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CEO CHARACTERISTICS, DISCLOSURE QUALITY, and INNOVATION
by
Hila Fogel-Yaari
A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy
Joseph L. Rotman School of Management University of Toronto
© Copyright by Hila Fogel-Yaari (2016)
ii
CEO Characteristics, Disclosure Quality, and Innovation
Hila Fogel-Yaari
Doctor of Philosophy
Joseph L. Rotman School of Management
University of Toronto
2016
Abstract
Innovation is an important driver of economic growth. In this dissertation, I bring together two
main streams of literature on the firm-level determinants of innovation: (1) CEO characteristics
(CEOs directly influence internal processes that culminate in corporate innovation) and (2)
information asymmetry between CEOs and investors (reduced information asymmetry may
improve monitoring and increase corporate innovation). I show that disclosure quality plays a
role in both cases: (1) a reduction of information asymmetry through higher disclosure quality is
associated with more innovation; and (2) disclosure quality serves as a mechanism through
which CEO characteristics also affect innovation (i.e., CEO characteristics’ “indirect effect”).
Based on a path analysis, the indirect effect is shown to be statistically and economically
significant, and accounts for as much as 33% of a CEO’s total effect on innovation. This implies
that CEOs affect corporate performance not only by shaping internal processes, but also by
utilizing disclosure quality to raise financial capital. Overall, my findings show the importance of
disclosure quality for corporate innovation.
iii
Acknowledgments
I thank my supervisors, Jeffrey Callen and Hai Lu, for their continuous guidance during
my doctoral studies. I also thank the other members of my dissertation committee—Aida Sijamic
Wahid, and Alberto Galasso—for providing excellent comments and suggestions, and Gordon
Richardson for his invaluable insights. I am also grateful for my colleagues who provided
assistance and patience. I dedicate this dissertation to my family, whose continued and
unwavering support helped to make this dissertation possible.
iv
Table of Contents
Abstract ........................................................................................................................................... ii
Acknowledgments .......................................................................................................................... iii
Table of Contents ........................................................................................................................... iv
List of Tables ................................................................................................................................. vi
List of Appendices ........................................................................................................................ vii
Chapter 1 - Introduction .................................................................................................................. 1
Chapter 2 – Literature Review and Hypothesis Development ........................................................ 8
2.1. Literature Review ................................................................................................................ 8
2.2. First Hypothesis ................................................................................................................ 11
2.3. Second Hypothesis ............................................................................................................ 14
Chapter 3 - Research Design ......................................................................................................... 17
3.1. Main Variables .................................................................................................................. 17
3.1.1. Innovation ............................................................................................................. 17
3.1.2. Disclosure Quality ................................................................................................ 19
3.1.3. CEO Characteristics .............................................................................................. 20
3.2. Regression Models ............................................................................................................ 21
3.2.1. Test of H1 ............................................................................................................. 21
3.2.2. Test of H2 ............................................................................................................. 24
Chapter 4 - The Sample ................................................................................................................ 27
Chapter 5 - Results ........................................................................................................................ 31
5.1. Test of H1 ......................................................................................................................... 31
5.2. Test of H2 ......................................................................................................................... 36
5.2.1. CEO Characteristics and Innovation ..................................................................... 36
v
5.2.2. CEO Characteristics and Disclosure Quality ........................................................ 40
5.2.3. Test of H2 ............................................................................................................. 42
Chapter 6 - Robustness Tests ........................................................................................................ 46
6.1. Alternative Measures of Disclosure Quality ..................................................................... 46
6.2. Alternative Measures of Innovation .................................................................................. 49
6.3. Alternative Regression Specifications .............................................................................. 51
6.4. Innovation as Measured by R&D Expenditure ................................................................. 53
6.5. The Mechanism through which Disclosure Quality Affects Innovation .......................... 54
Chapter 7 - Conclusion ................................................................................................................. 59
References ..................................................................................................................................... 62
vi
List of Tables
Table 1: Sample selection ............................................................................................................. 76
Table 2: Descriptive statistics ....................................................................................................... 77
Table 3: The direct effect of disclosure quality on innovation (H1) ............................................. 82
Table 3b: The direct effect of disclosure quality on innovation (H1) ........................................... 84
Table 4: The relation between disclosure quality and innovation ................................................ 87
Table 5: The importance of CEO characteristics for innovation .................................................. 89
Table 6: The importance of CEO characteristics for disclosure quality ....................................... 93
Table 7: CEO characteristics’ indirect effect on innovation through disclosure quality (H2) ..... 94
Table 8: Alternative measures of disclosure quality – mean of industry-year normalized ranking
..................................................................................................................................................... 100
Table 9: Alternative measures of disclosure quality – error in analyst forecasts ....................... 106
Table 10: Alternative measures of innovation – three-year-ahead patent data ........................... 112
Table 11: Alternative regression specifications – Poisson and negative binomial ..................... 115
Table 12: R&D expenditure ........................................................................................................ 120
Table 13: Cost of Equity Capital ................................................................................................ 122
vii
List of Appendices
Appendix A: Variable definitions ................................................................................................. 69
Appendix B: Patent Data .............................................................................................................. 74
1
Chapter 1 - Introduction
Innovation is an important driver of economic growth, supplying “half or more of
economic growth these days” (The Economist, 2014). At the microeconomic level, innovation is
important for firm value as a determinant of expected future cash flows, credit risk, and discount
rates (Hall, Jaffe, and Trajtenberg, 2001, 2005; Hegde and Mishra, 2014; Plumlee, Xie, Yan, and
Yu, 2015). In his extensive literature review, Cohen (2010) notes that we know more about the
effects of industry-level variables on corporate innovation than we do about the impact of firm
characteristics.
Prior research on firm-level determinants of innovation has focused on either (1) the role
of information asymmetry between management and shareholders (Aghion, Van Reenen, and
Zingales, 2013; Chang, Hilary, Kang, and Zhang, 2013; He and Tian, 2013) or (2) the role of
Chief Executive Officers’ (CEOs’) characteristics (Daellenbach, McCarthy, and Schoenecker,
1999; Cannella, Finkelstein, and Hambrick, 2009; Galasso and Simcoe, 2011; Bereskin and Hsu,
2014). The former stream of literature theorizes and shows that a reduction in information
asymmetry improves monitoring and is associated with more innovation (e.g., Aghion et al.,
2013). I contribute to this literature by documenting a positive association between disclosure
quality and innovation. The latter stream of literature contends that CEO characteristics, such as
overconfidence, experience, and attention, are primary determinants of corporate innovation
(Hirshleifer, Low, and Teoh, 2012; Kaplan, 2008; Custódio, Ferreira, and Matos, 2014).1 I
contribute to this literature as well by showing that disclosure quality is a mechanism through
which CEO characteristics impact innovation. Taken together, my results bridge these two
streams of literature on the firm-level determinants of innovation by showing that disclosure
quality plays a role in both of them.
1 In general, Bertrand and Schoar (2003) show that CEOs affect firm performance.
2
Disclosure quality plays a potentially important role in mitigating the information
asymmetry between managers and investors. While innovation is a risky investment that requires
a long-term orientation (Hall, 2009; Manso, 2011; Tian and Wang, 2014), information
asymmetry increases the demand for reporting strong short-term results (Graham, Harvey, and
Rajgopal, 2005; Reed, 2005), increases the cost of financing (Francis, LaFond, Olsson, and
Schipper, 2004), and weakens external stakeholders’ ability to monitor CEOs (Biddle, Hilary,
and Verdi, 2009). Improved disclosure quality reduces information asymmetry. Therefore, I
predict a positive association between disclosure quality and innovation. 2
In contrast, the literature on CEOs relies on the “upper echelons” theory, which maintains
that corporate performance reflects the choices made by upper management (Hambrick and
Mason, 1984; Hambrick, 2007; Cannella et al., 2009), and the differences in corporate
innovation stem from differences in CEOs’ tendency to innovate. For example, CEOs with
technical backgrounds are more likely to increase innovation than CEOs with legal backgrounds
(Daellenbach et al., 1999). While those with technical backgrounds are more likely to focus on
and comprehend the technical, operational, and financial implications of a proposed innovation,
those with legal backgrounds are more likely to focus on increasing short-term efficiency at the
expense of long-term innovation (henceforth, CEO characteristics’ “direct effect”).
CEO characteristics matter not only for innovation but also for disclosure quality. For
example, managers with legal backgrounds tend to guide expectations downwards, and managers
with financial backgrounds provide more precise earnings forecasts (Bamber, Jiang, and Wang,
2010). CEOs with a high tendency for innovation may either increase disclosure quality to
finance their innovative aspirations, or may inadvertently reduce disclosure quality since
increased innovation leads to more uncertainty about future performance. Taken together with a
hypothesized association between disclosure quality and innovation, CEO characteristics may
also affect innovation through disclosure quality. Therefore, I posit that disclosure quality is a
2 It may be that firms increase disclosure quality to compensate for the weaker information environment associated
with innovation. Therefore, I employ a three-stage least squares simultaneous regressions model to account for the
possibly endogenous relationship between disclosure quality and innovation.
3
mechanism through which CEO characteristics influence innovation (henceforth, CEO
characteristics’ “indirect effect”).
To test the relation among CEO characteristics, disclosure quality, and innovation, I
employ a sample of 24,134 CEO-firm-year observations between 1996 and 2010. Innovation is
measured by one-year-ahead patent data—specifically, the number of patent applications that
were eventually granted, and the number of forward citations received by patents filed in a given
year.3 Regarding CEO characteristics, I capture CEOs’ tendency to innovate as measured by
CEO fixed effects beyond the firm-level and time-variant variables known to impact corporate
innovation (these are the estimated CEO coefficients similar to Bertrand and Schoar, 2003). I
supplement my tests with observable CEO backgrounds, which are a manifestation of the
characteristics that shape CEOs’ tendency for innovation. Disclosure quality is a composite
measure. It is measured as the principal component of financial reporting quality (accruals
earnings quality, and 10-K readability and length) and management guidance quality (number of
management guidance).
In what follows, I first estimate the relation between disclosure quality and innovation. I
regress innovation on different aspects of disclosure quality and find that financial disclosure
quality has a positive association with innovation. However, it is possible that innovation leads to
more disclosure to the extent CEOs want to reduce the higher uncertainty that comes with
uncertain future payoffs arising from innovation activities.4 To address this possible concern of
reverse causality, I use three-stage least squares estimation, which allows for simultaneity
between innovation and disclosure quality thereby indicating the direction of causality. This
simultaneous equations modeling shows that while disclosure quality increases innovation,
3 I am using patent data to measure innovation, but firms may be innovative without employing patent protection.
Therefore, I restrict my sample to firms that utilized patent protection at least once in the patent grant files. The
patent grant files span a time period longer than my sample, so that some firms in my sample are potentially non-
innovative.
4 To address a potential self-selection bias, I use the relationships between patent data and variables that explain
innovation to predict the number of patent applications and patent citations of the firms that do not utilize patent
protection. Untabulated tests confirm that the relation between disclosure quality and innovation does not change
when I add the predicted observations to my sample.
4
innovation reduces disclosure quality. The result that innovation reduces disclosure quality is
consistent with innovation being uncertain. Innovation’s uncertain nature entails uncertain
expected earnings, which, in turn, hinder high quality disclosure. Hence, innovation has a
negative coefficient in the regression of disclosure quality on innovation. The result that
disclosure quality increases innovation is consistent with disclosure quality playing a monitoring
role. Disclosure quality’s monitoring role deters myopic decisions, which consequently improves
innovation. Hence, disclosure quality has a positive coefficient in the regression of innovation on
disclosure quality. This evidence is consistent with high disclosure quality increasing innovation.
Given these results, I re-examine the relation between CEO characteristics and
innovation. Empirically, regressing innovation on CEO characteristics yields estimates of
characteristics’ total association with innovation, including both the direct relation between them
and the mechanism (the indirect relation) through which CEO characteristics affect innovation.
Simultaneous equations model allows me to conduct a path analysis to distinguish between these
direct and indirect relations. I model innovation as a function of both CEO characteristics and
disclosure quality. The results indicate that disclosure quality serves as an indirect channel
through which CEO characteristics affect innovation. Specifically, CEO tendency for innovation
has a positive association with disclosure quality, and this higher disclosure quality is an indirect
channel that increases the total effect of the CEO on innovation.5 Furthermore, I show that this
indirect effect is more pronounced for CEOs with financial backgrounds and technical
backgrounds. The indirect effect on patent quality is almost 20% (33%) of the total effect on
innovation for CEOs with financial (technical) backgrounds, suggesting that disclosure quality
may be a significant mechanism through which CEO characteristics affect innovation.6
Lastly, I run sensitivity tests and show that my results are robust to alternative measures
of disclosure quality, regression specifications, and innovation proxies. Alternative measures of
5 In contrast, CEO’s financial background has negative direct and indirect effects on innovation.
6 To facilitate discussion of these results I use the term effect to describe the relation between CEO characteristics
and innovation. This term is consistent with the stream of literature on the effects of CEO characteristics on firm
performance. However, in my work I cannot rule out an endogenous matching between CEOs and firms. Therefore,
the path analysis can only demonstrate associations, not causal effects.
5
disclosure quality include replacing the principal component measure with the mean of the
normalized ranking of the disclosure quality components and with the absolute value of the error
in analyst forecasts. I also show that the results hold for Poisson and negative binomial
regressions whenever the dependent variable involves patent count data. Furthermore, I replace
one-year-ahead patent measures with three-year-ahead patent measures and demonstrate that
while CEOs affect corporate innovation at all stages of the innovative process, disclosure quality
has a stronger effect at the initial stages. Finally, I test whether cost of equity capital is the
mechanism through which disclosure quality affects innovation and show that the magnitude of
the effect of cost of equity capital is less than half of that of disclosure quality.
My dissertation makes several contributions to the literature. First, this study bridges two
streams of literature regarding firm-level effects on innovation: the characteristics of the firm’s
CEO (Daellenbach et al., 1999; Cannella et al., 2009; Galasso and Simcoe, 2011; Bereskin and
Hsu, 2014), and the role of information asymmetry between management and shareholders
(Aghion et al., 2013; Chang et al., 2013; He and Tian, 2013). While one stream of literature
assumes that the main driver of innovation is innate CEO characteristics (mediated to some
extent by bounded rationality), the other assumes that the main driver of innovation is the
relation with external constituencies (and all CEOs are completely and uniformly rational). This
study shows that improved disclosure quality is associated with more innovation under both
perspectives.
Second, I contribute to the literature on the effect of information asymmetry on
innovation. Recent papers in finance and accounting have documented the effects of institutional
ownership, analyst following, and conservative reporting on innovation (Aghion et al., 2013;
Chang et al., 2013; He and Tian, 2013). In contrast, this study focuses on reduction of
information asymmetry between CEOs and investors by improving disclosure quality. This is
among the first studies to show that firms with higher-quality disclosure have higher levels of
innovation.
Third, this dissertation contributes to the literature regarding the effects of CEO
characteristics on firm performance. Bertrand and Schoar (2003) document the general effect of
CEO characteristics on firm performance. For specific CEO characteristics, Daellenbach,
6
McCarthy, and Schoenecker (1999) and Barker and Mueller (2002) show that a CEO’s technical
background is associated with the corporation’s level of research and development (R&D)
spending. Galasso and Simcoe (2011) provide evidence that CEO overconfidence is associated
with higher innovation quality. I test the relation between CEO’s background (technical,
financial, and legal) and corporate innovation. Furthermore, previous studies have utilized small
samples from specific industries. This research study contributes to the literature by using a large
sample to confirm that CEOs with a technical background are more innovative, and to document
that CEOs with a legal background are less innovative.7
Fourth, I show that CEO characteristics have an indirect effect on innovation through the
connection between their characteristics and disclosure quality. This effect accounts for as much
as 33% of their total effect on innovation. This indirect effect suggests that CEOs utilize
disclosure quality to raise capital necessary for corporate innovation. While the current literature
focuses on a CEO’s choice of investment projects and influence on internal processes as the
mechanisms through which CEOs affect innovation, my findings imply that CEOs affect
corporate performance also through their influence on the firm’s ability to raise required capital.
Fifth, I also contribute to the research on the relation between CEO background and
disclosure quality. Bamber, Jiang, and Wang (2010) show that accounting and finance
backgrounds are associated with more precise management guidance. This study provides
evidence that CEOs with a technical background are also associated with higher financial
disclosure quality (but lower frequency of management guidance); while CEOs with a finance
background have lower disclosure quality as measured by higher unsigned discretionary
accruals.
Finally, this dissertation contributes to the literature on the economic consequences of
disclosure quality. Biddle, Hilary, and Verdi (2009) report the positive association between
financial reporting quality and investment efficiency, where investment efficiency is measured
7 The literature has also shown that CEO’s age and tenure affect R&D spending. The results remain unchanged if I
include CEO age and tenure in my regressions, and they are included in the calculation of innovation-related CEO
fixed effects.
7
by comparing the expected corporate spending to the spending of other firms in the same
industry and year. They find that their results are stronger for R&D activities than for capital
expenditure’s component of investment (p. 113). This research study lends support to their
findings by documenting a positive association between patent citation and disclosure quality
while controlling for R&D spending. Patent citations capture a unique aspect of investment
efficiency in the sense that investing more funds in innovation may lead to a greater number of
patents, but if those patents have few citations, the low number of citations may indicate that the
investment in innovation would have been better spent in a different direction. Similarly, patent
citations are also positively associated with market value (Hall, Jaffe, and Trajtenberg, 2005) and
future earnings (Gu, 2005).
The next sections are as follows. Chapter 2 is a review of the literature, and develops the
hypotheses. Chapter 3 presents the research design. Chapter 4 describes the sample. Chapter 5
details the empirical results. Chapter 6 explains the results of additional robustness tests, and
Chapter 7 concludes.
8
Chapter 2 – Literature Review and Hypothesis Development
2.1. Literature Review
Innovation is a process that creates new ideas, devices, or methods. It is an important
driver of economic growth, supplying “half or more of economic growth these days” (The
Economist, 2014). At the micro level, innovation is an important strategic decision that is crucial
for the firm’s survival and success because it creates a strategic advantage over competitors and
determines firm’s growth and long-term survival (Hall, 1987; Griliches, 1990; Banbury and
Mitchell, 1995; Roberts, 1999; Cefis and Marsili, 2005; Hall, Jaffe, and Trajtenberg, 2005). Hall
(1987) finds a positive correlation between innovation and firm’s growth and survival rate that
exceeds the impact of physical investment. Banbury and Mitchell (1995) research the U.S.
implantable pacemaker market and show that the incumbent firms that are the first to adapt the
incremental product innovation enjoy a larger market share than their competitors. Roberts
(1999) shows that propensity for innovation is associated with superior profitability (but not with
persistent profitability). Cefis and Marsili (2005) show that the expected survival time of an
innovative firm is about 11 percent higher than that of a non-innovative firm (in the
Netherlands). Hall, Jaffe, and Trajtenberg (2005) find that an extra citation per patent boosts
market value by 3%.
The research into corporate innovation began with studies on the extent external forces
that shape firms’ decisions to innovate, such as intellectual property (IP) protection (Mansfield,
1986; Pacheco-de-Almeida and Zemsky, 2012) and industry competition (Scherer, 1967;
Banbury and Mitchell, 1995; Aghion, Bloom, Blundell, Griffith, and Howitt, 2005). The
literature on IP protection debates whether IP protection mechanisms, such as patents, are
beneficial for innovation. On one hand, patent laws create incentive to invent and encourage
economic growth (Khan and Sokoloff, 1993). On the other hand, they may deter future
generations from investing in R&D (Scotchmer, 1991) and may be used strategically to hinder
competition and extract license fees (Moser, 2013). Mansfield (1986) shows that overall patent
protection is beneficial for innovation and that firms do not prefer to rely on trade secrets when
patent protection is possible. Furthermore, to understand firms’ decision to innovate, the
9
literature has looked at industry conditions and competitive pressures. Pacheco-de-Almeida and
Zemsky (2012), show that in some cases, this sharing of information is a strategic decision by
firms to reduce competition by inducing competitors to spend their resources on imitation instead
of continued innovation. Papers such as this and Banbury and Mitchell (1995) focus on “first
mover advantage” and the importance of the timing of innovation. Aghion et al. (2005) merge
the positive effect of competition in spurring innovation when competition is low, with the
Schumpeterian effect of firms focusing on increased profit margins when competition is high.
They show that the two effects result in an inverted-U shape relationship between competition
and innovation.
Only more recently, the literature has started exploring firm-level effects. One stream of
literature focuses on managers’ influence on corporate innovation. This literature relies on the
upper echelons theory, which explains that corporate performance is a reflection of the choices
made by the corporation’s top-most figures (Hambrick and Mason, 1984; Hambrick, 2007;
Cannella et al., 2009). Therefore, top management’s strategic choices and performances are
shaped by their innate characteristics, such as their background and personality traits, which in
turn shape managers’ perceptions and values. The general relation between CEOs and firm
performance was documented by Bertrand and Shoar (2003), who tested the importance of CEO
fixed effects for corporate R&D spending.8 Similarly, Kaplan (2008) shows that CEO’s attention
(as measured by investment decisions) shapes corporate response to technical changes. Specific
characteristics that affect R&D spending include age, tenure at the firm, functional background
(Barker and Mueller, 2002), and implicit motives (Veenstra, 2013). Baker and Mueller (2002)
show that younger CEOs invest less in R&D, and CEOs’ career experience in marketing or
engineering is correlated with increased R&D spending, while a legal career or a career in
production or operations is associated with decreased R&D spending. Furthermore, CEO
characteristics are more strongly correlated with R&D spending for CEOs with longer tenures.
Looking at patents, which are a more direct measure of innovation, studies show that CEO
8 R&D spending is required for corporate innovation, and is sometimes used as a measure of innovation. For
example, Bereskin, Hsu, and Rotenberg (2015) provide empirical evidence that abnormal cuts in R&D spending
lead to decreased innovation performance in the following years.
10
personality traits influence innovation. For example, firms with overconfident CEOs have higher
levels of innovation, probably due to greater risk-taking, as these CEOs under-estimate the
probability of failure (Galasso and Simcoe, 2011; Hirshleifer, Low, and Teoh, 2012).
A separate stream of literature focuses on the role of information asymmetry between
management and shareholders (Aghion et al., 2013; Chang, Hilary, Kang, and Zhang, 2013; He
and Tian, 2013).9 Information about innovation is important to investors as the growth expected
from innovation affects firm valuation, and innovation success impacts risk-assessment by
creditors. On the one hand, increased information asymmetry is detrimental to innovation in that
it restricts monitoring, which allows CEOs to engage in myopic behavior, and increases costs of
capital. Aghion, Van Reenen, and Zingales (2013) show that a higher percentage of institutional
ownership is associated with more innovation for firms that rely on patent protection. Similarly,
Chen, Huang, and Lao (2015) show that earnings guidance is associated with more innovation.
On the other hand, reduced information asymmetry may result in undue pressure on CEOs to
meet short-term goals at the expense of long-term investments in innovation (He and Tian, 2013;
Chang et al., 2013; Mao, Tian, and Yu, 2015). He and Tian (2013) explain that analysts put
pressure on the firm to meet short term goals and the expense of long term innovation.10 Mao,
Tian, and Yu (2015) study the effect of venture capital staging on entrepreneurs’ innovation
productivity, and show that IPO firms are less innovative when venture capitalists have greater
influence through a larger number of venture capital rounds. Specifically for accounting, Chang
et al. (2013) show that conservatism is associated with less innovation. They explain that the
additional monitoring through conservatism results makes it harder for firms to meet targets, so
that managers become more myopic and less willing to assume the risk associated with
innovation.
Financial accounting plays both a monitoring and a contracting role. While the literature
described above has shown that monitoring can either increase or decrease innovation,
9 Kerr and Nanda (2015) review this emerging literature.
10 Clarke, Dass, and Patel (2014) cast doubts on the results in He and Tian’s (2013) study, showing that He and
Tian’s results are driven by sample selection decisions, so that they hold only for firms that are “poor innovators”.
11
accounting’s contracting role should provide firms with better access to financing and therefore
increase innovation. By focusing on overall disclosure quality, I not only contribute to the debate
on the effect of monitoring on corporate innovation, but I also utilize CEOs’ ability to influence
disclosure quality to bridge the two streams of literature on firm-level effects on innovation.
2.2. First Hypothesis
The decision to invest in innovation is a unique investment decision with certain
distinguishing characteristics. Like other investments, it is positively associated with growth
(Hall, 2009) and is costly (Hall, 2010).11 However, unlike routine tasks, innovation involves a
long-term, uncertain process that has a high probability of failure (Holmstrom, 1989). Therefore,
investment in innovation is highly risky (Hall, 2009)12 and requires a focus on long-term
performance and a tolerance for short-term failure (Manso, 2011; Tian and Wang, 2014). The
fulfillment of these requirements is influenced both by the information asymmetry between the
CEO and investors, and by the characteristics of the CEO. For example, CEO characteristics,
such as the CEO’s risk tolerance, may indicate a CEO’s long-term focus and tolerance for short-
term failure. Alternatively, a reduction in information asymmetry between the CEO and investors
can help investors ensure that the CEO focuses on long-term goals and set incentives that
increase the CEO’s tolerance for short-term failure. In these ways, a reduction in information
asymmetry would increase innovation.
Information about innovation is important to investors because the growth expected from
innovation affects firm valuation, and because investment in innovation impacts risk-assessment
by shareholders and creditors. Innovation affects a firm’s expected future cash flows, credit risk,
and discount rate (Hall, Jaffe, and Trajtenberg, 2001, 2005; Plumlee, Xie, Yan, and Yu, 2015;
Hegde and Mishra, 2014). However, the information asymmetry between CEOs and shareholders
11 For example, the European Community Innovation Survey Wave 4 (CIS4) mentions that innovation in large
public companies requires “massive expenditure on Research and Development spending (R&D), design and
marketing expenses for bringing a new product to market, investment in the necessary new capital equipment, and
investment in training.”
12 Because investment in innovation is highly risky, some studies treat R&D intensity as a control for risk (Huddart
and Ke, 2007; Custódio and Metzger, 2013).
12
may be harmful to innovation because it increases both the demand for reporting strong short-
term results (Graham et al. 2005; Reed, 2005), and increases the cost of financing (Holmstrom,
1989; Hall, 2010). Empirically, the literature on the relation between information asymmetry and
innovation shows that a reduction of information asymmetry between CEOs and investors
(measured by high institutional ownership) is associated with more innovation (Aghion et al.,
2013).13
A direct means to reduce information asymmetry is to improve disclosure quality.14
Disclosure quality not only reduces information asymmetry, but it also improves capital
investment efficiency (Biddle, Hilary, and Verdi, 2009; Hope and Thomas, 2008; Biddle and
Hilary, 2006). The improvement in investment efficiency follows from both enabling investors to
monitor CEOs’ investment decisions and from improving financing efficiency. High disclosure
quality reduces under- and over-investment. High disclosure quality reduces under-investment
by helping financially constrained firms raise the capital necessary for innovative projects. When
a firm under-invests in R&D, future corporate innovation suffers since innovation is not possible
without R&D spending. The consequences of low levels of innovation are lower market value
and deterioration in a firm’s sustainable, long-term competitive advantage (Bereskin, Hsu, and
Rotenberg, 2015). Furthermore, high disclosure quality reduces over-investment by reducing the
likelihood that a firm obtains excess funds due to temporary mispricing, and it deters firms with
high liquidity from investing in value-decreasing activities. When a firm over-invests in R&D,
the firm runs the risk of insufficient funding for future innovation.
13 In contrast, He and Tian (2013) find that analyst following is negatively associated with innovation. However,
their results hold only for their subsample of firms, which includes firms that do not utilize patent protection and
excludes firms with no analyst following (Clarke, Dass, and Patel, 2014).
The association between analyst following and innovation is positive in a sample that focuses on firms that utilize
patent protection (to overcome the limitation that not all innovative firms patent their innovations). Such a sample
excludes firms that have never filed for patent protection and includes also smaller firms which utilize patent
protection but do not attract analyst following.
14 There are many factors that affect disclosure decisions. Examples include earnings and returns volatility, industry
reporting standards, government subsidies (Jones, 1991), and employee ownership (Bova, Dou, and Hope, 2015).
13
Overall, if we ignore the differences in CEOs’ ability to evaluate innovative projects,
firms with higher-quality disclosure are expected to have higher levels of innovation. Innovation
should benefit more from high quality disclosures than other types of investments (Palmon and
Yezegel, 2012) because information asymmetry between the firm and investors is greater with
regard to innovation due to the high proprietary costs, complexity, and uncertain outcomes
associated with innovation (Bhattacharya and Ritter, 1983; Holmstrom, 1989). Therefore, the
first hypothesis (in the alternative form) is:
H1: Disclosure quality is positively associated with innovation.
Innovation may lead to more disclosure to the extent CEOs may attempt to reduce the
higher uncertainty that comes with uncertain future payoffs arising from innovative activities.
However, the greater uncertainty associated with innovation may increase earnings volatility and
make it harder to forecast earnings, so that efforts to reduce the uncertainty could reduce
disclosure quality. For example, if the CEO tries to increase disclosure by providing more
information in the 10-K filings, these disclosures could decrease readability of the 10-K by
making it harder to read, especially if innovation is hard to explain. Similarly, an attempt to
reduce uncertainty by managing earnings, would result in greater use of accruals earnings
management. Overall, I expect innovation to decrease disclosure quality and disclosure quality to
increase innovation.
It should be noted that my work diverges from the extant accounting research on R&D
expenditures and disclosure quality. As described in the review above, the existing literature on
intangibles and disclosure quality focuses on the accounting policy choice (expensing versus
capitalizing R&D expenditures), on the value relevance of reported R&D expenditures, and on
factors that influence R&D reporting (Lev and Sougiannis, 1996; Lev, Sarath, and Sougiannis,
2005; Skaife et al., 2013; and many others); that is, the extant literature on R&D spending and
disclosure quality focuses on the effect of R&D spending on the quality of the information
provided by the accounting number. In contrast, this study focuses on how disclosure decisions
impact the decision to invest in innovation. Moreover, I look at disclosure quality in general
(earnings quality, readability and management guidance) rather than at the quality of R&D
reporting.
14
2.3. Second Hypothesis
In light of the proposed relation between disclosure quality and innovation, I re-examine
the relation between CEO characteristics and innovation. Previous works that studied the effects
of CEO characteristics on innovation relied on the upper echelons theory (Hambrick and Mason,
1984). This theory explains how CEO characteristics affect both innovation and disclosure
quality. The literature has provided empirical evidence of both direct effects. Given the
hypothesized relation between disclosure quality and innovation, disclosure quality may serve as
an indirect channel through which CEO characteristics affect innovation, so that CEO
characteristics have both direct and indirect effects on innovation.
The CEO, as head of the company, influences the extent of investment in innovation. As
detailed above, CEOs’ decisions are affected by their innate characteristics; as such, these
characteristics impact corporate innovation (Bertrand and Shoar, 2003). In this dissertation, I
distinguish among CEOs based on their tendency for innovation. Therefore, I examine both the
total effects measured by CEOs’ fixed effects and the expected effects implied by the CEOs’
functional background.15, 16
CEOs’ functional backgrounds shape the way they define the issues, goals, and actions in
the decision-making process (Barker and Mueller, 2002). While CEOs with finance and law
backgrounds are more likely to quantify the technological issues regarding innovation using
financial terms, CEOs with technical backgrounds are more likely to focus on and comprehend
the technical, operational, and financial implications of a proposed investment in product
innovation (Daellenbach et al., 1999). Consequently, Barker and Mueller (2002) document a
positive (negative) relation between a CEO’s technical (legal) background and R&D spending,
which they explain as resulting from the CEO’s focus on innovation. While the above discussion
15 I capture CEOs’ complete effect on innovation by utilizing Bertrand and Schoar’s (2003) method of measuring
CEOs’ fixed effects beyond the firm-level and time-variant variables known to impact corporate innovation. I
supplement my tests with the observable CEO background, which is a manifestation of the characteristics that shape
CEOs’ tendency for innovation.
16 The literature also examines other CEO characteristics such as overconfidence, experience, and attention
(Galasso and Simcoe, 2011; Hirshleifer, Low, and Teoh, 2012; Kaplan, 2008; Custódio, Ferreira, and Matos, 2014).
15
suggests negative associations between innovation and financial and legal backgrounds, a case
could also be made for positive associations.17 A CEO with a legal background may stress the
importance of patent protection, and therefore be associated with an increase in patent
applications. A CEO with an accounting or finance background may be better equipped to raise
the capital necessary for continued corporate innovation, as he or she is more likely to have the
financial savvy to weather financial downturns (Custódio and Metzger, 2014) and more capable
of translating the importance of current innovation for future financial performance. These are all
examples of the direct effect of CEO characteristics on innovation.
CEO characteristics are also expected to affect disclosure quality. CEOs with a high
tendency for innovation may either increase disclosure quality to finance their innovative
aspirations, or may inadvertently reduce disclosure quality since increased innovation leads to
more uncertainty about future performance, which would reduce disclosure quality.
The upper echelons theory based literature explains the influence of specific CEO
characteristics on disclosure quality. For example, managers with legal backgrounds are
probably more concerned with litigation risk, and consequently they tend to guide expectations
downward (Bamber, Jiang, and Wang, 2010). Similarly, managers with financial backgrounds
tend to provide more precise earnings forecasts, probably due to their being more conservative
(Bamber et al., 2010). By contrast, CEOs with technical backgrounds may have a lower
frequency of management guidance, as they probably choose to provide information on product
development rather than on financial forecasts. With regard to mandatory disclosure, I expect
that CEOs with legal backgrounds will be associated with lengthier 10-K filings and with higher
Fog indices because they are more likely to expand risk disclosures to reduce litigation risk.18,19
17 Barker and Mueller (2002) document that the relation between financial background and R&D spending is
insignificant.
18 However, I expect CEOs with a legal background to have an overall negative effect on innovation, so that the
firm would become less risky (innovation is risky). The reduction in risk would warrant shorter risk disclosures,
which would contrast with a lawyer’s tendency to expand that section of the 10-K.
19 The Fog index is influenced by firm and industry complexity, so I include firm and industry fixed effects in my
regressions.
16
In comparison with the CEOs with technical and legal backgrounds, CEOs with finance or
accounting backgrounds would be the most likely to influence earnings-reporting quality, as they
would have the expertise to do so (Ge, Matsumoto, and Zhang, 2011). Moreover, CEOs with a
financial background should have the strongest effect on earnings quality because often they will
have been Chief Financial Officers (CFOs) in the past, and CFOs are more likely to directly
manage accruals than the average CEO (Dejong and Ling, 2013).
These effects of CEO characteristics on disclosure quality have consequences for the
monitoring of the firm and its ability to finance investment in innovation. Therefore, given my
first hypothesis, disclosure quality may be a mechanism through which CEO characteristics
affect innovation (i.e., CEO characteristics’ indirect effect on innovation). Taken together, CEO
characteristics affect innovation both directly and indirectly through their effect on disclosure
quality. Thus, my second hypothesis (in the alternative form) is:
H2: CEO characteristics have an indirect effect on innovation through disclosure quality.
17
Chapter 3 - Research Design
3.1. Main Variables
3.1.1. Innovation
There are many ways to define innovation (Baregheh, Rowley, and Sambrook. 2009).
The definitions may include: (1) successful, ground-breaking products; (2) risk taking; and (3)
flexibility or incremental improvements. Similar to other studies on innovation, I measure
innovation with reference to patent data (e.g., Griliches, Pakes, and Hall, 1988; Hall et al., 2001;
Kaplan, 2008; He and Tian, 2013; Bereskin et al., 2015). Granted patent applications capture
innovative action (Griliches, 1990). Because patents include both ground-breaking products and
incremental improvements, they capture the physical manifestation of a corporation’s risk-taking
and flexibility.
The use of patent data as a measure of innovation has the following advantages when
compared to R&D expenses: (1) patents capture the process, while R&D expenses measure the
input; (2) R&D expenses may not be an accurate measure of the investment in innovation due to
either misreporting (Koh and Reeb, 2015) or classification shifting (Shen, 2013; Skaife,
Swenson, and Wangerin, 2013; McVay 2006) 20; and (3) the immediate expensing of R&D costs
disconnects the timing of this spending from its contribution to innovation.21 Other advantages
of patent data are that patent applications are a reliable measure of innovation considering the
20 R&D classification shifting is the reporting of other operating expenses as R&D expenses, often to justify
missing earnings targets, and to enhance the market’s perception of future profits. As a consequence, R&D expenses
may sometimes indicate more innovation than is actually taking place. Alternatively, managers may omit R&D to
mislead competitors and reduce product market competition, or to meet analysts’ pro forma forecasts (McVay,
2006). Firms are more likely to engage in this classification shifting rather than other forms of reporting
manipulation due to the difficulty of detecting R&D manipulation (Xu and Yan, 2013)
21 Additionally, there is a measurement issue with using Compustat reported R&D expenditures. There are many
cases of missing R&D expenditures in Compustat, which researchers often replace with zeros, thereby inducing
noise that may bias the estimation and lead to incorrect conclusions (Koh and Reeb, 2015). Koh and Reeb (2015,
page 76) suggest to “incorporate a dummy variable for missing R&D and to alternatively replace missing with the
industry average R&D and then with zeros (and if patent data is available using a Pseudo-Blank dummy as well).”
18
incentives provided by the patent system to file quickly, and the number of citations is reliable
due to the assurance provided by the patent examiner that future patents include all relevant
citations.22 Furthermore, since patents are granted to a large variety of products from different
industries, and the U.S. Patent and Trademarks Office (USPTO) provides assurance that the
innovation is new, many studies use patent data to measure innovation (Hall et al., 2005; Galasso
and Simcoe, 2011; Aghion et al., 2013; He and Tian, 2013; Bereskin et al., 2015; Kerr and
Nanda, 2015).
Patent data can be used to measure the quantity of innovation, the quality of innovation,
innovation productivity, innovation breadth, and innovation originality (Hall et al., 2005; He and
Tian, 2013). Intuitively, a greater focus on innovation should yield a higher number of patent
applications. However, these patent applications may not be successful and may not make a
significant contribution to innovation. Rather, the number of patent applications should capture a
firm’s intent and effort to innovate, while future citations of these patents likely indicate that the
firm is actually being innovative. The connection between patent citations and successful
innovation is also evident from the correlation between firm value and these citations (Hall,
Jaffe, and Trajtenberg, 2005). Therefore, I focus on both the quantity and the quality of
innovation, where the quantity of innovation is measured by the number of firm patent
applications eventually granted, while the quality of innovation is measured by the number of
citations per patent.
The focus of my work is on innovation in the sense of the process of creating something
new. Thus, my variable of interest is the internal decision-making that is crucial for innovation.
These decisions are unobservable and include decisions both at the start of the innovative
project, such as the choice of project, and during the life of the project, such as continued
funding, corporate culture, and employee incentives. Since these decisions are unobservable,
22 Since this study deals with large public firms and focuses only on firms that utilize patent protection, if R&D
spending does not result in patents (which are usually components of a finished product), then it was not properly
spent on innovation.
19
patent data provide the most accurate indication of innovation related decisions.23 There is a time
lag between these decisions and the patent applications; for example, the legal department needs
time to submit the documents to the patent office. Even decisions that have immediate
consequences are expected to manifest in patent applications in the next time period. For that
reason, I use patent data at time t+1 to measure innovation at time t.24 In sensitivity tests, I also
measure current innovation decisions with patent data at time t+3.
3.1.2. Disclosure Quality
I measure disclosure quality by financial disclosure quality, management guidance
quality, and their principal component. Financial disclosure quality includes both accruals
earnings quality (unsigned discretionary accruals) and 10-K readability (the Fog index and the
length of the 10-K). Unsigned discretionary accruals are the absolute value of the difference
between actual total accruals and the total accruals estimated from the cross-sectional Jones
(1991) model. I include financial disclosure quality because investors use financial information
to monitor managers (Bushman and Smith, 2001). The existing literature documents a positive
relation between financial disclosure quality and investment efficiency (Biddle et al., 2009), and
documents earnings quality being determined by CFOs (Feng, Ge, Luo, and Shevlin, 2011; Ge,
Matsumoto, and Zhang, 2011).25 I add management guidance quality for the following reasons:
(1) The nature of innovation may require more frequent updates than annual reports, so more
frequent management guidance should be more important for innovation than for other aspects of
firm performance; (2) CEOs have more control over management guidance than on the content
of annual financial disclosures. Furthermore, the existing literature on managers’ effects on
disclosure documents a relation between CEOs and management guidance (Bamber, Jiang, and
23 Furthermore, a CEO can set the tone at the top as to encourage innovation, while R&D expenditure is a sticky
cost that does not afford much flexibility. “Firms therefore tend to smooth R&D spending over time to avoid having
to lay off their research scientists and knowledge workers, leading R&D spending at the firm level to behave as if it
has high adjustment costs (e.g., Hall, Griliches, and Hausman 1986).” (Kerr and Nanda, 2015, p.448)
24 For these reasons Gunny and Zhang (2014) use patent citations in year t+1 to measure managers’ private
information in their decision to manage earnings to meet analysts’ forecasts in year t.
25 Francis, Huang, Rajgopal, and Zang (2008) examined CEO reputation, which is not necessarily a characteristic.
20
Wang, 2010). Since earnings quality, 10-K readability, and management guidance quality each
measure a different aspect of disclosure quality, I also calculate the principal component of these
disclosure metrics to provide a more complete picture of overall corporate transparency.
3.1.3. CEO Characteristics
The literature on CEO characteristics and innovation indicates that CEOs’ functional
backgrounds affect R&D spending. A CEO’s career experience in output functions
(technological background) is associated with more innovation since these functions are focused
on growth through new products. On the other hand, career experience in throughput functions
(such as financial, administration, and legal backgrounds) is associated with less innovation since
these functions focus on the efficiency inside the firm, such as cost cutting and reduction in R&D
spending. For example, Daellenbach, McCarthy, and Schoenecker (1999) show that having a
technological background is positively associated with R&D spending, while a legal background
is negatively associated with R&D spending. The literature also shows how personality traits that
determine a CEO’s attitude toward risk (such as overconfidence) have an effect on innovation
(Galasso and Simcoe, 2011; Hirshleifer, Low, and Teoh, 2012). Each characteristic provides part
of the mosaic. Therefore, apart from looking at specific characteristics, this study examines the
CEO tendency for innovation as a measure that may include additional characteristics that have
not yet been studied, such as CEO’s creativity skills or intuition for consumer trends.26
Background characteristics include whether the CEO has a technical background, a
finance background, or a legal background. Each one of these variables is an indicator variable
that equals one if the CEO had a background in that field, and zero otherwise.
FinanceBackground equals one if the CEO holds financial or accounting credentials (such as
CPA), served as a CFO or Controller, or holds a degree in finance or accounting. Most of the
CEOs with financial or accounting backgrounds were CFOs before becoming CEOs.
TechnicalBackground equals one if the CEO is an engineer, a doctor, or a pharmacist, holds a
26 The literature has also shown that CEO’s age and tenure affect R&D spending. This study’s results remain
unchanged if I include CEO age and tenure in my regressions, and they are included in the calculation of innovation-
related CEO fixed effects.
21
degree in natural or exact sciences, or served as a Chief Science Officer or Chief Technical
Officer. Legal equals one if the CEO has an Esq. suffix, is identified as a legal professional, or
served as a Chief Legal Officer, Chief Counsel, or General Counsel.
Similar to Bertrand and Schoar (2003), CEOs’ overall tendency for innovation in this
study is captured by the coefficient on a CEO identifier indicator variable included as a regressor
in a regression of innovation on its determinants.27 This regression requires a subsample of firms
with changes in CEOs to distinguish between firm and CEO effects. I validate this measure by
examining the correlations of the fixed-effects coefficients with specific CEO characteristics
known to affect innovation.
3.2. Regression Models
3.2.1. Test of H1
To test my first hypothesis, I regress innovation on disclosure quality and control
variables shown to affect innovation and that are commonly used in the literature (e.g., He and
Tian, 2013).
𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑡 = 𝜶𝟏𝑫𝒊𝒔𝒄𝒍𝒐𝒔𝒖𝒓𝒆 𝑸𝒖𝒂𝒍𝒊𝒕𝒚𝒕 + 𝛾1𝐿𝑛𝐴𝑠𝑠𝑒𝑡𝑠𝑡 + 𝛾2𝑅𝐷𝐴𝑠𝑠𝑒𝑡𝑠𝑡 + 𝛾3𝐿𝑛𝐹𝑖𝑟𝑚𝐴𝑔𝑒𝑡
+ 𝛾4𝑅𝑂𝐴𝑡 + 𝛾5𝑃𝑃𝐸𝐴𝑠𝑠𝑒𝑡𝑠𝑡 + 𝛾6𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑡 + 𝛾7𝐶𝑎𝑝𝑒𝑥𝐴𝑠𝑠𝑒𝑡𝑠𝑡 + 𝛾8𝑀𝑡𝑜𝐵𝑡
+ 𝛾9𝐿𝑛𝐴𝑛𝑎𝑙𝑦𝑠𝑡𝑠𝑡 + 𝛾10𝐼𝑛𝑠𝑡𝑖𝑡 𝑂𝑤𝑛𝑒𝑟𝑠𝑡 + 𝐹𝑖𝑟𝑚 + 𝑌𝑒𝑎𝑟 + 𝜖𝑡.
(1)
Innovation at year t is the natural logarithm of one plus the measure of innovation—either
patent quantity or patent quality at year t+1 or year t+3 (similar to He and Tian, 2013).
27 Tendency for innovation is measured by the coefficient on the CEO indicator variable (1), similar to the fixed
effects coefficients in Bertrand and Schoar (2003). The coefficient measures the specific CEO’s contribution to
corporate innovation beyond the expected innovation level considering observable firm characteristics and CEO’s
time-variant characteristics:
𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑡 = 𝜶𝟏𝑪𝑬𝑶𝒕 + 𝛾1𝐿𝑛𝐴𝑠𝑠𝑒𝑡𝑠𝑡 + 𝛾2𝑅𝐷𝐴𝑠𝑠𝑒𝑡𝑠𝑡 + 𝛾3𝐿𝑛𝐴𝑔𝑒𝑡 + 𝛾4𝑅𝑂𝐴𝑡 + 𝛾5𝑃𝑃𝐸𝐴𝑠𝑠𝑒𝑡𝑠𝑡
+ 𝛾6𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑡 + 𝛾7𝐶𝑎𝑝𝑒𝑥𝐴𝑠𝑠𝑒𝑡𝑠𝑡 + 𝛾8𝑀𝑡𝑜𝐵𝑡 + 𝛾9𝐿𝑛𝐴𝑛𝑎𝑙𝑦𝑠𝑡𝑠𝑡 + 𝛾10𝐼𝑛𝑠𝑡𝑖𝑡_𝑜𝑤𝑛𝑒𝑟𝑡
+ 𝛾11𝐶𝐸𝑂𝐴𝑔𝑒𝑡 + 𝛾12𝐶𝐸𝑂𝐴𝑔𝑒2𝑡
+ 𝛾13𝐶𝐸𝑂𝑇𝑒𝑛𝑢𝑟𝑒𝑡 + 𝛾14𝐶𝐸𝑂𝑇𝑒𝑛𝑢𝑟𝑒2𝑡 + 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 + 𝐹𝑖𝑟𝑚
+ 𝑌𝑒𝑎𝑟 + 𝜖𝑡 .
22
Disclosure Quality is either 10-K readability (10-K Fog or 10-K Length), earnings quality
(DiscAccruals), management guidance quality (MF Count), or their principal component (DQ).
As explained earlier for the first hypothesis, I expect 1, the coefficient for Disclosure Quality,
to be positive because innovation should benefit from the reduction in information asymmetry
brought about by higher disclosure quality.
LnAssets is the natural logarithm of one plus the firm’s total assets. I expect the
coefficient to be positive because larger firms have the resources to invest more in innovation.
RDAssets is R&D expenditures as a percentage of total assets. I expect the coefficient to be
positive because firms that invest more in research and development should have more
innovation. Profitability, ROA, enables firms to engage in innovative projects. Similarly,
innovation should be positively correlated with growth opportunities, so I expect the coefficient
on MtoB (market-to-book ratio) to be positive. LnFirmAge, the age of the firm, is expected to
have a negative relation with innovation because older firms are more likely to be in the mature
stage of the life cycle, and treated as a “cash cow” rather than investing cash flows in long-term
projects. PPEAssets is net PP&E scaled by total assets, and CapexAssets is capital expenditures
scaled by total assets. These variables control for whether the firm has the facilities for research,
and continues to invest in the equipment and resources required for patentable innovation.
Financial risk (Leverage) limits a firm’s ability to innovate, so I expect the coefficient to be
negative. LnAnalysts and Instit Owners are natural logarithms of one plus the number of analysts
following the firm, and the percentage of institutional ownership, respectively. They control for
governance, and I expect them to have a positive association with innovation. Controls also
include firm and year fixed effects to account for differences across firms, and for possible time-
series fluctuations in the ease of filing patent applications and in incentives to file for patents
(due to changes in the protection provided to intellectual property).
Next, I consider the endogenous relation between disclosure quality and innovation. On
the one hand, corporate disclosure should improve monitoring by external stakeholders and put
pressure on the firm to increase innovation, similar to disclosure quality’s positive impact on
investment efficiency (Biddle, Hilary, and Verdi, 2009). On the other hand, the decision to
disclose may be motivated by the manager’s privately held information about innovations. For
example, Gunny and Zhang (2014) show that when managers have private information about
23
upcoming patent applications, they manage earnings upwards (this is interpreted as lower
disclosure quality in this dissertation). 28
These concerns are addressed by using a simultaneous equation model where innovation
is a function of disclosure quality, and disclosure quality is a function of innovation. Specifically,
I use three-stage least squares (3SLS). The advantage of 3SLS is that it combines the two-stage
least squares approach (2SLS) with seemingly unrelated regressions estimation, which allows for
possible cross-equation covariance and allows for inter-temporal correlations between error
terms. The 3SLS estimate is consistent and, in general, asymptotically more efficient than the
2SLS. Unfortunately, a specification error in the 3SLS model will be propagated throughout the
system (Greene, 2003 page 413). Furthermore, 3SLS yields biased estimators in small samples,
which is irrelevant for my sample. To alleviate concerns of a specification error, in Table 4 I
include the results of the second stage of the 2SLS.29
𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑡 = 𝜶𝟏𝑫𝒊𝒔𝒄𝒍𝒐𝒔𝒖𝒓𝒆 𝑸𝒖𝒂𝒍𝒊𝒕𝒚𝒕 + 𝛼2𝐿𝑛𝐴𝑠𝑠𝑒𝑡𝑠𝑡 + 𝛼3𝑅𝐷𝐴𝑠𝑠𝑒𝑡𝑠𝑡 + 𝛼4𝑃𝑃𝐸𝐴𝑠𝑠𝑒𝑡𝑠𝑡
+ 𝛼5𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑡 + 𝛼6𝐶𝑎𝑝𝑒𝑥𝐴𝑠𝑠𝑒𝑡𝑠𝑡 + 𝛼7𝑀𝑡𝑜𝐵𝑡 + 𝛼8𝐿𝑛𝐹𝑖𝑟𝑚𝐴𝑔𝑒𝑡 + 𝛼9𝑅𝑂𝐴𝑡
+ 𝛼10𝐿𝑛𝐴𝑛𝑎𝑙𝑦𝑠𝑡𝑠𝑡 + 𝛼11𝐼𝑛𝑠𝑡𝑖𝑡 𝑂𝑤𝑛𝑒𝑟𝑠𝑡 + 𝜖𝐼𝑛𝑛𝑜𝑣.
(2a)
𝐷𝑖𝑠𝑐𝑙𝑜𝑠𝑢𝑟𝑒 𝑄𝑢𝑎𝑙𝑖𝑡𝑦𝑡
= 𝜷𝟏𝑰𝒏𝒏𝒐𝒗𝒂𝒕𝒊𝒐𝒏𝒕 + 𝛽2𝐿𝑛𝐴𝑠𝑠𝑒𝑡𝑠𝑡 + 𝛽3𝐿𝑛𝐹𝑖𝑟𝑚𝐴𝑔𝑒𝑡 + 𝛽4𝑅𝑂𝐴𝑡 + 𝛽5𝐶𝐹 𝑉𝑜𝑙𝑡
+ 𝛽6𝑆𝑎𝑙𝑒𝑠 𝑉𝑜𝑙𝑡 + 𝛽7𝐿𝑖𝑡𝑖𝑔𝑎𝑡𝑖𝑜𝑛 𝑅𝑖𝑠𝑘𝑡 + 𝛽8𝑆𝑎𝑙𝑒𝑠 𝐺𝑟𝑜𝑤𝑡ℎ𝑡 + 𝛽9𝑆𝑡𝑜𝑐𝑘 𝑅𝑒𝑡𝑢𝑟𝑛𝑡
+ 𝛽10𝑆𝐸𝑂𝑡 + 𝛽11𝐿𝑜𝑠𝑠𝑡 + 𝛽12𝐿𝑛𝐴𝑛𝑎𝑙𝑦𝑠𝑡𝑠𝑡 + 𝛽13𝐼𝑛𝑠𝑡𝑖𝑡 𝑂𝑤𝑛𝑒𝑟𝑠𝑡 + 𝜖𝐷𝑄
(2b)
28 In this work, I restrict my sample to firms that utilized patent protection. It may be that firms that choose to file
patents are systematically different from firms that innovate but choose not to file patents. To address this potential
self-selection bias, I use the relationships between patent data and variables that explain innovation to predict the
number of patent applications and patent citations of the firms that do not utilize patent protection. The results of the
3SLS do not change when I add these predicted observations to my sample.
29 I use Eq. (2b) in the first stage of the 2SLS, excluding Innovationt and including RDAssetst, to predict Disclosure
Qualityt. The predicted Disclosure Qualityt replaces the observed Disclosure Qualityt in the second stage of 2SLS.
24
In these regressions, I also add sales growth and stock return to more fully control for
litigation risk, as suggested by Kim and Skinner (2012). These variables are specific for
securities litigation risk, and therefore may be used as instrumental variables. Moreover, one of
the reasons that Kim and Skinner provide for adding these variables to more accurately capture
securities litigation risk is that the industry based litigation risk proxy may capture aspects of
industry characteristics that affect disclosure due to those industries’ innovation intensity.
Similarly, PPEAssets and CapexAssets are instrumental variables for innovation, as they are
relevant for innovation but not for disclosure quality.
3.2.2. Test of H2
To test the second hypothesis, I use path analysis to measure CEOs’ direct and indirect
effects on innovation (Bushee and Miller, 2012; Lu, Richardson, and Salterio, 2011).
Empirically, regressing innovation on CEO characteristics estimates characteristics’ total
association with innovation, which includes both the direct relation between them and the
mechanism through which CEO characteristics affect innovation, i.e., the indirect relation. The
path analysis uses a simultaneous equation model to model innovation as a function of CEO
characteristics, disclosure quality and controls, and to model disclosure quality as a function of
these same CEO characteristics. The advantage of the structural equation model is its flexibility
in modeling complex simultaneous relations among variables, enabling researchers to examine
direct and indirect effects while taking into account measurement errors in both dependent and
independent variables.30 This structural equation model makes it possible to conduct a path
analysis in which to simultaneously test: (1) the direct effects of CEO characteristics and
disclosure quality on innovation, and (2) the indirect effects of CEO characteristics on innovation
through their influence on disclosure quality. Formally, I estimate the following simultaneous
equations:
30 In general, an additional advantage of the structural equation model is the inclusion of latent constructs. However,
I calculated the principal component of disclosure quality separately rather than including it as a latent variable.
25
𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑡 = 𝜶𝟏𝑪𝑬𝑶 𝑪𝒉𝒂𝒓𝒂𝒄𝒕𝒆𝒓𝒊𝒔𝒕𝒊𝒄𝒔𝒕 + 𝜶𝟐𝑫𝒊𝒔𝒄𝒍𝒐𝒔𝒖𝒓𝒆 𝑸𝒖𝒂𝒍𝒊𝒕𝒚𝒕 + 𝛼3𝐿𝑛𝐴𝑠𝑠𝑒𝑡𝑠𝑡
+ 𝛼4𝑅𝐷𝐴𝑠𝑠𝑒𝑡𝑠𝑡 + 𝛼5𝑃𝑃𝐸𝐴𝑠𝑠𝑒𝑡𝑠𝑡 + 𝛼6𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑡 + 𝛼7𝐶𝑎𝑝𝑒𝑥𝐴𝑠𝑠𝑒𝑡𝑠𝑡
+ 𝛼8𝑀𝑡𝑜𝐵𝑡 + 𝛼9𝐿𝑛𝐹𝑖𝑟𝑚𝐴𝑔𝑒𝑡 + 𝛼10𝑅𝑂𝐴𝑡 + 𝛼11𝐿𝑛𝐴𝑛𝑎𝑙𝑦𝑠𝑡𝑠𝑡
+ 𝛼12𝐼𝑛𝑠𝑡𝑖𝑡 𝑂𝑤𝑛𝑒𝑟𝑠𝑡 + 𝜖𝐼𝑛𝑛𝑜𝑣 .
(3a)
𝐷𝑖𝑠𝑐𝑙𝑜𝑠𝑢𝑟𝑒 𝑄𝑢𝑎𝑙𝑖𝑡𝑦𝑡
= 𝜷𝟏𝑪𝑬𝑶 𝑪𝒉𝒂𝒓𝒂𝒄𝒕𝒆𝒓𝒊𝒔𝒕𝒊𝒄𝒔𝒕 + 𝛽2𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑡 + 𝛽3𝐿𝑛𝐴𝑠𝑠𝑒𝑡𝑠𝑡
+ 𝛽4𝐿𝑛𝐹𝑖𝑟𝑚𝐴𝑔𝑒𝑡 + 𝛽5𝑅𝑂𝐴𝑡 + 𝛽6𝐶𝐹 𝑣𝑜𝑙𝑡 + 𝛽7𝑆𝑎𝑙𝑒𝑠 𝑣𝑜𝑙𝑡
+ 𝛽8𝐿𝑖𝑡𝑖𝑔𝑎𝑡𝑖𝑜𝑛 𝑅𝑖𝑠𝑘𝑡 + 𝛽9𝑆𝐸𝑂𝑡 + 𝛽10𝐿𝑜𝑠𝑠𝑡 + 𝛽11𝐿𝑛𝐴𝑛𝑎𝑙𝑦𝑠𝑡𝑠𝑡
+ 𝛽12𝐼𝑛𝑠𝑡𝑖𝑡 𝑂𝑤𝑛𝑒𝑟𝑠𝑡 + 𝜖𝐷𝑄
(3b)
CEO_characteristics are the CEO characteristics indicated above: functional background
and tendency for innovation. The controls in Eq. (3a) are the same as in Eq. (1). The controls in
Eq. (3b) are firm size, age, and performance. I also add controls known to affect earnings
quality—cash flow volatility and sales volatility—which are positively correlated with unsigned
discretionary accruals (Hribar and Nichols, 2007). In addition, I control for litigation risk, stock
issuance, and loss because they would affect disclosure quality in general. Firms increase
disclosure prior to stock issuance to attract investors and decrease the cost of capital. Both bad
news (loss) and litigation risk affect firm disclosure decisions, and firms tend to increase
disclosure to avoid litigation risk when they have bad news (Cao and Narayanamoorthy, 2011). I
also include Innovationt as a control to account for the possibility that the CEO has private
information regarding innovation, which influences the disclosure decision (Gunny and Zhang,
2014).31
31 CEO’s private information regarding innovation has a significant effect on disclosure, so that if I do not control
for it, finance background’s indirect effect is significant only at the one-tailed level.
26
The direct effect of CEO characteristics on innovation is captured by the coefficient 1 in
Eq. (3a). Had innovation not been included as an explanatory variable in Eq. (3b), the indirect
effect would have been 𝛽1 ∙ 𝛼2, i.e., the effect of CEO characteristics on disclosure quality
multiplied by the effect of disclosure quality on innovation. The inclusion of innovation in Eq.
(3b) is a recursive term in the indirect effect. The total effect is the sum of the direct and indirect
effects.
In my model, I also account for the possibility that analyst following and institutional
ownership are affected by the firm’s recent innovation performance; thus, they are modeled as a
function of the number of patent applications in the current year (Counts) and the number of
citations of those patents (Cites). I control for other factors which may affect analysts and
institutional owners’ decision to follow, or invest, in a firm, including current patent measures
which reflect past innovative efforts (which are manifested in the current number of patent
applications and future citations) and past disclosure quality. For example, Lang and Lundholm
(1996) document a positive association between disclosure quality and analyst following.32
𝐿𝑛𝐴𝑛𝑎𝑙𝑦𝑠𝑡𝑠𝑡 = 𝛾1𝐶𝑜𝑢𝑛𝑡𝑠𝑡 + 𝛾2𝐶𝑖𝑡𝑒𝑠𝑡 + 𝛾3𝐷𝑖𝑠𝑐𝑙𝑜𝑠𝑢𝑟𝑒 𝑄𝑢𝑎𝑙𝑖𝑡𝑦𝑡−1 + 𝛾4𝐿𝑛𝐴𝑠𝑠𝑒𝑡𝑠𝑡
+ 𝛾5𝑅𝐷𝐴𝑠𝑠𝑒𝑡𝑠𝑡 + 𝛾6𝐿𝑛𝐹𝑖𝑟𝑚𝐴𝑔𝑒𝑡 + 𝛾7𝑅𝑂𝐴𝑡 + 𝛾8𝑀𝑡𝑜𝐵𝑡 + 𝜖𝐴𝑛𝑎𝑙𝑦𝑠𝑡𝑠.
(3c)
𝐼𝑛𝑠𝑡𝑖𝑡 𝑂𝑤𝑛𝑒𝑟𝑠𝑡
= 𝛿1𝐶𝑜𝑢𝑛𝑡𝑠𝑡 + 𝛿2𝐶𝑖𝑡𝑒𝑠𝑡 + 𝛿3𝐷𝑖𝑠𝑐𝑙𝑜𝑠𝑢𝑟𝑒 𝑄𝑢𝑎𝑙𝑖𝑡𝑦𝑡−1 + 𝛿4𝐿𝑛𝐴𝑠𝑠𝑒𝑡𝑠𝑡
+ 𝛿5𝑅𝐷𝐴𝑠𝑠𝑒𝑡𝑠𝑡 + 𝛿6𝐿𝑛𝐹𝑖𝑟𝑚𝐴𝑔𝑒𝑡 + 𝛿7𝑅𝑂𝐴𝑡 + 𝛿8𝑀𝑡𝑜𝐵𝑡 + 𝜖𝐼𝑛𝑠𝑡𝑖𝑡 𝑜𝑤𝑛𝑒𝑟 .
(3d)
32 My results do not change significantly if I remove disclosure quality for this equation.
27
Chapter 4 - The Sample
My sample includes CEO-firm-year observations between 1996 and 2010. The main
dependent variable, innovation, is based on patent data obtained from the United States Patent
and Trademark Office (USPTO). Disclosure quality includes unsigned discretionary accruals, the
Fog index and length of the 10-K filing, and the management guidance count from FirstCall.
CEO identity and characteristics were obtained from Execucomp and CaptialIQ. Additional CEO
observations are based on the 8-K filings in the Audit Analytics database of changes in directors
and executives and firms’ 10-K filings on EDGAR. Firm-level control variables are obtained
from Compustat, I/B/E/S, and the Thomson Reuters Institutional (13F) Holdings Database. First
Call data restricts the sample to begin in 1996, and patent data restricts the sample to end in 2010
(to account for the time lag between patent applications and their grants).
Patent data are from the patent grant publications obtained from USPTO’s website.33 The
USPTO explains that “[t]here are three types of patents: (1) Utility patents may be granted to
anyone who invents or discovers any new and useful process, machine, article of manufacture, or
composition of matter, or any new and useful improvement thereof; (2) Design patents may be
granted to anyone who invents a new, original, and ornamental design for an article of
manufacture; and (3) Plant patents may be granted to anyone who invents or discovers and
asexually reproduces any distinct and new variety of plant.” 34 In accordance with the previous
literature, this study uses only utility patents, which constitute over 90% of the patents granted
every year (i.e., throughout this dissertation, “patents” refers only to utility patents). Because
there is an average three-year lag between patent applications and patent grants, I obtain patent
33 Patents were matched to firm identifiers based on the NBER patent data project, which matches patent number to
Compustat gvkey until 2006. I also utilize the data from Kogan, Papanikolaou, Seru, and Stoffman (2012) obtained
from Noah Stoffman’s website to help match the firm names to unique identifiers for firm names that did not appear
before 2006.
The identification assigns subsidiaries’ patents to the parent companies, so that firms that acquire firms that continue
to innovate are also considered innovative.
34 http://www.uspto.gov/patents/resources/general_info_concerning_patents.jsp.
28
grants through 2013. However, this study only uses patent applications up until 2010. This also
helps to address a possible truncation issue with future citations (Aghion et al., 2013).35 Due to
the right-skewed distribution of the patent counts and citations, I follow He and Tian’s (2013)
method and use one plus the natural logarithm of these variables as the main measures of
innovation.
I use a number of sources for the CEO data: ExecuComp, CaptialIQ, AuditAnalytics, and
SEC 10-K filings. Given that each CEO has a unique identifier, it is possible to trace the
movement of CEOs across companies using ExecuComp.36 The data range from 1992 until 2013
is limited to S&P 1500 firms. The latter database also provides the CEO name, year of birth, date
the CEO joined the company, and date the CEO became a CEO. I use the titles of the executives
to identify their background. Background data are also obtained from CapitalIQ, which has the
CEO’s prior experience if she was an executive covered by the database before becoming a
CEO. For smaller firms, I rely on EDGAR 10-K filings and AuditAnalytics proxy filings for
CEO names. AuditAnalytics provides names, degree, and professional suffixes from proxy
statements of changes in executive positions. Most of the CEO identifiers from AuditAnalytics
are between 2004 and 2011, with an annual average of around 2,000 CEOs identified in those
years.
The initial sample is comprised of 99,735 CEO-firm-year observations, with 13,596 firms
and 20,271 CEOs between the years 1996 and 2010. The time period is chosen based on data
availability. Out of the initial sample, 20,892 observations are excluded due to missing
Compustat items; 12,042 observations in the financial and utilities industries (SIC between 6000
and 6999, and between 4900 and 4999) are excluded because their accounting reporting is
different and I require the financial reporting for disclosure quality construct. Finally, I delete
42,667 observations for firms that do not utilize patent protection (i.e., did not apply for a single
35 Aghion et al. (2013, pages 281-282) utilize citation data till 2002 to measure future citations for patent
applications only till 1999. This allows for a three-year window of future citations for the last cohort of patents in
their data, thereby addressing the issue of censoring.
36 I am able to trace the movement of some of the CEOs also to non-S&P 1500 firms by using EDGAR 10-K filings
and AuditAnalytics proxy filings and matching by CEO names and suffixes.
29
patent during the entire sample period) and do not have analyst following. The final sample
comprises 24,134 CEO-firm-year observations (3,263 firms and 4,770 CEOs). Table 1
summarizes the sample selection.
For tests based on CEO fixed effects, I use a subsample of firms with more than one
CEO, and firms whose CEOs also served as CEOs at another firm during the sample period with
a tenure of at least three years at each firm (Bertrand and Schoar, 2003; Veenstra, 2013). These
restrictions help distinguish CEO fixed effects from firm-level fixed effects. First, the firms with
more than one CEO provide data on changes in firms when there is a change in CEO. Similarly,
the sample of CEOs who server at more than one firm enables testing of a CEO carrying her
style to other firms. Without either of these options, the CEO’s fixed effect would be identical to
the firm fixed effect. Second, the tenure requirement excludes the effect of previous
management. The latter subsample is comprised of 7,064 firm-year observations (917 firms and
1,942 CEOs).
Table 2 Panel A presents the descriptive statistics for the variables. A detailed description
of each variable is found in Appendix A. I winsorize all continuous control variables at the top
and bottom 1%. The average one-year-ahead number of patents (Countst+1) is 15.371. However,
the distribution of the patent count is highly skewed, with a median of 2 patents. While the
skewedness of the sample is similar to He and Tian (2013), the firms in my sample have on
average a larger number of patents and spend more on R&D since, unlike He and Tian, I focus
on firms that utilize patent protection. The firms in their sample have a mean of 9.785 patents per
year and 0.050 R&D per assets, while the firms in my sample have a mean of 15.371 patents per
year and 0.094 R&D per assets. Of the CEOs in the sample, 20.1% have finance or accounting
backgrounds, 11.5% have a technical background, and only 1.8% have a legal background. My
sample is comparable to the one used by Biddle, Hilary, and Verdi’s (2009) to test the real
effects of financial disclosure quality. They report a sample with a mean (median) Fog index of
19.31 (19.15), while my sample has a mean (median) 19.574 (19.43). The firms in this current
study’s sample are slightly larger with a mean (median) of the natural logarithm of total assets of
6.229 (6.021) and have more analysts following the firm with a mean (median) of 8.679 (6), as
compared to 5.59 (2) in Biddle, Hilary, and Verdi (2009).
30
Panel B describes the Pearson’s correlations among the main variables. As expected,
there is a high correlation between the number of patents and the number of citations (0.78, p-
value 0.00). Consistent with existing literature, there are a positive correlation between technical
background and innovation, and a negative correlation between legal background and
innovation. The correlation between technical background and the number of patents (citations)
is 0.04 (0.06). The correlation between legal background and the number of patents (citations) is
-0.05 (-0.05). As for the associations among the functional background, there is no significant
relationship between technical and finance backgrounds (p-value -0.47), but both finance and
technical backgrounds have a negative association with a legal background. Both correlations are
-0.03. These relationships are consistent with some CEOs having both financial and technical
backgrounds, but CEOs with legal backgrounds having neither financial nor technical additional
backgrounds. Lastly, in support of my first hypothesis, disclosure quality, DQ, has a positive
association with innovation. The correlation with the number of patent applications (citations) is
0.09 (0.03) and it is significant at the 1% confidence level.
Panel C details the industry distribution of the patent data. As expected, most of the
observations are in the technology, pharmaceutical (2,916 observations), and services industries
(2,793 observations). However, there are also observations in almost all of the other industries,
including industries which rely less on innovation, such as textiles (137 observations), precious
metals (73 observations), and tobacco products (29 observations). Interestingly, the industry with
the highest average of annual patent applications is the aircraft industry (average of 44.59 annual
patent applications), followed by agriculture (average of 37.47 annual patent applications) and
defense (average of 30.58 annual patent applications). While the computers industry doesn’t
have the largest average of annual patent applications, it is among the top three industries with
regards to future patent citations (average of 260.40 annual future citations). The only industries
with a higher average of annual future citations are the aircraft and agriculture industries (347.30
and 323.32 averages of annual future citations, respectively).
31
Chapter 5 - Results
5.1. Test of H1
Table 3 regresses innovation on disclosure quality. Panel A presents the results for
innovation measured by patent quantity (the number of patent applications), and Panel B presents
the results for innovation measured by patent quality (the number of future citations). For both
measures of innovation, the 10-K Fog index and unsigned discretionary accruals (which indicate
low disclosure quality) have a negative association with innovation. In Panel A (patent quantity),
the coefficient for 10-K Fog is -0.014 (t-statistic -2.896), while the coefficient for DiscAccruals
is -0.278 (t-statistic -4.329). One standard deviation increase in DiscAccruals (10-K Fog) is
associated with a 3% (2%) decrease in the number of annual patent applications.37 In Panel B
(patent quality), the coefficient for 10-K Fog is -0.068 (t-statistic -6.444), and the coefficient for
DiscAccruals is -0.348 (t-statistic -2.658). One standard deviation increase in DiscAccruals (10-
K Fog) is associated with a 4% (9%) decrease in the number of annual patent citations. The
length of the 10-K filing (which again indicates low disclosure quality; see Li, 2008) has a
statistically negative association with innovation only for patent quality (Panel B, coefficient -
0.307, t-statistic -4.147), but it has a statistically insignificant coefficient with the expected sign
for patent quantity (Panel A, coefficient -0.042, t-statistic -1.093). Overall, the measures of
financial reporting quality support the first hypothesis of a positive association between
disclosure quality and innovation.
Besides financial disclosure quality, I also include management guidance quality as
measured by the number of management guidance. Columns (4) in both panels report negative
coefficients for MF Count, but it is not statistically significant for patent quantity (Panel A). In
Panel A, for patent quantity, the coefficient is -0.002 (t-statistic -0.513), and in Panel B, for
patent quality, the coefficient is -0.033 (t-statistic -3.285). The negative coefficients are
37 For example, DiscAccruals has a standard deviation of 0.115. When it is multiplied by its coefficient of -0.278,
then the natural logarithm of one plus the number of patent applications is -0.03, which indicates a decrease of 3% in
patent applications.
32
inconsistent with a working paper by Chen, Huang, and Lao (2015), who document a positive
association between management guidance frequency and innovation. However, when I partition
the sample by whether a firm reported a loss or not, I find that loss reporters have a positive
association between guidance frequency and innovation. This implies that managers, who utilize
management guidance to mitigate the negative market reaction to loss reporting, are better able
to continue supporting corporate innovation.38
Additionally, I develop a composite disclosure quality measure, DQ, based on principal
component analysis of the other disclosure measures: 10-K readability (10-K Fog and 10-K
Length), earnings quality (DiscAccruals),39 and management guidance frequency (MF Count).
This principal component provides a measure of overall corporate transparency, while each one
of the previous variables captures only certain aspects of transparency. In Panel A, for patent
quantity, the coefficient on DQ is 0.011 (t-statistic 3.719), and in Panel B, for patent quality, the
coefficient on DQ is 0.006 (t-statistic 1.081). One standard deviation increase in DQ is
associated with a 3% increase in the number of annual patent applications, and 1.5% increase in
the number of citations.40 The low t-statistic in Panel B stems from the negative coefficient for
MF Count. If I exclude MF Count and calculate the principal component from the financial
38 It is possible that the negative coefficient is due to firms with losses increasing the number of management
guidance to avoid litigation, and that these losses hurt future innovation, so that the loss drives both MF Count and
innovation. However, when I partition my sample by loss and non-loss firms, untabulated results show that the
coefficient is negative only for non-loss firms, and it is positive (and statistically significant at 5% confidence level)
for firms that report a loss in the current year. This suggests that firms that report a loss and increase their
management guidance frequency may be better at communicating with investors and may have better corporate
governance, which enables them to continue innovating despite the loss. In contrast, for the profitable firms, the
increase in the frequency of management guidance may indicate their greater susceptibility to market pressures to
meet short-term targets at the expense of continued long-term innovation.
39 In the principal component analysis, I also include a few modifications of the Jones model to minimize potential
measurement error in the original Jones model. The modifications include the lagged and forward-looking Dechow,
Richardson, and Tuna (2003) models, McNichols (2002) modification, and modified version of these models. In the
modified version the change in sales decomposed to sales, lagged sales, expenses, and lagged expenses. However,
the modifications of the Jones model provide similar results to those of the original model, so for the sake of brevity
I include only the results of the original Jones model.
40 DQ has a standard deviation of 2.445. When it is multiplied by DQ’s coefficient of 0.011 (0.006) from the
regression of LnCountst+1 (LnCitest+1) on disclosure quality, then the natural logarithm of one plus the number of
patent applications (citations) is 0.03 (0.015), which indicates an increase of 3% (1.5%) in patent applications
(citations).
33
disclosure measures only, the coefficient is positive and statistically significant for both
measures of innovation. The positive association of disclosure and patent quantity supports the
notion that disclosure quality helps in raising capital for innovation.41 The positive association of
disclosure with patent quality suggests that disclosure quality helps to provide monitoring that
discourages firms from over-investing in innovation and wasting resources. Overall, the results
presented in Table 3 support my first hypothesis by showing that disclosure quality is associated
with greater innovation—both in terms of patent quantity and patent quality.
With regard to the controls, the signs of the coefficients are in the expected direction. As
expected, firm size is positively associated with innovation, as larger firms have more resources
to invest in innovation. Similarly, RDAssets and PPEAssets have positive coefficients, as they
indicate that firms invest in R&D and have the facilities to conduct the research and development
(respectively). While having the facilities is beneficial for both innovation quality and quantity,
firm size and higher R&D spending per assets have positive associations with innovation
quantity, but not quality. The insignificant coefficient in the regression of patent citations on firm
size is consistent with the results in He and Tian (2013), in which firm size as a positive
association with patent quantity, but not with patent quality. In their sample, R&D spending has
a positive association with both patent quantity and patent quality due to their sample selection
criteria (Clarke, Dass, and Patel, 2014). Market-to-book ratio, MtoB, is also positively associated
with innovation, indicating that firms with growth opportunities innovate. Older firms usually
have fewer growth opportunities, consistent with a negative association between firm age and
innovation. Finally, the relation between analyst following and innovation is positive, which is
consistent with the positive association between monitoring and innovation (Clarke et al., 2014).
In Table 3, the institutional investors control has a negative coefficient, which seems to
contradict Aghion et al. (2013). In Table 3b, I distinguish among institutional investors based on
their type: dedicated (Instit Owners – DEDt), transient (Instit Owners – TRAt), or quasi-indexer
41 Further support for the relation between disclosure quality and capital-raising is provided by the results in Table
10. The table shows that the relation between disclosure quality and innovation is stronger for three-year-ahead
patents, which suggests that the effect of the disclosure quality of innovation is mostly at the initiation stage when
the firm raises project capital.
34
(Instit Owners – QIXt). Aghion et al. (2013) show that the positive association between
institutional investors and innovation holds for dedicated and transient investors, but not for
quasi-indexer investors. In their sample, both dedicated and transient investors have a positive
association with innovation, while the relation between quasi-indexer investors and innovation is
not statistically significant. They explain that dedicated investors may influence corporate
decisions by having directors on the firm’s board of directors, and transient investors may
influence firm decision through their threat to sell the stock. Both types of investors’ monitoring
activities encourage long-term investment horizons by reducing managers’ career concerns
associated with the risky investment in innovation. Consistent with Aghion et al.’s (2013) results,
in Table 3b I find that dedicated and transient investors have a positive association with
innovation. The coefficients are positive and statistically significant for both patent quantity
(Panel A) and patent quality (Panel B). However, for quasi-indexer investors I find a negative
association with innovation. Quasi-indexer investors may encourage myopic behavior because
their passive “buy-and-hold” strategy reduces monitoring (Porter, 1992). Table 2 Panel A shows
that quasi-indexer investors account for most of the institutional investors in my sample.
Therefore, in my paper, institutional owners have an overall negative association with
innovation.
Next, I consider the endogenous relation between disclosure quality and innovation via a
simultaneous equation model and simultaneously regress innovation on disclosure quality and
disclosure quality on innovation. Table 4 presents the results for the 3SLS estimation and second
stage of 2SLS. Columns (1) and (2) are the results of the 3SLS for patent quantity, and columns
(3) and (4) are for patent quality. Column (5) presents the results of the second stage of the 2SLS
for patent quantity, and column (6) for patent quality. The advantage of the 3SLS is that it allows
us to distinguish between the effect of innovation on disclosure quality (columns (1) and (3)) and
the effect of disclosure quality on innovation (columns (2) and (4)). Disclosure quality increases
innovation while innovation decreases disclosure quality for both patent quantity (columns (1)
and (2)) and patent quality (columns (3) and (4)). For patent quantity, the coefficient on DQ is
0.081 (column (2), t-statistic 4.420), and the coefficient on LnCountst+1 is -0.673 (column (1), t-
statistic -11.122). For patent quality, the coefficient on DQ is 0.050 (column (4), t-statistic
1.720), and the coefficient on LnCitest+1 is -0.423 (column (3), t-statistic -10.455). The negative
coefficients in columns (1) and (3) are consistent with Gunny and Zhang’s (2014) evidence that
35
managers use private information about upcoming patent applications to manage earnings
upwards, a result consistent with lower disclosure quality. The positive coefficients in columns
(2) and (4) are consistent with the first hypothesis, that disclosure quality has a positive effect on
innovation.
While the signs of the coefficients in columns (2) and (4) are consistent with Table 3, the
magnitudes of the coefficients are greater when I separate the effect of disclosure quality on
innovation from the effect of innovation on disclosure quality. In Table 4, one standard deviation
increase in DQ is associated with a 22% increase in the number of annual patent applications,
and 13% increase in the number of citations, as opposed to 3% and 1.5%, respectively, in the
OLS regressions reported in Table 3.
In columns (5) and (6) I present the results of the second stage of the 2SLS. In the first
stage, I use Eq. (2b) (excluding Innovation and including RDAssets)42 to predict DQ. The
predicted DQ replaces the observed DQ in the second stage of 2SLS. The results are similar to
those of 3SLS: disclosure quality increase innovation. The coefficients for predicted DQ are
0.058 (t-statistic 4.260) and 0.036 (t-statistic 1.666) for patent quantity and quality, respectively.
The signs of the coefficients of the controls are in the expected direction. As expected,
firm size, R&D spending, and capital expenditures are positively associated with innovation, as
larger firms, with more resources, who invest more in innovation, are more innovative.
(Surprisingly, PPEAssets has a negative association with innovation, which may imply that given
capital investment and firm size, the investment required for innovation is not necessarily in
physical property.) Firm size is positively associated with disclosure quality, as the increased
visibility of large firms subjects them to greater scrutiny to improve its disclosure quality. Older
firms may have the procedures in place to improve disclosure quality or the need to do so to
maintain their stock prices (as indicated by the positive coefficients in columns (1) and (3)), but
firm age is not significantly associated with innovation. Firm performance, ROA, has a positive
42 If I do not add RDAssets, the significance of Predicted DQ is weaker for patent quality, but the coefficient is still
positive.
36
association with both disclosure quality and innovation. Better performers may have less of a
need to manage earnings (and hence higher disclosure quality) and have the mandate to continue
to take the risks associated with innovation. Market-to-book ratio, MtoB, is also positively
associated with innovation, indicating that firms with growth opportunities innovate. However,
leverage has a negative association as financial constraints make it more difficult to invest in
innovation. Analyst following’s associations with innovation and disclosure quality are positive,
which is consistent with the analysts’ monitoring role. As before, institutional ownership has a
negative association with innovation, since my sample is dominated by quasi-indexer investors.
However, institutional ownership has a positive association with disclosure quality. This positive
association is consistent with Boone and White’s (2015) finding that the positive association
between institutional ownership and disclosure quality is driven by quasi-indexer investors’
demand for higher disclosure quality.43 Lastly, performance volatility, sales growth, SEO, and
loss all have negative associations with disclosure quality. In this study, disclosure quality
includes the absolute value of discretionary accruals. Therefore, for these controls, firms may be
engaging in more earnings management to make their earnings more appealing to investors
(decreasing the volatility of reported earnings, smoothing earnings from “good” periods to “bad”,
and increasing reported earnings around SEOs).
5.2. Test of H2
5.2.1. CEO Characteristics and Innovation
Table 5 shows the relation between CEO characteristics and innovation. As described in
the sample selection above, to distinguish between CEO and firm fixed effects, the regressions
are estimated for a subsample of firms with more than one CEO, or firms whose CEOs were also
CEOs in other firms, and include observations with CEO tenure of at least three years. The
43 Boone and White (2015, page 511) explain that “quasi-indexers favor greater firm transparency and public
information production to lower information asymmetries, which reduces their transaction and monitoring costs.”
For these types of investors, firm transparency is especially important because “[e]xpending resources to acquire
private information is also less attractive for quasi-indexers because of their diverse holdings and limited ability to
trade on this information”.
37
regressions include industry and year fixed effects. However, since there is very little movement
of CEOs across firms, my tests are not powerful enough to pick up the effect of CEO
backgrounds when including firm fixed effects. An alternative way to control for firm fixed
effects is to regress innovation on CEO indicator variables and firm fixed effects, and then
regress the coefficients of the indicator variables on CEO background (Bertrand and Schoar,
2003). (The results of the alternative test are reported in Panels C and D.)
Panel A presents the regression of innovation on CEO functional background. In these
tests, I regress innovation on the indicator variables for CEOs’ functional backgrounds, the
control variables listed above, and time-variant CEO characteristics (CEO age and tenure at the
firm). Panel A shows that technical background is associated with an increase in innovation. The
coefficient on TechnicalBackground is 0.214 (t-statistic 4.162) for patent quantity in column (2),
and 0.164 (t-statistic 2.156) for patent quality in column (6). Therefore, having a CEO with a
technical background is associated with a 24% increase in the number of annual patent
applications, and 18% increase in the number of citations.44 This positive association is
consistent with the results in Barker and Mueller (2002) and Daellenbach, McCarthy, and
Schoenecker (1999) that CEOs with technical backgrounds spend more on R&D.
Furthermore, Panel A shows that legal background is associated with a decrease in
innovation. The coefficient on Legal is -0.415 (t-statistic -3.394) for patent quantity in column
(3), and -0.849 (t-statistic -4.746) for patent quality in column (7). A firm with a CEO who has a
legal background is associated with a 34% decrease in the number of annual patent applications,
and 57% decrease in the number of citations as compared to a firm whose CEO does not have a
legal background. The negative association between legal background and innovation is
consistent with the results in Barker and Mueller (2002) that CEOs with legal backgrounds spend
less on R&D.
44 The coefficient on the CEO characteristic indicator variable indicates the increase in the natural logarithm of one
plus the patent variable. Therefore, exponent of the coefficient minus one is the percentage increase of the patent
variable. The coefficient of 0.214 in column (2), indicates an increase of 24% in the number of annual patent
applications, since 24% = exp(0.214)-1.
38
The results also hold when controlling for all backgrounds simultaneously. In the latter
case, the coefficient on TechnicalBackground is 0.315 (t-statistic 4.254) for patent quantity in
column (4) and 0.267 (t-statistic 2.459) for patent quality in column (8). A firm with a CEO who
has a technical background is associated with a 37% increase in the number of annual patent
applications, and 31% increase in the number of citations. The coefficient on Legal is -0.404 (t-
statistic -3.308) for patent quantity in column (4) and -0.837 (t-statistic -4.677) for patent quality
in column (8). A firm with a CEO who has a legal background is associated with a 33% decrease
in the number of annual patent applications, and 57% decrease in the number of citations as
compared to a firm whose CEO does not have a legal background. These results are consistent
with the different focus required by each profession. While CEOs with a technical background
would be more focused on the product output and thus more likely to increase innovation, CEOs
with a legal background would be focused on the process and more likely to decrease spending
on innovation (Daellenbach, McCarthy, and Schoenecker, 1999).
With regard to the controls, the signs of the coefficients are in the expected direction for
the most part. LnAssets, RDAssets, ROA, and MtoB are positive, consistent with large, R&D
intensive, profitable, and growth firms being more innovative. PPEAssets is net PP&E scaled by
total assets, and CapexAssets is capital expenditures scaled by total assets. These variables
control for whether the firm has the facilities for research, and continues to invest in the
equipment and resources required for patentable innovation. While CapexAssets does have a
positive coefficient, PPEAssets now has a negative coefficient. As expected, Leverage has a
negative coefficient, consistent with financial risk limiting a firm’s ability to innovate. Similarly,
institutional ownership has a negative coefficient, since my sample is dominated by short-term
institutional investors, who probably put pressure on the firm to meet short-term goals at the
expense of long-term innovation.
Panel B shows the calculation of CEO tendency for innovation. Specifically, I regress
innovation on the control variables listed above, and add indicator variables for CEO identifiers,
similar to the CEO fixed-effects test in Bertrand and Schoar (2003). The regressions include firm
and year fixed effects and time-variant CEO characteristics (CEO age and tenure at the firm and
their squared terms, which control for CEOs’ experience and career concern incentives). The
estimated coefficients for the CEO indicator variables are used in the rest of this study as a
39
measure of each CEO’s overall tendency for innovation. Column (1) calculates CEO tendency
for patent quantity, LnCountst+1, and column (2) for patent quality, LnCitest+1. As shown in the
table, most of the coefficients have the expected sign. The one variable that sticks out is
Leveraget, which I expect to have a negative association with innovation, but has a positive
coefficient in Panel B. The coefficient is 0.248 (t-statistic 2.458) for patent LnCountst+1 and not
statistically significant (t-statistic 0.276) for LnCitest+1. The insignificant coefficient is consistent
with the previous tables, where Leveraget was either negative or statistically insignificant. It may
be that while usually debt constraints firms from continued investment in innovation, in some
cases sophisticated debt holders with access to private information (bank debt, for example) may
improve monitoring and deter myopic behavior, thereby increasing innovation.
Panels C and D present the associations of CEO tendency for innovation, based on patent
quality and quantity, respectively, with CEO functional backgrounds. The dependent variable in
Panel C is the coefficients calculated in Panel B column (1) and the dependent variable in Panel
D is the coefficients in Panel B column (2). Similar to the results in Panel A, CEOs with
technical backgrounds have a higher tendency for innovation (positive coefficients in columns
(2) and (4)), while CEOs with legal backgrounds have a lower tendency for innovation (negative
coefficients in columns (3) and (4)). More specifically, Panel C, column (2) shows that the
coefficient for technical background is 0.119 (t-statistic 2.222) and column (3) shows that the
coefficient for legal background is -0.404 (t-statistic -3.821). Having a CEO with a technical
background is associated with a 13% increase in a CEO’s tendency for patent quantity, while a
legal background is associated with 33% decrease in the CEO’s tendency.45 The results are
similar when controlling for the other functional backgrounds: in column (4), the coefficient for
technical background is 0.277 (t-statistic 3.612) and the coefficient for legal background is -
0.396 (t-statistic -3.761). While the economic significance for legal background remains the
same, technical background is now associated with a 32% increase in tendency for patent
45 The coefficient on the CEO characteristic indicator variable indicates the increase in the natural logarithm of one
plus the patent variable. Therefore, exponent of the coefficient minus one is the percentage increase of the patent
variable. The coefficient of 0.119 in column (2), indicates an increase of 13% in the number of annual patent
applications, since 13% = exp(0.119)-1.
40
quantity. The economic significances are much higher when I test the effects on CEO’s tendency
for patent quality, LnCitest+1. Panel D, column (2) shows that the coefficient for technical
background is 1.491 (t-statistic 8.150) and column (3) shows that the coefficient for legal
background is -1.529 (t-statistic -4.065). Having a CEO with a technical background is
associated with a 344% increase in a CEO’s tendency for patent quality, while a legal
background is associated with 78% decrease in the tendency for patent quality. The results are
similar when controlling for the other functional backgrounds: in column (4), the coefficient for
technical background is 0.810 (t-statistic 2.970) and the coefficient for legal background is -
1.503 (t-statistic -4.012). While the economic significance for legal background remains the
same, technical background is now associated with only a 125% increase in tendency for patent
quality. These results help to validate the CEO fixed-effects measure by showing that it is
correlated with observable CEO characteristics in the expected direction, thereby justifying the
use of these coefficients as a measure of CEO tendency for innovation.
5.2.2. CEO Characteristics and Disclosure Quality
Table 6 shows the relation between CEO characteristics and disclosure quality. To
distinguish the effect of the CEO, the same sample and specification as in Table 5 was used. The
table shows that CEOs with a finance background reduce disclosure quality by reducing the 10-
K’s readability and earnings quality (i.e., higher 10-K Fog index and higher discretionary
accruals). For overall disclosure quality, DQt, the coefficient on FinanceBackgroundt in column
(1) is -0.145 (t-statistic -2.015), which translates to CEOs having a financial background being
associated with a 13% decrease in disclosure quality. For 10-K Fog index and discretionary
accruals, the coefficients on FinanceBackgroundt are 0.141 (t-statistic 2.647) and 0.006 (t-
statistic 2.697) in columns (2) and (4), respectively, which translate into a 15% increase in 10-K
Fogt and 0.6% increase in DiscAccrualst. The relation between FinanceBackgroundt and
earnings quality is consistent with the findings in Ge, Matsumoto, and Zhang (2011) that CFOs’
personal styles manifest in the firm’s earnings quality.
However, CEOs with a technical background improve disclosure quality by improving
earnings quality (lower discretionary accruals) and shortening the 10-K. For overall disclosure
quality, DQt, the coefficient on TechnicalBackgroundt in column (1) is 0.515 (t-statistic 3.806),
which translates into a 67% increase in DQt. For 10-K length and discretionary accruals, the
41
coefficients on TechnicalBackgroundt are -0.030 (t-statistic -2.075) and -0.012 (t-statistic -2.645)
in columns (3) and (4), respectively, which translate into a 3% decrease in 10-K Fogt and 1%
decrease in DiscAccrualst. This CEO characteristic is also associated with a lower frequency of
management guidance, as shown by the negative coefficient in column (5) (coefficient -0.383, t-
statistic -2.948, which is a 32% reduction in MF Countt).
Lastly, legal background does not have an overall effect on disclosure quality, but it is
associated with improved readability (lower Fog index) and higher management guidance
frequency. For overall disclosure quality, DQ, the coefficient on Legal in column (1) is not
statistically different from zero (t-statistic -0.159). For 10-K Fog index and management
guidance frequency, the coefficients on Legal are -0.371 (t-statistic -2.324) and 0.345 (t-statistic
1.675) in columns (2) and (5), respectively. The result is consistent with Bamber, Jiang, and
Wang’s review (2010), which documents a positive (but statistically insignificant) relation
between CEOs’ legal background and management guidance frequency.
The signs of the coefficients of the controls are in the expected direction. As expected,
firm size is positively associated with disclosure quality, as the increased visibility of large firms
subjects them to greater scrutiny to improve its disclosure quality. Larger firms may also have
more complicated operations, which result in longer and less readable 10-K filings. Older firms
may have the procedures in place to improve disclosure quality or the need to do so to maintain
their stock prices (as indicated by the positive coefficients for management guidance and
negative coefficients for 10-K readability and DiscAccrualst). Like before, firm performance,
ROA, has a positive association with disclosure quality. Institutional ownership has a positive
association with disclosure quality, which implies that institutional owners prefer high disclosure
quality, while analyst following has a positive association only with management guidance, as it
may be that analysts are more sensitive to management’s willingness to communicate than to
other aspects of disclosure quality. Lastly, performance volatility, sales growth, SEO, and loss all
have negative associations with disclosure quality. The negative association with disclosure
quality is mainly from the positive association with DiscAccrualst. Firms may be engaging in
more earnings management to make their earnings more appealing to investors (decreasing the
volatility of reported earnings, smoothing earnings from “good” periods to “bad”, and increasing
reported earnings around SEOs).
42
5.2.3. Test of H2
Table 7 presents the results for my second hypothesis which is tested using a structural
equations model.46 The model separates the direct and indirect effects of CEO characteristics on
innovation. Panel A presents the results for CEO tendency for quantity of innovation. Panel B
shows the results for CEO tendency for quality of innovation. Panel C presents the results
relating CEOs’ functional backgrounds to the quantity of innovation. Panel D gives the results
relating CEOs’ functional backgrounds to the quality of innovation. In all panels, columns (1)–
(3) present the results for the direct and indirect CEO effects on the quantity of innovation and
column (4) presents results for disclosure quality. To facilitate the comparison of the
coefficients, they are all standardized, so that they are in standard deviation units.
Columns (1) in Panels A and B report positive coefficients for the direct effect of CEO
characteristics on innovation (coefficient of 0.134, Z-statistic 10.44 in Panel A; coefficient of
0.282, Z-statistic 20.76 in Panel B). The positive coefficients are not surprising because CEO
tendency for innovation has a positive association with innovation by construction.47 Columns
(1) in Panels A and B also support the finding in Table 3 of a positive association between
disclosure quality, DQ, and innovation (coefficient of 0.222, Z-statistic 6.03, in Panel A;
coefficient 0.224, Z-statistic 5.32, in Panel B).
Columns (2) in Panels A and B show that CEO tendency for innovation has an indirect
effect on innovation; the coefficient for CEO is 0.005 (Z-statistic 1.68) for patent quantity in
column (2) Panel A, and the coefficient for CEO is 0.005 (Z-statistic 1.57) for patent quality in
column (2) Panel B. Columns (4) in Panels A and B demonstrate that the indirect effect of CEO
46 I use the sem function in Stata, which carries out linear structural equation modeling. The program runs
generalized method of moments (GMM) to estimate the standardized coefficients and calculates the direct, indirect,
and total effects. Structural equation modeling fits the first and second moments of the distribution of observed
variables (means, variances and covariances) to estimate the coefficients. This helps to mitigate possible
measurement error in the observed variables.
47 As noted above, CEO tendency for innovation is the firm’s patent activity that is not explained by firm-level
effects. Since it is constructed as the coefficients for CEO indicator variables from the regression of patent variables
on firm-level variables, the tendency for innovation is positive when the specific CEO increases innovation as
compared to other CEOs, and is negative when the specific CEO decreases innovation as compared to other CEOs.
43
tendency for innovation is through disclosure quality, as the coefficient on CEO is 0.063 for
patent quantity (Z-statistic 4.14 in column (4) Panel A), and the coefficient on CEO is 0.106 for
patent quality (Z-statistic 5.32 in column (4) Panel B). The indirect effect of DQ on innovation
(columns (2) in Panels A and B) stems from the modeling of disclosure quality as being also a
function of innovation, as CEOs’ privately held information regarding innovation also affects
their disclosure decisions (Gunny and Zhang, 2014). The results in these panels confirm that
CEOs have both a direct and indirect effect on innovation, and the indirect effect is almost 4% of
the direct effect. As expected, a CEO’s tendency for innovation has a positive association with
corporate innovation. The new result is that CEOs’ tendency for innovation also has a positive
association with disclosure quality as shown in columns (4) in Panels A and B.
In an untabulated analysis, I replace overall disclosure quality with its components and
find that the strongest indirect channel is through the frequency of management guidance. For
MF Count, the indirect effect of CEO tendency for innovation on patent quantity, LnCountst+1, is
0.017 (Z-statistic 2.38), and the total effect is 0.176 (Z-statistic 9.41), so that the indirect effect is
almost 10% of the total effect. For the effect on patent quality, LnCitest+1, it is even higher and
reaches 14% of the total effect (indirect effect 0.041 and total effect 0.291). Overall, a CEO’s
tendency for innovation is significantly associated with improved disclosure quality, which in
turn is associated with an increase in innovation.
Panels C and D present the results for CEO background and innovation. As in Panels A
and B, columns (1)–(3) present the results for the direct and indirect effects on innovation and
column (4) presents results for disclosure quality. Panel C is based on the quantity of innovation,
and Panel D on the quality of innovation. Panels C and D show that CEOs with a technical
background are associated with higher disclosure quality, CEOs with a finance background have
lower disclosure quality, and CEOs with a legal background do not influence overall disclosure
quality. Column (2) in Panels C and D show that a CEO’s financial background has an indirect
effect of -0.005 on patent quantity (column (2) Panel C, Z-statistic -1.76) and -0.006 on patent
quality (column (2) Panel D, Z-statistic -1.74). This indirect effect is almost 20% of the total
effect on innovation (column (3) Panel C, standardized coefficient -0.029, Z-statistic -2.27;
44
column (3) Panel D, standardized coefficient -0.031, Z-statistic -2.31).48 Untabulated tests show
that most of the indirect effect of a CEO’s financial background is through earnings quality.
Column (2) in Panels C and D also show that a CEO’s technical background has an indirect
effect of 0.009 on patent quantity (column (2) Panel C, Z-statistic 2.59) and 0.010 on patent
quality (column (2) Panel D, Z-statistic 2.57). This indirect effect is as much as 33% of the total
effect on innovation (column (3) Panel C, standardized coefficient 0.033, Z-statistic 2.59; column
(3) Panel D, standardized coefficient 0.030, Z-statistic 2.20).49
As before, the controls have the expected associations with the dependent variables,
consistent with the results in Table 4 for the simultaneous relation between disclosure quality and
innovation. In all the panels, firm size, R&D spending, market-to-book ratio, and capital
expenditures are positively associated with innovation, while PPEAssets has a negative
association with innovation, which may imply that given capital investment and firm size and
growth opportunities, the investment required for innovation is not necessarily in physical
property. Firm size and age are positively associated with disclosure quality, as the increased
visibility of large firms subjects them to greater scrutiny to improve its disclosure quality, and
older firms may have the procedures in place to improve disclosure quality. Firm performance,
ROA, has a positive association with both disclosure quality and innovation. Better performers
may have less of a need to manage earnings (and hence higher disclosure quality) and have the
mandate to continue to take the risks associated with innovation. However, leverage has a
negative association as financial constraints make it more difficult to invest in innovation.
Analyst following’s associations with innovation is positive, which is consistent with the
analysts’ monitoring role. As before, institutional ownership has a negative association with
innovation and a positive association with disclosure quality, which is consistent with quasi-
indexed investors enabling myopic behavior at the expense of long term innovation, while
48 In Panel C, the indirect effect on patent quantity is -0.005, out of -0.029 total effect, which is 17%. In Panel D,
the indirect effect on patent quality is -0.006 out of -0.031 total effect, which is 19%. I am able to compare the
coefficients directly because they are standardized.
49 In Panel C, the indirect effect is 0.009/0.033 of the total effect on patent quantity. In Panel D, the indirect effect is
0.010/0.030 of the total effect on patent quality.
45
preferring high disclosure quality (Boone and White, 2015). Lastly, performance volatility, SEO,
and loss all have negative associations with disclosure quality, as these conditions may
incentivize firms to engage in more earnings management to make their earnings more appealing
to investors.
These results support the second hypothesis and show the existence of an indirect effect
of CEO characteristics on innovation. Both CEOs’ tendency for innovation and CEOs’ financial
and technical backgrounds have direct and indirect effects on innovation, where the indirect
effect on disclosure quality increases the total effect on innovation. The fact that CEOs with
financial backgrounds have the strongest effects on earnings quality is consistent with the
intuition that a financial background provides the manager with both a focus on reported
earnings and with the expertise to influence earnings quality.
46
Chapter 6 - Robustness Tests
6.1. Alternative Measures of Disclosure Quality
The results hold also for alternative measures of disclosure quality. Untabulated tests
confirm that the results for earnings quality are similar when the Jones model is replaced by its
modifications, such as in McNichols (2002) and the lagged and forward-looking models
suggested by Dechow, Richardson, and Tuna (2003).
I also construct an aggregate disclosure score based on the mean of the normalized
ranking of the different components of disclosure quality, following Biddle, Hilary, and Verdi
(2009) and Bova, Dou, and Hope (2015). Specifically, I rank management guidance frequency,
Fog index, length of the 10-K, and earnings quality, so that a higher ranking indicates a higher
disclosure quality (i.e., while management guidance have higher ranking for a higher guidance
count, the Fog index, 10-K length, and earnings quality have a higher ranking for lower
numbers). The rankings are then normalized between zero and one by subtracting the minimum
ranking and dividing by the difference between the maximum and minimum rankings in each
industry-year. The final measure is the mean of the normalized rankings, so that a higher
observation indicates higher disclosure quality.
Table 8 presents the results. Panel A supports my first hypothesis. Columns (1) and (3)
show that the new aggregate disclosure measure has a positive coefficient (column (1),
coefficient 0.107, t-statistic 3.036, and column (3), coefficient 0.386, t-statistic 5.245). Because
the CEO is likely to have more influence on the Management Discussion and Analysis (MD&A)
section of the 10-K than on the rest of the 10-K, I also replace the Fog index of the 10-K with the
Fog index of only the MD&A. The new mean of the normalized rankings, DQ_Norm_fogMDA,
is used in columns (2) and (4) and provides further support for the first hypothesis. The
coefficient for patent quantity is 0.131 (t-statistic 3.563, column (2)), and for patent quality is
0.468 (t-statistic 5.895, column (4)). The standard deviations of DQ_Normt and
DQ_Norm_fogMDAt are 0.197 and 0.187, respectively (untabulated). Therefore, one standard
deviation change in DQ_Normt is associated with a 2% increase in the number of annual patent
47
applications and a 8% increase in the number of patent citations.50 Similarly, one standard
deviation change in DQ_Norm_fogMDAt is associated with a 2.5% increase in the number of
annual patent applications and a 9% increase in the number of patent citations. The association
with patent quantity is similar to the results in Table 3, which shows that DQt is associated with a
3% increase in patent quantity.
Panel B supports the second hypothesis and shows that a finance background has an
indirect effect on innovation through disclosure quality, comprising over 60% of the total effect
on innovation.51 Panels B1 and B2 provide the results for the indirect effects of CEO tendency
for innovation. Panel B1 reports that CEO tendency for patent quantity has an indirect effect
(2.5% of the total effect) on patent quantity: the indirect effect is 0.004 (column (2), Z-statistic
3.30) and the total effect is 0.163 (column (3), Z-statistic 13.77). Similarly, Panel B2 shows that
a CEO tendency for patent quality has a 2% indirect effect on patent quality: a financial
background has an indirect effect of 0.008 (Z-statistic 4.38 in column (2)) and a total effect of -
0.429 (Z-statistic 45.95 in column (3)). Panels B3 and B4 provide the results for the indirect
effects of specific CEO characteristics. Panel B3 reports that finance background has an indirect
effect on patent quantity: the indirect effect is -0.003 (column (2), Z-statistic -2.27) and the total
effect is -0.012 (column (3), Z-statistic -1.08). Similarly, Panel B4 shows that a finance
background has an indirect effect on patent quality which is 19% of its total effect: a financial
background has an indirect effect of -0.004 (Z-statistic -2.33 in column (2)) and a total effect of -
0.021 (Z-statistic -1.61 in column (3)).
I also replaced disclosure quality with the absolute value of the error in analyst forecasts
scaled by forecast dispersion, as an indirect measure of disclosure quality. This measure captures
overall corporate transparency by focusing on the extent to which sophisticated external parties
50 For example, a coefficient of 0.107 (0.386) implies that a one standard deviation in DQ_Normt increases the
natural logarithm of the number of patent applications (citations) plus one by 2.1% (7.6%). Therefore, it increases
the number of patent applications (citations) by 2.1% (7.9%).
51 As in Table 7, the coefficients are standardized to facilitate the comparison.
48
are able to glean information about the firm from their public communications.52 In that sense,
this indirect measure of disclosure quality includes non-financial disclosure and additional
aspects of firm transparency, such as stand-alone corporate social responsibility reports
(Dhaliwal, Radhakrishnan, Tsang, and Yang, 2012), product related disclosures (Nichols and
Wieland, 2009), presentations at technical conferences, body language, and other soft disclosures
that may affect the quality of information provided by the firm.
As Table 9 shows, this alternative measure continues to provide support for my first
hypothesis that innovation has a positive association with disclosure quality. Panel A
demonstrates that EPS forecast error has a negative influence on both patent quantity (column
(1), coefficient -0.006, t-statistic -2.812) and patent quality (column (2), coefficient -0.015, t-
statistic -3.132). High EPS forecast error indicates low disclosure quality, so the negative
coefficients indicate a positive relation between disclosure quality and innovation. Columns (3)
to (6) present the results for the 3SLS estimations. Columns (3) and (4) show that EPS forecast
error has a negative effect on patent quantity (column (4), coefficient -0.423, t-statistic -3.645)
while patent quantity does not have a significant effect on analyst forecast errors (column (3), t-
statistic -1.515). Columns (5) and (6) show that EPS forecast error has a negative effect on
patent quality (column (6), coefficient -0.389, t-statistic -2.403) while patent quality does not
have a significant effect on analyst forecast errors (column (5), t-statistic -1.256).
However, as presented in Panel B, the alternative measure provides weaker support for
the second hypothesis.53 Panels B1 and B2 show that CEOs’ tendency for innovation influence
analyst error (column (4), coefficients -0.037 and -0.035, Z-statistics -2.27 and -1.87, for patent
quantity and patent quality respectively), but the indirect effect (column (2), coefficients 0.001
52 Assuming that analyst forecast error is reduced by managers relaying accurate private information about
upcoming performance, forecast error captures the extent of private information conveyed by the firm. When
managers communicate more private information, analyst forecasts are expected to be closer to the actual reported
earnings. This is different from analyst forecast dispersion which captures analysts’ consensus. High levels of
innovation are associated with greater risk and uncertainty, which would serve to increase analyst forecast
dispersion. Therefore, to isolate the effect of a firm’s communications from the effect of the inherent risky nature of
innovation, I scale the forecast error by forecast dispersion.
53 As in Table 7, the coefficients are standardized to facilitate the comparison.
49
and 0.001, Z-statistics 1.51 and 1.14) is less than 1% of the total effect (column (3), coefficients
0.158 and 0.428, Z-statistics 13.20 and 45.46). Panel B3 shows that CEOs’ technical and legal
backgrounds influence analyst error (column (4), coefficients 0.025 and 0.03, Z-statistics 1.72
and 2.01), but the indirect effects (column (2), coefficients -0.001, Z-statistics -1.39 and -1.38)
are less than 1% of the total effect (column (3), coefficients 0.087 and -0.111, Z-statistics 7.98
and -10.05). Panel B4 also shows that CEOs’ technical and legal backgrounds influence analyst
error (column (4), coefficients 0.024 and 0.033, Z-statistics 1.59 and 2.23), but with indirect
effects which are statistically insignificant even at a one-tailed test (column (2), both coefficients
are -0.001, Z-statistics -1.23).
6.2. Alternative Measures of Innovation
As a sensitivity test, I also used three-year-ahead measures of innovation. Even if the
innovative process begins immediately, the innovation-related decision can only be observed
when manifested in the patent data. Recent accounting and finance literature, which study the
relation between information asymmetry and innovation, often use three-year-ahead patent data
to account for the delay between the investment in innovation and its realization as a patent
application (Bereskin and Hsu, 2014; He and Tian, 2013; Kaplan, 2008). This differs from the
literature on CEO characteristics, which tests one-year-ahead patent data probably because the
CEO may affect innovation at any stage of the innovative process, whether through the
investment decisions or through changing the firm’s strategic priorities and culture (Galasso and
Simcoe, 2011).54 Therefore, it is unsurprising that the tests with three-year-ahead patent data
yield results showing a stronger relation between disclosure quality and innovation but a similar
relation between CEO characteristics and innovation.
Table 10, Panel A presents the results for the relation between disclosure quality and
three-year-ahead patents. The coefficients for disclosure quality are 0.013 (column (1), t-statistic
3.983) and 0.021 (column (3), t-statistic 3.376) for three-year-ahead patents, as compared to
54 An example of a small change a CEO could implement that would likely have significant implications for
innovation would be to cut bonuses for successful patent applications. Such a decision would almost certainly result
in a drastic decline in corporate innovation.
50
0.011 (Table 3, Panel A, column (5), t-statistic 3.756) and 0.006 (Table 3, Panel B, column (5), t-
statistic 1.044) for the one-year-ahead patents. While the effect on patent quantity appears robust
to both innovation horizons, the results for patent quality are clearly stronger for the three-year
horizon than for the one-year horizon. The stronger results for the three-year horizon are also
apparent when using the alternative disclosure quality variable of the sum of the disclosure
quality component quartiles, DQ_Norm_fogMDA. The coefficients for DQ_Norm_fogMDA are
0.268 (column (2), t-statistic 6.578) and 0.575 (column (4), t-statistic 6.824) for three-year-ahead
patent quantity and quality respectively, as compared to 0.131 (Table 8, Panel A, column (2), t-
statistic 3.563) and 0.468 (Table 8, Panel A, column (4), t-statistic 5.895) for one-year-ahead
patent quantity and quality.
Panel B of Table 10 shows that CEO characteristics affect three-year-ahead patents just
as much as they do the one-year horizon (Table 5, Panel A). Table 10 replicates Table 5 (Panel
A) with three-year-ahead patent data replacing the one-year-ahead horizon. There is very little
difference between the tables with respect to the relation between CEO characteristics and
innovation. For example, column (4) in both tables presents the results for the relation between
CEOs’ backgrounds and patent quantity. In Table 5 (Panel A), for one-year-ahead patent
quantity, the coefficients for finance background, technical background, and legal background
are 0.014 (t-statistic 0.327), 0.297 (t-statistic 4.057), -0.403 (t-statistic -3.294). In Panel B of
Table 10, for three-year-ahead patent quantity, the coefficients for finance background, technical
background, and legal background are 0.028 (t-statistic 0.609), 0.290 (t-statistic 3.701), -0.414 (t-
statistic -3.149), respectively. Similarly, for patent quality reported in columns (8) of both tables,
in Table 5 (Panel A), for one-year-ahead patent quality, the coefficients for finance background,
technical background, and legal background are 0.037 (t-statistic 0.610), 0.254 (t-statistic 2.376),
-0.838 (t-statistic -4.673), respectively. In Panel B of Table 10, for three-year-ahead patent
quality, the coefficients for finance background, technical background, and legal background are
0.068 (t-statistic 1.058), 0.238 (t-statistic 2.177), -0.675 (t-statistic -3.679), respectively.
Overall, these results imply that while disclosure quality is more important for the initial
stages of financing innovation, CEOs intervene at all stages of development.
51
6.3. Alternative Regression Specifications
Most of the results on innovation hold when estimating Poisson and negative binomial
regressions with patent count dependent variables instead of OLS.55 These alternative
specifications are based on the fact that patent applications and citations are count variables. In
accordance with previous literature, I include the average beginning value of innovation to
account for firm fixed effects (Galasso and Simcoe, 2011).
As shown in Table 11 Panel A, the main result in Table 3 remains the same with these
alternative specifications: disclosure quality has a positive association with innovation. In the
Poisson specification reported in Table 11 Panel A, the coefficients for disclosure quality are
0.016 (column (1), Z-statistic 12.678) for patent quantity and 0.012 (column (2), Z-statistic
29.453) for patent quality. In the negative binomial specification, the coefficients for disclosure
quality are 0.018 (column (3), Z-statistic 5.321) for patent quantity and 0.012 (column (2), Z-
statistic 2.613) for patent quality. Interestingly, the Poisson and negative binomial regressions
show a positive association also with innovation quality, Citest+1, while the coefficient is not
significant in the OLS regression.
Similarly, the associations between CEO backgrounds and innovation are significant for
the Poisson and negative binomial specifications as for the OLS regressions presented in Table 5
(Panel A). The only difference is that in the Poisson (negative binomial) regressions,
FinanceBackground has a positive association with innovation quantity (quality), Countst+1
(Citest+1). Table 11 Panels B and C show the relations between CEO background and innovation
for the Poisson specification and the negative binomial, respectively. Columns (1) to (4) report
the relation with innovation measured by patent quantity, and columns (5) to (8) report the
relation with innovation measured by patent quality.
55 In my main tests I use OLS regressions for the sake of consistency, since the regressions of continuous disclosure
quality variables on CEO characteristics required the OLS specification. Many studies use the OLS regressions with
the natural logarithm of one plus the patent-based variables (for example, He and Tian, 2013; Bereskin and Hsu,
2014; Custódio, Ferreira, and Matos, 2014).
52
Table 11 confirms that technical background is associated with an increase in innovation.
Panel B shows that with the Poisson regressions, the coefficient on TechnicalBackground is
0.260 (t-statistic 29.612) for patent quantity in column (2), and 0.085 (t-statistic 29.282) for
patent quality in column (6). These results are similar controlling for the other CEO
characteristics: for patent quantity, column (4) shows that the coefficient for technical
background is 0.310 (Z-statistic 25.482), and for patent quality, column (8) shows that the
coefficient is 0.175 (Z-statistic 39.358). Panel C shows that with the negative binomial
regressions, the coefficient on TechnicalBackground is 0.365 (Z-statistic 5.895) for patent
quantity in column (2), and 0.301 (Z-statistic 3.485) for patent quality in column (6). These
results are again similar controlling for the other CEO characteristics: for patent quantity, column
(4) shows that the coefficient for technical background is 0.595 (Z-statistic 6.546), and for patent
quality, column (8) shows that the coefficient is 0.516 (Z-statistic 3.924).
Additionally, Table 11 confirms that legal background is associated with a decrease in
innovation. Panel B shows that with the Poisson regressions, the coefficient on Legal is -1.055
(Z-statistic -26.385) for patent quantity in column (3), and -2.465 (Z-statistic -73.304) for patent
quality in column (7). These results are similar controlling for the other CEO characteristics: for
patent quantity, column (4) shows that the coefficient for legal background is -1.036 (Z-statistic -
25.904), and for patent quality, column (8) shows that the coefficient is -2.462 (Z-statistic -
73.192). Panel C shows that with the negative binomial regressions, the coefficient on Legal is -
0.710 (Z-statistic -4.390) for patent quantity in column (3), and -1.468 (Z-statistic -6.706) for
patent quality in column (7). These results mimic the results of controlling for the other CEO
characteristics: for patent quantity, column (4) shows that the coefficient for legal background is
-0.679 (Z-statistic -4.225), and for patent quality, column (8) shows that the coefficient is -1.384
(Z-statistic -6.335).
In summary, the relations between innovation and finance background, and between
innovation and legal background are stronger for the Poisson and negative binomial
specifications than for the OLS specification.
53
6.4. Innovation as Measured by R&D Expenditure
This research focuses on the innovative process measured from patent data. However,
some studies use the ratio of R&D expenditure-to-sales (Daellenbach, McCarthy, and
Schoenecker, 1999) or R&D spending per employee (Barker and Mueller, 2002), which capture
the investment in innovation. As discussed above, patent data are a more reliable measure of
innovation for the subsample of firms that utilize patent protection.56
Table 12 replicates the patent-based tests with R&D spending. Panel A replicates Table 3
and shows that not only is disclosure quality positively associated with innovation, but that all of
the disclosure quality components have the expected coefficients. More specifically, while the
coefficients for earnings quality and disclosure quality are still in the same direction (-4.384 for
DiscAccruals, t-statistic -4.170, column (3) and 0.209 for DQ, t-statistic -4.279, column (5)), the
coefficients for low readability are now negative: the coefficient for the Fog index is -0.211 (t-
statistic -2.436, column (1)) and the coefficient for the 10-K’s length is -0.699 (t-statistic -1.157,
column (2)). Similarly, the results for management guidance frequency also changes and the
coefficient is now positive (0.152, t-statistic 2.057, column (4)). With R&D-to-sales, readability
now has a positive association with innovation, as does management guidance frequency.
In Panel B, I replicate Table 5 (Panel A) but replace the patent based innovation measures
with R&D-to-sales. While technical background still has a positive association with innovation
(coefficients 3.274 and 1.332, t-statistics 5.990 and 1.898, columns (2) and (4)) and finance
background is still insignificant (t-statistics -1.013 and -0.934, columns (1) and (4)), legal
background’s negative association with innovation is statistically significant only at the one-
tailed level (coefficients -2.167 and -2.162, t-statistics -1.538 and -1.538, columns (3) and (4)).
56 Furthermore, a CEO can set the tone at the top as to encourage innovation, while R&D expenditure is a sticky
cost that does not afford much flexibility. “Firms therefore tend to smooth R&D spending over time to avoid having
to lay off their research scientists and knowledge workers, leading R&D spending at the firm level to behave as if it
has high adjustment costs (e.g., Hall, Griliches, and Hausman 1986).” (Kerr and Nanda, 2015, p.448)
54
6.5. The Mechanism through which Disclosure Quality Affects Innovation
Disclosure quality should affect innovation by enhancing the firm’s ability to raise capital
necessary to finance innovative projects. Specifically, higher disclosure quality should reduce the
cost of raising funds for “good” projects and increase the cost of raising funds for “bad” projects.
Information asymmetry between managers and investors places investors at a disadvantage in
assuring that their invested funds are being used in their best interest. Information asymmetry
allows managers to shirk from effort or extract personal rents, both of which come at the expense
of firms’ future prospects and reduce shareholders’ welfare. Without additional information,
investors have a hard time distinguishing between investments that will increase firm value (i.e.,
“good” projects with positive NPV) and those that decrease it (i.e., “bad” projects with negative
NPV), resulting in “good” projects being undervalued and “bad” projects being overvalued.
Consequently, in this pooling equilibrium, managers are less able to raise capital for “good”
projects and find it easier to raise capital for “bad” projects, which lead to underinvestment in
positive NPV projects and overinvestment in negative NPV projects. A reduction in information
asymmetry enables investors to price securities more accurately, which reduces the
underinvestment in positive NPV projects and overinvestment in negative NPV projects (Biddle,
Hilary and Verdi, 2009).
To empirically test this mechanism, the path analysis described in Eq. (3) was expanded
to include the following simultaneous equations (Eq. (3c) and (3d) remain unchanged):
𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛𝑡 = 𝜶𝟏𝑪𝑬𝑶 𝑪𝒉𝒂𝒓𝒂𝒄𝒕𝒆𝒓𝒊𝒔𝒕𝒊𝒄𝒔𝒕 + 𝜶𝟐𝑫𝒊𝒔𝒄𝒍𝒐𝒔𝒖𝒓𝒆 𝑸𝒖𝒂𝒍𝒊𝒕𝒚𝒕
+ 𝜶𝟑𝑪𝒐𝒔𝒕 𝒐𝒇 𝑪𝒂𝒑𝒕𝒊𝒂𝒍𝒕 + 𝛼4𝐿𝑛𝐴𝑠𝑠𝑒𝑡𝑠𝑡 + 𝛼5𝐿𝑛𝐹𝑖𝑟𝑚𝐴𝑔𝑒𝑡 + 𝛼6𝑅𝑂𝐴𝑡
+ 𝛼7𝑅𝐷𝐴𝑠𝑠𝑒𝑡𝑠𝑡 + 𝛼8𝑃𝑃𝐸𝐴𝑠𝑠𝑒𝑡𝑠𝑡 + 𝛼9𝐿𝑒𝑣𝑒𝑟𝑎𝑔𝑒𝑡 + 𝛼10𝐶𝑎𝑝𝑒𝑥𝐴𝑠𝑠𝑒𝑡𝑠𝑡
+ 𝛼11𝑀𝑡𝑜𝐵𝑡 + 𝛼12𝐿𝑛𝐴𝑛𝑎𝑙𝑦𝑠𝑡𝑠𝑡 + 𝛼13𝐼𝑛𝑠𝑡𝑖𝑡 𝑂𝑤𝑛𝑒𝑟𝑠𝑡 + 𝜖𝐼𝑛𝑛𝑜𝑣.
(4a)
55
𝐶𝑜𝑠𝑡 𝑜𝑓 𝐶𝑎𝑝𝑡𝑖𝑎𝑙𝑡
= 𝝋𝟏𝑪𝑬𝑶 𝑪𝒉𝒂𝒓𝒂𝒄𝒕𝒆𝒓𝒊𝒔𝒕𝒊𝒄𝒔𝒕 + 𝝋𝟐𝑫𝒊𝒔𝒄𝒍𝒐𝒔𝒖𝒓𝒆 𝑸𝒖𝒂𝒍𝒊𝒕𝒚𝒕 + 𝜑3𝑏𝑒𝑡𝑎𝑡
+ 𝜑4𝐵𝑜𝑜𝑘 𝑡𝑜 𝑀𝑎𝑟𝑘𝑒𝑡𝑡−1 + 𝜑5𝑆𝑖𝑧𝑒𝑡−1 + 𝜑6𝑆𝑡𝑜𝑐𝑘 𝑅𝑒𝑡𝑢𝑟𝑛𝑡 + 𝜑7𝐿𝑛𝐴𝑛𝑎𝑙𝑦𝑠𝑡𝑠𝑡
+ 𝜑8𝐼𝑛𝑠𝑡𝑖𝑡 O𝑤𝑛𝑒𝑟𝑠𝑡 + 𝜑9𝐶𝑜𝑢𝑛𝑡𝑠𝑡 + 𝜑10𝐶𝑖𝑡𝑒𝑠𝑡 + 𝜖𝐶𝑜𝑠𝑡 𝑜𝑓 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 .
(4b)
Eq. (4a) is Eq. (3a) with the firm’s cost of capital as an additional explanatory variable.57
This formulation allows disclosure quality and cost of equity capital to affect innovation
separately. No changes were made in the equations for disclosure quality, analyst following, and
institutional ownership. Therefore, Eq. (3b), (3c), and (3d) are included as before. Eq. (4b) is
added to the simultaneous equations model to include the additional link between disclosure
quality and cost of capital; that is, I add an indirect effect of disclosure quality on innovation
through its effect on the cost of capital. By including CEO Characteristics in Eq. (4b), I account
not only for the effect of disclosure quality on cost of capital, but also for CEOs affecting costs
of capital through other means (such as through other channels of disclosure, which are not
measured here, or through investment decisions which change the risk profile of the firm). I
control for the usual firm characteristics expected to affect cost of capital: beta, beginning period
book to market, firm size, and stock returns. I also add the information environment (analyst
following and institutional ownership) which may decrease cost of equity capital. Lastly, I add
the firm’s innovative efforts (Counts and Cites, i.e., number of patents and number of citations)
57 Cost of capital is measured here as the mean of the available cost of capital measures from the following sources:
- Ohlson and Juettner-Nauroth (2005): both short-term and long-term based cost of capital calculation. The
difference between the two methods is in the way long-term growth is calculated. While the short-term
version calculates long-term growth based on the forecasted growth between years 2 and 1, the long-term
version uses the mean of the growth between years 5 and 4 and between 3 and 2.
- Easton (2004): modified price-earnings growth (i.e., MPEG)
- Claus and Thomas (2001)
- Gebhardt, Lee, and Swaminathan (2001)
where the earnings forecasts are generated by the cross-sectional model suggested by Hou, van Dijk, and Zhang
(2012). See Appendix A for the equations.
56
since the patent grants reduce investor uncertainty and signal a higher probability of successful
innovation, which reduces the cost of equity capital (Hegde and Mishra, 2014). The direct effect
of cost of equity capital on innovation is captured by the coefficient 3 in Eq. (4a), and the effect
of disclosure quality on cost of capital is captured by the coefficient 2 in Eq. (4b).
Table 13 shows that disclosure quality lowers costs of capital (column (4) in all of the
panels), and lower costs of capital in turn increase innovation quality (as measured by patent
citations – column (1) in Panel B), but not innovation quantity (as measured by patent count –
column (1) in Panel A).58 The latter result is consistent with lower cost of capital encouraging
“good” investment decisions. Panels A and B incorporate CEOs tendency for innovation, and
Panels C and D include CEOs functional backgrounds. Panels A and C analyze innovation
quantity, LnCountst+1, and Panels B and D analyze innovation quality, LnCitest+1. In all the
panels, column (4) shows that disclosure quality, DQ, has a negative association with the cost of
equity capital, CoC. The coefficients are -0.213 (Z-statistic -14.62) in Panel A, -0.211 (Z-statistic
-14.63) in Panel B, -0.180 (Z-statistic -13.31) in Panel C, and -0.188 (Z-statistic -13.63) in Panel
D. This result is consistent with the negative association between cost of capital and earnings
quality presented by Francis, LaFond, Olsson, and Schipper (2004). Similarly, all the panels
show (in column (1)) that disclosure quality, DQ, increases innovation regardless of the effect of
cost of capital. The coefficients for DQ are 0.171 (Z-statistic 4.74) in Panel A, 0.124 (Z-statistic
3.18) in Panel B, 0.198 (Z-statistic 5.55) in Panel C, and 0.204 (Z-statistic 5.01) in Panel D.
These results suggest that disclosure quality influences innovation beyond its effect on cost of
capital. Higher disclosure quality increases corporate transparency, which in turn disciplines
managers’ use of funds. The increased transparency not only allows shareholders to reduce cost
of capital for innovation, but also allows other stakeholders to put pressure on the firm to invest
in long-term innovation.
58 As in Table 7, the coefficients are standardized to facilitate the comparison.
57
Table 13 shows that cost of capital has a much weaker effect on innovation quantity than
on innovation quality.59 For innovation quantity, LnCountst+1, in Panel A, the Z-statistic is -0.97
and in Panel C it is -2.48 (coefficient -0.043, which is 22% of the coefficient for DQ). For
innovation quality, LnCitest+1, Panels B and D show the negative association between cost of
capital and innovation. In Panel B, the coefficient for CoC is -0.038 (Z-statistic -2.26, and is 31%
of the coefficient for DQ), and in Panel D it is -0.092 (Z-statistic -4.97, and is 45% of the
coefficient for DQ). Increased innovation quantity is usually considered beneficial for future firm
performance. However, higher patent application count includes both patents with high impact
and patents which are insignificant and have few, if any, future citations. A large number of
insignificant patent applications may indicate an inefficient allocation of resources. The
difference in cost of capital’s effect on innovation quantity and quality highlights the possibility
that lower cost of capital is associated with both a lower moral hazard problem of CEOs shirking
from effort (the quality of innovation increases), and a lower agency problem of over-allocation
of resources (there is no significant effect on innovation quantity as it does not distinguish
between efficient and inefficient allocation of resources).
Furthermore, the results suggest that the disclosure quality increases innovation only
partly through its effect on cost of capital. Since the coefficients are standardized, all the panels
show that even when cost of capital affects innovation, its magnitude is less than half of that of
disclosure quality. In Panel B, the coefficient for CoC is -0.038 (Z-statistic -2.26), as compared
to 0.124 (Z-statistic 3.18) for DQ. In Panel D, the coefficient for CoC is -0.092 (Z-statistic -4.97),
as compared to 0.204 (Z-statistic -5.01) for DQ. These results are consistent with prior research,
which demonstrates that disclosure quality contributes to external parties’ ability to monitor and
influence firm performance, apart from its contribution to shareholders’ ability to influence stock
price. Examples include the impact of disclosure on product market competition (Darrough,
1993; Darrough and Stoughton, 1990; Wagenhofer, 1990), covenant design by debt holders
(Sridhar and Magee, 1996), credit lines from suppliers (Raman and Shahrur, 2008), salary
59 I focus on the direct effect described in column (1), since it makes up most of the total effect reported in column
(3), and the indirect effect in column (2) is a byproduct of the reciprocal relation between disclosure quality and
innovation.
58
negotiations with employees (Bova, Dou, and Hope, 2015), and attraction and retention of
customers (Raman and Shahrur, 2008).
Overall, Table 13 provides evidence that while cost of capital reduces the agency and the
moral hazard problems, disclosure quality affects innovation through other channels as well.
59
Chapter 7 - Conclusion
In this dissertation, I study the relation between corporate disclosure quality and
innovation. Innovation is an important driver of economic growth, and is important for a firm’s
growth and survival. I measure innovation by the number of patent applications that were
eventually granted and their future citations. An extra citation per patent boosts market value by
3% (Hall, Jaffe, and Trajtenberg, 2005).
The patenting process is regulated. When a firm files for a patent, its application goes
through a due process where the firm is required to make sufficient disclosure to justify patent
protection (a patent’s life is for 20 years with some possibility to get an extension), and the
USPTO patent officer checks that the application indeed applies to a patent that is new and
useful and that all relevant patents are cited. Patent data yield better measures of the firm’s
innovation than R&D spending not only because of the external assurance provided by the
USPTO, but also because the immediate expensing of R&D costs disconnects the timing of this
spending from its contribution to innovation.
To test the first hypothesis, that disclosure quality is positively associated with
innovation, I examine different aspects of disclosure quality (earning quality, 10-K readability,
management guidance frequency, and their principal component) and show that innovation
benefits from higher disclosure quality. This relation holds also when using three-stage least
squares to account for the endogenous relation between innovation and disclosure decisions.
These simultaneous regressions show that while disclosure quality increases innovation,
innovation reduces disclosure quality. These results are consistent with improved disclosure
quality allowing the firm to finance innovation, while innovation increases uncertainty, which
reduces disclosure quality.
Prior studies show that CEO characteristics affect disclosure quality as well as
innovation. Based on that evidence and the results above, it may be that disclosure quality is
utilized to impact continued innovation. I hypothesize and show that CEO characteristics also
affect innovation indirectly though their effect on disclosure quality. Specifically, I examine the
60
relation between innovation and the following CEO characteristics: professional background
(technical, financial, and legal) and the CEO’s tendency for innovation. First, I validate my
measures and show that technical (legal) background affects innovation favorably (unfavorably).
These results are consistent with previous research, which found similar associations between
functional backgrounds and R&D expense. Next, I use a simultaneous equation model to conduct
a path analysis to separate the direct and indirect effects of CEO characteristics on innovation.
The results suggest that CEO characteristics affect innovation not only directly but also
indirectly through their influence on disclosure quality. While CEOs with technical and legal
backgrounds indirectly affect innovation through their influence on management guidance
frequency, the indirect effect of CEOs with financial backgrounds is primarily through earnings
quality. The difference most likely stems from CEOs with financial backgrounds having the
financial sophistication to have a stronger influence on the quality of reported earnings. The
indirect effects on innovation may be as high as 33% of the total effect, which implies that
disclosure quality is a significant mechanism through which CEO characteristics affect
innovation.
While I endeavor to separate CEO and firm effects, I acknowledge that my empirical
design does not completely rule out endogenous CEO-firm matching.60 Ideally, I should look at
a subsample of exogenous CEO turnovers, such as sudden deaths (Fee, Hadlock, and Pierce,
2013). Unfortunately, there are too few exogenous turnovers in my sample to afford a
meaningful empirical analysis, especially given the large sample requirement of the structural
equations modeling (used to test the second hypothesis). Nevertheless, the main conclusions of
this research study are that: (1) disclosure quality matters for innovation; and (2) this relation is
strategically utilized by upper management.
60 There is an ongoing debate on whether CEO characteristics are exogenous or whether they are a manifestation of
the board of directors’ preferences. This debate is part of a broader discussion on the balance of power between the
board and management and whether it is the CEOs who actually control the board by choosing outside directors and
controlling the flow of information to the board, for example. Ronen and Yaari (2008, Chapter 5.4) provide a review
of that literature.
61
62
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69
Appendix A: Variable definitions
Variable Name Description
Innovation
Countst Patent quantity in year t, as measured by the number of utility patents
for that a firm applied during a year and that were eventually granted.
A prefix Ln indicates that the variable is the natural logarithm of the
patent count plus one.
Citest Patent quality in year t, as measured by the number of future citations
for the patents for which the firm applied during a year and were
eventually granted. A prefix Ln indicates that the variable is the
natural logarithm of the number of citations plus one.
CEO
CEOt The CEO tendency for innovation in year t, measured as the fixed-
effects coefficients from regressing innovation on control variables,
including firm and year fixed effects.
FinanceBackgroundt Finance background in year t. An indicator variable that equals one if
the CEO holds financial or accounting credentials (such as CPA),
served as a CFO or Controller, or holds a degree in finance or
accounting.
TechnicalBackgroundt Technical background in year t. An indicator variable that equals one
if the CEO is an engineer, a doctor, a pharmacist, holds a degree in
natural or exact sciences, or served as a Chief Science Officer or
Chief Technical Officer.
Legalt Legal background in year t. An indicator variable that equals one if
the CEO either has an Esq. suffix, is identified as a legal
professional, or served as a Chief Legal Officer, Chief Counsel, or
General Counsel.
CEOAget The age of the CEO in year t.
CEOTenuret The number of years in year t since the CEO became CEO at the
firm.
Disclosure Quality
MF Countt The number of management guidance issued by the firm during fiscal
year t regarding the firm’s annual performance, calculated from
FirstCall.
10-K Fogt The Fog index for the 10-K in year t.
10-K Lengtht The number of words in the 10-K in year t, scaled by 100,000.
70
Variable Name Description
DiscAccrualst Unsigned discretionary accruals in year t as estimated by the model
in Jones (1991): 𝑇𝐴𝑡
𝐴𝑠𝑠𝑒𝑡𝑠𝑡−1= 𝛽1
1
𝐴𝑠𝑠𝑒𝑡𝑠𝑡−1+ 𝛽2
∆𝑆𝑎𝑙𝑒𝑠𝑡
𝐴𝑠𝑠𝑒𝑡𝑠𝑡−1+ 𝛽3
𝑃𝑃𝐸𝑡
𝐴𝑠𝑠𝑒𝑡𝑠𝑡−1+ 𝜖𝑡.
Discretionary accruals are total accruals minus the predicted accruals
from industry-year regressions. (Each industry-year has at least 15
observations.) Unsigned discretionary accruals are the absolute value
of the discretionary accruals.
DQt The principal component of the other disclosure quality measures in
year t.
DQ_Normt The mean of the normalized ranking of the specific disclosure quality
measures in year t, where a higher ranking indicates higher disclosure
quality.
DQ_Norm_fogMDAt DQ_Normt where the 10-K Fog index is replaced by the Fog index of
the MD&A section of the 10-K.
EPS forecast errort The absolute value of the error in analyst forecasts in year t, scaled
by the standard deviation of analyst forecast.
Firm level controls
LnAssetst Natural logarithm of total assets in year t (at) plus one.
RDAssetst Investment in intangible assets—research and development
expenditure in year t deflated by total assets (xrd/at).
LnFirmAget Natural log of one plus the firm age in year t, where the age is based
on the number of years the firm has existed in CRSP monthly stock
return files.
ROAt Profitability—return on assets in year t, defined as operating income
before depreciation divided by total assets (oibdp / at).
PPEAssetst Asset tangibility—Property Plant and Equipment in year t deflated by
total assets ( ppent / at).
Leveraget Book value of debt in year t deflated by total assets ( (dltt + dlc) / at).
CapexAssetst Capital expenditure in year t deflated by total assets (capx / at).
MtoBt Market to book in year t (mkvalt / bkvlps).
Analystst Natural logarithm of one plus the number of analysts following the
firm in year t. The number of analysts is the variable analysts in
I/B/E/S. If firm i is not in I/B/E/S, the number of analysts is assumed
to be zero.
Instit Ownerst The percentage of shares owned by institutional investors in year t
calculated from the Thomson Reuters Institutional (13f) Holdings.
71
Variable Name Description
Instit Owners – DEDt The average percentage of shares owned by dedicated institutional
investors in year t. The institutional ownership data are from
Thomson Reuters Institutional (13f) Holdings and the classification
of these investors is from Brian Bushee’s website
(http://acct.wharton.upenn.edu/faculty/bushee/IIclass.html).
“Dedicated investors are characterized by large average investments
in portfolio firms and extremely low turnover, consistent with a
‘relationship investing’ role“ (Bushee, 2001, p. 214).
Instit Owners – TRAt The average percentage of shares owned by transient institutional
investors in year t. The institutional ownership data are from
Thomson Reuters Institutional (13f) Holdings and the classification
of these investors is from Brian Bushee’s website
(http://acct.wharton.upenn.edu/faculty/bushee/IIclass.html).
“’Transient’ institutions are characterized as having high portfolio
turnover and highly diversified portfolio holdings.“ (Bushee, 2001, p.
214).
Instit Owners – QIXt The average percentage of shares owned by quasi-indexer
institutional investors in year t. The institutional ownership data are
from Thomson Reuters Institutional (13f) Holdings and the
classification of these investors is from Brian Bushee’s website
(http://acct.wharton.upenn.edu/faculty/bushee/IIclass.html). “Quasi-
indexers are also characterized by low turnover, but they tend to have
diversified holdings, consistent with a passive, buy-and-hold strategy
of investing portfolio funds in a broad set of firms“ (Bushee, 2001, p.
214).
CF Volt Volatility of cash flows in year t, measured as the volatility of
operating cash flows (oancf) over the previous five years, scaled by
the average of total assets over those same five years.
Sales Volt Volatility of sales in year t, measured as the volatility of sales (sale)
over the previous five years, scaled by the average of total assets over
those same five years.
Litigation Riskt An indicator variable that equals one if the firm belongs to a litigious
industry in year t (SICs 2833–2836, 3570–3577, 3600–3674, 7370–
7374, 5200–5961, 8731–8734).
Sales Growtht Percentage change in sales in year t (sale) as compared to the
previous year.
Stock Returnt Annual return calculated at the end of fiscal year t based on
Compustat variable prcc_f.
SEOt An indicator variable that equals one if the firm’s outstanding shares
increased by more than 10% in year t compared with the previous
year.
Losst An indicator variable that equals one if the firm incurred a loss in
year t, measured as net income (ni) being negative.
72
Variable Name Description
LogOperCyclet Natural logarithm of the operating cycle in year t, which is calculated
as the sum of days receivables and days inventory ((urect/sale)*365
+ (invt/cogs)*365).
BGV Beginning value of patent variable. This is the average of the 10
years before the sample period of the dependent variable in the
Poisson regressions, and is used to account for firm fixed effects.
CoCt Cost of Equity Capital in year t. CoC is measured as the mean of the
available cost of capital measures from the following sources:
- Ohlson and Juettner-Nauroth (2005): both short-term and long-term
based cost of capital calculation. The difference between the two
methods is in the way long-term growth is calculated. While the
short-term version calculates long-term growth based on the
forecasted growth between years 2 and 1, the long-term version uses
the mean of the growth between years 5 and 4 and between 3 and 2.
𝑅 = 𝐴 + √𝐴2 +𝐸𝑡[𝐸𝑡+1]
𝑀𝑡× (�̂� − (𝛾 − 1))
Where:
𝐴 = 0.5 ((𝛾 − 1) +𝐸𝑡[𝐷𝑡+1]
𝑀𝑡)
𝑔 = 0.5 (𝐸𝑡[𝐸𝑡+3] − 𝐸𝑡[𝐸𝑡+2]
𝐸𝑡[𝐸𝑡+2]+
𝐸𝑡[𝐸𝑡+5] − 𝐸𝑡[𝐸𝑡+4]
𝐸𝑡[𝐸𝑡+4])
- Easton (2004): modified price-earnings growth (i.e., MPEG)
𝑀𝑡 =𝐸𝑡[𝐸𝑡+2] + 𝑅 × 𝐸𝑡[𝐷𝑡+1] − 𝐸𝑡[𝐸𝑡+1]
𝑅2
- Claus and Thomas (2001)
𝑀𝑡 = 𝐵𝑡 + ∑𝐸𝑡[(𝑅𝑂𝐸𝑡+𝑘 − 𝑅) × 𝐵𝑡+𝑘−1]
(1 + 𝑅)𝑘
5
𝑘=1
+𝐸𝑡[(𝑅𝑂𝐸𝑡+5 − 𝑅) × 𝐵𝑡+4](1 + 𝑔)
(𝑅 + 𝑔)(1 + 𝑅)5
- Gebhardt, Lee, and Swaminathan (2001)
𝑀𝑡 = 𝐵𝑡 + ∑𝐸𝑡[(𝑅𝑂𝐸𝑡+𝑘 − 𝑅) × 𝐵𝑡+𝑘−1]
(1 + 𝑅)𝑘
11
𝑘=1
+𝐸𝑡[(𝑅𝑂𝐸𝑡+12 − 𝑅) × 𝐵𝑡+11]
𝑅 × (1 + 𝑅)11
Where, the earnings forecasts are generated by the cross-sectional
model suggested by Hou, van Dijk, and Zhang (2012).
73
Variable Name Description
Betat Firm’s beta in year t, calculated from annual returns for the previous
ten years.
BMt Ratio of book value to market value in year t.
Sizet Firm size, measured as natural logarithm of the firm’s market value
(mkvalt) in year t.
Definitions of Compustat variables (shown in italics).
* TA are changes in non-cash working capital, measured as net income before extraordinary
items (Compustat item ibc) minus cash flow from operations (Compustat item oancf); Sales is
the annual change in sales, measured as the change in Compustat item sale; PPE is year-end
property, plant, and equipment (Compustat item ppegt); Assets are the total assets (Compustat
item at); CFO is cash flows from operations (Compustat item oancf); REC is the annual change
in accounts receivables (Compustat item rect). McN, DRT_lag, and DRT_fwd require items from
the statement of cash flows and can therefore be calculated only after 1988. Jones can also be
calculated for earlier years. For those years, total accruals, TA, is calculated as the change in
current assets (change in Compustat item act) minus the change in current liabilities (change in
Compustat item lct) and change in cash (change in Compustat item che) plus the change in debt
in current liabilities (change in Compustat item dlc).
74
Appendix B: Patent Data
Due to the importance of innovation to economic growth, policy makers have been
implementing mechanisms for intellectual property (“IP”) protection. Moser (2011) details the
historical development of IP protection going as far back as 1474. One of the main mechanisms
used to protect IP and encourage innovation is the provision of patent protection. Today, the
United States Patent and Trademark Office (USPTO) is designated with patent protection, and
grant utility patents to “anyone who invents or discovers any new and useful process, machine,
article of manufacture, or composition of matter, or any new and useful improvement thereof".61
USPTO officers grant utility patents to inventions that are both new and useful and ensure that
the patents cite all other relevant patents and patent applications. Patents provide information on
the innovators, organization, location, dates (application and grant), and technology codes which
enable categorization of the patents themselves, and facilitate connections with other data sets.
USPTO has been working on making the data more accessible to researchers by publishing files
with historical patent grants and updating patent grants data files on a weekly basis. Similarly,
the National Bureau of Economic Research (“NBER”) encourages research into innovation by
publishing their patent data project, which matches patent number to Compustat gvkey from
1976 until 2006. The availability of the data together with the ease of matching them to other
datasets, the long time series, and large dataset, enables researchers to conduct large sample
studies on innovation. Moreover, patents are granted to a large variety of products from different
industries62 and the U.S. Patent and Trademarks Office (USPTO) provides assurance that the
innovation is new.
Patent data can be used to measure the quantity of innovation, the quality of innovation,
innovation productivity, innovation breadth, and innovation originality (Hall et al., 2005; He and
Tian, 2013). These attributes of innovation are usually based on patent applications and citations,
which are reliable considering the incentives provided by the patent system to file quickly and
61 http://www.uspto.gov/patents/resources/general_info_concerning_patents.jsp.
62 For example, Starbucks has a patent for the “[m]ethod of making beverages with enhanced flavors and aromas”
(patent grant 8043645).
75
the assurance provided by the patent examiner that future patents include all relevant citations.
Intuitively, a greater focus on innovation should yield a higher number of patent applications.
However, these patents may not have an impact because they may not make a significant
contribution to future patents. This implies that the number of patent applications may capture a
firm’s intent and effort to innovate, while future citations of these patents may indicate that the
firm is actually being innovative. The connection between patent citations and successful
innovation is also evident from the correlation between firm value and these citations (Hall,
Jaffe, and Trajtenberg, 2005).
Nowadays, patent data is commonly used in the literature to measure innovation.63 The
first paper to use patent data was Pakes and Griliches (1980). Since then, Hall, Jaffe, and
Trajtenberg (2001) have provided guidance on the use of the patent data, which set the stage for
later studies using patent data to conduct large sample studies on innovation. As a result of the
availability of the dataset and its richness, many studies use patent data to measure innovation
(for example, Hall, Jaffe, and Trajtenberg, 2005; Galasso and Simcoe, 2011; Aghion, Van
Reenen, and Zingales, 2013; He and Tian, 2013; Bereskin, Hsu, and Rotenberg, 2015).
The main limitation of using patent data as a proxy for innovation is that patents may not
capture all innovative efforts. For example, one of the main alternative methods of protecting IP
is through trade secrets (Bhattacharya and Guriev, 2006; Masayuki, 2014), so that looking only
at patent data may result in incorrect inferences about the factors influencing innovation and
lumping together non-innovative firms with innovative firms. Therefore, in my work I focus only
on the firms that utilize patent protection.
63 Recent accounting literature has also started using patent data to capture private information on a firm’s future
performance. For example, Plumlee et al. (2015) use notice of patent pending as a measure of private information
used in negotiation of bank loans, and Gunny and Zhang (2014) use patent citations in the following year to measure
managers’ private information in their decision to manage earnings to meet analysts’ forecasts in the current year.
76
Table 1: Sample selection
Number of
Observations
Number of
Firms
Number of
CEOs
CEO sample from 1996 to 2010 99,735 13,596 20,271
Excluding:
Missing Compustat data 20,892 2,232 2,488
Financial and utility industries (SIC
between 6000 and 6999, and between
4900 and 4999) 12,042 1,985 2,654
Firm did not have even one patent
application granted during the entire
sample, or had no analyst following 42,667 6,116 10,359
Total number of observations with CEO
identity 24,134 3,263 4,770
Sample of firms with at least two CEOs (or
CEOs that appear in more than one firm)
with a tenure of at least 3 years per CEO
7,064 917 1,942
77
Table 2: Descriptive statistics
This table reports the summary statistics of the variables included in my main regressions. Panel
A presents the descriptive statistics. Panel B describes the Pearson correlations among the main
variables. The statistically significant correlations are in bold, and p-values are in parentheses.
Panel C details the distribution of the patent data by industry. Countst+1 is the one-year-ahead
number of patents for which the firm applied during the year and were eventually granted.
Citest+1 is the one-year-ahead number of citations. See Appendix A for the full list of variable
definitions.
Panel A: Descriptive statistics
Variable # Obs mean sd p25 p50 p75
Countst+1 22,861 15.371 34.453 0.000 2.000 10.000
Citest+1 22,861 132.402 359.067 0.000 4.000 57.000
FinanceBackground 6,014 0.201 0.401 0.000 0.000 0.000
TechnicalBackground 6,346 0.115 0.319 0.000 0.000 0.000
Legal 5,819 0.018 0.132 0.000 0.000 0.000
DiscAccruals 21,651 -0.025 0.115 -0.061 -0.012 0.029
MF Count 22,861 0.923 1.865 0.000 0.000 1.000
Fog 18,106 19.574 1.432 18.617 19.433 20.334
Length
(words/100,000) 18,106 0.315 0.208 0.179 0.263 0.390
DQ 16,790 0.257 2.445 -0.298 1.137 1.819
LnAssets 22,861 6.229 2.013 4.661 6.021 7.640
RDAssets 22,861 0.094 0.136 0.006 0.045 0.123
FirmAge (years) 22,861 14.162 2.242 8.000 14.000 27.000
ROA 22,861 0.043 0.241 0.016 0.110 0.170
PPEAssets 22,861 0.214 0.179 0.077 0.162 0.299
Leverage 22,861 0.178 0.189 0.005 0.133 0.290
CapexAssets 22,861 0.050 0.046 0.020 0.036 0.064
MtoB 22,861 2.493 2.132 1.268 1.776 2.834
Analysts 22,861 8.679 7.785 3.000 6.000 12.000
Instit Owners 22,861 0.487 0.289 0.238 0.506 0.732
Instit Owners – DED 22,672 0.075 0.130 0.000 0.016 0.097
Instit Owners – TRA 22,672 0.264 3.044 0.092 0.182 0.318
Instit Owners – QIX 22,672 0.429 7.106 0.128 0.288 0.499
78
Panel B: Table of correlations
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19)
(1) Countst+1 1.00
(2) Citest+1 0.78
(0.00)
(3) Finance
Background -0.02 0.00
(-0.10) (-0.76)
(4) Technical Background
0.04 0.06 0.01
(0.00) (0.00) (-0.47)
(5) Legal -0.05 -0.05 -0.03 -0.03
(0.00) (0.00) (-0.01) (-0.01)
(6) DiscAccruals 0.00 0.00 -0.01 -0.02 0.00
(-0.99) (-0.54) (-0.63) (-0.11) (-0.88)
(7) MF Count 0.16 0.06 -0.05 -0.17 0.03 0.00
(0.00) (0.00) (0.00) (0.00) (-0.03) (-0.71)
(8) Fog 0.03 -0.02 0.05 0.02 -0.03 -0.03 0.01
(0.00) (-0.02) (0.00) (-0.17) (-0.02) (0.00) (-0.11)
(9) Length 0.14 0.08 -0.01 -0.02 0.04 -0.06 0.11 0.31
(0.00) (0.00) (-0.53) (-0.14) (-0.01) (0.00) (0.00) (0.00)
(10) DQ 0.09 0.03 -0.03 -0.09 0.03 0.31 0.17 -0.04 -0.01
(0.00) (0.00) (-0.02) (0.00) (-0.05) (0.00) (0.00) (0.00) (-0.07)
(11) LnAssets 0.48 0.32 -0.07 -0.16 0.04 0.07 0.31 -0.02 0.28 0.31
(0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (-0.02) (0.00) (0.00)
(12) RDAssets -0.04 -0.02 0.02 0.24 -0.06 -0.21 -0.18 0.12 -0.02 -0.32 -0.47
(0.00) (0.00) (-0.14) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
(13) FirmAge 0.23 0.13 0.01 -0.17 0.04 0.14 0.24 -0.04 0.09 0.27 0.52 -0.31
(0.00) (0.00) (-0.31) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
(14) ROA 0.15 0.11 -0.07 -0.22 0.03 0.24 0.23 -0.12 -0.02 0.30 0.48 -0.73 0.28
(0.00) (0.00) (0.00) (0.00) (-0.02) (0.00) (0.00) (0.00) (-0.02) (0.00) (0.00) (0.00) (0.00)
79
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19)
(15) PPEAssets 0.02 0.03 -0.05 -0.09 0.01 0.13 -0.05 -0.09 0.02 0.18 0.34 -0.30 0.24 0.25
(0.00) (0.00) (0.00) (0.00) (-0.48) (0.00) (0.00) (0.00) (-0.01) (0.00) (0.00) (0.00) (0.00) (0.00)
(16) Leverage 0.03 0.01 0.04 -0.02 0.03 0.00 0.03 -0.02 0.17 0.07 0.30 -0.16 0.21 0.05 0.32
(0.00) (-0.07) (0.00) (-0.18) (-0.05) (-0.46) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
(17) CapexAssets 0.04 0.09 -0.05 -0.02 0.00 0.04 -0.06 -0.07 -0.01 -0.01 0.08 -0.11 -0.02 0.14 0.60 0.09
(0.00) (0.00) (0.00) (-0.10) (-0.74) (0.00) (0.00) (0.00) (-0.05) (-0.31) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
(18) MtoB 0.02 0.09 0.01 0.14 -0.03 -0.07 -0.10 0.02 -0.03 -0.19 -0.25 0.35 -0.22 -0.23 -0.21 -0.16 0.03
(-0.01) (0.00) (-0.56) (0.00) (-0.02) (0.00) (0.00) (-0.01) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
(19) Analysts 0.42 0.33 -0.10 -0.08 0.00 -0.04 0.34 0.05 0.23 0.13 0.60 -0.14 0.27 0.24 0.11 0.06 0.08 0.04
(0.00) (0.00) (0.00) (0.00) (-0.79) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)
(20) Instit Owners 0.06 -0.01 -0.07 -0.22 0.03 0.03 0.40 0.05 0.14 0.25 0.32 -0.22 0.33 0.29 0.01 0.06 -0.07 -0.13 0.40
(0.00) (-0.27) (0.00) (0.00) (-0.03) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (-0.40) (0.00) (0.00) (0.00) (0.00)
80
Panel C: Distribution by industry
Fama French Industry Mean Countst+1 Mean Citest+1 # Obs
Electronic Equipment 29.53 237.08 2,936
Pharmaceutical Products 12.35 81.08 2,916
Business Services 9.63 105.29 2,793
Medical Equipment 8.87 139.95 1,566
Computers 25.63 260.40 1,517
Machinery 17.59 134.52 1,355
Measuring and Control Equipment 11.23 90.99 984
Chemicals 24.90 148.06 751
Automobiles and Trucks 23.35 204.37 652
Petroleum and Natural Gas 17.52 139.46 624
Electrical Equipment 10.67 76.95 598
Consumer Goods 19.89 161.07 539
Business Supplies 15.47 124.97 498
Communication 15.09 128.35 465
Construction Materials 6.65 46.62 435
Retail 1.69 26.70 408
Steel Works 5.50 33.11 358
Food Products 3.56 22.16 356
Wholesale 2.35 27.52 337
Apparel 4.32 33.89 292
Recreation 18.21 191.04 265
Rubber and Plastic Products 3.41 24.83 258
Transportation 1.70 9.84 203
Aircraft 44.59 347.30 179
Healthcare 1.68 27.94 161
Textiles 2.98 15.83 137
Entertainment 15.20 199.75 114
Shipping Containers 5.47 40.85 110
Fabricated Products 1.06 7.08 98
Personal Services 0.27 2.99 97
Restaurants, Hotels, Motels 0.53 7.14 94
Defense 30.58 199.87 91
Non-Metallic and Industrial Metal 2.77 20.24 82
Printing and Publishing 0.95 8.29 80
Construction 1.60 5.29 77
Precious Metals 0.47 2.95 73
Beer & Liquor 8.67 61.70 63
Shipbuilding, Railroad Equipment 8.04 83.56 57
Agriculture 37.47 323.32 47
Tobacco Products 4.59 10.52 29
81
Fama French Industry Mean Countst+1 Mean Citest+1 # Obs
Candy & Soda 0.50 0.90 10
Total 15.25 130.85 22,705
82
Table 3: The direct effect of disclosure quality on innovation (H1)
This table presents the OLS regression results of innovation on disclosure quality proxies.
Innovation is measured either as patent quantity (LnCounts) in Panel A or patent quality
(LnCites) in Panel B. See Appendix A for variable definitions. The regressions include industry,
firm and year fixed effects. t-statistics are in parentheses. Reported standard errors are clustered
by firm. *** p<0.01, ** p<0.05, * p<0.1
Panel A: Patent quantity
(1) (2) (3) (4) (5)
LnCountst+1 LnCountst+1 LnCountst+1 LnCountst+1 LnCountst+1
10-K Fogt -0.014***
(-2.896)
10-K Lengtht -0.042
(-1.093)
DiscAccrualst -0.278***
(-4.329)
MF Countt -0.002
(-0.513)
DQt 0.011***
(3.756)
LnAssetst 0.193*** 0.195*** 0.194*** 0.192*** 0.196***
(7.564) (7.645) (8.169) (8.678) (7.162)
RDAssetst 0.232* 0.226* 0.277* 0.203* 0.255
(1.718) (1.671) (1.894) (1.718) (1.519)
LnFirmAget -0.405*** -0.411*** -0.383*** -0.381*** -0.441***
(-12.193) (-12.440) (-12.092) (-13.133) (-11.568)
ROAt 0.002 -0.002 -0.022 -0.015 -0.006
(0.027) (-0.039) (-0.356) (-0.265) (-0.081)
Leveraget -0.059 -0.055 -0.034 -0.067 -0.042
(-0.910) (-0.845) (-0.552) (-1.140) (-0.608)
PPEAssetst 0.666*** 0.670*** 0.646*** 0.626*** 0.696***
(4.299) (4.327) (4.412) (4.657) (4.052)
CapexAssetst -0.174 -0.167 -0.255 -0.220 -0.207
(-0.803) (-0.768) (-1.194) (-1.086) (-0.887)
MtoBt 0.017*** 0.018*** 0.017*** 0.016*** 0.018***
(4.287) (4.402) (4.229) (4.392) (3.849)
LnAnalystst 0.055*** 0.055*** 0.080*** 0.069*** 0.060***
(2.729) (2.744) (4.207) (3.791) (2.809)
Instit Ownerst -0.039 -0.043 -0.070 -0.040 -0.069
(-0.701) (-0.772) (-1.352) (-0.788) (-1.184)
Observations 17,987 17,987 21,503 22,705 16,681
Number of Firms 2,484 2,484 3,009 3,144 2,346
Adjusted R2 0.049 0.048 0.043 0.044 0.048
83
Panel B: Patent quality
(1) (2) (3) (4) (5)
LnCites t+1 LnCites t+1 LnCites t+1 LnCites t+1 LnCites t+1
10-K Fogt -0.068***
(-6.444)
10-K Lengtht -0.307***
(-4.147)
DiscAccrualst -0.348***
(-2.658)
MF Countt -0.033***
(-3.285)
DQt 0.006
(1.044)
LnAssetst -0.017 -0.001 0.010 0.030 -0.009
(-0.356) (-0.027) (0.216) (0.725) (-0.172)
RDAssetst -0.458* -0.488* -0.314 -0.278 -0.669**
(-1.739) (-1.847) (-1.117) (-1.186) (-2.125)
LnFirmAget -1.755*** -1.784*** -1.737*** -1.691*** -1.946***
(-24.048) (-24.483) (-25.373) (-27.030) (-23.951)
ROAt 0.189 0.161 0.133 0.163 0.142
(1.487) (1.260) (1.010) (1.426) (0.996)
Leveraget -0.065 -0.038 -0.004 -0.083 -0.045
(-0.469) (-0.275) (-0.033) (-0.654) (-0.308)
PPEAssetst 2.197*** 2.207*** 2.205*** 2.065*** 2.258***
(6.811) (6.812) (7.183) (7.330) (6.414)
CapexAssetst 0.314 0.355 0.020 0.205 0.136
(0.690) (0.778) (0.047) (0.503) (0.278)
MtoBt 0.047*** 0.049*** 0.056*** 0.046*** 0.059***
(5.431) (5.660) (6.268) (5.830) (5.913)
LnAnalystst 0.199*** 0.198*** 0.243*** 0.230*** 0.193***
(4.823) (4.793) (6.425) (6.353) (4.457)
Instit Ownerst -0.609*** -0.628*** -0.667*** -0.627*** -0.620***
(-5.081) (-5.223) (-5.984) (-5.786) (-4.981)
Observations 17,987 17,987 21,503 22,705 16,681
Number of Firms 2,484 2,484 3,009 3,144 2,346
Adjusted R2 0.231 0.230 0.217 0.220 0.232
84
Table 3b: The direct effect of disclosure quality on innovation (H1)
This table presents the OLS regression results of innovation on disclosure quality proxies. In this
table, the institutional investors control variable is separated by investor type: dedicated (Instit
Owners – DEDt), transient (Instit Owners – TRAt), or quasi-indexer (Instit Owners – QIXt).
Innovation is measured either as patent quantity (LnCounts) in Panel A or patent quality
(LnCites) in Panel B. See Appendix A for variable definitions. The regressions include industry,
firm and year fixed effects. t-statistics are in parentheses. Reported standard errors are clustered
by firm. *** p<0.01, ** p<0.05, * p<0.1
Panel A: Patent quantity
(1) (2) (3) (4) (5)
LnCountst+1 LnCountst+1 LnCountst+1 LnCountst+1 LnCountst+1
10-K Fogt -0.014***
(-2.877)
10-K Lengtht -0.048
(-1.254)
DiscAccrualst -0.291***
(-4.518)
MF Countt -0.002
(-0.401)
DQt 0.011***
(3.838)
Instit Owners – DEDt 0.152** 0.147** 0.122** 0.106** 0.184***
(2.571) (2.490) (2.261) (2.058) (2.918)
Instit Owners – TRAt 0.079* 0.088** 0.010*** 0.009*** 0.102**
(1.896) (2.105) (9.005) (8.585) (2.410)
Instit Owners – QIXt -0.034** -0.037*** -0.005*** -0.004*** -0.041***
(-2.442) (-2.581) (-7.354) (-6.812) (-2.798)
LnAssetst 0.201*** 0.203*** 0.197*** 0.197*** 0.205***
(7.913) (7.995) (8.190) (8.737) (7.547)
RDAssetst 0.251* 0.245* 0.286* 0.212* 0.290*
(1.835) (1.791) (1.954) (1.774) (1.729)
LnFirmAget -0.391*** -0.398*** -0.391*** -0.387*** -0.432***
(-12.032) (-12.329) (-12.521) (-13.364) (-11.796)
ROAt -0.009 -0.013 -0.023 -0.016 -0.014
(-0.136) (-0.211) (-0.366) (-0.289) (-0.194)
Leveraget -0.052 -0.047 -0.029 -0.060 -0.035
(-0.796) (-0.725) (-0.467) (-1.011) (-0.504)
PPEAssetst 0.677*** 0.681*** 0.664*** 0.642*** 0.709***
(4.340) (4.370) (4.497) (4.726) (4.120)
CapexAssetst -0.160 -0.152 -0.253 -0.218 -0.212
(-0.737) (-0.700) (-1.183) (-1.072) (-0.911)
MtoBt 0.017*** 0.018*** 0.017*** 0.016*** 0.018***
(4.354) (4.443) (4.146) (4.343) (3.940)
85
(1) (2) (3) (4) (5)
LnCountst+1 LnCountst+1 LnCountst+1 LnCountst+1 LnCountst+1
LnAnalystst 0.045** 0.045** 0.066*** 0.058*** 0.046**
(2.265) (2.259) (3.631) (3.300) (2.194)
Observations 17,929 17,929 21,352 22,516 16,649
Number of Firms 2,466 2,466 2,985 3,117 2,336
Adjusted R2 0.050 0.049 0.044 0.045 0.050
Panel B: Patent quality
(1) (2) (3) (4) (5)
LnCites t+1 LnCites t+1 LnCites t+1 LnCites t+1 LnCites t+1
10-K Fogt -0.067***
(-6.318)
10-K Lengtht -0.327***
(-4.436)
DiscAccrualst -0.305**
(-2.325)
MF Countt -0.033***
(-3.379)
DQt 0.005
(0.942)
Instit Owners – DEDt 0.591*** 0.563*** 0.570*** 0.481*** 0.774***
(4.132) (3.936) (4.342) (3.809) (5.111)
Instit Owners – TRAt 0.620*** 0.665*** 0.023** 0.022** 0.699***
(6.358) (6.851) (2.378) (2.291) (7.376)
Instit Owners – QIXt -0.245*** -0.260*** -0.010** -0.009** -0.260***
(-4.188) (-4.175) (-2.107) (-2.010) (-4.439)
LnAssetst -0.021 -0.006 -0.011 0.014 -0.005
(-0.439) (-0.123) (-0.239) (0.328) (-0.090)
RDAssetst -0.394 -0.422 -0.323 -0.292 -0.552*
(-1.485) (-1.585) (-1.161) (-1.234) (-1.778)
LnFirmAget -1.785*** -1.817*** -1.853*** -1.814*** -1.962***
(-24.873) (-25.368) (-27.498) (-29.300) (-24.657)
ROAt 0.116 0.086 0.091 0.111 0.071
(0.913) (0.672) (0.691) (0.977) (0.494)
Leveraget -0.023 0.005 0.031 -0.045 -0.014
(-0.166) (0.035) (0.226) (-0.348) (-0.097)
PPEAssetst 2.292*** 2.304*** 2.332*** 2.187*** 2.352***
(7.063) (7.074) (7.505) (7.643) (6.678)
CapexAssetst 0.343 0.386 -0.000 0.165 0.086
(0.755) (0.848) (-0.000) (0.400) (0.178)
86
(1) (2) (3) (4) (5)
LnCites t+1 LnCites t+1 LnCites t+1 LnCites t+1 LnCites t+1
MtoBt 0.045*** 0.047*** 0.054*** 0.044*** 0.058***
(5.221) (5.386) (6.048) (5.642) (5.841)
LnAnalystst 0.125*** 0.122*** 0.169*** 0.160*** 0.114***
(3.067) (2.994) (4.534) (4.504) (2.665)
Observations 17,929 17,929 21,352 22,516 16,649
Number of Firms 2,466 2,466 2,985 3,117 2,336
Adjusted R2 0.236 0.234 0.220 0.223 0.238
87
Table 4: The relation between disclosure quality and innovation
This table presents the three-stage least squares (3SLS) and two-stage least squares (2SLS)
estimation results for the simultaneous equations model relating disclosure quality and
innovation. Columns (1), (2), and (5) measure innovation by patent quantity and columns (3),
(4), and (6) by patent quality. Columns (1) through (4) present the results for 3SLS. Columns (5)
and (6) present the second stage results for 2SLS. See Appendix A for variable definitions. The
regressions include industry and year indicator variables. t-statistics are in parentheses. ***
p<0.01, ** p<0.05, * p<0.1
(1) (2) (3) (4) (5) (6)
VARIABLES DQt LnCountst+1 DQt LnCitest+1 LnCountst+1 LnCitest+1
DQt 0.081*** 0.050*
(4.420) (1.720)
LnCountst+1 -0.673***
(-11.112)
LnCites t+1 -0.423***
(-10.455)
Predicted DQt 0.058*** 0.036* (from 1st stage of 2SLS) (4.260) (1.666)
LnAssetst 0.414*** 0.520*** 0.345*** 0.665*** 0.511*** 0.631***
(12.721) (51.355) (12.036) (40.949) (72.282) (56.126)
LnFirmAget 0.065** -0.013 0.062* -0.025 -0.008 0.005
(2.022) (-0.813) (1.927) (-0.953) (-0.584) (0.221)
ROAt 0.935*** 0.184*** 0.973*** 0.331*** 0.147*** 0.170*
(7.225) (2.690) (7.601) (3.031) (2.608) (1.888)
LnAnalystst 0.159*** 0.156*** 0.153*** 0.243*** 0.102*** 0.201***
(3.780) (8.246) (3.600) (8.030) (6.973) (8.654)
Instit Ownerst 0.266*** -0.373*** 0.334*** -0.395*** -0.199*** -0.126**
(2.885) (-7.964) (3.674) (-5.249) (-5.444) (-2.170)
CF Volt -3.629*** -3.648***
(-12.213) (-12.326)
Sales Volt -0.922*** -0.813***
(-10.180) (-9.286)
Litigation Riskt -0.054 -0.096
(-0.706) (-1.293)
Sales Growtht -0.488*** -0.462***
(-12.971) (-12.538)
Stock Returnt -0.027 -0.022
(-1.094) (-0.910)
SEOt -0.394*** -0.369***
(-8.616) (-8.231)
Losst -0.686*** -0.698***
(-13.864) (-14.310)
88
(1) (2) (3) (4) (5) (6)
VARIABLES DQt LnCountst+1 DQt LnCitest+1 LnCountst+1 LnCitest+1
RDAssetst 3.215*** 4.063*** 2.574*** 3.091***
(24.933) (19.910) (24.267) (18.325)
Leveraget -0.236*** -0.373*** -0.763*** -1.191***
(-4.360) (-4.391) (-10.216) (-10.021)
PPEAssetst -0.814*** -1.346*** -0.354*** -0.530***
(-9.014) (-9.449) (-7.615) (-7.172)
CapexAssetst 1.850*** 3.445*** 2.303*** 3.949***
(6.522) (7.713) (9.489) (10.231)
MtoBt 0.078*** 0.138*** 0.076*** 0.136***
(14.105) (15.726) (16.245) (18.148)
Observations 16,360 16,360 16,360 16,360 21,331 21,331
Adjusted R2 0.126 0.446 0.130 0.431 0.452 0.430
89
Table 5: The importance of CEO characteristics for innovation
This table presents the OLS regression results of innovation on CEO characteristics, where innovation is measured as patent quantity
(LnCounts) or patent quality (LnCites). Panel A presents the results of the relation between CEO professional background and
innovation. Panel B presents the results of calculation of CEO tendency for innovation. The regressions include indicator variables for
each CEO. The coefficients for these indicator variables are the measure of CEO tendency for innovation. Panels C and D present the
correlations between the coefficients from the regressions in Panel B and specific CEO characteristics. The dependent variable in
Panel C is the coefficients calculated in Panel B column (1) and the dependent variable in Panel D is the coefficients in Panel B
column (2). See Appendix A for variable definitions. The regressions in Panel A include industry and year indicator variables. In
Panel B, the regressions include industry, firm and year fixed effects. t-statistics are in parentheses. *** p<0.01, ** p<0.05, * p<0.1
Panel A: CEO professional background and innovation
(1) (2) (3) (4) (5) (6) (7) (8)
LnCountst+1 LnCountst+1 LnCountst+1 LnCountst+1 LnCites t+1 LnCites t+1 LnCites t+1 LnCites t+1
FinanceBackgroundt 0.028 0.010 0.062 0.033
(0.674) (0.237) (1.040) (0.533)
TechnicalBackgroundt 0.214*** 0.315*** 0.164** 0.267**
(4.162) (4.254) (2.156) (2.459)
Legalt -0.415*** -0.404*** -0.849*** -0.837***
(-3.394) (-3.308) (-4.746) (-4.677)
LnAssetst 0.597*** 0.599*** 0.593*** 0.589*** 0.715*** 0.708*** 0.709*** 0.706***
(35.275) (37.572) (34.602) (34.293) (28.798) (30.088) (28.265) (28.061)
RDAssetst 3.310*** 3.017*** 3.204*** 3.202*** 3.540*** 3.288*** 3.374*** 3.374***
(14.890) (14.829) (14.314) (14.319) (10.843) (10.949) (10.300) (10.303)
LnFirmAget 0.019 0.021 -0.005 0.006 -0.013 -0.001 -0.045 -0.037
(0.662) (0.740) (-0.172) (0.214) (-0.317) (-0.034) (-1.053) (-0.851)
ROAt 0.818*** 0.759*** 0.793*** 0.810*** 0.920*** 0.871*** 0.891*** 0.905***
(6.523) (6.525) (6.258) (6.396) (4.999) (5.076) (4.807) (4.883)
Leveraget -0.458*** -0.490*** -0.438*** -0.456*** -0.731*** -0.740*** -0.696*** -0.714***
(-4.535) (-5.142) (-4.288) (-4.461) (-4.924) (-5.263) (-4.654) (-4.768)
PPEAssetst -0.945*** -0.918*** -0.912*** -0.888*** -1.600*** -1.568*** -1.538*** -1.508***
(-5.913) (-5.932) (-5.605) (-5.433) (-6.818) (-6.864) (-6.462) (-6.298)
90
(1) (2) (3) (4) (5) (6) (7) (8)
LnCountst+1 LnCountst+1 LnCountst+1 LnCountst+1 LnCites t+1 LnCites t+1 LnCites t+1 LnCites t+1
CapexAssetst 1.872*** 1.884*** 1.854*** 1.799*** 3.584*** 3.540*** 3.498*** 3.444***
(3.362) (3.507) (3.240) (3.148) (4.382) (4.465) (4.179) (4.114)
MtoBt 0.082*** 0.077*** 0.081*** 0.080*** 0.137*** 0.128*** 0.137*** 0.136***
(8.265) (8.393) (8.067) (8.003) (9.407) (9.421) (9.318) (9.271)
LnAnalystst 0.021 0.015 0.021 0.026 0.054 0.056 0.058 0.063
(0.600) (0.443) (0.595) (0.733) (1.059) (1.149) (1.143) (1.242)
Instit Ownerst -0.338*** -0.297*** -0.342*** -0.330*** -0.463*** -0.386*** -0.487*** -0.475***
(-4.069) (-3.729) (-4.072) (-3.924) (-3.794) (-3.286) (-3.960) (-3.858)
CEOAget -0.035 -0.031 -0.026 -0.026 -0.043 -0.033 -0.025 -0.024
(-1.499) (-1.373) (-1.081) (-1.085) (-1.257) (-1.005) (-0.728) (-0.706)
CEOAge2t 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
(1.026) (0.863) (0.602) (0.574) (0.890) (0.654) (0.343) (0.305)
CEOTenuret 0.019*** 0.015*** 0.017*** 0.015** 0.030*** 0.023*** 0.027*** 0.026***
(3.345) (2.736) (2.852) (2.547) (3.544) (2.908) (3.159) (2.996)
CEOTenure2t -0.000 -0.000 -0.000 -0.000 -0.001* -0.000 -0.001* -0.001*
(-1.416) (-0.987) (-1.382) (-1.202) (-1.762) (-1.275) (-1.825) (-1.727)
Observations 5,893 6,213 5,698 5,698 5,893 6,213 5,698 5,698
Adjusted R2 0.485 0.489 0.472 0.473 0.504 0.504 0.489 0.489
91
Panel B: Calculation of CEO tendency for innovation
Expected (1) (2)
sign LnCountst+1 LnCitest+1
LnAssetst + 0.182*** 0.386***
(5.435) (5.637)
RDAssetst + 0.476* 1.068**
(1.914) (2.098)
LnFirmAget - -0.070 -0.291
(-0.800) (-1.622)
ROAt + 0.499*** 0.368
(3.967) (1.427)
PPEAssetst + 0.740*** 0.878*
(3.327) (1.926)
Leveraget - 0.248** 0.276
(2.458) (1.339)
CapexAssetst -0.179 -0.063
(-0.504) (-0.087)
MtoBt + 0.019*** 0.057***
(2.671) (3.814)
LnAnalystst 0.021 -0.025
(0.729) (-0.421)
Instit Ownerst 0.169** 0.296*
(2.208) (1.889)
CEOAget 0.073* 0.038
(1.876) (0.473)
CEOAge2t -0.000 -0.000
(-1.497) (-0.497)
CEOTenuret -0.098*** -0.295***
(-6.678) (-9.803)
CEOTenure2t 0.002*** 0.003***
(6.900) (5.236)
Indicator variable for
each CEO
Included Included
Observations 4,929 4,929
Adjusted R2 0.307 0.516
92
Panel C: Validation of the measure of CEO tendency for innovation based on patent
quantity (LnCountst+1)
(1) (2) (3) (4)
FinanceBackground 0.024 -0.054*
(0.719) (-1.859)
TechnicalBackground 0.119** 0.277***
(2.222) (3.612)
Legal -0.404*** -0.396***
(-3.821) (-3.761)
Observations 2,024 2,051 1,868 1,868
Adjusted R2 -0.000 0.002 0.007 0.015
Panel D: Validation of the measure of CEO tendency for innovation based on patent
quality (LnCitest+1)
(1) (2) (3) (4)
FinanceBackground 0.225** -0.283***
(1.961) (-2.721)
TechnicalBackground 1.491*** 0.810***
(8.150) (2.970)
Legal -1.529*** -1.503***
(-4.065) (-4.012)
Observations 2,024 2,051 1,868 1,868
Adjusted R2 0.001 0.031 0.008 0.016
93
Table 6: The importance of CEO characteristics for disclosure quality
This table presents the OLS regression results of disclosure quality on CEO functional
background. See Appendix A for variable definitions. The regressions include industry and year
indicator variables. t-statistics are in parentheses. *** p<0.01, ** p<0.05, * p<0.1
(1) (2) (3) (4) (5)
DQt 10-K Fogt 10-K Lengtht DiscAccrualst MF Countt
FinanceBackgroundt -0.145** 0.141*** 0.011 0.006*** 0.038
(-2.015) (2.647) (1.490) (2.697) (0.533)
TechnicalBackgroundt 0.515*** -0.143 -0.030** -0.012*** -0.383***
(3.806) (-1.421) (-2.075) (-2.645) (-2.948)
Legalt -0.033 -0.371** 0.025 0.001 0.345*
(-0.159) (-2.324) (1.122) (0.083) (1.675)
LnAssetst 0.067** 0.122*** 0.056*** -0.001 0.242***
(2.196) (5.320) (17.264) (-0.958) (8.549)
LnFirmAget 0.084 -0.188*** -0.033*** -0.005*** 0.186***
(1.540) (-4.561) (-5.647) (-2.814) (3.540)
ROAt 0.860*** -0.521*** -0.097*** -0.015** 0.375*
(3.928) (-3.507) (-4.597) (-2.132) (1.917)
CF Volt -5.963*** 0.426 0.092* 0.154*** -0.542
(-10.627) (1.118) (1.704) (9.516) (-1.403)
Sales Volt -0.492*** 0.143 0.023 0.020*** 0.136
(-3.118) (1.235) (1.408) (3.817) (0.892)
Litigation Riskt -0.094 0.209** -0.018 0.004 -0.384***
(-0.778) (2.272) (-1.383) (1.044) (-3.095)
Sales Growtht -0.612*** 0.030 0.008 0.023*** -0.080
(-9.030) (0.709) (1.349) (10.186) (-1.422)
Stock Returnt -0.051 -0.045 -0.000 0.000 -0.092**
(-1.224) (-1.455) (-0.073) (0.327) (-2.282)
SEOt -0.248*** -0.037 -0.003 0.015*** -0.115
(-3.215) (-0.648) (-0.325) (5.859) (-1.500)
Losst -0.661*** 0.196*** 0.043*** 0.033*** -0.713***
(-8.365) (3.362) (5.191) (12.572) (-9.115)
LnAnalystst -0.021 -0.046 -0.005 0.002 0.468***
(-0.347) (-1.002) (-0.844) (1.005) (8.125)
Instit Ownerst 0.474*** 0.000 0.042*** -0.014*** 0.521***
(3.285) (0.005) (2.752) (-3.023) (3.650)
CEOAget 0.006 -0.001 -0.001 -0.000*** 0.017***
(1.415) (-0.161) (-1.304) (-2.814) (3.893)
CEOTenuret 0.005 0.003 -0.001 -0.000 -0.030***
(0.987) (0.712) (-0.930) (-0.587) (-5.986)
Observations 4,723 5,081 5,081 5,539 5,723
Adjusted R2 0.225 0.109 0.174 0.206 0.316
94
Table 7: CEO characteristics’ indirect effect on innovation through disclosure
quality (H2)
This table presents the structural equations model results of the direct and indirect effects of
CEO characteristics on innovation. Panels A and B include CEO tendency for innovation and
Panels C and D include CEO functional background. In each panel, columns (1)-(3) present the
results for the direct and indirect effects of CEO characteristics and disclosure quality on
innovation. Column (4) presents the results for the effect of CEO characteristics on disclosure
quality. Panels A and C are with innovation quantity and Panels B and D are with innovation
quality. See Appendix A for variable definitions. To facilitate the comparison of the coefficients,
they are all standardized. Z-statistics are in parentheses. *** p<0.01, ** p<0.05, * p<0.1
Panel A: CEO tendency for innovation and innovation quantity
Direct Effect Indirect Effect Total Effect
LnCountst+1 LnCountst+1 LnCountst+1 DQt
(1) (2) (3) (4)
CEOt 0.134*** 0.005* 0.140*** 0.063***
(10.44) (1.68) (11.21) (4.14)
DQt 0.222*** -0.013*** 0.209***
(6.03) (-6.03) (6.03)
LnAssetst 0.492*** 0.072*** 0.564*** 0.187***
(19.70) (3.92) (35.05) (6.49)
LnFirmAget 0.031* 0.001 0.032** 0.105***
(1.82) (0.12) (2.07) (5.59)
ROAt 0.101*** 0.022*** 0.123*** 0.114***
(5.50) (3.82) (7.17) (6.00)
RDAssetst 0.403*** -0.01 0.394***
(21.23) (-1.16) (22.52)
PPEAssetst -0.153*** 0.009*** -0.144***
(-8.22) (2.91) (-8.44)
Leveraget -0.042*** 0.002** -0.039***
(-2.94) (2.28) (-2.93)
CapexAssetst 0.085*** -0.005** 0.08***
(4.81) (-2.55) (4.88)
MtoBt 0.076*** 0.011*** 0.087***
(5.08) (2.65) (6.36)
CF Volt -0.021*** -0.021*** -0.1***
(-4.72) (-4.72) (-5.66)
Sales Volt -0.023*** -0.023*** -0.112***
(-4.30) (-4.30) (-6.89)
Litigation Riskt -0.011*** -0.011*** -0.05***
(-3.13) (-3.13) (-3.01)
SEOt -0.012*** -0.012*** -0.056***
(-3.53) (-3.53) (-3.88)
Losst -0.026*** -0.026*** -0.126***
(-5.16) (-5.16) (-7.09)
95
Direct Effect Indirect Effect Total Effect
LnCountst+1 LnCountst+1 LnCountst+1 DQt
(1) (2) (3) (4)
LnAnalystst 0.081*** 0.001 0.081*** 0.026
(3.88) (0.14) (4.02) (1.09)
Instit Ownerst -0.041*** 0.01*** -0.032** 0.035***
(-2.93) (2.91) (-2.31) (2.23)
LnCountst+1 -0.059*** -0.059*** -0.282***
(-6.59) (-6.59) (-6.59)
# patentst 0.002 0.002
(1.14) (1.14)
# citest 0.003** 0.003**
(2.12) (2.12)
DQt-1 -0.001 -0.001
(-1.13) (-1.13)
Panel B: CEO tendency for innovation and innovation quality
Direct Effect Indirect Effect Total Effect
LnCitest+1 LnCitest+1 LnCitest+1 DQt
(1) (2) (3) (4)
CEOt 0.282*** 0.005 0.287*** 0.106***
(20.76) (1.57) (21.94) (5.32)
DQt 0.224*** -0.014*** 0.210***
(5.32) (-5.32) (5.32)
LnAssetst 0.39*** 0.048** 0.438*** 0.171***
(15.14) (2.51) (26.46) (6.11)
LnFirmAget -0.015 0.005 -0.01 0.094***
(-0.82) (0.65) (-0.60) (5.07)
ROAt 0.096*** 0.011* 0.107*** 0.115***
(4.99) (1.81) (6.05) (6.04)
RDAssetst 0.33*** -0.014* 0.317***
(16.80) (-1.68) (17.69)
PPEAssetst -0.141*** 0.009*** -0.132***
(-7.32) (2.61) (-7.54)
Leveraget -0.015 0.001 -0.014
(-1.06) (1.02) (-1.06)
CapexAssetst 0.124*** -0.008*** 0.116***
(6.79) (-2.59) (6.95)
MtoB 0.100*** 0.016*** 0.116***
(6.40) (3.46) (8.15)
CF Volt -0.022*** -0.022*** -0.104***
(-4.53) (-4.53) (-5.87)
Sales Volt -0.021*** -0.021*** -0.098***
(-3.98) (-3.98) (-6.21)
96
Direct Effect Indirect Effect Total Effect
LnCitest+1 LnCitest+1 LnCitest+1 DQt
(1) (2) (3) (4)
Litigation Riskt -0.012*** -0.012*** -0.057***
(-3.42) (-3.42) (-3.43)
SEOt -0.01*** -0.01*** -0.048***
(-3.12) (-3.12) (-3.24)
Losst -0.028*** -0.028*** -0.131***
(-4.80) (-4.80) (-7.41)
LnAnalystst 0.072*** 0 0.072*** 0.02
(3.38) (-0.04) (3.46) (0.83)
Instit Ownerst -0.107*** 0.01*** -0.096*** 0.018***
(-7.23) (2.95) (-6.66) (1.12)
LnCitest+1 -0.061*** -0.061*** -0.289***
(-6.15) (-6.15) (-6.15)
# patentst 0.006*** 0.006***
(2.77) (2.77)
# citest 0.008*** 0.008***
(3.54) (3.54)
DQt-1 -0.006*** -0.006***
(-2.88) (-2.88)
Panel C: CEO background and innovation quantity
Direct Effect Indirect Effect Total Effect
LnCountst+1 LnCountst+1 LnCountst+1 DQt
(1) (2) (3) (4)
FinanceBackgroundt -0.023* -0.005* -0.029** -0.032**
(-1.81) (-1.76) (-2.27) (-2.25)
TechnicalBackgroundt 0.024* 0.009*** 0.033*** 0.048***
(1.85) (2.59) (2.59) (3.38)
Legalt -0.057*** 0.001 -0.057*** -0.01
(-4.48) (0.21) (-4.52) (-0.70)
DQt 0.212*** -0.01*** 0.202***
(5.78) (-5.78) (5.78)
LnAssetst 0.493*** 0.045** 0.537*** 0.158***
(19.33) (2.41) (31.73) (5.42)
LnFirmAget -0.003 -0.006 -0.009 0.035*
(-0.18) (-0.99) (-0.57) (1.95)
ROAt 0.159*** -0.003 0.156*** 0.047**
(7.86) (-0.55) (7.93) (2.48)
RDAssetst 0.366*** -0.01 0.356***
(17.54) (-1.43) (18.07)
PPEAssetst -0.193*** 0.009*** -0.184***
(-9.91) (2.66) (-10.19)
97
Direct Effect Indirect Effect Total Effect
LnCountst+1 LnCountst+1 LnCountst+1 DQt
(1) (2) (3) (4)
Leveraget -0.023* 0.001 -0.022*
(-1.65) (1.54) (-1.65)
CapexAssetst 0.072*** -0.003** 0.069***
(4.05) (-2.23) (4.08)
MtoBt 0.079*** 0.01** 0.089***
(5.23) (2.43) (6.38)
CF Volt -0.038*** -0.038*** -0.187***
(-5.73) (-5.73) (-10.74)
Sales Volt -0.016*** -0.016*** -0.077***
(-3.45) (-3.45) (-4.87)
Litigation Riskt -0.006*** -0.006*** -0.03***
(-1.93) (-1.93) (-1.85)
SEOt -0.01*** -0.01*** -0.049***
(-3.11) (-3.11) (-3.46)
Losst -0.029*** -0.029*** -0.142***
(-5.26) (-5.26) (-8.14)
LnAnalystst 0.054*** 0.002 0.056*** 0.021
(2.60) (0.39) (2.73) (0.92)
Instit Ownerst -0.067*** 0.009*** -0.058*** 0.029***
(-4.56) (2.78) (-4.01) (1.86)
LnCountst+1 -0.045*** -0.045*** -0.224***
(-5.31) (-5.31) (-5.31)
# patentst 0.002* 0.002*
(1.84) (1.84)
# citest 0.005*** 0.005***
(2.74) (2.74)
DQt-1 -0.005*** -0.005***
(-3.19) (-3.19)
98
Panel D: CEO background and innovation quality
Direct Effect Indirect Effect Total Effect
LnCitest+1 LnCitest+1 LnCitest+1 DQt
(1) (2) (3) (4)
FinanceBackgroundt -0.025* -0.006* -0.031** -0.032**
(-1.84) (-1.74) (-2.31) (-2.25)
TechnicalBackgroundt 0.020 0.010*** 0.030** 0.048***
(1.44) (2.57) (2.20) (3.36)
Legalt -0.067*** 0.001 -0.066*** -0.012
(-4.92) (0.19) (-4.98) (-0.84)
DQt 0.232*** -0.011*** 0.221***
(5.47) (-5.47) (5.47)
LnAssetst 0.365*** 0.026 0.391*** 0.134***
(13.48) (1.32) (21.62) (4.83)
LnFirmAget -0.058*** -0.008 -0.066*** 0.024
(-3.27) (-1.14) (-3.91) (1.30)
ROAt 0.148*** -0.021*** 0.127*** 0.059***
(6.90) (-3.10) (6.10) (3.00)
RDAssetst 0.242*** -0.011 0.23***
(10.90) (-1.64) (11.00)
PPEAssetst -0.18*** 0.009** -0.171***
(-8.69) (2.45) (-8.97)
Leveraget 0.001 0 0.001
(0.06) (-0.06) (0.06)
CapexAssetst 0.141*** -0.007** 0.134***
(7.48) (-2.47) (7.57)
MtoBt 0.13*** 0.017*** 0.147***
(8.15) (3.19) (9.90)
CF Volt -0.042*** -0.042*** -0.19***
(-5.48) (-5.48) (-10.93)
Sales Volt -0.015*** -0.015*** -0.066***
(-3.25) (-3.25) (-4.27)
Litigation Riskt -0.01*** -0.01*** -0.044***
(-2.73) (-2.73) (-2.81)
SEOt -0.01*** -0.01*** -0.044***
(-2.89) (-2.89) (-3.08)
Losst -0.033*** -0.033*** -0.147***
(-5.06) (-5.06) (-8.42)
LnAnalystst 0.061*** 0.002 0.063*** 0.023
(2.74) (0.39) (2.88) (0.97)
Instit Ownerst -0.171*** 0.009** -0.161*** 0.005
(-10.97) (2.45) (-10.34) (0.28)
LnCitest+1 -0.048*** -0.048*** -0.219***
(-4.94) (-4.94) (-4.94)
# patentst 0.007** 0.007**
(2.47) (2.47)
99
Direct Effect Indirect Effect Total Effect
LnCitest+1 LnCitest+1 LnCitest+1 DQt
(1) (2) (3) (4)
# citest 0.017*** 0.017***
(5.19) (5.19)
DQt-1 -0.012*** -0.012***
(-4.45) (-4.45)
100
Table 8: Alternative measures of disclosure quality – mean of industry-year
normalized ranking
This table presents the OLS regression and structural equations model results of innovation on
disclosure quality, where disclosure quality is measured as the mean of its components’ industry-
year normalized rankings. Innovation is measured as patent quantity (LnCounts) or patent quality
(LnCites). In Panel A, the OLS regressions include industry, firm and year fixed effects, t-
statistics are in parentheses and reported standard errors are clustered by firm. Panel B presents
the structural equations model results and includes coefficients which are all standardized and Z-
statistics are in parentheses. See Appendix A for variable definitions. *** p<0.01, ** p<0.05, *
p<0.1
Panel A: Patent quantity and quality
(1) (2) (3) (4)
LnCountst+1 LnCountst+1 LnCites t+1 LnCites t+1
DQ_Normt 0.107*** 0.386***
(3.036) (5.245)
DQ_Norm_fogMDAt 0.131*** 0.468***
(3.563) (5.895)
LnAssetst 0.189*** 0.190*** 0.004 0.007
(8.577) (8.613) (0.095) (0.161)
RDAssetst 0.198* 0.200* -0.330 -0.323
(1.672) (1.690) (-1.410) (-1.379)
LnFirmAget -0.385*** -0.386*** -1.710*** -1.717***
(-13.281) (-13.339) (-27.295) (-27.413)
ROAt -0.018 -0.021 0.154 0.146
(-0.326) (-0.368) (1.346) (1.271)
PPEAssetst 0.617*** 0.615*** 2.064*** 2.055***
(4.568) (4.553) (7.285) (7.260)
Leveraget -0.061 -0.061 -0.049 -0.047
(-1.036) (-1.028) (-0.390) (-0.377)
CapexAssetst -0.216 -0.214 0.221 0.226
(-1.069) (-1.058) (0.543) (0.556)
MtoBt 0.017*** 0.017*** 0.048*** 0.049***
(4.537) (4.561) (6.167) (6.211)
LnAnalystst 0.065*** 0.064*** 0.213*** 0.211***
(3.594) (3.556) (5.885) (5.830)
Instit Ownerst -0.046 -0.045 -0.670*** -0.666***
(-0.910) (-0.890) (-6.224) (-6.198)
Observations 22,704 22,705 22,704 22,705
Number of Firms 3,144 3,144 3,144 3,144
Adjusted R2 0.045 0.045 0.220 0.221
101
Panel B: Direct and indirect effects
Panel B1: CEO tendency for innovation and innovation quantity
Direct Effect Indirect Effect Total Effect
LnCountst+1 LnCountst+1 LnCountst+1 DQ_Norm_fogMDAt
(1) (2) (3) (4)
CEOt 0.159*** 0.004*** 0.163*** 0.072***
(13.31) (3.30) (13.77) (5.49)
DQ_Norm_fogMDAt 0.069*** -0.0004*** 0.069***
(3.87) (-3.87) (3.87)
LnAssetst 0.61*** 0.0152** 0.625*** -0.122***
(63.56) (2.54) (80.61) (-9.22)
LnFirmAget -0.011 -0.0037 -0.015** 0.116***
(-1.46) (-1.33) (-2.10) (15.47)
ROAt 0.098*** 0.005** 0.103*** 0.122***
(9.48) (2.06) (10.63) (13.60)
RDAssetst 0.298*** 0.004** 0.301***
(31.04) (2.17) (31.71)
PPEAssetst -0.203*** 0.001** -0.202***
(-24.14) (2.03) (-24.06)
Leveraget -0.055*** 0.0003** -0.055***
(-8.41) (2.03) (-8.39)
CapexAssetst 0.105*** -0.001** 0.104***
(13.46) (-2.00) (13.46)
MtoBt 0.082*** 0.007*** 0.09***
(12.31) (5.60) (13.75)
CF volt -0.006*** -0.006*** -0.08***
(-3.79) (-3.79) (-10.70)
Sales volt -0.00001 -0.00001 0
(-0.01) (-0.01) (-0.01)
Litigation Riskt 0.002** 0.002** 0.029***
(2.45) (2.45) (4.16)
SEOt -0.006*** -0.006*** -0.08***
(-3.74) (-3.74) (-12.71)
Losst -0.01*** -0.01*** -0.147***
(-3.85) (-3.85) (-18.68)
LnAnalystst 0.043*** 0.008*** 0.05*** 0.113***
(5.23) (12.15) (6.20) (12.66)
Instit Ownerst -0.052*** 0.01*** -0.042*** 0.138***
(-7.07) (19.23) (-5.73) (18.59)
LnCountst+1 -0.006*** -0.006*** -0.086***
(-4.08) (-4.08) (-4.08)
# patentst 0.0003 0.0003
(0.34) (0.34)
# citest 0.003*** 0.003***
(4.60) (4.60)
102
Panel B2: CEO tendency for innovation and innovation quality
Direct Effect Indirect Effect Total Effect
LnCitest+1 LnCitest+1 LnCitest+1 DQ_Norm_fogMDAt
(1) (2) (3) (4)
CEO 0.421*** 0.008*** 0.429*** 0.14***
(43.66) (4.38) (45.95) (8.67)
DQ_Norm_fogMDAt 0.101*** -0.0014*** 0.099***
(5.00) (-5.00) (5.00)
LnAssets 0.404*** 0.0278*** 0.432*** -0.122***
(34.66) (4.24) (43.40) (-10.66)
LnAge -0.032*** -0.0119*** -0.044*** 0.118***
(-3.34) (-3.74) (-4.86) (15.05)
ROA 0.066*** 0.002 0.068*** 0.107***
(4.92) (0.70) (5.27) (11.14)
RDAssets 0.226*** 0.005** 0.231***
(17.95) (2.44) (18.50)
PPEAssets -0.179*** 0.002*** -0.177***
(-17.82) (2.73) (-17.64)
Leverage -0.032*** 0.0004** -0.031***
(-3.93) (2.42) (-3.92)
CapexAssets 0.115*** -0.002*** 0.113***
(11.73) (-2.65) (11.73)
MtoB 0.103*** 0.013*** 0.115***
(12.10) (7.78) (13.97)
CF_vol -0.008*** -0.008*** -0.081***
(-4.74) (-4.74) (-10.84)
Sales_vol 0 0 0.004
(0.53) (0.53) (0.53)
Litigation Risk 0.003*** 0.003*** 0.034***
(3.13) (3.13) (4.88)
SEO -0.008*** -0.008*** -0.082***
(-4.80) (-4.80) (-12.81)
Loss -0.015*** -0.015*** -0.149***
(-4.87) (-4.87) (-18.79)
LnAnalysts 0.077*** 0.011*** 0.088*** 0.121***
(8.83) (11.50) (10.22) (12.80)
Instit_owner -0.102*** 0.015*** -0.087*** 0.132***
(-12.94) (19.11) (-11.01) (17.05)
LnCitest+1 -0.014*** -0.014*** -0.139***
(-5.62) (-5.62) (-5.62)
# patents 0.002 0.002
(1.58) (1.58)
# cites 0.005*** 0.005***
(5.22) (5.22)
103
Panel B3: CEO background and innovation quantity
Direct Effect Indirect Effect Total Effect
LnCountst+1 LnCountst+1 LnCountst+1 DQ_Norm_fogMDAt
(1) (2) (3) (4)
FinanceBackgroundt -0.009 -0.003** -0.012 -0.033***
(-0.82) (-2.27) (-1.08) (-2.69)
TechnicalBackgroundt 0.095*** -0.0008 0.094*** 0.001
(8.85) (-0.76) (8.74) (0.04)
Legalt -0.105*** -0.0025** -0.108*** -0.038***
(-9.54) (-2.11) (-9.74) (-3.08)
DQ_Norm_fogMDAt 0.091*** -0.0009*** 0.09***
(5.02) (-5.02) (5.02)
LnAssetst 0.614*** 0.014** 0.627*** -0.111***
(64.27) (2.29) (81.74) (-8.14)
LnFirmAget -0.018** -0.002 -0.02*** 0.113***
(-2.47) (-0.67) (-2.95) (14.94)
ROAt 0.105*** 0.007*** 0.112*** 0.123***
(10.52) (3.02) (12.03) (13.70)
RDAssetst 0.279*** 0.003* 0.282***
(30.30) (1.90) (31.18)
PPEAssetst -0.193*** 0.002** -0.191***
(-23.25) (2.48) (-23.18)
Leveraget -0.049*** 0.0005** -0.048***
(-7.58) (2.45) (-7.55)
CapexAssetst 0.107*** -0.001** 0.106***
(13.96) (-2.44) (13.97)
MtoBt 0.074*** 0.008*** 0.082***
(11.37) (5.86) (12.96)
CF Volt -0.008*** -0.008*** -0.085***
(-4.84) (-4.84) (-11.55)
Sales Volt 0.0002 0.0002 0.002
(0.28) (0.28) (0.28)
Litigation Riskt 0.003*** 0.003*** 0.029***
(2.80) (2.80) (4.19)
SEOt -0.007*** -0.007*** -0.078***
(-4.74) (-4.74) (-12.41)
Losst -0.013*** -0.013*** -0.144***
(-4.96) (-4.96) (-18.41)
LnAnalystst 0.044*** 0.01*** 0.054*** 0.115***
(5.43) (12.13) (6.72) (12.77)
Instit Ownerst -0.057*** 0.013*** -0.044*** 0.134***
(-7.72) (18.88) (-6.00) (18.08)
LnCountst+1 -0.009*** -0.009*** -0.104***
(-4.74) (-4.74) (-4.74)
104
Direct Effect Indirect Effect Total Effect
LnCountst+1 LnCountst+1 LnCountst+1 DQ_Norm_fogMDAt
(1) (2) (3) (4)
# patentst 0.0001 0.0001
(0.17) (0.17)
# citest 0.003*** 0.003***
(4.78) (4.78)
Panel B4: CEO background and innovation quality
Direct Effect Indirect Effect Total Effect
LnCitest+1 LnCitest+1 LnCitest+1 DQ_Norm_fogMDAt
(1) (2) (3) (4)
FinanceBackgroundt -0.017 -0.004** -0.021 -0.033***
(-1.28) (-2.33) (-1.61) (-2.68)
TechnicalBackgroundt 0.095*** -0.0013 0.093*** 0.003
(7.53) (-0.74) (7.40) (0.22)
Legalt -0.132*** -0.004** -0.136*** -0.045***
(-10.48) (-2.25) (-10.77) (-3.58)
DQ_Norm_fogMDAt 0.143*** -0.0025*** 0.141***
(6.44) (-6.44) (6.44)
LnAssetst 0.409*** 0.025*** 0.434*** -0.124***
(39.02) (3.62) (51.55) (-10.87)
LnFirmAget -0.077*** -0.014*** -0.091*** 0.104***
(-9.44) (-4.00) (-12.10) (13.32)
ROAt 0.115*** 0.006* 0.12*** 0.131***
(10.39) (1.71) (11.78) (14.32)
RDAssetst 0.18*** 0.005** 0.185***
(17.93) (2.36) (18.82)
PPEAssetst -0.163*** 0.003*** -0.16***
(-18.08) (2.95) (-17.99)
Leveraget -0.021*** 0.0004** -0.021***
(-3.00) (2.26) (-3.00)
CapexAssetst 0.15*** -0.003*** 0.147***
(18.08) (-2.94) (18.04)
MtoBt 0.124*** 0.014*** 0.137***
(17.44) (7.66) (20.15)
CF Volt -0.012*** -0.012*** -0.086***
(-5.93) (-5.93) (-11.63)
Sales Volt 0.001 0.001 0.006
(0.82) (0.82) (0.82)
Litigation Riskt 0.004*** 0.004*** 0.025***
(3.01) (3.01) (3.73)
105
Direct Effect Indirect Effect Total Effect
LnCitest+1 LnCitest+1 LnCitest+1 DQ_Norm_fogMDAt
(1) (2) (3) (4)
SEOt -0.011*** -0.011*** -0.075***
(-6.07) (-6.07) (-11.75)
Losst -0.02*** -0.02*** -0.146***
(-6.26) (-6.26) (-18.47)
LnAnalystst 0.083*** 0.016*** 0.099*** 0.123***
(9.32) (11.62) (11.34) (12.94)
Instit Ownerst -0.127*** 0.02*** -0.107*** 0.124***
(-15.74) (17.96) (-13.14) (15.75)
LnCitest+1 -0.018*** -0.018*** -0.126***
(-5.30) (-5.30) (-5.30)
# patentst 0.003** 0.003**
(2.31) (2.31)
# citest 0.006*** 0.006***
(5.18) (5.18)
106
Table 9: Alternative measures of disclosure quality – error in analyst forecasts
This table presents the OLS regression and structural equations model results of innovation on
the error in analyst forecasts. Panel A columns (1) and (2) present the OLS regression results.
The regressions include industry, year and firm fixed effects. In columns (3) to (6), the three-
stage least squares regressions include industry and year indicator variables. t-statistics are in
parentheses. Panel B presents the structural equations model results. The coefficients are
standardized to facilitate the comparison of the coefficients. Z-statistics are in parentheses. See
Appendix A for variable definitions. *** p<0.01, ** p<0.05, * p<0.1
Panel A: Disclosure quality and innovation
(1) (2) (3) (4) (5) (6)
LnCountst+1 LnCites t+1 EPS forecast
errort
LnCounts t+1 EPS forecast
errort
LnCites t+1
EPS forecast errort -0.006*** -0.015*** -0.423*** -0.389**
(-2.812) (-3.132) (-3.645) (-2.403)
LnCountst+1 -0.072
(-1.515)
LnCites t+1 -0.041
(-1.256)
LnAssetst 0.193*** 0.015 0.043* 0.524*** 0.030 0.643***
(8.572) (0.359) (1.750) (61.863) (1.403) (54.473)
RDAssetst 0.198 -0.336 2.409*** 3.092***
(1.636) (-1.403) (20.777) (17.912)
LnFirmAget -0.386*** -1.726*** -0.079*** -0.031 -0.082*** -0.018
(-13.068) (-27.031) (-3.339) (-1.546) (-3.472) (-0.654)
ROAt -0.007 0.168 -0.178* 0.180*** -0.093 0.174*
(-0.126) (1.457) (-1.928) (2.733) (-0.981) (1.862)
PPEAssetst 0.601*** 2.007*** -0.781*** -1.224***
(4.328) (6.907) (-9.130) (-9.187)
Leveraget -0.072 -0.077 -0.353*** -0.552***
(-1.191) (-0.597) (-6.908) (-6.826)
CapexAssetst -0.228 0.322 2.340*** 4.020***
(-1.116) (0.791) (9.312) (10.093)
MtoBt 0.016*** 0.048*** 0.078*** 0.140***
(4.385) (6.080) (15.487) (17.997)
LnAnalystst 0.059*** 0.210*** -0.334*** -0.046 -0.328*** 0.068
(3.185) (5.688) (-11.794) (-1.100) (-11.244) (1.173)
Instit Ownerst -0.059 -0.698*** 0.310*** -0.025 0.317*** 0.026
(-1.158) (-6.409) (4.821) (-0.456) (4.971) (0.344)
CF Volt -0.366** -0.342*
(-2.276) (-1.823)
Sales Volt 0.448*** 0.358***
(7.618) (5.507)
Litigation Riskt -0.166*** -0.126**
(-3.271) (-2.154)
107
(1) (2) (3) (4) (5) (6)
LnCountst+1 LnCites t+1 EPS forecast
errort
LnCounts t+1 EPS forecast
errort
LnCites t+1
Sales Growtht 0.073*** 0.046*
(3.451) (1.913)
Stock Returnt 0.024 0.027
(1.543) (1.476)
SEOt 0.160*** 0.158***
(5.139) (4.587)
Losst -0.027 0.024
(-0.787) (0.640)
LogOperCyclet 0.035** 0.049**
(2.045) (2.509)
Observations 22,098 22,098 20,381 20,381 20,381 20,381
Number of Firms 3,114 3,114
Adjusted R2 0.044 0.223 0.022 0.170 0.024 0.339
108
Panel B: Direct and indirect effects
Panel B1: CEO tendency for innovation and innovation quantity
Direct Effect Indirect Effect Total Effect
LnCountst+1 LnCountst+1 LnCountst+1 EPS forecast errort
(1) (2) (3) (4)
CEOt 0.157*** 0.001 0.158*** -0.037**
(13.06) (1.51) (13.20) (-2.27)
EPS forecast errort -0.032* 0.00003* -0.032*
(-1.73) (1.73) (-1.73)
LnAssetst 0.601*** 0.0268*** 0.628*** 0.004
(64.67) (5.32) (81.62) (0.24)
LnFirmAget -0.006 -0.0096*** -0.015** -0.066***
(-0.77) (-4.29) (-2.17) (-8.13)
ROAt 0.111*** -0.003** 0.107*** 0.014
(11.68) (-2.18) (11.38) (1.48)
RDAssetst 0.291*** 0.005*** 0.297***
(30.24) (3.50) (30.84)
PPEAssetst -0.205*** 0.0002 -0.205***
(-24.23) (0.68) (-24.27)
Leveraget -0.058*** 0.0001 -0.058***
(-8.93) (0.68) (-8.92)
CapexAssetst 0.104*** -0.0001 0.104***
(13.30) (-0.68) (13.30)
MtoBt 0.076*** 0.008*** 0.084***
(11.61) (6.05) (13.01)
CF volt -0.000004 -0.000004 0
(-0.02) (-0.02) (0.02)
Sales volt -0.001 -0.001 0.017**
(-1.24) (-1.24) (2.23)
Litigation Riskt 0.0004 0.0004 -0.014*
(1.05) (1.05) (-1.78)
SEOt -0.001 -0.001 0.032***
(-1.63) (-1.63) (4.48)
Losst -0.001 -0.001 0.024***
(-1.53) (-1.53) (2.87)
LnAnalystst 0.046*** 0.004*** 0.051*** -0.137***
(5.56) (13.73) (6.12) (-13.89)
Instit Ownerst -0.041*** -0.001*** -0.042*** 0.035***
(-6.00) (-4.17) (-6.16) (4.31)
LnCountst+1 -0.001 -0.001 0.027
(-1.09) (-1.09) (1.09)
# patentst 0.0003 0.0003
(0.29) (0.29)
# citest 0.003*** 0.003***
(4.62) (4.62)
109
Panel B2: CEO tendency for innovation and innovation quality
Direct Effect Indirect Effect Total Effect
LnCitest+1 LnCitest+1 LnCitest+1 EPS forecast errort
(1) (2) (3) (4)
CEO 0.427*** 0.001 0.428*** -0.035*
(45.26) (1.14) (45.46) (-1.87)
EPS forecast errort -0.034* 0.00005* -0.034*
(-1.65) (1.65) (-1.65)
LnAssets 0.391*** 0.046*** 0.437*** 0.004
(34.03) (8.40) (44.14) (0.28)
LnAge -0.023** -0.022*** -0.045*** -0.065***
(-2.51) (-8.86) (-5.00) (-7.82)
ROA 0.085*** -0.008*** 0.077*** 0.017*
(6.59) (-4.50) (5.94) (1.69)
RDAssets 0.218*** 0.008*** 0.226***
(16.39) (4.50) (16.98)
PPEAssets -0.184*** 0.0002 -0.184***
(-17.89) (0.77) (-17.93)
Leverage -0.041*** 0.0001 -0.041***
(-5.02) (0.78) (-5.01)
CapexAssets 0.11*** -0.0001 0.11***
(10.97) (-0.77) (10.97)
MtoB 0.088*** 0.014*** 0.102***
(10.22) (9.18) (11.96)
CF_vol -0.00006 -0.00006 0.002
(-0.21) (-0.21) (0.21)
Sales_vol -0.001 -0.001 0.015**
(-1.24) (-1.24) (2.09)
Litigation Risk 0.0005 0.0005 -0.014*
(1.11) (1.11) (-1.92)
SEO -0.001 -0.001 0.031***
(-1.59) (-1.59) (4.41)
Loss -0.001 -0.001 0.024***
(-1.44) (-1.44) (2.84)
LnAnalysts 0.085*** 0.005*** 0.089*** -0.139***
(9.54) (13.03) (10.11) (-13.41)
Instit_owner -0.086*** -0.001*** -0.087*** 0.037***
(-11.84) (-4.02) (-12.01) (4.43)
LnCitest+1 -0.001 -0.001 0.04
(-1.42) (-1.42) (1.42)
# patents 0.002 0.002
(1.48) (1.48)
# cites 0.005*** 0.005***
(5.26) (5.26)
110
Panel B3: CEO background and innovation quantity
Direct Effect Indirect Effect Total Effect EPS forecast
errort LnCountst+1 LnCountst+1 LnCountst+1
(1) (2) (3) (4)
FinanceBackgroundt -0.009 -0.00002 -0.009 0.001
(-0.79) (-0.04) (-0.79) (0.06)
TechnicalBackgroundt 0.096*** -0.001 0.095*** 0.025*
(8.97) (-1.39) (8.89) (1.72)
Legalt -0.103*** -0.001 -0.104*** 0.03**
(-9.30) (-1.38) (-9.42) (2.01)
EPS forecast errort -0.037* 0.00004* -0.036*
(-1.90) (1.90) (-1.90)
LnAssetst 0.602*** 0.029*** 0.631*** 0.001
(65.37) (5.81) (82.67) (0.04)
LnFirmAget -0.009 -0.01*** -0.019*** -0.061***
(-1.29) (-4.48) (-2.79) (-7.36)
ROAt 0.124*** -0.003** 0.12*** 0.017*
(13.47) (-2.12) (13.14) (1.71)
RDAssetst 0.274*** 0.006*** 0.279***
(29.77) (3.85) (30.47)
PPEAssetst -0.193*** 0.0002 -0.193***
(-23.27) (0.66) (-23.34)
Leveraget -0.052*** 0.00005 -0.052***
(-8.08) (0.67) (-8.08)
CapexAssetst 0.105*** -0.0001 0.105***
(13.85) (-0.66) (13.86)
MtoBt 0.068*** 0.009*** 0.076***
(10.65) (6.50) (12.19)
CF Volt -0.0001 -0.0001 0.003
(-0.32) (-0.32) (0.32)
Sales Volt -0.001 -0.001 0.016**
(-1.29) (-1.29) (2.07)
Litigation Riskt 0.0005 0.0005 -0.013*
(1.07) (1.07) (-1.67)
SEOt -0.001* -0.001* 0.03***
(-1.74) (-1.74) (4.24)
Losst -0.001 -0.001 0.023***
(-1.59) (-1.59) (2.67)
LnAnalystst 0.05*** 0.005*** 0.055*** -0.138***
(5.95) (13.75) (6.58) (-13.92)
Instit Ownerst -0.042*** -0.001*** -0.043*** 0.037***
(-6.18) (-4.49) (-6.38) (4.63)
# patentst -0.001 -0.001 0.027
(-1.00) (-1.00) (1.00)
# citest 0.0001 0.0001
(0.10) (0.10)
111
Panel B4: CEO background and innovation quality
Direct Effect Indirect Effect Total Effect EPS forecast
errort LnCitest+1 LnCitest+1 LnCitest+1
(1) (2) (3) (4)
FinanceBackgroundt -0.017 -0.00002 -0.018 0.001
(-1.33) (-0.03) (-1.33) (0.06)
TechnicalBackgroundt 0.095*** -0.001 0.094*** 0.024
(7.56) (-1.23) (7.50) (1.59)
Legalt -0.132*** -0.001 -0.133*** 0.033**
(-10.37) (-1.23) (-10.48) (2.23)
EPS forecast errort -0.036 0.00004 -0.036
(-1.48) (1.48) (-1.48)
LnAssetst 0.39*** 0.049*** 0.439*** 0.004
(38.95) (8.91) (52.48) (0.34)
LnFirmAget -0.062*** -0.027*** -0.089*** -0.059***
(-7.91) (-10.36) (-11.85) (-6.86)
ROAt 0.144*** -0.011*** 0.133*** 0.015
(14.41) (-5.68) (13.24) (1.49)
RDAssetst 0.173*** 0.008*** 0.181***
(17.16) (4.28) (17.94)
PPEAssetst -0.163*** 0.0002 -0.163***
(-18.06) (0.59) (-18.12)
Leveraget -0.026*** 0.00003 -0.026***
(-3.70) (0.60) (-3.70)
CapexAssetst 0.148*** -0.0002 0.148***
(17.93) (-0.59) (17.94)
MtoBt 0.115*** 0.016*** 0.13***
(16.51) (9.67) (18.96)
CF Volt -0.0001 -0.0001 0.003
(-0.34) (-0.34) (0.35)
Sales Volt -0.001 -0.001 0.014*
(-1.17) (-1.17) (1.95)
Litigation Riskt 0.0004 0.0004 -0.012
(0.98) (0.98) (-1.58)
SEOt -0.001 -0.001 0.029***
(-1.46) (-1.46) (4.08)
Losst -0.001 -0.001 0.023***
(-1.31) (-1.31) (2.72)
LnAnalystst 0.094*** 0.005*** 0.099*** -0.139***
(10.28) (12.80) (10.89) (-13.12)
Instit Ownerst -0.105*** -0.001*** -0.106*** 0.039***
(-14.24) (-4.19) (-14.44) (4.54)
# patentst -0.001 -0.001 0.029
(-0.98) (-0.98) (0.98)
# citest 0.003** 0.003**
(2.25) (2.25)
112
Table 10: Alternative measures of innovation – three-year-ahead patent data
This table presents the OLS regression results of innovation on disclosure quality and CEO
characteristics, where innovation is measured by three-year-ahead patent quantity (LnCountst+3)
and patent quality (LnCitest+3). See Appendix A for variable definitions. Panel A presents the
OLS regression results of innovation on disclosure quality. The regressions include industry,
firm, and year fixed effects, t-statistics are in parentheses, and reported standard errors are
clustered by firm. Panel B presents the OLS regression results of innovation on CEO
characteristics. The regressions include industry and year indicator variables and t-statistics are
in parentheses. *** p<0.01, ** p<0.05, * p<0.1
Panel A: Disclosure quality and innovation
(1) (2) (3) (4)
LnCountst+3 LnCountst+3 LnCites t+3 LnCites t+3
DQt 0.013*** 0.021***
(3.983) (3.376)
DQ_Norm_fogMDAt 0.268*** 0.575***
(6.578) (6.824)
LnAssetst 0.085*** 0.074*** -0.195*** -0.184***
(2.939) (3.244) (-3.657) (-4.185)
RDAssetst -0.018 -0.039 -0.809** -0.492**
(-0.096) (-0.305) (-2.403) (-2.068)
LnFirmAget -0.559*** -0.503*** -1.796*** -1.644***
(-13.088) (-15.303) (-20.360) (-24.006)
ROAt 0.079 0.049 0.313* 0.264**
(0.937) (0.755) (1.946) (2.043)
PPEAssetst 0.844*** 0.745*** 2.465*** 2.232***
(4.766) (5.308) (6.888) (7.539)
Leveraget 0.017 -0.017 0.067 -0.004
(0.212) (-0.239) (0.402) (-0.027)
CapexAssetst 0.048 0.043 0.899* 0.914**
(0.206) (0.204) (1.846) (2.185)
MtoBt 0.025*** 0.018*** 0.063*** 0.047***
(4.833) (4.655) (6.000) (5.687)
LnAnalystst 0.054** 0.068*** 0.215*** 0.235***
(2.338) (3.512) (4.901) (6.304)
Instit Ownerst -0.136** -0.119** -0.856*** -0.806***
(-1.982) (-2.046) (-6.371) (-7.052)
Observations 14,718 19,910 14,718 19,910
Number of Firms 2,017 2,739 2,017 2,739
Adjusted R2 0.075 0.074 0.275 0.269
113
Panel B: CEO characteristics and innovation
(1) (2) (3) (4) (5) (6) (7) (8)
LnCountst+3 LnCountst+3 LnCountst+3 LnCountst+3 LnCites t+3 LnCites t+3 LnCites t+3 LnCites t+3
FinanceBackgroundt 0.052 0.028 0.110* 0.068
(1.163) (0.609) (1.766) (1.058)
TechnicalBackgroundt 0.232*** 0.290*** 0.223*** 0.238**
(4.199) (3.701) (2.869) (2.177)
Legalt -0.427*** -0.414*** -0.691*** -0.675***
(-3.247) (-3.149) (-3.765) (-3.679)
LnAssetst 3.243*** 2.992*** 3.139*** 3.133*** 3.552*** 3.346*** 3.405*** 3.404***
(13.417) (13.492) (12.904) (12.892) (10.491) (10.721) (10.033) (10.032)
RDAssetst -0.479*** -0.473*** -0.457*** -0.471*** -0.657*** -0.653*** -0.618*** -0.635***
(-4.256) (-4.463) (-4.017) (-4.142) (-4.165) (-4.379) (-3.894) (-3.998)
LnFirmAget -0.971*** -0.981*** -0.922*** -0.895*** -1.591*** -1.624*** -1.524*** -1.483***
(-5.631) (-5.866) (-5.255) (-5.076) (-6.587) (-6.905) (-6.225) (-6.025)
ROAt 1.666*** 1.767*** 1.528** 1.462** 3.331*** 3.513*** 3.127*** 3.058***
(2.780) (3.044) (2.481) (2.375) (3.968) (4.301) (3.638) (3.557)
Leveraget 0.086*** 0.081*** 0.085*** 0.084*** 0.146*** 0.136*** 0.145*** 0.145***
(8.062) (8.212) (7.851) (7.810) (9.806) (9.783) (9.658) (9.625)
PPEAssetst 0.600*** 0.599*** 0.597*** 0.593*** 0.713*** 0.702*** 0.705*** 0.702***
(33.144) (35.117) (32.568) (32.292) (28.107) (29.262) (27.572) (27.402)
CapexAssetst -0.000 0.012 -0.024 -0.016 0.018 0.049 -0.011 -0.007
(-0.008) (0.409) (-0.791) (-0.536) (0.437) (1.204) (-0.256) (-0.156)
MtoBt 1.005*** 0.986*** 0.979*** 0.991*** 1.165*** 1.179*** 1.131*** 1.141***
(7.264) (7.640) (7.005) (7.099) (6.014) (6.491) (5.804) (5.858)
LnAnalystst 0.064* 0.054 0.058 0.064* 0.096* 0.093* 0.087* 0.094*
(1.703) (1.518) (1.545) (1.693) (1.836) (1.845) (1.663) (1.788)
Instit Ownerst -0.463*** -0.431*** -0.467*** -0.454*** -0.579*** -0.526*** -0.587*** -0.572***
(-5.169) (-5.037) (-5.159) (-5.010) (-4.615) (-4.367) (-4.647) (-4.528)
114
(1) (2) (3) (4) (5) (6) (7) (8)
LnCountst+3 LnCountst+3 LnCountst+3 LnCountst+3 LnCites t+3 LnCites t+3 LnCites t+3 LnCites t+3
Observations 5,477 5,767 5,299 5,299 5,477 5,767 5,299 5,299
Adjusted R2 0.492 0.497 0.479 0.480 0.518 0.518 0.500 0.500
115
Table 11: Alternative regression specifications – Poisson and negative binomial
This table presents the Poisson and negative binomial regressions results. In Panel A, innovation
is regressed on disclosure quality and in Panel B innovation is regressed on CEO characteristics.
Innovation is measured as patent quantity (Counts) or patent quality (Cites). The regressions
include fixed effects and indicator variables consistent with the OLS specifications in previous
tables. See Appendix A for variable definitions. Z-statistics are in parentheses. *** p<0.01, **
p<0.05, * p<0.1
Panel A: Disclosure quality and innovation
(1) (2) (3) (4)
Poisson Poisson Negative binomial Negative binomial
Countst+1 Cites t+1 Countst+1 Cites t+1
DQt 0.016*** 0.012*** 0.018*** 0.012***
(12.678) (29.453) (5.321) (2.613)
LnAssetst 0.284*** 0.077*** 0.144*** 0.248***
(44.718) (36.005) (11.015) (19.536)
RDAssetst 0.882*** -0.669*** 0.382*** 1.330***
(15.060) (-33.796) (2.949) (9.605)
LnFirmAget -0.444*** -1.681*** -0.275*** -0.457***
(-36.216) (-384.504) (-10.879) (-18.701)
ROAt -0.044 -0.510*** 0.051 0.489***
(-1.465) (-55.960) (0.733) (5.949)
PPEAssetst 0.830*** 3.121*** 0.596*** 0.118
(18.049) (193.094) (5.448) (1.112)
Leveraget -0.090*** 0.070*** 0.036 -0.062
(-4.188) (9.444) (0.650) (-0.931)
CapexAssetst -0.291*** -0.967*** 0.107 1.648***
(-3.053) (-32.346) (0.431) (5.273)
MtoBt 0.014*** 0.040*** 0.024*** 0.083***
(9.255) (90.886) (6.043) (16.124)
LnAnalystst 0.169*** 0.327*** 0.123*** 0.120***
(22.265) (126.666) (6.334) (5.118)
Instit Ownerst -0.039** -0.810*** -0.140*** -0.884***
(-2.158) (-131.867) (-2.914) (-16.408)
Observations 15,739 15,436 15,739 15,436
116
Panel B: CEO backgrounds and innovation - Poisson
(1) (2) (3) (4) (5) (6) (7) (8)
Countst+1 Countst+1 Countst+1 Countst+1 Cites t+1 Cites t+1 Cites t+1 Cites t+1
FinanceBackgroundt 0.071*** 0.062*** 0.004 0.004
(9.544) (7.571) (1.634) (1.413)
TechnicalBackgroundt 0.260*** 0.310*** 0.085*** 0.175***
(29.612) (25.482) (29.282) (39.358)
Legalt -1.055*** -1.036*** -2.465*** -2.462***
(-26.385) (-25.904) (-73.304) (-73.192)
LnAssetst 0.557*** 0.570*** 0.573*** 0.566*** 0.426*** 0.435*** 0.443*** 0.437***
(158.475) (170.971) (159.843) (158.047) (335.832) (359.349) (333.767) (327.294)
RDAssetst 3.182*** 3.068*** 3.091*** 3.018*** 2.002*** 1.895*** 1.840*** 1.782***
(68.510) (67.643) (65.081) (63.156) (111.839) (108.683) (99.028) (94.942)
LnFirmAget 0.032*** 0.011** -0.014** -0.017*** 0.038*** 0.029*** -0.006*** -0.008***
(6.201) (2.325) (-2.535) (-3.171) (21.733) (17.126) (-3.340) (-4.320)
ROAt 0.906*** 1.098*** 0.899*** 0.926*** 0.490*** 0.637*** 0.420*** 0.432***
(30.034) (37.586) (29.014) (29.703) (46.847) (62.024) (38.391) (39.278)
Leveraget -1.090*** -1.143*** -1.224*** -1.212*** -2.264*** -2.258*** -2.560*** -2.563***
(-32.129) (-33.891) (-34.541) (-33.850) (-185.667) (-185.938) (-194.192) (-192.814)
PPEAssetst -0.184*** -0.204*** -0.186*** -0.193*** 0.019** -0.009 -0.010 -0.022***
(-8.626) (-9.725) (-8.455) (-8.787) (2.348) (-1.092) (-1.164) (-2.594)
CapexAssetst 2.346*** 2.156*** 2.541*** 2.456*** 5.481*** 5.220*** 6.108*** 6.068***
(20.814) (19.327) (21.579) (20.807) (149.120) (143.318) (157.574) (156.245)
MtoBt 0.041*** 0.034*** 0.040*** 0.039*** 0.042*** 0.039*** 0.042*** 0.041***
(21.281) (18.048) (19.953) (19.435) (73.748) (69.271) (71.888) (70.200)
LnAnalystst 0.162*** 0.136*** 0.143*** 0.157*** 0.237*** 0.226*** 0.222*** 0.226***
(23.137) (20.260) (20.208) (22.110) (94.057) (92.297) (86.167) (87.191)
Instit Ownerst 0.139*** 0.122*** 0.165*** 0.212*** -0.069*** -0.013** -0.008 0.009
(8.097) (7.381) (9.407) (12.062) (-11.180) (-2.218) (-1.283) (1.362)
BGV 0.001*** 0.001*** 0.001*** 0.001*** 0.000*** 0.000*** 0.000*** 0.000***
(32.926) (29.938) (33.520) (35.640) (160.282) (154.081) (169.566) (174.088)
117
(1) (2) (3) (4) (5) (6) (7) (8)
Countst+1 Countst+1 Countst+1 Countst+1 Cites t+1 Cites t+1 Cites t+1 Cites t+1
Observations 5,942 6,270 5,747 5,747 5,942 6,270 5,747 5,747
Pseudo R2 0.604 0.612 0.601 0.604 0.660 0.663 0.655 0.656
118
Panel C: CEO backgrounds and innovation – negative binomial
(1) (2) (3) (4) (5) (6) (7) (8)
Countst+1 Countst+1 Countst+1 Countst+1 Cites t+1 Cites t+1 Cites t+1 Cites t+1
FinanceBackgroundt -0.044 -0.002 0.157** 0.223***
(-0.860) (-0.033) (2.178) (2.915)
TechnicalBackgroundt 0.365*** 0.595*** 0.301*** 0.516***
(5.895) (6.546) (3.485) (3.924)
Legalt -0.710*** -0.679*** -1.468*** -1.384***
(-4.390) (-4.225) (-6.706) (-6.335)
LnAssetst 0.677*** 0.680*** 0.671*** 0.671*** 0.603*** 0.594*** 0.597*** 0.600***
(27.483) (29.379) (27.016) (26.864) (17.932) (18.684) (17.631) (17.607)
RDAssetst 3.944*** 3.597*** 3.818*** 3.838*** 2.099*** 1.946*** 1.987*** 2.035***
(12.791) (12.824) (12.296) (12.493) (4.902) (4.936) (4.612) (4.749)
LnFirmAget -0.113*** -0.118*** -0.140*** -0.120*** -0.237*** -0.227*** -0.240*** -0.238***
(-3.163) (-3.420) (-3.881) (-3.318) (-4.613) (-4.553) (-4.666) (-4.612)
ROAt 0.873*** 0.834*** 0.838*** 0.797*** -0.016 -0.118 -0.118 -0.169
(5.222) (5.321) (4.962) (4.752) (-0.069) (-0.535) (-0.497) (-0.717)
Leveraget -1.656*** -1.517*** -1.468*** -1.421*** -2.241*** -2.176*** -2.163*** -2.025***
(-7.893) (-7.441) (-6.810) (-6.605) (-7.816) (-7.699) (-7.372) (-6.903)
PPEAssetst -0.346*** -0.405*** -0.366*** -0.474*** -0.209 -0.211 -0.213 -0.310*
(-2.825) (-3.467) (-2.951) (-3.777) (-1.236) (-1.311) (-1.246) (-1.799)
CapexAssetst 2.265*** 1.988*** 1.963*** 1.835** 4.953*** 4.713*** 4.863*** 4.627***
(3.138) (2.859) (2.664) (2.501) (4.895) (4.731) (4.677) (4.480)
MtoBt 0.083*** 0.080*** 0.082*** 0.085*** 0.137*** 0.126*** 0.134*** 0.139***
(6.344) (6.523) (6.109) (6.437) (7.068) (6.879) (6.824) (7.092)
LnAnalystst 0.107** 0.104** 0.128*** 0.130*** 0.238*** 0.243*** 0.266*** 0.281***
(2.499) (2.515) (2.939) (3.001) (3.882) (4.054) (4.298) (4.553)
Instit Ownerst 0.095 0.187* 0.083 0.146 0.297* 0.411*** 0.272* 0.355**
(0.874) (1.815) (0.760) (1.327) (1.930) (2.800) (1.753) (2.271)
BGV 0.003*** 0.003*** 0.003*** 0.003*** 0.000*** 0.000*** 0.000*** 0.000***
(8.890) (8.868) (8.869) (8.824) (11.640) (11.697) (11.445) (11.649)
119
(1) (2) (3) (4) (5) (6) (7) (8)
Countst+1 Countst+1 Countst+1 Countst+1 Cites t+1 Cites t+1 Cites t+1 Cites t+1
Observations 5,942 6,270 5,747 5,747 5,942 6,270 5,747 5,747
Pseudo R2 0.0986 0.101 0.0987 0.0999 0.0755 0.0756 0.0764 0.0769
120
Table 12: R&D expenditure
This table presents the OLS regression results of innovation on disclosure quality and CEO
characteristics, where patent based innovation measures are replaced with the ratio of R&D to
sales. In Panel A, the regressions include industry, firm and year fixed effects, t-statistics are in
parentheses and reported standard errors are clustered by firm. In Panel B, the regressions
include industry and year indicator variables and t-statistics are in parentheses. See Appendix A
for variable definitions. *** p<0.01, ** p<0.05, * p<0.1
Panel A: R&D expenditure and Disclosure Quality
(1) (2) (3) (4) (5)
RD_Salest RD_Salest RD_Salest RD_Salest RD_Salest
10-K Fogt -0.211**
(-2.436)
10-K Lengtht -0.699
(-1.157)
DiscAccrualst -4.384***
(-4.170)
MF Countt 0.152**
(2.057)
DQt 0.209***
(4.279)
LnAssetst 0.127 0.168 -0.052 0.147 -0.118
(0.285) (0.381) (-0.136) (0.372) (-0.271)
LnFirmAget -5.138*** -5.234*** -4.334*** -5.337*** -4.718***
(-6.714) (-6.779) (-7.502) (-8.224) (-6.521)
ROAt -4.600*** -4.646*** -1.185 -4.169*** -2.511
(-2.667) (-2.685) (-0.892) (-2.853) (-1.545)
PPEAssetst -1.796 -1.736 0.465 0.014 0.066
(-0.589) (-0.569) (0.181) (0.005) (0.022)
Leveraget -4.701*** -4.635*** -3.979*** -5.206*** -3.649**
(-2.790) (-2.751) (-2.857) (-3.505) (-2.198)
CapexAssetst -4.613 -4.576 -4.921* -5.248 -7.061**
(-1.190) (-1.183) (-1.647) (-1.537) (-2.019)
MtoBt 0.185* 0.191* 0.156** 0.217** 0.181*
(1.879) (1.939) (1.972) (2.456) (1.929)
LnAnalystst -1.347*** -1.346*** -1.010*** -1.116*** -1.216***
(-3.563) (-3.557) (-3.515) (-3.476) (-3.450)
Instit Ownerst -2.603** -2.652** -3.241*** -3.010*** -2.876***
(-2.426) (-2.469) (-3.809) (-3.137) (-2.944)
Observations 15,788 15,788 18,875 19,888 13,816
Number of Firms 2,211 2,211 2,713 2,813 2,020
Adjusted R2 0.071 0.071 0.068 0.071 0.073
121
Panel B: R&D expenditure and CEO characteristics
(1) (2) (3) (4)
RD_Salest RD_Salest RD_Salest RD_Salest
FinanceBackgroundt -0.404 -0.388
(-1.013) (-0.934)
TechnicalBackgroundt 3.274*** 1.332*
(5.990) (1.898)
Legalt -2.167 -2.162
(-1.538) (-1.534)
LnAssetst 0.787*** 0.925*** 0.824*** 0.795***
(4.867) (5.357) (4.992) (4.805)
LnFirmAget -1.222*** -1.275*** -1.268*** -1.202***
(-4.357) (-4.154) (-4.426) (-4.169)
ROAt -11.072*** -13.281*** -11.357*** -11.320***
(-12.004) (-13.720) (-12.106) (-12.065)
Leveraget -2.273** -1.251 -2.326** -2.374**
(-2.340) (-1.200) (-2.355) (-2.402)
PPEAssetst -3.482** -4.492** -3.830** -3.871**
(-2.134) (-2.493) (-2.297) (-2.311)
CapexAssetst 3.684 7.341 4.537 4.358
(0.638) (1.161) (0.768) (0.737)
MtoBt 0.504*** 0.447*** 0.483*** 0.480***
(5.612) (4.674) (5.262) (5.225)
LnAnalystst -1.954*** -2.253*** -1.968*** -1.964***
(-5.831) (-6.183) (-5.765) (-5.740)
Instit Ownerst 2.543*** 3.263*** 2.665*** 2.689***
(3.154) (3.710) (3.245) (3.273)
Observations 5,071 5,399 4,945 4,945
Adjusted R2 0.495 0.434 0.497 0.497
122
Table 13: Cost of Equity Capital
This table presents the structural equation model results of the direct and indirect effects of CEO
characteristics on innovation. These models include an additional indirect effect of disclosure
quality on innovation through cost of equity capital. Panels A and B are with CEO tendency for
innovation and Panels C and D are with CEO functional background. In each panel, columns (1)-
(3) present the results for the direct and indirect effects on innovation and column (4) presents
the results for the regression for disclosure quality. Panels A and C are with innovation quantity
and Panels B and D are with innovation quality. See Appendix A for variable definitions. To
facilitate the comparison of the coefficients, they are all standardized. Z-statistics are in
parentheses. *** p<0.01, ** p<0.05, * p<0.1
Panel A: CEO tendency for innovation and innovation quantity
Direct Effect Indirect Effect Total Effect
LnCountst+1 LnCountst+1 LnCountst+1 CoCt
(1) (2) (3) (4)
CEOt 0.104*** 0.005* 0.109*** -0.037***
(7.99) (1.79) (8.54) (-2.81)
DQt 0.171*** -0.003** 0.168*** -0.213***
(4.74) (-2.14) (4.84) (-14.62)
CoCt -0.016 0.001 -0.016
(-0.97) (0.97) (-0.97)
LnAssetst 0.485*** 0.071*** 0.556***
(19.13) (3.82) (33.15)
LnFirmAget 0.042** -0.007 0.035**
(2.45) (-1.05) (2.23)
ROAt 0.102*** 0.023*** 0.125***
(5.31) (4.13) (6.93)
RDAssetst 0.41*** -0.001 0.409***
(21.58) (-0.17) (22.73)
PPEAssetst -0.147*** 0.005** -0.141***
(-7.82) (2.39) (-7.98)
Leveraget -0.041*** 0.001** -0.039***
(-2.83) (1.98) (-2.83)
CapexAssetst 0.087*** -0.003** 0.084***
(4.89) (-2.18) (4.95)
MtoBt 0.061*** 0.013*** 0.074***
(3.77) (3.24) (4.86)
CF Volt -0.017*** -0.017***
(-4.16) (-4.16)
Sales Volt -0.018*** -0.018***
(-3.76) (-3.76)
Litigation Riskt -0.008*** -0.008***
(-2.86) (-2.86)
Sales Growtht -0.018*** -0.018***
(-4.31) (-4.31)
123
Direct Effect Indirect Effect Total Effect
LnCountst+1 LnCountst+1 LnCountst+1 CoCt
(1) (2) (3) (4)
Stock Returnt 0.001 0.001 -0.266***
(0.12) (0.12) (-19.39)
SEOt -0.007** -0.007**
(-2.51) (-2.51)
Losst -0.02*** -0.02***
(-4.32) (-4.32)
LnAnalystst 0.078*** 0.001 0.079*** -0.086***
(3.73) (0.16) (3.81) (-3.69)
Instit Ownerst -0.042*** 0.01*** -0.032** -0.06***
(-2.92) (3.90) (-2.23) (-4.25)
LnCountst+1 -0.036*** -0.036***
(-5.32) (-5.32)
# patentst 0.001 0.001 0.07***
(0.33) (0.33) (3.91)
# citest 0.004** 0.004** -0.05***
(2.19) (2.19) (-2.90)
DQt-1 -0.002 -0.002
(-1.35) (-1.35)
Betat 0.001 0.001 -0.053***
(0.92) (0.92) (-3.45)
BMt-1 0.001 0.001 -0.07***
(0.95) (0.95) (-5.31)
Sizet-1 0.006 0.006 -0.357***
(0.97) (0.97) (-15.42)
124
Panel B: CEO tendency for innovation and innovation quality
Direct Effect Indirect Effect Total Effect
LnCitest+1 LnCitest+1 LnCitest+1 CoCt
(1) (2) (3) (4)
CEOt 0.348*** 0.007** 0.355*** -0.117***
(26.16) (2.47) (27.21) (-8.72)
DQt 0.124*** 0.005*** 0.128*** -0.211***
(3.18) (4.20) (3.38) (-14.63)
CoCt -0.038** 0.001** -0.037**
(-2.26) (2.26) (-2.26)
LnAssetst 0.371*** 0.037** 0.408***
(14.75) (2.03) (24.47)
LnFirmAget 0.003 -0.002 0
(0.16) (-0.40) (0.02)
ROAt 0.079*** 0.011* 0.09***
(4.12) (1.92) (5.02)
RDAssetst 0.325*** -0.003 0.323***
(17.23) (-0.44) (18.01)
PPEAssetst -0.127*** 0.003* -0.124***
(-6.80) (1.80) (-6.93)
Leveraget -0.009 0 -0.009
(-0.61) (0.59) (-0.61)
CapexAssetst 0.109*** -0.003* 0.106***
(6.14) (-1.79) (6.24)
MtoBt 0.079*** 0.014*** 0.093***
(4.93) (3.50) (6.12)
CF Volt -0.014*** -0.014***
(-3.25) (-3.25)
Sales Volt -0.012*** -0.012***
(-2.93) (-2.93)
Litigation Riskt -0.007*** -0.007***
(-2.86) (-2.86)
Sales Growtht -0.014*** -0.014***
(-3.32) (-3.32)
Stock Returnt 0.007 0.007 -0.266***
(1.40) (1.40) (-19.63)
SEOt -0.005** -0.005**
(-2.34) (-2.34)
Losst -0.015*** -0.015***
(-3.28) (-3.28)
LnAnalystst 0.054*** 0.001 0.055*** -0.076***
(2.57) (0.44) (2.67) (-3.29)
Instit Ownerst -0.081*** 0.01*** -0.071*** -0.078***
(-5.64) (4.80) (-4.97) (-5.50)
LnCitest+1 -0.025*** -0.025***
(-4.41) (-4.41)
125
Direct Effect Indirect Effect Total Effect
LnCitest+1 LnCitest+1 LnCitest+1 CoCt
(1) (2) (3) (4)
# patentst 0.002 0.002 0.062***
(1.05) (1.05) (3.47)
# citest 0.008*** 0.008*** -0.044***
(3.55) (3.55) (-2.59)
DQt-1 -0.004** -0.004**
(-2.53) (-2.53)
Betat 0.002* 0.002* -0.046***
(1.77) (1.77) (-3.03)
BMt-1 0.003** 0.003** -0.07***
(2.09) (2.09) (-5.36)
Sizet-1 0.013** 0.013** -0.352***
(2.24) (2.24) (-15.34)
Panel C: CEO background and innovation quantity
Direct Effect Indirect Effect Total Effect
LnCountst+1 LnCountst+1 LnCountst+1 CoCt
(1) (2) (3) (4)
FinanceBackgroundt -0.016 -0.007** -0.023* 0.026**
(-1.22) (-2.18) (-1.76) (2.06)
TechnicalBackgroundt 0.034*** 0.009*** 0.043*** 0.004
(2.56) (2.68) (3.33) (0.34)
Legalt -0.058*** -0.001 -0.058*** 0.045***
(-4.41) (-0.18) (-4.53) (3.57)
DQt 0.198*** -0.001 0.198*** -0.18***
(5.55) (-0.35) (5.76) (-13.31)
CoCt -0.043** 0.002** -0.041**
(-2.48) (2.48) (-2.48)
LnAssetst 0.47*** 0.043** 0.513***
(17.76) (2.22) (28.80)
LnFirmAget 0.009 -0.006 0.004
(0.54) (-0.89) (0.22)
ROAt 0.158*** 0 0.158***
(7.42) (0.01) (7.64)
RDAssetst 0.379*** -0.009 0.37***
(17.81) (-1.32) (18.07)
PPEAssetst -0.183*** 0.007** -0.175***
(-9.14) (2.56) (-9.37)
Leveraget -0.016 0.001 -0.016
(-1.11) (1.09) (-1.11)
CapexAssetst 0.074*** -0.003** 0.071***
126
Direct Effect Indirect Effect Total Effect
LnCountst+1 LnCountst+1 LnCountst+1 CoCt
(1) (2) (3) (4)
(4.06) (-2.18) (4.10)
MtoBt 0.061*** 0.011*** 0.072***
(3.72) (2.58) (4.67)
CF Volt -0.036*** -0.036***
(-5.60) (-5.60)
Sales Volt -0.016*** -0.016***
(-3.51) (-3.51)
Litigation Riskt -0.004 -0.004
(-1.23) (-1.23)
Sales Growtht -0.025*** -0.025***
(-4.96) (-4.96)
Stock Returnt 0.011* 0.011* -0.304***
(1.94) (1.94) (-23.51)
SEOt -0.006** -0.006**
(-1.96) (-1.96)
Losst -0.026*** -0.026***
(-4.95) (-4.95)
LnAnalystst 0.05** 0.004 0.055** -0.055**
(2.34) (0.85) (2.56) (-2.50)
Instit Ownerst -0.072*** 0.015*** -0.057*** -0.135***
(-4.81) (4.64) (-3.87) (-9.92)
LnCountst+1 -0.04*** -0.04***
(-5.04) (-5.04)
# patentst 0 0 0.063***
(-0.18) (-0.18) (4.07)
# citest 0.007*** 0.007*** -0.049***
(3.24) (3.24) (-3.17)
DQt-1 -0.004*** -0.004*** 0.131***
(-2.87) (-2.87) (10.17)
Betat 0.002** 0.002** -0.055***
(2.09) (2.09) (-4.04)
BMt-1 -0.005** -0.005** 0.131***
(-2.41) (-2.41) (10.17)
Sizet-1 0.015** 0.015** -0.351***
(2.47) (2.47) (-15.63)
127
Panel D: CEO background and innovation quality
Direct Effect Indirect Effect Total Effect
LnCitest+1 LnCitest+1 LnCitest+1 CoCt
(1) (2) (3) (4)
FinanceBackgroundt -0.02 -0.008** -0.028** 0.025**
(-1.39) (-2.40) (-2.02) (2.01)
TechnicalBackgroundt 0.026* 0.01** 0.035*** 0.005
(1.82) (2.51) (2.57) (0.37)
Legalt -0.064*** -0.003 -0.067*** 0.045***
(-4.62) (-0.80) (-4.89) (3.58)
DQt 0.204*** 0.008*** 0.212*** -0.188***
(5.01) (4.04) (5.40) (-13.63)
CoCt -0.092*** 0.004*** -0.088***
(-4.97) (4.97) (-4.97)
LnAssetst 0.337*** 0.022 0.358***
(11.97) (1.04) (18.81)
LnFirmAget -0.048*** -0.006 -0.054***
(-2.62) (-0.89) (-3.10)
ROAt 0.134*** -0.015** 0.12***
(5.90) (-2.19) (5.44)
RDAssetst 0.266*** -0.012* 0.254***
(11.80) (-1.72) (11.73)
PPEAssetst -0.166*** 0.007** -0.159***
(-7.81) (2.37) (-8.01)
Leveraget 0.014 -0.001 0.013
(0.88) (-0.80) (0.88)
CapexAssetst 0.144*** -0.006** 0.138***
(7.41) (-2.41) (7.50)
MtoBt 0.095*** 0.017*** 0.112***
(5.44) (3.35) (6.83)
CF Volt -0.039*** -0.039***
(-5.30) (-5.30)
Sales Volt -0.015*** -0.015***
(-3.34) (-3.34)
Litigation Riskt -0.007** -0.007**
(-1.97) (-1.97)
Sales Growtht -0.027*** -0.027***
(-4.76) (-4.76)
Stock Returnt 0.024*** 0.024*** -0.304***
(3.86) (3.86) (-23.43)
SEOt -0.005 -0.005
(-1.62) (-1.62)
Losst -0.028*** -0.028***
(-4.74) (-4.74)
LnAnalystst 0.049** 0.007 0.056** -0.057***
(2.15) (1.31) (2.50) (-2.61)
128
Direct Effect Indirect Effect Total Effect
LnCitest+1 LnCitest+1 LnCitest+1 CoCt
(1) (2) (3) (4)
Instit Ownerst -0.173*** 0.021*** -0.152*** -0.132***
(-10.94) (5.58) (-9.57) (-9.71)
LnCitest+1 -0.043*** -0.043***
(-4.82) (-4.82)
# patentst 0.001 0.001 0.063***
(0.28) (0.28) (4.06)
# citest 0.02*** 0.02*** -0.049***
(5.63) (5.63) (-3.17)
DQt-1 -0.008*** -0.008*** 0.132***
(-3.35) (-3.35) (10.27)
Betat 0.005*** 0.005*** -0.056***
(3.11) (3.11) (-4.09)
BMt-1 -0.012*** -0.012*** 0.132***
(-4.46) (-4.46) (10.27)
Sizet-1 0.031*** 0.031*** -0.347***
(4.79) (4.79) (-15.45)
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