RE-EXAMINING ACCOUNTING CONSERVATISM:
THE IMPORTANCE OF ADJUSTING FOR FIRM HETEROGENEITY
ALAN G. HUANG
School of Accounting and Finance, University of Waterloo, Ontario, Canada N2L 3G1
Email: [email protected]
YAO TIAN
School of Business, University of Alberta, Alberta, Canada T6G 2R6
Email: [email protected]
TONY S. WIRJANTO
School of Accounting and Finance and Department of Statistics and Actuarial Science,
University of Waterloo, Ontario, Canada N2L 3G1
Email: [email protected]
Version: March 2011
Abstract
In this paper, we examine the role of firm heterogeneity of earnings in measuring accounting
conservatism. Prior studies have documented the extent of accounting conservatism (in the form
of the asymmetric response of accountings earnings to news) and its tendency to increase over
time. However, many of these studies implicitly assume that unobserved firm-specific
characteristics (known as firm heterogeneity or firm-specific fixed-effects) are unimportant to
earnings determination. We find that after allowing for firm-specific fixed-effects in the earnings
determination, (i) the level of accounting conservatism is smaller in magnitude than previously
documented, and (ii) conservatism does not increase monotonically over time as has been
claimed in prior studies. Our results echo recent studies that call for firm-level measures of
conservatism, and emphasize the importance of allowing for firm heterogeneity in measuring
accounting conservatism.
Keywords: returns; earnings; firm-specific fixed-effects; asymmetry; conservatism.
* We are solely responsible for all remaining errors.
1.
INTRODUCTION
Accounting conservatism is an important characteristic of the Generally Accepted Accounting
Principles (GAAP). It refers to “the differential verifiability required for recognition of profits
versus losses” (Watts, 2003). Many empirical studies have attempted to quantify the extent of
accounting conservatism. In a seminal publication, Basu (1997) studies the timeliness of earnings
recognition with respect to stock returns (as a measure for news) in a piecewise linear regression
model. He finds that earnings reflect “bad news” faster than “good news”, providing empirical
evidence of accounting conservatism. Employing Basu’s (1997) measure as well as a number of
other measures, Givoly and Hayn (2000) show that accounting conservatism has increased over
time.1
In this paper, we reexamine the appropriateness of the model specifications used in Basu (1997)
in the context of firm heterogeneity. We focus on the Basu (1997) measure, since it is the most
widely used conservatism measure in the literature (Ryan, 2006). Basu (1997) and many
subsequent papers on accounting conservatism typically use a pooled ordinary least squares
(OLS) approach to estimate their empirical models of accounting conservatism, where earnings
is regressed on stock returns and negative/positive return regimes. This approach inadvertently
neglects the panel structure of the data and treats unobserved and unobservable firm-specific
characteristics as homogeneous and, thus, unimportant to the determination of earnings. In
particular, the OLS measure obscures the cross-sectional variation in conservatism by assuming
that all firms are homogeneous. 2 Our findings show that there are significant cross-sectional
variations (due to unobserved firm heterogeneity or firm-specific fixed-effects) in earnings. By
ignoring firm heterogeneity, the pooled OLS regression model essentially forces return to be the
sole determining factor of the cross-sectional variations in earnings. This can result in an omitted
1
Studies of accounting conservatism that focus on high-tech versus low-tech firms include Kwon, Qin, and Han
(2006), and studies that focus on the banking industry include Alali and Jaggi (2011) and Anandarajan, Francis,
Hasan, and John (2011). In addition, non-US studies of conservatism include Elbannan (2011) for an emerging
market, and Anandarajan, Francis, Hasan, and John (2011) for a large number of international markets.
2
Some studies control for industry fixed-effects (for example, Ahmed et al. 2002, Ahmed and Duellman 2007,
Francis et al. 2009, Khan and Watts, 2009). Doing so ameliorates the cross-sectional variation problem but does not
remove it. Controlling for industry fixed-effects assumes that firms within an industry are homogenous and thus
ignores the cross-sectional variation of conservatism within the industry. The adjustment of firm- and time-fixedeffects is also used in other settings in accounting. For example, Beaver and Ryan (2000) use fixed-effects to model
the persistent bias in book-to-market ratios due primarily to unconditional conservatism, and use time-specific
effects to capture economy wide temporal variation.
1
variable bias in the parameter estimates of the regression model, and thus can lead to an
erroneous inference about the extent of and trend in accounting conservatism.
Our study is motived by recent studies that call for controlling for firm heterogeneity in
estimating measures of conservatism. The literature suggests that conservatism is associated with
firm- and economy-specific factors such as contracts (including debt and compensation
contracts), litigation, taxation and regulation (Watts, 2003). Adjusting for these firm-level
heterogeneities yields a firm-level measure of conservatism. However, traditional conservatism
measures are not designed to capture these firm-level variations. Notably, Ryan (2006)
comments that “(s)uch measures are currently not available and are desperately needed in order
to address many research questions empirically.” It is not until recently that firm-level
conservatism measures were introduced in the literature. 3 For example, based on the return
decomposition model of Vuolteenaho (2002), Callen, Segal, and Hope (2010) define the ratio of
unexpected current earnings to total earnings shocks as a firm-level conservatism measure. Khan
and Watts (2009) specify the asymmetric earnings timeliness coefficient in Basu (1997) as a
linear function of firm-specific characteristics of size, market-to-book and leverage, and
substitute these characteristics into the Basu regression to arrive at a firm-level conservatism
measure.
In this paper we provide another refinement of Basu (1997) for firm-level conservatism. Since
there are potentially many factors that affect variation in conservatism,4 and since the literature is
inconclusive on which factors to use, we use a parsimonious setting of firm fixed-effects to
control for firm-specific heterogeneity. The literature suggests that fixed-effects provide a
mechanism to control for unobservable firm-specific effect in a number of settings. For example,
observing that leverage is stable over time for individual firms, Lemmon, Roberts and Zender
(2008) find that the unobserved firm-specific effects (as measured by fixed-effects) are
responsible for the majority of cross-sectional variation in capital structure. In particular, related
3
Controlling for fixed-effects is a popular way to derive firm-specific measures. For example, Ramirez and Hachiya
(2006) use fixed-effects to measure firm-specific organizational capital.
4
For example, Watts’s (2003) survey paper suggests that conservatism is associated with contracts, litigation,
taxation and regulation, and Khan and Watts (2009) choose size, market-to-book and leverage as the primary factors
that affect conservatism.
2
to this paper, Ball, Kothari and Nikolaev (2011) recently recommend that researchers interested
in measuring conservatism in the Basu (1997) framework should control for firm-specific effects
in order to avoid potentially spurious inferences.
To provide evidence of firm heterogeneity in the earnings regression, we show that a substantial
part of the total variation in earnings is attributed to cross-sectional differences. In fact, the
unidentified cross-sectional fixed-effects account for more variation in earnings as the traditional
returns-earnings specification. The adjusted R-square from a regression of earnings on just firmspecific fixed-effects is 13% in our constant sample of 691 firms from 1976 to 2005. In contrast,
the adjusted R-square from the traditional earnings regression (Basu, 1997) is 10% only.5 These
results are in line with Lemmon, Roberts and Zender (2008), who call for a fixed-effects study,
and provide empirical support for Ball, Kothari and Nikolaev’s (2011) recommendation to
control for fixed-effects in estimating the Basu (1997) asymmetric earnings timeliness.
Due to the strength of the cross-sectional variations, and hence the importance of firm
heterogeneity in earnings determination, we explicitly incorporate firm-specific fixed-effects into
Basu’s regression model. We then use this refined model to reexamine the extent of accounting
conservatism and its trend over time. We find that, after allowing for firm-specific fixed-effects
in the earnings regression, the level of accounting conservatism is much smaller in magnitude
than previously documented. In addition, contrary to Givoly and Hayn (2000), we do not find
clear evidence to support the assertion that, over time, there is a monotonic increase in
accounting conservatism.
We also conduct a number of sensitivity analyses to ensure that our results are robust to different
model specifications and alternative measures of accounting conservatism, such as employing
different samples, and using cash flows from operations as a proxy for news (Ball and
Shivakumar 2006). Overall, our results highlight the importance of allowing for firm
heterogeneity in the regression models that use asymmetric response of accounting earnings to
news as a conservatism measure.
5
Consistent with these results, model specification tests conducted in this paper reject the assumption of firm
homogeneity.
3
Our econometric refinements of the Basu (1997) specifications are intended to provide an
agnostic firm-level measure of conservatism. As such, this paper joins several recent studies in
addressing the efficacy of the Basu measure. Dietrich, Muller and Riedl (2007) show that the
Basu specification gives rise to evidence consistent with accounting conservatism even in the
absence of accounting conservatism; that is, Basu (1997) overstates the occurrence of accounting
conservatism. Patatoukas and Thomas (2009, 2010) further support the views espoused by
Dietrich, Muller and Riedl (2007) by arguing that the scale variable used in the Basu (1997)
regressions entails an effect that biases the Basu estimator. Contrasting these views, Givoly,
Hayn and Natarajan (2007) claim that Basu’s (1997) measure understates accounting
conservatism. In addition, Ball, Kothari and Nikolaev (2011) challenge the views of Patatoukas
and Thomas (2010) by arguing that the bias in the Basu estimator is in fact caused by the
correlation between the expected values of earnings and return. Ball, Kothari and Nikolaev
(2011) advocate for using fixed-effects in the Basu (1997) regression to correct for this bias. Our
paper takes this suggestion seriously: we explicitly control for firm fixed-effects. We also
contribute to the literature by showing that controlling for fixed-effects considerably weakens the
trend of conservatism.
The remainder of the paper is organized as follows. In section 2, we briefly review studies on
accounting conservatism and present the pooled OLS earnings regression model originally
employed in Basu (1997) to measure the extent of accounting conservatism. In section 3, we
discuss the potential shortcomings of the pooled OLS regression model, and introduce the fixedeffect model to overcome some of these problems. In Section 4, we first demonstrate the
importance of firm heterogeneity in earnings determination, and then report the findings on the
extent of accounting conservatism and its trend over time after allowing for firm-specific fixedeffects in the earnings regression. We conduct four sets of sensitivity analysis in Section 5 to
demonstrate the robustness of our results. Finally, Section 6 concludes.
2.
ACCOUNTING CONSERVATISM
Over the years, a considerable amount of research has investigated the extent of accounting
conservatism, its impact on the quality of financial reporting, and its trend over time. In these
4
studies, researchers have introduced a number of definitions of conservatism. For instance,
Belkaoui (1985) broadly defines conservatism as “reporting the lowest values of assets and
revenues and the highest values of liabilities and expenses.” Taking a balance-sheet perspective,
Penman and Zhang (2002) define conservative accounting as “choosing accounting methods and
estimates that keep the book values of net assets relatively low.” Basu (1997) argues that
“conservatism in the balance sheet is of dubious value if attained at the expense of conservatism
in the income statement, which is far more significant”. Taking an income-statement perspective,
he defines accounting conservatism as “the practice of reducing earnings in response to ‘bad
news’, but not increasing earnings in response to ‘good news’.” In this paper, we adopt Basu’s
convention and define accounting conservatism in terms of the asymmetric timeliness of
earnings to reflect “good news” and “bad news”.
To measure the extent of accounting conservatism, Basu (1997) proposes a piecewise linear
regression:
EPSit / Pit 1   0   1Rit   2 Dit   3 ( Dit  Rit )  uit
(1)
In the above regression, EPS it is the earnings per share of firm i in fiscal year t, Pit 1 is the price
per share of firm i at the beginning of the fiscal year t, Rit is the discretely compounded annual
return of firm i in fiscal year t, and Dit is a dummy variable set equal to 1 when Rit  0 (bad
news) and to 0 when Rit  0 (good news); and u it is the unobserved zero-mean error term. In (1),
the slope coefficient,  3 , measures the incremental responsiveness of earnings to bad news over
earnings to good news. It is expected to be positive and significant under a conservative
reporting system. Basu (1997) estimates this model as a pooled cross-sectional regression and
finds that earnings is about four and a half times ( ( 1   3 ) /  1  4.66 ) as sensitive to negative
returns as it is to positive returns. These results provide strong evidence for accounting
conservatism.
Since the publication of Basu (1997), the above framework has been widely adopted to measure
the extent of accounting conservatism. It is, in fact, the “most widely used conservatism measure
5
in the literature” (Ryan, 2006). Givoly and Hayn (2000) use Basu’s (1997) measure as well as
other measures to examine the trend in conservatism over time. Estimating the model in a pooled
OLS regression model using the intersection of firms in Standard and Poor’s Compustat and the
Center for Research in Security Prices (CRSP) from 1950 to 1998, these authors find that the
timely recognition of “bad news” relative to “good news” increased over time. In other words,
the authors document that there is an increasing trend in accounting conservatism.
Like most studies on earnings-returns relation, Basu (1997) and Givoly and Hayn (2000)
estimate the returns-earnings relation by using the standard pooled OLS approach. It is important
to note that the pooled OLS estimator in equation (1) treats sample observations as being serially
uncorrelated for a given firm with homoskedastic errors across firms and time periods. However,
as we will show empirically in Section 4 of this paper, this assumption is rather restrictive. In
fact, we find there are significant cross-sectional variations (i.e., firm heterogeneity) in earnings
determination. In the presence of this firm heterogeneity, the pooled OLS estimates of
  ( 0 ,  1 ,  2 ,  3 ) ' in (1) are biased and inconsistent. This is our underlying motivation to refine
Basu’s (1997) model by explicitly allowing for firm-specific fixed-effects. We then use the
refined model to reexamine the extent of accounting conservatism and its trend over time.
3.
MODEL DEVELOPMENT—ACCOUNTING FOR FIRM HETEROGENEITY
There are two methods to account for firm heterogeneity: the fixed-effects model and the
random-effects model. On one hand, the fixed-effects model can be estimated using three
alternative approaches: between-firms, within-firms, and Least Squares Dummy Variable
(LSDV) estimations. This model is particularly suitable when the differences between individual
firms can be reasonably viewed as parametric shifts in the regression function itself. For
instance, an example for this is, when the cross-sections used in the estimation represent a
broadly exhaustive sample of the population of firms. On the other hand, if the cross-sections are
drawn from a larger population, so that the sample firms may not be reasonably considered
exhaustive, then it is more appropriate to view firm-specific terms in the sample as randomly
distributed over the full cross-sections of firms, and to instead apply the random-effects model.6
6
See, for example, Greene’s (2003) textbook for an illustration of fixed-effects and random-effects models.
6
In untabulated results, 7 we perform a series of model specification tests in our sample to
determine the most appropriate panel-data regression model for equation (1). Our test results
confirm the presence of firm-specific effects and suggest that estimates of the parameters in the
linear piece-wise earnings regression in equation (1) obtained by the pooled OLS are biased and
inconsistent. Furthermore, our test results favor the fixed-effects model with the LSDV
estimation approach over the RE model and other fixed-effects models. Consequently, our
subsequent analyses in the remainder of the paper will focus on estimates obtained from the
LSDV/FE specification.
Let i=1,…,N denote firms, and t  1,..., T denote time periods. The fixed-effects model is
specified as follows:
N
EPS it / Pit 1 =  0   1 Rit   2 Dit   3 ( Dit  Rit )    j F jit + uit ,
(2)
j 2
where Fijt is the firm dummy variable (i.e., Fijt  1 for j=i and Fijt  0 for i  j ; i,j=2,…N),  j
is the individual firm-specific effect which is assumed to be time invariant, and u it is the
unobserved zero-mean error term. In the context of our study,  j includes firm-specific
characteristics of a company that are candidates for additional explanatory variables in the
model. If there is no firm heterogeneity in the data, this model would reduce to the original Basu
model as  j  0 for all j. Note that the regression model in equation (2) can be extended to
include time fixed-effects as follows:
N
T
j 2
s 2
EPSit / Pit 1 =  0   1 Rit   2 Dit   3 ( Dit  Rit )    j F jit   sYsti + uit ,
(3)
where Ysti is the time dummy ( Ysti = 1 for s = t and Ysti = 0 for s  t ; s,t = 2,…T). Consistent with
the literature that calls for firm-level conservatism measures (e.g., Ball, Kothari and Nikolaev,
2011), in this paper we choose to present results from regression (2) to emphasize the importance
of allowing for firm heterogeneity in measuring accounting conservatism. We note that adding
7
The results are available from the authors upon request.
7
time fixed-effects does not change the qualitative results appreciably. The estimation results are
available upon request.
4.
EMPIRICAL RESULTS
In this section, we will first describe the sample, and provide empirical evidence on the
importance of firm heterogeneity. We will then use the refined model in equation (2) to account
for firm-specific fixed-effects and reexamine the extent of accounting conservatism and its trend
over time.
4.1
Data
Our sample covers NYSE/AMEX/NASDAQ firms for the period of 1976-2005. We first
consider a constant sample where firms have non-missing observations in all years of the entire
sample period. In robustness checks, we use an alternate sample that allows for new listings and
de-listings of firms. The accounting data used in this study are retrieved from Compustat, and the
return data are from CRSP. The dependent variable in equation (1), EPSit / Pit 1 , is calculated as
earnings per share (Compustatvariable EPSPX) divided by the lagged-one fiscal-year-end
closing price (Compustat variable PRCC_F). Firm’s annual stock return is defined as the
cumulative monthly returns for the period from 9 months before the fiscal year end to 3 months
after the fiscal year end. Finally, as in Basu (1997), we winsorize the top and bottom 1% of the
earnings and returns variables to exclude outliers. This results in a final sample of 691 firms for
the time period from 1976 to 2005.
In addition, in robustness checks, we use cash flows from operation (Compustat variable
OANCF) to proxy for economic gains/losses. Since cash flows from operation is only available
after 1988, we construct a constant sample of firms over the period from 1988 to 2005 by
requiring firms to have a non-missing value in accruals and cash flows from operations every
year. This alternative sample has a total of 995 firms.
4.2
Evidence of the Importance of Firm Heterogeneity
8
We now show that the variations in earnings and returns for the pooled sample are attributed
mainly to firm-specific cross-sectional variations. Table 1 reports the full-sample standard
deviations, the time-series mean of cross-sectional standard deviations, and the cross-sectional
mean of time-series standard deviations of individual firms on earnings and stock returns. We
observe that the means of the cross-sectional volatilities for both variables are almost as large as
the pooled volatility, whereas the means of the time-series volatilities of the individual firms are
much smaller. As a further illustration of this point, Figure 1 plots the ratios of the crosssectional volatility of each variable to its pooled counterpart over time. As shown in the figure,
the high ratios between the means of the cross-sectional sample volatilities and pooled sample
volatility are not due to any particular sub-periods, as the ratios remain high across time.
Collectively, Table 1 and Figure 1 show that it is the between-firm differences instead of withinfirm differences that drive the pooled sample variations.
[Table 1 about here.]
[Figure 1 about here.]
The fact that the sample variation of return is mainly driven by cross-sectional variation justifies
the use of LSDV/FE.8 However, it is important to point out that the original Basu model does
not account for the between-firm differences. By ignoring the firm-specific fixed-effects, the
pooled OLS model essentially forces the returns to be the sole factor determining the crosssectional variations (firm heterogeneity) in earnings. This can lead to an omitted variable bias in
the returns-earnings regression, and consequently an erroneous interpretation of the extent of
accounting conservatism. In our refined model, we capture this unobserved firm heterogeneity
using the firm-specific fixed-effects in equation (2).
Before we estimate the fixed-effects regression of equation (2), it is necessary to examine how
much of the documented “conservatism” effect (as captured by the returns variable) in Basu’s
pooled OLS model is attributable to the unobserved firm-specific fixed-effects. To this end, we
8
If there is little cross-sectional variation in return, then a between-firms regression, which averages variables over
time across firms, would also be appropriate, since in this case what matters is the time-series changes in variables.
This is the approach taken by Grambovas, Giner and Christodoulou (2006).
9
use the analysis of covariance (ANCOVA) to decompose the variations in the earnings due to the
different factors based on the following piecewise linear fixed-effects regressions in (a) to (f).9
N
EPS it / Pit 1 =  0    j F jit + u it
(a)
j 2
T
EPS it / Pit 1 =  0    sYsti + u it
(b)
s2
N
T
j 2
s2
EPSit / Pit 1 =  0   j F jit   sYsti + u it
(c)
EPSit / Pit 1 =  0   1Rit   2 Dit   3 ( Dit  Rit ) + uit
(d)
N
EPS it / Pit 1 =  0   1 Rit   2 Dit   3 ( Dit  Rit )    j F jit  u it
(e)
j 2
N
T
j 2
s 2
EPS it / Pit 1 =  0   1 Rit   2 Dit   3 ( Dit  Rit )    j F jit   s Ysti + uit
(f)
The ANCOVA results are reported in Table 2. Each column in Table 2 corresponds to a different
specification in (a)-(f). The numbers in each column, excluding the last row, are fractions of total
Type III partial sum of squares for each model. That is, the partial sum of squares for each effect
is scaled by the aggregate partial sum of squares of all effects in the model, to sum each column
to unity. As a result, the values in the table correspond to the fractions of the model’s sum of
squares due to particular effects. A special case arises when there is only one effect in the
model. In this case, the total explained sum of squares is due to that effect only. For instance, in
column (a), when only firm-specific fixed-effects are included in the model, the estimate takes
on a value of 1.00 (or 100%).
[Table 2 about here.]
The last row of Table 2 reports the value of the adjusted R-square for each specification. Column
(a) in the table shows that firm-specific fixed-effects alone account for 13% of the observed
9
Lemmon, Roberts and Zender (2008) use a similar method in the context of capital structure determinants and find
that the traditional determinants for capital structure explain little of firms’ actual leverage once firm fixed-effects
are controlled for.
10
variation in earnings. Column (d) represents the results from the pooled OLS model in Basu
(1997). The value of the adjusted R-square for this particular specification is 10%, lower than
that from the model with only firm-specific fixed-effects. Column (e) shows our proposed
specification, which adds the firm-specific fixed-effects to the Basu model in column (d). The
adjusted R-square rises from 10% in column (d) to 20% after including the firm-specific fixedeffects. In addition, a substantial 82% of the explanatory power in this model is captured solely
by the firm-specific fixed-effects.10 In summary, the results of the variance decomposition show
that earnings contain a nontrivial firm-specific component that is not captured by the pooled OLS
specification in Basu (1997). Since a substantial part of the total variation in earnings emanates
from the cross-sectional differences, there is a need to control for firm fixed-effects in the Basu
(1997) model.
4.3
Empirical Results on the Extent of Accounting Conservatism and its Trends
4.3.1 The Extent of Accounting Conservatism
Given the presence of firm-specific fixed-effects in the returns-earnings relation, we adopt a
panel-data methodology to estimate the refined Basu model to account for firm-specific fixedeffects and reexamine the extent of accounting conservatism. For comparison purposes, we also
estimate the original Basu model using the pooled OLS approach, and report the estimation
results from both models in Table 3.11
[Table 3 about here]
As Table 3 shows, the refined model has a much larger adjusted R-square than the original
model (i.e., 0.196 versus 0.097), suggesting a better fit for the refined model. Also, both models
produce statistically significant estimates for the parameters (  0 ,  1 , and  3 ), all with expected
10
We also observe in Table 2 that the contribution from the year fixed-effects is significant. In this paper, we follow
the literature and focus on the effect of firm heterogeneity in earnings determination. As previously noted, including
the year fixed-effects in the earnings regression does not change the qualitative results.
11
The standard R-squared value is not an appropriate goodness-of-fit measure for panel-data regressions since it can
yield numerical values outside the interval [0,1]. Instead, a generalization of the R-squared value due to Theil (1961)
is reported for LSDV/FE regressions. The Theil R-squared value is defined as one minus the sum of squared errors
divided by the sum of squares of the (transformed) dependent variable.
11
signs. Recall that  0 captures the current recognition of unrealized gains from the prior period.
The estimated coefficients of  0 are positive and highly significant in both models, suggesting a
delayed recognition of gains in a conservatism accounting system. The estimates of  2 in both
models are very small in magnitude and statistically insignificant, suggesting that the current
recognition of prior-period gains (as captured by the constant term,  0 ) does not change with the
type of news received in the current period.
The estimates of the coefficient  3 (which measures the incremental responsiveness of earnings
to bad news) are positive and statistically significant in both models, providing evidence for
accounting conservatism. However, the estimated  3 from the refined model is much smaller in
magnitude than that from the original model (0.139 versus 0.205). A test of the hypothesis that
 3 = 0.205 in the refined model is strongly rejected at below the 1% significance level, indicating
that the refined model gives different levels of accounting conservatism than the original model
does. In particular, according to the original model, earnings is about 8.59 (= (0.027 +
0.205)/0.027) times as sensitive to negative returns as it is to positive returns, while the refined
model suggests that earnings is about 4.48 ( = (0.040 + 0.139) /0.040) times as sensitive to
negative returns as it is to positive returns. A comparison of the estimate of  3 and the ratio of
( 1   3 ) /  1 between the pooled OLS model and their counterparts in the LSDV/FE model
reveals that they are significantly different at the 1% level. Overall, these results suggest that
after correcting for firm heterogeneity, the actual extent of accounting conservatism is only about
one half of the level as previously documented.
4.3.2 The Trend in Accounting Conservatism
In examining the trend in conservatism over time, Givoly and Hayn (2000) estimate the Basu
(1997) model using pooled OLS and find that the timely recognition of “bad news” relative to
“good news” has increased over time. Their results provide evidence for an increasing trend in
accounting conservatism.12
12
In the Appendix, we replicate Givoly and Hayn’s (2000) analysis based on Basu’s measure in our sample. Using
their sample period, our replication results are very close to what they have reported. However, their findings appear
12
Given the ubiquity of the Basu model to measure conservatism (Ryan, 2006), and given the
significant difference in our findings regarding the extent of accounting conservatism after
allowing for firm-specific fixed-effects, it is worthwhile to reexamine the trend in accounting
conservatism by using the refined Basu model that explicitly accounts for firm heterogeneity. To
this end, we divide the full sample period into sub-periods with a five year interval, as well as
sub-periods corresponding to business cycles defined as periods from NBER business recession
to NBER business peak. We estimate both the original pooled OLS model and our refined
LSDV/FE model over these various sub-periods. We report the estimation results in Table 4.
[Table 4 about here.]
A number of observations warrant discussion. First, contrary to the findings in Givoly and Hayn
(2000), the refined model does not show an increasing trend in conservatism, as evidenced in
both the estimate of  3 and the ratio of ( 1   3 ) /  1 . As for  3 , its estimates from the refined
model are positive and significant in each of the five-year intervals from 1976 to 2005, providing
evidence of accounting conservatism in each sub-period. However, the estimated values of  3
from the refined model show an overall declining trend over time. This indicates that, the first
half of the sample period has a larger estimate of  3 than the second half. In particular, the
estimate of  3 drops from 0.132 in the sub-period of 1976-1980 to 0.033 in the sub-period of
1996-2000. This indicates a significant reduction in asymmetry during this period. An exception
is the sub-periods of 1986-1990 and 2001-2005, during which the estimate rose from 0.072 in
1981-1985 to 0.147 in 1986-1990, and from 0.033 in 1996-2000 to 0.065 in 2001-2005
respectively. As for ( 1   3 ) /  1 , the conservatism trend as measured by this ratio in the refined
model is relatively stable across these sub-periods. The ratio, which measures the sensitivity of
earnings to bad news relative to their sensitivity to good news, always exceeds unity, suggesting
a greater tendency of firms to recognize bad news in a more timely fashion than good news.
Once firm-specific fixed-effects are incorporated into the regression specification, there is no
to be both sample specific and estimation-method specific. In particular their main findings are reversed once the
sample period is extended to cover the period of 1995-2004.
13
clear evidence that the incremental responsiveness of earnings to bad news is stronger in later
years than in early years. Overall, our results cast serious doubts on the assertion that the trend of
conservatism is increasing over time.
Second, similar to the results reported in Table 3, the estimated values of  3 and ( 1   3 ) /  1 in
the refined model are consistently smaller than their counterparts in the original model in all subperiods, providing evidence that the results reported in Table 3 for the whole sample is not likely
caused by a particular sub-period. The estimated values in the former are usually one half or a
third of those in the original model, confirming that after incorporating firm heterogeneity in the
earnings regression, the measured degree of accounting conservatism is weaker than previously
reported.
Finally, the estimate value of  1 , which measures earnings’ responsiveness to good news, in the
refined model is larger than that in the original Basu model. A comparison between the first half
and the second half of the sample period reveals that the estimate of  1 tends to decrease over
time in both the original pooled OLS and the refined LSDV/FE models. These results indicate
that (i) controlling for firm heterogeneity results in earnings being more responsive to good news
than is the original Basu (1997) setting and (ii) not only is earnings less responsive to bad news
over time, it is also less responsive to good news over time—that is, earnings is less sensitive to
news. Overall, these results further weaken the argument of asymmetric timeliness of earnings of
Basu (1997), in that earnings’ responsiveness to news is now less asymmetric.
When the sample period is partitioned according to the NBER business cycles, the results are
similar. The estimated value of  1 in the refined model shows a steady decline from 0.076 in
1976-1979 to 0.023 in 2001-2005, which is again consistent with the idea that earnings reflect
positive returns on a less timely basis over the business cycles. The corresponding estimates
of  3 show a similar pattern to those obtained for the five-year intervals. In addition, the estimate
of ( 1   3 ) /  1 over the business cycles shows a similar pattern to the ratio obtained over the subperiods with the five-year interval. Again, the evidence corroborates that earnings are also less
timely in reflecting bad news over time.
14
5.
5.1
SENSITIVITY ANALYSES
An Alternate Sample Allowing for New Listings and Delistings
Our previous results use the sample filtering procedure of Givoly and Hayn (2000), which
require all firm-year observations to be non-missing. While this sample selection procedure
makes our study more comparable to Givoly and Hayn (2000), we note that Basu (1997) does
not impose this condition. We now confirm the robustness of our results by using the Basu
(1997) sample selection procedure that allows for new listings and delistings of firms.
Specifically, to allow for meaningful fixed-effects regression, we require that each firm has at
least 10 observations in our sample period of 30 years. This sample covers 6,041 firms, with an
average cross section of 3,623 firms. Compared with the 691 firms in Tables 3 and 4, this sample
is about four times larger. We then repeat the exercises of Tables 3 and 4 with this sample. The
results are presented in Table 5. We observe that the results are similar to, if not better than,
those presented in Tables 3 and 4—the percentage reduction in the estimate of  3 in Table 5 is
generally larger than that in Tables 3 and 4. In other words, for the full sample, after controlling
for firm fixed-effects, (1) the degree of conservatism is much smaller in both the full-sample
(Panel A) and each of the five-year sub-periods (Panel B) as indicated by the estimated values of
 3 and ( 1   3 ) /  1 ,13 and (2) conservatism is decreasing over time as suggested by the estimates
of  3 or remains relatively stable as suggested by the estimates of ( 1   3 ) /  1 . In sum, these
results reiterate the findings in Tables 3 and 4.
[Table 5 about here.]
5.2
The Returns-Cash Flow Regression
The basic reasoning in Basu (1997) is that the parameter  3 in equation (2) or (3) captures a
more sensitive relationship between earnings and returns when returns are negative. However, it
13
For each of the five-year sub-periods, we further require that firms have three non-missing observations in that
sub-period for the fixed-effects adjustment to be meaningful.
15
is possible that a significant estimate of  3 is driven instead by the changes in returns.
Abarbanell and Bernard (1992), Sloan (1996) and Ball and Bartov (1996), for instance, argue
that the market may not fully understand the earnings process. In particular, Abarbanell and
Bernard (1992) reason that the market tends to over-react to bad news. Therefore, it is possible
that the market believes that negative earnings changes are more permanent than positive
earnings changes. This would give rise to the estimate of  3 being driven by changes in returns
rather than being a measure of earnings conservatism as suggested by Basu (1997). The key
question here is whether the market properly interprets earnings changes. To facilitate this
interpretation, we follow Basu (1997) and decompose earnings into its cash flow and accrual
components. This leads to a piecewise linear cash flows regression with firm-specific fixedeffects:
N
CFOit / Pit 1 =  0   1 Rit   2 Dit   3 ( Dit  Rit )    j F jit + uit ,
(4)
j 2
where CFO denotes cash flows from operation. If the significance of the coefficient  3 in
equation (2) is due to accruals anticipating bad news, then when cash flows is used as the
dependent variable in the regression as in equation (4), the significance of  3 in equation (4)
should be diminished or, better, vanished. The results in Table 6 confirm that this is the case.
[Table 6 about here.]
Table 6 shows that the magnitudes of the estimates of  3 are invariably much smaller than those
in the earnings regression. This is particularly the case for the LSDV/FE estimates reported in the
table. Moreover, for the sub-periods of 1994-2000 and 2001-2005, the LSDV/FE estimates of  3
are not significant. This is in line with the above accruals hypothesis of earnings conservatism:
Current period cash flow is more likely to be affected by poor performance of firms, and, thus,
the observed increased sensitivity between current period cash flow and current returns are, to
some extent, driven by the changing nature of the returns.14
14
We note that the estimate of
3
for the sub-period of 1988-1993 is significant (with a t-statistic of 3.20).
However, at 0.070, the magnitude of the estimate is small.
16
We further provide an alternative measure of earnings conservatism: the implied effect of
accruals, defined as the difference between the estimated coefficient of  3 from the earnings
regression and the estimated coefficient of  3 from the cash flows regression. If the accrual
hypothesis of conservatism is true, the implied effect of accruals should be small and a large
effect would indicate the existence of conservatism. The results are reported in the Table 7.
[Table 7 about here.]
The estimates of the pooled OLS model provide evidence of accounting conservatism, as
illustrated by a large and significant difference between the estimates in columns 1 and 2.
However, the LSDV/FE estimates indicate that the differences between the two estimates are
small in magnitude and not statistically significant at the 5% level. Interestingly, the difference
in the sub-period of 1988-1993 is negative, implying that earnings may have been boosted by
accruals when firms’ poor performance is anticipated by returns. However, this difference has a
t-statistic of 0.45 only. Thus, after controlling for the firm-specific fixed-effects, the evidence of
accounting conservatism diminishes. Tables 6 and 7 are consistent with our previous findings
that accounting conservatism is much weaker than has been documented.
5.3
Including Lagged Returns in Basu’s (1997) Model
Kothari and Sloan (1992), Collins, Kothari, Shanken and Sloan (1994), and Beaver and Ryan
(2000), among others, examine how well current and past returns explain current earnings. They
find that returns in all three previous periods contain information about current earnings. Based
on this argument, we extend the specification in equation (2) to allow for past returns to explain
current earnings.15 The inclusion of the firm-specific fixed-effects leads to the following paneldata specification:
EPSit / Pit 1 =  0   10 Rit   11Rit 1   12 Rit  2   13 Rit  3   20 Dit   21Dit 1   22 Dit  2   23 Dit  3
N
  30 ( Dit  Rit )   31 ( Dit 1  Rit 1 )   32 ( Dit 2  Rit 2 )   33 ( Dit 3  Rit 3 )    i Fijt + uit
j 2
15
Ryan and Zarowin (2003) study a similar specification without incorporating firm-specific fixed-effects.
17
(5)
In equation (5), the parameters ( 10 ,  11 ,  12 ,  13 ) measure the impacts of current and past returns
on current earnings. The parameters ( 20 ,  21,  22 ,  23 ) are the shifting coefficients when there is
bad news in returns in the period, and the parameters ( 30 ,  31,  32 ,  33) are the shifting coefficients
on returns when there is bad return news in the period. If the accounting system is conservative
and earnings reflect news for up to three prior periods, we should expect each of the estimates in
( 30 ,  31,  32 ,  33) to be positive. Moreover, the sum of coefficients,  30   31   32   33 , measures
the aggregate degree of conservatism (i.e., the incremental responsiveness of earnings to
negative news above that to positive news in the three years after the news occurs).
The estimation results of equation (5) are provided in Table 8. A comparison between the pooled
OLS and LSDV/FE results again re-emphasizes the importance of controlling for firm-specific
fixed-effects in the earnings regression. In particular, the extent of accounting conservatism as
measured by the pooled OLS estimates  30 ,  31 ,  32 ,  33 is uniformly greater than their LSDV/FE
counterparts, both in the full sample (Panel A) and in sub-periods of 1980-1989, 1990-2000, and
2001-2005 (Panel B).
Furthermore, there is no evidence of an increasing trend of conservatism from the LSDV/FE
regression. First, recall that the estimate of the coefficient  30 measures the shift in the
association between returns and earnings when returns are negative. Between 1980 and 2000,
there is a decreasing trend in the incremental responsiveness of earnings to bad news; the
coefficient estimate then stabilizes in the period of 2001-2005. Second, the responsiveness of
earnings to lagged returns displays a similar declining trend, as shown in the smaller estimates of
 31 ,  32 and  33 over time. Consequently, the aggregate indicator of accounting
conservatism,  30   31   32   33 , also declines over time. In sum, the results in Table 8
corroborate our previous findings that conservatism is weaker than has been documented once
firm fixed-effects are controlled for.
5.4
Alternative measures of Accounting Conservatism
18
Dietrich, Muller and Riedl (2007) argue that Basu’s (1997) approach to assess asymmetric
timeliness can yield biased estimates when earnings information affects returns. In light of such
criticisms, we follow Ball and Shivakumar (2006) to use alternative models to measure
accounting conservatism. These models do not rely on the returns-earnings relation to measure
conditional conservatism. Rather, they rely on the relationship between accruals and cash flows.
Operating cash flows are used to determine good news versus bad news. The asymmetrically
timely recognition of bad news would result in a greater association between accruals and cash
flows relative to good news. This argument suggests that we can use Ait (the total accruals of
firm i in fiscal year t scaled by total assets) as the dependent variable in the piecewise linear
panel-data regression. The independent variable, CFOit , is the operating cash flows of firm i in
fiscal year t (Compustat variable OANCF) scaled by total assets (Compustat variable AT). This
leads to the following three piecewise linear accruals panel-data regressions. The first one is
based on the cash flow model:
N
Ait   0   1CFOit   2 Dit   3 ( Dit  CFOit )    j F jit  uit
(6)
j 2
where Dit is a dummy variable set equal to 1 if CFOit is negative and 0 otherwise. In the second
and third models, we control for other variables that could potentially affect accruals. In
particular, the second model is based on the linear accrual model proposed by Dechow and
Dichev (2002):
N
Ait   0   1CFOit   2 Dit   3 ( Dit  CFOit )  1CFOit 1   2CFOit 1    j F jit  uit ,
(7)
j 2
and the third model is based on the linear accrual model proposed by Jones (1991):
N
Ait   0   1CFOit   2 Dit   3 ( Dit  CFOit )  1REVit   2GPPEit    j F jit  uit ,
(8)
j 2
where REVit is the change in revenue ( REVit , measured as Compustat variable SALE) in year t,
REVit  REVit 1 , and GPPEit is the gross property, plant, and equipment (Compustat variable
PPEGT). Each variable in equations (6), (7) and (8) is scaled by total assets.
19
As a further robustness check, we follow Ball and Shivakumar (2006) and consider, instead of
the level, the change in cash flow, CFOit  CFOit  CFOit 1 , as another financial reporting
measure to proxy for good and bad news. We repeat the estimation of equations (6), (7) and (8)
with CFOit in place of CFOit and define Dit based on CFOit .
The results from estimating the above three models are reported in Table 9 for the pooled OLS
and LSDV/FE specifications. In Panel A, the level of CFO is used to proxy for economic loss. In
Panel B, the change in CFO is used to proxy for economic loss. The asymmetry implies that the
coefficient on the interaction terms ( Dit  CFOit in Panel A and Dit  CFOit in Panel B) should be
positive. This is confirmed in Table 9. That is, in panel A, the incremental loss coefficients are
all positive, ranging from 0.627 to 0.735 across the three accrual models for the pooled OLS
specification, and from 0.600 to 0.655 for the LSDV/FE specification. The LSDV/FE estimates
of the asymmetry are consistently smaller than those from the pooled OLS model. However the
F-test rejects the pooled OLS specification in favor of the LSDV/FE specification at the 5% level
of significance. In Panel B, again all of the incremental loss coefficient estimates are positive,
ranging from 0.071 to 0.199 for the pooled OLS specification, and from 0.032 to 0.104 for the
LSDV/FE specification, with the LSDV/FE estimates uniformly smaller than the pooled OLS
estimates. Once again, the pooled OLS specification is rejected at the 5% level of significance in
favor of the LSD/FE specification. These results are consistent with the earlier results using
stock return serving as a proxy for economic loss.
[Table 9 about here.]
Overall, the results obtained from Table 9 confirm the findings that a timely loss-recognition
plays an important role in accounting for accruals when we use either the level of, or the change
in, cash flows as a proxy for gains and losses. More importantly, the magnitude of asymmetric
responsiveness of earnings to bad news declines once firm-specific fixed-effects are controlled
for. The decline is particularly substantial with the change in cash flows serving as a proxy for
economic gains or losses. For example, in Panel B, the Jones model has a coefficient estimate on
20
D  CFO of 0.119 in the pooled OLS regression. The coefficient estimate reduces to only 0.032
in the LSDV/FE regression, amounting to a reduction of over 70%.
In summary, the results emerging from our previous sensitivity analyses reinforce the importance
of incorporating firm specific fixed-effects in the models intended for measuring accounting
conservatism.
6.
CONCLUSION
In this paper, we demonstrate that a significant part of the total variation in the earnings is
attributed to the cross-sectional differences. Given the importance of firm heterogeneity, we
propose to refine the original Basu (1997) model of accounting conservatism to explicitly
account for firm-specific fixed-effects. We then use the refined model to reexamine the extent of
accounting conservatism and its trend over time. We find that the estimated incremental
responsiveness of earnings to bad news is not as strong as previously documented, and that there
is no clear trend of an increase of accounting conservatism over time. We conduct sensitivity
analyses to demonstrate that our results are robust to different model specifications.
Our results echo recent studies that call for firm-level measures of conservatism. Noting that
conservatism is associated with a number of firm-specific factors (Watts, 2003), Ryan (2006)
comments that firm-specific measures are “desperately needed” for empirical studies. In a
response to Ryan (2006), Ball, Kothari and Nikolaev (2011) recommend that researchers
interested in measuring conservatism in the Basu (1997) framework should control for firmspecific effects in order to avoid potentially spurious inferences. We provide empirical supports
for the call in Ball, Kothari and Nikolaev (2011) by showing that a substantial part of the total
variation in earnings is attributed to cross-sectional differences. Furthermore, focusing on the
most widely used conservatism measure of Basu (1997) (Ryan, 2006), we show that accounting
conservatism does not appear to show an increasing trend, contrary to what is previously
believed. In sum, our study demonstrates the importance of adjusting for firm heterogeneity in
examining accounting conservatism.
21
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24
Table 1
Full-sample
volatility
0.105
Pooled and Cross-sectional Standard Deviations of Earnings and Returns
Earnings
Mean of
cross-sectional
volatilities
0.098
Mean of
time-series
volatilities
0.053
Full-sample
volatility
0.392
Return
Mean of
cross-sectional
volatilities
0.363
Mean of
time-series
volatilities
0.132
This table reports the full-sample standard deviations, the time-series means of cross-sectional standard deviations,
and the cross-sectional means of time-series standard deviations of individual firms on earnings and stock returns.
Earnings are computed as earnings per share (Compustat variable EPSPX) divided by lagged fiscal year-end closing
price (Compustat variable PRCC_F), and returns are measured as the cumulative monthly returns for the period of 9
months before the fiscal year end to 3 months after the fiscal year end. Our constant sample consists of 691 firms
from 1976 to 2005 from Compustat. A constant sample requires no missing observation in each of the sample year
for each firm.
25
Table 2 Variance Decompositions of Returns-Earnings Relation
Variable
Firm FE (  j )
(a)
1.00
Year FE (  s )
Specification
(c)
(d)
0.53
(b)
1.00
(e)
0.82
0.47
(f)
0.44
0.47
Returns ( Rit )
0.16
0.09
0.04
Dummy ( Dit )
0.00
0.00
0.00
0.84
0.09
0.05
0.10
0.20
0.30
Returns*Dummy ( Dit
 Rit )
2
0.13
Adjusted R
0.11
0.24
This table presents the variance decomposition for model specifications with or without firm/year fixed-effects. The
regression equations are:
N
EPS it / Pit 1 =  0    j F jit + u it
(a)
j 2
T
EPS it / Pit 1 =  0    sYsti + u it
(b)
s2
N
T
j 2
s2
EPSit / Pit 1 =  0   j F jit   sYsti + u it
(c)
EPSit / Pit 1 =  0   1 Rit   2 Dit   3 ( Dit  Rit ) + uit
(d)
N
EPS it / Pit 1 =  0   1 Rit   2 Dit   3 ( Dit  Rit )    j F jit  u it
(e)
j 2
N
T
j 2
s 2
EPS it / Pit 1 =  0   1 Rit   2 Dit   3 ( Dit  Rit )    j F jit   s Ysti + uit
where EPS = earnings per share, P = price, R = return, 
j
(f)
is the firm dummy,  s is the year dummy, and Dit equals
1 if Rit< 0 and 0 otherwise. In each specification, we compute the type III partial sum of square for each variable and
then normalize the square of each variable by the sum of the type III squares of the specification. Both firm and time
effects are modeled as parametric shifts in the intercept terms of the regressions as in the LSDV framework.
26
Table 3 Extent of Accounting Conservatism
Estimation
Method
Pooled OLS (1)
LSDV/FE (2)
0
1
2
3
0.078
[64.01]
0.103
[5.87]
0.027
[10.93]
0.040
[16.29]
0.001
[0.42]
0.004
[1.59]
0.205
[25.25]
0.139
[16.94]
R2
0.097
0.196
This table reports the estimation results from both the original and refined Basu’s model of accounting conservatism.
Basu’s (1997) model is specified as:
EPSit / Pit 1 =  0   1 Rit   2 Dit   3 ( Dit  Rit )  uit
The refined Basu model after accounting for firm-specific fixed-effects is specified as:
N
EPS it / Pit 1 =  0   1 Rit   2 Dit   3 ( Dit  Rit )    j F jit  u it
j 2
In these specifications, EPS = earnings per share, P = price, R = return,
Rit< 0 and 0 otherwise. In Pooled OLS,
i
is the firm dummy, and Dit equals 1 if
 i = 0 for all firms. LSDV/FE refers to least square dummy variable method
with fixed-effects. R-square for the LSDV/FE model is calculated based on Theil (1961). The numbers in square
brackets are the t-statistics.
27
Table 4 Trend in Accounting Conservatism
Pooled OLS
Sub-period
1976-1980
1981-1985
1986-1990
1991-1995
1996-2000
2001-2005
1
0.038
[6.91]
0.018
[3.29]
0.032
[4.39]
0.033
[5.58]
0.006
[1.28]
-0.005
[-0.91]
3
( 1   3 ) /  1
0.256
[8.39]
0.215
[10.84]
0.292
[16.44]
0.259
[10.98]
0.076
[6.46]
0.247
[13.27]
LSDV/FE
7.74
Adj. R2
0.092
12.94
0.12
10.13
0.16
8.85
0.09
13.67
0.048
-48.40
0.088
3
1
0.051
[9.25]
0.021
[4.25]
0.055
[7.48]
0.051
[8.35]
0.008
[1.69]
0.023
[3.74]
0.132
[4.49]
0.072
[3.77]
0.147
[7.99]
0.089
[3.62]
0.033
[2.69]
0.065
[3.47]
Pooled OLS
By economic
cycle
1976-1979
1980-1989
1990-2000
2001-2005
1
0.053
[7.72]
0.035
[8.97]
0.018
[5.00]
-0.005
[-0.91]
3
( 1   3 ) /  1
0.226
[6.98]
0.248
[16.98]
0.122
[11.58]
0.247
[13.27]
( 1   3 ) /  1
3.59
Theil’s R2
0.496
4.43
0.524
3.67
0.52
2.75
0.453
5.13
0.384
3.83
0.474
LSDV/FE
5.26
Adj. R2
0.105
8.09
0.139
7.78
0.065
-48.40
0.088
3
1
0.076
[10.82]
0.052
[13.50]
0.028
[7.55]
0.023
[3.74]
( 1   3 ) /  1
0.097
[3.10]
0.108
[7.44]
0.054
[5.05]
0.065
[3.47]
2.28
Theil’s R2
0.57
3.08
0.368
2.93
0.267
3.83
0.474
This table reports the estimation results on the trend in accounting conservatism over time using both the original
and refined Basu’s model of accounting conservatism. The original Basu (1997)’s model is specified as:
EPSit / Pit 1 =  0   1 Rit   2 Dit   3 ( Dit  Rit )  uit
The refined Basu’s model after accounting for firm-specific fixed-effects is specified as:
N
EPS it / Pit 1 =  0   1 Rit   2 Dit   3 ( Dit  Rit )    j F jit  u it
j 2
where EPS = earnings per share, P = price, R = return,
otherwise. In Pooled OLS,
i
is the firm dummy, and Dit equals 1 if Rit< 0 and 0
 i = 0 for all firms. The numbers in square brackets are t-statistics.
28
Table 5 Estimating Conservatism with an Alternate Sample
Allowing for New Listings and Delistings of Firms
Panel A: Full-sample regression results
Pooled OLS
LSDV/FE
0
1
2
3
0.033
[22.31]
0.004
[0.05]
-0.002
[-0.84]
0.023
[0.82]
0.023
[8.91]
0.028
[10.71]
0.457
[69.26]
0.281
[39.93]
R2
0.073
0.244
Panel B: Regression results by sub-periods
Pooled OLS
Sub-period
1976-1980
1981-1985
1986-1990
1991-1995
1996-2000
2001-2005
LSDV/FE
1
3
( 1   3 ) /  1
0.036
[9.18]
0.020
[4.57]
0.001
[0.11]
-0.003
[-0.70]
-0.017
[-4.29]
-0.032
[-6.86]
0.446
[21.13]
0.344
[21.87]
0.468
[31.23]
0.502
[31.23]
0.361
[29.42]
0.426
[26.00]
13.39
Adj. R2
0.099
18.20
0.078
469.00
0.087
-166.33
0.074
-20.24
0.053
-12.31
0.065
1
0.053
[13.50]
0.032
[7.03]
0.025
[3.99]
0.027
[6.01]
-0.001
[-0.21]
0.010
[2.10]
3
0.237
[11.24]
0.087
[12.62]
0.206
[12.62]
0.174
[9.99]
0.150
[10.99]
0.036
[2.04]
( 1   3 ) /  1
5.47
Theil’s R2
0.486
3.72
0.414
9.24
0.455
7.44
0.447
-149.00
0.387
4.60
0.424
This table reports the estimation results of accounting conservatism using both the original Basu model (Pooled
OLS) and the refined model of fixed-effects (LSDV/FE) using an alternate sample that allows for firm new listings
and delistings. We require that each firm has at least 10 observations in our sample period of 30 years. This sample
covers 6,041 firms, with an average cross section of 3,623 firms. In Panel B, we further require that firms have three
non-missing observations in each of the five-year sub-periods. The numbers in square brackets are t-statistics.
29
Table 6
The Returns-Cash Flows Relation
Pooled OLS
Sub-period
1988-1993
1994-1999
2000-2005
LSDV/FE
1
3
1
3
0.035
0.141
0.050
0.070
[5.85]
[6.26]
[9.00]
[3.20]
0.005
[1.08]
0.125
[7.01]
0.036
[8.45]
0.011
[0.69]
0.050
[9.19]
0.053
[2.86]
0.050
[10.10]
-0.010
[-0.59]
Notes: The regression equation is:
N
CFOit / Pit 1 =  0   1 Rit   2 Dit   3 ( Dit  Rit )    j F jit + uit ,
j 2
where CFO = cash flows from operations per share, P = price, R = return,
Rit< 0 and 0 otherwise. In Pooled OLS,
 i = 0 for all firms.
i
is the firm dummy, and Dit equals 1 if
The CFO variable is only available after 1988. The
constant sample is between 1988 and 2005 and consists of 995 firms in total. The numbers in square brackets are
the t-statistics.
30
Table 7
The Implied Effect of Accruals
Pooled OLS,
3
LSDV/FE,
3
Earnings
Cash Flows
Earning
Cash Flows
regression
regression
Regression
Regression
Column
Column
Column
Column
Column
Column
Sub-period
(1)
(2)
(1)-(2)
(1)
(2)
(1)-(2)
1988-1993
0.441
0.141
0.300
0.043
0.070
-0.026
[9.35]
[6.26]
[5.73]
[0.79]
[3.20]
[0.45]
0.188
0.125
0.062
0.062
0.011
0.051
[9.31]
[7.01]
[2.32]
[2.70]
[0.69]
[1.83]
0.357
0.053
0.303
-0.009
-0.010
0.001
[4.15]
[2.86]
[3.45]
[-0.10]
[-0.59]
[0.07]
1994-1999
2000-2006
Notes: For the earnings regression, the regression equation is:
N
EPSit / Pit 1 =  0   1 Rit   2 Dit   3 ( Dit  Rit )   j F jit  uit ,
j 2
and for the cash flows regression, the equation is:
N
CFOit / Pit 1 =  0   1 Rit   2 Dit   3 ( Dit  Rit )    j F jit + uit ,
j 2
where EPS = earnings per share, CFO = cash flows from operations per share, P = price, R = return,
dummy, and Dit equals 1 if Rit< 0 and 0 otherwise. In Pooled OLS,
i =
i
is the firm
0 for all firms. The estimate of
is
reported in the table. The constant sample is between 1988 and 2005 and consists of 995 firms in total. The numbers
in square brackets are the t-statistics.
31
Table 8 Estimation Results for the Extended Basu model with Lagged Returns
Panel A: Full sample, 1979-2005
Method
Pooled OLS
LSDV/FE
0
 10
 11
 12
 13
 20
 21
 22
 23
 30
 31
 32
 33
30 31
32 33
0.08
[40.75]
0.09
[5.64]
0.04
[17.87]
0.05
[19.71]
0.02
[9.17]
0.03
[10.95]
0.01
[4.62]
0.02
[6.41]
0.01
[3.39]
0.01
[5.39]
0.01
[2.35]
0.01
[3.04]
0.01
[6.05]
0.01
[6.79]
0.01
[4.35]
0.01
[5.20]
0.01
[2.33]
0.01
[3.32]
0.15
[19.01]
0.12
[16.08]
0.21
[27.02]
0.19
[24.43]
0.13
[17.04]
0.11
[14.50]
0.1
[12.36]
0.08
[9.69]
0.59
{1907.8}
0.5
{1096.9}
Panel B: By economic cycle, 1980-2005
1980-1989
Method
Pooled OLS
LSDV/FE
1990-2000
Pooled OLS
LSDV/FE
2001-2005
Pooled OLS
LSDV/FE
0
 10
 11
 12
 13
 20
 21
 22
 23
 30
 31
 32
 33
30 31
32 33
0.1
[33.17]
0.15
[5.67]
0.06
[21.67]
0.06
[[2.74]
0.06
[11.38]
0.04
[0.93]
0.05
[12.22]
0.05
[13.81]
0.03
[8.34]
0.04
[9.71]
0.05
[8.05]
0.06
[9.08]
0.01
[4.05]
0.02
[5.24]
0.02
[4.83]
0.02
[6.22]
0.03
[5.44]
0.04
[6.78]
0.01
[2.10]
0.01
[3.41]
0.01
[3.69]
0.02
[4.36]
0.01
[1.19]
0.02
[2.50]
-0.01
[-1.90]
0
[0.09]
0.02
[4.56]
0.02
[4.96]
0.01
[2.02]
0.01
[1.18]
0
[-0.23]
0.01
[1.84]
0
[0.25]
0
[-0.95]
0.01
[1.52]
0.01
[1.76]
0.01
[1.77]
0.01
[2.99]
0.01
[4.35]
0.01
[3.22]
0.02
[4.15]
0.03
[4.66]
0
[1.15]
0.01
[2.07]
0.01
[4.15]
0.01
[3.51]
0.01
[1.36]
0.01
[1.56]
0
[-0.52]
0
[0.72]
0.01
[3.55]
0.01
[3.16]
0.01
[1.85]
0.01
[1.44]
0.19
[13.69]
0.14
[9.79]
0.1
[9.68]
0.06
[6.20]
0.16
[8.90]
0.06
[3.34]
0.23
[16.24]
0.18
[12.34]
0.14
[12.91]
0.11
[9.60]
0.25
[15.11]
0.17
[9.06]
0.14
[9.72]
0.09
[6.25]
0.1
[8.97]
0.07
[6.25]
0.14
[8.53]
0.05
[2.97]
0.11
[7.43]
0.05
[3.42]
0.08
[6.37]
0.05
[3.75]
0.07
[4.68]
0.01
[0.76]
0.67
{703.8}
0.46
{190.9}
0.42
{467.3}
0.29
{141.3}
0.62
{490.6}
0.29
{39.6}
Notes: The regression equation is:
EPSit / Pit 1 =  0   10 Rit   11Rit 1   12 Rit  2   13 Rit  3   20 Dit   21Dit 1   22 Dit  2   23 Dit  3
(Adj.)
R2
0.2155
0.2892
(Adj.)
R2
0.2572
0.412
0.145
0.3119
0.2452
0.5238
N
  30 (Dit  Rit )   31 ( Dit 1  Rit 1 )   32 ( Dit 2  Rit 2 )   33 (Dit 3  Rit 3 )  i Fijt + uit , where EPS = earnings per share, P = price, R = return,  i is the firm
j 2
dummy, and Dit (Dit-1 /Dit-2/Dit-3) equals 1 if Rit, (Rit-1 /Rit-2 /Rit-3) is smaller than 0 and 0 otherwise. The data are from 1979 to 2005. In Pooled OLS,
 i = 0 for all
firms. In the sub-period analysis, we drop year 1979 and report only three business cycles. Numbers in square brackets are t-statistics, and numbers in curly
brackets are chi-statistics from the Wald tests.
Table 9 Estimation Results from Alternative Models of Accounting Conservatism
Panel A: Proxy for economic loss, the Level of Cash flow (CFO) < 0
CFO Model
Pooled OLS
Intercept
CFOt
LSDV/FE
DD Model
Pooled OLS
Jones Model
LSDV/FE
-0.058
-0.009
-0.069
0.008
0.042
[-4.85]
[-3.27]
[-8.60]
[-3.95]
[6.83]
[2.47]
-0.477
-0.584
-0.558
-0.611
-0.471
-0.581
[-55.97]
[-56.31]
[-63.59]
[-59.05]
[-57.36]
[-59.21]
0.067
0.057
[20.18]
[16.84]
0.063
0.050
[18.05]
[14.21]
0.095
0.099
[41.41]
[43.82]
-0.034
-0.068
[-27.73]
[-24.25]
CFOt+1
∆REVt
GPPEt
DtCFOt
R-squared (%)
F-test for FE
LSDV/FE
-0.005
CFOt-1
Dt
Pooled OLS
0.031
0.028
0.024
0.026
0.025
0.027
[14.33]
[12.70]
[11.52]
[11.66]
[12.15]
[12.99]
0.735
0.655
0.627
0.600
0.714
0.610
[59.01]
[41.52]
[49.15]
[37.84]
[60.25]
[40.90]
18.15
35.4
21.54
36.97
27.15
42.86
4.27
3.90
4.40
Table 9 cont’d
Panel B: Proxy for economic loss, change in cash flow (∆CFO) < 0
DD Model
CFO Model
Intercept
CFOt
Pooled OLS
LSDV/FE
Pooled OLS
-0.042
-0.104
-0.044
[-40.79]
[-5.76]
-0.176
[-33.39]
Jones Model
Pooled OLS
LSDV/FE
-0.110
-0.037
-0.025
[-43.98]
[-6.13]
[-31.80]
[-1.36]
-0.298
-0.334
-0.379
[-41.63]
[-47.67]
[-47.88]
0.088
0.064
[23.54]
[16.70]
0.080
0.059
[21.68]
[16.17]
0.089
0.098
[36.69]
[40.48]
-0.052
-0.087
[-42.20]
[-29.25]
CFOt-1
CFOt+1
LSDV/FE
∆REVt
GPPEt
∆CFOit
Dt
Dt∆CFOt
R-squared (%)
-0.122
-0.060
-0.116
-0.075
[-25.20]
[-12.46]
[-25.34]
[-15.88]
0.026
0.022
0.020
0.018
0.033
0.034
[21.01]
[19.02]
[16.39]
[15.47]
[28.94]
[31.34]
0.199
0.104
0.071
0.047
0.119
0.032
[26.23]
[13.14]
[12.23]
[8.18]
[17.54]
[4.16]
11.23
31.8
14.25
33.21
19.07
34.6
F-test for FE
4.83
4.52
3.80
In Panel A, the regression equation for the CFO model is:
N
Ait   0   1CFOit   2 Dit   3 ( Dit  CFOit )   j F jit  uit
j 2
The equation for the DD model is:
N
Ait   0   1CFOit   2 Dit   3 ( Dit  CFOit )  1CFOit 1   2CFOit 1   j F jit  uit
j 2
And the equation for the Jones model is:
N
Ait   0   1CFOit   2 Dit   3 ( Dit  CFOit )  1REVit   2GPPEit   j F jit  uit
j 2
In the above equations, A = accruals, CFO = cash flow from operations, REV is the change in revenue, GPPE is
the gross property, plant, and equipment, and Dit equals 1 if CFOit< 0 and 0 otherwise. All these variables are scaled
by lagged total assets. In Pooled OLS,  i = 0 for all firms. In Panel B, CFO is replaced by the change in CFO
(∆CFO), and Dit= 1 if ∆CFOit< 0 and 0 otherwise. The numbers in square brackets are the t-statistics.
34
Figure 1 The Ratios of Cross-sectional Volatility
over Pooled Volatility for Earnings and Returns over Time
35
Appendix: Replication of Givoly and Hayn’s (2000) Results Based on Basu’s Measure and
Discussion on Firm Composition of Full Sample
Using pooled OLS regressions and the full sample of firms in the intersection of Compustat and
CRSP from 1950 to 1998, Givoly and Hayn (2000)—hereafter “GH”—show that the asymmetric
response of earnings to return using Basu’s (1997) measure has increased over time. As a reality
check, Table A-1 presents Basu’s (1997) results for five-year sub-periods, using GH’s full
sample derived from the intersection of Compustat and CRSP from 1963 to 2004 and estimation
method.
[Table A-1 here.]
A general observation from the Table is that the asymmetric response of earnings to return ( 3 )
shows an increasing trend during the sample period studied in GH. This finding is consistent
with GH. However, in the last two sub-periods of the sample (1995-2004), the asymmetric
response is declining. Thus, the statement of an increase in the asymmetric response of earnings
to return appears to be sample-specific, in addition to being estimation-method specific.
In showing the increase in accounting conservatism, GH use not only Basu’s (1997) measure, but
also accruals level to proxy for conservatism. GH present the accruals level results using a
constant sample. Part of the reason for using a constant sample is the changing composition of
firms in the full sample during the sample period, which is shown in Figure A-1.
[Figure A-1 here.]
From Figure A-1, we observe that the number of firms included in the sample varies
substantially over time: the sample started with 803 firms in 1963, peaked to 5,570 firms in
1997, and dropped to 3,397 firms in 2005. A comparison of Table A-1 with Table 4, where the
trend in the asymmetric response of earnings to return is shown for a constant sample, reveals
that much of the increase in the asymmetric response documented in GH is attributed to new
36
listings. Given the degree of the changing composition of firms, we believe that focusing on a
constant sample in studying the returns-earnings relation retains a consistent sample and,
therefore, helps us to address the question of whether the returns-earnings relation has changed
over time for the same firms. Therefore, we rely on a constant sample most of the time in this
paper.
37
Table A-1 Replication of Givoly and Hayn (2000)
Sub-period
0
1
2
3
Adj. R2
1963-1964
0.072
-0.004
0.023
0.137
0.144
1965-1969
0.068
-0.003
0.016
0.067
0.172
1970-1974
0.094
0.007
0.033
0.105
0.081
1975-1979
0.144
-0.017
0.057
0.346
0.123
1980-1984
0.073
-0.021
0.008
0.250
0.109
1985-1989
0.036
-0.004
-0.009
0.407
0.142
1990-1994
0.014
0.014
-0.015
0.449
0.083
1995-1999
0.021
0.007
-0.021
0.293
0.086
2000-2004
0.000
0.005
-0.033
0.363
0.057
Notes: The regression equation is:
EPSit / Pit 1   0   1 Rit   2 Dit   3 ( Dit  Rit )  uit , where EPS = earnings
per share, P = price, R = return, and Dit equals 1 if Rit< 0 and 0 otherwise. We strictly follow Givoly and Hayn
(2000) to derive a sample of firms from the intersection of Compustat and CRSP from 1963 to 2004. The numbers
in square brackets are the t-statistics.
38
Figure A-1
The Number of Firms over Time in the Full Sample
39