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Financial Reporting Quality and Corporate Innovation
KoEun Park1
University of Massachusetts Boston
Abstract: This paper examines the effects of financial reporting on corporate innovation. After
controlling for other determinants of corporate innovation, I find that firms with higher-quality
financial reporting exhibit greater innovation. I show that the relation between financial reporting
quality and innovation is more pronounced for firms that have greater growth opportunities,
those that operate in innovation-intensive industries, and those that have high institutional
ownership. Further, I find some evidence that high-quality financial reporting is associated with
shorter patent approval time for first-time patent applicants and that it is related to greater R&D
performance. Overall, my evidence suggests that high-quality financial reporting facilitates
corporate innovation by reducing the information risk inherent in firms that undertake risky but
value-enhancing projects.
Keywords: innovation, financial reporting quality, accruals quality, real activities manipulation
1 The author is an Assistant Professor of Accounting and Finance at the College of Management, University of
Massachusetts Boston. The author would like to thank the seminar participants at the 2015 annual meeting of the
AAA for their helpful comments.
Address for correspondence: KoEun Park, Department of Accounting and Finance, College of Management,
University of Massachusetts Boston, 100 Morrissey Blvd., Boston, MA 02125-3393. E-mail:
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1. INTRODUCTION
“In a period of rapid structural change, the only ones who survive are those who innovate and
create change (Drucker, 2008, p. 357).” Today’s firms strive for growth through innovation.
What drives innovation is of great interest to various stakeholders, including investors,
employees, suppliers, customers, and regulators. There is a growing body of literature that
supports the critical role of highly developed financial markets in fostering innovation (e.g., Hsu
et al., 2014). In particular, developed financial markets can promote innovation by reducing
market frictions, such as adverse selection and moral hazard, and thus enhancing the efficiency
of resource allocation. Despite a high degree of uncertainty inherent in firms undertaking
innovative activities and the role of financial reporting quality in reducing the risk of information
about the firm, the existing literature has provided little insight into the direct relation between
financial reporting quality and firm innovation. The goal of this paper is to investigate whether
firms with higher financial reporting quality exhibit greater innovation.
Innovation requires a long-term commitment to the exploration of new, untested
approaches with a high probability of failure (Holmstrom, 1989). Innovation also requires heavy
investment, a long-term focus, and tolerance for experimentation and mistakes. Firms investing
heavily in innovative projects are more likely to be undervalued and at high risk of hostile
takeovers (Stein, 1988). Given the need for external financing and high uncertainty associated
with innovative activities, information problems between managers and outside capital suppliers
arise. On the one hand, high risk associated with innovation might dissuade risk-averse managers
from investing in innovation. On the other hand, managers who desire empire building in firms
with ample capital might engage in routine tasks or even value-destroying activities that impede
firm innovation. As such, firms are likely to undertake sub-optimal levels of innovation.
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Improving financial reporting quality, which is a key ingredient of financial markets, is
one potential solution to such distortions of investments in innovation. One of the objectives of
corporate financial reporting is to provide useful information to investors, creditors, and other
users in making rational investment decisions and assessing the expected future cash flows
(FASB, 1978). Prior studies have examined the economic consequences of financial reporting
quality based on the reduction of information risk. Bushman and Smith (2001) suggest that
financial accounting information can affect the investments, productivity, and value added of
firms through three channels: (1) by helping managers and investors identify good versus bad
projects (project identification); (2) by disciplining managers to direct resources toward projects
identified as good and away from projects identified as bad (governance channel); and (3) by
reducing information asymmetry among investors and liquidity risk (adverse selection).
If high financial reporting quality reduces information risk, suppliers of capital can
estimate future performance of a firm better. To the extent that high-quality financial reporting
allows firms to attract capital investment in risky but value-enhancing innovative projects and
curbs managerial incentive to undertake value-destroying activities that may impede innovation,
I expect firms with higher-quality financial reporting to exhibit greater innovation. Existing
papers show that financial reporting quality is associated with corporate investment (e.g., Biddle
and Hilary, 2006; McNichols and Stubben, 2008; Biddle et al., 2009) and do not speak directly to
whether high-quality financial reporting ultimately contributes to greater corporate innovation. I
focus on innovation because corporate innovation is a major driver of firm- and national-level
economic growth and because firms that undertake innovative projects are more likely to suffer
from information problems.
4
I consider different proxies of financial reporting quality. Prior studies use accruals-based
metrics as proxies for financial reporting quality (e.g., Jones, 1991; Aboody et al., 2005; Francis
et al., 2005; Kim et al., 2012; Pietro and Wagenhofer, 2014). I use three alternative measures of
accruals quality. Recent studies suggest that firms use RAM, which has a real cash flow effect,
as an alternative tool for earnings management (e.g., Roychowdhury, 2006; Cohen et al., 2008;
Cohen and Zarowin, 2010; Mizik, 2010; Francis et al., 2011; Zang, 2012). Therefore, I also use
three different measures of RAM. Following previous studies on the innovation literature, I
measure corporate innovation by patenting activity that captures how effectively a firm has
utilized its innovation inputs. Specifically, I use the number of patent applications a firm files in
a year that are eventually granted and the number of citations subsequently received by each
patent applied for during a given year as main proxies to assess the success of long-term
investment in innovation (e.g., Hirshleifer et al., 2012; Aghion et al., 2013; He and Tian, 2013;
Tian and Wang, 2014; Fang et al., 2014; Cornaggia et al., 2015).
My baseline results show a positive relation between financial reporting quality and
innovation. Specifically, firms with higher accruals quality and those with less RAM have
greater innovation, suggesting that high-quality financial reporting fosters firm innovation. In
order to test whether high financial reporting quality increases the effectiveness of the firm in
generating innovation, rather than increasing innovation by means of value-reducing
overspending on research and development (R&D), I include R&D expenditures to the set of
controls (Hirshleifer et al., 2012). My evidence thus indicates that high-quality financial
reporting increases the effectiveness of generating innovation after controlling for the amounts of
R&D expenditures. To alleviate the endogeneity concern, I investigate the effect of financial
reporting quality on innovation three years ahead (e.g., He and Tian, 2013), use a fixed effects
approach (e.g., Hsu et al., 2014), control for pre-sample innovation success (e.g., Chang et al.,
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2015), and employ a Heckman two-stage model (e.g., Cohen and Zarowin, 2010) and a two-
stage least squares (2SLS) regression model. My results still hold.
I next examine a more specific issue, whether high-quality financial reporting allows
firms to translate growth opportunities into innovation outcomes. I find that the relation between
financial reporting quality and innovation is more pronounced for firms that have greater growth
opportunities and those that operate in industries where good opportunities for innovation are
available. I also find that the relation between financial reporting quality and innovation is
stronger for firms with high institutional ownership, particularly when information asymmetry is
low. The results suggest that financial reporting and institutional ownership are complements in
facilitating corporate innovation. Lastly, I find some evidence that high-quality financial
reporting enables first-time patent applicants to shorten the length of time it takes for a patent to
be approved. Higher-quality financial reporting also appears to allow firms to transform R&D
spending into greater innovation output and long-term firm value.
My paper offers additional insight into the real effects of financial markets and is related
to three streams of literature. First, it contributes to the literature on financial development and
growth. Since Schumpeter (1911), an extensive literature has examined the relation between
financial development and economic growth. My study extends this literature by identifying a
mechanism through which financial development facilitates economic growth, namely, high
financial reporting quality that fosters firm innovation. Prior literature has examined how
financial reporting quality, which is one of key ingredients in financial markets, affects firm
investment. For example, Biddle et al. (2009) find that financial reporting quality is negatively
related to under- and over-investment. However, these studies address neither whether financial
reporting quality ultimately affects innovation, which is directly linked to economic growth, nor
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whether it impacts the properties of a firm’s innovation activities, including patent approval time
and associated long-term firm value.
Second, this study contributes to the growing literature on corporate innovation. Recent
studies provide evidence on the relation between innovation and firm characteristics including
institutional ownership (Aghion et al., 2013), investors’ greater tolerance for failure (Tian and
Wang, 2014), and anti-takeover provisions (Chemmanur and Tian, 2013). Extending this
literature, I provide evidence that firms with high financial reporting quality, which can reduce
the information risk inherent in firms undertaking innovation projects, have greater innovation
and that they are better at transforming growth opportunities into actual innovation.
Lastly, this paper contributes to the literature on the interaction between financial
reporting quality and other corporate control mechanisms. The importance of financial
accounting information varies based on other control mechanisms, which can be important
channels through which financial accounting information enhances governance and economic
performance (Bushman and Smith, 2001). In addition to investigating the overall effect of
financial reporting quality on innovation, I investigate how its effects are conditioned by
institutional investors who can serve as an external monitor. My findings suggest that the
governance role of financial reporting and institutional ownership are complementary in
fostering innovation, particularly when information asymmetry is low.
The remainder of this paper is organized as follows. Section 2 reviews prior literature and
develops hypotheses. Section 3 describes my research design and Section 4 presents my results.
Section 5 concludes.
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2. PRIOR LITERATURE AND HYPOTHESES
In the absence of market imperfections, firms obtain financing for positive net present value
projects and invest until the marginal benefit of capital investment equals the marginal cost,
adjusting for installment costs (e.g., Yoshikawa, 1980; Hayashi, 1982). However, economists
have realized the possibility that firms deviate from the optimal investment policy. The U.S.
system for allocating investment capital does not direct capital effectively to the most value-
enhancing investment projects and some firms waste capital on investments that have limited
financial or social rewards (Porter, 1992). Information and incentive problems impede efficient
allocation of resources in a capital market economy (Healy and Palepu, 2001). For instance, if
managers are better informed than investors about a firm’s prospects, they may attempt to sell
overpriced securities, which in turn may lead to overinvestment if they are successful or to
underinvestment if investors ration capital ex ante (e.g., Baker et al., 2003; Myers and Majluf,
1984). If managers attempt to maximize their personal welfares at the expense of capital
suppliers, such moral hazard can lead to sub-optimal investment decisions (e.g., Jensen and
Meckling, 1976; Jensen, 1986; Hoshi et al., 1991). The financial market’s role in allocating
capital to the highest value use by reducing such frictions is essential to promote economic
growth (Schumpeter, 1911).
High-quality financial reporting is critical for well-functioning financial markets. High-
quality financial reporting can reduce adverse selection, liquidity risk, and information risk (e.g.,
Leuz and Verrecchia, 2000; Easley and O’Hara, 2004; Lambert et al., 2007). It can also aid
corporate control mechanisms in monitoring managerial investment decisions (e.g., Bushman
and Smith, 2001; Lambert, 2001; Lambert et al., 2007). Although measuring the optimal level of
investment has been controversial, several empirical studies show that financial reporting quality
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is associated with the optimal level of investment. For example, Biddle and Hilary (2006) show
that higher financial reporting quality is associated with lower investment-cash flow sensitivity.
McNichols and Stubben (2008) find that restating firms overinvest substantially during the
misreporting period and no longer overinvest following the misreporting period.
To compete effectively in globalized markets, firms must continuously innovate and
upgrade their competitive advantages (Porter ,1992). A growing literature studies various factors
that affect firm innovation. Manso (2011) suggests that managerial compensation contracts that
tolerate short-term failure and reward long-term success are best suited for motivating
innovation. Hirshleifer et al. (2012) find that firms with overconfident CEOs exhibit greater
innovation. Atanassov (2013) finds a drop in innovation for firms incorporated in states that pass
antitakeover laws. Aghion et al. (2013) show that firms with higher institutional ownership
exhibit greater innovation. He and Tian (2013) document that firms covered by a large number of
analysts have less innovation. Chaemmanur et al. (2014) find that corporate venture capital-
backed firms are more innovative. Fang et al. (2014) find that an increase in liquidity causes a
reduction in future innovation. Chang et al. (2015) show the positive effect of non-executive
employee stock options on innovation.
Innovation involves a long process that is full of uncertainty and has a high probability of
failure (Holmstrom, 1989). Efficient capital allocation is critical for the growth of innovation.
Since innovation often requires heavy investment, firms that undertake innovative projects are
likely to be dependent on external finance. If investors perceive that managers engaging in
innovation exploit their private information or to pursue their own interests at the expense of
investors, investors will withhold capital or require a higher rate of return. All of these
imperfections are produced by the existence of information asymmetry between managers and
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outside suppliers of capital. If firms that undertake innovative projects provide high-quality
financial reporting that reduces information risk, outsiders would not ration capital for fear of
buying at an inflated price and less agency problem would arise, which in turn will promote firm
innovation.
In this study, I focus on the role of financial reporting quality in promoting innovation. I
define financial reporting quality as the precision with which financial reporting conveys
information about the firm’s operation, in particular its expected cash flows (Biddle et al., 2009).
There is extensive research that provides empirical evidence that higher financial reporting
quality reduces information risk and influences the estimates of future cash flows. According to
Francis et al. (2004), accruals quality is the attribute of earnings that is most associated with the
reduction of information risk. Pietro and Wagenhofer (2014) find evidence supporting the use of
accruals-based metrics as a measure of earnings quality.2 Prior studies also show that managers
engage in RAM by altering the timing or structuring of real operations to meet short-term
earnings targets. For example, Graham et al. (2005) find that executives are likely to cut
discretionary spending on R&D, advertising, and maintenance to meet their earnings targets.3
Such practices distort information about the firm’s expected future cash flows, decreasing
financial reporting quality.
High-quality financial reporting reduces information risk, particularly for the firm with a
high degree of uncertainty that undertakes innovation projects. Thus, it enables capital suppliers
to estimate future performance of such a firm better and aids corporate control mechanism. As a
2 There is an alternative view that abnormal accruals are the means of communicating private information. However,
Pietro and Wagenhofer (2014) show that, using a stock-price-based measure for assessing the quality of earnings
quality measures, accruals-based metrics are the best measures of earnings quality. 3 Although Gunny (2010) suggest that firms engaging in RAM to just meet zero or last year’s earnings targets
credibly signal superior future performance, a majority of studies suggest that RAM, which is a departure from
normal operational practices, has a negative impact on cash flows and long-term firm value (e.g., Ewert and
Wagenhofer, 2005; Graham et al., 2005; Cohen et al., 2008; Cohen and Zarowin, 2010; Mizik, 2010; Francis et al.,
2011; Zang, 2012; Flip et al., 2015).
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result of reducing information risk, firm innovation would increase. The above discussion leads
to my first hypothesis:
H1: Corporate financial reporting quality is positively associated with corporate
innovation.
If high-quality financial reporting allows capital suppliers to better identify and allocate
their resources to those firms that engage in risky but value-enhancing innovative projects, it
would allow firms with good opportunities for growth to translate their potential into realized
innovation outcomes. I thus expect the effect of financial reporting quality on innovative
outcomes to be larger for firms that have greater growth opportunities or firms that operate in
innovation-intensive industries. This argument leads to my second hypothesis:
H2: The relation between financial reporting quality and innovation is more pronounced
for firms with greater innovation potential.
Finally, I examine a mechanism through which financial reporting quality affects
corporate innovation. Several studies suggest that large stockholdings and the relative
sophistication of institutional investors allow managers to focus on long-term value. For
example, Bushee (1998) shows that institutional investors monitor and discipline managers,
ensuring that managers choose investments to maximize long-term value. Aghion et al. (2013)
find the positive role of institutional investors in the governance of innovation. Previous research
examines whether corporate disclosures and corporate governance are substitutes or
complements (e.g., Bushman and Smith, 2001; Beyer et al., 2010). If institutional investors
facilitate interpretation of corporate financial reporting, acting as a complement, in monitoring
managerial innovation activities, the relation between financial reporting quality and innovation
will strengthen when institutional ownership is high. Alternatively, if institutional investors
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produce information about a firm’s innovation activities by using other private sources,
substituting for financial reporting, the relation between financial reporting quality and
innovation will weaken when institutional ownership is high. Furthermore, due to reduced
information advantage, institutional investors are less incentivized to search for private
information when information asymmetry is low than when information asymmetry is high. This
argument leads to my third hypothesis:
H3: The relations between financial reporting quality and innovation are different for
firms with high institutional ownership and low institutional ownership.
3. RESEARCH DESIGN
(i) Sample Construction
I compile my data set from several sources. I obtain financial data from the Compustat, stock
return data from the Center for Research in Security Prices (CRSP),4 institutional ownership data
from the Thompson’s CDA/Spectrum database (form 13f), and analyst data from the Institutional
Brokers’ Estimate System (I/B/E/S). I obtain firm-year patent and citation data from the latest
version of the National Bureau of Economics Research (NBER) Patent Citation Data File.5 This
database is originally developed by Hall et al. (2001) and contains information on all U.S.
patents granted by U.S. Patent and Trademark Office (USPTO) between 1976 and 2006.
4 I merge Compustat and CRSP following Beaver et al. (2007).
5 See http://www.nber.org/patents/.
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I begin constructing my sample by including all firms in Compustat over the period 1990-
2003.6 Following prior research on financial reporting quality, I exclude firms in utilities and
financial industries (SIC 4400-5000 and SIC 6000-6999, respectively). Also excluded are firms
with missing values for financial reporting quality and control variables employed in the
regressions. These restrictions result in a final sample that consists of 39,615 firm-year
observations.
(ii) Measurement of Accruals Quality
Numerous studies use discretionary accruals as proxies for earnings quality (e.g., Jones, 1991;
Dechow et al., 1995; Dechow and Dichev, 2002; McNichols, 2002; Biddle et al., 2009). To
enhance comparability with prior studies, I employ three different measures of discretionary
accruals that focus on the accuracy with which accruals convey information about a firm’s
fundamentals and cash flows to inform stakeholders (see Appendix A for details). First, using the
Jones (1991) model, I estimate the normal level of accruals as a linear function of change in sales
and contemporaneous property, plant and equipment (PPE). The residual from the model reflects
accruals unexplained by change in sales and PPE. I use the absolute value of the residual
(DACC) as an inverse measure of accruals quality (e.g., Aboody et al., 2005; Kim et al., 2012).
Second, following Kothari et al. (2005), I augment the modified Jones model by including
contemporaneous return on assets to avoid potential misspecification for well-performing or
poorly-performing firms. I use the absolute value of the residual from the model (DACCP).
Third, I estimate discretionary accruals using the Dechow and Dichev (2002) model augmented
6 The sample ends in 2003 because NBER database contains updated patent and citation information up to 2006 and
I link financial reporting quality in year t to innovation proxies measured three years ahead (t+3).
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by the fundamental variables in the Jones (1991) model as suggested by McNichols (2002) and
use the standard deviation of the estimated residuals calculated over the period t-4 to t (AQ).
(iii) Measurement of Real Activities Manipulation
RAM is defined as management actions that deviate from normal business practices undertaken
for purposes of meeting or beating earnings thresholds (Roychowdhury, 2006). Following prior
studies (e.g., Roychowdhury, 2006; Cohen et al., 2008; Cohen and Zarowin, 2010; Zang, 2012;
Kim et al., 2012), I estimate and examine three types of RAM (see Appendix A for details): (1)
abnormal cash flows from operations (ABCFO), (2) abnormal production costs (ABPROD), and
(3) abnormal discretionary expenses (ABDEXP). For example, managers may provide temporary
incentive for customers to buy more products, overproduce to reduce cost of goods sold (COGS)
in the current period, or cut discretionary expenses that do not generate immediate revenues to
inflate their reported earnings. Certain manipulating activities are possibly optimal actions in
certain economic circumstances (Roychowdhury, 2006). However, if managers engage in this
type activities more extensively than is normal given their economic circumstances, I assume
they engage in RAM.
(iv) Measurement of Innovation
Following the existing literature, I use patenting activity to capture firm innovativeness. While
R&D expenditures, which are used in earlier studies, capture only one particular observable
innovation input, patent-based measures capture how effectively a firm has used both observable
and unobservable innovation inputs (e.g., Fang et al., 2014). Thus, patenting activity is
14
considered a better proxy than R&D expenditures. My first measure of innovation is the number
of patents because innovation is usually officially introduced to the public in the form of
approved patents (Hirshleifer et al., 2013). I use the number of patent applications a firm files in
a year that are eventually granted (e.g., He and Tian, 2013; Fang et al., 2014; Cornaggia et al.,
2015; Chang et al., 2015). However, patents differ greatly in their technological and economic
importance (Hirshleifer et al., 2012). Thus, my second measure of innovation is the number of
citations each patent receives in subsequent years, which captures its quality. Hall et al. (2005)
find that patent citations are an accurate measure of the value of innovation.
Following the existing innovation literature, I adjust the two measures of innovation to
address the truncation problems (e.g., Hall et al., 2001, 2005; Fang et al., 2014). The patents
appear in the NBER database only after they are granted. There is a time lag between the date
when inventors file for patents and the date when patents are granted. Many patent applications
filed during the latter years in the sample were still under review and had not been granted by
2006. To correct for the truncation bias, I first estimate the application-grant lag distribution,
which is defined as the percentage of patents applied for in a given year that are granted in s
years, for the patents filed and granted between 1995 and 2000. I then calculate the following
truncation-adjusted patent counts for years 2001 to 2006:
t
s s
raw
adj
W
PATPAT
2006
0
(1)
where rawPAT is the unadjusted number of patent applications in year t from 2001 to 2006;
sW is
the application-grant lag distribution.
The truncation bias of the citation counts arises because a patent can keep receiving
citations over a long period of time, but the NBER database provides observations up to 2006.
15
Following the innovation literature (e.g., Hall et al., 2001, 2005; Fang et al., 2014), I correct for
the bias by multiplying the unadjusted citation counts by the adjustment factor (hjtwt) provided
by the NBER patent database, which is constructed by the shape of the citation lag distribution.
Following previous innovation studies, if a firm does not have available patent or citation
information in the NBER database, both innovation variables are assumed to be zero (e.g.,
Hirshleifer et al., 2012; He and Tian, 2013; Fang et al., 2014; Cornaggia et al., 2015). Due to the
right-skewed distributions of patent counts and citations per patent, I use the natural logarithm of
one plus the truncation-adjusted values.
(v) Control Variables
Following the innovation literature, I control for a vector of firm and industry characteristics that
could affect a firm’s future innovation. In particular, in order to test whether high financial
reporting quality increases the effectiveness in generating innovation given the amount of
innovative investment, I control for R&D expenditures scaled by lagged total assets.7 To control
for the substitutive nature of two earnings management methods, I include DACC, a proxy for
accruals quality, as a control variable in the RAM regressions and total RAM (TRAM), which is
calculated as ABPROD minus the sum of ABCFO and ABDEXP, as a control variable in the
accruals quality regressions (e.g., Cohen et al., 2008; Kim et al., 2012). Other control variables
include profitability [return on assets (ROA)], asset tangibility [net property, plants, and
equipment (PPE) scaled by total assets], leverage, capital expenditures, growth opportunities
(Tobin’s Q), financial constraints [Kaplan and Zingales (1997) KZ Index], industry
concentration (Herfindahl Index based on sales), firm size (the natural logarithm of total sales),
7 Since discretionary expenses include R&D expenditures, the R&D control is excluded in the regressions that use
ABDEXP as a proxy for financial reporting quality to avoid multicollinearity.
16
firm age (the number of years listed on Compustat), and institutional ownership. I provide
detailed variable definitions in Appendix B
(vi) Empirical Model
To examine the relation between financial reporting quality and innovation, I estimate the
following ordinary least squares (OLS) regression:
ittitititit YearIndustryZFRQInnovation 3 (2)
where 3itInnovation refers to a firm’s innovation, measured as the natural logarithm of one plus
the number of patents filed and eventually granted (LnPAT) or the natural logarithm of one plus
the number of citations per patent (LnCITE) three years ahead (t+3); the key explanatory variable
is itFRQ , accruals quality or RAM measures;
itZ represents the set of control variables defined
above.
To address the long-term nature of the innovation process, I examine the effect of a
firm’s financial reporting quality in year t on its patenting activities in year t+3 (e.g., He and
Tian, 2013). The residuals in the regression may be correlated since each firm can enter the
sample several times, thereby overstating t-statistics due to the cluster sample problem. I correct
for this problem by employing adjusted standard errors that account for possible correlations
between residuals for observations for a firm (Petersen, 2009). I also control for industry fixed
effects based on two-digit SIC in all regressions because innovation can be more intensive in
certain industries. Lastly, I include year fixed effects to account for intertemporal variation that
may affect the relation between financial reporting quality and innovation.
17
4. EMPIRICAL RESULTS
(i) Descriptive Statistics
Panel A of Table 1 presents descriptive statistics (see Appendix B for variable definitions). All
continuous variables are winsorized at the top and bottom 1 percent of their distributions. On
average, a firm in my final sample has 6.051 granted patents per year and each patent receives
2.757 citations. It shows the mean values of 0.116, 0.087 and 0.136 for DACC, DACCP, and AQ,
respectively. The mean value of ABCFO, ABPROD, and ABDEXP are 0.015, -0.005, and -0.020,
respectively. Panel B of Table 1 presents the Pearson correlation coefficients among variables.
Overall, innovation is negatively correlated with DACC, DACCP, AQ, and ABPROD while it is
positively correlated with ABCFO and ABDEXP.
[Insert Table 1 Here]
(ii) Accruals Quality and Firm Innovation
Table 2 presents the results of the multivariate regression analysis using accruals quality as a
proxy for financial reporting quality. I find a positive relation between accruals quality and
innovation. Specifically, the estimated coefficients on DACC, DACCP, and AQ are negative and
significant in the LnPAT regressions (columns 1-3) and the coefficients on DACC and DACCP
are negative and significant in the LnCITE regressions (columns 4 and 5), indicating that higher-
accruals quality is associated with greater innovation even after controlling for the amount of
innovative investment (R&D expenditures).
[Insert Table 2 Here]
18
(iii) Real Activities Manipulation and Firm Innovation
Table 3 presents the results of the multivariate regression analysis using RAM as a proxy for
financial reporting quality. Similarly, I find a negative relation between RAM and innovation.
Specifically, in the LnPAT regression, the estimated coefficient on ABCFO is significantly
positive (column 1), while the coefficient on ABPROD is significantly negative (column 2). In
the LnCITE regression, the coefficients on ABCFO and ABDEXP are significantly positive
(columns 4 and 6), while the coefficient on ABPROD is significantly negative (column 5).
Overall, the baseline results suggest that financial reporting quality is positively related to a
firm’s innovation, consistent with my first hypothesis.
[Insert Table 3 Here]
(iv) Identification
While the baseline results are consistent with the hypothesis that higher financial reporting
quality encourages firm innovation, important concerns are that causation might run from
innovation to financial reporting quality (reverse causality concern) and that omitted factors
correlated with both financial reporting quality and innovation could be the true underlying cause
(omitted variable concern). In the baseline model, I attempt to address the problem by
investigating the effect of financial reporting quality on innovation three years ahead, including
an extensive set of control variables, and using a fixed effects approach that captures both time
series and cross-sectional dynamics between financial reporting quality and innovation. To
further alleviate the concerns, I employ the following identification strategies.
First, I control for pre-sample average innovation measures to explicitly account for past
innovation success. Given that I have a long pre-sample history of innovation data, I construct
the pre-sample means of innovation measures calculated over the period 1976-1989. This can be
19
used to control for entry innovation stock of each firm (e.g., Aighon et al., 2013; Chang et al.,
2015). The untabulated results are similar to those reported in Tables 2 and 3 except that the
coefficient on DACCP is negative but insignificant in the LnPAT regression, indicating that
reverse causality arising from past innovation success is not the main driver of my results.
Second, I use a two-stage Heckman model to control for the self-selection (e.g., Cohen
and Zarowin, 2010; Chan et al., 2015; Farrell et al., 2014). I identify financial reporting quality
conditioning on the firm being “suspect,” that is the firm that just beats or meets earnings targets
where earnings manipulation is more likely to occur.8 In the first-stage, I estimate a selection
model using all the sample firms. Given that a firm has decided to manipulate reported earnings,
I next test whether financial reporting quality is related to innovation in the second-stage.
More specifically, building on prior research, I first model a firm’s decision to
manipulate reported earnings as a function of capital market incentive, capital structure,
performance, size, growth opportunities, and industry and year fixed effects using the following
probit model:
ititititit
itititit
MTBSIZEROALEVStockIssue
ShareAnalrHabitBeatesuspectPROB
876514
3210
) (
(3)
where itsuspect is an indicator variable for the suspect firm-year that takes the value of one if
the firm’s earnings before extraordinary items divided by lagged total assets are between 0 and
0.01, its change in earnings before extraordinary items divided by lagged total assets is between
0 and 0.01, or its actual earnings minus the most recent analysts’ consensus forecast prior to
earnings announcement are between 0 and 0.01, and zero otherwise (e.g., Kim and Park, 2014);
itrHabitBeate is the number of times analysts’ forecast consensus had been met or beaten over the
8 Previous studies suggest that firm-years with earnings right at or just above benchmarks are likely to manipulate
their earnings to meet such benchmarks (e.g., Burgstahler and Dichev, 1997; Degeroge et al., 1999; Bartov et al.,
2002; Graham et al., 2005).
20
past four quarters; itAnal is the number of analysts following the firm during the year; itShare is
the natural logarithm of the number of shares outstanding; 1itStockIssue is an indicator variable
that takes the value of one if the company’s net proceeds from equity financing is positive in
year t+1, and zero otherwise; itLEV is the leverage ratio; itROA is return on assets; itSIZE is the
natural logarithm of market capitalization; itMTB is the market to book ratio.
After estimating Eq. (3), I compute the inverse Mills ratio (IMR) and include it to
reestimate Eq. (2). In the full sample, 7,948 firm-years can be classified as suspect firm-years.
Panel A in Table 4 reports the result of the first-stage regression.9 Bartov et al. (2002) and
Kasznik and McNichols (2002) suggest that firms that have repeatedly beaten earnings targets
have stronger interests to keep doing so. Consistent with previous findings, the likelihood of
manipulating reported earnings is positively related to a prior history of meeting or beating
earnings benchmarks (HabitBeater). The results of the second-stage regression for accruals
quality in Panel B of Table 4 are generally similar to those reported in Table 2 except that the
coefficient on DACC in the LnPAT regression becomes insignificant. The results of second-stage
regression for RAM in Panel C of Table 4 are similar to those reported in Table 3.
Using the sample of firm-year observations where earnings manipulation is likely to
occur, I find that poor financial reporting quality leads to less innovation. These results are
consistent with the findings of Chang et al. (2015) who investigate the relation between
accounting conservatism and corporate innovation, in that managerial myopia curbs corporate
innovation. They argue that conditional conservatism exacerbates the effect of myopic
managerial behavior that reduces R&D activities because the asymmetric accounting treatment
of good and bad news increases the likelihood of missing earnings targets and thus increases the
9 The number of observations decreases from 39,615 because of missing values for control variables in the first-
stage regression.
21
propensity to reduce R&D efforts. They find that firms with a greater degree of conditional
conservatism exhibit fewer patents, suggesting that conservative reporting hinders corporate
innovation, particularly in the presence of myopia. However, my analysis in this section is
different, in that it focuses on managerial myopia in financial reporting, which affects the
information risk inherent in firms that undertake risky but value-enhancing projects.
[Insert Table 4 Here]
Third, I use a 2SLS regression approach to further correct for the potential endogeneity
bias. I first compute the predicted values of financial reporting quality by estimating the first-
stage regression in which each financial reporting quality variable is the dependent variable and
independent variables include all the exogenous variables from the second-stage innovation
regressions and the chosen instrument. The predicted values of financial reporting quality are
then employed as independent variables in lieu of their actual values in the second-stage
regressions. I use contemporaneous innovation values rather than those values three years ahead
in this section.
Instruments include a change in regulatory regime for accruals quality and the operating
cycle for RAM. The Securities and Exchange Commission issued Staff Accounting Bulletin
(SAB) No. 101 in 1999, which reduces the timeliness of revenue recognition and affects
accounting information (e.g., Altamuro et al., 2005; Crawford et al., 2011). I use this exogenous
shock to accounting adjustments as an instrument.10
On the other hand, firms with higher
accounting flexibility have a lower level of RAM. Longer operating cycles provide firms with
more accounting flexibility because these firms have larger accrual accounts and take longer to
10
Specifically, I use an indicator variable that takes the value of one after the enactment of SAB 101. All public
companies filing with the SEC had to conform to SAB 101 by the first fiscal year ending after December 15, 2000.
22
reverse accruals (Zang, 2012).11
At the same time, it is unlikely that these instruments have a
direct systematic effect on a firm’s innovation. Therefore, these instruments provide reasonable
exogenous variations that help to identify the direction of causality.12
In addition to selecting
instruments based on economic arguments, I require them to pass an array of relevance and
validity conditions.13
Table 5 presents the results of the second-stage of the 2SLS regression. The
coefficients on financial reporting quality are consistent with those in Tables 2 and 3 except for
ABDEXP.
[Insert Table 5 Here]
Although I follow prior studies to justify instrument variable choices, conduct
specification tests, and carefully design the Heckman selection model, I acknowledge that due to
inherent limitation in these models, the reported results could be affected by the endogeneity bias
(e.g., Larcker and Rusticus, 2010; Lenox et al., 2012). However, collective evidence from this
section is consistent with the notion that higher financial reporting quality leads to greater
innovation.
(v) Financial Reporting Quality, Innovation Potential, and Firm Innovation
I have found that high-quality financial reporting increases patent applications and patent
citations. But, the question remains as to whether high-quality financial reporting allows firms
for which good opportunities are available to translate innovation potential into innovation
11
I use an indicator variable that takes the value of one if the firm’s operating cycle at the beginning of the year is
above the annual median. 12
Since it is difficult to identify a completely exogenous instrument in earnings management studies, I acknowledge
that other endogenous interactions may still exist. 13
First, the coefficients on individual instruments are all statistically significant in the appropriate first-stage
regressions, indicating that the instruments are individually relevant. Second, the instrument also passes the
relevance test as the F-statistics from the joint test of excluded instruments are significant. Lastly, the differences in
the Sargan-Hansen statistics reject the null hypothesis that financial reporting quality is exogenous to innovation,
indicating that the use of the 2SLS methodology is appropriate.
23
outcomes. I therefore examine whether the relation between financial reporting quality and
innovation is more pronounced for firms with greater growth opportunities.
To that end, I first use the firm market-to-book (MTB) ratio as a proxy for firm growth
opportunities. I generate an indicator variable for a firm with greater firm growth opportunities
(ABMTB) that takes the value of one if the firm has the MTB above the annual median. I then
add an interaction term between financial reporting quality and the indicator variable to the
regressions. Table 6 presents the results. To save space, I suppress the coefficients of all the
control variables. Interactions between ABMTB and DACC, DACCP, or AQ are negative and
significant in both LnPAT and LnCITE regressions (Panel A). Interactions between ABMTB and
ABCFO are significantly positive, while interactions between ABMTB and ABPROD are
significantly negative in both LnPAT and LnCITE regressions (Panel B).
[Insert Table 6 Here]
I also use the industry price to earnings (PE) ratio as an alternative, a more exogenous
proxy for growth opportunities (Bekaert et al., 2007). I calculate the industry PE ratio as the
logarithmic transformation of the ratio of the industry’s total market capitalization to its total
earnings. To minimize the noise in the PE ratio that is influenced by discount rate changes, I
subtract the 5-year moving average of the PE ratio (Bekaert et al., 2007; Hirshleifer et al., 2012).
I generate an indicator variable for a firm with greater industry growth opportunities (ABIPE)
that takes the value of one if the firm has the industry PE ratio above the annual median. I then
add an interaction term between financial reporting quality and the indicator variable to the
regressions. In general, the results in Table 7 are similar to those reported in Table 6.
[Insert Table 7 Here]
24
Finally, I expect the effect of financial reporting quality on innovation to be larger for
firms in industries where good opportunities for innovation are available (e.g., Hirshleifer et al.,
2012). Innovation-intensive industries should contain more good risky growth opportunities. I
thus examine whether the relation between financial reporting quality and innovation is more
pronounced for firms in innovation-intensive industries. To test this, I calculate the average
number of citations per patent for each industry-year and then take the median average for each
industry. I define an industry as innovation-intensive if the industry median is in the top decile of
all industries, where industries are classified at the two-digit SIC level. Innovation-intensive
industries in my sample include electrical equipment (SIC=36), measuring, analyzing, and
controlling instruments (SIC=38), commercial machinery (SIC=35), and furniture and fixtures
(SIC=25). I generate an indicator variable for high innovation opportunities (HICITE) that takes
the value of one if the firm operates in an innovation-intensive industry. I then add an interaction
term between financial reporting quality and the indicator variable to the regressions. The results
in Table 8 are consistent with those reported in Tables 6 and 7. Overall, high-quality financial
reporting enables firms to translate innovation potential into innovation outcomes, consistent
with my second hypothesis.
[Insert Table 8 Here]
(vi) Effect of Institutional Investors
Several studies suggest that institutional investors play a significant role as external corporate
governance (e.g., Bushee, 1998; Rajgopal et al., 1999; Aghion et al., 2013). Institutional
investors are likely to provide effective monitoring due to their large ownership stake in firms as
well as their ability in information processing and production. Previous research examines
25
whether corporate disclosures and corporate governance are substitutes or complements (e.g.,
Bushman and Smith, 2001; Beyer et al., 2010). In this section, I investigate how financial
reporting quality and institutional investors interact with each other in facilitating corporate
innovation.
To test this, I first add an interaction term between financial reporting quality and an
indicator variable (ABIOR) that takes the value of one if the firm’s institutional ownership is
above the annual median to the regressions. Panels A and B of Table 9 present the results. To
save space, I suppress the coefficients of all the control variables. In general, interactions
between ABIOR and accruals quality measures are significantly negative (Panel A). Interactions
between ABIOR and ABCFO or ABDEXP are significantly positive, while those between ABIOR
and ABPROD are significantly negative (Panel B). The results imply that institutions are
sophisticated users of financial reports who can amplify the effect of financial reporting quality
on innovation.
I then examine how this complement effect between financial reporting quality and
institutional ownership depends on the information environment of the firm. Firms with high
information asymmetry provide opportunities for profitable private information acquisition
activities. Barth et al. (2001) find that analysts have greater incentives to cover firms with more
information asymmetry. Similarly, institutional investors are less incentivized to search for
private information in monitoring managerial innovation activities when information asymmetry
is low. I classify sample firms into either a low information asymmetry group (below-annual
median) or a high information asymmetry group (above-annual median). I aggregate four
measures of information asymmetry, including bid-ask spread (e.g., Richardson, 2000), stock
turnover (e.g., Mohd, 2005), analyst following, and analyst forecast errors (e.g., Xiao, 2015) to
26
mitigate the random error component in individual measures. More specifically, I first rank firms
into deciles based on each information asymmetry measure and create a composite score
measure, which is computed as the average of ranked values of the four variables.14
I then rank
sample firms based on the composite measure and generate an indicator variable that takes the
value of one if the firm has information asymmetry above the annual median. Panel C of Table 9
reports the coefficients of interactions between financial reporting quality and ABIOR. In
general, the complement effect between financial reporting quality and institutional ownership is
more pronounced for firms with low information asymmetry except for DACCP.
[Insert Table 9 Here]
(vii) Alternative Measures of Innovative Success
While the number of patents and patent citations are used in the literature as proxies for
corporate innovation, I also adopt other approaches to examine the variations in success with
respect to innovation as a robustness check. First, patent approval time could be used as a
process-related measure of the success of innovation. Popp et al. (2003) find that firms with
more experience in the patent process have shorter approval time. Gans et al. (2008) suggest that
efficient technology transfer can be impeded when patent lags are too long. In particular, firms
that do not have past patenting experience are more likely to be affected by innovation risk and
information asymmetry in the patent process (Jia and Tian, 2015). Higher-quality financial
reporting can facilitate their patent processes by enhancing information transparency to outsiders
including patent examiners, which in turn may result in shorter approval time. I thus examine the
relation between financial reporting quality and the length of time in the patent process.
14
I multiply stock turnover and analyst following by minus one before ranking so that, they are increasing with
information asymmetry.
27
To test this, I measure the patent grant lag as the natural logarithm of one plus the number
of months between the application date and the grant date, averaged across all the patents applied
in the year.15
I regress the lag on financial reporting quality and an interaction between financial
reporting quality and an indicator variable (FIRST) that takes the value of one for a first-time
patent applicant (i.e., it has never filed a patent before the year).16
Since the patent grant lag
varies across sectors (Popp et al., 2003), I focus on innovation-intensive industries (electrical
equipment, measuring, analyzing, and controlling instruments, and commercial machinery).17
Table 10 presents the results. To save space, I suppress the coefficients of all the control
variables. In general, interactions between FIRST and DACC, DACCP, AQ, or ABPROD are
positive, while interactions between FIRST and ABCFO are negative. These results provide some
evidence that high-quality financial reporting enables first-time patent applicants to reduce their
patent approval time. In contrast, the estimated coefficients on accruals quality or RAM variables
are inconsistent with the prediction. It is likely that there are countering factors that influence
patent approval time for non-first-time patent applicants. For example, Johnson and Popp (2003)
suggest that more significant inventions take longer to go through the application process. Non-
first-time patent applicant firms with high-quality financial reporting could undertake significant
inventions due to reduced information asymmetry, which in turn may lead to longer patent
approval time.
[Insert Table 10 Here]
Next, financial reporting quality can affect the relationship between the innovation input
(R&D spending) and the innovation output (approved patents). Firms invest heavily in R&D
15
I obtain patent application date data from the Harvard Business School (HBS) Patent Network Dataverse. 16
To be consistent with prior tests, I examine the effect of a firm’s financial reporting quality in year t on its patent
lag in year t+3. 17
The furniture and fixtures industry is excluded in this test because it does not have any first-time patent applicant.
28
activities to promote technological advantages. Examining the relationship between the input and
the output is another way to assess the quality of innovation (e.g., Yanadori and Cui, 2013). I
thus include an interaction term between financial reporting quality and R&D spending (RD) in
the regressions. Table 11 presents the results.18
To save space, I suppress the coefficients of all
the control variables. Interactions between RD and DACC, DACCP, or AQ are negatively
significant, while interactions between RD and ABCFO are positively significant. The results
indicate that higher-quality financial reporting allows firms to translate R&D spending into
greater innovation outcome.
[Insert Table 11 Here]
Lastly, higher-quality financial reporting is likely to allow managers to undertake risky
but valuable innovation, which in turn may result in higher long-term firm value. I thus examine
how firms with high- and low-quality financial reporting differ in translating R&D spending into
long-term firm value. To test this, I split my sample firms into firms with high- (above-annual
median) and low-quality (below-median) financial reporting and examine the relation between
R&D spending and long-term firm value. The measure of firm value is the five-year moving
average of Tobin’s Q.19
Table 12 presents the results.20
To save space, I suppress the coefficients
of all the control variables. In general, the magnitudes of the coefficients on RD are larger for
firms with high-quality financial reporting than those for firms with low-quality financial
reporting. The results suggest that high-quality financial reporting enhances the success of
corporate R&D spending.
[Insert Table 12 Here]
18
I exclude the test for discretionary expenses in this section to avoid multicollinearity. 19
It is measured over the five years subsequent to patenting activities. 20
I exclude the test for discretionary expenses in this section to avoid multicollinearity. The sample size is reduced
to 31,404 firm-years for which future firm value data are available.
29
(viii) Alternative Measures of Real Activities Manipulation
In this section, I examine several alternative measures of RAM as a robustness check. First, I
decompose discretionary expenses into R&D and selling, general, and administrative (SG&A)
expenses, and estimate abnormal levels of these components (Gunny, 2010).21
Table 13 presents
the results. The coefficients on ABRD (abnormal R&D expenses) are positively significant
(columns 1 and 4), while the coefficients on ABSGA (abnormal SG&A expenses) are negatively
significant (columns 2 and 5). 22
To further understand the relation between abnormal SG&A
expenses and innovation, I split my sample firms into firms with high (above-annual median)
and low (below-annual median) abnormal R&D expenses and find that the relation between
abnormal SG&A expenses and innovation is driven mainly by firms with high abnormal R&D
expenses (untabulated). The results suggest that a reduction in SG&A expenses including
employee training, maintenance, and travel, which is a type of RAM, is less likely to negatively
affect corporate innovation and that the reduction can even foster innovation for firms that could
increase R&D expenses instead.
Second, I examine how a reduction in advertising intensity, another type of RAM, is
related to corporate innovation. Advertising intensity reflects a firm’s brand assets, a critical
intangible resource contributing to a firm’s market value (e.g., He and Wang, 2009). Managers
may give up or defer establishing brand assets to meet more immediate earnings targets in the
current period. I estimate abnormal levels of advertising expenses (ABADV) following
Roychodhury (2006). Column 3 of Table 13 provides evidence that a reduction in advertising
expenditures is associated with fewer patent applications.
21
See Appendix A for details. 22
The sample size is reduced to 26,921 firm-years for which independent variable data in the estimation model are
available.
30
[Insert Table 13 Here]
5. CONCLUSION
Prior research has suggested that high-quality financial reporting can improve investment
efficiency by reducing information asymmetry that gives rise to market frictions, such as adverse
selection and moral hazard. I extend this research by examining whether financial reporting
quality ultimately facilitates innovation, which is a main driver of economic growth.
Specifically, I test whether high-quality financial reporting is associated with greater firm
innovation and whether high-quality financial reporting allows firms with good opportunities to
transfer their innovation potential to innovation outcomes.
My results support the hypotheses. Financial reporting quality is positively related to firm
innovation. In addition, I find that the relation between financial reporting quality and innovation
is more pronounced for firms that have greater growth opportunities and those that operate in
innovation-intensive industries. The link between financial reporting and innovation is stronger
for firms with high institutional ownership, particularly when information asymmetry is low.
Overall, my findings are consistent with the idea that financial reporting quality serves a role in
reducing the information risk inherent in firms that undertake risky but value-enhancing projects,
which in turn promotes innovation.
Given the importance of innovation for firm- and national-level growth, it is paramount
to understand what determines innovation at the firm level. This paper attempts to do so by
studying the role financial reporting quality, which is a critical ingredient in financial markets,
plays in fostering firm innovation. My study offers additional insight into the real effects of
31
financial market development on the economy by showing an economic channel through which
well-functioning financial markets exert an influence on innovation.
32
APPENDIX A
Measurement of Financial Reporting Quality
Accruals Quality
I use three different proxies for accruals quality: (1) discretionary accruals from the Jones (1991)
model, (2) discretionary accruals from the modified Jones model augmented by including ROA
(Dechow et al., 1995; Kothari et al., 2005), and (3) standard deviations of discretionary accruals
from the Dechow and Dichev (2002) model augmented by the fundamental variables in the Jones
model (e.g., McNichols, 2002; Biddle et al., 2009).
Using the Jones model, I estimate accruals as a linear function of change in sales and
contemporaneous PPE:
it
it
it
it
it
itit
it
TA
PPE
TA
SALE
TATA
ACC
1
3
1
2
1
10
1
1 (4)
where itACC is earnings before extraordinary items minus cash flows from operations (Collins
and Hribar 1999); 1itTA is beginning total assets; 1 ititit SALESALESALE ; itSALE is net
sales; itPPE is gross property, plant, and equipment.
Eq. (4) is estimated cross-sectionally for each two-digit SIC-year with at least 15
observations. I use the absolute value of the estimated residual from the regression (DACC).
Second, following Kothari et al. (2005), I estimate accruals by using the following
modified Jones model augmented by including ROA:
itit
it
it
it
itit
itit
it ROATA
PPE
TA
RECSALE
TATA
ACC
4
1
3
1
2
1
10
1
1 (5)
where 1 ititit RECRECREC ; itREC is receivables; itROA is current return on assets.
33
Eq. (5) is estimated for each two-digit SIC-year with at least 15 observations. I use the
absolute value of the estimated residual from the regression (DACCP).
Lastly, I estimate accruals using the Dechow and Dichev model augmented by the
fundamental variables in the Jones model as suggested by McNichols (2002):
it
it
it
it
it
it
it
it
it
it
it
it
it
TA
PPE
TA
SALE
TA
CFO
TA
CFO
TA
CFO
TA
ACC
1
5
1
4
1
1
3
1
2
1
1
10
1
(6)
where itCFO is cash flows from operations.
Eq. (6) is estimated for each two-digit SIC-year with at least 15 observations. I use the
standard deviation of the estimated residuals from the regression during the years of t-4 to t
(AQ).
A lower level of DACC, DACCP, or AQ implies higher financial reporting quality.
Real Activities Manipulation
I follow Roychowdhury (2006) in measuring RAM: (1) abnormal cash flows from operations, (2)
abnormal production costs, and (3) abnormal discretionary expenses. Subsequent studies provide
evidence of the construct validity of these proxies (e.g., Cohen et al., 2008; Cohen and Zarowin,
2010; Zang, 2012; Kim et al., 2012; Kim and Park, 2014).
I model cash flows from operations as a linear function of contemporaneous sales and
change in sales:
it
it
it
it
it
itit
it
TA
SALE
TA
SALE
TATA
CFO
1
3
1
2
1
10
1
1 (7)
Eq. (7) is estimated for each two-digit SIC-year with at least 15 observations. The
abnormal level of cash flows from operations (ABCFO) is the estimated residual from the
regression.
34
Production costs (PROD) are defined as the sum of COGS and change in inventory
during the year. I model COGS as a linear function of contemporaneous sales:
it
it
it
itit
it
TA
SALE
TATA
COGS
1
2
1
10
1
1 (8)
where itCOGS is cost of goods sold.
Next, I model inventory growth as a linear function of contemporaneous and lagged
change in sales.
it
it
it
it
it
itit
it
TA
SALE
TA
SALE
TATA
INV
1
1
3
1
2
1
10
1
1 (9)
where 1 ititit INVINVINV ; itINV is inventory; 211 ititit SALESALESALE .
Using Equations (8) and (9), I estimate production costs from the following regression:
it
it
it
it
it
it
it
itit
it
TA
SALE
TA
SALE
TA
SALE
TATA
PROD
1
1
4
1
3
1
2
1
10
1
1 (10)
Eq. (10) is estimated for each two-digit SIC-year with at least 15 observations. The
abnormal level of production costs (ABPROD) is the estimated residual from the regression.
Discretionary expenses (DEXP) are defined as the sum of R&D, advertising, and SG&A
expenses. I model discretionary expenses as a function of lagged sales because modeling
discretionary expenses as a function of contemporaneous sales creates a mechanical problem
(Roychowdhury, 2006).23
it
it
it
itit
it
TA
SALE
TATA
DEXP
1
12
1
10
1
1 (11)
where itDEXP is the sum of R&D, advertising, and SG&A expenses.
23
If a firm manages sales upward in the current year, it results in abnormally low residual in the model with current
sales.
35
Eq. (11) is estimated for each two-digit SIC-year with at least 15 observations. The
abnormal level of discretionary expenses (ABDEXP) is the estimated residual from the
regression.
A lower (higher) level of ABPROD (ABCFO or ABDEXP) implies higher financial
reporting quality.
Next, I decompose discretionary expenses into R&D and SG&A expenses. Following
Gunny (2010), I estimate R&D expenses by using the following model:
it
it
it
it
it
itit
itit
it
TA
RD
TA
INTFTOBINQSIZE
TATA
RD
1
1
5
1
432
1
10
1
1 (12)
where itINTF is internal funds, measured as the sum of income before extraordinary items, R&D
expenses, and depreciation and amortization.
Eq. (12) is estimated cross-sectionally for each two-digit SIC-year with at least 15
observations. The abnormal level of R&D expenses (ABRD) is the estimated residual from the
regression.
I estimate SG&A expenses by using the following model (Gunny, 2010):
it
it
it
it
it
it
ititit
itit
it
DDTA
SALE
TA
SALE
TA
INTFTOBINQSIZE
TATA
SGA
1
6
1
5
1
432
1
10
1
1
(13)
where itDD is an indicator variable that takes the value of one if the firm’s total sales decreases
between year t-1 to year t, and zero otherwise.
Eq. (13) is estimated cross-sectionally for each two-digit SIC-year with at least 15
observations. The abnormal level of SG&A expenses (ABSGA) is the estimated residual from the
regression.
I estimate advertising expenses by using the following model:
36
it
it
it
itit
it
TA
SALE
TATA
ADV
1
1
2
1
10
1
1 (14)
where itADV is advertising expenses.
Eq. (14) is estimated cross-sectionally for each two-digit SIC-year with at least 15
observations. The abnormal level of advertising expenses (ABADV) is the estimated residual
from the regression.
A higher level of ABRD, ABSGA or ABADV implies higher financial reporting quality.
37
APPENDIX B
Variable Definitions
Variable
Definition
Innovation Measures
LnPAT
Natural logarithm of one plus the total number of patents filed (and
eventually granted).
LnCITE
Natural logarithm of one plus the total number of citations received on the
firm's patents filed (and eventually granted) scaled by the number of the
patents filed (and eventually granted).
LnLAG
Natural logarithm of one plus the number of months between the application
date and the grant date, averaged across all the patents applied in the year.
AvgTOBINQ
Five-year moving average of Tobin’s Q, measured over the five years
subsequent to patenting activities.
Financial Reporting Quality Measures
DACC
Absolute abnormal accruals computed as the difference between a company's
total accruals and its normal accruals estimated by the Jones (1991) model.
DACCP
Absolute abnormal accruals computed as the difference between a company's
total accruals and its normal accruals estimated by the augmented modified
Jones model (Kothari et al., 2005).
AQ
Standard deviation of the estimated residuals from the Dechow and Dichev
(2002) model augmented by the fundamental variables in the Jones model as
suggested by McNichols (2002), calculated over the period t-4 to t.
ABCFO
Abnormal cash flows from operations computed as the estimated residual
from the Roychowdhury (2006) model.
ABPROD
Abnormal production costs computed as the estimated residual from the
Roychowdhury (2006) model.
ABDEXP
Abnormal discretionary expenses computed as the estimated residual from
the Roychowdhury (2006) model.
ABRD
Abnormal R&D expenses computed as the estimated residual from the
Gunny (2010) model.
ABSGA
Abnormal SG&A expenses computed as the estimated residual from the
Gunny (2010) model.
ABADV
Abnormal advertising expenses computed as the estimated residual from the
Roychowdhury (2006) model.
Control Variables
ROA
Return on assets, defined as operating income before depreciation divided by
lagged total assets.
RD
R&D expenses divided by lagged total assets.
PPE
Property, plant, equipment divided by lagged total assets.
38
APPENDIX B
Continued
LEV
Total debt divided by (book value of total assets minus book value of equity
plus market value of equity).
CAPEX
Capital expenditures divided by lagged total assets.
TOBINQ
Market-to-book ratio, calculated as (market value of equity plus book value of
assets minus book value of equity minus balance sheet deferred taxes) divided
by book value of assets.
KZ
Kaplan and Zingales index, measured as -1.002*cash flow plus 0.283*Tobin's
Q plus 3.139*leverage minus 39.368*dividends minus 1.315*cash holdings.
HHI
Hirfindahl index, defined as the sum of squared market shares for each four-
digit SIC-year group. Market shares are computed based on the firms’ sales
from Compustat.
LnSALE
Natural logarithm of sales.
AGE
Age of the firm, approximated by the number of years listed on Compustat.
IOR
Institutional holdings, calculated as the mean quarterly institutional holdings
during the year divided by the total number of shares outstanding.
TRAM
Total RAM calculated as ABPROD minus the sum of ABCFO and ABDEXP.
39
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44
Table 1
Descriptive Statistics
Panel A: Summary Statistics
Variable N Mean Median Standard
deviation
25th
percentile
75th
percentile
Dependent Variables
PAT 39,615 6.051 0.000 27.378 0.000 0.000
CITE 39,615 2.757 0.000 7.886 0.000 0.000
Variables of Interests
DACC 39,615 0.116 0.064 0.202 0.027 0.130
DACCP 39,615 0.087 0.056 0.100 0.024 0.111
AQ 39,615 0.136 0.069 0.218 0.036 0.142
ABCFO 39,615 0.015 0.025 0.178 -0.050 0.105
ABPROD 39,615 -0.005 -0.004 0.248 -0.132 0.115
ABDEXP 39,615 -0.020 -0.052 0.314 -0.196 0.092
Control Variables
ROA 39,615 0.082 0.121 0.232 0.032 0.196
RD 39,615 0.061 0.006 0.118 0.000 0.072
PPE 39,615 0.303 0.237 0.251 0.116 0.414
LEV 39,615 0.365 0.327 0.244 0.159 0.542
CAPEX 39,615 0.069 0.044 0.080 0.022 0.084
TOBINQ 39,615 2.006 1.403 1.843 1.037 2.179
KZ 39,615 -4.024 -0.371 15.370 -3.684 1.168
HHI 39,615 0.225 0.178 0.165 0.101 0.294
lnSALE 39,615 4.877 4.875 2.218 3.344 6.407
AGE 39,615 10.315 9.000 3.821 7.000 13.000
IOR 39,615 0.337 0.297 0.264 0.090 0.556
45
Table 1
Continued
Panel B: Correlations
Variable 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18.
1. DACC
2. DACCP 0.49
3. AQ 0.50 0.43
4. ABCFO -0.14 -0.24 -0.14
5. ABPROD 0.04 0.05 0.02 -0.38
6. ABDEXP 0.10 0.23 0.14 -0.35 -0.46
7. PAT -0.04 -0.03 -0.05 0.11 -0.07 -0.02
8. CITE -0.06 -0.02 -0.08 0.04 -0.06 0.05 0.27
9. ROA -0.22 -0.30 -0.31 0.59 -0.31 -0.22 0.10 0.08
10. RD 0.19 0.36 0.29 -0.26 -0.00 0.43 0.05 0.12 -0.50
11. PPE -0.07 -0.11 -0.14 0.06 0.03 -0.04 0.01 -0.02 0.23 -0.21
12. LEV -0.04 -0.16 -0.13 -0.10 0.19 -0.16 -0.05 -0.11 0.01 -0.34 0.12
13. CAPEX 0.02 0.05 -0.02 0.05 -0.03 0.08 0.03 0.04 0.17 -0.02 0.68 -0.11
14. TOBINQ 0.15 0.32 0.26 -0.12 -0.08 0.26 0.07 0.08 -0.24 0.46 -0.11 -0.51 0.08
15. KZ -0.08 -0.15 -0.14 -0.02 0.04 -0.07 0.01 0.00 0.08 -0.19 0.22 0.26 0.08 -0.17
16. HHI -0.02 -0.04 -0.06 0.00 0.04 -0.05 0.01 -0.01 0.05 -0.14 -0.08 0.07 -0.08 -0.08 0.01
17. lnSALE -0.18 -0.28 -0.24 0.23 -0.06 -0.13 0.31 0.11 0.50 -0.33 0.16 0.22 0.04 -0.20 0.14 0.04
18. AGE -0.01 -0.06 0.01 0.11 0.00 -0.10 0.06 -0.13 0.09 -0.10 0.01 0.04 -0.06 -0.06 0.01 0.06 0.24
19. IOR -0.12 -0.17 -0.13 0.24 -0.09 -0.10 0.21 0.12 0.32 -0.09 0.10 -0.10 0.09 -0.00 0.02 -0.02 0.68 0.20
Notes:
This table presents descriptive statistics for variables used in the full sample. Utilities and financial
industries (SIC 4400-5000 and 6000-6999, respectively) are excluded. The sample period is from 1990 to
2003. All continuous variables are winsorized at the 1st and 99th percentiles. Variables are defined in
Appendix B. In panel B, bold amounts represent correlations significant at the 0.05 level.
46
Table 2
Accruals Quality and Firm Innovation
Dependent
Variable: LnPAT
LnCITE
(1) (2) (3) (4) (5) (6)
DACC -0.098***
-0.105***
(-3.68)
(-4.00)
DACCP
-0.202***
-0.208***
(-3.23)
(-3.75)
AQ
-0.074**
-0.021
(-2.23)
(-0.78)
ROA -0.418*** -0.419*** -0.421***
-0.114*** -0.114*** -0.108**
(-7.76) (-7.74) (-7.73)
(-2.72) (-2.73) (-2.56)
RD 0.908*** 0.924*** 0.905***
0.978*** 0.994*** 0.968***
(8.52) (8.62) (8.49)
(10.64) (10.74) (10.50)
PPE 0.045 0.043 0.043
0.023 0.021 0.022***
(0.73) (0.70) (0.71)
(0.51) (0.47) (0.48)
LEV -0.449*** -0.450*** -0.452***
-0.342*** -0.343*** -0.347***
(-9.01) (-9.03) (-9.08)
(-9.73) (-9.78) (-9.89)
CAPEX 0.105 0.118 0.097
0.279*** 0.292*** 0.263**
(0.87) (0.98) (0.80)
(2.70) (2.83) (2.55)
TOBINQ 0.041*** 0.042*** 0.041***
0.021*** 0.022*** 0.021***
(6.71) (6.91) (6.75)
(4.48) (4.73) (4.35)
KZ -0.001 -0.001 -0.001
-0.001 -0.001 -0.001
(-1.30) (-1.38) (-1.30)
(-1.49) (-1.58) (-1.38)
HHI 0.186** 0.187** 0.185**
0.106** 0.106** 0.105**
(2.42) (2.43) (2.41)
(2.00) (2.01) (1.98)
LnSALE 0.293*** 0.293*** 0.294***
0.157*** 0.157*** 0.157***
(19.72) (19.68) (19.73)
(22.80) (22.77) (22.85)
AGE 0.005* 0.005* 0.005*
-0.005** -0.005** -0.005**
(1.94) (1.95) (1.89)
(-2.49) (-2.45) (-2.44)
IOR -0.070 -0.071 -0.071
0.122*** 0.121*** 0.125***
(-1.04) (-1.06) (-1.06)
(2.82) (2.79) (2.89)
TRM -0.083*** -0.083*** -0.084***
-0.048*** -0.048*** -0.048***
(-4.43) (-4.46) (-4.48)
(-3.06) (-3.08) (-3.06)
N 39,615 39,615 39,615
39,615 39,615 39,615
Adjusted R² 0.349 0.349 0.349 0.276 0.276 0.275
Notes:
This table presents the OLS regressions of innovation on accruals quality. The dependent variables are
LnPAT in columns 1-3 and LnCITE in columns 4-6, respectively. Accruals quality is measured by DACC,
DACCP, and AQ in columns 1/4, 2/5, and 3/6, respectively. Other variables are defined in Appendix B.
All regressions include year and industry fixed effects and all statistics and significance levels are based
on standard errors adjusted for firm clustering. ***, **, and * indicate significance at the 1%, 5%, and
10% level, respectively.
47
Table 3
Real Activities Manipulation and Firm Innovation
Dependent
Variable: LnPAT
LnCITE
(1) (2) (3) (4) (5) (6)
ABCFO 0.729***
0.231***
(12.59)
(5.44)
ABPROD
-0.257***
-0.136***
(-6.54)
(-4.35)
ABDEXP
0.022
0.117***
(0.74)
(4.55)
ROA -0.686*** -0.467*** -0.530***
-0.178*** -0.136*** -0.237***
(-11.21) (-8.39) (-9.98)
(-3.98) (-3.21) (-5.94)
RD 1.134*** 0.965***
1.083*** 1.014***
(10.81) (9.19)
(11.69) (11.12)
PPE 0.024 0.059 0.007
0.011 0.029 0.009
(0.40) (0.95) (0.12)
(0.24) (0.65) (0.21)
LEV -0.446*** -0.443*** -0.521***
-0.346*** -0.340*** -0.390***
(-9.00) (-8.92) (-10.26)
(-9.89) (-9.68) (-10.96)
CAPEX 0.159 0.128 0.269**
0.305*** 0.293*** 0.377***
(1.32) (1.07) (2.17)
(2.94) (2.83) (3.58)
TOBINQ 0.045*** 0.040*** 0.055***
0.023*** 0.021*** 0.031***
(7.52) (6.60) (8.77)
(4.89) (4.40) (6.61)
KZ 0.000 -0.001 -0.001**
0.000 -0.001 -0.001**
(-1.00) (-1.45) (-2.10)
(-1.37) (-1.58) (-2.36)
HHI 0.195** 0.193** 0.120
0.107** 0.109** 0.051
(2.56) (2.51) (1.55)
(2.02) (2.06) (0.95)
LnSALE 0.302*** 0.296*** 0.290***
0.160*** 0.159*** 0.153***
(20.19) (19.82) (19.41)
(23.13) (22.93) (21.97)
AGE 0.004 0.006** 0.004
-0.005*** -0.005** -0.006***
(1.59) (2.01) (1.27)
(-2.71) (-2.44) (-3.14)
IOR -0.138** -0.082 -0.042
0.099** 0.115*** 0.164***
(-2.03) (-1.22) (-0.62)
(2.28) (2.67) (3.72)
DACC -0.058** -0.092*** -0.072***
-0.092*** -0.102*** -0.090***
(-2.21) (-3.48) (-2.77)
(-3.52) (-3.89) (-3.53)
N 39,615 39,615 39,615
39,615 39,615 39,615
Adjusted R² 0.355 0.350 0.342 0.276 0.276 0.269
Notes:
This table presents the OLS regressions of innovation on RAM. The dependent variables are LnPAT in
columns 1-3 and LnCITE in columns 4-6, respectively. RAM is measured by ABCFO, ABPROD, and
ABDEXP in columns 1/4, 2/5, and 3/6, respectively. Other variables are defined in Appendix B. Since
discretionary expenses include R&D expenditures, the R&D control is excluded in the regressions that
use ABDEXP as a proxy for financial reporting quality to avoid multicollinearity. All regressions include
year and industry fixed effects and all statistics and significance levels are based on standard errors
adjusted for firm clustering. ***, **, and * indicate significance at the 1%, 5%, and 10% level,
respectively.
48
Table 4
Heckman Two-Stage Model
Panel A. First-Stage Regression - Decision to Manipulate Reported Earnings
suspect
HabitBeater
0.044***
(6.50)
Anal
0.026*
(1.89)
Share
-0.053***
(-4.70)
StockIssue
-0.084***
(-5.45)
LEV
0.231***
(5.84)
ROA
0.931***
(17.40)
SIZE
0.091***
(10.37)
MTB
-0.018***
(-8.40)
N
39,429
Pseudo R² 0.0578
49
Table 4
Continued
Panel B. Second-Stage Model - Effect of Accruals Quality on Innovation
Dependent
Variable: LnPAT
LnCITE
(1) (2) (3) (4) (5) (6)
DACC -0.087
-0.102*
(-1.54)
(-1.92)
DACCP
-0.311*
-0.359**
(-1.87)
(-2.50)
AQ
-0.169**
-0.033
(-2.41)
(-0.58)
ROA -0.708*** -0.694*** -0.722***
-0.094 -0.078 -0.104
(-3.58) (-3.50) (-3.64)
(-0.62) (-0.51) (-0.68)
RD 1.839*** 1.870*** 1.880***
1.587*** 1.623*** 1.585***
(6.06) (6.14) (6.24)
(5.95) (6.06) (5.91)
PPE 0.173 0.166 0.174
0.132 0.124 0.130
(1.47) (1.41) (1.47)
(1.62) (1.53) (1.60)
LEV -0.282*** -0.281*** -0.282***
-0.287*** -0.286*** -0.288***
(-2.83) (-2.82) (-2.83)
(-4.01) (-4.00) (-4.01)
CAPEX 0.446* 0.474** 0.444*
0.446** 0.479** 0.437**
(1.85) (1.97) (1.84)
(2.07) (2.23) (2.02)
TOBINQ 0.049*** 0.050*** 0.050***
0.014 0.015 0.014
(3.50) (3.58) (3.61)
(1.20) (1.35) (1.23)
KZ -0.001 -0.001 -0.001
0.000 0.000 0.000
(-1.13) (-1.17) (-1.22)
(-0.28) (-0.34) (-0.26)
HHI 0.272** 0.273** 0.272**
0.160* 0.161* 0.160*
(2.13) (2.14) (2.12)
(1.77) (1.79) (1.78)
LnSALE 0.336*** 0.336*** 0.336***
0.156*** 0.156*** 0.156***
(15.01) (14.98) (15.00)
(12.56) (12.55) (12.52)
AGE 0.008* 0.008* 0.008*
-0.003 -0.003 -0.003
(1.71) (1.69) (1.68)
(-1.07) (-1.09) (-1.02)
IOR -0.347*** -0.347*** -0.351***
0.007 0.007 0.007
(-3.43) (-3.43) (-3.46)
(0.10) (0.10) (0.10)
TRM -0.190*** -0.190*** -0.192***
-0.106*** -0.106*** -0.107***
(-4.95) (-4.94) (-5.01)
(-3.26) (-3.26) (-3.31)
IMR -0.374** -0.359** -0.371**
-0.246* -0.229* -0.253*
(-2.10) (-2.00) (-2.08)
(-1.87) (-1.73) (-1.91)
N 7,948 7,948 7,948
7,948 7,948 7,948
Adjusted R² 0.412 0.412 0.412 0.323 0.323 0.323
50
Table 4
Continued
Panel C. Second-Stage Model - Effect of RAM on Innovation
Dependent
Variable: LnPAT
LnCITE
(1) (2) (3) (4) (5) (6)
ABCFO 1.027***
0.193*
(8.34)
(1.84)
ABPROD
-0.496***
-0.239***
(-6.54)
(-3.86)
ABDEXP
0.213***
0.285***
(3.28)
(5.21)
ROA -0.745*** -0.767*** -0.822***
-0.001 -0.102 -0.265*
(-3.87) (-3.89) (-4.41)
(-0.01) (-0.67) (-1.92)
RD 2.378*** 1.934***
1.876*** 1.671***
(8.24) (6.65)
(7.35) (6.48)
PPE 0.097 0.177 0.124
0.101 0.130 0.125
(0.83) (1.50) (1.05)
(1.24) (1.60) (1.54)
LEV -0.292*** -0.274*** -0.407***
-0.309*** -0.287*** -0.350***
(-2.95) (-2.76) (-4.03)
(-4.32) (-4.02) (-4.87)
CAPEX 0.456* 0.494** 0.657***
0.458** 0.471** 0.566***
(1.89) (2.05) (2.64)
(2.12) (2.18) (2.60)
TOBINQ 0.052*** 0.048*** 0.077***
0.015 0.013 0.031***
(3.83) (3.44) (5.37)
(1.36) (1.17) (2.84)
KZ -0.001 -0.001 -0.001
0.000 0.000 0.000
(-0.83) (-1.13) (-0.92)
(-0.18) (-0.26) (-0.20)
HHI 0.273** 0.281** 0.185
0.155* 0.163* 0.103
(2.14) (2.20) (1.43)
(1.72) (1.80) (1.14)
LnSALE 0.345*** 0.340*** 0.333***
0.157*** 0.158*** 0.152***
(15.40) (15.17) (14.62)
(12.63) (12.72) (12.12)
AGE 0.007 0.008* 0.006
-0.004 -0.003 -0.005
(1.51) (1.71) (1.22)
(-1.15) (-1.08) (-1.47)
IOR -0.374*** -0.357*** -0.292***
-0.002 0.001 0.054
(-3.69) (-3.54) (-2.85)
(-0.02) (0.02) (0.77)
DACC -0.053 -0.095* -0.080
-0.100* -0.107** -0.096*
(-0.96) (-1.68) (-1.43)
(-1.92) (-2.01) (-1.80)
IMR -0.201 -0.352** -0.305*
-0.209 -0.234* -0.236*
(-1.15) (-1.99) (-1.69)
(-1.59) (-1.79) (-1.79)
N 7,948 7,948 7,948
7,948 7,948 7,948
Adjusted R² 0.416 0.414 0.400 0.322 0.324 0.317
51
Notes:
This table presents the results of the Heckman two-stage model. Panel A reports probit regression
estimates of the first-stage. The dependent variable is an indicator variable for the suspect firm-year that
takes the value of one if the firm’s earnings before extraordinary items divided by lagged total assets are
between 0 and 0.01, its change in earnings before extraordinary items divided by lagged total assets is
between 0 and 0.01, or its actual earnings minus the most recent analysts’ consensus forecast prior to
earnings announcement are between 0 and 0.01, and zero otherwise. HabitBeater is the number of times
analysts’ forecast consensus had been met or beaten over the past four quarters. Anal is the number of
analysts following the firm during the year. Share is the natural logarithm of the number of shares
outstanding. StockIssue is an indicator variable that takes the value of one if the company’s net proceeds
from equity financing is positive in the following year, and zero otherwise. LEV is the leverage ratio. ROA
is return on assets. SIZE is the natural logarithm of market capitalization. MTB is the market to book ratio.
Panels B and C report OLS regression estimates of the second-stage. The dependent variables are LnPAT
in columns 1-3 and LnCITE in columns 4-6, respectively. In Panel B, accruals quality is measured by
DACC, DACCP, and AQ in columns 1/4, 2/5, and 3/6, respectively. In Panel C, RAM is measured by
ABCFO, ABPROD, and ABDEXP in columns 1/4, 2/5, and 3/6, respectively. IMR is the inverse Mills
ratio computed from the first-stage probit regression. Other variables are defined in Appendix B. All
regressions include year and industry fixed effects and all statistics and significance levels are based on
standard errors adjusted for firm clustering. ***, **, and * indicate significance at the 1%, 5%, and 10%
level, respectively.
52
Table 5
Second-Stage of Two-Stage Least Squares Regression
Panel A. Accruals Quality
Dependent
Variable: LnPAT
LnCITE
(1) (2) (3) (4) (5) (6)
DACC -1.838***
-9.087***
(-4.44)
(-10.72)
DACCP
-11.942***
-59.035***
(-3.55)
(-4.96)
AQ
-0.930***
-4.596***
(-4.64)
(-15.12)
ROA -0.793*** -1.220*** -0.775***
-1.210*** -3.317*** -1.118***
(-10.06) (-5.83) (-10.63)
(-7.31) (-4.25) (-10.19)
RD 1.262*** 2.647*** 1.140***
2.517*** 9.363*** 1.914***
(9.68) (5.46) (9.60)
(8.69) (5.32) (9.89)
PPE 0.022 -0.033 0.002
0.012 -0.260 -0.088
(0.33) (-0.39) (0.03)
(0.10) (-0.93) (-1.14)
LEV -0.346*** -0.188* -0.433***
0.132 0.915*** -0.294***
(-5.95) (-1.91) (-8.25)
(1.41) (2.76) (-4.92)
CAPEX 0.413*** 1.875*** 0.169
2.075*** 9.303*** 0.869***
(2.78) (3.48) (1.36)
(7.10) (4.85) (5.28)
TOBINQ 0.047*** 0.145*** 0.045***
0.105*** 0.587*** 0.094***
(6.50) (4.40) (6.63)
(7.03) (4.98) (8.82)
KZ -0.001 -0.005*** 0.000
-0.004*** -0.024*** -0.002***
(-1.13) (-2.89) (-0.71)
(-3.00) (-3.93) (-2.69)
HHI 0.164** 0.225** 0.148*
0.063 0.363 -0.014
(2.06) (2.44) (1.91)
(0.66) (1.57) (-0.18)
LnSALE 0.319*** 0.288*** 0.323***
0.183*** 0.030 0.206***
(21.03) (15.80) (21.14)
(15.15) (0.64) (19.35)
AGE 0.003 0.005 0.002
-0.003 0.008 -0.007*
(1.01) (1.30) (0.79)
(-0.67) (0.61) (-1.84)
IOR -0.087 -0.323*** -0.069
-0.092 -1.255*** -0.001
(-1.18) (-2.80) (-0.94)
(-1.08) (-3.63) (-0.01)
TRM -0.100*** -0.130*** -0.109***
-0.127*** -0.273** -0.170***
(-4.72) (-4.10) (-5.27)
(-3.07) (-2.18) (-5.97)
N 39,615 39,615 39,615 39,615 39,615 39,615
53
Table 5
Continued
Panel B. Real Activities Manipulation
Dependent
Variable: LnPAT
LnCITE
(1) (2) (3) (4) (5) (6)
ABCFO 4.423***
2.778***
(7.23)
(5.13)
ABPROD
-2.320***
-1.457***
(-6.97)
(-5.10)
ABDEXP
-7.575***
-4.093**
(-3.06)
(-2.54)
ROA -2.679*** -1.735*** -2.946***
-1.605*** -1.012*** -1.739***
(-8.76) (-9.31) (-4.05)
(-6.00) (-6.44) (-3.68)
RD 1.793*** 0.551***
1.927*** 1.147***
(12.45) (3.12)
(13.69) (7.58)
PPE -0.005 0.311*** -1.223***
-0.040 0.159** -0.715***
(-0.07) (3.66) (-3.00)
(-0.67) (2.24) (-2.69)
LEV -0.360*** -0.264*** -1.905***
-0.387*** -0.327*** -1.283***
(-6.33) (-4.03) (-4.20)
(-8.00) (-6.07) (-4.35)
CAPEX 0.243* 0.025 4.564***
0.304** 0.167 2.752***
(1.65) (0.18) (3.21)
(2.25) (1.29) (2.98)
TOBINQ 0.053*** 0.015** 0.293***
0.037*** 0.014** 0.177***
(7.70) (2.02) (3.59)
(5.62) (2.02) (3.34)
KZ 0.001* -0.001 -0.003
0.001** 0.000 -0.001
(1.79) (-0.91) (-1.44)
(2.50) (0.68) (-0.93)
HHI 0.234*** 0.259*** -0.633**
0.072 0.088 -0.458**
(2.95) (3.14) (-2.24)
(1.07) (1.29) (-2.56)
LnSALE 0.377*** 0.354*** 0.413***
0.235*** 0.221*** 0.248***
(21.87) (21.65) (10.82)
(22.09) (23.21) (10.70)
AGE 0.004 0.013*** -0.029**
-0.004 0.002 -0.023**
(1.36) (3.64) (-2.00)
(-1.20) (0.67) (-2.44)
IOR -0.382*** -0.064 -0.942***
0.086 0.285*** -0.157
(-4.20) (-0.85) (-2.75)
(1.18) (4.86) (-0.71)
DACC 0.142*** -0.055 0.799***
0.017 -0.107*** 0.374*
(2.77) (-1.40) (2.61)
(0.37) (-2.98) (1.91)
N 39,615 39,615 39,615 39,615 39,615 39,615
54
Notes:
This table presents the results of the second-stage of two-stage least squares (2SLS) regressions. The
dependent variables are contemporaneous LnPAT in columns 1-3 and contemporaneous LnCITE in
columns 4-6, respectively. In Panel A, accruals quality is measured by DACC, DACCP, and AQ in
columns 1/4, 2/5, and 3/6, respectively. In Panel B, RAM is measured by ABCFO, ABPROD, and
ABDEXP in columns 1/4, 2/5, and 3/6, respectively. All the financial reporting quality variables are the
predicted values computed from the first-stage. The instrument for the endogeneous accruals quality
variable is the post-SAB101 period, which is an indicator variable that takes the value of one after the
enactment of SAB 101, and the instrument for the endogeneous RAM is the operating cycle, which is an
indicator variable that takes the value of one if the firm’s operating cycle at the beginning of the year is
above the annual median. Other variables are defined in Appendix B. All regressions include year and
industry fixed effects and all statistics and significance levels are based on standard errors adjusted for
firm clustering. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively.
55
Table 6
Financial Reporting Quality, Firm Growth Opportunities, and Innovation
Panel A. Accruals Quality
Dependent Variable: LnPAT
LnCITE
(1) (2) (3)
(4) (5) (6)
FRQ = DACC DACCP AQ DACC DACCP AQ
FRQ -0.012 -0.063 0.067*
-0.006 -0.107 0.116***
(-0.38) (-0.84) (1.69)
(-0.19) (-1.44) (3.35)
FRQ×ABMTB -0.183*** -0.246** -0.242***
-0.209*** -0.171* -0.233***
(-3.53) (-2.25) (-4.87)
(-5.45) (-1.74) (-5.96)
ABMTB 0.083*** 0.083*** 0.094***
0.025 0.016 0.031*
(4.06) (3.61) (4.37)
(1.53) (0.87) (1.83)
Control Y Y Y
Y Y Y
N 39,615 39,615 39,615
39,615 39,615 39,615
Adjusted R² 0.350 0.350 0.350 0.276 0.276 0.276
Panel B. Real Activities Manipulation
Dependent Variable: LnPAT
LnCITE
(1) (2) (3)
(4) (5) (6)
FRQ = ABCFO ABPROD ABDEXP ABCFO ABPROD ABDEXP
FRQ 0.462*** -0.180*** 0.083**
0.108** -0.076** 0.130***
(8.12) (-4.56) (2.53)
(2.13) (-1.98) (4.12)
FRQ×ABMTB 0.439*** -0.122** -0.104**
0.198*** -0.099** -0.022
(6.01) (-2.22) (-2.44)
(3.26) (-1.99) (-0.58)
ABMTB 0.071*** 0.063*** 0.067***
0.004 0.002 0.004
(3.86) (3.43) (3.63)
(0.27) (0.14) (0.30)
Control Y Y Y
Y Y Y
N 39,615 39,615 39,615
39,615 39,615 39,615
Adjusted R² 0.357 0.351 0.343 0.276 0.276 0.269
Notes:
This table presents the OLS regressions of innovation on financial reporting quality for firms with
different firm growth opportunities. The dependent variables are LnPAT in columns 1-3 and LnCITE in
columns 4-6, respectively. In Panel A, accruals quality is measured by DACC, DACCP, and AQ in
columns 1/4, 2/5, and 3/6, respectively. In Panel B, RAM is measured by ABCFO, ABPROD, and
ABDEXP in columns 1/4, 2/5, and 3/6, respectively. ABMTB is an indicator variable that takes the value
of one if the firm has the MTB above the annual median, and zero otherwise. To save space, I suppress
the coefficients of all the control variables. All regressions include year and industry fixed effects and all
statistics and significance levels are based on standard errors adjusted for firm clustering. ***, **, and *
indicate significance at the 1%, 5%, and 10% level, respectively.
56
Table 7
Financial Reporting Quality, Industry Growth Opportunities, and Innovation
Panel A. Accruals Quality
Dependent Variable: LnPAT
LnCITE
(1) (2) (3)
(4) (5) (6)
FRQ = DACC DACCP AQ DACC DACCP AQ
FRQ -0.032 0.015 0.084**
-0.079 -0.069 0.091**
(-0.68) (0.21) (2.11)
(-1.58) (-0.92) (2.49)
FRQ ×ABIPE -0.099** -0.407*** -0.299***
-0.045 -0.269*** -0.230***
(-2.05) (-4.00) (-7.38)
(-0.90) (-2.81) (-6.48)
ABIPE 0.035*** 0.059*** 0.061***
0.006 0.024* 0.030***
(3.49) (5.04) (6.28)
(0.57) (1.92) (2.73)
Control Y Y Y
Y Y Y
N 39,615 39,615 39,615
39,615 39,615 39,615
Adjusted R² 0.350 0.350 0.350 0.271 0.271 0.271
Panel B. Real Activities Manipulation
Dependent Variable: LnPAT
LnCITE
(1) (2) (3)
(4) (5) (6)
FRQ = ABCFO ABPROD ABDEXP ABCFO ABPROD ABDEXP
FRQ 0.589*** -0.222*** -0.014
0.155*** -0.109*** 0.073**
(9.10) (-5.13) (-0.47)
(2.88) (-2.89) (2.56)
FRQ×ABIPE 0.354*** -0.099** 0.046
0.206*** -0.060 0.057*
(5.88) (-2.38) (1.45)
(3.59) (-1.49) (1.74)
ABIPE 0.017** 0.023*** 0.025***
-0.002 0.001 0.004
(2.07) (2.89) (3.09)
(-0.26) (0.10) (0.38)
Control Y Y Y
Y Y Y
N 39,615 39,615 39,615
39,615 39,615 39,615
Adjusted R² 0.358 0.351 0.342 0.272 0.272 0.264
Notes:
This table presents the OLS regressions of innovation on financial reporting quality for firms in industries
with different growth opportunities. The dependent variables are LnPAT in columns 1-3 and LnCITE in
columns 4-6, respectively. In Panel A, accruals quality is measured by DACC, DACCP, and AQ in
columns 1/4, 2/5, and 3/6, respectively. In Panel B, RAM is measured by ABCFO, ABPROD, and
ABDEXP in columns 1/4, 2/5, and 3/6, respectively. ABIPE is an indicator variable that takes the value of
one if the firm has the industry PE ratio above the annual median, and zero otherwise. To save space, I
suppress the coefficients of all the control variables. All regressions include year and industry fixed
effects and all statistics and significance levels are based on standard errors adjusted for firm clustering.
***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively.
57
TABLE 8
Financial Reporting Quality, Industry Innovativeness, and Innovation
Panel A. Accruals Quality
Dependent Variable: LnPAT
LnCITE
(1) (2) (3)
(4) (5) (6)
FRQ = DACC DACCP AQ DACC DACCP AQ
FRQ -0.036 -0.063 0.055*
-0.022 -0.060 0.096***
(-1.48) (-1.07) (1.89)
(-0.92) (-1.22) (3.93)
FRQ×HICITE -0.448*** -0.522*** -0.637***
-0.445*** -0.413*** -0.698***
(-5.39) (-3.76) (-6.73)
(-6.81) (-3.57) (-8.74)
HICITE 1.177*** 1.172*** 1.198***
0.572*** 0.558*** 0.598***
(20.46) (19.77) (20.78)
(17.18) (16.01) (17.77)
Control Y Y Y
Y Y Y
N 39,615 39,615 39,615
39,615 39,615 39,615
Adjusted R² 0.330 0.330 0.331 0.274 0.274 0.276
Panel B. Real Activities Manipulation
Dependent Variable: LnPAT
LnCITE
(1) (2) (3)
(4) (5) (6)
FRQ = ABCFO ABPROD ABDEXP ABCFO ABPROD ABDEXP
FRQ 0.346*** -0.151*** 0.047*
0.046 -0.067** 0.093***
(6.68) (-4.14) (1.66)
(1.17) (-2.29) (3.82)
FRQ×HICITE 0.723*** -0.280*** -0.055
0.362*** -0.199*** 0.065
(7.86) (-3.13) (-0.90)
(5.08) (-2.83) (1.25)
HICITE 1.062*** 1.135*** 1.172***
0.500*** 0.529*** 0.592***
(20.25) (20.50) (21.47)
(15.52) (16.38) (18.10)
Control Y Y Y
Y Y Y
N 39,615 39,615 39,615
39,615 39,615 39,615
Adjusted R² 0.337 0.331 0.323 0.275 0.274 0.267
Notes:
This table presents the OLS regressions of innovation on financial reporting quality for firms in industries
with different innovation intensiveness. The dependent variables are LnPAT in columns 1-3 and LnCITE
in columns 4-6, respectively. In Panel A, accruals quality is measured by DACC, DACCP, and AQ in
columns 1/4, 2/5, and 3/6, respectively. In Panel B, RAM is measured by ABCFO, ABPROD, and
ABDEXP in columns 1/4, 2/5, and 3/6, respectively. HINNOV is an indicator variable that takes the value
of one if the firm operates in an innovation-intensive industry, and zero otherwise. To save space, I
suppress the coefficients of all the control variables. All regressions include year and industry fixed
effects and all statistics and significance levels are based on standard errors adjusted for firm clustering.
***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively.
58
Table 9
Effect of Institutional Investors
Panel A. Accruals Quality
Dependent Variable: LnPAT
LnCITE
(1) (2) (3)
(4) (5) (6)
FRQ = DACC DACCP AQ DACC DACCP AQ
FRQ -0.007 -0.196*** 0.052
-0.004 -0.129** 0.125***
(-0.21) (-3.31) (1.55)
(-0.13) (-2.12) (4.23)
FRQ*ABIOR -0.248*** -0.018 -0.400***
-0.279*** -0.243** -0.471***
(-4.67) (-0.11) (-5.65)
(-6.71) (-1.98) (-8.85)
ABIOR 0.027 0.002 0.046
0.130*** 0.119*** 0.153***
(0.80) (0.06) (1.37)
(4.53) (4.02) (5.23)
Control Y Y Y
Y Y Y
N 39,615 39,615 39,615
39,615 39,615 39,615
Adjusted R² 0.349 0.349 0.350 0.277 0.276 0.278
Panel B. Real Activities Manipulation
Dependent Variable: LnPAT
LnCITE
(1) (2) (3)
(4) (5) (6)
FRQ = ABCFO ABPROD ABDEXP ABCFO ABPROD ABDEXP
FRQ 0.507*** -0.078** -0.074***
0.209*** -0.027 0.026
(10.66) (-2.44) (-2.89)
(4.75) (-0.85) (0.98)
FRQ*ABIOR 0.673*** -0.475*** 0.273***
0.061 -0.286*** 0.260***
(5.53) (-5.60) (3.98)
(0.69) (-4.55) (4.92)
ABIOR -0.007 0.001 0.005
0.101*** 0.102*** 0.105***
(-0.21) (0.04) (0.16)
(3.60) (3.63) (3.69)
Control Y Y Y
Y Y Y
N 39,615 39,615 39,615
39,615 39,615 39,615
Adjusted R² 0.357 0.353 0.343 0.277 0.278 0.271
Panel C. Effect of Information Asymmetry
Dependent Variable: LnPAT
(1) (2)
(3) (4)
(5) (6)
FRQ = DACC
DACCP
AQ
Information Asymmetry low high low high low high
FRQ×ABIOR -0.366*** -0.121***
-0.168 -0.306**
-0.475*** -0.253***
(-3.70) (-3.09) (-0.81) (-2.37) (-4.93) (-4.38)
59
Table 9
Continued
Dependent Variable: LnCITE
(1) (2)
(3) (4)
(5) (6)
FRQ = DACC
DACCP
AQ
Information Asymmetry low high low high low high
FRQ×ABIOR -0.280*** -0.208***
-0.301* -0.358**
-0.474*** -0.369***
(-3.83) (-4.36) (-1.83) (-2.00) (-6.80) (-5.23)
Dependent Variable: LnPAT
(1) (2)
(3) (4)
(5) (6)
FRQ = ABCFO
ABPROD
ABDEXP
Information Asymmetry low high low high low high
FRQ×ABIOR 0.545*** 0.079
-0.513*** -0.094
0.456*** 0.060
(3.65) (0.89)
(-4.78) (-1.50)
(5.18) (1.24)
Dependent Variable: LnCITE
(1) (2)
(3) (4)
(5) (6)
FRQ = ABCFO
ABPROD
ABDEXP
Information Asymmetry low high low high low high
FRQ×ABIOR 0.021 -0.150
-0.283*** -0.073
0.312*** 0.101
(0.18) (-1.35) (-3.46) (-0.89) (4.64) (1.52)
Notes:
This table presents the OLS regressions of innovation on financial reporting quality for firms with
different institutional ownership. The dependent variables are LnPAT (columns 1-3) and LnCITE
(columns 4-6) in Panels A and B. In Panel A, accruals quality is measured by DACC, DACCP, and AQ in
columns 1/4, 2/5, and 3/6, respectively. In Panel B, RAM is measured by ABCFO, ABPROD, and
ABDEXP in columns 1/4, 2/5, and 3/6, respectively. ABIOR is an indicator variable that takes the value of
one if the firm’s institutional ownership is above the annual median, and zero otherwise. Panel C presents
the coefficients on interactions between financial reporting quality and ABIOR. To save space, I suppress
the coefficients of all the control variables. I split sample firms into firms with below-median (low) and
above-median (high) information asymmetry, which is measured by a composite score aggregating bid-
ask spread, stock turnover, analyst following, and analyst forecast error. All regressions include year and
industry fixed effects and all statistics and significance levels are based on standard errors adjusted for
firm clustering. ***, **, and * indicate significance at the 1%, 5%, and 10% level, respectively.
60
Table 10
Financial Reporting Quality and Patent Grant Lag
Dependent Variable: LnLAG
(1) (2) (3) (4) (5) (6)
FRQ = DACC DACCP AQ ABCFO ABPROD ABDEXP
Panel A. Electrical Equipment (SIC=36; N=4,068)
FRQ -0.363** -0.504* -0.175 0.743*** -0.335* -0.395**
(-2.40) (-1.86) (-0.87) (4.14) (-1.91) (-2.08)
FRQ×FIRST 1.698* 3.060** 3.329*** -1.684** 0.561 0.146
(1.78) (2.35) (5.37) (-2.29) (1.09) (0.34)
FIRST 2.184*** 2.071*** 1.977*** 2.337*** 2.352*** 2.373***
(13.40) (10.67) (11.95) (17.14) (17.85) (18.39)
Panel B. Measuring, Analyzing, and Controlling Instruments (SIC=38; N=3,672)
FRQ -0.256 -0.205 0.036 0.506** -0.263 -0.122
(-1.24) (-0.70) (0.13) (2.44) (-1.61) (-0.87)
FRQ×FIRST -0.797 0.252 2.669** 0.266 -0.066 0.240
(-0.80) (0.20) (2.31) (0.52) (-0.18) (0.77)
FIRST 2.223*** 2.128*** 1.890*** 2.132*** 2.152*** 2.158***
(17.18) (13.20) (12.59) (19.49) (19.38) (18.73)
Panel C. Commercial Machinery (SIC=35; N=3,807)
FRQ -0.284 -0.125 -0.128 0.246 -0.204 0.010
(-1.59) (-0.46) (-0.71) (1.32) (-1.15) (0.06)
FRQ×FIRST 3.239*** 2.931** 5.374*** -0.871 1.191*** -0.363
(2.89) (2.49) (3.06) (-0.81) (2.74) (-0.76)
FIRST 1.873*** 1.917*** 1.720*** 2.182*** 2.188*** 2.160***
(12.34) (13.11) (9.82) (16.48) (16.35) (16.03)
Notes:
This table presents the OLS regressions of the patent grant lag on financial reporting quality for first-time
and non-first-time patent applicants. The dependent variables are LnLAG. Financial reporting quality is
measured by DACC, DACCP, AQ, ABCFO, ABPROD, and ABDEXP in columns 1, 2, 3, 4, 5, and 6,
respectively. FIRST is an indicator variable that takes the value of one for a first-time patent applicant,
and zero otherwise. Panel A reports the results for the electrical equipment industry (SIC=36), Panel B
reports the results for the measuring, analyzing, and controlling instruments industry (SIC=38), and Panel
C reports the results for the commercial machinery industry (SIC=35). To save space, I suppress the
coefficients of all the control variables. All regressions include year and industry fixed effects and all
statistics and significance levels are based on standard errors adjusted for firm clustering. ***, **, and *
indicate significance at the 1%, 5%, and 10% level, respectively.
61
Table 11
Financial Reporting Quality, R&D Spending, and Patenting Activities
Panel A. Accruals Quality
Dependent Variable: LnPAT
LnCITE
(1) (2) (3)
(4) (5) (6)
FRQ = DACC DACCP AQ DACC DACCP AQ
FRQ 0.014 0.164** 0.096**
0.006 0.049 0.141***
(0.50) (2.28) (2.46)
(0.23) (0.81) (4.83)
FRQ×RD -0.829*** -2.390*** -1.137***
-0.822*** -1.680*** -1.088***
(-7.25) (-7.99) (-7.57)
(-8.48) (-7.12) (-8.73)
RD 1.182*** 1.475*** 1.334***
1.249*** 1.381*** 1.379***
(9.33) (9.94) (9.45)
(11.36) (11.00) (11.37)
Control Y Y Y
Y Y Y
N 39,615 39,615 39,615
39,615 39,615 39,615
Adjusted R² 0.350 0.351 0.351 0.277 0.277 0.278
Panel B. Real Activities Manipulation
Dependent Variable: LnPAT
LnCITE
(1) (2)
(3) (4)
FRQ = ABCFO ABPROD ABCFO ABPROD
FRQ 0.475*** -0.269***
0.059 -0.120***
(8.25) (-6.08)
(1.33) (-2.89)
FRQ×RD 1.811*** 0.086
1.226*** -0.112
(10.29) (0.64)
(8.35) (-0.96)
RD 1.440*** 0.955***
1.290*** 1.026***
(11.68) (8.71)
(12.33) (10.74)
Control Y Y
Y Y
N 39,615 39,615
39,615 39,615
Adjusted R² 0.359 0.350 0.278 0.276
Notes:
This table presents the OLS regressions of innovation on financial reporting quality and an interaction
between financial reporting quality and R&D expenses. In Panel A, the dependent variables are LnPAT in
columns 1-3 and LnCITE in columns 4-6, respectively, and accruals quality is measured by DACC,
DACCP, and AQ in columns 1/4, 2/5, and 3/6, respectively. In Panel B, the dependent variables are
LnPAT in columns 1 and 2 and LnCITE in columns 3 and 4, respectively, and RAM is measured by
ABCFO and ABPROD in columns 1/3 and 2/4, respectively. To save space, I suppress the coefficients of
all the control variables. All regressions include year and industry fixed effects and all statistics and
significance levels are based on standard errors adjusted for firm clustering. ***, **, and * indicate
significance at the 1%, 5%, and 10% level, respectively.
62
Table 12
Financial Reporting Quality, R&D Spending, and Firm Value
Panel A. Accruals Quality
Dependent Variable: AvgTOBINQ
(1) (2)
(3) (4)
(5) (6)
FRQ = DACC
DACCP
AQ
Reporting Quality high low high low high low
RD 2.010*** 0.949**
2.002*** 0.888**
2.147*** 1.045**
(4.26) (2.11)
(3.91) (1.98)
(3.67) (2.31)
Control Y Y
Y Y
Y Y
N 15,852 15,552
15,773 15,631
15,978 15,426
Adjusted R² 0.209 0.220
0.184 0.219
0.162 0.216
Subsample comparison of coefficients on RD
p-value 0.035 0.035 0.116
Panel B. Real Activities Manipulation
Dependent Variable: AvgTOBINQ
(1) (2)
(3) (4)
FRQ = ABCFO
ABPROD
Reporting Quality high low high low
RD 2.056*** 0.576
1.295*** 0.981
(4.93) (1.04)
(2.93) (1.56)
Control Y Y
Y Y
N 16,109 15,295
15,881 15,523
Adjusted R² 0.133 0.274
0.162 0.276
Subsample comparison of coefficients on RD
p-value 0.018 0.658
Notes:
This table presents the OLS regressions of long-term firm value on R&D expenses. The dependent
variable is AvgTOBINQ, which is measured as the five-year moving average of Tobin’s Q. I split samples
firms into firms with high (below-median for DACC, DACCP, AQ, and ABPROD, or above-median for
ABCFO) and low (above-median for DACC, DACCP, AQ, and ABPROD, or below-median for ABCFO)
financial reporting quality. To save space, I suppress the coefficients of all the control variables. Control
variable include firm size, return on assets, leverage, capital expenditures, and sales. All regressions
include year and industry fixed effects and all statistics and significance levels are based on standard
errors adjusted for firm clustering. ***, **, and * indicate significance at the 1%, 5%, and 10% level,
respectively.
63
Table 13
Alternative Measures of Real Activities Manipulation and Firm Innovation
Dependent
Variable: LnPAT
LnCITE
(1) (2) (3) (4) (5) (6)
ABRD 0.385***
0.335***
(4.61)
(4.26)
ABSGA
-0.245***
-0.064*
(-5.88)
(-1.81)
ABADV
0.830***
0.372
(2.64)
(1.53)
Control Y Y Y
Y Y Y
N 26,921 26,921 39,615
26,921 26,921 39,615
Adjusted R² 0.393 0.398 0.348 0.307 0.312 0.275
Notes:
This table presents the OLS regressions of innovation on alternative RAM measures. The dependent
variables are LnPAT in columns 1-3 and LnCITE in columns 4-6, respectively. RAM is measured by
ANRD, ABSGA, and ABADV in columns 1/4, 2/5, and 3/6, respectively. To save space, I suppress the
coefficients of all the control variables. All regressions include year and industry fixed effects and all
statistics and significance levels are based on standard errors adjusted for firm clustering. ***, **, and *
indicate significance at the 1%, 5%, and 10% level, respectively.
