0.84)FY
36,78275.26%-0.312(-3.52)
4.825(15.37)-0.027(-2.07)0.044(2.65)-0.241(-1.83)-0.005(-0.09)-0.023(-0.68)0.077(1.01)-0.159(-2.78)0.052(0.48)
0.036(6.76)-0.038(-2.93)0.544(1.87)-0.507(-1.86)-0.047(-1.24)0.000(0.01)
4.745(6.39)
FY
36,78247.40%-0.214(-2.07)
6.123(20.70)-0.091(-7.19)0.061(4.66)-0.351(-3.51)0.115(2.44)-0.012(-0.50)0.041(0.65)-0.111(-2.32)0.139(1.72)
0.033(8.50)-0.027(-2.84)0.771(3.13)-0.733(-2.93)-0.009(-0.29)0.008(0.80)
3.928(7.14)
FY
36,78260.63%

Table 10
News coverage and alternative growth measures
This table presents the tests on the association between news coverage and alternative growth measures. The dependent
variables are four proxies of firm growth, the principal component of following three growth metrics (Growth Overall), theindustry adjusted growth of cash flow from operation measured in year t+3 (Growth CFO), the discretionary selling,
general and administrative expenses measured in year t (Growth SG&A)., and the discretionary research and development
expenses measured in year t (Growth R&D). News coverage (News) is the number of news articles estimated in year t. The
models include other control variables and unreported firm- and year-fixed effects (FY). Panel A presents the results of
OLS regression. Panel B presents the results from the second stage of the two-stage instrumental variable analysis while
the first stage results are the same as reported in Table 4. The full panel sample comprises 36,782 firm-year observations
for news coverage variables from 2000 to 2010 and innovation outcome variables from 2002 to 2012, which varies
according to the availability of the growth metrics and is further restricted in the instrumental variable analysis. Variable
definitions are detailed in the Appendix. Key results are highlighted in bold. The t-statistics shown in parentheses are
based on standard errors adjusted for heteroskedasticity.
Panel A: OLS Regression
Growth Overall
M1

Growth CFO t+3
M2

Growth SG&A t
M3

Growth R&D t
M4

News t-0.041(-2.40)-0.003(-2.46)-0.005(-2.16)-0.005(-3.58)Assets t

0.243(6.30)
5.700(14.05)-0.717(-5.42)
0.637(7.29)
0.202(1.07)
0.021(0.20)
0.255(0.89)
0.119(9.13)-0.330(-6.10)0.040(0.10)
0.261(0.69)-0.322(-4.62)-0.089(-3.72)-0.180(-0.12)FY
17,00068.30%-0.001(-0.49)-0.058(-1.72)-0.019(-2.50)-0.018(-2.11)-0.011(-0.75)-0.012(-1.36)-0.004(-0.15)-0.001(-0.76)-0.001(-0.15)-0.002(-0.05)0.011(0.34)-0.010(-1.81)-0.001(-0.32)-0.117(-0.82)FY
29,84613.60%

0.014(3.18)
0.379(7.47)-0.085(-6.25)0.043(3.78)
0.012(0.50)
0.025(2.05)
0.098(3.16)
0.021(12.21)-0.032(-5.15)-0.116(-2.00)0.129(2.53)-0.038(-4.49)-0.011(-3.72)-0.199(-1.11)FY
30,31770.25%

0.038(12.01)
0.615(21.84)-0.056(-5.20)0.059(9.10)-0.025(-1.40)-0.014(-1.48)0.018(0.71)
0.010(10.33)-0.029(-7.73)0.056(1.60)-0.026(-0.82)-0.026(-4.36)-0.013(-5.79)0.174(1.39)
FY
21,82662.01%

Variable

R&D t
Age t
ROAt
PPE t
Leverage t
Capex tTobinQ t
KZIndex t
HIndex t
HIndex2 t
InstOwn t
Analystt
Spread t
Fixed Effects
Observations
R2Adj

56

Panel B: Instrument Variable Analysis
Growth Overall
M1

Growth CFO t+3
M2

Growth SG&A t
M3

Growth R&D t
M4

News Predict t-1.570(-2.87)-0.034(-2.04)-0.125(-1.42)-0.135(-3.65)

Assets t

0.550(3.27)
7.742(30.15)0.080(0.73)
0.522(5.57)-1.118(-7.01)-0.463(-2.22)2.633(5.85)
0.227(5.42)-0.229(-4.96)-0.639(-2.04)0.940(2.63)-0.471(-2.56)0.189(2.42)
18.983(2.91)

0.010(1.99)-0.012(-0.84)0.003(0.87)-0.036(-5.71)-0.007(-1.19)-0.027(-4.05)0.044(2.51)
0.004(3.25)-0.005(-1.69)-0.031(-2.90)0.038(3.10)-0.012(-2.06)0.008(2.94)
0.431(2.05)

0.034(1.24)
0.736(17.67)0.001(0.07)-0.008(-0.59)-0.172(-7.18)-0.047(-1.47)0.381(6.26)
0.034(5.12)-0.025(-3.69)-0.144(-2.98)0.157(2.66)-0.041(-1.37)0.027(2.14)
1.123(1.09)

0.054(4.73)
0.798(44.24)0.008(1.08)
0.089(13.81)-0.059(-5.07)-0.046(-3.32)0.165(5.27)
0.014(5.17)-0.019(-6.27)-0.026(-1.17)0.051(2.08)-0.044(-3.55)0.011(2.04)
1.697(3.87)

FY
17,00034.55%

FY
29,8460.77%

FY30,31719.53%

FY
21,82641.12%

Variable

R&D t
Age t
ROAt
PPE t
Leverage t
Capex tTobinQ t
KZIndex t
HIndex t
HIndex2 t
InstOwn t
Analystt
Spread t
Fixed Effects
Observations
R2Adj

57

Internet Appendix“Does the media spotlight burn or spur innovation?”
This online appendix provides additional tables for “Does the media spotlight burn or spur innovation?” We summarize the content as follows:

Table IA1: Variable definitions and data sources in the Internet appendix
Table IA2: Alternative measures of innovation outcome
Table IA3: Scaled measures of news coverage and innovation outcome
Table IA4: Alternative sample selection excluding firms with limited news coverage or truncated patent information
Table IA5: Alternative clustering techniques
Table IA6: Additional tests on news originality and press release
Table IA7: Additional tests based on Thomson Reuters News Analytics (TRNA) data

Internet Appendix, Page 1

Internet Appendix 1
Variable definitions and data sources in Internet appendix
This appendix presents variable definitions and data sources for metrics used only in the internet appendix.
Variable

Definition

Innovation outcome variables
Citation

Number of citations of patents for patents granted in one year based on the data provided by KPSS (Kogan, Papanikolaou, Seru, and Stoffman, 2017) and the datacollected from Google USPTO Bulk Downloads. The number of citations per patent is adjusted for truncation, where the raw value is divided by the sample
annual mean (Hall, Jaffe, and Trajtenberg, 2001). Log value of Citation is taken in the regression analysis.

PValue Raw

Sum of patent values for patents granted in one year based on the data provided by KPSS (2017) and the data collected from Google USPTO Bulk Downloads.
The patent value is estimated based on the stock return following patent grant date using the approach from KPSS (2017). Log value of PValue Raw is taken in the
regression analysis.

PValue Ave

Average patent value for patents granted in one year based on the data provided by KPSS (2017) and the data collected from Google USPTO Bulk Downloads.
The patent value is estimated based on the stock return following patent grant date using the approach from KPSS (2017). Log value of PValue Ave is taken in the
regression analysis.

Innovation MAdj

Scaled measures of the average number of citations per patent (CitaPat MAdj), sum of patent values (PValue MAdj - same as PValue in main tables), number ofpatents (Patent AAdj) by market value of equity for patents granted in one year based on the data provided by KPSS (2017) and the data collected from Google
USPTO Bulk Downloads as well as Compustat Annual. PValue MAdj is essentially the same as PValue in the main analysis, which is noted differently in Internet
Appendix merely for sake of presentation consistency. Log value of Innovation MAdj is taken in the regression analysis.

Innovation AAdj

Scaled measures of the average number of citations per patent (CitaPat AAdj), sum of patent values (PValue AAdj), number of patents (Patent AAdj) by total assets forpatents granted in one year based on the data provided by KPSS (2016) and the data collected from Google USPTO Bulk Downloads as well as Compustat
Annual. Log value of Innovation AAdj is taken in the regression analysis.

Innovation EAdj

Scaled measures of the average number of citations per patent (CitaPat EAdj), sum of patent values (PValue EAdj), number of patents (Patent EAdj) by number of
employees, for patents granted in one year based on the data provided by KPSS (2017) and the data collected from Google USPTO Bulk Downloads as well as
Compustat Annual. Log value of Innovation EAdj is taken in the regression analysis.

News coverage variables
News Madj

Scaled measures of the number of news articles by market value of equity in one year based on RavenPack.

News AAdj

Scaled measures of the number of news articles by total assets in one year based on RavenPack.

News EAdj

Scaled measures of the number of news articles by number of employees in one year based on RavenPack.

Internet Appendix, Page 2

Internet Appendix 1 - Continued
Variable

Definition

News coverage variables
News Repeated

Number of repeated news articles in one year based on RavenPack and divided by 100 in regression analysis.

News Original

Number of original news articles in one year based on RavenPack.

News Press Release

Number of press releases issued by a firm in one year based on RavenPack.

News TRNA

Number of news articles in one year based on Thomson Reuters News Analytics (TRNA).

News TRNA+RavenPack , 1

Number of news articles in one year based on RavenPack, which is complemented by the TRNA data if the RavenPack information is missing.

News TRNA+RavenPack, 2

Sum of numbers of news articles in one year from both RavenPack and TRNA databases.

Internet Appendix, Page 3

Internet Appendix 2
Alternative measures of innovation outcome
This table presents regressions of corporate innovation outcome variables on news coverage, using alternative measures ofinnovation outcome, and including other control variables and unreported firm- and year-fixed effects (FY). From M1 to
M3, the corporate innovation outcome variables are the log values of the average number of citations per patent (CitaPat),
the sum of patent values scaled by market value of equity (PValue), and the number of patents (Patent) measured in year
t+3. From M4 to M6, the corporate innovation outcome variables are the logarithm values of the number of citations(Citation), the sum of patent values (PValue Raw), and the average patent value (PValue Ave) for patents granted in year t+2.
News coverage (News) is the number of news articles estimated in year t. The full panel sample comprises 36,782 firmyear observations for news coverage variable from 2000 to 2010 and innovation outcome variables from 2002 to 2012.
Variable definitions are detailed in the Appendix. Key results are highlighted in bold. The t-statistics shown in parentheses
are based on standard errors adjusted for heteroskedasticity and firm-level clustering.
Variable
News tAssets t
R&D t
Age t
ROAt
PPE t
Leverage t
Capex tTobinQ t
KZIndex t
HIndex t
HIndex2 t
InstOwn t
Analystt
Spread t
Fixed Effects
Observations
R2Adj

CitaPatt+3
M1-0.067(-5.53)

PValuet+3
M2-0.007(-8.42)

Patentt+3
M3-0.038(-1.75)

Citation t+2
M4-0.162(-5.51)

PValue Raw t+2
M5-0.373(-8.67)

PValue Ave t+2
M6-0.186(-8.43)0.068(4.38)-0.127(-1.12)0.049(0.88)-0.034(-1.25)0.109(1.53)-0.157(-2.99)0.133(1.33)
0.036(7.91)-0.024(-2.39)0.703(2.56)-0.690(-2.58)-0.038(-1.03)-0.006(-0.48)5.295(7.99)

0.004(6.18)-0.010(-2.15)0.010(3.50)-0.002(-2.10)0.001(0.38)-0.006(-2.56)0.015(3.85)
0.002(8.17)-0.001(-1.24)0.051(3.95)-0.051(-3.95)-0.001(-0.81)0.001(1.86)
0.239(8.40)

0.111(6.09)-0.198(-1.71)-0.205(-3.23)-0.008(-0.29)0.117(1.37)-0.230(-3.46)0.104(1.09)
0.028(6.15)-0.004(-0.45)0.839(2.72)-1.014(-3.24)-0.048(-0.97)-0.024(-1.67)1.463(2.24)

0.188(6.58)-0.371(-1.80)0.164(1.59)-0.093(-1.87)0.276(2.03)-0.375(-3.78)0.057(0.35)
0.072(9.23)-0.049(-2.63)1.704(3.25)-1.678(-3.17)-0.085(-1.19)0.038(1.71)
9.591(8.15)

0.092(2.90)-0.332(-2.00)1.004(7.09)-0.173(-4.10)0.163(1.13)-0.252(-2.27)0.101(0.64)
0.069(8.61)-0.052(-3.45)
1.894(3.03)-1.486(-2.35)-0.155(-1.98)0.072(3.12)
12.151(9.96)

0.010(0.62)-0.132(-1.49)0.479(6.94)-0.097(-4.07)0.047(0.60)-0.091(-1.60)0.002(0.02)
0.030(6.87)-0.031(-3.78)0.548(1.74)-0.290(-0.93)-0.077(-1.90)0.015(1.21)
5.510(8.92)

FY
36,78242.25%

FY
36,78248.07%

FY
36,78272.25%

FY
36,78266.64%

FY
36,78261.97%

FY
36,78254.06%

Internet Appendix, Page 4

Internet Appendix 3
Scaled measures of news coverage and innovation outcome
This table presents regressions of corporate innovation outcome variables on news coverage, using alternative scaled measures of news coverage and innovation outcome, andincluding other control variables and unreported firm- and year-fixed effects (FY). The corporate innovation outcome variables are logarithm values of the average number ofcitations per patent, the sum of patent values scaled by market value of equity, and the number of patents scaled by market value of equity (CitaPat MAdj, PValue MAdj, and Patent
MAdj), book value of total assets (CitaPat AAdj, PValue AAdj, and Patent AAdj), and number of employees (CitaPat EAdj, PValue EAdj, and Patent EAdj) measured in year t+2. The main
variables of interest are the number of news articles scaled by market value of equity (News Madj), book value of total assets (News AAdj), and number of employees (News EAdj)
estimated in year t. The full panel sample comprises 36,782 firm-year observations for news coverage variables from 2000 to 2010 and innovation outcome variables from 2002 to
2012. Variable definitions are detailed in the Appendix. Key results are highlighted in bold. The t-statistics shown in parentheses are based on standard errors adjusted forheteroskedasticity and firm-level clustering.

Variable
News Madj t

CitaPat MAdj t+2
M1

PValue MAdj t+2
M2

Patent MAdj t+2
M3-0.048(-4.76)-0.024(-3.28)-0.048(-4.76)

News AAdj t

CitaPat AAdj +2
M4

PValue AAdj t+2
M5

Patent AAdj t+2
M6-0.116(-4.79)-0.088(-4.31)-0.116(-4.79)News EAdj t

Control
Variables
Fixed
Observations
Effects
R2Adj

Yes

FY
36,78229.96%CitaPat EAdj t+2
M7

PValue EAdj t+2
M8

Patent EAdj t+2
M9-0.084(-4.91)-0.083(-4.52)-0.084(-4.91)

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

FY
36,78230.93%

FY
36,78229.96%

FY
36,78237.69%

FY
36,78237.10%

FY
36,78237.69%

FY
35,87143.06%

FY
35,87140.02%

FY
35,87143.06%

Internet Appendix, Page 5

Internet Appendix 4
Alternative sample selection excluding firms with limited news coverage or truncated patent information
This table presents regressions of corporate innovation outcome variables on news coverage, using alternative sample
selections by excluding firms with limited news coverage, patenting activities, or patent information, and including othercontrol variables and unreported firm- and year-fixed effects (FY). The corporate innovation outcome variables are the log
values of the average number of citations per patent (CitaPat), the summed value of patents scaled by market value ofequity (PValue), and the number of patents (Patent) measured in year t+2. News coverage (News) is the number of news
articles estimated in year t. The full panel sample comprises 36,782 firm-year observations for news coverage variablefrom 2000 to 2010 and innovation outcome variables from 2002 to 2012. We restrict the sample by requiring news
coverage in year t to be positive (News > 0), or focusing on the sub-sample period (Year ~ [2000, 2007]). Variable
definitions are detailed in the Appendix. Key results are highlighted in bold. The t-statistics shown in parentheses are
based on standard errors adjusted for heteroskedasticity and firm-level clustering.

Variable
News tAssets t
R&D t
Age t
ROAt
PPE t
Leverage t
Capex tTobinQ t
KZIndex t
HIndex t
HIndex2 t
InstOwn t
Analystt
Spread t
Fixed Effects
Observations
R2Adj

CitaPat t+2
M1-0.065(-4.74)

News t > 0
PValue t+2
M2-0.145(-9.22)

Patent t+2
M3-0.075(-3.34)

CitaPat t+2
M4-0.034(-2.16)

Year ~ [2000, 2007]
PValue t+2
M5-0.108(-6.84)

Patent t+2
M6-0.145(-6.75)0.091(4.75)-0.161(-1.15)0.002(0.03)-0.070(-1.85)0.112(1.23)-0.183(-2.89)0.079(0.61)
0.046(8.01)-0.036(-2.55)0.803(2.44)-0.722(-2.30)-0.072(-1.66)0.010(0.72)
6.279(7.69)

0.119(7.45)-0.261(-2.33)0.147(2.39)-0.058(-2.18)0.087(1.09)-0.147(-2.56)0.139(1.46)
0.044(10.06)-0.023(-2.23)1.061(3.48)-0.984(-3.15)-0.044(-1.12)0.021(1.68)
6.207(9.41)

0.153(8.34)-0.156(-1.35)-0.280(-4.27)-0.037(-1.30)0.105(1.19)-0.164(-2.53)0.122(1.25)
0.028(5.95)-0.013(-1.28)0.809(2.55)-0.910(-2.74)-0.061(-1.35)-0.004(-0.28)0.123(0.18)

0.081(3.75)-0.062(-0.37)-0.041(-0.52)-0.056(-1.18)0.065(0.64)-0.210(-3.00)0.115(0.74)
0.037(5.88)-0.041(-2.22)0.486(1.44)-0.435(-1.33)-0.068(-1.37)-0.015(-0.99)4.687(4.73)

0.101(6.84)-0.150(-1.26)0.091(1.54)-0.038(-1.34)0.015(0.20)-0.174(-3.33)0.243(2.39)
0.032(7.70)-0.024(-1.91)0.679(2.67)-0.658(-2.46)-0.013(-0.34)-0.007(-0.57)3.580(5.54)

0.105(6.36)-0.154(-1.42)0.290(4.14)-0.069(-2.46)0.073(0.91)-0.195(-3.42)0.126(1.26)
0.035(7.97)-0.026(-2.45)0.791(2.73)-0.748(-2.41)-0.018(-0.44)0.017(1.39)
3.353(5.04)

FY
33,86645.12%

FY
33,86655.76%

FY
33,86674.75%

FY
27,16551.08%

FY
27,16568.72%

FY
27,16581.96%

Internet Appendix, Page 6

Internet Appendix 5
Alternative clustering techniquesThis table presents regressions of corporate innovation outcome variables on news coverage, using alternative clustering
techniques, and including other control variables and unreported firm- and year-fixed effects (FY). The corporate
innovation outcome variables are the log values of the average number of citations per patent (CitaPat), the summed valueof patents scaled by market value of equity (PValue), and the number of patents (Patent) measured in year t+2. News
coverage (News) is the number of news articles estimated in year t. The full panel sample comprises 36,782 firm-year
observations for news coverage variable from 2000 to 2010 and innovation outcome variables from 2002 to 2012.
Variable definitions are detailed in the Appendix. Key results are highlighted in bold. The t-statistics shown in parentheses
are based on standard errors adjusted for heteroskedasticity and firm-level clustering.

Variable
News tAssets t
R&D t
Age t
ROAt
PPE t
Leverage t
Capex tTobinQ t
KZIndex t
HIndex t
HIndex2 t
InstOwn t
Analystt
Spread t
Fixed Effects
Observations
R2Adj

CitaPat t+2
M1-0.062(-3.88)Industry Clustering
PValue t+2
M2-0.127(-5.50)Patent t+2
M3-0.078(-3.87)

CitaPat t+2
M4-0.062(-4.28)

State Clustering
PValue t+2
M5-0.127(-7.92)

Patent t+2
M6-0.078(-2.69)0.085(1.99)-0.181(-1.11)
0.036(0.56)-0.060(-1.61)0.128(1.76)-0.185(-3.74)0.038(0.28)
0.042(4.09)-0.034(-1.79)0.740(0.90)-0.670(-0.91)-0.068(-1.53)0.010(0.70)
6.266(5.10)

0.113(2.82)-0.276(-1.99)0.175(2.23)-0.059(-1.39)0.108(1.80)-0.146(-2.85)0.121(1.01)
0.042(5.55)-0.022(-1.67)1.022(1.58)-0.940(-1.72)-0.037(-0.93)0.022(1.07)
5.909(3.90)

0.134(4.59)-0.186(-1.63)-0.270(-2.73)-0.040(-1.00)0.096(1.14)-0.163(-2.56)0.146(1.56)
0.027(6.80)-0.012(-0.88)0.817(1.47)-0.900(-1.85)-0.033(-1.10)-0.005(-0.33)0.220(0.24)

0.085(4.04)-0.181(-1.09)0.036(0.38)-0.060(-1.50)0.128(1.46)-0.185(-2.18)0.038(0.42)
0.042(7.25)-0.034(-2.61)0.740(3.35)-0.670(-2.94)-0.068(-0.85)0.010(0.63)
6.266(2.50)

0.113(7.61)-0.276(-2.47)0.175(1.86)-0.059(-2.32)0.108(1.78)-0.146(-2.37)0.121(1.13)
0.042(8.30)-0.022(-2.03)1.022(3.30)-0.940(-2.83)-0.037(-0.59)0.022(1.41)
5.909(2.92)

0.134(4.26)-0.186(-2.05)-0.270(-3.26)-0.040(-1.71)0.096(1.35)-0.163(-2.15)0.146(1.69)
0.027(7.61)-0.012(-1.22)0.817(3.33)-0.900(-3.45)-0.033(-0.81)-0.005(-0.45)0.220(0.26)

FY
36,78245.37%

FY
36,78255.91%

FY
36,78274.83%

FY
36,78245.37%

FY
36,78255.91%

FY
36,78274.83%

Internet Appendix, Page 7

Internet Appendix 6
Additional tests on news originality and press release
This table presents regressions of corporate innovation outcome variables on additional news coverage measures based onnews originality and press release, including other control variables and unreported firm- and year-fixed effects (FY). The
corporate innovation outcome variables are the log values of the average number of citations per patent (CitaPat), thesummed value of patents scaled by market value of equity (PValue), and the number of patents (Patent) measured in yeart+2. News coverage (News) is the number of news articles estimated in year t. The additional news coverage measures are
the number of repeated (News Repeated), original news articles (News Original), and the number of press releases by firm (NewsPress Release) estimated in year t. The full panel sample comprises 36,782 firm-year observations for news coverage variablesfrom 2000 to 2010 and innovation outcome variables from 2002 to 2012. Variable definitions are detailed in the
Appendix. Key results are highlighted in bold. The t-statistics shown in parentheses are based on standard errors adjusted
for heteroskedasticity and firm-level clustering.
CitaPat t+2
PValue t+2
Patent t+2
CitaPat t+2
PValue t+2
Patent t+2
Variable
M1
M2
M3
M4
M5
M6
-0.050-0.170-0.210News Repeated t(-1.50)(-4.65)(-4.03)-0.115-0.123
0.018
News Original t(-4.53)(-4.25)(0.50)
News Press Release-0.0700.081-0.269t(-0.85)(1.01)(-2.74)-0.081-0.139-0.053
News t(-5.65)(-7.91)(-2.34)
Assets t
R&D t
Age t
ROAt
PPE t
Leverage t
Capex tTobinQ t
KZIndex t
HIndex t
HIndex2 t
InstOwn t
Analystt
Spread t
Fixed Effects
Observations
R2Adj-0.0500.090-0.1700.115-0.2100.130(5.22)-0.178(-1.35)0.035(0.58)-0.059(-1.68)0.129(1.62)-0.189(-3.20)0.044(0.40)
0.043(8.10)-0.033(-2.51)0.745(2.42)-0.673(-2.30)-0.065(-1.58)0.012(0.93)
6.328(8.24)(7.79)-0.277(-2.63)0.182(3.16)-0.059(-2.39)0.110(1.56)-0.148(-2.76)0.122(1.46)
0.042(10.30)-0.021(-2.18)1.033(3.60)-0.946(-3.22)-0.038(-1.01)0.022(1.91)
5.958(9.69)(7.57)-0.190(-1.74)-0.268(-4.45)-0.041(-1.56)0.097(1.21)-0.160(-2.64)0.138(1.61)
0.027(5.84)-0.013(-1.38)0.820(2.77)-0.902(-2.95)-0.040(-0.93)-0.008(-0.60)0.233(0.36)

0.089(5.14)-0.178(-1.35)0.035(0.58)-0.061(-1.72)0.128(1.61)-0.186(-3.16)0.041(0.38)
0.043(8.03)-0.033(-2.52)0.743(2.41)-0.671(-2.29)-0.066(-1.61)
0.012(0.93)
6.331(8.24)

FY
36,78245.43%

FY
36,78255.83%

FY
36,78274.82%

FY
36,78245.42%

Internet Appendix, Page 8

0.113(7.63)-0.278(-2.64)0.185(3.20)-0.058(-2.35)0.112(1.59)-0.149(-2.79)0.124(1.48)
0.042(10.29)-0.021(-2.21)
1.038(3.61)-0.949(-3.23)-0.038(-1.01)0.022(1.90)
5.947(9.66)
FY
36,78255.79%

0.135(7.83)-0.184(-1.68)-0.257(-4.29)-0.041(-1.54)0.096(1.20)-0.156(-2.59)0.141(1.65)
0.026(5.71)-0.012(-1.22)0.826(2.79)-0.904(-2.95)-0.030(-0.69)-0.002(-0.18)0.213(0.33)
FY
36,78274.81%

Internet Appendix 7
Additional tests based on Thomson Reuters News Analytics data
This table presents regressions of corporate innovation outcome variables on additional news coverage measures estimated by incorporating the Thomson Reuters News Analytics(TRNA) data, including other control variables and unreported firm- and year-fixed effects (FY). The corporate innovation outcome variables are the log values of the average number ofcitations per patent (CitaPat), the summed value of patents scaled by market value of equity (PValue), and the number of patents (Patent) measured in year t+2. News coveragemeasures are the number of news articles extracted from TRNA database (News TRNA), the number of news articles extracted from RavenPack database and if zero, then complemented by
TRNA data (News TRNA + RavenPack, 1), and sum of the number of news articles from both RavenPack and TRNA databases (News TRNA + RavenPack, 2) estimated in year t. The full panel sample
comprises 36,782 firm-year observations for news coverage variables from 2000 to 2010 and innovation outcome variables from 2002 to 2012. From M1 to M6, the sample period fornews coverage variables spans from 2003 to 2010 because the TRNA data is only available from 2003. Variable definitions are detailed in the Appendix. Key results are highlighted inbold. The t-statistics shown in parentheses are based on standard errors adjusted for heteroskedasticity and firm-level clustering.

Variable
News TRNA t

CitaPat t+2
M1

PValue t+2
M2

Year ~ [2003, 2010]
Patent t+2
CitaPat t+2
M3
M4-0.025(-3.58)-0.061(-5.03)0.004(0.26)

News TRNA + RavenPack, 1, t

PValue t+2
M5

Patent t+2
M6-0.074(-4.85)-0.055(-2.91)-0.029(-3.00)News TRNA + RavenPack, 2, t

Year ~ [2000, 2010]
CitaPat t+2
PValue t+2
Patent t+2
M7
M8
M9

-0.026(-4.51)-0.063(-8.36)-0.020(-2.69)Control Variables

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Fixed Effects
Observations
R2Adj

FY
26,813FY
26,813FY
26,813FY
26,813FY
26,813FY
26,813FY
36,782FY
36,782FY
36,78237.32%

45.49%

70.92%

37.27%

45.20%

70.98%

45.42%

56.50%

74.79%

Internet Appendix, Page 9