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.

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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

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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

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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.

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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)