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THE INNOVATION CONSEQUENCES OF FINANCIAL REGULATION FOR YOUNG
LIFE-CYCLE FIRMS*
Abigail Allen**
Marriott School of Management
Brigham Young University
Melissa F. Lewis-Western
Marriott School of Management
Brigham Young University
Kristen Valentine
McCombs School of Business
University of Texas, Austin
February 2019
*The authors gratefully acknowledge the financial support of the Marriott School of Business and the McCombs
School of Business. The authors wish to recognize Vishal Balria (AAA discussant), Mike Drake, James Hansen,
Michelle Lowry, Tim Seidel, Amy Sheneman, Jake Thornock, workshop participants from Brigham Young
University, The University of Florida and The University of Texas at Austin, and audience participants at the 2017
American Accounting Association annual meeting for insightful comments that enhanced the paper. Kristen Valentine
gratefully acknowledges support from a Deloitte Foundation Doctoral Fellowship.
**Corresponding Author; Email: [email protected]; Phone: 801-422-4194; Mailing Address: Brigham Young
University, 515 Tanner Bldg., Provo, Utah 84602.
THE INNOVATION CONSEQUENCES OF FINANCIAL REGULATION FOR YOUNG
LIFE-CYCLE FIRMS
ABSTRACT: The last several decades have witnessed a striking uptick in reactionary financial regulation
intended to curb financial misreporting by requiring increased external monitoring and centralized
decision-making. We provide evidence that young life-cycle stage firms, characterized by low
levels of financial slack and heavy investment in explorative innovation, are particularly
vulnerable to negative innovation consequences from such regulation. Using SOX as a backdrop
to test our predictions, we document a significant reduction in both R&D spending and innovation
outputs for young life-cycle stage firms after regulation, relative both to their more mature
counterparts and to young life-cycle stage firms exempt from full regulatory compliance.
Additional tests indicate that the decline in innovation manifests both from a diversion of scarce
resources and from the imposition of an organizational structure mismatched to the pursuit of
explorative innovation. Importantly, we find no evidence that innovation declines are offset by
other ensuing benefits to young life-cycle stage firms; across several measures, we fail to detect
evidence of improved financial reporting quality. Moreover, an event study analysis suggests that
market participants expected financial regulation to be incrementally value decreasing for young
life-cycle stage firms, and post-regulation returns analysis corroborates this expectation.
Supplemental tests of other regulatory settings yield consistent inferences. Collectively, our results
support the notion that financial regulation places a heavy burden on innovative, young life-cycle
stage firms.
Keywords: Firm Life-Cycle; Innovation; Explorative Innovation, R&D; Financial Regulation;
Corporate Governance; Financial Reporting Quality
Data Availability: All data are available from public sources identified in the paper.
1
THE INNOVATION CONSEQUENCES OF FINANCIAL REGULATION FOR YOUNG
LIFE-CYCLE FIRMS
“It is important that our rules and regulatory actions create an
environment that fosters innovation and growth.” 1
INTRODUCTION
The last several decades have witnessed a striking uptick in regulatory requirements
predominantly oriented toward improving the reliability of financial information by increasing
external monitoring and centralizing decision making (hereafter “financial regulation”).2 Often
sparked by financial reporting scandal, the assumption implicit in such reactionary financial
regulation is that it benefits the economy via improved financial reporting quality, a proposition
that is generally borne out in the extant literature (e.g., Persons 2005; Altamuro and Beatty 2010).
However, there are also real costs to such regulation, both direct (e.g. resources expended on
implementation, attestation, litigation) and indirect (e.g. modified firm incentives/behavior). Prior
research on these costs has examined an average effect; we propose, however, that the costs of
financial regulation are not borne equally across all firms and that young life-cycle stage firms
constitute a population particularly vulnerable to negative consequences. Because young life-cycle
stage firms are a vital segment for national economic growth (e.g., Henrekson and Johansson 2010)
and innovation is the lifeblood of the economy, in this study we examine how financial regulation
affects the innovative capacity of young life-cycle stage firms.
The unique characteristics of young life-cycle stage firms suggest that implementation of
financial regulations may have detrimental consequences on innovation for two reasons. First,
1 Speech by former SEC Chair Mary Jo White in March of 2016: https://www.sec.gov/news/speech/chair-white-
silicon-valley-initiative-3-31-16.html#_ftn 2 e.g., Sarbanes Oxley Act of 2002, Dodd-Frank Wall Street Reform and Consumer Protection Act, and exchange
based requirements on characteristics of firms’ board structure.
2
young life-cycle stage firms are characterized by large investments in R&D and high levels of
explorative innovation as they attempt to establish new markets and deter entry of competitors
(Spence 1997, 1979, 1981).3 Such innovation thrives in a decentralized environment where
managers are afforded significant flexibility in decision-making (e.g. Holmstrom 1989; Shadab
2008). Financial regulation, while intending to reduce the opportunistic use of discretion through
increased external monitoring and centralized decision-making, may reduce both opportunities and
incentives for risky investments that are the precursor to explorative innovation (Balsmeier,
Fleming, and Manso 2017; Bargeron, Lehn, and Zutter 2010; Bernstein 2015; Cohen, Dey, and
Lys 2013). Essentially, financial regulation requires the adoption of an organizational structure ill-
suited for the exploratory aims of young life-cycle firms’ innovation strategies. Consequently, we
expect more severe indirect innovation consequences from financial regulation for young life-
cycle stage firms relative to their more mature counterparts. We refer to this channel as innovation
hindrance.
Second, mandatory investments in financial reporting initiatives are costly (e.g. Zhang 2007,
Iliev 2010, Engel Hayes and Wang 2010) and divert scarce resources away from potential
investments in innovation. Young life-cycle stage firms are characterized by low levels of financial
slack and heavy reliance on external financing (Dickinson 2011) and are therefore financially
constrained relative to their more mature counterparts. Prior research suggests that financial slack
has a critical impact on managers’ ability and incentives to innovate (Cyert and March, 1963;
Nohria and Gulati, 1996). Accordingly, we expect that the direct costs associated with financial
3 Explorative innovation is directed at developing new ideas, processes or customers and is best facilitated in an
environment that promotes non-routine problem solving and deviance from existing knowledge (Jansen, Van Den
Bosh, and Volberda 2006). By contrast, more mature firms more frequently engage in exploitative innovation which
leverages existing technology and firm product lines to achieve incremental improvements for existing customers.
3
regulation incrementally cumber innovation for young life-cycle stage firms. We refer to this
channel as resource diversion.
Understanding the innovation consequences of financial regulation on young life-cycle
stage firms is important because these firms are a critical segment for national economic growth
(Henrekson and Johansson 2010; Van Praag and Versloot 2008); as such, declines in their ability
to innovate and grow could prove detrimental to the economy more broadly. For example, young
life-cycle stage firms represent only 20% of Compustat firms, but comprise 64% of aggregate
R&D expenditure.4 Moreover, while often thought of as small startups, young life-cycle stage
firms, particularly in recent years, are often large firms and/or high-growth firms that generate
substantial innovation and employment opportunities.5 Recognizing their strategic importance to
the economy, regulators have occasionally taken specific actions designed to shelter young life-
cycle firms from the burden of excess regulation (e.g., the Jumpstart Our Business Startups Act
“JOBS”, Review of Regulation S-K). However, discussion of firm life-cycle stage has been
noticeably absent in the context of reactionary financial regulations.6
We investigate the influence of financial regulation on innovation for young life-cycle
stage firms using a sample of US firms from 2001-2007 who were required to comply with the
Sarbanes Oxley Act of 2002 (SOX). We choose SOX over other settings because prior research
identifies it as providing a powerful shock to firm resources and operations that may negatively
affect innovation (Balsmeier et al. 2017; Coates and Srinivasan 2014; Faleye, Hoitash, and Hoitash
4 We calculate this statistic for 2004, the year of implementation of SOX, and the statistic sums R&D scaled by assets
for two groups: (1) young life-cycle stage firms, and (2) all other Compustat firms. 5 For example, “unicorns” are young life-cycle stage firms with valuations of more than $1 billion. 6 Regulators do often consider firm size as a relevant distinction when weighing the costs/benefits of regulation (e.g.
FCPA, Sarbanes Oxley Act of 2002, Dodd-Frank). In particular, it is generally understood that the cost of
implementing new regulations is disproportionately burdensome to small firms; accordingly, size is almost always
included as a control variable in academic studies of regulatory impact. Firm life-cycle however is both conceptually
and empirically distinct from firm size (Dickinson 2011).
4
2011).7 Generally, SOX provisions were intended to induce “more objective monitoring by
outsiders” and to reduce “subjective decision making by insiders” (Shadab 2008). The internal
control provisions of SOX 404(b), coupled with the provisions for increased director independence
and management certification, induced significant financial strain, and organizational reorientation
towards more centralized decision-making and formalized processes. In defining pre- and post-
regulation periods, we employ a conservative approach by demarcating the implementation of
section 404(b) as the point from which to look for changes in firms’ innovation outcomes.8
Following Dickinson (2011), we identify young life-cycle stage firms as those having
negative operating and investing cash flows, and positive financing cash flows.9 Following prior
research (Gunny and Zhang 2014; Koh and Reeb 2015; Hall, Jaffe, Trajtenberg 2001), we measure
innovation with R&D intensity, the number of patents, the number of patent citations, and the
number of claims made in the patent. In supplemental tests, we also examine patent originality as
a proxy for explorative innovation.
To isolate the effects of financial regulation, we use a difference-in-difference design that
compares the effect of financial regulation on young life-cycle firms to two different control
samples. The first is U.S. firms required to comply with SOX at a more mature life-cycle stage.
We expect that firms in later life-cycle stages fare better following financial regulation because
they have higher free cash flows (less resource diversion) and generally pursue an exploitative
innovation strategy, which is less likely to be hampered by regulation-imposed changes to
7 SOX also has several empirical advantages relative to other possible settings such as firm IPOs, and the JOBS act.
Section 5 provides supplemental descriptive analysis of these settings, while noting the empirical limitations that
prevent more causal analysis. Overall, the results of Section 5 are consistent with those obtained using SOX as a
setting suggesting broader applicability of our findings. 8 For most firms, the audit of internal controls became effective for fiscal years ending after November 15, 2004. To
the extent that firms made changes earlier in anticipation of mandated compliance with regulation, those investments
should bias against us finding results. 9 These firms have also been referred to in as “introduction” firms (Dickenson 2011; Gort and Klepper 1982).
5
monitoring and decision-making processes (less innovation hindrance). The second control
sample consists of young life-cycle stage firms who were exempt from the full requirements of
SOX, namely non-accelerated filers.10 This analysis isolates the effect of implementing financial
regulation within young life-cycle stage firms, and provides an estimate of the overall (as opposed
to relative) cost of such implementation.11 We find that young life-cycle stage firms experience a
significant decline in innovation after SOX relative to both control samples. Specifically, we
estimate declines to R&D intensity and patents for young-life cycle stage firms that range from
11-15% of pre-SOX levels.
The observed declines to innovation may occur exclusively because of resource diversion
or may also result from innovation hindrance. We provide exploratory evidence via an analysis of
changes in innovation strategy following SOX. We find that patent originality, a proxy for
explorative innovation, declines for young life-cycle stage firms after SOX; this result suggests
that financial regulation changes young life-cycle stage firms’ innovation strategy and provides
evidence of greater innovation hindrance for young versus mature life-cycle stage firms. We also
find evidence that the decline in innovation persists following initial SOX implementation,
suggesting that the effects are not confined to the year of greatest resource diversion.
We conduct several additional analyses to corroborate our primary results. First, to ensure
that our results stem from implementation of financial regulation, we replicate our tests for two
10 Non-accelerated filers were initially granted a 2-year delay for compliance with the provisions of SOX 404(b).
Subsequent extensions were granted and ultimately, non-accelerated filers were exempted altogether as part of the
Dodd-Frank Act in 2010. To the extent that some of these non-accelerated filers may have invested during our sample
period in anticipation of mandatory compliance with 404 provisions, this could diminish the power of our tests (i.e.
bias against our ability to detect innovation consequences). 11 We considered using young life-cycle stage UK and Canadian firms not subject to the provisions of SOX as a third
potential control group. However, as pointed out by Leuz and Wysocki (2016), using a foreign benchmark can be
problematic to the SOX setting as it is hard to satisfy parallel trends assumptions. Pragmatically, there were also too
few young life-cycle stage firms with data available on Compustat Global to make that analysis tractable.
6
pseudo-investment dates and find no evidence of changes in innovation. Second, we explicitly
control for a differential SOX impact on innovation for small firms. Results suggest that the effect
of firm life-cycle is economically incremental to the effect of firm size.12 Third, while our results
suggest that implementation of financial regulation resulted in negative net present value
investments for young life-cycle stage firms as it pertains to innovation outcomes, it is possible
that other benefits outweigh these costs. We vet this possibility by replicating our tests using
several different financial reporting quality (FRQ) measures including restatements, the Dechow
and Dichev (2002) accruals quality metric, the Financial Statement Divergence Score of Amiram,
Bozanic and Rouen (2015), total accruals, and abnormal revenue (Stubben 2010). These analyses
do not provide evidence of improvement in FRQ for young life-cycle stage firms.
Notwithstanding, it remains possible that some other benefit accrues or that due to
imperfect measurement we are simply unable to detect the FRQ effect. Thus, we conduct an event
study investigating whether the market anticipated the net costs associated with SOX compliance
to be positive or negative for young life-cycle stage firms. Results support the latter; we find
incrementally negative market reactions for young life-cycle stage firms associated with events
increasing the likelihood that SOX would become law relative to their more mature counterparts.
We also examine post-SOX returns to determine if some ensuing benefit manifests which was
unanticipated by the market; we find no evidence to this effect. These results support the notion
that financial regulation imposes heavy net costs on innovative, young life-cycle stage firms.
Finally, although our primary analysis utilizes SOX as a powerful setting to test our
predictions, we are interested in speaking more broadly to the consequences of financial regulation.
Accordingly, we supplement our main analysis with tests of innovation consequences associated
12 Across our measures of innovation, we find that the differential innovation consequences for young life-cycle stage
firms relative to mature firms is 0.7 to 3.9 times the differential consequences for small firms relative to large firms.
7
with IPO decisions and the passage of the JOBS Act. Both settings pose empirical challenges to
clean identification, however, results are consistent with our main analysis.
Our results contribute to the literature examining the influence of financial regulation on
innovative outcomes (Balsmeier et al. 2017; Bargeron et al. 2010; Cohen et al. 2013; Faleye et al.
2011; Kang, Liu and Qi 2010). This research provides mixed evidence and does not consider if
firm life-cycle moderates the effect. Our results indicate that prior conclusions are incomplete. The
decline in innovation from financial regulation is significantly more severe for young life-cycle
stage firms than mature life-cycle stage firms. These differences appear to occur both because
young life-cycle stage firms are more sensitive to the direct costs of regulation (resource diversion)
and because financial regulation imposes formalized processes and controls mismatched to young
life-cycle stage firms’ innovation strategies (innovation hindrance). We also contribute to the
literature examining organizational structure and innovative outcomes, which predicts that
increased external monitoring and centralized decision making will typically reduce innovation
(e.g., Holmstrom 1989; Eisenhardt 1985; Turner and Makhija 2006; Jansen et al. 2006). Our results
support these predictions and indicate amplified effects for young life-cycle stage firms. Our
results also illustrate the influence of financial regulation on the innovative success of young life-
cycle stage firms, who are the backbone of our economy. On average, regulators appear to have
taken the position that the financial reporting benefits associated with financial regulation
outweigh the costs. Our results suggest the contrary for young life-cycle stage firms.
8
LITERATURE REVIEW AND PREDICTIONS
Impact of Financial Regulation on Innovation
A frequent concern of both scholars and policy makers is that financial regulation may
have detrimental consequences for corporate risk taking and innovation.13 Innovation requires
companies to invest in long-term risky projects (R&D) that often require substantial coordination
and that are facilitated by a de-centralized decision making process that emphasizes strategic
objectives rather than financial controls (e.g., Foss and Laursen 2005; Grant 1996; Hitt, Hoskisson,
Johnson and Moesel 1996; Jansen et al. 2006; Mueller 1972; Nickerson and Zenger 2004).
Essentially, innovation-fostering environments prevent managers from focusing too much on
short-term quantifiable performance metrics and instead encourage focus on longer-term strategic
objectives that lead to innovation (i.e., they reduce incentives for myopic behavior). This structure
generates innovation by allowing the employee to make decisions quickly, encouraging longer-
term focus and coordination, and by discouraging investment in projects with only short-term
payoffs. One cost of innovation-fostering environments, however, is that, by necessity, they allow
employees greater flexibility in decision making, which may increase opportunities for the
manager to benefit himself at the expense of the company (e.g. Holmstrom 1989; Shadab 2008).
Implementation of financial regulation designed to curb such managerial opportunism may reduce
both managerial opportunities and incentives for risk taking and induce a myopic focus on short-
term performance. Consistent with this concern, Bargeron et al. (2010) and Cohen et al. (2013)
provide evidence that firms significantly reduced R&D spending and risky investments in response
to SOX. Bernstein (2015) provides evidence that firms experience significant declines in
13 See for example statements from former SEC Chairman William Donaldson (Michaels 2003), former Federal
Reserve Chairman Alan Greenspan (Greenspan 2003) and from famed economist Milton Freedman (Gerstein 2006)
in relation to Sarbanes Oxley.
9
innovation output subsequent to the IPO, where regulations typically require increased monitoring
and centralization of decisions.
Life-cycle Stage as a Moderating Factor between Financial Regulation and Innovation
To understand why financial regulation may differentially affect young life-cycle stage
firms consider the following. First, the type of innovation pursued by young life-cycle stage firms
differs on average from that of mature firms. Young life-cycle stage firms most often engage in
“explorative innovation”, directed at new products and customers. Explorative innovation is best
facilitated in an environment that promotes non-routine problem solving and deviance from
existing knowledge or processing. Because financial regulation tends to increase centralization of
decision making and formalization of rules, processes, and communications, it may negatively
impact both the quality and quantity of “explorative innovation” (Jansen et al 2006). By contrast,
more mature firms often leverage existing technology and firm product lines to achieve
incremental improvements for its existing customer base, that is, “exploitative innovation”.14
Because “exploitative innovation” relies on existing processes and structure, centralization of
control and formalized processes, rules and communication channels may serve to increase the
efficacy and efficiency of “exploitative innovation” (Burns and Stalker 1961, McGrath and
MacMillan 2000). Thus, compared to their younger counterparts, mature life-cycle stage firms are
less likely to suffer innovation consequences (and may even benefit) from financial regulation.
Second, prior research suggests that financial slack has a critical impact on managers’
ability and incentives to innovate (Cyert and March, 1963; Nohria and Gulati, 1996) and that
mandatory investments in financial reporting initiatives are costly (e.g. Zhang 2007, Iliev 2010,
14 Research suggests that ambidextrous firms that balance “explorative” and “exploitative” innovation achieve superior
performance (He and Wong 2004, Tushman and O’Reilly 1996), however, obtaining such ambidexterity is difficult.
On average larger and more mature firms have excelled at sustaining (exploitative) innovation but have struggled to
achieve disruptive (explorative) innovation, relative to their younger counterparts (Christensen 1997, March 1991).
10
Engel et al. 2010). Young life-cycle stage firms are financially constrained relative to their more
mature counterparts (e.g. Dickinson 2011). Thus, we expect the impact of regulation induced
spending to have a greater downward impact on the R&D spending of young life-cycle stage firms
relative to their more mature and less financially constrained counterparts.
In summary, financial regulation is likely to create an environment more hostile to
explorative innovation—the type of innovation most often pursued by young life-cycle stage firms
(innovation hindrance). Also, the diversion of scarce resources to financial reporting initiatives is
likely more detrimental to innovation for cash-constrained, young life-cycle stage firms (resource
diversion). Thus, we expect financial regulation to generate more severe innovation consequences
for young life-cycle stage firms relative to more mature life-cycle stage firms.
SAMPLE AND DATA
Setting
SOX provides a powerful setting to test for differential innovation consequences to young
life-cycle stage firms. SOX was a regulatory response to revelations of widespread fraud among
U.S. firms and its provisions were intended to change organizational structure to prevent fraud.
The internal control provisions of SOX 404(b) and the requirements for increased director
independence and management certification required investments in controls and governance as
well as more centralized decision making processes, all of which are integral to our hypothesis of
life-cycle dependent innovation outcomes.15 Prior research has shown significant effects of SOX
on firm investments and risk taking (Bargeron et al. 2010; Cheng, Dhaliwal, and Zhang 2013;
Cohen et al. 2013) suggesting that, on average, compliance with SOX did influence firm decisions.
15 For a review of academic research on changes induced by SOX see Coates and Srinivasan (2014).
11
Given that SOX-induced changes within firms likely happened over time, true
identification of when firms implemented the regulation is challenging, especially considering
presumed heterogeneity in compliance prior to these regulatory changes. In defining pre- and post-
regulation periods, we employ a conservative approach by demarcating the implementation of
section 404(b) as the point from which to look for changes in firms’ innovation outcomes.16 To
the extent that firms made investments prior to mandatory compliance with section 404(b), those
investments bias against us finding post-regulation changes.
Sample Selection
We employ two samples to test our predictions. For our first sample, we start with all U.S.
firms (FIC = “USA”) included in Compustat from 2001-2007 with positive total assets. We
exclude financial firms and utilities because the operating decisions of firms in regulated industries
differ from those of firms in non-regulated industries (Badertscher, Shroff, and White 2013). We
also exclude observations missing the data necessary to calculate firm life-cycle stage or control
variables. Koh and Reeb (2015) note that not all innovative firms separately report R&D; thus,
missing R&D does not indicate abstention from innovative activities. As such, we do not exclude
from our sample firms with missing R&D.17 We exclude firms in the decline and shakeout life-
cycle stages as of implementation of SOX as these firms are characterized by very low levels of
innovation and do not represent a helpful counterfactual. We require firms to have data for the
three years prior to and after implementation of SOX 404(b). We remove observations with
changes in fiscal year end during the sample period as the fiscal year end determines the SOX
implementation date. Finally, we include only firms that have an internal control opinion for all
16 Section 404(b) requires an audit of internal control effectiveness and became effective for US firms classified as
accelerated filers for fiscal years ending after November 15, 2004 (see Coates and Srinivasan 2014). 17 Koh and Reeb 2015 note several specifications checks that should be conducted to ensure appropriate conclusions
related to innovative activities; as discussed in footnote 25, our results are robust to these procedures.
12
three years after SOX implementation. This requirement ensures that sample firms complied with
SOX section 404(b). Table 1 outlines these procedures. Our final sample that compares young to
mature life-cycle stage firms consists of 9,582 firm-years from 2001-2007 reflecting 1,400 firms
of which 197 are classified as young life-cycle firms.
To identify our second sample that compares accelerated to non-accelerated young life-
cycle stage firms, we follow the same sample selection procedures, except that we no longer
exclude firms without internal control opinions post-SOX (non-accelerated filers), and we retain
only young life-cycle stage firms. To identify non-accelerated filing status, we take advantage of
the fact that accelerated filers are required to have an internal control audit opinion in the post
period (2004-2006 or 2005-2007 depending on a firm’s fiscal year end), whereas non-accelerated
filers are not required to have an internal control audit opinion. We therefore classify a firm as a
non-accelerated filer if an internal control opinion is absent in all three post years. These
procedures yield a sample of 2,460 non-accelerated filer firm-years and 1,182 accelerated filer
firm-years, all of which are young life-cycle stage firms.
Data and Variable Definitions
Life-Cycle Stage
Gort and Klepper (1982) define five distinct phases of firms’ life-cycle: 1) introduction, 2)
growth, 3) maturity, 4) shake-out, and 5) decline. Extant research has measured life-cycle stage
using various proxies including firm age, firm size, and cash flow patterns (e.g., Bradshaw, Drake,
Myers and Myers 2011; Dickinson 2011; Doyle, Ge and McVay 2007; Klein and Marquardt 2006;
Wasley and Wu 2006). We measure life-cycle stage using cash flow patterns because Dickinson
(2011) validates the cash flow measure as an effective proxy for firm life-cycle and notes its
theoretical superiority over age and size. Following Dickinson (2011) we define a firm’s life-cycle
13
stage based on the firm’s pattern of cash flows. The earliest life-cycle stage is the introductory
stage, which is defined as years with operating cash flows (OCF) and investing cash flows (ICF)
less than zero, and financing cash flows (FCF) greater than zero. When these cash flow patterns
are present, we set YoungLifeCycle equal to one. YoungLifeCycle is set equal to zero for one growth
and maturity stage firms.18 In the growth stage, firms report positive OCF, negative ICF, and
positive FCF, while the mature stage firms report positive OCF, negative ICF, and negative FCF.
SOX 404(b) compliance for accelerated filers was effective for fiscal years ending after November
15, 2004 and we aim to measure life-cycle stage at the time of implementation. Thus, we assign
firms to life-cycle stages based on fiscal year 2004 financials for firms whose fiscal year ends in
November through May and based on fiscal year 2005 financials for firms with fiscal year ends in
June through October.19
Innovation
Our main tests examine four firm-year measures of innovation that have been used
extensively in prior research. R&D Intensity is measured as the ratio of R&D expense scaled by
total assets, where missing values of R&D are set to zero (Gunny and Zhang (2014) and Chan,
Lakonishok, and Sougiannis (2001)). Patent based measures, Log(#Patents), Log(Citations), and
Log(#Claims) are measured following Gunny and Zhang (2014) and Chan, Lakonishok, and
Sougiannis (2001). Specifically, Log(#Patents) is the number of patents a firm applied for in year
t. Log(Citations) is the log of the average forward patent citations received through 2006 for all
patents a firm applied for in year t multiplied by a factor to adjust for the fact that patents later in
18 We exclude from our sample firms in the decline and shakeout life-cycle stages. Decline and shakeout firms are
expected to reduce investment over time, thereby limiting their usefulness as control firms because declining business
prospects or SOX could account for any observed changes in innovation. 19 In robustness tests, we alternatively assigned firms to life-cycle stages using the sum of three years cash flows
leading up to and including the year of SOX implementation. Results are robust to this alternative definition.
14
the series have fewer years in which to receive citations (Log(Citations)). Log(#Claims) is the log
of the average number of claims made in a patent for all patents a firm applied for in year t.20
In supplemental tests, we also consider patent originality as a proxy for explorative
innovation. Originality is a measure of how many different technology areas (referred to as fields)
an inventor draws from to create the innovation in a patent document. It is calculated as 1 minus
the concentration of the patent’s technology fields, where concentration is measured as Herfindahl
concentration index (Hall et al. 2001; Koh and Reeb 2015). Specifically, Originality is calculated
as 𝑂𝑟𝑖𝑔𝑖𝑛𝑎𝑙𝑖𝑡𝑦𝑖 = 1 − 𝛴𝑗𝑛𝑖𝑠𝑖𝑗
2 , where 𝑠𝑖𝑗 is the percentage of citations a patent document, i, makes
to patents in technology area j, out of 𝑛𝑖technology areas (Hall et al. 2001). We take the average
value of Originality across all patents a firm applies for in year t as our measure. We expect
explorative innovation to lead to more original patents. We also examine patent Generality, which
captures the breadth of fields that subsequently cite the patent a firm applied for in year t. The idea
is that the more general an innovation, the greater the number of technological areas that will cite
the patent. We expect that exploitative innovation is more likely to lead to patents that are cited by
a greater breadth of technologies, but we refrain from placing too much weight on this measure
because it suffers from the same measurement problems as does the Log(Citations), namely, there
is not much hindsight in the data to obtain reliable forward citations.
20 Because the dataset of corporate patent holdings is only available through 2006, when using patent-based measures
as dependent variables, we remove firms with a fiscal 2005 implementation year as patent data is not available for all
of their post period (i.e. data is unavailable in 2007). Furthermore, Hall et al. (2001) note the Log(Citations) variable
isn’t precisely measured for 2004-2006 as there is too short a time between when a patent was applied for and the end
of the measurement period to reliably measure actual cites. Because of the noise in forward citations-based measures
in later time periods, we may fail to detect an effect using these measures even if one exists. We include this measure
for completeness as it is prevalent in the literature.
15
Financial Reporting Quality
The purported benefit of financial regulation is improved quality of financial reports and
disclosures. Thus, in supplemental tests we examine changes in young life-cycle stage firms’ FRQ,
as a potential offsetting benefit to any observed decline in innovation. To measure FRQ, we
examine the firm’s frequency of restatements, accrual quality, financial statement error, total
accruals and abnormal revenue. Restatement is set to one when a firm subsequently restates the
results of year t and zero otherwise. Restatement data is obtained from Audit Analytics. Poor
Accrual Quality is captured using the firm-year Dechow and Dichev (2002) model as modified by
McNichols (2002), specifically it is the absolute value of the residual from regressing the change
in working capital on past, present and future cash flows, the change in sales, and net PPE at the
industry-year level. Higher levels correspond to worse accrual quality. The Financial Statement
Divergence Score (FSD), calculated as described in Amiram et al. (2015), serves as our third
measure of financial reporting quality. Benford’s law describes the expected frequency of leading
digits in data sets. The FSD score is based on Benford’s law. Essentially, errors in data sets change
the frequency of leading digits. The FSD score exploits this characteristic to detect errors in
financial statements. Thus, higher values of FSD correspond to greater error. Total accruals has
been used by prior research as a measure of poor financial reporting quality (Dechow, Ge, and
Schrand 2010); we measure this variable as the absolute value of the difference between income
before extraordinary items and OCF (Abs(Total Accruals)) and with the signed equivalent (Total
Accruals). Our final measure is the absolute value of Abnormal Revenue following Stubben (2010).
Control Variables
The SEC anticipated the costs of complying with SOX to increase in firm size, an
expectation corroborated by Bargeron et al. (2010). Thus, we control for size with the log of total
16
assets (Size).21 Firm age has also been linked to numerous firm outcomes including innovation.
Given that our primary design focuses on within-firm changes over time, however, we do not
control for age as doing so would be tantamount to including a time trend variable.22 Growth
opportunities have been shown to influence innovation (Biddle and Hilary 2006; Biddle, Hilary
and Verdi 2009); thus, we control for the firm’s book-to-market ratio (Book-to-Market)23. We also
include a measure of leverage to control for financial constraints (Asker, Farre-Mensa and
Ljungqvist 2014; Biddle and Hilary 2006; Biddle et al. 2009), which may bear on a company’s
ability to innovate or comply with regulation (Leverage). Auditor quality has been shown to
influence FRQ (DeFond and Jiambalvo, 1991; Francis, Michas and Yu 2013) and, as such, we
control for whether or not a firm has a Big N auditor (BigNAuditor). In the R&D models, we follow
Koh and Reeb (2015) by including a dummy variable when a firm’s R&D spending is missing in
Compustat (R&D Missing).24 Finally, when patent variables are used as a dependent variable, we
include R&D Intensity as a control, to account for a firm’s innovative efficiency (Hirshleifer, Hsu
and Li 2013). Please refer to Appendix A for detailed variable definitions.
Descriptive Statistics
Table 2 presents descriptive statistics for pre- and post-SOX implementation years. Panel
21 As discussed in section 4, we include an interaction between PostSOX × Size to further ensure that our results are
attributable to firm life-cycle and not any coincident changes in size around SOX. 22 As a practical matter, our results are robust to including in models the years a firm has been listed on Compustat. 23 One concern with including Book-to-Market as a control variable is firms may have managed market values
downward to avoid complying with SOX 404(b) provisions (see Gao, Wu and Zimmerman 2009). Therefore, Book-
to-Market may also be changing as a result of the treatment. To test if this or any other control variable might result
in a “bad controls” issue, we re-estimate our primary regressions without control variables. Inferences are robust. 24 Koh and Reeb 2015 note that “unfortunately, the true spending of the missing R&D firms in practice is unobservable,
suggesting that researchers should carefully consider their setting and determine whether their results are sensitive to
eliminating the blank observations, treating them as zero or treating them with the industry average (both of the latter
with blank dummies) and including a Pseudo-Blank dummy variable.” Thus, we examine the sensitivity of our results
to each alternative for dealing with missing R&D. Specifically, we 1) replace missing R&D values with zero and
include indicator variables for both R&D missing and pseudo-blank R&D (where there is patent activity, but R&D is
missing), 2) replace missing R&D values with the 2-digit SIC industry average, and 3) drop observations with missing
R&D. In each of these cases, our results are consistent with those reported.
17
A compares young life-cycle stage firms to mature life-cycle stage firms. Young life-cycle stage
firms’ R&D intensity declined significantly post-SOX while mature firms experienced a
statistically positive but economically insignificant increase. This finding is consistent with the
premise that mature firms tend to conduct exploitative innovation, which is more likely to benefit
from formalized processes and centralization of controls than explorative innovation, the type of
innovation for which young life-cycle stage firms are more likely to pursue. Examination of
differences in innovation for mature relative to young life-cycle stage firms post-SOX suggest that
young life-cycle stage firms experience significantly greater declines in R&D intensity and the
number of patent claims following SOX than mature life-cycle stage firms.
Panel B present similar statistics, but compares young life-cycle stage firms that are
required to comply with SOX to young life-cycle firms exempt from SOX compliance. The results
provide evidence of a decline in innovation for young life-cycle stage firms required to comply
with SOX. Overall, the results in Table 2 support the prediction that that the innovation costs of
financial regulation are greater for young life-cycle stage firms relative to both mature firms
required to comply with SOX and relative to young life-cycle firms exempt from SOX compliance.
RESEARCH DESIGN AND EMPIRICAL RESULTS
Young Life-Cycle versus Mature Life-Cycle Firms
To test our prediction, we implement a generalized differences-in-differences research
design that includes firm and year fixed effects (equation 1) and a traditional differences-in-
differences research design (equation 2). The generalized model is preferred when the effect is not
expected to be instantaneous. The models are shown below:
18
Innovationi,t = α + β1YoungLifeCyclei × PostSOX + β2Sizei,t + β3Book-to-Marketi,t +
β4Leveragei,t +5BigNAuditori,t + β6R&DMissingi,t + ∑ 𝛿𝑖𝐹𝑖𝑟𝑚𝑖𝑖=1𝑡𝑜𝑗 +
∑ 𝜉𝑚𝑌𝑒𝑎𝑟𝑚𝑚=1𝑡𝑜𝑘 + εi,t (1),
Innovationi,t = α + β1YoungLifeCyclei × PostSOX + β2YoungLifeCyclei + β3PostSOX +
β4Sizei,t + β5Book-to-Marketi,t + β6Leveragei,t + β7BigNAuditori,t +
β8R&DMissingi,t +εi, (2).
Innovation is measured as R&D Intensity, Log(#Patents), Log(Citations), or Log(#Claims).
YoungLifeCycle is the indicator variable for young life-cycle stage firms.25 PostSOX is an indicator
variable set to one for fiscal years following implementation of SOX 404(b), and zero otherwise.26
The firm fixed effects control for time invariant unobservables and the year fixed effects control
for macroeconomic shocks. We include Size, Book-to-Market, Leverage and BigNAuditor to
account for time varying factors.
Our primary variable of interest is the interaction of YoungLifeCycle stage with PostSOX,
(i.e., 𝛽1). The 𝛽1 coefficient reflects the change in innovation for young life-cycle stage firms
following SOX relative to the change in innovation for mature life-cycle stage firms following
SOX. Mature life-cycle firms act as a counterfactual for what the innovation consequences of
implementing financial regulation would have been for young life-cycle firms had they been
allowed to delay implementing until a more mature stage. If the effect of SOX is similar, the
coefficient on 𝛽1 will not differ significantly from zero. Alternatively, if financial regulation results
in more significant declines in innovation for young life-cycle stage firms than for more mature
firms, then the coefficient on 𝛽1 will be negative. Because the generalized difference-in-difference
25 Firm fixed effects make the inclusion of the main effect of YoungLifeCycle collinear as it does not vary within a
firm; thus, this variable is excluded from equation 1. 26 Because some firms implement SOX in 2004 and others in 2005, PostSOX is not collinear with year fixed effects
in equation 1 when R&D Intensity is the dependent variable. However, because the NBER patent data is available
only through 2006, firms who implement SOX in 2005 are excluded from our patent-based tests because they lack
patent-based measures for the entire post-SOX measurement window. Therefore, when patent-based measures are the
dependent variable for equation 1, the main effect of PostSOX is perfectly collinear with year fixed effects. For
consistency, in presenting results we exclude PostSOX in both estimations of equation 1. Results for R&D Intensity
are not sensitive this this decision and remain if we include PostSOX in estimations.
19
model includes year and firm fixed effects, the α coefficient in equation 1 reflects the average of
the dependent variable in the omitted year, which is 2001 in our models. The α coefficient in
equation 2 reflects the average of the dependent variable for mature firms in the pre-period.
Table 3, Panel A presents the estimation of equation 1, with columns for each of the
innovation variables (R&D Intensity, Log(#Patents), Log(Citations), and Log(#Claims)). We find
that innovation as measured by R&D Intensity and Log(#Claims) declines for young life-cycle
stage firms following SOX. Because prior research has suggested that the impacts of SOX are
moderated by firm size, in Panel B, we include a control for Size x PostSOX. Results confirm that
size is an important moderator and that that firm-life cycle has an incremental effect over firms
size; across all four measures we find evidence of a decline in innovation for young life-cycle
firms. To benchmark the economic impact of life-cycle versus size, in untabulated analyses we
create an indicator for small firms defined as those in the bottom size quartile each year, and we
estimate the results replacing size with the small firm indicator. The results suggest that the young
life-cycle stage effect is 0.7 to 3.9 times the effect of firm size; thus, the life-cycle stage effect is
economically significant. Table 3, Panel C presents results using a traditional difference-in-
difference design. In this specification, we find that the coefficient for YoungLifeCycle × PostSOX
is significant and negative in the R&D Intensity and Log(#Claims) regressions. The results in Table
3 suggest that young life-cycle stage firms experienced more severe innovation consequences from
SOX than mature life-cycle stage firms.
SOX Compliance vs. No SOX Compliance within Young Life-Cycle Stage Firms
In this section, we examine our second counterfactual, namely, young life-cycle stage firms
20
that were exempt from full compliance with SOX (non-accelerated filers).27 The non-accelerated
filer control sample allows us to make comparisons of the effects of financial regulation among
firms in the same life-cycle stage, but comes at the cost of making comparisons among firms of
different sizes.28 Using this sample, we estimate the following model:
Innovationi,t = α + β1Accelerated Fileri × PostSOX + β2Sizei,t + β3Book-to-Marketi,t +
β4Leveragei,t + β5R&DMissingi,t +β6BigNAuditori,t + ∑ 𝛿𝑖𝐹𝑖𝑟𝑚𝑖𝑖=1𝑡𝑜𝑗 +
∑ 𝜉𝑚𝑌𝑒𝑎𝑟𝑚𝑚=1𝑡𝑜𝑘 + εi,t (3).
The results from the estimation of equation 3 are provided in Table 4. We find that innovation
declines for young life-cycle firms required to comply with SOX across all four measures of
innovation. As an estimate of economic magnitude, consider R&D Intensity. Average R&D
Intensity in the pre-SOX period for young life-cycle stage firms was 19%. Thus, the coefficient of
–0.029 reported in Table 4 indicates a decline in R&D intensity of over 15 percent (0.029/0.19).
Overall, the results in Table 3 and 4 provide evidence that young life-cycle stage firms experience
significant innovation consequences from financial regulation and that the consequences of
financial regulation are greater for young life-cycle stage firms than mature firms.
Innovation Type
Financial regulation can impact innovation through both resource diversion and innovation
hindrance. In this section, we explore the influence of these channels via an analysis of the types
of innovation that declined as well as a time trend analysis. Patent originality and generality can
27 The SEC defines public float as the market value of common equity owned by nonaffiliates. Determining shares
held by affiliates involves judgment and can potentially be manipulated by the firm (Gao et al. 2009). Rather than
using market capitalization as a noisy proxy for public float, we use the presence of an internal control opinion to
identify our treatment firms. 28 Importantly, however, the accelerated filers are larger than the non-accelerated filers. Thus, in these tests the
treatment firms are larger than control firms and any effect we document is unlikely to be attributable to treatment
firms being systematically smaller than control firms. Notwithstanding, Table 4 includes size as a control variable and
in supplemental tests (untabulated) we included the interaction term, Size*PostSOX, to confirm that firm life-cycle
effect is distinct from any size effect. Results from these supplemental tests are consistent with our primary
conclusions. Specifically, when including Size*PostSOX we continue to find a highly significant decrease in
innovation output (patent-based variables) for young life-cycle stage firms post SOX implementation (p-values<.01).
21
be used as proxies for a firm’s innovation strategy. To the extent that innovation declines result
exclusively from reallocation of scarce resources (resource diversion) we would expect to see a
decline across all measures of firms’ innovation strategy because less resources are devoted to all
innovative activities. If, however, SOX also imposed organizational constraints detrimental to a
culture of explorative innovation (innovation hindrance) we expect to observe greater declines for
patent originality for young life-cycle stage firms relative to mature life-cycle stage firms.
We replicate Table 3, Panel A for measures of Originality and Generality. The results are
presented in Table 5 and are consistent with the decline in innovation that we document in Table
3 being attributable, at least in part, to a decline in the originality of patents. Although not
definitive, declining originality is consistent with imposed organizational changes negatively
affecting the innovation environment more severely for young life-cycle stage firms (i.e., the
innovation hindrance channel), and suggests that declining innovation is not exclusively
attributable to resource diversion. We also present the results comparing young life-cycle stage
firms required to comply with SOX to young life-cycle stage firms exempt from full compliance
(Panel B, Table 5). The results indicate that young life-cycle stage firms required to comply with
SOX experienced a significant decline in both patent Originality and Generality post-SOX,
although the negative effect appears to be larger for patent Originality.
Time Trend Analysis
The direct costs of SOX (internal controls implementation, testing, and attestation fees)
steadily decline after the initial year of compliance. Thus, to the extent that resource diversion is
solely responsible for the observed decline in innovation, we should expect the effect to taper off
after the initial year of implementation. By contrast, if declining innovation is also a product of
innovation hindrance, we expect the effect to persist beyond the initial year of compliance. We
22
provide evidence to this distinction by replicating our main findings from Table 3, Panel A and in
Table 4 except instead of interacting YoungLifeCycle with Post-SOX, we replace Post-SOX with
event-time indicators, specifically YoungLifeCycle × t-2 through YoungLifeCycle × t+2 where
YoungLifeCycle × t-3 is the omitted group.29 The results are presented in Table 6. In Panel A,
which compares young to mature life-cycle firm, the results are mixed. In the case of R&D
Intensity, Log(#Patents) and Log(Citations) we do observe a tapering off of the effect by t+2;
however, for Log(#Claims) the effect persists and is heightened by t+2 relative to time t. In Panel
B, which compares young life-cycle firms required to comply to young life-cycle firms exempt
from full compliance, the effect remains persistent across all measures through year t+2.
Collectively we interpret these findings, coupled with those on innovation type, as suggestive that
innovation is negatively impacted both through resource diversion and innovation hindrance.
Pseudo-event Test
A necessary assumption in our analysis is that treatment and control firms have parallel
trends in the pre-period which would have continued had it not been for SOX. We do observe
some pre-trend differences between young and mature life-cycle firms leading up to our assumed
year of implementation (year t). For example, we observe in Table 6 a decline in innovation
beginning in year t-1. One explanation for this difference is that because firms were aware of
impending SOX implementation, they may have begun implementing changes in anticipation of
mandatory compliance. To the extent that firms implemented the mandates of SOX sooner than
was required, this biases against us finding results in the period following mandated compliance.
We further mitigate concerns that pre-existing differences in trends between young and
29 Years t-3 to t-1 represent the pre-event window and years t to t+2 represent the implementation year and two post-
event windows. While we considered including additional post-event years in our analysis, we are limited in our
patent-based innovation data that concludes in 2006. Furthermore, the longer the time horizon employed, the less
reliably we feel we can attribute differences to our event of interest.
23
mature life-cycle firms are driving our results through pseudo-event tests. If our results are
attributable to unobservable differences between young and mature life-cycle stage firms, then we
would expect to observe similar results when we utilize a pseudo-event date. In our pseudo-event
test we estimate equation (1), but use 1995-2001 as the sample period, where the pseudo-
investment date is 1998 or 1999 depending on a firm’s fiscal year end.30 Because 1995-2001 also
includes the Internet bubble period and may therefore influence innovation outcomes, we also
analyze a pseudo-event period from 1990-1996 with 1993 or 1994 as the pseudo-implementation
year. Panel A (Panel B) of Table 7 presents the findings for the 1995-2001 (1990-1996) sample
period. In contrast to the decline in innovation that we document following SOX, we do not
observe changes in innovation following the pseudo-event dates. These results suggest that our
results in Table 3 are attributable to implementation of financial regulation, rather than reflective
of pre-existing trend differences. Notwithstanding, we acknowledge that, similar to other studies
using SOX as a shock, we cannot perfectly rule out the possibility that other economic or
regulatory changes around the same time period may contribute to our findings (see Leuz and
Wysocki 2016 for a review and discussion).
Potentially Offsetting Benefits from SOX
Our results suggest that implementation of SOX resulted in negative net present value
(NPV) investments for young life-cycle stage firms as it pertains to innovation outcomes. Yet, it
is possible that some other benefit ensues which outweighs such costs. To provide evidence on
this issue we conduct three tests.
30 The only modification to sample selection criteria from those used in our main tests is that we do not have the
benefit of knowing which firms will ultimately have to comply with SOX and therefore we cannot use the presence
of an internal control opinion in the post period to identify our sample firms. Thus, we include in our sample firms
with market value of equity greater than $75 million and assume that these are the firms who would have to comply
with SOX had it been required during that pseudo-event period (see Bargeron et al. 2010).
24
Financial Reporting Quality
First, because the aim of SOX was to improve FRQ, it is possible that financial reporting
benefits outweigh the innovation costs. Prior research has documented improvement in FRQ
following financial regulation (e.g., Abbott, Parker, and Peters 2004; Ashbaugh-Skaife et al. 2008;
Beasley 1996; Brunynseels and Cardinaels 2014; Doyle et al. 2007; Ege 2014; Farber 2005;
Hoitash, Hoitash, Bedard 2009; Klein 2002; Krishnan 2005; Vafeas 2005), although the strength
and persistence of this relation is open to debate (e.g., Bushman, Piotroski and Smith 2004;
Larcker, Richardson and Tuna 2007; Krishnan and Visvanathan 2008). The results documented in
prior research, however, may not generalize to young life-cycle stage firms who are systematically
distinct from their more mature counterparts when it comes to ex ante misstatement risk factors.
Intentional misstatements result from agency conflicts between managers and capital
providers which are likely to manifest differentially dependent on firm life-cycle stage. Agency
conflicts often manifest in the form of managerial pursuit of growth rather than stockholder wealth
(e.g. Mueller 1972) and are exacerbated by the presence of free cash flows (e.g. Jensen 1986).
Young life-cycle stage firms have less free cash flow than do mature life-cycle stage firms and are
more reliant on debt financing (e.g., Diamond 1991; Dickinson 2011; Myers 1977; Myers 1984;
Barclay and Smith 2005), which can provide external constraints on managerial opportunism (e.g.
Jensen 1986). Moreover, they tend to have less diversified ownership structures, attenuating the
distance between management and owners and thereby ameliorating manager’s incentives to
maximize stockholder wealth (Ang, Cole and Lin 2000; Huyghebaert and Van de Guncht 2007).
To the extent that financial regulation reduces the risk of intentional misstatements that originate
from agency conflicts that do not typify young life-cycle stage firms, then the regulation is unlikely
to improve their FRQ (Filatotchev, Toms and Wright 2006; O’Connor and Byrne 2015a, 2015b).
25
To examine if financial regulation influences FRQ for young life-cycle stage firms, we
replicate the analyses reported in Table 3 and 4, but we examine measures of FRQ rather than
innovation (Restatement, Poor Accrual Quality, FSD, Abs(Total Accruals), Total Accruals, and
Abnormal Revenue). We report these results in Table 8. Using either control group (i.e., mature
firms in Panel A or young life-cycle firms exempt from full SOX compliance in Panel B), we find
no robust evidence of improvement in FRQ.31 In Table 8, Panel A, we actually find evidence of a
decrease in FRQ; restatement rates as well as abnormal revenue (a proxy for accrual-based
earnings management) increase following SOX for young life-cycle stage firms relative to mature
firms. Collectively, the results suggest that financial regulation did not improve FRQ for young
life-cycle stage firms.
Market Expectations (Event Study)
Second, having failed to detect evidence of improvement in FRQ, we examine whether, on
average, the market anticipated differential (negative) outcomes for young-life-cycle firms
required to comply with SOX. Zhang (2007) and Engel et al. (2007) document negative returns
around events that increased the likelihood that SOX would pass. We add to their model a young
life-cycle indicator variable. We test for incrementally negative market reactions to events
increasing the likelihood that SOX would become law for YoungLifeCycle firms relative to their
more mature counterparts.32 Finding such evidence would be consistent with market participants
anticipating incremental net costs from SOX compliance for young life-cycle firms.
31 In Panel B of Table 8, we find evidence of decreased absolute value of total accruals post-SOX for young life-cycle
stage firms required to comply with SOX compared with non-accelerated filers (exempt firms). However, when we
examine signed total accruals, we find that income-increasing total accruals increase post-SOX suggesting that the
decline in the absolute value of discretionary accruals is not reflective of an improvement in FRQ. 32 We do not benchmark to non-accelerated filers as the market was not aware of this exemption until after the dates
we study.
26
Our sample selection procedures mirror that of Zhang (2007) and Engel et al. (2007) except
that we require cash flow data available to compute our measure of life-cycle stage and we exclude
firms in the shakeout and decline life-cycle stage. Furthermore, firms must be covered by CRSP
and have trading data available for our event periods. Of the 17 events that Zhang 2007 examines,
only four are individually statistically significant (see Zhang 2007, Table 2). In column (1) of
Table 9, we use the cumulative abnormal return for these four events as our first market reaction
measure (CAR1).33 In column (2) of Table 9, we alternatively consider the event dates used in
Engel et al. (2007) (this compares to their AR_SOX variable) (CAR2). We include a description
of the events and the event windows employed in Appendix B.34 Across both measures of returns
(CAR1 and CAR2), Table 9 provides evidence that young life-cycle stage firms experience
cumulative abnormal returns that are 3.9% lower than more mature firms (p-value<.05). This
suggests that the market anticipated that, on average, the costs of compliance with SOX would
exceed the benefits and that this effect would be more pronounced for young life-cycle stage firms.
Future Market Performance
Finally, to test whether the innovation consequences borne by young life-cycle stage firms
are offset by some ensuing benefit unanticipated by the market, we examine abnormal returns post
SOX. Specifically, we calculate the annual abnormal buy-and-hold return for year t that
compounds the monthly excess return for the 12-month period ending three months after the
balance sheet date, where abnormal returns are the excess of the firm’s return over the return to
33 The reported results employ robust standard errors, though inferences are identical if we use bootstrapped standard
errors. Following Engel et al. (2007), we include controls for market value (L(MV)), book-to-market (Book-to-
Market), leverage (Lev), free cash flow (FreeCashFlow), ROA (ROA), share turnover (Turnover) and the standard
deviation of returns measured as of the end of the prior year (StdRet). Appendix A provides detailed variable
measurement descriptions. 34 On average, in our replication we find that the cumulative abnormal return using the Zhang 2007 dates is –0.033
(compare to –0.038 for Zhang 2007) and is –0.029 using the Engel et al. (2007) event dates (compare to –0.056 for
Engel et al., 2007). We attribute these differences to our more stringent sample restrictions noting that we achieve
parity in direction and statistical significance.
27
the monthly value-weighted market index. We report the results in Table 10. Column (1) compares
changes in the annual buy-and-hold returns for young life-cycle firms to relative to mature firms.
Column (2) compares changes in the annual buy-and-hold return for young life-cycle accelerated
filers to young life-cycle non-accelerated filers. Finally, Column (3) uses the same sample and
pseudo-event period as in Table 7, Panel A (1995-2001) and Column (4) uses the same sample and
pseudo-event period as in Table 7, Panel B 1990-1996. The results provide evidence that returns
performance is worse post-SOX for young life-cycle firms relative to both mature firms and young
life-cycle stage firms exempt from full SOX compliance. In contrast, in the 1995-2001 post
pseudo-event period, young life-cycle stage firms experience superior returns performance or no
difference in returns performance using the 1990-1996 pseudo-event period.
Confounding Events
We recognize that SOX 404(b) was but one of several changes required by SOX and that
changes to financial reporting processes were also required by regulations that preceded SOX. For
example, between 1999 and 2003 both the NYSE and NASDAQ required listed firms’ audit
committees to be comprised entirely of independent directors and the board to have a majority of
independent directors (Dunchin, Matsusaka and Ozbas 2010). Research suggests that board
members can serve in an advisory or monitoring capacity; independent directors increase the
boards monitoring capabilities, but at the expense of the board’s advisory capabilities (Baldenius,
Melumad and Meng 2014; Coles, Daniel and Naveen 2008; Faleye et al. 2011). As such, an
increased emphasis on monitoring at the board level is expected to harm innovative outcomes
because monitoring-focused boards spend less time (and have less capabilities) related to strategy
development and resource acquisition, which aid the innovation process (Faleye et al. 2011;
Flitotchev et al. 2006). Accordingly, board independence or other provisions of SOX that increased
28
monitoring and centralization of decisions other than SOX 404(b) might account for a portion of
our results. However, to the extent that regulation-induced changes occurred prior to SOX 404(b)
implementation, this would bias against finding post-SOX 404(b) effects. Although we are not
able to fully disentangle the relative contributions of various provisions on our results, two factors
alleviate concerns. First, our primary contribution is to highlight the role that a firm’s life-cycle
stage plays in the implementation of financial regulation—a contribution whose impact is the same
regardless of the specific provision that drives our results. Second, the preponderance of regulation
requiring changes to board structure occurred prior to 2004. As these years constitute our pre-event
period, and we find evidence of post-event changes, our findings point to SOX 404(b)
implementation as the likely cause of our findings.
ALTERNATIVE SETTINGS
In our primary analysis, we utilize SOX as a setting to test our predictions. However, there
are several other settings of current relevance, including firm IPOs and the JOBS Act, which
dramatically alter the landscape of financial regulation to which firms are subject. Both settings
present significant empirical challenges. A major challenge to utilizing the IPO setting is the issue
of self-selection—firms who choose to IPO presumably consider the benefits of going public to
outweigh the costs associated increased regulatory compliance. Because the primary aim of the
JOBS Act was to lessen the regulatory burden for small IPO firms, similar issues of self-selection
present. Additionally, analysis of the innovation consequences resultant from the JOBS act is
limited to an analysis of R&D intensity; NBER patent data is only available through 2006.35
35 The Dodd-Frank represents another significant shock to financial regulation that we considered for analysis.
However, because its regulations are primarily oriented toward financial firms it is less likely to have a direct impact
on the type of innovation we study; rather the effect is more likely to manifest indirectly (if at all) as a result of
tightened lending. Notwithstanding, we attempted to examine whether there is any change in R&D intensity for young
29
Notwithstanding, because we are interested in the effects of financial regulation broadly, we
provide supplemental analyses of the innovation consequences associated with these settings. The
results obtained should be viewed in the context of the above outlined limitations.
We begin with a generalized difference-in-difference analysis of IPO-induced changes in
R&D intensity for young life-cycle stage firms versus mature firms, which is similar to Table 3
except that we replace our PostSOX with a PostIPO_Years indicator, which takes a value of 1 for
firm-years after the IPO.36 In order to hold the regulatory environment constant, our sample is
comprised of all firms with an IPO date between 2006 (post–SOX 404(b) implementation) and
April 4, 2012 (pre-JOBS) that have an internal control opinion in at least one post-period. As
shown in column 1 of Table 11, we find a significant reduction in R&D intensity for young life-
cycle stage firms relative to their more mature counterparts following the IPO. This evidence is
consistent with the incremental regulatory requirements associated with public-firm status having
greater innovation consequences for young life-cycle firms. Given the endogeneity inherent to an
IPO decision, it is striking that this result still manifests.
We next examine whether the JOBS act, intended to decrease the regulatory burden for
newly public firms, lessens the negative innovation consequences associated with IPOs for young
life-cycle stage firms. This analysis focuses on young life-cycle stage firms and examines if post-
JOBS IPOs results in less severe innovation consequences relative to pre-JOBS IPOs. Table 11
(column 2) presents a generalized difference-in-difference estimation of changes in R&D intensity
life-cycle stage firms relative to more mature firms around the effective date of the independent executive
compensation committee provisions of Dodd-Frank (2013) as these provisions might be expected to have broader
implications for the type of innovation we study. Unfortunately, after merging our sample with ISS/Risk Metrics data
and eliminating those firms who had 100% independent compensation committees prior to Dodd-Frank (and were
therefore unaffected by Dodd-Frank provisions), we are left without a sufficient sample of young life-cycle stage firms
to estimate a regression. Thus, we are unable to speak to the life-cycle moderated impact of Dodd-Frank on innovation. 36 We include up to three years around the IPO date for each firm and require that a firm have at least one pre- and
one post-observation (omit singleton observations).
30
for young life-cycle stage firms associated with IPOs occurring pre-JOBS (4/5/2012) compared to
post-JOBS.37 We do not find evidence that the regulatory reductions embedded in JOBS
significantly moderated the innovation consequences for young life-cycle stage IPOs, perhaps
because reduced disclosure requirements increased information uncertainty, potentially resulting
in capital rationing and less innovation ex post (Barth, Landsman and Taylor 2017).
Because we cannot examine innovation outputs (i.e. patents) directly, we further examine
the cost/benefit implications of JOBS through a descriptive analysis of the proportion of young
life-cycle stage firms that decide to IPO in the post- versus pre-JOBS period. Dambra, Field and
Gustafson (2015) provide evidence that the JOBS Act led to significant increases in overall IPO
activity. To the extent that financial regulation is particularly cumbersome to young life-cycle
stage firms we expect that the increase in IPO volume may be skewed towards these firms. Table
12 presents descriptive statistics on firm life-cycle stage at the time of IPO from for the population
of IPOs occurring between 2006 and 2017. As shown in Table 12, young life-cycle stage firms
comprised 34% of IPOs in the pre-JOBS period and 53% in the post-JOBS period suggesting that
for young life-cycle firms the net benefits of an IPO incrementally improve (relative to mature
firms) as a result of JOBS. This difference is statistically significant (two-tailed p-value <0.01).
Collectively, the results of these analyses, caveated in the context of their empirical
limitations, provide evidence consistent with our primary results and suggest that financial
regulation in a variety of settings may generate incremental innovation consequences for young
life-cycle stage firms.
37In order to ensure that pre-JOBS firms were subject to similar levels of regulatory burden, we exclude pre-JOBS
firms from this analysis without at least one post-IPO internal control opinion. Similarly, we also exclude firms in
the JOBS period that did not qualify for EGC status to ensure our comparison group had uniformly lower regulatory
requirements.
31
CONCLUSION
We provide evidence that innovation outcomes resultant from financial regulation vary
based on the life-cycle stage of a firm. Specifically, we find that R&D intensity, patent quantity
and patent impact are severely impaired for young life-cycle stage firms required to comply with
SOX relative to both their more mature counterparts, and to a benchmark of young life-cycle stage
firms exempted from full SOX compliance. Supplemental analyses on innovation type and effect
persistence suggest that declines to innovation manifest as a consequence of both scarce resource
diversion and from organizational changes that hamper the innovation environment (innovation
hindrance). We fail to detect evidence of improved financial reporting quality for young life-cycle
stage firms nor do we observe any market-based evidence that other offsetting benefits may
compensate for lost innovation. Supplemental analyses that examine other settings aside from SOX
yield similar conclusions. Overall, our results indicate that the diversion of resources, increase in
external monitoring, and centralization of decision making required by most reactionary financial
regulation produces an organizational structure less conducive to innovation and that these changes
produce incremental costs for young life-cycle stage firms. These findings should be of interest to
market participants and regulators, and suggest that a “one-size-fits all” approach to regulation
may disproportionately harm young life-cycle stage firms, an important segment of our economy.
32
References
Abbott, L., S. Parker, and G. Peters. 2004. Audit Committee characteristics and restatements. Auditing 23:
69–87.
Altamuro, J., and A. Beatty. 2010. How does internal control regulation affect financial
reporting? Journal of Accounting and Economics 49 (1): 58–74.
Amiram, D., Z. Bozanic, and E. Rouen. 2015. Financial statement errors: evidence from the distributional
properties of financial statement numbers. Review of Accounting Studies 20(4): 1540–1593.
Ang, J., R. Cole, and J. Lin. 2000. Agency Costs and Ownership Structure. The Journal of Finance LV
(1): 81–106.
Ashbaugh-Skaife, H., D. Collins, W. Kinney Jr, and R. LaFond. 2008. The effect of SOX internal control
deficiencies and their remediation on accrual quality. The Accounting Review 83(1): 217–250.
Asker, J., J. Farre-Mensa, and A. Ljungqvist. 2014. Corporate investment and stock market listing: a
puzzle? Review of Financial Studies 28(2): 342–390.
Badertscher, B., N. Shroff, and H. White. 2013. Externalities of public firm presence: Evidence from
private firms’ investment decisions. Journal of Financial Economics 109(3): 682–706.
Baldenius, T., N. Melumad, and X. Meng. 2014. Board composition and CEO power. Journal of
Financial Economics, 112(1): 53–68.
Balsmeier, B. L. Fleming, and G. Manso. 2017. Independent boards and innovation. Journal of Financial
Economics 123: 536–557.
Barclay, M., and C. Smith, Jr. 2005. The capital structure puzzle: The evidence revisited. Journal of
Applied Corporate Finance 17(1): 8–17.
Bargeron, L., K. Lehn, and C. Zutter. 2010. Sarbanes-Oxley and corporate risk-taking. Journal of
Accounting and Economics 49(1): 34–52.
Barth, M. E., Landsman, W. R., & Taylor, D. J. 2017. The JOBS Act and information uncertainty in IPO
firms. The Accounting Review, 92(6), 25-47.
Beasley, M. 1996. An empirical analysis of the relation between the board of director composition and
financial statement fraud. Accounting Review 71(4): 443–465.
Bernstein, S. 2015. Does going public affect innovation? Journal of Finance 70(4): 1365–1403.
Biddle, G., and G. Hilary. 2006. Accounting quality and firm-level capital investment. The Accounting
Review 81(5): 963–982.
Biddle, G., G. Hilary, and R. Verdi. 2009. How does financial reporting quality relate to investment
efficiency? Journal of Accounting and Economics 48(2): 112–131.
Bradshaw, M., M. Drake, J. Myers, and L. Myers. 2012. A re-examination of analysts’ superiority over
time-series forecasts of annual earnings. Review of Accounting Studies 17(4): 944–968.
Bruynseels, L., and E. Cardinaels. 2014. The audit committee: Management watchdog or personal friend
of the CEO? The Accounting Review 89(1): 113–145.
Burns, T., and G. Stalker. 1961. The Management of Innovation. Oxford University Press Inc., New
York.
Bushman, R., J. Piotroski, and A. Smith. 2004. What determines corporate transparency? Journal of
Accounting Research 42(2): 207–252.
Chan, L., J. Lakonishok, and T. Sougiannis. 2001. The Stock Market Valuation of Research and
Development Expenditures. The Journal of Finance 56(6): 2,431–2,456.
Cheng, M., D. Dhaliwal., and Y. Zhang. 2013. Does investment efficiency improve after the disclosure of
material weaknesses in internal control over financial reporting? Journal of Accounting and
Economics 56:1–18.
Christensen, C. 1997. The Innovator’s Dilemma. HBS Press, Boston, MA
Coates, J., and S. Srinivasan. 2014. SOX after ten years: A multidisciplinary review. Accounting
Horizons 28(3): 627–671.
Cohen, D., A. Dey, and T. Lys. 2013. Corporate governance reform and executive incentives:
Implications for investments and risk taking. Contemporary Accounting Research 30(4): 1296–1332.
33
Coles, J., N. Daniel, and L. Naveen. (2008). Boards: Does one size fit all? Journal of Financial
Economics, 87(2): 329–356.
Cyert, R., and J. March. 1963. A behavioral theory of the firm. Englewood Cliffs, NJ 2.
Dambra, M., Field, L.C., and Gustafson, M.T., 2015. The JOBS Act and IPO volume: Evidence that
disclosure costs affect the IPO decision. Journal of Financial Economics, 116(1), pp.121–143.
Dechow, P., and I. Dichev. 2002. The quality of accruals and earnings: The role of accrual estimation
errors. The Accounting Review 77(Supplemental): 35–59. Dechow, P., W. Ge, and C. Schrand. 2010. Understanding earnings quality: A review of the proxies, their
determinants and their consequences. Journal of Accounting and Economics 50: 344–401.
DeFond, M., and J. Jiambalvo. 1991. Incidence and circumstances of accounting errors. The Accounting
Review 66(3): 643–655.
Demerjian, P., B. Lev, M. Lewis, and S. McVay. 2013. Managerial ability and earnings quality. The
Accounting Review 88(2): 463–498.
Diamond, D. 1991. Monitoring and reputation: The choice between bank loans and directly placed debt.
Journal of Political Economy 99(4): 689–721.
Dickinson, V. 2011. Cash flow patterns as a proxy for firm life cycle. The Accounting Review 86(6):
1969–1994.
Doyle, J., W. Ge, and S. McVay. 2007. Determinants of weaknesses in internal control over financial
reporting. Journal of Accounting and Economics 44(1): 193–223.
Ege, M. 2014. Does internal audit function quality deter management misconduct? The Accounting
Review 90(2): 495–527.
Eisenhardt, K. 1985. Control: Organizational and economic approaches. Management Science 31(2):
134–149.
Engel, E., R. Hayes, and X. Wang. 2007. The Sarbanes–Oxley Act and firms’ going-private decisions.
Journal of Accounting and Economics 44(1):116–145.
Faleye, O., R. Hoitash, and U. Hoitash. (2011). The costs of intense board monitoring. Journal of
Financial Economics, 101(1): 160–181.
Farber, D. 2005. Restoring trust after fraud: Does corporate governance matter? The Accounting Review
80: 344–401.
Filatotchev, I., S. Toms and M. Wright. 2006. The firm's strategic dynamics and corporate governance
life-cycle. International Journal of Managerial Finance 2(4): 256–279.
Foss, N., and K. Laursen. 2005. Performance pay, delegation and multitasking under uncertainty and
innovativeness: An empirical investigation. Journal of Economic Behavior & Organization 58(2):
246–276.
Francis, J., P. Michas, and M. Yu. 2013.Office size of Big 4 auditors and client restatements.
Contemporary Accounting Research 30(4): 1626–1661.
Gao, F., J. Wu, and J. Zimmerman. 2009. Unintended consequences of granting small firms exemptions
from securities regulation: Evidence from the Sarbanes-Oxley Act. Contemporary Accounting
Research 47(2):459–506.
Gerstein, J. 2006. Friedman, 93, set to unleash power of choice. New York Sun, March 22, 2006.
Gort, M., and S. Klepper. 1982. Time paths in the diffusion of product innovation. Economic Journal
92(367): 630–653.
Grant, R. 1996. Prospering in dynamically-competitive environments: Organizational capability as
knowledge integration. Organization Science 7(4): 375–387.
Greenspan, A. 2003. Testimony before the Committee on Financial Services. U.S. House of
Representatives. Washington, DC: Government Printing Office.
Gunny, K., and T. Zhang. 2014. Do managers use meeting analyst forecasts to signal private information?
Evidence from patent citations. Journal of Business Finance & Accounting 41(7-8): 950–973.
Hall, B., A. Jaffe, and M. Trajtenberg. 2001. The NBER patent citation data file: Lessons, insights and
methodological tools (No. w8498). National Bureau of Economic Research.
34
He, Z., and P. Wong. 2004. Exploration and exploitation: An empirical test of the ambidexterity
hypothesis. Organization Science 15(4) 481–494.
Henrekson, M., and D. Johansson. 2010. Gazelles as job creators: a survey and interpretation of the
evidence. Small Business Economics 35(2): 227–244.
Hirshleifer, D., P. Hsu, and D. Li. 2013. Innovative efficiency and stock returns. Journal of Financial
Economics 107(3): 632–654.
Hitt, M., R. Hoskisson, R. Johnson, and D. Moesel. 1996. The market for corporate control and firm
innovation. Academy of Management Journal 39(5): 1084–1119.
Hoitash, U., R. Hoitash, and J. Bedard. 2009. Corporate governance and internal control over financial
reporting: A comparison of regulatory regimes. The Accounting Review 84(3): 839–867.
Holmstrom, B. 1989. Agency costs and innovation. Journal of Economic Behavior & Organization 12(3):
305–327.
Huyghebaert, N., and L. Van de Gucht. 2007. The determinants of financial structure: new insights from
business start-ups. European Financial Management 13(1): 101–133.
Iliev, P. 2010.The effect of SOX Section 404: Costs, earnings quality, and stock prices. The Journal of
Finance 65(3): 1163–1196.
Jansen, J., F. Van Den Bosch, and H. Volberda. 2006. Exploratory innovation, exploitative innovation,
and performance: Effects of organizational antecedents and environmental moderators. Management
Science 52(11): 1661–1674.
Jensen, M. C. 1986. Agency cost of free cash flow, corporate finance, and takeovers. American
Economic Review 76(2): 323–329.
Kang, Q., Q. Liu, and R. Qi. 2010. The Sarbanes-Oxley Act and Corporate Investment: A Structural
Assessment. Journal of Financial Economics 96: 291–305.
Klein, A. 2002. Audit committee, board of director characteristics, and earnings management. Journal of
Accounting and Economics, 33(3): 375–400.
Klein, A., and C. Marquardt. 2006. Fundamentals of accounting losses. The Accounting Review 81 (1):
179–206.
Krishnan, J. 2005. Audit committee quality and internal control: An empirical analysis. The Accounting
Review 80(2): 649–675.
Koh, P., and D. Reeb. 2015. Missing R&D. Journal of Accounting and Economics 60(1): 73–94.
Krishnan, G., and G. Visvanathan. 2008. Does the SOX definition of an accounting expert matter? The
association between audit committee directors' accounting expertise and accounting
conservatism. Contemporary Accounting Research 25(3): 827–858.
Larcker, D., S. Richardson, and I. Tuna. 2007. Corporate governance, accounting outcomes, and
organizational performance. The Accounting Review 82(4): 963–1008.
Leuz, C., and P. Wysocki. 2016. The economics of disclosure and financial reporting regulation:
Evidence and suggestions for future research. Journal of Accounting Research 54(2): 525–622.
March, J. 1991. Exploration and exploitation in organizational learning. Organization Science 2(1): 71–
87.
McNichols, M. 2002. Discussion of: The quality of accruals and earnings: The role of accrual estimation
errors. The Accounting Review 77 (4): 61–69.
McGrath, R., and I. MacMillan. 2000. The Entrepreneurial Mindset: Strategies for Continuously Creating
Opportunity in an Age of Uncertainty. Harvard Business Press. ISBN: 0875848346
Michaels, A. 2003. After a year of U.S. corporate clean-up, William Donaldson calls for a return
to risk taking. FinancialTimes.com (July 24).
Mueller, D. 1972. A life cycle theory of the firm. The Journal of Industrial Economics 20(3): 199–219.
Myers, S. 1977. Determinants of corporate borrowing. Journal of Financial Economics 5(2): 147–175.
Myers, S. 1984. The capital structure puzzle. Journal of Finance 39(3): 575–592.
Nohria, N., and R. Gulati. 1996. Is Slack Good or Bad for Innovation? The Academy of Management
Journal 39(5): 1245–1264
35
Nickerson, J., and T. Zenger. 2004. A knowledge-based theory of the firm—The problem-solving
perspective. Organization Science 15(6): 617–632.
O'Connor, T., and J. Byrne. 2015a. Governance and the corporate life-cycle. International Journal of
Managerial Finance 11(1): 23–43.
O'Connor, T., and J. Byrne, 2015b. When does corporate governance matter? Evidence from across the
corporate life-cycle. Managerial Finance 41 (7): 673–691.
Persons, O. 2005. The relation between the new corporate governance rules and the likelihood of financial
statement fraud. Review of Accounting and Finance 4(2): 125–148.
Shadab, H. 2008. Innovation and corporate governance: The impact of Sarbanes-Oxley. University of
Pennsylvania Journal of Business and Employment Law 10(4): 955–1008
Spence, M. 1977. Entry, capacity, investment, and oligopolistic pricing. Bell Journal of Economics 8(2):
534–544.
Spence, M. 1979. Investment strategy and growth in a new market. Bell Journal of Economics 10(1): 1–
19.
Spence, M. 1981. The learning curve and competition. Bell Journal of Economics 12(1): 49–70. Stubben, S. 2010. Discretionary revenues as a measure of earnings management. The Accounting Review
85(2): 695–717.
Turner, K., and M. Makhija. 2006. The role of organizational controls in managing knowledge. Academy
of Management Review 31(1): 197–217.
Tushman, M., and C. O’Reilly. 1996. Ambidextrous organizations: Managing evolutionary and
revolutionary change. California Management Review 38:8–29.
Vafeas, N. 2005. Audit committees, boards, and the quality of reported earnings. Contemporary Accounting
Research 22(4): 1093–1122.
Van Praag, M., and Versloot, P. 2008. The economic benefits and costs of entrepreneurship: A review of
the research. Foundations and Trends in Entrepreneurship Research 4(2): 65–154.
Wasley, C., and J. Wu. 2006. Why do managers voluntarily issue cash flow forecasts? Journal of
Accounting Research 44(2): 389–429.
Zhang, I. 2007. Economic consequences of the Sarbanes–Oxley Act of 2002. Journal of Accounting and
Economics 44(1): 74–115.
36
Appendix A - Variable Definitions
Variable Description Definition
DEPENDENT VARIABLES
R&D Intensity Innovation The ratio of R&D expense to total assets (XRD/AT). Missing
values are set to zero. Obtained from Compustat.
Log(#Patents) Innovation Log of the total number of patents a firm applied for in year t.
Obtained from the NBER database available at
https://sites.google.com/site/patentdataproject/Home.
Log(Citations) Innovation Log of the average lifetime patent forward citations available
through 2006 for patents applied for in year t.
Log(#Claims) Innovation Log of the average number of claims made by a firm’s patents
applied for in year t. Obtained from the NBER database.
Originality Innovation Type Our proxy for explorative innovation. 𝑂𝑟𝑖𝑔𝑖𝑛𝑎𝑙𝑖𝑡𝑦𝑖 = 1 −
𝛴𝑗𝑛𝑖𝑠𝑖𝑗
2 , where 𝑠𝑖𝑗 is the percentage of citations a patent
document, i, makes to patents in technology area j, out of
𝑛𝑖technology areas (Hall et al. 2001). We take the average
value of Originality across all patents a firm applies for in year
t as our measure.
Generality Innovation Type Our proxy for exploitative innovation. It is calculated similarly
to Originality except it uses the forward citations a patent
document receives. 𝐺𝑒𝑛𝑒𝑟𝑎𝑙𝑖𝑡𝑦𝑖 = 1 − 𝛴𝑗𝑛𝑖𝑠𝑖𝑗
2 , where 𝑠𝑖𝑗 is the
percentage of citations a patent document, i, receives from
patents in technology area j, out of 𝑛𝑖technology areas (Hall et
al. 2001). We take the average value of Generality across all
patents a firm applies for in year t as our measure.
Restatement Poor Financial
Reporting Quality
Takes on the value of one if a firm restates the financial
statements related to year t whether due to error or fraud and
zero otherwise. Obtained from Audit Analytics Non-Reliance
Restatements dataset.
Poor Accrual
Quality
Poor Financial
Reporting Quality
The absolute value of the annual Dechow and Dichev (2002)
accruals quality metric as used in Demerjian, Lewis and McVay
(2013). It is the residual from running the following OLS
regression by industry and year:
𝛥𝑊𝑜𝑟𝑘𝑖𝑛𝑔 𝑐𝑎𝑝𝑖𝑡𝑎𝑙𝑡
= 𝑐𝑎𝑠ℎ 𝑓𝑙𝑜𝑤 𝑓𝑟𝑜𝑚 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑠𝑡−1
+ 𝑐𝑎𝑠ℎ 𝑓𝑙𝑜𝑤 𝑓𝑟𝑜𝑚 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑠𝑡
+ 𝑐𝑎𝑠ℎ 𝑓𝑙𝑜𝑤 𝑓𝑟𝑜𝑚 𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑜𝑛𝑠𝑡+1
+ 𝛥𝑠𝑎𝑙𝑒𝑠𝑡−1,𝑡 + 𝑛𝑒𝑡 𝑝𝑝𝑒𝑡 + 𝑒𝑡
37
Abs(Total
Accruals)
Poor Financial
Reporting Quality
The absolute value of the difference between income before
extraordinary items and cash flows from operations, scaled by
average total assets. (IB-OANCF)/Average AT.
Total Accruals Poor Financial
Reporting Quality
The signed difference between income before extraordinary
items and cash flows from operations, scaled by average total
assets. (IB-OANCF)/Average AT.
Abnormal
Revenue
Poor Financial
Reporting Quality
The absolute value of the residual from the regression of the
change in accounts receivable (RECCH/Average AT) on the
annual change in sales for the fourth quarter and quarters one
through three, where changes in sales are calculated as follows:
(SALE-lag(SALE))/Average AT).
FSD Poor Financial
Reporting Quality
The Financial Statement Divergence Score, calculated as
described in Amiram, Bozanic and Rouen (2015).
CAR1 Cumulative
abnormal return
Cumulative abnormal return associated with events increasing
the likelihood that SOX would become law, where the CRSP
value-weighted return is the market index. The events used to
calculate CAR1 are the significant events identified by Zhang
(2007). See Appendix B for event windows used.
CAR2 Cumulative
abnormal return
Cumulative abnormal return associated with events increasing
the likelihood that SOX would become law, where the CRSP
value-weighted return is the market index. CAR2 includes the
events associated with increased SOX passage likelihood as
identified by Engel et al. (2007) (i.e., events used to calculate
their AR_SOX measure). See Appendix B for event windows
used.
Buy-and-hold
Return
Performance The annual buy-and-hold return that compounds the monthly
excess return for the 12-mo. period ending three months after
the balance sheet date, where returns are calculated in excess of
the monthly stock index value weighted return (VWRETD).
Obtained from CRSP.
INDEPENDENT VARIABLES
YoungLife
Cycle
Young Life-Cycle
Stage Firm
Indicator
Young life-cycle firm indicator, which takes on the value of one
if all of the following are true in the year of assumed
implementation:
1) operating cash flows (OANCF) are less than zero,
2) investing cash flows (IVNCF) are less than zero, and
3) financing cash flows (FINCF) are greater than zero
and zero otherwise, following Dickinson (2011). Obtained
from Compustat.
38
Control Variables
Size Firm Size The log of total assets (AT), obtained from Compustat.
BigNAuditor Audited by Big N
Audit Firm
Takes on the value of one if a firm is audited by a BigNAuditor
and zero otherwise. Obtained from Compustat (AU codes 1-8,
namely Arthur Anderson, PricewaterhouseCoopers, Ernst &
Young, Deloitte & Touche, KPMG and their pre-merger
names).
R&D Missing Indicator for
missing R&D data
Takes on the value of one if the Compustat variable XRD is
missing and zero otherwise. Obtained from Compustat.
L(MV)t-1 The logarithm of
the firm’s market
value of equity
The log of the market value of equity: log(CSHO*PRCC_F).
Obtained from Compustat.
Book-to-Market The Ratio of the
Firm’s Book Value
to its market value
The book to market ratio calculated as total assets divided by
total assets less equity plus market value [AT/(AT-CEQ +
(CSHO*PRCC_F))]. Obtained from Compustat.
Leverage Leverage The leverage ratio calculated as total liabilities divided by total
assets (DLTT+DLC)/AT. Obtained from Compustat.
FreeCash
Flowt-1
Excess cash Free cash flow calculated as (operating income before
depreciation - income tax expense + deferred taxes - dividends
to preferred and common)/assets (OIBDP - TXT + TXDB -
DV)/AT. Obtained from Compustat.
ROAt-1 Return on Assets Return on assets computed as operating income before
depreciation divided by total assets OIBDP/AT. Obtained from
Compustat.
Turnovert-1 Monthly share
turnover
The average monthly share turnover calculated as share volume
divided by the number of shares outstanding
(VOL*100)/(SHROUT*1,000). Obtained from CRSP.
StdRett-1 Standard deviation
of returns
The standard deviation of a firm’s monthly holding period
return (RET), calculated using monthly returns. Obtained from
CRSP.
39
Appendix B – Event Dates
This table outlines the events used to calculate the measures of cumulative abnormal returns
associated with events increasing the likelihood that SOX would become law. CAR1 calculates
returns using the significant events as indicated by Zhang (2007). CAR2 calculates returns using
the events associated with increased SOX passage likelihood as identified by Engel et al. (2007)
(i.e., events used to calculate their AR_SOX measure).
Event Description
(Taken from Zhang 2007)
Zhang (2007)
Event Window
Engel et al. (2007)
Event Window
Treasury Secretary called for changes in rules
governing corporations
2/1–2/4 Not included in
calculating CAR2.
SEC proposed rules to require executives to certify
financial reports
Not an individually
significant event.
6/11–6/13
Senate Banking Committee passed Sarbanes’ bill Not an individually
significant event.
6/17–6/19
WorldCom admitted that they understated expenses
by $3.8 billion
Not an individually
significant event.
6/25–6/28
Senate debated Sarbanes’ bill
Bush delivered a speech on corporate reforms;
passage of Sarbanes’ bill likely
Senate passed a tough amendment to strengthen
criminal penalties 97 to 0
7/8–12 7/7–7/13
Senate passed Sarbanes’ bill
House passed bill to strengthen criminal penalties
Not an individually
significant event.
7/14–7/17
House Republican leaders reportedly retreated from
efforts to dilute the Senate’s tough bill
Conference committee started negotiations to merge
bills and Senate’s bill became the framework;
negotiation continued over the weekend
Bush pushed to speedup rulemaking in a radio
address
Lobbyists reportedly lost their impact
7/18–23 Not included in
calculating CAR2.
Senate and House agreed on the final rule
Senate and House passed SOX
7/24–26 7/23–7/26
40
Table 1
Sample Selection
Panel A: Sample Selection Criteria
Criteria Number of firm-year
observations
US firms with positive assets from 2001-2007 93,281
Excluding financial firms and utilities 75,333
Observations with non-missing cash flow data 71,144
Observations with non-missing data necessary for the calculation
of the control variables
62,879
Exclude shake-out and decline firms 52,768
Limit sample years to six consecutive years around
implementation.
16,518
Exclude firms that changed fiscal year ends during the sample
period
16,152
Exclude firms that changed filing status during the sample period. 14,988
Include only accelerated filers and young life-cycle stage, non-
accelerated filers
12,042
Panel B: Samples Employed
More mature life-cycle, accelerated filer firm-years 8,400
Young life-cycle, accelerated filer firm-years 1,182
Total: Young vs. Mature Life-Cycle Sample 9,582 Young life-cycle, accelerated filer firm-years 1,182
Young life-cycle, non-accelerated filer firm-years 2,460
Total: Accelerated vs. Non-accelerated Young Life-Cycle Sample 3,642
Note: The sample includes Compustat firms with internal control audit opinions for the entire post period and data
available for all six years of our sample period (2001-2007) and excludes firms in the decline and shakeout life-cycle
stages as of the SOX implementation year.
41
Table 2
Descriptive Statistics
Panel A: Young vs. Mature Life-Cycle Firms
Life-cycle Stage = Life-cycle Stage = Difference-in
Young More Mature Difference
Pre Post Difference Pre Post Difference
R&D Intensity 0.19 0.15 –0.04 ** 0.03 0.03 0.00 *** –0.04 *
R&D Missing 0.22 0.20 –0.02 0.39 0.39 0.00 –0.02
Log(#Patents) 0.72 0.16 –0.56 *** 0.80 0.23 –0.57 *** 0.01
Log(Citations) 0.49 0.02 –0.47 *** 0.47 0.04 –0.43 *** –0.04
Log(#Claims) 1.36 0.39 –0.97 *** 1.08 0.41 –0.67 *** –0.30 ***
Restatement 0.18 0.16 –0.02 0.22 0.13 –0.09 *** 0.07 **
Poor Accrual Quality 0.06 0.06 0.00 0.03 0.03 0.00 *** 0.00
FSD 0.03 0.03 0.00 0.03 0.03 0.00 *** 0.00
Abs(Total Accruals) 0.16 0.13 –0.03 ** 0.09 0.07 –0.02 *** –0.01
Total Accruals –0.12 –0.08 0.04 *** -0.08 -0.06 0.02 *** 0.02
Abnormal Revenue 0.04 0.04 0.00 0.03 0.02 –0.01 *** 0.01 *
Size 4.86 5.29 0.43 *** 6.57 6.93 0.36 *** 0.07
Book-to-market 0.59 0.50 –0.09 *** 0.64 0.57 –0.07 *** –0.02
Leverage 0.21 0.21 0.00 0.22 0.20 –0.02 *** 0.02
Big N Auditor 0.87 0.78 –0.09 *** 0.95 0.90 –0.05 *** –0.04 *
Firm Age 12.16 15.16 3.00 *** 21.09 24.09 3.00 *** 0.00
42
Table 2
Descriptive Statistics, cont.
Panel B: Accelerated Filer Young Life-Cycle vs. Non-accelerated Filer Young Life-Cycle Firms
Life-cycle Stage = Young Life-cycle Stage = Young Difference-in
Difference Mandated Investment = Yes Mandated Investment = No
Pre Post Difference Pre Post Difference
R&D Intensity 0.19 0.15 –0.04 ** 0.20 0.20 0.00 –0.04 *
R&D Missing 0.22 0.20 –0.02 0.31 0.30 –0.01 –0.01
Log(#Patents) 0.72 0.16 –0.56 *** 0.12 0.02 –0.10 *** –0.46 ***
Log(Citations) 0.49 0.02 –0.47 *** 0.09 0.00 –0.09 *** –0.38 ***
Log(#Claims) 1.36 0.39 –0.97 *** 0.31 0.07 –0.24 *** –0.73 ***
Restatement 0.18 0.16 –0.02 0.13 0.11 –0.02 0.00
Accrual Quality 0.06 0.06 0.00 0.12 0.11 –0.01 0.01
FSD 0.03 0.03 0.00 0.04 0.04 0.00 0.00
Abs(Total Accruals) 0.16 0.13 –0.03 ** 0.63 0.73 0.10 ** –0.13 ***
Total Accruals –0.12 –0.08 0.04 *** –0.53 –0.61 –0.08 * 0.12 ***
Abnormal Revenue 0.04 0.04 0.00 0.07 0.07 0.00 0.00
Size 4.86 5.29 0.43 *** 1.65 1.95 0.30 *** 0.13
Book-to-market 0.59 0.50 –0.09 *** 0.49 0.42 –0.07 *** –0.02
Leverage 0.21 0.21 0.00 0.60 0.67 0.07 –0.07
Big N Auditor 0.87 0.78 –0.09 *** 0.34 0.15 –0.19 *** 0.10 ***
Firm Age 12.16 15.16 3.00 *** 12.15 15.15 3.00 *** 0.00 Notes: This table presents mean sample descriptive statistics for the pre- and post- SOX periods by life-cycle stage.
Panel A includes a comparison of young to mature life-cycle firms, while Panel B compares accelerated filer young
life-cycle firms to non-accelerated filer young life-cycle firms. Appendix A provides variable definitions.
43
Table 3
Innovation, Young vs. More Mature Life-Cycle Firms
Panel A: Generalized Difference-in-Difference Design
Innovationi,t = α + β1YoungLifeCyclei × PostSOX + β2Sizei,t + β3Book-to-Marketi,t + β4Leveragei,t
+5BigNAuditori,t + β6R&DMissingi,t + ∑ 𝛿𝑖𝐹𝑖𝑟𝑚𝑖𝑖=1𝑡𝑜𝑗 + ∑ 𝜉𝑚𝑌𝑒𝑎𝑟𝑚𝑚=1𝑡𝑜𝑘 + εi,t
(Equation 1)
Dependent Variable =
R&D Intensity Log(#Patents) Log(Citations) Log(#Claims)
Variables (1) (2) (3) (4)
YoungLifeCycle × PostSOX –0.023** –0.052 –0.064 –0.417***
(–2.415) (–0.899) (–1.041) (–4.727)
Size –0.056*** 0.230*** 0.125*** 0.159***
(–7.975) (5.787) (3.539) (3.236)
Book-to-Market 0.002 –0.258*** –0.173*** –0.204**
(0.340) (–4.501) (–2.755) (–2.479)
Leverage 0.072*** –0.013 –0.060 0.003
(3.904) (–0.145) (–0.834) (0.023)
BigNAuditor 0.007 –0.219*** –0.124** –0.072
(1.367) (–3.892) (–2.115) (–0.757)
R&D Missing –0.011** –0.058 –0.009 –0.040
(–2.378) (–0.599) (–0.090) (–0.288)
R&D Intensity 0.480*** 0.476** 0.597**
(2.841) (2.418) (2.085)
Constant 0.388*** –0.187 0.108 0.415
(9.274) (–0.738) (0.479) (1.277)
N 9,582 6,522 6,522 6,522
Adjusted R2 0.146 0.256 0.219 0.207
Year Fixed Effects YES YES YES YES
Firm Fixed Effects YES YES YES YES
44
Table 3
Innovation, Young vs. More Mature Life-Cycle Firms, cont.
Panel B: Generalized Difference-in-Difference Design Including Size x PostSOX
Dependent Variable =
R&D Intensity Log(#Patents) Log(Citations) Log(#Claims)
Variables (1) (2) (3) (4)
YoungLifeCycle × PostSOX –0.022** –0.306*** –0.165** –0.425***
(–2.374) (–4.849) (–2.498) (–4.514)
Size × PostSOX 0.001** –0.156*** –0.062*** –0.004
(2.223) (–8.569) (–4.654) (–0.234)
Size –0.056*** 0.226*** 0.124*** 0.159***
(–7.992) (5.845) (3.505) (3.234)
Book-to-Market 0.001 –0.195*** –0.148** –0.202**
(0.219) (–3.554) (–2.363) (–2.437)
Leverage 0.072*** –0.012 –0.060 0.003
(3.908) (–0.166) (–0.890) (0.023)
BigNAuditor 0.005 –0.071 –0.065 –0.068
(1.107) (–1.276) (–1.119) (–0.717)
R&D Missing –0.011** –0.043 –0.003 –0.039
(–2.459) (–0.444) (–0.034) (–0.285)
R&D Intensity 0.641*** 0.540*** 0.601**
(3.613) (2.685) (2.108)
Constant 0.390*** –0.355 0.042 0.410
(9.306) (–1.418) (0.184) (1.263)
N 9,582 6,522 6,522 6,522
Adjusted R2 0.146 0.292 0.226 0.207
Year Fixed Effects YES YES YES YES
Firm Fixed Effects YES YES YES YES
45
Table 3
Innovation, Young vs. More Mature Life-Cycle Firms, cont.
Panel C: Traditional Difference-in-Difference Design
Innovationi,t = α + β1YoungLifeCyclei × PostSOX + β2YoungLifeCyclei + β3PostSOX + β4Sizei,t
+ β5Book-to-Marketi,t + β6Leveragei,t + β7BigNAuditori,t + β8R&DMissingi,t +εi,t
(Equation 2)
Dependent Variable =
R&D Intensity Log(#Patents) Log(Citations) Log(#Claims)
Variables (1) (2) (3) (4)
YoungLifeCycle × PostSOX –0.028*** –0.010 –0.047 –0.383***
(–2.796) (–0.171) (–0.765) (–4.261)
YoungLifeCycle 0.113*** –0.006 –0.001 0.316***
(9.165) (–0.082) (–0.013) (2.894)
PostSOX –0.003*** –0.671*** –0.485*** –0.715***
(–3.143) (–19.419) (–20.562) (–22.327)
Size –0.016*** 0.276*** 0.081*** 0.218***
(–7.896) (13.117) (9.915) (12.849)
Book-to-Market –0.080*** –0.279*** –0.078* –0.332***
(–9.106) (–3.993) (–1.957) (–4.209)
Leverage 0.035* –0.389*** –0.193*** –0.383***
(1.695) (–4.836) (–4.678) (–3.820)
BigNAuditor 0.017** –0.045 0.036 0.122*
(1.991) (–0.940) (1.363) (1.664)
R&D Missing –0.053*** –0.732*** –0.322*** –0.902***
(–19.024) (–16.039) (–14.287) (–17.399)
R&D Intensity 1.526*** 0.614*** 1.604***
(6.607) (4.274) (5.211)
Constant 0.190*** –0.433*** 0.136*** 0.153
(9.740) (–4.110) (2.600) (1.250)
N 9,582 6,522 6,522 6,522
Adjusted R2 0.344 0.313 0.202 0.258 Notes: This table reports the results from the regression of Innovation on YoungLifeCycle× PostSOX, and control
variables. T-statistics are presented below the coefficients. Panels A and B use a generalized difference-in-difference
design, Panel C employs a traditional difference-in-difference design. See Appendix A for variable definitions. ***,
**,* denotes a two-tailed p-value of less than 0.01, 0.05, and 0.10, respectively. To control for time-series correlation
in the error term, t-statistics are based on robust standard errors that are clustered by firm.
46
Table 4
Innovation, Accelerated Filer Young Life-Cycle vs. Non-accelerated Filer Young Life-Cycle
Firms
Innovationi,t = α + β1Accelerated Fileri × PostSOX + β2Sizei,t + β3Book-to-Marketi,t +
β4Leveragei,t + β5R&DMissingi,t +β6BigNAuditori,t + ∑ 𝛿𝑖𝐹𝑖𝑟𝑚𝑖𝑖=1𝑡𝑜𝑗 +
∑ 𝜉𝑚𝑌𝑒𝑎𝑟𝑚𝑚=1𝑡𝑜𝑘 + εi,t
(Equation 3)
Dependent Variable =
R&D Intensity Log(#Patents) Log(Citations) Log(#Claims)
Variables (1) (2) (3) (4)
Accelerated Filer × PostSOX –0.025* –0.464*** –0.414*** –0.764***
(–1.887) (–8.378) (–6.693) (–8.316)
Size –0.089*** 0.051*** 0.020 0.106***
(–8.522) (3.661) (1.206) (3.732)
Book-to-Market –0.017 –0.023 –0.011 –0.108
(–1.079) (–0.567) (–0.190) (–1.460)
Leverage 0.033*** 0.016 –0.002 0.030
(2.646) (1.363) (–0.119) (1.107)
BigNAuditor 0.045*** –0.050 –0.025 –0.031
(2.950) (–1.309) (–0.637) (–0.391)
R&D Missing –0.211*** –0.026 –0.001 –0.030
(–6.143) (–0.613) (–0.024) (–0.360)
R&D Intensity 0.128*** 0.080 0.253**
(2.640) (1.640) (2.585)
Constant 0.456*** 0.319*** 0.335*** 0.599***
(13.134) (6.419) (5.610) (5.843)
N 3,642 2,436 2,436 2,436
Adjusted R2 0.196 0.243 0.169 0.197
Year Fixed Effects YES YES YES YES
Firm Fixed Effects YES YES YES YES Notes: This table only considers young life-cycle stage firms (i.e., other life-cycle stage firms are excluded from the
analysis). This table uses a generalized difference-in-difference model and reports the results from the regression of
innovation on Accelerated Filer× PostSOX, control variables, firm and year fixed effects. Accelerated Filer firms are
the young life-cycle firms required to comply with SOX. Non-accelerated filers were exempt from full compliance.
See Appendix A for variable definitions. ***, **,* denotes a two-tailed p-value of less than 0.01, 0.05, and 0.10,
respectively. To control for time-series correlation in the error term, t-statistics are based on robust standard errors
that are clustered by firm.
47
Table 5 Innovation Type
Panel A: Young vs. Mature Life-Cycle Firms
InnovationTypei,t = α + β1YoungLifeCyclei × PostSOX + β2Sizei,t + β3Book-to-Marketi,t +
β4Leveragei,t +β5BigNAuditori,t + β6R&DMissingi,t + ∑ 𝛿𝑖𝐹𝑖𝑟𝑚𝑖𝑖=1𝑡𝑜𝑗 +
∑ 𝜉𝑚𝑌𝑒𝑎𝑟𝑚𝑚=1𝑡𝑜𝑘 + εi,t
(Equation 4)
Dependent Variable =
Originality Generality
Variables (1) (2)
YoungLifeCycle × PostSOX –0.085*** –0.004
(–4.322) (–0.223)
N 6,522 6,522
Adjusted R2 0.165 0.128
Year & Firm Fixed Effects YES YES
Controls Included YES YES
Panel B: Accelerated Filer Young Life-Cycle vs. Non-accelerated Filer Young Life-Cycle Firms
InnovationTypei,t = α + β1Accelerated Fileri × PostSOX + β2Sizei,t + β3Book-to-Marketi,t +
β4Leveragei,t + β5R&DMissingi,t +β6BigNAuditori,t + ∑ 𝛿𝑖𝐹𝑖𝑟𝑚𝑖𝑖=1𝑡𝑜𝑗 +
∑ 𝜉𝑚𝑌𝑒𝑎𝑟𝑚𝑚=1𝑡𝑜𝑘 + εi,t
(Equation 5)
Dependent Variable =
Originality Generality
Variables (1) (2)
Accelerated Filer × PostSOX –0.142*** –0.073***
(–6.945) (–4.626)
N 2,436 2,436
Adjusted R2 0.155 0.086
Year & Firm Fixed Effects YES YES
Controls Included YES YES Notes: This table uses a generalized difference-in-difference model and reports the results from the regression of
Innovation Type on YoungLifeCycle× PostSOX (Panel A) and Accelerated Filer × PostSOX (Panel B), control
variables, and firm and year fixed effects. T-statistics are presented below the coefficients. See Appendix A for
variable definitions. ***, **,* denotes a two-tailed p-value of less than 0.01, 0.05, and 0.10, respectively. To control
for time-series correlation in the error term, t-statistics are based on robust standard errors that are clustered by firm.
48
Table 6
Time Trend Analysis, Generalized Difference-in-Difference Design
Panel A: Mature versus Young Life-Cycle Stage Firms
Dependent Variable =
R&D Intensity Log(#Patents) Log(Citations) Log(#Claims)
Variables (1) (2) (3) (4)
YoungLifeCycle × t-2 0.025** –0.012 –0.098 –0.061
(2.185) (–0.215) (–1.169) (–0.523)
YoungLifeCycle × t-1 –0.049*** –0.175*** –0.160** –0.218
(–3.814) (–2.686) (–2.001) (–1.621)
YoungLifeCycle × t –0.035** –0.138** –0.199** –0.285**
(–2.561) (–2.207) (–2.146) (–2.252)
YoungLifeCycle × t+1 –0.038*** –0.164** –0.137 –0.623***
(–2.774) (–2.022) (–1.456) (–4.583)
YoungLifeCycle × t+2 –0.023 –0.047 –0.118 –0.629***
(–1.569) (–0.483) (–1.249) (–4.577)
N 9,582 6,522 6,522 6,522
Adjusted (Pseudo) R2 0.167 0.257 0.219 0.209
Year & Firm Fixed Effects YES YES YES YES
Controls Included YES YES YES YES
Panel B: Accelerated Filer Young Life-Cycle vs. Non-accelerated Filer Young Life-Cycle Firms
Dependent Variable =
R&D Intensity Log(#Patents) Log(Citations) Log(#Claims)
Variables (1) (2) (3) (4)
YoungLifeCycle × t-2 0.002 –0.087 –0.214** –0.064
(0.126) (–1.500) (–2.435) (–0.497)
YoungLifeCycle × t-1 –0.026 –0.286*** –0.440*** –0.265*
(–1.559) (–4.192) (–5.159) (–1.836)
YoungLifeCycle × t –0.020 –0.385*** –0.608*** –0.437***
(–1.094) (–5.978) (–6.306) (–3.172)
YoungLifeCycle × t+1 –0.046** –0.644*** –0.649*** –0.930***
(–2.323) (–7.915) (–6.697) (–6.393)
YoungLifeCycle × t+2 –0.039* –0.742*** –0.646*** –1.259***
(–1.896) (–8.097) (–6.633) (–8.686)
N 3,642 2,436 2,436 2,436
Adjusted (Pseudo) R2 0.196 0.268 0.188 0.218
Year & Firm Fixed Effects YES YES YES YES
Controls Included YES YES YES YES Notes: Panel A reports the results from the regression of Innovation on YoungLifeCycle × t-2 through
YoungLifeCycle× t+2, control variables, firm and year fixed effects. Panel B reports the results from the regression of
Innovation on Accelerated Filer × t-2 through Accelerated Filer × t+2, control variables, firm and year fixed effects.
T-statistics are presented below the coefficients. See Appendix A for variable definitions. ***, **,* denotes a two-
tailed p-value of less than 0.01, 0.05, and 0.10, respectively. To control for time-series correlation in the error term, t-
statistics are based on robust standard errors that are clustered by firm.
49
Table 7
Pseudo Event Dates, Generalized Difference-in-Difference Design
Innovationi,t = α + β1YoungLifeCyclei × Post + β2Sizei,t + β3Book-to-Marketi,t + β4Leveragei,t +
β5R&DMissingi,t +β6BigNAuditori,t + ∑ 𝛿𝑖𝐹𝑖𝑟𝑚𝑖𝑖=1𝑡𝑜𝑗 + ∑ 𝜉𝑚𝑌𝑒𝑎𝑟𝑚𝑚=1𝑡𝑜𝑘 + εi,t
(Equation 6)
Panel A: Pseudo-event period 1995-2001
Dependent Variable =
R&D Intensity Log(#Patents) Log(Citations) Log(#Claims)
Variables (1) (2) (3) (4)
YoungLifeCycle × Post –0.008 0.022 0.012 0.110
(–1.438) (0.341) (0.136) (0.975)
Size –0.019*** 0.138*** 0.156*** 0.107**
(–7.848) (4.409) (3.208) (2.317)
Book-to-Market 0.008*** –0.150** –0.042 –0.144
(3.067) (–2.551) (–0.427) (–1.465)
Leverage 0.002 –0.050 –0.218 –0.277
(0.253) (–0.514) (–1.238) (–1.613)
BigNAuditor 0.000 –0.005 0.035 –0.085
(0.086) (–0.034) (0.204) (–0.580)
R&D Missing –0.018*** 0.171* 0.201 0.028
(–4.734) (1.920) (1.077) (0.194)
R&D Intensity 1.006* 1.204 0.403
(1.949) (1.635) (0.587)
Constant 0.158*** 0.187 0.067 0.726**
(10.140) (0.772) (0.189) (2.235)
N 7,062 4,440 4,440 4,440
Adjusted (Pseudo) R2 0.099 0.020 0.016 0.010
Year Fixed Effects YES YES YES YES
Firm Fixed Effects YES YES YES YES
50
Table 7, cont.
Pseudo Event Dates, Generalized Difference-in-Difference Design
Panel B: Pseudo-event period 1990-1996
Dependent Variable =
R&D Intensity Log(#Patents) Log(Citations) Log(#Claims)
Variables (1) (2) (3) (4)
YoungLifeCycle × Post 0.004 –0.004 0.144 0.192
(0.868) (–0.046) (0.799) (1.156)
Size –0.005*** 0.207*** 0.045 0.103
(–3.661) (3.898) (0.696) (1.570)
Book-to-Market 0.007*** 0.051 0.201* 0.202*
(2.963) (0.628) (1.810) (1.735)
Leverage –0.004 0.018 –0.253 –0.073
(–1.129) (0.153) (–1.384) (–0.388)
BigNAuditor 0.009** –0.042 0.108 0.128
(1.963) (–0.522) (0.569) (0.799)
R&D Missing –0.021*** –0.047 –0.165 –0.107
(–3.787) (–0.567) (–1.085) (–0.883)
R&D Intensity 0.219 –2.399* –0.383
(0.152) (–1.692) (–0.320)
Constant 0.056*** –0.241 0.839 0.333
(5.523) (–0.651) (1.621) (0.678)
N 5,832 3,534 3,534 3,534
Adjusted (Pseudo) R2 0.057 0.032 0.003 0.006
Year Fixed Effects YES YES YES YES
Firm Fixed Effects YES YES YES YES Notes: This table reports the results from the estimation of a generalized difference-in-difference regression of innovation on
YoungLifeCycle× Post-Pseudo Date, control variables, and firm and year fixed effects. In Panel A, the sample period is 1995-
2001, with pseudo-event dates of 1998 or 1999 depending on a firm’s fiscal year end. In Panel B, the sample period is 1990-
1996, with pseudo-event dates of 1993 or 1994 depending on a firm’s fiscal year end. The sample compares young life-cycle
stage firms to mature life-cycle stage firms. T-statistics are presented below the coefficients. See Appendix A for variable
definitions. ***, **,* denotes a two-tailed p-value of less than 0.01, 0.05, and 0.10, respectively. To control for time-series
correlation in the error term, t-statistics are based on robust standard errors that are clustered by firm.
51
Table 8
Financial Reporting Quality
Panel A: Young vs. Mature Life-Cycle Firms; Generalized Difference-in-Difference Design
Poor Financial Reporting Qualityi,t = α + β1YoungLifeCyclei × PostSOX + β2Sizei,t + β3Book-to-Marketi,t + β4Leveragei,t +β5BigN Auditori,t
+ ∑ 𝛿𝑖𝐹𝑖𝑟𝑚𝑖𝑖=1𝑡𝑜𝑗 + ∑ 𝜉𝑚𝑌𝑒𝑎𝑟𝑚𝑚=1𝑡𝑜𝑘 + εi,t
(Equation 7)
Dependent Variable =
Restatement
clogit
Resatement
OLS
PoorAccr.
Quality FSD
Abs(Total
Accruals)
Total
Accruals
Abnormal
Revenue
Variables (1) (2) (3) (4) (5) (6) (7)
YoungLifeCycle × PostSOX 0.653** 0.051** 0.000 0.001 –0.004 0.013 0.005*
(2.315) (2.199) (0.070) (1.092) (–0.287) (0.833) (1.950)
Size 0.241 0.032** –0.008*** –0.002*** –0.035** 0.033** –0.002
(1.258) (2.004) (–3.086) (–7.648) (–2.228) (2.209) (–1.076)
Book-to-Market –0.168 –0.013 –0.018*** 0.000 0.034*** –0.029** –0.005**
(–0.614) (–0.502) (–4.342) (0.610) (2.897) (–2.324) (–2.046)
Leverage 0.249 0.008 0.002 –0.002** 0.117* –0.126** 0.001
(0.577) (0.243) (0.363) (–2.524) (1.880) (–2.352) (0.186)
BigNAuditor 0.185 0.032 0.004 –0.001 0.020 –0.013 0.006***
(0.624) (1.232) (1.430) (–0.943) (1.551) (–1.140) (2.613)
Constant –0.037 0.096*** 0.046*** 0.271*** –0.254*** 0.047***
(–0.356) (6.048) (22.414) (3.311) (–3.155) (4.976)
N 3,504 9,582 9,109 9,582 9,581 9,581 9,130
Adjusted (Pseudo) R2 0.101 0.034 0.026 0.015 0.055 0.060 0.041
Year & Firm Fixed Effects YES YES YES YES YES YES YES
52
Table 8 Financial Reporting Quality, cont.
Panel B: Accelerated Filer Young Life-Cycle vs. Non-accelerated Filer Young Life-Cycle Firms; Generalized Difference-in-difference
Design
Poor Financial Reporting Qualityi,t = α + β1Accelerated Fileri × PostSOX + β2Sizei,t + β3Book-to-Marketi,t + β4Leveragei,t +
β5BigNAuditori,t + ∑ 𝛿𝑖𝐹𝑖𝑟𝑚𝑖𝑖=1𝑡𝑜𝑗 + ∑ 𝜉𝑚𝑌𝑒𝑎𝑟𝑚𝑚=1𝑡𝑜𝑘 + εi,t
(Equation 8)
Dependent Variable =
Restatement
clogit
Resatement
OLS
PoorAccr.
Quality FSD
Abs(Total
Accruals)
Total
Accruals
Abnormal
Revenue
Variables (1) (2) (3) (4) (5) (6) (7)
Accelerated Filer × PostSOX –0.123 –0.023 0.008 –0.000 –0.098** 0.081** –0.002
(–0.384) (–0.900) (1.249) (–0.218) (–2.560) (2.204) (–0.371)
Size 0.190 0.014 –0.025*** –0.002*** –0.226*** 0.171*** –0.006**
(1.338) (1.233) (–5.583) (–6.206) (–6.810) (5.419) (–2.009)
Book-to-Market –0.477 –0.033 –0.017** –0.001 –0.063 0.126*** –0.003
(–1.579) (–1.316) (–2.479) (–1.365) (–1.366) (2.944) (–0.539)
Leverage –0.060 –0.007 0.016*** –0.000 0.234*** –0.230*** 0.011***
(–0.524) (–0.778) (3.026) (–0.678) (5.231) (–5.550) (3.871)
BigNAuditor 0.885** 0.060** 0.003 0.000 0.145*** –0.121*** 0.011**
(2.383) (2.398) (0.513) (0.163) (3.353) (–2.847) (2.510)
Constant 0.048 0.182*** 0.044*** 0.934*** –0.777*** 0.067***
(1.277) (12.216) (34.989) (9.503) (–8.124) (7.271)
N 1,302 3,642 2,903 3,420 3,627 3,627 3,404
Adjusted (Pseudo) R2 0.106 0.031 0.066 0.028 0.163 0.148 0.024
Year & Firm Fixed Effects YES YES YES YES YES YES YES Notes: This table reports the results from the regression of Financial Reporting Quality on YoungLifeCycle× PostSOX, and control variables. We use OLS regression
except in the case where Restatement is the dependent variable, in which case we use conditional logit. T-statistics are presented below the coefficients. Panel A
uses a generalized difference-in-difference design, Panel B includes an additional interaction term, Size x PostSOX. See Appendix A for variable definitions. ***,
**,* denotes a two-tailed p-value of less than 0.01, 0.05, and 0.10, respectively. To control for time-series correlation in the error term, t-statistics are based on
robust standard errors that are clustered by firm.
53
Table 9
Event Study for Political Events Increasing the Likelihood of SOX Passage
CARi = α + β1YoungLifeCyclei,t + β2L(MV)i,t-1 + β3Book-to-Marketi,t + β4Leveragei,t-1 +
β5FreeCashFlowi,t-1 +β6ROAi,t-1 +β6Turnoveri,t-1 +β6StdReti,t-1 + εi
(Equation 9)
Dependent Variable = CAR1 CAR2
Variables (1) (2)
YoungLifeCyclet –0.039** –0.039**
(–2.486) (–2.549)
L(MV)t-1 –0.009*** –0.002
(–3.650) (–0.885)
Book-to-Markett-1 –0.009 –0.024*
(–0.684) (–1.719)
Leveraget-1 –0.033 –0.002
(–1.599) (–0.118)
FreeCashFlowt-1 –0.148*** –0.036
(–2.999) (–0.752)
ROAt-1 0.122*** –0.016
(2.652) (–0.356)
Turnovert-1 –0.209*** 0.064*
(–4.798) (1.688)
StdRett-1 –0.300*** –0.054
(–5.362) (–1.251)
Constant 0.027 0.012
(1.044) (0.494)
N 2,947 2,949
Adjusted R2 0.071 0.008
Robust Standard Errors YES YES Notes: The sample used for this table includes all firms from Compustat in 2002 (time t) that were in the
young, growth or mature life-cycle stages and had the requisite data available. This table reports the results
from the regression of volume-weighted, cumulative abnormal returns around political events that increased
the likelihood of the passage of SOX on an indicator for young life-cycle firms (YoungLifeCycle) and controls.
CAR1 calculates cumulative abnormal returns using the significant events as indicated by Zhang (2007).
CAR2 calculates cumulative abnormal returns using the events associated with increased SOX passage
likelihood as identified by Engel et al. (2007) (i.e., events used to calculate their AR_SOX measure). For
additional details, please see Appendix B. T-statistics are presented below the coefficients. See Appendix A
for variable definitions. ***, **,* denotes a two-tailed p-value of less than 0.01, 0.05, and 0.10, respectively.
T-statistics are based on robust standard errors.
54
Table 10
Market Performance, Generalized Difference-in-Difference Design
Annual Buy-and-Hold Returni,t = α + β1Treati × PostSOX + β2Sizei,t + β3Book-to-Marketi,t + β4Leveragei,t +β5BigN Auditori,t +
∑ 𝛿𝑖𝐹𝑖𝑟𝑚𝑖𝑖=1𝑡𝑜𝑗 + ∑ 𝜉𝑚𝑌𝑒𝑎𝑟𝑚𝑚=1𝑡𝑜𝑘 + εi,t
(Equation 10)
Variables
Annual Buy-and-Hold
Return
Young vs. Mature
(1)
Annual Buy-and-Hold
Return
AF vs. NAF
(2)
Annual Buy-and-Hold
Return
Pseudo-event period
1995-2001
(3)
Annual Buy-and-Hold
Return
Pseudo-event period
1990-1996
(4)
YoungLifeCycle * PostSOX –0.356*** –0.248*** 0.159** –0.016
(–6.610) (–3.348) (2.088) (–0.277)
Size –0.292*** –0.123*** –0.139*** –0.205***
(–10.105) (–2.924) (–3.556) (–6.057)
Book-to-Market –1.047*** –1.061*** –1.513*** –1.018***
(–19.592) (–10.709) (–20.682) (–20.305)
Leverage 0.072 –0.237** 0.151 0.268***
(0.756) (–2.528) (1.135) (2.641)
BigNAuditor 0.059 0.151* –0.004 –0.109
(1.065) (1.885) (–0.025) (–1.262)
Constant 2.709*** 1.136*** 1.809*** 2.162***
(14.608) (5.889) (6.740) (9.268)
Observations 9,169 2,081 6,901 5,699
Adjusted R2 0.190 0.287 0.178 0.152
Year & Firm Fixed Effects YES YES YES YES Notes: The dependent variable is the annual buy-and-hold return that compounds the monthly excess return for the 12-month period ending three months after the
balance sheet date, where returns are calculated in excess of the monthly stock index value weighted return. This table reports the results from the estimation of a
generalized difference-in-difference regression of the buy-and-old return on Treat×Post, control variables, and firm and year fixed effects. Column (1) includes
the sample as described in Tables 3 and 4 with Treat taking on the value of one for young life-cycle firms and zero otherwise. Column (2) includes the sample from
Table 5 with Treat taking on the value of one for accelerated-filers and zero for non-accelerated filers. Column (3) includes the sample from Table 6 where Treat
takes on the value of one for young life-cycle firms in the pseudo-event period and zero otherwise. T-statistics are presented below the coefficients. See Appendix
A for variable definitions. ***, **,* denotes a two-tailed p-value of less than 0.01, 0.05, and 0.10, respectively. To control for time-series correlation in the error
term, t-statistics are based on robust standard errors that are clustered by firm
55
Table 11
Innovation Changes, Post-IPO and Post-JOBS Act
Innovationi,t = α + β1YoungLifeCyclei × PostIPO_Years + β2PostIPO_Years + β3Sizei,t + +
β4Leveragei,t +β5BigNAuditori,t + β6R&DMissingi,t + ∑ 𝛿𝑖𝐹𝑖𝑟𝑚𝑖𝑖=1𝑡𝑜𝑗 +
∑ 𝜉𝑚𝑌𝑒𝑎𝑟𝑚𝑚=1𝑡𝑜𝑘 + εi,t
Innovationi,t = α + β1PostJOBSi × PostIPO_Years + β2PostIPO_Years + β3Sizei,t + β4Leveragei,t
+β5BigNAuditori,t + β6R&DMissingi,t + ∑ 𝛿𝑖𝐹𝑖𝑟𝑚𝑖𝑖=1𝑡𝑜𝑗 + ∑ 𝜉𝑚𝑌𝑒𝑎𝑟𝑚𝑚=1𝑡𝑜𝑘 + εi,t
(Equations 11 and 12)
Variables R&D Intensity
(1)
R&D Intensity
(2)
YoungLifeCycle × PostIPO_Years –0.117***
(–4.558)
PostJOBS_IPO × PostIPO_Years 0.0166
(0.295)
PostIPO_Years 0.019 0.113*
(1.260) (1.944)
Size –0.094*** –0.334***
(–4.450) (–8.362)
Leverage 0.102** 0.255***
(2.046) (4.187)
BigNAuditor 0.038** –0.0415
(1.981) (–0.466)
R&D Missing –0.057*** –0.522***
(–3.317) (–2.724)
N 1,778 1,902
Adjusted R2 0.719 0.579
Year & Firm Fixed Effects YES YES
Sample
Young Life-Cycle versus
More Mature Life-Cycle
IPOs Occurring After SOX,
but Before JOBS
Pre-JOBS Young Life-
Cycle IPOs versus Post-
JOBS Young Life-Cycle
IPOs
Sample Years 2006-4/4/2012 2006-2017 Notes: This table reports the results from the regression of R&D Intensity on YoungLifeCycle× PostIPO_Years in
column (1) or PostJOBS_IPO × PostIPO_Years in column (2) and control variables using a generalized difference-
in-difference design. T-statistics are presented below the coefficients. See Appendix A for variable definitions. ***,
**,* denotes a two-tailed p-value of less than 0.01, 0.05, and 0.10, respectively. To control for time-series correlation
in the error term, t-statistics are based on robust standard errors that are clustered by firm.
56
Table 12
Percentage of Young Life-Cycle IPOs Before and After Implementation of the JOBS Act
Pre-JOBS IPOs Post-JOBS IPOs t - Test
Young Life-Cycle IPO 163 338
More Mature Life-Cycle IPO 322 294
Total 485 632
Percentage Young Life-Cycle 34% 53% t = 6.80; p <0.01 Notes: This table includes all firms with an IPO date between 2006 and 2017, excluding financial/utility firms and
shakeout/decline firms, that have cash flow and other data required to compute control variables available on
Compustat. We determine a firm’s life-cycle status as of the IPO year. The t-statistic is a two-sample t-test with
unequal variances.
