how does corporate governance affect firm … does corporate governance affect firm behavior? ......
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How Does Corporate Governance Affect Firm Behavior?
Panel Data versus Shock-Based Methods
Bernard Black Northwestern University, Law School and Kellogg School of Management
Woochan Kim Korea University Business School
Julia Nasev University of Cologne
Draft May 2015
Northwestern University School of Law Law and Econ Research Paper Number 12-13
This paper can be downloaded without charge from the Social Science Research Network electronic library at:
http://ssrn.com/abstract=2133283
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How Does Corporate Governance Affect Firm Behavior?
Panel Data versus Shock-Based Methods
Bernard Black, Northwestern University
Woochan Kim, Korea University Business School
Julia Nasev, University of Cologne
Abstract
Most of the literature on the effect of corporate governance on firms’ behavior, including financial reporting, investment and growth, provides evidence on association, not causation. How likely are these results to survive, if one could apply causal methods? We provide evidence on that question, by comparing “classic” panel data research designs (pooled OLS, firm random effects and firm fixed effects), simpler causal designs (simple difference-in-differences (DiD), regression discontinuity (RD), instrumental variables (IV), and year-by-year DiD), and combined causal designs (combining simple DiD, IV, and year-by-year DiD with RD), estimates. We use a case study of Korea, where under 1999 legal reforms, large firms (assets over 2 trillion won, about US$2 billion) face a major, exogenous shock to board structure, with no similar shock to smaller firms. This shock predicts improved scores on an overall disclosure index with both panel data and causal methods. The shock predicts lower investment, slower growth, lower absolute abnormal accruals, and more extensive MD&A disclosure with panel data methods, but these results fall away when we apply causal methods. In some instances, results with simpler causal methods are not robust to use of more careful methods, especially combined designs. The shock has limited impact on other outcomes with both classic and causal methods. Our case study provides evidence that classic panel methods can provide a weak guide to causation, and that simpler causal methods can also be unreliable. JEL classifications: ...
Keywords: financial reporting quality, earnings management, conservatism, accrual quality, audit committees, corporate governance, Korea, causal inference.
We thank seminar participants at McCombs School of Business (July 2012), Chicago Booth School of
Business (Nov. 2012), 7th International Conference on Asia-Pacific Financial Markets (Dec. 2012),
American Accounting Association annual meeting (August 2013), Financial Management Association
annual meeting (2013), Shuping Chen, Martin Dierker, Dain Donelson, Zi Jia, Christian Leuz, Jim
Naughton, Joshua Ronen, and Ira Yeung for comments.
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1. Introduction
To what extent does corporate governance affect financial reporting and firm
behavior, such as investment and growth decisions? These questions are often
studied, but most studies are subject to concerns with endogeneity, because firm-
level governance, financial reporting, and investment and growth are all firm-
choices. Armstrong et al. (2010), Ball (2000), and others stress the difficulty in
assessing causation in corporate governance research. Many authors use an
instrumental variable (IV) strategy to address endogeneity, but the IVs are rarely
convincing (Larcker and Rusticus, 2010; Atanasov and Black, 2015a, 2015b). These
problems are leading researchers to rely more often on natural experiments.
But often, natural experiments are hard to find. How much reliance should we
place on results using classic panel data designs (pooled ordinary least squares
(pooled OLS), firm random effects (RE), and firm fixed effects (FE)), let alone the
simple cross-sectional OLS regressions that dominate the empirical literature?
When a natural experiment can be found, what differences in results are there
between classic panel methods and causal methods, or between different causal
methods, which rely on the same shock?
We address these questions using a natural experiment in Korea – a large,
exogenous legal shock to the board structure of large, public Korean firms (assets
over 2 trillion won, about US$2 billion, below, “2T”), with no similar shock to
smaller firms. Korean rules, adopted in 1999, which became effective partly in 2000
and fully in 2001, require large firms to have at least 50% outside directors, an audit
committee (with at least 2/3 outside directors and an outside chair), and an outside
director nominating committee (with at least 50% outside directors). Before the
reforms, essentially no Korean firm had any of these governance elements. Thus,
these rules greatly affect two core governance institutions – outside directors and
audit committees – which plausibly affect financial reporting and other firm
outcomes, and have been found to do so in much, but not all, prior research. This
shock has a large impact on the market value of large firms (Black and Kim, 2012).
Thus, it is plausible that it also affects the outcomes we study here – various
measures of financial reporting quality, investment, and growth.
Using classic panel methods over 1998-2005, a period which spans the shock,
we find that better corporate governance, measured using a broad Korea Corporate
Governance Index (KCGI), lagged one period, predicts improved firm behavior on a
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variety of measures: Lagged KCGI predicts higher scores on an overall disclosure
index, lower absolute abnormal accruals (abs(AA)), and higher word count in firms’
“MD&A” disclosure. Turning from disclosure measures to investment and growth,
lagged KCGI predicts lower sales growth, in an environment where many large firms
had emphasized growth at the expense of profits. Non-lagged KCGI (but not lagged
KCGI) predicts reduced investment, measured as capex/assets, in an environment
where many large firms had likely been overinvesting. Below, we call these our
“panel outcomes.” Because these results rely on firm FE, they are stronger than
most results in the prior literature, many of which use only OLS, applied to either
cross-sectional or panel data.
We find no evidence that KCGI predicts several other measures of financial
reporting, including earnings smoothing, abs(raw accruals), or signed accruals
(either abnormal or raw). Below, we call these our “panel non-outcomes.”
We then apply several “simpler” causal methods that directly exploit the
governance shock, including: (i) “panel” difference-in-differences” (“panel DiD”), in
which we compare after-minus-before changes in outcomes for large firms to after-
minus-before changes to “small” Korean public firms (assets < 2T); (ii) regression
discontinuity (RD), in which we limit the sample to large and mid-sized firms within
a moderate size range (“bandwidth”) around the 2T threshold, study post-shock
outcomes, and look for a jump in the outcome variables at the threshold; (iii) “panel”
IV, in which we use the legal shock, interacted with a large firm dummy, as an
instrument for corporate governance; and (iv) yearly DiD. The results for the
disclosure index survive in all four specifications, but results vanish for the other
disclosure outcomes (abs(AA) and MD&A word count). The capex/assets and sales
growth results are mixed. The capex/assets results survive with panel DiD and RD,
but not with panel IV or yearly DiD. The sales growth results survive only with
panel DiD. The results for abs(AA) and MD&A word count largely disappear. The
“panel non-outcomes” remain insignificant with these simpler causal methods.
We then exploit more careful, “combined” causal methods, in which we
combine RD with panel DiD, panel IV, and yearly DiD, by applying the DiD and IV
methods within a bandwidth around the 2T threshold. The disclosure results
survive with a broader [0.5T, 8T] bandwidth, but become insignificant with a
narrower [1T, 4T] bandwidth. The capex/assets results survive only with panel
IV/RD. All other outcomes are insignificant.
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Abs(AA) provide a good example of the differences across designs.
Consistent with most (though not all) prior research in developed markets, we find
that higher KCGI scores predict lower abs(AA) in panel regressions, including firm
FE. This is already a stronger design than most prior studies, which (even if they
have panel data) do not use FE. But these results disappear when we use causal
research designs, which directly exploit the legal shock. Our sample of large firms is
small, but the non-results do not appear to reflect low statistical power. Instead
there is no evidence of a causal effect at all. Across specifications, the estimated
effect of the shock is positive (opposite from predicted) as often as negative. In the
most informative specification -- year-by-year DiD regressions within the RD
bandwidth -- the coefficient on the interaction between post-shock dummy and
large firm dummy rises steadily over 2000-2004 (opposite from predicted), and is
positive (opposite from predicted) in each year from 2001-2005.
It is possible that one could fail to find significant results using classic panel
methods, yet find them with causal methods. Our results suggest that this is likely
to be uncommon. The only outcome that is significant with simpler causal methods,
but not with panel methods, is capex/assets. And capex/assets largely loses
significance with combined causal designs.
Results from an emerging market such as Korea, in which almost all firms
have a controlling family and many large firms belong to business groups, may not
carry over to developed countries. Still, the disappearance of results with classic
panel methods, when we switch to using causal methods, provides a warning for the
reliability of classic panel data studies elsewhere.
This paper proceeds as follows. Part 2 discusses the setting for our study. Part 3
provides an overview of the research designs and the outcomes we study. Part 4
applies classic panel methods to our outcome measures. Parts 5 and 6 apply
simpler and combined causal methods, respectively. Part 7 concludes.
2. Korean Setting
2.1. Korean Governance Shock
We use the 1999 legal shock to the governance of large Korean firms to provide
the setting for us to compare classic panel methods to causal methods and to assess
what difference choice of method makes for one’s results. In response to the 1997-
1998 East Asian financial crisis, Korea required that “large” firms (assets > 2 trillion
won, below “2T”) have (i) a minimum of 50% outside directors; (ii) a minimum of
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three outside directors, (iii) an audit committee with an outside director as chair
and at least two-thirds outside members; and (iv) an outside director nominating
committee (with at least 50% outside members) to select new outside directors.
Smaller firms were only required to have at least 25% outside directors. Prior to
the reforms, almost no firms had 50% outside directors and none had an audit
committee; indeed, Korean Company Law did not allow boards to have committees.
This shock appears to be exogenous (see § 2.3). It caused a large change in two core
governance institutions – the board of directors and the audit committee -- that
could plausibly affect financial reporting, investment, and firm growth (the
outcomes we study).
The rules came into force over 2000-2001. Large firms had to have at least three
outside directors and the two committees in 2000, and had to have 50% outside
director in 2001.1 We expect any impact on firm behavior to appear with a lag, thus
in 2001 or later. Black and Kim (2012) report, using both simple and combined
causal methods, evidence that this legal shock strongly increases the market value
of large firms Large firm share prices increase by about 30% relative to mid-sized
firms (0.5 trillion won < assets < 2T). Black, Kim, Jang, and Park (2015) report
evidence that this shock reduces tunneling by insiders of chaebol (Korean business
group) firms through related-party transactions. Thus, this shock was economically
important, which makes it plausible that it could affect financial reporting.
2.2. Korean Corporate Governance Index
For our non-causal analyses, we rely on a broad Korea Corporate Governance
Index (KCGI), developed in Black and Kim (2012), and summarized in Table 1. We
construct KCGI over 1998 to 2004, for the vast majority of public companies listed
on the Korea Stock Exchange. KCGI (0 ~ 100) consists of five equally weighted
subindices: Board Structure (5 elements), Disclosure (3 elements), Shareholder
Rights (4 elements), Board Procedure (14 elements), and Ownership Parity (one
element). Within each subindex, all elements are equally weighted, except that
Board Structure Subindex is composed of Board Independence Subindex (2
elements, 0 ~ 10), and Board Committee Subindex (3 elements, 0 ~ 10). For details
on index construction, see Black and Kim (2012).
1 A rule change in 2003 requires large firms to have a majority of outside directors beginning in 2005
(exactly 50% is no longer allowed).
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The subindices and elements are Korea-specific. They cover aspects of
governance which we judged to be important in Korea during this period, and for
which we have data. Almost all Korean firms had a controlling shareholder or group,
so takeover defenses were unimportant. As a result, KCGI is quite different from
U.S.-centric indices, which focus heavily on takeover defenses (e.g., Gompers et al.
2003; Bebchuk et al., 2009), and its elements are quite different from those studied
by Larcker et al. (2007).
Table 2, Panel A provides summary statistics for KCGI and each subindex; Panel B
provides correlation coefficients. All subindices correlate strongly with each other,
except for Ownership Parity.
2.3 Evidence on Exogeneity
We summarize here evidence that the 1999 reform can be reasonably treated as
exogenous; Black and Kim (2012) provide additional details. First, the reforms
cause a major change in board structure at large firms. Figure 1 shows how Board
Structure Subindex – the part of KCGI directly affected by the reforms -- changes
over 1998-2004 for large and mid-sized firms. We exclude banks and former state-
owned enterprises (SOEs) from the sample; and exclude all financial firms when
using capital expenditures as the outcome variable. The vertical line shows the 2T
threshold; the horizontal line is at 11.67, which is the minimum Board Structure
Subindex score for firms that comply with the large-firm rules. No firms have any of
the three reform elements at year-end 1998; only one mid-sized and one large firm
have adopted any of these elements at year-end 1999. Some large firms comply
with the new rules in 2000, ahead of the spring 2001 deadline; all comply by 2001.
Some overcomply and are above the horizontal line.
Some mid-sized firms voluntarily adopt board structure changes; the tendency
for voluntary adoption increases over 2000-2004. The discontinuity at 2T is thus
“fuzzy”, and increasingly so in later years.
We search for and find no evidence that firms reduce or limit their size to avoid
the rules. There is no bunching of firms just below the threshold. Compare Iliev
(2010) who finds bunching below the $75 million free float threshold for U.S. firms
to comply with § 404 of the Sarbanes-Oxley Act.2
2 We examine individually the seven firms that are large at year-end 1999 but then shrink below the 2T threshold. Four retain the reforms, one soon grows and becomes large again, and one continues to shrink. Only one large firm shrinks to moderately below the threshold, remains there, and abandons the large-firm reforms, and thus might have shrunk for this purpose.
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2.4. Background on Korean Firms, Korean Accounting Rules, and Covariates
Most Korean firms have a controlling family. Many firms, especially large firms
belong to business groups, known as chaebol. The incentives of these firms’
managers in making decisions on financial disclosure, investment, and growth could
differ from those at firms without a controlling shareholder; similarly, incentives
could differ between chaebol and non-chaebol firms. But it is not obvious why any
differences between Korean and, say, U.S. firms should affect our core research
questions, which involve the robustness of classic panel and simpler causal research
designs, versus more careful designs.
We measure Korean governance over1998-2004 and outcomes with a one-year
lag, over 1999-2005. Our causal research designs assume that the 1999 governance
reforms to large firms is the only external shock affecting large firm outcomes,
relative to those for smaller firms. We therefore note the principal changes to
Korean accounting rules during this period. Korea adopted an analog to Regulation
FD (Fair Disclosure) in 2002, and an analog to SOX in 2003; most of the provisions
became effective in 2004. Both of these reforms apply to both large and small firms,
so should not compromise our research design.
We use an extensive set of control variables to limit omitted variable bias, and
thus strengthen our panel data designs. Table 3 defines the principal outcome and
control variables we use in this study. Our data comes from various sources. We take
balance sheet, income, cash flow statement data, foreign ownership data, related-party
transactions, and original listing year from the TS2000 database maintained by the
KLCA; information on chaebol groups from annual reports by the Korea Fair Trade
Commission (KFTC); other stock market data from the KSE; information on ADRs from
JP Morgan and Citibank websites; and industry classification from the Korea Statistics
Office (KSO). Share ownership comes from the KSE for financial institutions and from a
hand-collected database for other firms.
A limitation of this study is the modest number of large firms. Our main RD
results use mid-sized and large Korean public firms, within a size band from 0.5-8
trillion won in assets. This size band reflects a compromise between desire for a
narrow band, to make treated and control firms more similar, and need for a
reasonable sample size. The tradeoff between bandwidth and sample size is
common in RD designs, but is acute for us because of the limited number of large
firms. The modest number of large firms limits statistical power and could lead to
failure to reject the null even if a governance effect is present.
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3. Panel and Causal Research Designs; Outcome Variables
A principal goal of this paper is to compare estimates across classic panel data
designs, simpler shock-based designs, and combined shock-based designs. We
summarize here the designs that we examine.
3.1. Classic Panel Data Designs
We use three classic panel data designs: pooled OLS, RE, and FE. To reduce
the influence of outliers, we exclude observations as outliers if a studentized
residual from regressing the outcome variable on KCGI > |1.96|. We use an
unbalanced panel, and cluster standard errors on firm. We generally lag KCGI one
period, because it seems likely that governance changes will not immediately affect
our outcome variables.
These panel models are well-known, we review here aspects that are
relevant for our study. The pooled OLS model is:
0 1 , 1 2 , ,it i t i t t i ty KCGI g β x (1)
Here xi,t is a vector of covariates, which we assume to be exogenous, gt are year
dummies. A firm effects model adds firm effects fi (Wooldridge, 2010, § 10.2):
0 1 , 1 2 , ,( )it i t i t t i i ty KCGI g f β x (2)
The FE model can be seen as a “time-demeaned” specification. Let
, ,( )i t
dm
i t i x x x , and similar for other variables. The FE model is:
, ,1 , 1 2 i t i t
dm dm dm dm dm
it i t ty KCGI g β x (3)
RE leads to a “quasi-demeaned” feasible GLS estimate. Let σε and σf be the
standard deviations of εi,t and fi, T be the number of periods, and define:
2 2
1* fT
and quasi-demeaned variables , ,( * )
i t
qdm
i t i x x x and similar for other variables.
The RE model is:
, ,1 , 1 2 i t i t
qdm qdm qdm qdm qdm qdm
it i t t iy KCGI g f β x (4)
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The pooled OLS and RE models makes a “strict exogeneity” assumption; one form
of this assumption is that the firm effects are uncorrelated with the covariates in all
time periods: Cov(fi, xi,t) = 0 t. The FE estimator is consistent even if the firm
effects are correlated with KCGI and other covariates. However, FE estimates rely
only on within-firm variation, which reduces power. Governance often changes
slowly over time, so the power loss can be substantial.
Exogeneity requires, among other things that KCGI does not influence future x’s.
This is unlikely to be strictly true, but one might hope that it is a reasonable
approximation, especially with RE. First, for Korean firms, time varying
characteristics only weakly predict CGI (Black, Jang and Kim, 2006). Bhargava and
Sargan (1983) suggest that assuming exogeneity is more reasonable if one uses RE
to address unobserved heterogeneity, the data has a “short” time dimension, and the
principal variable of interest (here, KCGI) is time-persistent. Both FE and RE will be
inconsistent if there are omitted time-varying firm covariates that are correlated
with both KCGI and the outcome. Still, there is reason to hope that RE and,
especially FE specifications may be reasonable choices, when an external shock
cannot be found. A principal purpose of this paper is to assess how well RE and FE
perform, in a setting where an external shock exists and can be used for comparison.
Given sufficient time variation in governance, FE is ordinarily preferred
because one avoids the need to assume strict exogeneity. However, RE has greater
power than FE, due to larger effective sample size and ability to exploit both within-
firm and across-firm variation. Also, the RE estimator converges to the FE estimator
as λ approaches 1. Thus, the additional bias of RE estimates, relative to FE, should
be limited if λ is close to 1. Pooled OLS is unlikely to be superior to RE, we report it
below principally for comparison, because many prior governance studies report
only cross-sectional or pooled OLS results. If strict exogeneity is not satisfied, both
pooled OLS and RE will be inconsistent. As λ approaches 1, RE should be preferred
to pooled OLS. For low values of λ, it is more plausible that the bias in RE estimates
could be greater than in pooled OLS estimates. However, we are aware of no
simulation or other studies comparing the bias from the two models. Actual median
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λ values in our study are 0.17 for sales growth; 0.44 for capex; 0.48 for abs(AA), and
0.78 for word count and earnings smoothing.
3.2. Simple Causal Designs
The shock to large-firm governance permits several distinct research designs.
First, we can use DiD, and compare the after-minus-before change in financial
reporting outcomes for large firms to the change for smaller firms. The panel DiD
regression specification is:
{panelDiD}: ( * )it i t DiD it ity f g w (5)
Here the treatment dummy wit = 1 for treated firms after the shock, 0 otherwise.
As for the classic panel specifications above, we cluster standard errors on firm. The
core assumption for DiD validity is “parallel trends”: But for the treatment, there
would have been parallel changes in the outcome variable for treated and control
firms. This assumption is untestable, but an important plausibility check is to assess
whether outcomes for the two groups were parallel during the pre-treatment period.
We do so using the leads-and-lags model, discussed below.
Second, we can use RD, and compare outcomes for large firms, just above the 2T
threshold, to mid-sized firms, just below the threshold. A core decision in
implementing RD is how one controls for the running variable. A relative simple
specification, with a linear control for the running variable, ln(assets), that allows
for different slopes above and below the threshold, is:
{RD}: * *ln( / 2 ) * *(ln( / 2 )it t RD i i ity g w assets T w assets T (6)
We present results below using a bandwidth of [0.5T, 2T] for the control group of
“mid-sized” firms and [2T, 8T] for the treatment group. This bandwidth reflects a
compromise between the reduced credibility of inference as we stray farther from
the discontinuity, and loss of sample size as we narrow the bandwidth. We present
selected results with a narrower overall bandwidth of [1T, 4T].
Third, we can use IV, with large firm dummy as an instrument for governance.
The regression model is classic two-stage least squares (2SLS).
Fourth, instead of using “simple” DiD, for which eqn. (5) assumes a one-time
jump from before to after the shock, we can use either a leads-and-lags specification,
which allows the effect of the shock to vary in each pre- and post-shock year, or a
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“distributed lag” specification, which allows the effect to vary in the post-shock
period. We use the leads-and-lags specification here. It is:
2
1
{leads and lags}: *n
k k
it i t DiD i it
k n
y f g w
(7)
Here the model includes n lags (where n ≤ the number of post-shock periods. Each k
iw turns on for treated firms in period k and then off again. One period must be
omitted and becomes a reference period; we omit 1999. The wik should be small and
insignificant during the pre-treatment period, with no apparent trend. During the
post-treatment period, they will map out the treatment effect over time. The leads-
and-lags model lends itself to graphical interpretation. One can plot each annual
coefficient, plus an associated confidence interval.
3.3. Combined Causal Designs
We can also use combined causal designs in several ways. First, we can run
“simple” DiD within an RD bandwidth. This can be a more compelling design than
DiD alone, because limiting the sample to a bandwidth around the discontinuity
should ensure that treated and control firms are similar on both observed and
unobserved covariates, except for the RD “running variable (here, ln(assets)). The
model is again eqn. (5), the only change is to the sample.
Second, when the governance law is adopted in 1999, the discontinuity at the 2T
threshold is very close to being “sharp”: essentially no firms, either large or smaller,
comply with any of the large firm rules prior to 1999; yet all large firms must do so
by 2001. Over time, however, some large firms go beyond the legal minimum, while
some smaller firms voluntarily adopt some of the large-firm reforms. This creates
something close to a “fuzzy RD” setting: the probability of compliance does not
jump from 0 to 1 at the threshold.3 One can then use the discontinuity as an
instrument for governance, for a sample limited to a bandwidth around the
discontinuity. This is a combined RD/IV design. In effect, the sharp RD design
provides an “intent-to-treat” estimate, in which we compare large firms (which the
Korean government intended to regulate) to smaller firms (which it did not
regulate), while the RD/IV design provides a “local average treatment effect” (LATE)
3 On sharp and fuzzy RD designs and the use of a combined RD/IV design to address a fuzzy RD setting,
see, e.g., Angrist and Pischke (2009), ch. 6.
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for “compliers” –firms who would only adopt the large-firm rules if required to do
so.4 The regression model is again 2SLS, within the RD bandwidth.
Third, we can used the leads-and-lags DiD specification in eqn. (7) within an RD
bandwidth.
3.4. Outcome Measures
We use the following outcome measures.5
3.4.1. Disclosure Subindex
One part of KCGI is a Disclosure Subindex, which consists of three elements: Firm
conducted investor relations activity in last year; firm website includes resumes of
board members; and firm provides English language financial disclosure. In panel
regressions where we use KCGI to predict Disclosure Subindex, we remove
Disclosure Subindex from KCGI.
3.4.2. Absolute and Signed Abnormal Accruals
Accruals are accounting adjustments that turn cash flow into earnings. Variation
in accruals across industries should reflect industry specific conditions. In contrast,
variation across firms within an industry is likely to reflect a mix of the firm’s
specific circumstances and managerial discretion. A large accounting literature uses
firm-level accruals as a proxy for earnings management.
A core problem in using accruals to proxy for earnings management is estimating
“unmanaged” accruals. Theory provides little guidance. To ensure that our results
are not driven by choice of accruals model, we use four models. In the simplest, we
assess whether governance predicts raw accruals, controlling for a broad set of firm
4 The “intent to treat” language comes from medical trials, in which some patients are offered treatment
and others are not, but some of those offered treatment will decline, while some controls may find another
way to be treated. In our setting, large firms are assigned to treatment; smaller firms are assigned to control.
the language of causal IV, we have “one-sided noncompliance”: our sample includes “always takers”
(firms which would take the treatment – the large firm reforms -- whether large or not) and “compliers”
(who will be treated only if assigned to treatment), but since all large firms comply, we have no “defiers”
(firms who would be treated if assigned to control but would avoid the treatment if assigned to it or “never
takers” (firms who would not take the treatment, whether assigned to treatment or control). See Angrist,
Imbens and Rubin (1996). We discuss below the imperfect fit between our setting and the binary causal IV
setting in which the LATE estimate and concepts were developed.
5 We considered a number of other measures of financial reporting quality but do not study them here
because they are not compatible with a DiD research design. These include conditional conservatism,
timeliness and its close relative, value relevance; avoidance of small losses; and earnings persistence.
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and industry characteristics (compare Leuz, Nanda and Wysocki, 2003). We study
both signed raw accruals and the absolute value of raw accruals.
The other models separate accruals into normal and abnormal components. The
simplest is the Jones-Dechow model (Jones, 1991; extended by Dechow, Sloan, and
Sweeney, 1995). In this model, we first regress accruals within each industry-year
on changes in sales and in property, plant and equipment (eqn. (8)). We use these
regression coefficients to predict “normal” accruals for each firm (eqn. (9)). The
remaining “abnormal” accruals reflect a combination of firm-specific circumstances
and earnings management.
We also estimate two models which include additional control variables in the
first-stage, predictive regression, with the goal of better predicting normal accruals.
Following Larcker, Richardson and Tuna. (2007), we add controls for book-to-
market ratio and for operating cash flow scaled by lagged total assets; we term this
the Jones-Dechow-Larcker model. We investigate whether adjusted R2 in predicting
normal accruals would improve if we include squares or interactions of the terms in
this model; this leads us to add (cash flow/lagged assets)2 to a “full predictive
model.” We present below results using the full predictive model. Results with the
Jones-Dechow-Larcker model are similar to those from the full predictive model;
results with the simpler Jones-Dechow model are consistent in sign but often
weaker.
We have a joint modeling decision to make: how fine to make the industry
classifications, what minimum number of firms to require for each industry-year,
and how many variables to use in the predictive model. We use two-digit Korean
industry classification codes for industries other than manufacturing, four-digit
codes for manufacturing and require a minimum of 10 firms per industry-year.6 The
mean (median) number of firms per industry-year is 34 (32).7
6 Most U.S. studies use two-digit SIC industry codes. The two-digit Korean Standard Industrial
Classification (KSIC) code gives 15 non-financial industries, but 75% of firm-years are in manufacturing.
The four-digit classification comprises 52 industries, but outside manufacturing, many have very few
observations. In unreported robustness checks, we obtain similar results if we use 4-digit industries.
7 The full predictive model involves 7 parameters, so with “out-of-sample” estimation, we have a
minimum of 2 degrees of freedom per industry-year. This is uncomfortable, but the median values are
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Eqn. (8) shows the first stage regression. The Jones-Dechow model uses the
terms in square brackets; the Jones-Dechow-Larcker model uses all terms except
(cash flow/assets)2; and the full predictive model uses all terms. Ppe is property,
plant and equipment; btm is book-to-market ratio, and cfo is cash flow from operations.
We estimate eqn. (8) separately for each firm, excluding that firm from its industry-
year group.
, , ,
1 2 3 4 ,
, 1 , 1 , 1 , 1
2
, ,
5 6 ,
, 1 , 1
1i t i t i t
i i i i i i t
i t i t i t i t
i t i t
i i i t
i t i t
accruals sales ppebtm
assets assets assets assets
cfo cfo
assets assets
(8)
We then use the parameters estimated from eqn. (8) to predict normal accruals for
each firm:
, , ,
, 1 2 3
, 1 , 1 , 1
, ,
4 , 5 6
, 1 , 1
1ˆ ˆ ˆˆ( )
ˆ ˆ ˆ
i t i t i t
i t i i i i
i t i t i t
i t i t
i i t i i
i t i t
sales receivables ppenormal accruals na
assets assets assets
cfo cfobtm
assets assets
2
(9)
For each model, abnormal accruals are:
,
, ,
, 1
( )i t
i t i t
i t
accrualsabnormal accruals aa normal accruals
assets
(10)
We study both the absolute value of abnormal accruals (abs(AA)) and signed
abnormal accruals.
3.4.3. MD&A Word Count
We obtain financial statements for the firms in our sample from the Korean
DART (Data Analysis, Retrieval, and Transfer) database, and count the number of
words in the MD&A (management’s discussion and analysis) section of these reports,
to assess whether board structure predicts the length of the MD&A disclosure. Or
dependent variable is ln(MD&A words).
reasonable, at 24 degrees of freedom per industry-year and a parameters/observations ratio of 7/32 = 22%.
In robustness checks, we obtain similar results with a minimum of 15 firms per industry-year. For our
causal methods results, we use 8 firms per industry-year to increase the number of usable large firms, but
obtain similar results with a 10-firm minimum.
14
3.4.4. Earnings Smoothing
Firms can use accruals to smooth earnings. We therefore assess whether
governance predicts earnings smoothing. We adapt the measure in Leuz, Nanda,
and Wysocki (2003) to the Korean data, and define for a multiyear time period k as:
( / )
( / )
k it iti
it it
earnings assetssmooth
EBITDA assets
(11)
This measure requires multiple years of data (a reasonable minimum is 3 years) to
estimate the standard deviations. Thus, annual estimates are not feasible. We use
one pre-shock period (1998-2000) and one post-shock period (2001-2004). If
governance reduces earnings smoothing, we expect a positive coefficient on KCGI in
panel regressions, and on the treatment dummy in DiD regressions.
3.4.5. Sales Growth and Investment
In the period leading up to the East Asian financial crisis, many Korean chaebol
groups emphasized growth over profitability (e.g., Campbell and Keys, 2002). Many
chaebol firms had Tobin’s q values well below 1, implying that a dollar of invested
capital was producing less than a dollar of market value on average. This suggests
that stronger governance might reduce growth and capital expenditures. We assess
whether governance predicts fractional sales growth, defined as:
1
1
sales growth t tit
t
revenue revenue
revenue
We also assess whether governance predicts capital investment (capex), defined as
capex/lagged assets (capext/assetst-1).
4. Classic Panel Results
4.1. Overview
In this section, we present results with classic panel methods for the five
outcome variables for which we find statistically significant results with either
classic panel methods or causal methods: Disclosure Subindex, abs(AA), MD&A
word count, sales growth, and investment. For the other outcome variables we
study -- earnings smoothing, signed abnormal accruals, the absolute value of raw
accruals, and signed raw accruals we obtain statistically insignificant results with
both panel and causal methods. We summarize those results in Table 5, but do not
separately report them.
15
Table 5 provides an overview of the outcomes for which we find that KCGI
significant predicts these outcome. A high-level overview: KCGI, lagged one period,
predicts significantly higher scores on Disclosure Subindex, lower abs(AA), higher
MD&A Word Count across all three panel methods (pooled OLS, firm RE, and firm
FE); and lower sales growth (only marginally significant with FE). If we do not lag
KCGI, then KCGI predicts significantly lower sales growth with FE as well. If we use
Board Structure Subindex as the governance measure (controlling for the rest of
KCGI), we would find that both lagged and non-lagged Board Structure Subindex
predicts significantly lower sales growth across all three methods.
Lagged KCGI does not predict capital investment. However, if we use Board
Structure Subindex as the governance measure, lagged Board Structure Subindex
predicts significantly lower investment with pooled OLS and RE (but not FE), and
non-lagged Board Structure Subindex predicts lower investment with all three
methods.
Thus, it would be easy for a researcher, using classic panel methods, to report
that either KCGI as a whole, or Board Structure Subindex, predicts a number of
outcomes which suggest that governance improves financial reporting (measured
by Disclosure Subindex, MD&A word count, and abs(AA)), investment efficiency
(measured by sales growth and capex/assets, in an environment where other it was
likely that many large firms were overinvesting) or both. Yet, as we show below,
most of those results fall away with causal methods – the only surviving causal
result is that the 1999 legal shock to board structure predicts improved disclosure,
measured by Disclosure Subindex.
4.2. Impact of Board Structure Reforms on Overall Disclosure
We first examine whether lagged KCGI (less Disclosure Subindex) predicts
Disclosure Subindex with classic panel methods, and report results in Table 6.
Lagged KCGI does so, and strongly, across methods. With firm FE, lagged KCGI takes
a coefficient of 0.088 (t = 4.44). Coefficients are slightly larger in the pooled OLS and
RE specifications. In Table 5, we also report results for all covariates. We suppress
results for covariates in later tables, to save space.
4.3. Absolute Abnormal Accruals
We present results in Table 7 for abs(AA, MD&A word count, sales growth, and
capex/assets. In Panel A, lagged KCGI predicts lower abs(AA) across methods. The
coefficient with firm FE is -0.035 (t = 2.20); coefficients are similar in the pooled OLS
16
and RE specifications. Thus, a ten unit increase in KCGI predicts about a .0035 drop
in absolute abnormal accruals. This is economically significant; it is about 12% of
the median level of around .03.
The finding that governance predicts lower abs(AA) is broadly consistent with
most, but not all, prior research on the impact of governance on accruals. For
example, Klein (2002) finds that greater board and audit committee independence
predict lower abs(AA) for S&P 500 firms over 1992 and 1993; Vafaes and Theodorou
(1998) and Weir et al. (2003) find similar results for UK firms. Larcker, Richardson
and Tuna (2007) find more mixed results.
4.4. Other Reported Outcomes
In Table 7, Panel B, we report results for MD&A word count. Lagged KCGI
predicts higher word count, across methods> However, the coefficient estimate is
sensitive to method, falling from 0.0038 (t = 2.73) with pooled OLS to only 0.0017 (t
= 2.09) with firm FE.
In Table 7, Panel C, we report results for sales growth. Lagged KCGI predicts
lower sales growth across methods. The coefficients are statistically significant at
the conventional 5% level with pooled OLS and RE. The firm FE coefficient is only
marginally significant with FE, but is larger in magnitude than the pooled OLS and
RE coefficients (at -0.0017 for FE versus -0.0013 for RE). This suggests that the
lower t-statistic with FE likely reflects the weaker statistical power of FE, rather
than the firm effects soaking up the apparent association between KCGI and sales
growth.
In Table 7, panel D, we report results for capex. Lagged KCGI does not predict
capex. However, recall that the governance shock hits Board Structure Index
directly. Any effect of the shock on other subindices is indirect. In unreported
results, we therefore investigate whether Board Structure Subindex predicts capex.
We find that lagged Board Structure Subindex predicts significantly lower
investment with pooled OLS and RE (but not FE), and non-lagged Board Structure
Subindex predicts lower investment with all three methods.
5. Simpler Causal Methods
We turn next to simpler causal methods, and assess which of the panel data
results survive.
17
5.1. Panel DiD
Our first approach is simple panel DiD. We divide the sample into large firms
(assets > 2T) and smaller firms (assets < 2T), and ask whether the outcome changes
for large firms in the treatment period (2001-2004), relative to the pre-treatment
period (1998-2000), relative to the after-minus-before change for smaller firms. We
choose 1998-2000 as the pre-treatment period because: (i) the 1999 legal rules
came into force partly in 2000 and partly in 2001; and (ii) we expect any impact of
governance on outcomes to appear with a lag. We present results in Table 8.
The results for Disclosure Subindex remain strong. The coefficient on the
interaction between post-reform dummy and large firm dummy is 4.01 (t = 6.35),
indicating that large firms increase their scores on Disclosure Subindex after reform
by around 4 points. Disclosure Subindex as a whole runs from 0~20; this increase is
large compared to the pre-shock mean for large firms of 4.5.
In contrast, the results for abs(AA) disappear entirely. The coefficient on the
interaction term is still negative, but is economically small and not close to being
significant (t = 0.47).
The results for MD&A word count and sales growth generally survive. The shock
to large firm governance predicts a 6.2% increase in MD&A words (e0.06 = .062).
However, the coefficient is only marginally statistically significant (t = 1.88). The
shock also predicts an 8.8% drop in sales growth (t = 2.26), which is economically
important relative to a sample mean of 12.4%.
The shock also predicts about a 1% drop in capex/assets (t = 2.10), compares to a
sample mean of around 6%, in contrast to the mixed results we found with panel
methods (significant for Board Structure Subindex but insignificant for KCGI).
However a trouble sign for this result: The sum of the coefficient on post-reform
dummy and the coefficient on the interaction term is near-zero. We find a relative
drop in capex/assets for large firms not because large firms reduce capex/assets,
but instead because small firms increase capex/assets. This puts great stress on the
parallel trends assumption – here, that large firms would have increased capex by
an amount similar to small firms, but for the governance shock.
5.2. Post-Shock RD
We present RD results, using a sample that is pooled over the post-treatment
period (2001-2004), in Figure 2. Consider first the figures for Disclosure Subindex,
show in Panel A. The left figure uses a [0.5T, 8T] bandwidth; the right figure uses a
18
narrower [1T, 4T] bandwidth. In each, we show two sets of lines: (i) a set of flat
lines with a jump at the 2T threshold (that is, without controlling for ln(assets)); and
(ii) a set of sloped lines which include a linear control for ln(assets) with different
slopes above and below the threshold, following eqn. (6). In the left figure, the two
above-threshold lines sit on top of each other, so appear as a single line.
With the broader bandwidth, there is a significant jump at the threshold with
both sets of lines. The jump is 4.4 points (t = y.yy) with flat lines and 2.5 (t = z.zz)
with sloped lines. With the narrower bandwidth, there is a significant jump with flat
lines, but the jump disappears if we allow sloped lines. In our experience, this is a
common problem with RD designs, with modest sample sizes. As one narrows the
bandwidth, there is a tendency for the control for the running variable to absorb the
jump at the threshold, even when other evidence, such as the Panel DiD/RD results
and Panel IV/RD results presented below, suggest that the jump is likely to be
“really there.” This concern with narrowing the bandwidth also exists with DiD/RD,
but is more severe with cross-sectional data, because there are no firm effects, and
hence more potential “play” in the coefficient on the running variable. Overall, we
see the RD design as supporting the existence of an effect of the governance shock
on Disclosure Subindex.
For the other four outcomes, in contrast, there is no evidence of a jump at the
threshold. The estimated jumps are insignificant, both with and without slopes, in
all cases.
5.3. Panel IV
Our third causal design is panel IV. We use the full data period from 1998-2004,
and use the interaction of post-reform dummy (=1 for 2001 on) and large firm
dummy as an instrument and report results in Table 9. In Panel A, we assume that
the instrumented variable is lagged KCGI as a whole. In Panel B, we instead assume
that the instrumented variable is lagged Board Structure Subindex, and control for
the rest of lagged KCGI. we adopt this approach because it is not clear whether we
should assume that the IV affects the outcome only through Board Structure Index
(as we assume in Panel B), or more generally through all of KCGI. The legal shock
directly affects only on Board Structure Subindex, but the change in board structure
could lead to changes in other subindices. Indeed, we find evidence of such an effect
for Disclosure Subindex. Thus, there is no clear reason to prefer KCGI versus Board
Structure Subindex as the instrumented variable. We report both stages of the 2SLS
regressions.
19
In the first stage, the IV strongly predicts both KCGI and Board Structure
Subindex, with t-statistics ranging from 11.59 to 17.43. We clearly have no reason
to worry about instrument strength. Note that the first stage regressions are the
same for all outcomes except Disclosure Subindex, the only difference is in the
available sample. We control for ln(assets) and report the coefficient on this
variable, but suppress the coefficients on other covariates.
In the second stage, the instrumented variable strongly predicts Disclosure
Subindex, regardless of which instrumented variable we choose. However, results
for the other outcomes are not strong. For abs(AA), with KCGI as the instrumented
variable, the coefficient has the predicted sign and is marginally significant (coeff. = -
0.11; t = 1.87), but is not significant with Board Structure Subindex as the
instrumented variable. For the other three outcomes, the coefficient on the
instrumented variable is insignificant in both Panels.
Panel DiD and Panel IV are similar specifications. If the instrumented variable is
binary, then the DiD coefficients can be understood as an “intent-to-treat” effect,
while the IV coefficients can be understood as providing a “local average treatment
effect” for “complier” firms, whose behavior was changed by the instrument
(Atanasov and Black, 2015a, 2015b). The correspondence is less close for a
continuous instrumented variable, but we should still expect DiD and IV to have
similar statistical strength. That expectation holds in this case.
5.4. Year-by-year DiD
Our fourth “simple” causal design is year-by-year DiD. We report results in
graphical form, using the leads-and-lags model in eqn. (7), with year 2000 values
pegged to zero. In Figure 3, we report full-sample results in the left-hand figures. In
the right-hand figures, we limit the sample to assets [0.5T, 8T]. The right-hand
figures provide a combined yearly DiD/RD design. We discuss the results from that
combined design in Part 6.
For Disclosure Subindex, the values are reasonably constant over 1998-2000, and
then rise steadily through the end of the sample period. We would ideally want to
have a longer pre-shock period, in which to assess parallel trends. Still, these graphs
provide strong evidence of a causal effect of the shock on Disclosure Subindex.
Disclosure Subindex rises when it should, if the rise were caused by the legal
reforms which came into force in 2000 and 2001, with a lag to allow the board
structure reforms to affect disclosure.
20
The year-by-year DiD graphs for the other outcomes provide no support for a
causal effect of the shock. The abs(AA) graphs show no significant time trend.
MD&A word count tilts up in 2004. Longer MD&A disclosures by large firms in 2004,
drive the positive, marginally significant coefficient on the interaction term in Table
7 (Panel DiD). Yet there is no reason to think this delayed effect is caused by the
legal shock.
Sales growth falls during the post-shock period, but the drop begins in 2000.
This explains why we found stronger results in panel regressions with non-lagged
KCGI. We would similarly find stronger results with panel DiD if we allowed the
shock to turn on in 2000, instead of 2001. But if we believe, on “science” grounds,
that a lag is more plausible, the year-by-year graphs provide evidence that the trend
toward slower growth at large firms began too early – we do not have a solid basis
to believe that it was caused by the legal shock. Capex for large firms, relative to
small firms, falls over 1998-2001, then begins to rise. There is no evidence from the
year-by-year graph that the legal shock led to lower capex in the post-shock period.
5.5. Overview: Causal versus Panel Designs; Multiple Causal Designs
Across all four of our simpler causal methods, an effect of the legal shock on
Disclosure Subindex looks quite robust. For the other measures, several are
significant or marginally significant with panel DiD, but significance falls away with
the other designs. There is clear value in using multiple designs to exploit the same
shock, and assessing whether any results are consistent across designs. The year-
by-year DiD results are particularly useful. They let us assess both: (i) the existence
or absence of parallel trends during the pre-treatment period; and (ii) whether post-
shock differences appear when (relative to the shock) they should, if they are
genuinely caused by the shock.
We also see that results that appear strong with panel data can vanish, when we
apply causal methods to the same data. The panel data results for abs(AA) and
MD&A word count were consistent across panel methods, and the results for sales
growth were nearly so. Yet all three sets of results fall away with causal methods.
The reverse is theoretically possible: Results that are not seen with panel data
methods could be found with causal methods. We see a hint of this possibility for
capex/assets, which is insignificant with panel methods, but takes a significant
negative coefficient with panel DiD. On the whole though, results that are
insignificant with panel methods remain so with causal methods. This pattern also
applies to several other outcomes which we studied, but do not report details on.
21
The tendency is thus for panel data results to provide false positives but not false
negatives, as to the actual existence of a causal effect.
6. Combined Causal Methods
In this part, we combine RD with other methods. We limit the sample to a
bandwidth around the 2T threshold, and apply panel DiD, panel IV, and year-by-year
DiD to the limited sample. We present results with two bandwidths: a broader
[0.5T, 8T] bandwidth and a narrower [1T, 4T] bandwidth. We measure size at year-
end 2000, except as otherwise noted.
Using combined research designs, within a bandwidth around the 2T threshold,
enhances credibility, at the cost of reduced sample size. Consider DiD. The parallel
trends assumption will be more plausible if the sample exhibits “covariate balance”
– if the treated and control firms are similar on a range of covariates prior to
treatment. Treated and control firms necessarily differ on the RD running variable,
which is assets. But within the RD bandwidth, one can expect reasonable balance on
other covariates. With a larger sample, it could be valuable to systematically vary
the RD bandwidth, and show graphically how the coefficients from, say, a DiD
analysis vary with bandwidth (Atanasov and Black, 2015b; Lee and Lemieux, 2010).
That approach is not feasible with our dataset.
6.1. Combined Panel DiD/RD
In Table 10, we present panel DiD results with our two RD bandwidths. Panel A
shows results for the broader [0.5T, 8T] bandwidth; Panel B shows results for the
narrower [1T, 4T] bandwidth. Consider first Disclosure Subindex. In Panel A, the
interaction term between post-reform period and large firm dummy continues to
predict significantly higher Disclosure Subindex values. However the predicted
increase in Disclosure Subindex falls from 4.0 points in Table 9 to 2.3 points in Panel
A. In Panel B, the coefficient falls further and becomes insignificant.
The only other outcomes which shows a significant coefficient with the broader
bandwidth, in the predicted direction, is sales growth. This too weakens in Panel B.
We defer assessing how much weight to put on the results in Panel A until we
review results using the other combined designs.
6.2. Panel IV/RD
In Table 11, we present panel IV results, within an RD bandwidth. Panel A
presents results with the [0.5T, 8T] bandwidth; Panel B presents results with the
22
narrower [1T, 4T] bandwidth. We instrument for lagged KCGI, similar to Table 9,
Panel A.
In column (1), instrumented lagged (KCGI – Disclosure Subindex) predicts higher
Disclosure Subindex scores with the broader bandwidth. The coefficient of 0.33,
multiplied by the first-stage coefficient of 8.78, predicts a 2.9 point increase in
Disclosure Subindex, somewhat larger than the DiD/RD estimate of a 2.3 point
increase, in Table 10, Panel A. However, this coefficient weakens and becomes
insignificant in Panel B, with the narrower bandwidth.
For sales growth, the coefficients on instrumented lagged KCGI are negative and
marginally significant in both panels. For the other outcomes, the coefficients are
insignificant in Panel A, and remain so in Panel B, except for a marginally significant,
negative (opposite from predicted) coefficient for MD&A word count in Panel B.
6.3. Year-by-Year DiD/RD
The last research design we example is year-by-year DiD, within the broader RD
bandwidth. We show results in the right hand figures within Figure 3. In Panel A,
the rise in Disclosure Subindex remains significant in 2003 and 2004. We continue
to have reasonable evidence for parallel pre-treatment trends, albeit for a limited
pre-treatment period. The large-firm rules came into force partly in 2000 and partly
in 2001, so 2000 can be seen as a partial-treatment year, with 2001 as the first full
treatment year.
For abs(AA), there is no evidence of a post-shock decline. Instead, the yearly
coefficient values rise (opposite from predicted) over 1998-2004. MD&A word
count shows no time trend over 1998-2003 and rises in 2004 – the wrong time for
this to be plausibly caused by the legal shock. The capex coefficient bounces a fair
bit, and ends in 2003-2004 about where it was in 1999. Finally, the coefficient on
sales growth falls over 1999-2002, then flattens out. While this is possibly causal,
our judgment is that a drop in 2000 is too early to be caused by the legal shock,
which affected board structure in 2000 and 2001.
6.4. Overall Assessment
On the whole, we retain moderate confidence that there is a true causal effect of
the shock to board structure on Disclosure Index, at the 2T threshold, but we have
little confidence on magnitude, given that the coefficient becomes small and
insignificant with the narrow bandwidth. For sales growth, there are hints of a post-
shock decline, but the coefficients are only marginally significant, and the decline
23
begins in 2000 – earlier than we would expect for a true causal effect. For the other
three outcomes (abs(AA), MD&A word count, and capex), there is no evidence that
the shock affects anything. In unreported results, there is also no evidence that the
shock affects earnings smoothing, signed abnormal accruals, or absolute or signed
raw accruals.
7. Discussion
We examine here, using a case study of Korea, the reliability of classic panel
methods, as a guide to whether a change in governance causes changes in a variety
of outcomes related to disclosure, investment, and growth. We study Korea because
1999 large-firm reforms provide a large exogenous shock to the board structures of
these firms – both board independence and existence of audit committees. This
shock is economically important – share prices of large firms rise by around 30%,
versus mid-sized firms (Black and Kim, 2012). We can exploit the shock using a
variety of causal research designs, including DiD, IV, and RD, as well as combined
designs.
With classic panel methods, we find that higher scores for a broad Korean
Corporate Governance Index predict improved disclosure (measured by a
Disclosure Subindex and by MD&A word count), lower abs(AA), and lower sales
growth (in an environment where large firms were likely seeking to grow at the
expense of profitability). These methods, although “non-causal”, are stronger than
those in many studies of the effect of governance on accruals, earnings smoothing,
and other accounting and financial outcomes. We find no significant associate
between KCGI and a number of other outcomes, including capital expenditures,
earnings smoothing, signed AA, and absolute or signed raw accruals.
But as we shift to simpler and then combined causal designs, all of the results we
found with panel methods fall away, save for the increase in Disclosure Subindex.
We find mild evidence, with some causal designs, that the legal shock leads to
reduced capital expenditures, but our best judgment considering all designs
together, is that the decline begins too early (in 2000) to be reliably attributed to the
shock.
Overall, we provide evidence supporting two main propositions: First, classic
panel methods can be an unreliable guide to causal inference. We all know that
association is not causation, but might provide a clue. Our evidence suggests that, at
least for governance research, the clue is a mild one. Second, results using simpler
24
causal research designs can also be unreliable, and ought to be tested using a variety
of designs, including combined designs where feasible.
The extent to which our results generalize beyond Korea and this particular
shock is, of course, unknown. A promising avenue for future research will to use our
approach of “compare results across an array of research designs” with other shocks
to governance or accounting rules, in other countries, and see what emerges.
Nonetheless, our results suggest caution in interpreting non-causal research designs
as providing meaningful evidence that board or audit committee independence, or
indeed audit committee existence, causally predicts a range of outcomes of interest.
25
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- 27 -
1 Figures and Tables
Figure 1: Board Structure Index and Asset Size
The scatter plots show the relationship between ln(assets in billion Won) and Board Structure Index (0~20) from 1998–2004. The 1999 reforms require large firms (assets > 2 trillion Won; ln(assets) = 7.60) to have a minimum Board Structure Index value ≥ 11.7 (5 points for 50% outside directors; 6.7 points for audit and outside director nomination committees). Audit committee is required in 2000; 50% outside directors and outside director nominating committee in 2001. Sample excludes banks and SOEs. Vertical line indicates 2 trillion Won; horizontal line indicates minimum Index value for large firms. Firm size is measured separately for each year.
28
Figure 2. Post-Shock Regression Discontinuity
Figures show scatter plots of indicated outcome variables versus ln(assets) over 2001-2004. Left figure shows firms
in asset bandwidth [0.5T, 8T]; right figure shows narrower [1T, 4T] bandwidth. Vertical line is at 2T. Each
figure shows two sets of fitted lines. The first set assumes the outcome variable is constant within the bandwidth
except for a jump at 2T; the second set allows a linear relationship between the outcome variable and ln(assets),
different slopes below and above 2T, and a jump at 2T. t-statistics for slopes and jump at 2T are from following
regressions with firm clusters. Without slopes: y = a + b*large firm dummy. With slopes: y = a + b*large firm
dummy + c* [ln(Assets/2T)] + d*large firm dummy*ln(Assets/2T]
Panel A. Disclosure Subindex
0.5-8T bandwidth (left figure): Jump without slopes = 4.4x (t = x.xx); jump with slopes = 2.48 (t = x.xx); n = 378/180 below/above 2T. 1-4T bandwidth (right figure): Jump with no slopes = 3.76 (t = x.xx); jump with slopes = y.yy (t = x.xx); n = xxx/yyy below/above 2T.
Panel B. Absolute Abnormal Accruals
0.5-8T bandwidth (left figure): Jump without slopes = 0.33 (t = 2.00); with slopes = 0.33 (t = 0.40); n = xxx/yyy
below/above 2T; 180 above 2T. 1-4T bandwidth (right figure): Jump without slope = 0.15 (t = 0.27); with slopes =
-0.82 (t = 0.75); n = 496/249 below/above 2T. Abs(AA) is winsorized at 99%.
29
Panel C. MD&A Word Count
0.5-8T bandwidth (left figure): Jump without slopes = 0.82 (t = 2.00); with slopes = -0.77 (t = 0.98); n = 1,278/386
below/above 2T; 180 above 2T. 1-4T bandwidth (right figure): Jump without slope = 0.15 (t = 0.27); with slopes =
-0.82 (t = 0.75); n = 496/249 below/above 2T.
Panel D. One-Year Sales Growth
0.5-8T bandwidth (left figure): Jump without slopes = 0.011 (t = 0.35); with slopes = -0.062 (t = 0.90); n = 384/188
below/above 2T; 180 above 2T. 1-4T bandwidth (right figure): Jump without slope = -0.034 (t = 0.73); with slopes
= -0.074 (t = 0.88); n = 158/116 below/above 2T.
-.2
0.2
.4
1-y
ear
Sa
les G
row
th
6 7 8 9Log of Asset Size
-.2
0.2
.4
1-y
ear
Sa
les G
row
th
7 7.5 8 8.5Log of Asset Size
Panel E. (Capex/Assets)*100
Regressions include all firms within bandwidth, but figure is limited to 3 < [(capex/assets) *100] < 8. 0.5-8T bandwidth (left figure): Jump without slopes = 0.36 (t = 0.60); with slopes = -0.78 (t = 0.87); n = 307/142 below/above 2T; 180 above 2T. 1-4T bandwidth (right figure): Jump without slope = 0.002 (t = 0.00); with slopes = -1.20 (t = 0.92); n = 125/94 below/above 2T.
02
46
81
0
(Cap
ex/A
ssets
)x10
0
6 7 8 9Log of Asset Size
02
46
81
0
(Cap
ex/A
ssets
)x10
0
7 7.5 8 8.5Log of Asset Size
30
Figure 3. Yearly DiD Results
Figures present coefficients on large firm dummy from year-by-year OLS regressions of change in outcome variable on large-firm dummy, constant, and changes
in covariates (same variables as in Table 6). Changes are relative to 2000. Dashed lines show 95% confidence interval, using heteroskedasticity-robust standard
errors. Sample is firms with assets at year-end 2000. No bandwidth for figures on the left and a [0.5T, 8T] bandwidth for figures on the right.
Panel A. Disclosure Subindex.
-2.00
0.00
2.00
4.00
6.00
8.00
10.00
19
98
19
99
20
00
20
01
20
02
20
03
20
04
Coeff
Low
High
Panel B. Absolute Abnormal Accruals
31
Panel C. MD&A Word Count
-0.30
-0.20
-0.10
0.00
0.10
0.20
0.30 1
99
8
19
99
20
00
20
01
20
02
20
03
20
04
Coeff
Low
High
Panel D. One-Year Sales Growth
32
Panel E. (Capex/assets) * 100
33
Table 1: Construction of KCGI
This table shows the governance elements included in KCGI. For details on data sources and element and index
construction, see Black and Kim (2012).
Shareholder Rights Index (A)
Firm permits cumulative voting for election of directors.
Firm permits voting by mail.
Firm discloses director candidates to shareholders in advance of shareholder meeting.
Board approval required for related party transactions (required 2000 for top 10 chaebol, mid-2001 for all
chaebol, 2001 on for large and chaebol firms )
Board Structure Index (B)
Firm has at least 50% outside directors (rule adopted 1999 required beginning mid-2001 for large firms )
Firm has more than 50% outside directors (director database except as indicated)
Firm has outside director nominating committee (rule adopted 1999, required from mid-2001 for large firms ).
Audit committee of the board of directors exists (rule adopted 1999, required from mid-2001 for large firm )
firm has compensation committee
Board Procedure Index (C)
Directors’ positions on board meeting agenda items are recorded in board minutes.
Board chairman is an outside director or (from 2003) firm has outside director as lead director.
A system for evaluating directors exists.
A bylaw to govern board meetings exists.
Firm holds four or more regular board meetings per year.
Firm has one or more foreign outside directors.
Shareholders approve outside directors’ aggregate pay (separate from all directors' pay).
Outside directors attend at least 70% of meetings, on average
Board meeting solely for outside directors exists.
100% outside directors on audit committee
Bylaws governing audit committee (or internal auditor) exist.
Audit committee includes person with expertise in accounting
Audit committee (or internal auditor) approves the appointment of the internal audit head.
Audit committee meets ? 4 times per year
Disclosure Index (E)
Firm conducted investor relations activity in last year
Firm website includes resumes of board members
English financial disclosure exists
Ownership Parity (P)
Ownership Parity = (1 - ownership disparity); disparity = ownership by all affiliated shareholders - ownership
by controlling shareholder and family members
34
Table 2: Summary statistics and correlations
Panel A: Summary Statistics for KCGI and its subindices
All Large Small
1998 484 24.23 33.17 23.05 23.33 6.72 10.6 64.1
2000 535 31.54 49.55 28.82 29.18 10.47 7.76 84.8
2002 466 43.05 66.84 38.84 39.73 13.64 14 97.1
2004 512 44.89 72.07 40.8 42.03 13.74 20.1 98.8
1998 511 0.25 1.69 0.03 0 1.54 0 10
2004 513 3.81 15.75 2.01 0 5.83 0 20
1998 516 17.63 17.51 17.64 18.89 2.97 3.63 20
2004 520 17.03 17.41 16.98 18.69 3.6 4.2 20
1998 523 4.56 4.48 0.71 4.44 2.82 0 17.5
2004 521 9.1 13.82 5.17 9.09 2.99 1.43 18.8
1998 535 1.17 6.65 4.22 0 3.15 0 20
2004 521 6.3 12.78 8.55 6.67 5.87 0 20
1998 516 0.82 3.74 0.36 0 2.89 0 20
2004 521 8.65 12.03 8.14 6.67 3.23 5 20
Board
Procedure
Shareholder
Rights
Min Max
KCGI
Board
Structure
Ownership
Parity
Disclosure
Index Year Obs.Mean
MedianStd.
Dev.
Panel B presents Pearson correlation coefficients for KCGI and subindices. *, **, and *** indicate significance at 10%, 5%,
and 1% levels.
Panel B: Correlations
KCGIBoard
Structure
Ownership
ParityDisclosure
Board
Procedure
Shareholde
r Rights
KCGI 1
Board
Structure0.78*** 1
Ownership
Parity0.20*** 0.01 1
Disclosure 0.74*** 0.44*** -0.03** 1
Board
Procedure0.70*** 0.50*** -0.07*** 0.40*** 1
Shareholder
Rights0.75*** 0.45*** -0.02 0.43*** 0.46*** 1
35
Table 3: Variable Definitions
Definitions of principal variables. Amounts are in billion Won. Balance sheet amounts are measured at fiscal year-end and income and cash flow amounts are for fiscal year, unless otherwise specified.
dependent variables description Disclosure Subindex See Table 1 (construction of KCGI) capex/assets (capital expenditures/assets) * 100 one-year sales growth percentage sales growth = [(salest/salest-1)-1]*100 abs(AA) absolute value of abnormal accruals signed(AA) signed value of abnormal accruals raw accruals accruals in t/assets in t-1; accruals=net income less cash flow abs(raw accruals) Absolute value of raw accruals
earnings smoothing standard deviation(operating income in t/ assets in t-1)/standard deviation(operating cash flow in t/assets in t-1)
log(word count) log(number of words in the MD&A section of financial statements) main independent variable
description
kcgi Korean Corporate Governance Index other variables description advertising/sales advertising expenditures/sales; missing treated as 0 ln(assets) natural logarithm of assets btm (book value of equity/market value of equity) capex/ppe capital expenditures/property, plant and equipment cash flow/lagged assets cash flowt/(assetst-1) debt/mvce book value of debt/market value of common equity ebit/sales earnings before interest and taxes/sales exports/sales export revenue/sales; missing treated as 0 foreign ownership common shares held by foreign investors/common shares outstanding
large large firm dummy equals 1 if book value of assets>2 trillion won at end of 2000 and zero otherwise (or as specified in the text)
ln(mvce) Natural logarithm of market value of common equity market share firm’s share of sales by all firms in the same 4-digit industry listed on KSE.
post post reform dummy equals 1 if fiscal year >=2001 and zero otherwise (or as specified in the text)
ppe/sales property, plant and equipment/sales R&D/sales research and development expenditures/sales; missing treated as 0
five-year sales growth geometric mean growth during past five fiscal years (or available period if shorter)
sole ownership common shares held by controlling shareholder and family members/common shares outstanding
turnover common shares traded during the year / common shares held by public shareholders
ln(years listed) natural logarithm of number of years listed on Korean Stock Exchange
36
Table 4: Descriptive Statistics
Descriptive statistics for principal dependent and control variables, for in regressions of disclosure sample on (rest of KCGI) and control variables. For this regression, dependent variable is measured over 1998-2003; independent variables over 1999-2004; sample size is 2,691 firm-years and 647 firms. Sample size varies slightly for other regressions.
dependent variables Mean S.D. Min Median Max
Disclosure Subindex 3.214 4.932 0 0 20
(capex/assets)*100 6.161 6.180 0 4.104 31.423
one-year sales growth 0.124 0.256 -0.635 0.103 1.635
abs(AA) 4.227 4.064 0.000 3.172 44.098
signed(AA) 0.104 5.692 -41.222 0.077 29.085
raw accruals -0.005 0.062 -0.209 -0.006 0.246
abs(raw accruals) 0.048 0.039 0.000 0.039 0.246
earnings smoothing 1.897 4.276 0.021 0.985 104.517
log(MD&A word count) 7.984 0.388 6.671 7.957 9.387
main independent variable Mean S.D. Min Mdn Max
kcgi 35.592 12.773 7.048 33.267 98.571
control variables Mean S.D. Min Mdn Max
advertising/sales 0.009 0.021 0.000 0.001 0.211
ln(assets) 5.654 1.482 2.068 5.399 11.075
btm 2708.85 2299.74 29.80 2155.18 34820.07
capex/ppe 0.140 0.158 0.000 0.089 2.045
cash flow/lagged assets 0.061 0.075 -0.478 0.056 0.530
debt/mvce 4.985 11.415 0.014 2.191 238.525
ebit/sales 0.041 0.616 -30.780 0.058 0.965
exports/sales 0.267 0.305 0.000 0.125 1.000
foreign ownership 8.664 14.622 0.000 1.240 94.110
five-year sales growth 0.116 0.603 -0.646 0.073 22.659
market share 0.068 0.161 0.000 0.012 1.000
ln(mvce) 17.913 1.629 14.260 17.564 24.943
ppe/sales 0.502 0.658 0.001 0.380 16.466
R&D/sales 0.015 0.160 0.000 0.002 7.694
sole ownership 20.043 16.555 0.000 19.890 78.600
turnover 7.209 11.071 0.030 4.337 206.246
ln(years listed) 2.629 0.687 0.000 2.708 3.892
37
Table 5: Comparison of Results across Research Designs
Table summarizes relation between KCGI and indicated outcomes, across indicated causal and non-causal research designs. “Yes” and boldface means statistically significant relationship (at 5% level or better), in predicted direction; “Marg” “and italics means marginally significant relationship (at 10% level), in predicted direction; “No” means results are insignificant; “—“ means not tried, because results from other methods were consistent. AA = abnormal accruals. See text for details on methods.
Methods Classic panel methods Simpler causal methods Combined causal methods
(bandwidth = [0.5T, 8T]
Outcome variable pooled
OLS RE FE
Panel
DiD
Post-shock
RD
Panel
IV
Yearly
DiD
Panel
DiD/RD
Panel
IV/RD
Yearly
DiD/RD
Disclosure Subindex Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Abs(AA) Yes Yes Yes No No Marg No No No No
MD&A Word Count Yes Yes Yes Marg No No No No No No
Sales Growth Yes Yes Marg Yes No No No Marg Marg No
Investment (capex/assets) No No No Yes No No No No No No
Earnings smoothing No No No No -- No n.a. No No n.a.
Abs(Raw Accruals) No No No No -- No No No No No
Signed(AA or Raw Accruals) No No No No No -- No No -- No
Note: KCGI predicts higher Tobin’s q with all methods (see Black, Jang and Kim, 2006; Black and Kim, 2012). Black, Kim, Jang and Park (2015) find evidence, with causal methods, that control of related party transactions is one channel which can explain the effect of governance on Tobin’s q.
38
Table 6: Panel Regressions for Disclosure Subindex
Regressions (1)-(3) report coefficients from pooled OLS, firm random effects (RE), and firm fixed effects (FE)
regressions of disclosure subindex on lagged (KCGI less disclosure), indicated covariates, and year dummies, over
1998-2003 (for dependent variable). Last column reports FE regression of disclosure subindex on lagged board
structure subindex and lagged (KCGI less disclosure and board structure). Variables are defined in Table 2. All
regressions include year dummies; OLS and RE include industry dummies. t-statistics, with standard errors
clustered on firm, are in parentheses. No. of observations (firms) = 2,691 (647). *, **, *** indicate significance at
the 10%, 5% and 1% level; significant results (at 5% or better) are in boldface. R2 is adjusted R2 for OLS, overall R2
for RE and within R2 for FE. Median RE λ indicates whether RE results are closer to OLS (λ ~ 0) or FE ((λ ~ 1).
dependent variable Disclosure Subindex
method Pooled OLS RE FE
lagged (KCGI less Disclosure
Subindex) 0.0963*** 0.0894*** 0.0880***
(5.13) (5.36) (4.44)
ln(assets) 1.0145*** 0.7677*** 0.1102
(8.13) (6.13) (0.29)
ln(years listed) -0.6836*** -0.5731*** 1.1732
(-3.36) (-3.22) (1.18)
debt/mvce -0.0011 0.0001 0.0007
(-1.22) (0.20) (1.45)
sales growth -0.3139** -0.2681** -0.1823
(-2.39) (-2.48) (-0.63)
r&d/sales 0.4592 0.4058** 0.3780***
(1.30) (2.32) (2.59)
advertising/sales -1.4026 4.4784 9.7779
(-0.22) (0.61) (0.81)
exports/sales -0.1469 0.1601 -0.0867
(-0.30) (0.37) (-0.12)
ppe/sales -0.4083*** -0.2338** -0.2119
(-2.65) (-2.43) (-1.55)
capex/ppe 1.8593*** 0.5398 0.0490
(3.23) (1.07) (0.08)
ebit/sales 0.1635*** 0.1253*** -0.0403
(3.39) (2.62) (-0.07)
market share 0.9519 1.9724 0.8627
(0.59) (1.44) (0.46)
turnover 0.0099 -0.0011 -0.0066
(1.20) (-0.24) (-1.50)
foreign ownership 0.0458*** 0.0563*** 0.0639***
(3.80) (5.01) (4.71)
sole ownership -0.0208*** -0.0226*** -0.0126
(-2.69) (-3.06) (-0.92)
constant -5.4054*** -4.2655*** -4.8605
(-6.03) (-5.19) (-1.57)
year dummies Yes Yes Yes
industry dummies Yes Yes Absorbed
R2 0.442 0.4445 0.4269
median λ 0.60
39
Table 7: Panel Regressions: Other Outcome Variables
Panels A-D. Coefficients from pooled OLS, RE, and FE regressions on indicated governance variable and
covariates. Dependent variables are, respectively, [Abs(AA)*100], MD&A word count, one-year sales growth, and
[capex/assets*100]. Sample and covariates are same as Table 5 except: Panel A (covariates are cash flow/lagged
assets; (cash flow/lagged assets)2, btm, ln(mvce)); Panel C (drop five-year sales growth); Panel D (drop financial
firms, drop capex/ppe, add Tobin’s q). Coefficients on covariates are suppressed. R2 is adjusted R2 for OLS, overall
R2 for RE and within R2 for FE. Sample period for outcome variable is 1999-2004 (1999-2005 for abs(AA)).
Outliers are eliminated if studentized residual from regressing dependent variable on principal independent variable
> |1.96|. Number of observations (firms) is 2,307 (592) in Panel A; 2,767 (551) in Panel B; 2,684 (645) in Panel C;
2,524 (628) in Panel D. t-statistics, with standard errors clustered on firm, in parentheses. *, **, *** indicate
significance at the 10%, 5% and 1% level; significant results (at 5% or better) are in boldface.
Method Pooled OLS RE FE
Panel A. Dependent variable = abs(AA)*100
lagged (KCGI) -0.031*** -0.031** -0.035**
(2.67) (2.57) (2.20)
Panel B. Dependent variable = MD&A word count
lagged KCGI 0.0038*** 0.0023*** 0.0017**
(2.73) (3.13) (2.09)
Panel C. Dependent variable = one-year sales growth
lagged (KCGI) -0.0012** -0.0013*** -0.0017*
(2.53) (2.65) (1.74)
Panel D. Dependent variable = (capex/assets)*100
lagged (KCGI) -0.0048 -0.0071 -0.0002
(0.48) (0.76) (0.02)
40
Table 8: Panel DiD Regressions
Table reports regressions of indicated dependent variables on post-reform dummy (=1 for 2001 on), large firm
dummy (measured at year-end 2000; absorbed by firm FE), interaction of post reform and large firm dummies,
control variables, firm and year FE, and constant term. Covariates are same as in Table 6, coefficients are
suppressed. Sample period is 1998- 2004. t-statistics, with standard errors clustered on firm, are in parentheses. *,
**, *** indicate significance at the 10%, 5% and 1% level; significant results (at 5% or better) are in boldface.
dependent variable disclosure
subindex abs(AA)*100
MD&A word
count
one-year sales
growth
(capex/assets)*
100
post-reform * large firm
dummy 4.010*** -0.3074 0.060* -0.088** -1.085**
(6.35) (0.47) (1.88) (2.26) (2.10)
post-reform dummy 2.115*** 0.0833 0.287*** 0.004 0.918**
(7.02) (0.25) (15.86) (0.16) (2.20) covariates yes yes yes yes yes
within R2 0.4440 0.0399 0.3478 0.2250 0.0495
Number of obs (firms) 3,405 (635) 4126 (500) 3,399 (635) 3,108 (514) 2,839 (469)
41
Table 9: Panel Instrumental Variable Regressions
Table reports first- and second-stage 2SLS regressions of indicated dependent variables on indicated instrumented
variables, with post-reform dummy* large firm dummy as the instrumental variable. Covariates are same as Table 6,
coefficients are suppressed. All regressions include firm and year FE. Sample period is 1998- 2004. t-statistics,
with standard errors clustered on firm, are in parentheses. *, **, *** indicate significance at the 10%, 5% and 1%
level; significant results (at 5% or better) are in boldface.
Panel A. Instrument for KCGI (or KCGI – Disclosure Subindex)
Instrumented variable is KCGI or, in regression (1), (KCGI – disclosure subindex).
(1) (3) (4) (5) (2)
dependent variable disclosure
subindex abs(AA)*100
MD&A word
count
one-year sales
growth
(capex/assets)*
100
instrumented variable lagged (KCGI
– disclosure) lagged KCGI lagged KCGI lagged KCGI lagged KCGI
First stage
post-reform * large firm
dummy 11.09*** 16.00*** 14.64*** 12.84*** 13.61***
(12.60) (12.62) (14.38) (13.08) (11.59)
ln(assets) -0.44 0.41** -0.19 -0.2668 0.4248
(0.72) (1.92) (0.28) (0.41) (0.68)
covariates yes Yes yes yes yes
within R2 0.6412 0.6770 0.6693 0.7667 0.7958
Second stage
instrumented KCGI 0.41*** -0.11* 0.0006 -0.373 -0.008
(6.68) (-1.87) (0.36) (-1.51) (-0.24)
ln(assets) 0.13 0.76*** 0.04 12.4018*** -0.0186
(0.28) (2.81) (1.90) (4.51) (0.05)
covariates Yes yes yes yes yes
Number of obs (firms) 2606 (562) 2,936 (567) 2,600 (561) 2,437 (541) 2,209 (494)
Panel B. Instrument for Board Structure, Control for Other Subindices
Instrumented variable is lagged Board Structure Subindex, regression includes other subindices (in regression (1),
not Disclosure Subindex).
(1) (3) (4) (5) (2)
dependent variable disclosure
subindex abs(AA)*100
MD&A word
count
one-year sales
growth
(capex/assets)*
100
First stage
post-reform * large firm
dummy 8.32*** 8.81*** 8.17*** 8.95*** 8.91***
(15.10) (12.31) (14.12) (17.43) (14.16)
ln(assets) -0.10 0.14* -0.09 -0.1899 -0.3788
(0.24) (1.68) (0.23) (0.52) (1.31)
other subindices yes yes yes yes yes
covariates yes yes yes yes yes
within R2 0.3125 0.2757 0.3177 0.5512 0.5388
Second stage
instrumented lagged board
structure 0.54*** -0.17 -0.001 -0.483 -0.022
(7.19) (-1.52) (-0.43) (-1.34) (-0.41)
ln(assets) -0.03 0.74*** 0.04 12.1969*** -0.0519
(0.07) (2.78) (1.83) (4.36) (0.15)
other subindices yes yes yes yes yes
covariates yes yes Yes yes yes
Number of obs (firms) 2606 (562) 2918 (549) 2600 (561) 2,437 (541) 2,209 (494)
42
Table 10 Panel DiD with RD Bandwidth
Both Panels. Regressions of indicated dependent variables on post-reform dummy (=1 for 2001 on), large firm
dummy (measured at year-end 2000; absorbed by firm FE), interaction of post reform and large firm dummies,
control variables, firm and year FE, and constant term. Covariates are same as Table 6. Sample period is 1998-
2004. t-statistics, with standard errors clustered on firm, are in parentheses. *, **, *** indicate significance at the
10%, 5% and 1% level; significant results (at 5% or better) are in boldface. Panel A. Sample is firms with assets
[0.5T, 8T] at year-end 2000. Panel B. Sample is firms with assets [1T, 4T] at year-end 2000.
Panel A. Broader Bandwidth (assets [0.5T, 8T])
dependent variable disclosure
subindex
(capex/assets)*
100 abs(AA)*100
MD&A word
count
one-year sales
growth
post-reform * large firm
dummy 2.2978*** -0.2831 0.3586 -0.0026 -0.1148**
(2.91) (-0.45) (0.57) (-0.06) (-2.10)
post-reform dummy 6.9318*** 0.4740 -2.3706** 0.2274*** -0.0712
(9.17) (0.50) (-2.27) (5.96) (-1.30)
covariates yes yes yes yes yes
within R2 0.5649 0.0981 0.1103 0.3659 0.1878
Number of obs (firms) 905 (177) 686 (126) 894 (108) 896 (175) 868 (165)
Panel B. Narrower Bandwidth (assets [1T, 4T])
dependent variable disclosure
subindex
(capex/assets)*
100 abs(AA)*100
MD&A word
count
one-year sales
growth
post-reform * large firm
dummy
0.6915 0.0325 0.5058 -0.0237 -0.0342
(0.66) (0.03) (0.62) (-0.43) (-0.48)
post-reform dummy 3.8697*** -0.2145 -3.0415* 0.2619*** 0.0980
(4.25) (-0.15) (-1.78) (6.05) (0.92)
covariates yes yes yes yes yes
within R2 0.6618 0.1564 0.1172 0.3520 0.1953
Number of obs (firms) 433 (96) 335 (64) 452 (54) 435 (94) 422 (88)
43
Table 11 Panel IV/RD
Table reports first- and second-stage 2SLS regressions of indicated dependent variables on indicated instrumented
variables, with post-reform dummy* large firm dummy as the instrumental variable. Covariates are same as Table 6,
coefficients are suppressed. All regressions include firm and year FE. Sample period is 1998- 2004. t-statistics,
with standard errors clustered on firm, are in parentheses. *, **, *** indicate significance at the 10%, 5% and 1%
level; significant results (at 5% or better) are in boldface.
Panel A. Broader Bandwidth (assets [0.5T, 8T]
(1) (2) (3) (4) (5)
dependent variable disclosure
subindex
(capex/assets)*
100 abs(AA)*100
one-year sales
growth
MD&A word
count
instrumented variable lagged (KCGI
– disclosure) lagged KCGI lagged KCGI lagged KCGI lagged KCGI
First stage post-reform * large firm
dummy 8.78*** 10.4445*** 12.53*** 9.9671*** 10.98*** (11.15) (7.68) (7.98) (8.40) (11.89)
ln(assets) -3.61*** -3.3167* 0.42 -4.7819*** -3.45 (-2.56) (-1.80) (0.75) (-2.99) (-2.67)
covariates yes yes yes yes yes
within R2 0.7067 0.8153 0.6770 0.8039 0.8022
Second stage
instrumented variable 0.33*** 0.0043 -0.06 -0.6704* -0.002 (3.57) (0.09) (-1.02) (-1.74) (-0.66)
ln(assets) -0.72 1.1865 1.6** 14.4831* 0.07
(-0.64) (1.03) (2.01) (1.93) (1.58) covariates yes yes yes yes yes
Number of obs (firms) 688 (150) 548 (120) 643 (123) 684 (151) 701 (168)
Panel B. Narrower Bandwidth (assets [1T, 4T])
First stage
post-reform * large firm
dummy 5.51*** 6.5550*** 9.36*** 6.6876*** 5.99***
(4.19) (4.44) (5.52) (5.02) (-4.39)
ln(assets) -5.28*** -1.0137 1.12 -4.5134* -5.67**
(-2.27) (-0.41) (1.26) (-1.76) (-2.33)
covariates yes yes Yes yes yes
within R2 0.7788 0.8776 0.8296 0.8531 0.8306
Second stage
instrumented variable 0.23 -0.0483 -0.11 -1.2680* -0.014*
(1.30) (-0.55) (-1.22) (-1.73) (-1.81)
ln(assets) -0.37 -0.1857 1.86 5.1704 -0.03
(-0.24) (-0.11) (1.40) (0.51) (-0.31)
covariates yes yes yes yes yes
No. of obs. (firms) 322 (72) 269 (57) 316 (67) 329 (76) 316 (71)