Incentive Contracting and Value-Relevance of
Earnings and Cash Flows
Rajiv Banker*
Rong Huang†
Ram Natarajan**
*Temple University
†City University of New York- Baruch College
** The University of Texas at Dallas
June 11, 2008
We thank seminar participants at Temple University, City University of New York—Baruch
College, The University of Texas at Dallas, and the American Accounting Association 2006
Annual meeting for helpful comments and suggestions.
Abstract
Accounting performance measures such as earnings and cash flows are useful for
both valuation and performance evaluation purposes. However, little evidence exists on
whether there is any association between these two roles. In this study, we provide large
sample empirical evidence that the value-relevance of earnings explains a significant
amount of the cross-sectional variation in the pay-sensitivity of earnings and the
incremental value-relevance of cash flows explains variation in the marginal paysensitivity of cash flows. We document that while both value-relevance and
compensation weight on earnings decline from the sub period of 1993 to 1997 to the sub
period of 1998 to 2003, both value-relevance and compensation weight on cash flows
increase from the earlier sub period to the later sub period. Overall, our results provide
additional evidence that value-relevance of a performance measure plays a significant
role in its use for performance evaluation.
Keywords: Incentive contracting, value-relevance, earnings, cash flows, executive
compensation
2
I.
Introduction
This study examines the association between pay-sensitivity and value-relevance
of earnings and cash flows. Accounting performance measures such as earnings and cash
flows serve a variety of purposes in organizations and markets, including valuation and
performance evaluation. While a number of prior studies have examined the valuation
roles and incentive-contracting roles of accounting performance measures separately,
very little evidence exists on whether there is any association between these two roles.
Using CEO compensation and accounting data for a large number of U.S. firms, we
examine the empirical association between value and incentive relevance of earnings and
cash flows over an eleven year period from 1993 to 2003. The graph in Figure I shows
that the relative compensation weight on cash flows versus earnings tracks remarkably
closely to their relative valuation relevance for our sample period from 1993 to 2003.
The correlation between the relative compensation weight and the relative valuation
relevance is as high as 0.87. When we analyze the data more formally to control for
other influential factors at the firm level, the results confirm that compensation weight on
each of earnings and cash flows is higher for firms that exhibit high levels of valuerelevance for the performance measure. The evidence also indicates that both valuerelevance and compensation weight for earnings decline from the sub period of 19931997 to the sub period of 1998-2003, while both the value-relevance and the
compensation weight for cash flows increase from the earlier sub period to the later sub
period.
Gjesdal (1981) puts forth the basic premise that the incentive-informativeness of
performance measures that determines their compensation weights may be different from
3
their valuation-informativeness. He considers differential uses of accounting information
in organizations and shows that the ranking of information systems for valuation
purposes need not align with the ranking of those information systems for control
purposes. Lambert (1993) expands on this issue by remarking that valuing the firm is not
the same as evaluating the manager’s contribution to the value of the firm. The
observations made by Gjesdal (1981) and Lambert (1993) are based on single-action,
single-period settings where optimal compensation contracts assign lower (higher)
weights on performance measures that have lower (higher) sensitivity-to-noise ratio
(Banker and Datar 1989).
The implication of the above observations for empirical accounting researchers is
that the association between value-relevance and incentive-informativeness of
performance measures is context-specific and varies across agencies. However, it
appears that these implications may have been misunderstood as indicating instead that
there is no association between value-relevance and incentive-informativeness. A
notable exception is Bushman, Engel and Smith (2006) who examine linkages between
the weight placed on earnings in compensation contracts and the weight placed on
earnings in stock price formation. They show that valuation earnings coefficients and
compensation earnings coefficients are positively associated and point out that further
theoretical and empirical investigation of this association remains an interesting challenge
for future research.1 Our main motivation behind this study stems from a similar desire
to provide evidence on the positive association between the valuation roles and incentive
1
In a related context, Engel, Hayes and Wang (2003) find that the weight on earnings information in CEO
turnover decisions is increasing in the timeliness of earnings where timeliness is measured as the
contemporaneous association between earnings and stock returns.
4
contracting roles in settings where multiple accounting performance measures are used to
evaluate top managers.
We consider two primary accounting performance measures, earnings and cash
flows, for the purposes of this study. We focus on these performance measures for
several reasons. First, since cash flows is a component of earnings, the research setting
lends itself to examining incremental value-relevance and marginal pay-performance
sensitivity of cash flows when cash flows information is available in addition to earnings
for valuation and performance evaluation purposes. Second, a number of studies have
established that earnings and cash flows have differential implications for firm value
(Rayburn 1986, Bowen, Burgstahler and Daley 1986, Ali 1994, Sloan 1996). Prior
studies have also shown that the incremental value of cash flows over earnings varies
cross-sectionally depending on factors such as the persistence of earnings and cash flows
(Sloan 1996, Xie 2001, Richardson, Sloan, Soliman and Tuna 2005), the time interval
over which performance is measured, the volatility of the firm’s working capital
requirements, and the length of the firm’s operating cycle (Dechow 1994, Dechow,
Kothari and Watts 1998). Third, studies examining the stewardship value of components
of earnings have found that there is significant cross-sectional variation in the way cash
flows and earnings are used in determining top management compensation (Natarajan
1996, Nwaeze, Yang and Yin 2006). Fourth, very limited empirical evidence exists on
whether the incremental value-relevance and the marginal pay-performance sensitivity of
cash flows have changed over time since cash flow information was first made available
to shareholders in 1987 through SFAS 95.
5
To understand the structural factors influencing the context-specific nature of the
above-mentioned association, we first derive pay-sensitivities and value-relevance
measures using a highly stylized principal-agent setting characterized by two
performance measures.2 The two performance measures are each modeled as consisting
of a distinct managerial effort component, a common pay-off relevant noise term and a
specific non value-relevant noise component. We show that the context-specific nature
of the association between valuation weights and compensation weights is critically
dependent on the cross-sectional differences in the variances of the pay-off relevant noise
and idiosyncratic noise of the performance measures under consideration. The insights
provided by our stylized model are used to generate empirical proxies of the variances of
the relevant noise terms from the firm-specific variance-covariance matrix of earnings
and cash flows and to explicitly quantify the co-movement of the theoretical,
endogenously determined, valuation and compensation weights at various deciles of the
cross-section of a large sample of Compustat firms. The analysis based on estimated
values of the variances of the relevant noise terms suggests an expected positive
association between compensation weights and valuation weights when earnings and cash
flows are the performance measures under consideration.
We formally test this prediction using actual compensation, valuation and
performance measure data. We use a sample of 7,076 CEO-years spanning the period
1993 to 2003 in our empirical analysis. In the first stage of our analysis, we estimate
value-relevance of earnings and incremental value-relevance of cash flows for each firmyear by using a time-series of 8 to 10 years of past data on earnings, cash flows, stock
2
As observed by Bushman, Engel and Smith (2006), any economic model that links pay-sensitivities and
value-relevance measures should account for the fact that these constructs are endogenously determined.
We pay particular attention to this issue in our model development.
6
prices and adapting the metrics suggested in prior literature (Ohlson 1995, Collins,
Maydew and Weiss 1997, Barth, Beaver, Hand and Landsman 1999, Engel, Hayes and
Wang 2003, Bushman, Chen, Engel and Smith 2003) to our context. In the second stage,
we estimate cross-sectional yearly regressions that use CEO and firm level data on cash
compensation, earnings, cash flows, value-relevance of earnings and cash flows, as well
as a variety of control variables that have been identified in prior literature as
determinants of cross-sectional variation in pay-performance sensitivity of earnings and
cash flows. The regression coefficients from the second stage regression enable us to
estimate the average magnitude of the association between value-relevance and payperformance sensitivity during our sample period for both earnings and cash flows.
The empirical results support our predictions. The estimated association between
pay-earnings sensitivity and the value-relevance is significantly positive for both earnings
and cash flows. We evaluate the robustness of our findings by considering cash flows as
the primary performance measure and earnings as the supplementary performance
measure, by using total compensation instead of cash compensation and by employing
changes rather than levels in earnings and cash flows as performance measures. These
results also support our predictions.
Our study contributes to a stream of research that has examined the association
between the valuation and performance evaluation roles of accounting performance
measures. Two notable studies that belong to this stream are Bushman, Engel and Smith
(2006) and Engel, Hayes and Wang (2003). In contrast to Bushman, Engel and Smith
(2006) who empirically examine the association between the valuation and incentive
contracting role of accounting earnings, we focus on a pair of correlated accounting
7
performance measures namely, accounting earnings and cash flows. Our empirical
results confirm Bushman et al.’s (2006) findings that higher the value-relevance of
earnings, higher the pay-sensitivity of earnings. More importantly, we also provide
evidence that higher the incremental value-relevance of cash flows, higher the
incremental pay-sensitivity of cash flows.
Engel, Hayes and Wang (2003) employ a research design somewhat similar to
ours to examine the association between CEO turnover probability and accounting
earnings. They predict that CEO turnover probability is decreasing in the timelines of
earnings and find empirical evidence consistent with their predictions. “Timeliness” of
earnings is measured through its association with contemporaneous stock returns.
There are however, some significant differences between Engel, Hayes and Wang
(2003) and our study. Our study focuses on the compensation decision while Engel,
Hayes and Wang (2003) examine turnover decisions. The empirical model in Engel,
Hayes and Wang (2003) is similar to Sloan (1993) and Lambert and Larcker (1987) in
that the primary focus is on the relative weights on accounting earnings and stock returns
for performance evaluation. In contrast, we focus on the association between valuation
and performance evaluation roles of two accounting performance measures e.g., earnings
and cash flows. The empirical analysis in Engel, Hayes and Wang (2003) is based on the
predictions of a two-period model where current earnings, by construction, reflect only a
portion of current period managerial effort, and which predicts that relative weight on
earnings increases with timeliness of current period earnings. In contrast, our analytical
characterization underlines the influence of cross-sectional differences in the variances of
pay-off relevant noise and idiosyncratic noise of performance measures on the
8
association between compensation and valuation weights of performance measures and
does not make any directional predictions for this association.
To summarize, our study contributes in three ways to the existing literature on the
use of accounting performance measures in valuation and performance evaluation. First,
it confirms and quantifies the positive association between value-relevance and payperformance sensitivity for earnings and cash flows. Second, it quantifies the decline in
value-relevance and pay-performance sensitivity of earnings and corresponding increase
in value-relevance and pay-performance sensitivity of cash flows over the past decade.
Third, it provides evidence that value–relevance of performance measures plays a
significant role in the choice of accounting performance measures for performance
evaluation in organizations.
The remainder of this paper proceeds as follows. Section II develops the main
hypotheses. Section III discusses the research design and sample selection. Section IV
presents the empirical results. Finally, section V concludes the paper.
II.
Theory Development
Lambert’s (1993) remark that valuing the firm is not the same as evaluating the
manager’s contribution to the value of the firm is intuitively appealing but provides
limited guidance to empirical researchers on the similarities and differences in the way
value-relevance measures and pay-sensitivities are influenced by the underlying agency
and performance measure characteristics. Since both the pay-sensitivities and valuerelevance measures are endogenously determined and are functions of the characteristics
that differ across the agencies in the cross-section, it is important to understand the
9
impact of the variation of the various characteristics on the association between these
endogenous variables in the cross-section. Changes in some of the characteristics result
in changes in valuation and contracting weights that are of the same sign while changes
in other characteristics may have opposing effects on these weights. The stylized model
that we develop in this section and the analysis of performance measure data to quantify
the insights from the model are oriented towards understanding the dominant
characteristics that drive the cross-sectional distribution of the set of agencies (firms)
under consideration.
We formally consider a simple, single period, two-action, two-signal principalagent setting based on the LEN (Linear contract, Negative exponential utility and
Normally distributed random variables) framework to better understand the factors
influencing the association between value-relevance measures and pay-sensitivities of
performance measures. Complete details of the model are provided in the Appendix I
and we briefly describe the setting here.
The setting we consider involves a risk-averse manager who is in charge of two
distinct productive activities. These productive activities along with factors beyond the
control of the manager determine the unobservable outcome and generate two observable,
contractible, performance measures. Each performance measure is driven by one of the
productive activities while the outcome or value to the principal is influenced by both
activities. The two performance measures also contain a common as well as a specific
random component. From a valuation point of view, the performance measures are
useful because the common component they contain is a key component of the outcome.
Market participants, therefore, use these signals to update their beliefs about the outcome.
10
From a contracting point of view, the performance measures are useful because they are
informative about the agent’s unobservable activities. Without loss of generality, we
normalize the risk-aversion coefficient and the sensitivities of the performance measures
to managerial actions to unity to focus on a minimal set of parameters that vary across
agencies.3
While the setting we consider is highly stylized, it captures some necessary
elements of a contracting environment where accounting performance measures such as
earnings and cash flows are key determinants of executive compensation. The two
performance measures have a positive correlation by construction which is representative
of the empirically documented positive correlation between earnings and cash flows
(Dechow 1994). Productive managerial activities which may differentially impact cash
flows and earnings ultimately contribute to increases in firm value and this essential
aspect is also captured in the way the unobservable outcome is modeled. Investor beliefs
about firm value get updated on the release of information about realizations of earnings
and cash flows (Rayburn 1986, Bowen, Burgstahler, and Daley 1986) and we
operationalize this by modeling the random components of the performance measures as
garbled versions of the random component of the outcome. Our simplified set up,
however, does not capture the effect of prior period productive actions on current period
earnings and cash flows as well as the effect of earnings management practices on
reported accounting numbers.
In this model, we can completely characterize the pay-sensitivities and valuerelevance metrics of the performance measures in terms of the elements of the variance-
3
As we show later, empirical proxies of these agency-specific parameters can be estimated from the sample
variance-covariance matrix of the performance measures under consideration.
11
covariance matrix of the performance measures. The elements of the variance-covariance
matrix are functions of two specific variances and a common variance.4 In our model,
different agencies (firms) are characterized by different values for the three-component,
positive-valued, variance vector.
We show in the appendix that both the valuation and compensation weights
decrease when the total variance of the performance measure increases due to an increase
in the specific or idiosyncratic variance. However, if the source of the increase in the
total variance of a performance measure is the common variance then the performance
measure becomes more informative for valuation purposes but less informative for
contracting purposes. We also characterize the exact magnitudes of the change in
valuation and compensation weights in the neighborhood of a particular agency as
functions of the levels and changes of the common and specific variances. We point out
that if one is examining a set of agencies (or firms) where the variation across agencies is
primarily driven by the variation in those characteristics that have similar directional
influence on valuation and compensation weights, it is more likely that one would
observe a positive association between these weights. If, on the other hand, the set of
agencies primarily vary along those characteristics whose changes lead to opposing
effects in changes in valuation and contracting weights the cross-sectional association
will be, on average, negative.
We estimate firm-specific values of the common variance, specific variance of
earnings and specific variance of cash flows for a sample of 1,351 Compustat firms using
time-series data of EPS and CFPS and use the decile values of the estimated variances to
4
The total variance of any of the performance measure is the sum of the common variance and the specific
variance of that performance measure. The covariance between the two performance measures is equal to
the common variance.
12
construct representative firms that characterize the cross-sectional distribution of the
firms. Table A1 in appendix provides the estimated variances as well as the estimated
compensation and valuation weights for these firms.
As expected, when the specific as well as common variances increase the
compensation weights decline. What is interesting is that the value-relevance measures
also decline when we go from lower deciles to higher deciles. It appears that from a
valuation perspective, the reduction in the valuation weight triggered by the increase in
specific variance dominates the increase triggered by the increase in the common
variance leading to an overall decline. Overall, the empirical evidence based on the joint
distribution of earnings and cash flows suggests a possible positive association between
the valuation and compensation weights.
It is possible that factors other than the specific and common variances of the
performance measures can also play a role in determining the association between
valuation and compensation weights. For instance, Engel, Hayes and Wang (2003) and
Bushman, Engel and Smith (2006) describe alternative scenarios in which current
earnings do not fully reflect multi-period effects of managerial actions on firm value that
can result in compensation weights and valuation weights having a positive association.
Bushman, Engel and Smith (2006) also argue that a positive association between the two
roles can arise in a world where the marginal product of effort and the sensitivity of
earnings to managerial actions are positively correlated random variables.
To summarize, our theoretical setup enables us to gain insights into the contextspecific association between valuation and contracting roles in case of the possible use of
earnings and cash flows as performance measures. We analyze the performance measure
13
data using theoretical expressions for the change in compensation weight and change in
valuation weight in firm-specific neighborhoods for representative firms at various
deciles. The evidence is supportive of a positive association between the contracting and
valuation roles of earnings and cash flows. We formally test this prediction in our
subsequent empirical analysis using actual compensation and valuation data.
Next, we discuss the research design that we use in the study to examine the
above hypotheses and provide details on the sample used in the study.
III.
Research Design and Sample Selection
A direct way to test our hypotheses developed in the previous section is to
estimate pay-sensitivities and value-relevance measures on a firm-specific basis and
examine the association between these measures in the cross-section. However, this
approach suffers from a number of limitations especially in the estimation of firm-level
pay-performance sensitivity measures. Issues such as short time-series of firm-specific
compensation data, the unrealistic assumption of time-invariant pay-performance
sensitivity, and CEO-turnover, lead to substantial noise in the estimated firm level payperformance sensitivities. To address these issues, we follow previous compensation
studies in accounting (Sloan 1993, Baber, Janakiraman and Kang 1996, Baber, Kang and
Kumar 1998, Nwaeze, Yang and Yin 2006) and adopt a cross-sectional approach to test
our hypotheses.
Typical cross-sectional pay-performance studies model annual CEO
compensation as a function of the various performance measures as well as the
performance measures interacted with firm-level control variables to capture the cross-
14
sectional variation in pay-performance sensitivities across firms. We include additional
terms that interact the performance measures with their firm-specific value-relevance
counterparts.5 Specifically, the cross-sectional relationship we examine is:
Compensation  f(earnings, cash flows, earnings*value-relevance of earnings,
cash flows*incremental value-relevance of cash flows, valuerelevance of earnings, incremental value-relevance of cash
flows, earnings*control, cash flows*control)
We operationalize the above relationship in an OLS framework using annual CEO and
firm-level data.6 Specifically, we estimate, on an annual basis
log( COMPi )   0   e ( ROAi )   c (CFOAi )  we (vei * ROAi )  wc (vci * CFOAi )
  e vei   c vci   e vei   c vci   e Control * ROAi   c Control * CFOAi  u i
where, for firm i,
COMPi is CEO cash compensation (salary + bonus),
ROAi = Net income before extraordinary items (#18)/average total assets (#6)
CFOAi = Cash flows from operations (#308)/average total assets (#6)
vei = Value-relevance of earnings
vci = Incremental value-relevance of cash flows from operations, and,
Controli = Decile rank values for various control variables.
5
While there is no economic theory to motivate the inclusion of main effects of value relevance measures,
we include them in the empirical model for econometric reasons. Our results hold when we remove these
main effects from the empirical model.
We use a “level” specification similar to those employed in Core, Holthausen, and Larcker (1999) and
Smith and Watts (1992). We also repeat our analysis with a “change” specification in a later section as a
sensitivity check.
6
15
A significant positive value for we, the regression coefficient on vei* ROAi, is
expected to provide support for the argument that the association between valuerelevance of earnings and pay-sensitivity of earnings is positive and, in a similar vein, a
significant positive value for wc, provides support for the argument that the association
between incremental value-relevance of cash flows and marginal pay-sensitivity of cash
flows is positive.
Variable Measurement
We obtain measures for value-relevance of earnings and the incremental valuerelevance of cash flows from firm-year-specific estimation of the following time-series
regressions using data from a 10-year rolling window. We require that each firm has data
available for at least 8 years starting from 1980.
Pi ,t   0   1 BPS i ,t  eit
(1)
Pi ,t   0  1 EPS i ,t   2 BPS i ,t  u it
(2)
Pi ,t   0   1 EPSi ,t   2 CPS i ,t   3 BPS i ,t   it
(3)
Pit is price per share of firm i at the end of the third month after fiscal year-end t, EPSit is
earnings per share of firm i during year t, CPSit is cash flows per share of firm i during
year t and BPSit is book value per share of firm i during year t. The above models are
derived from Ohlson (1995, 1999) valuation model and modified from Collins, Maydew
and Weiss (1997). CPS (cash flow per share) can be interpreted as pertaining to “other
information” in Ohlson (1999) model. Following Collins, Maydew and Weiss (1997), we
first obtain coefficients of determination from equation (1), (2) and (3) denoted as R2bv ,
R2earnbv and R2total, respectively. Value-relevance of earnings (ve) is measured as
2
( Rearnbv
 Rbv2 ) /(1  Rbv2 ) and the incremental value-relevance of cash flows (vc) is
16
2
2
2
 Rearnbv
) /(1  Rearnbv
) . By construction, both of these measures take
measured as ( Rtotal
values in the range 0 to 1.
To accommodate the possibility that cash flows may be perceived as the primary
performance measure and earnings as the supplementary performance measure, we also
modify equation (2) to obtain value-relevance of cash flows and incremental valuerelevance of earnings as follows:
Pi ,t   0  1CPSi ,t   2 BPS i ,t  u it
(4)
Denoting the coefficient of determination of equation (4) as R2cfobv, we measure value2
relevance of cash flows (vc) as ( Rcfobv
 Rbv2 ) /(1  Rbv2 ) and the incremental value2
2
2
relevance of earnings (ve) as ( Rtotal
 Rcfobv
) /(1  Rcfobv
) for use in later analysis. By
construction, once again, both of these measures take values in the range 0 to 1.
Our R2 measures are related to the R2 measure used in Bushman, Chen, Engel and
Smith (2004) (equation 2 on page 173) except that 1) we use a “level” specification of
Ohlson (1995) model while Bushman, Chen, Engel and Smith (2004) adopt a “change
specification of Ohlson (1995) model, 2) we add cash flows per share as the “other
information” component in Ohlson (1999) since we are interested in the incremental
value-relevance of cash flows over and above earnings, and 3) we do not include earnings
level in our return valuation model to be consistent with the compensation specification
and to link our R2 measure more directly to pay-sensitivities.
An alternative R2 measure is used by Engel, Hayes and Wang (2003) to examine
the association between CEO turnover probability and earnings timeliness. Similar to
Engel, Hayes and Wang (2003), our paper seeks to provide directional prediction of how
a valuation-based R2 measure moderates the relation between the dependent variable and
17
performance measures. While the research designs in both Engel, Hayes and Wang
(2003) and our study are similar in the use of an interaction term involving accounting
earnings and a valuation-based R2 measure, our paper significantly differs from Engel,
Hayes and Wang (2003) in the following ways: 1) We are interested in compensation
decision while Engel, Hayes and Wang (2003) are interested in turnover decisions.
Therefore our dependent variable is compensation as contrasted to turnover in Engel,
Hayes and Wang (2003). 2) Our value-relevance measure is different from the timeliness
measure in Engel, Hayes and Wang (2003). 3) The empirical model in Engel, Hayes and
Wang (2003) is more related to Sloan (1993) and Lambert and Larcker (1987). They
consider one accounting performance measure, earnings, and one market performance
measure, market returns. Our empirical model is more closely related to Natarajan (1996)
by considering two accounting performance measures, earnings and cash flows. 4) In
their setup where earnings and returns are performance measures, Engel, Hayes and
Wang (2003) predict that relative weight on earnings increases with timeliness. However
in our setup the compensation weight on accounting performance measures can increase
or decrease with their value-relevance depending on the context.
The control variables that we include in our empirical specification are taken
from prior studies that have examined sensitivity of CEO pay to accounting performance
measures in the cross-section. We include proxies for investment opportunity sets (IOS),
leverage, performance measure noise, trading cycle, and performance measure
persistence. Growth opportunities reduce the pay-for-performance sensitivities of
accounting performance measures (Smith and Watts 1992, Gaver and Gaver 1993).
Leverage is expected to reduce the pay-sensitivity of earnings and increase the
18
incremental pay-sensitivity of cash flows (Natarajan 1996). Longer trading cycles
decrease the incremental stewardship value of cash flows (Dechow 1994, Natarajan
1996). Performance measure noise (Banker and Datar 1989, Lambert and Larcker 1987,
Sloan 1993) leads to a reduction in the compensation weights on performance measures.
Earnings persistence is shown to be positively related to the reliance of CEO
compensation on earnings (Baber, Kang and Kumar 1998). Finally, we include size and
stock returns as additional control variables and expect the level of CEO pay to be
positively associated with size and stock returns (Smith and Watts 1992, Core,
Holthausen and Larcker 1999).
Sample Selection
We obtain data from Compustat 2004, CRSP 2004 and ExecuComp 2004. We
impose the following restrictions on the sample: (1) No CEO change during the year, (2)
CEO served in the same company for at least two consecutive years, and (3) book value
and total assets are positive. The final sample contains 7,076 CEO-year observations
from 1993 to 2003. The number of observations varies from 410 in 1993 to 690 in 1999.
IV.
Empirical results
Table 1 shows descriptive statistics of sample characteristics. The mean and
median values of EPS (1.201 and 1.096) are close to those reported in Collins, Maydew
and Weiss (1997). CPS is higher than EPS, indicating that on average accruals are
income-decreasing. The empirical distributions of the control variables appear to be
similar to those documented in other executive compensation studies. Our sample is
19
biased towards large, profitable firms similar to many studies that have used ExecuComp
data.
Value-relevance of earnings and cash flows
We first obtain value-relevance measure of earnings and incremental valuerelevance measure of cash flows. Panel A of table 2 presents descriptive statistics of
pricing coefficients, coefficient of determination and value-relevance measures. Similar
to the results in Collins, Maydew and Weiss (1997), we observe that the pricing
coefficient on EPS (  1 ) is higher than the pricing coefficient on BPS (  2 ) in firmspecific regressions of stock price on earnings and book value. The mean and median
coefficients on EPS are 4.265 and 2.107, comparable to the pooled cross-sectional timeseries coefficient of 3.41 on EPS reported in Collins, Maydew and Weiss (1997). The
mean and median coefficients of determination for the earnings and book value
regressions are 0.568 and 0.598, comparable to the coefficient of determination of 0.536
in Collins, Maydew and Weiss (1997). The pricing coefficients and coefficients of
determination from estimating the regression of stock price on earnings, cash flows and
book value are comparable to Barth, Beaver, Hand and Landsman (1999), although we
use different estimation methods and focus on a different sample period. Combining the
coefficients of determination from the three different regression specifications, we
estimate the value-relevance of earnings (mean=0.262, median=0.198), and the
incremental value-relevance of cash flows (mean=0.195, median=0.115).
We also present descriptive statistics of pricing coefficients, coefficients of
determination and value-relevance measures using cash flows as the primary performance
measure and earnings as the supplementary performance measure in panel B of table 2.
20
The distribution of pricing coefficients, coefficients of determination, and consequently
the value-relevance measures is similar to those reported in panel A of table 2. The mean
and median values of value-relevance of cash flows are 0.195 and 0.117, respectively.
The mean and median values of incremental value-relevance of earnings are 0.258 and
0.186, respectively.
Cash compensation weight and value-relevance of earnings and cash flows
To examine the linkage between compensation weight and value-relevance for
earnings and cash flows, we run the analysis in a cross-sectional setting using logarithm
of cash compensation (salary plus bonus) as the dependent variable. We focus our
analysis on cash compensation following prior studies that have focused on paysensitivities of accounting performance measures (Lambert and Larcker 1987, Gaver and
Gaver 1993, Sloan 1993, Natarajan 1996). Cash compensation is more closely tied to
accounting performance measures than total compensation. Moreover, Core, Guay and
Verrecchia (2003) find that results from cash compensation support predictions from
standard agency models while results from total compensation do not seem to be
consistent with the predictions. We repeat our analysis using total compensation as a
robustness check. When estimating the model, we removed influential observations with
Studentized residuals greater than three or Cook’s D statistic greater than one (Belsley,
Kuh and Welsch 1980). We performed White’s (1980) test for heteroskedasticity and
found that heteroskedasticity was not a problem for our models. We tested for
multicollinearity using the Belsley, Kuh, and Welsch (1980) diagnostics. The condition
indices for all the interested explanatory variables were less than 10, well below the
suggested cutoff.
21
Table 3 shows the average association between pay-sensitivity and valuerelevance of earnings and that between the marginal pay-sensitivity of cash flows and the
incremental value-relevance of cash flows. We use earnings deflated by average total
assets and cash flow from operations deflated by average total assets as our performance
measures (Antle and Smith 1986, Sloan 1993). We use year-by-year regressions and
report the average regression coefficients and associated t-statistics. Our specification
also includes industry dummies defined at the one-digit SIC level to partially control for
differences in benchmarks for the performance measures across different industries
(Antle and Smith 1986, Janakiraman, Lambert and Larcker 1992). The mean coefficient
on ve* ROA is positive and significant after we control for other factors that may
influence the pay-for-performance sensitivities of earnings in the cross-section. The
yearly mean coefficient on ve* ROA is 2.146 (Fama-MacBeth t statistic = 2.79). This
result indicates that the compensation weight on earnings is increasing in value-relevance
of earnings. Similarly, the mean coefficient on vc*CFOA is positive and significant
(coefficient = 1.533, Fama-MacBeth t-statistic= 2.31). This is in support of the
conjecture that the marginal compensation weight on cash flows is positively related to
the incremental value-relevance of cash flows. Using information from table 2 and table
3, we calculate the total pay-sensitivity on ROA for a representative median firm in our
sample to be 1.487. The incremental pay-for-performance sensitivity on CFOA for a
representative median firm is –0.319. The net effect of the two is the total pay-sensitivity
on cash flows. This means that for a representative median firm, the total pay-sensitivity
on cash flows is 1.168. The results indicate that 13% of the pay-sensitivity of accruals of
a representative median firm can be attributed to the value-relevance of earnings and 10%
22
of the total pay-sensitivity of cash flows can be explained by the contribution of the
incremental value-relevance of cash flows.
The above results are based on value-relevance measures calculated under the
assumption that earnings is the primary performance measure and cash flows is the
supplementary performance measure. We also present results using cash flows as the
primary performance measure and earnings as the supplementary performance measure.
Our results in the third and fourth columns of panel A of table 3 provide support for the
positive association between pay-sensitivity and value-relevance of performance
measures. The yearly mean coefficient on vc* CFOA is 1.655 (Fama-MacBeth t statistic
= 1.97). This result indicates that the compensation weight on cash flows is increasing in
value-relevance of cash flows. Similarly, the mean coefficient on ve*ROA is positive and
significant (coefficient = 2.347, Fama-MacBeth t-statistic = 5.09). This is in support of
the notion that the marginal compensation weight on earnings is positively related to the
incremental value-relevance of earnings. Overall, the results in panel A of table 3
confirm a positive association between pay-sensitivity and value-relevance for both
earnings and cash flows independent of which one of these is designated as the primary
performance measure.
Total compensation weight and value-relevance of earnings and cash flows
Core, Guay and Verrecchia (2003) indicate that predictions from standard agency
theory find support when CEO cash compensation is used, but not when total
compensation is used. To address this concern, we repeat our analysis using total
compensation. Panel B of table 3 shows the average association between pay-forperformance sensitivities and value-relevance using logarithm of total compensation as
23
the dependent variable. We include additional control variables that have been shown to
influence equity incentives (Yermack 1995, Core and Guay 1999) in our specification.
Firms that have lower free cash flows and higher net operating loss carry-forwards use
more stock options as a substitute for cash pay (Yermack 1995, Matsunaga 1995,
Dechow, Hutton, and Sloan 1996). We measure the degree of cash flow shortfall as the
three-year average of [(common and preferred dividends + cash flows from investingcash flows from operations)/total assets]. Net operating loss is an indicator variable equal
to one if firm has net operating loss carry-forwards in any of the three previous years.
The use of stock options is more when a firm faces earnings constraints and has limited
ability to pay dividends. We categorize a firm as dividend-constrained if (retained
earnings at year-end + cash dividends and stock repurchases during the year)/the prior
year's cash dividends and stock repurchases, is less than two in any of the previous three
years. If the denominator is zero for all three previous years, we also categorize the firm
as dividend constrained (Dechow, Hutton, and Sloan 1996). We also control for the
potential relation between total CEO compensation and firm performance by including
current year and prior year stock returns (Baber, Janakiraman and Kang 1996, Core and
Guay 1999).
Our results in panel B of table 3 once again support the argument that a positive
association exists between pay-sensitivities on earnings and value-relevance of earnings.
The mean value of yearly regression coefficients on ve * ROA is positive and significant
after we add control variables (coefficient = 1.772; Fama-MacBeth t-statistic = 2.13).
We also find a positive linkage between the incremental pay-sensitivity of cash flows and
the incremental value-relevance of cash flows (coefficient = 2.699; Fama-MacBeth t-
24
statistic = 2.16). As before, these associations are not sensitive to designating earnings
rather than cash flows as the primary performance measure.
Value-relevance of change in earnings and change in cash flows
So far, our analysis considered the levels of earnings and cash flows as the
relevant performance measures. Several compensation studies have focused on change in
accounting performance measures based on the argument that the correct benchmark for
performance is immediate past performance (Lambert 1987, Sloan 1993, Baber,
Janakirman and Kang 1996, Core, Guay and Verrecchia 2003). Next, we test our
hypotheses assuming that change in earnings and change in cash flows are the
appropriate performance measures. Accordingly, we use an alternative model in the first
stage of our analysis where market-adjusted stock returns are regressed on change in
earnings and change in cash flows to calibrate the value-relevance of change in earnings
and the incremental value-relevance of change in cash flows (Bernard and Stober 1989,
Jennings 1990, Ali 1994). We construct the value-relevance measures of change in
earnings and change in cash flows from the following firm-specific regressions using a
10-year rolling window estimation method. We require that each firm has data available
for at least 8 years starting from 1980.
R2earn is obtained from
Ri ,t   0   1 EARN i ,t  eit
R2 total is obtained from: Ri ,t   0  1 EARN i ,t   2 CFOi ,t   it
(5)
(6)
2
Value-relevance measure of earnings ( ve ) is Rearn
and incremental value-relevance
2
2
2
 Rearn
) /(1  Rearn
) . We also obtain R2cfo from
measure of cash flows ( vc) is ( Rtotal
Ri ,t   0   1 CFOi ,t  eit
(7)
25
2
and estimate value-relevance measure of cash flows ( vc ) as Rcfo
and incremental value-
2
2
2
relevance measure of earnings ( ve) as ( Rtotal
 Rcfo
) /(1  Rcfo
) for the case where cash
flows is designated as the primary performance measure.
Table 4 reports descriptive statistics of variables and estimation coefficients from
firm-specific time-series regressions. Panel A shows results using earnings as the
primary performance measure and cash flows as the supplementary performance measure.
A positive ve implies that earnings information is value-relevant to the investors. A
positive vc implies that the market attaches incremental value to cash flows over and
above earnings. The mean and median values of ve (0.232 and 0.158) and the mean and
median values of vc (0.168 and 0.095) are lower in magnitude compared to their
counterparts estimated using levels. Panel B provides the descriptive statistics for the
value relevance of change in cash flows and the incremental value relevance of earnings
Pay-sensitivity and value-relevance of change in earnings and change in cash flows
Table 5 shows the results for year-by-year regressions of change in logarithm of
cash compensation on change in earnings, change in cash flows, value-relevance of
earnings, incremental value-relevance of cash flows and other control variables. We
obtain value-relevance measures from the analysis presented in table 4. Pane A presents
results using change in logarithm of cash compensation as the dependent variable. This
change specification focuses on innovation in earnings and cash flows that affects payfor-performance sensitivities (Baber, Janakiraman and Kang 1996). The mean
coefficients of ve*∆ROA (2.157) is positive and significant (Fama-MacBeth t-statistic =
2.43). This supports the conjecture that pay-sensitivity for change in earnings is
increasing in the value-relevance of change in earnings. The mean coefficient on
26
vc*∆CFOA (1.667) is positive and significant (Fama-MacBeth t-statistic = 2.20). We find
support for the notion that the incremental pay-sensitivity of change in cash flows is
increasing in the incremental value-relevance of change in cash flows. The results are
qualitatively the same when we use change in cash flows as the primary performance
measure and change in earnings as the supplementary performance measure.
Panel B of table 5 presents results of the analysis using change in logarithm of
total compensation as the dependent variable. Again, we find evidence in support of the
conjecture that pay-sensitivity is positively associated with value-relevance of change in
earnings (coefficient of ve*∆ROA = 3.865; Fama-MacBeth t-statistic = 1.97) and that
incremental pay-sensitivity is increasing in incremental value-relevance of change in cash
flows (coefficient of vc*∆CFOA = 1.621; Fama-MacBeth t-statistic = 1.77). The results
when using change in cash flows as the primary performance measure and earnings as the
supplementary performance are slightly weaker.
Industry-specific estimation of value-relevance and pay-sensitivity
As an additional robustness check, we also carry out industry-specific estimation
of regressions with market-adjusted stock returns and change in logarithm of cash
compensation as dependent variables and change in earnings and change in cash flows
scaled by market value of equity as common independent variables (Bushman, Engel and
Smith 2006). Further, we split the sample into two sub periods: 1993 to 1997 and 1998
to 2003 and let the regression coefficients vary across these sub periods. Specifically, we
estimate
Ri ,t    Ve * EARN i ,t  Ve * EARN 2 i ,t  Vc * CFOi ,t  Vc * CFO2 i ,t   i ,t (8)
and
27
 log( CASHCOMPi ,t )    We * EARN i ,t  We * EARN 2 i ,t  Wc * CFOi ,t
 Wc * CFO2 i ,t  Wr * RETi ,t  Wr * RET 2 i ,t  i ,t
EARN i ,t
Where EARN 2 i ,t  
0
CFOi ,t
CFO2 i ,t  
0
RETi ,t
RET 2 i ,t  
0
(9)
if t  1997
otherwise
if t  1997
otherwise
if t  1997
otherwise
We estimate the valuation equation (8) for each 2-digit SIC industry and report
the descriptive statistics of coefficient estimates in panel A of table 6. We require at least
20 observations for each industry-subperiod combination. Panel A of table 6 shows that
the valuation coefficients of earnings have declined from sub period 1 (1993 to 1997) to
sub period 2 (1998 to 2003). The mean and median coefficients for ∆Ve are -0.297 and 0.207, respectively, and the aggregate Z-statistic across the 29 industry groups is -2.83
(p<0.01). On the other hand, the incremental valuation coefficients of cash flows have
increased from sub period 1 to sub period 2. The mean and median coefficients for ∆Vc
are 0.156 and 0.148, respectively and the aggregate Z-statistic is 2.71 (p<0.01).
We then estimate the compensation equation (9) for each 2-digit SIC industry and
report the descriptive statistics of coefficient estimates in panel B of table 6. Consistent
with the temporal change in valuation coefficients, we find that the compensation weights
on earnings have declined from the earlier sub period to the later sub period (mean = 0.349, median = -0.231 and z-statistic = -3.88 with a two-sided p-value less than 0.01).
We also find that the incremental compensation weights on cash flows have increased
between the two sub periods (mean = 0.146, median = 0.062, and z-statistic = 2.30 with a
28
two-sided p-value of 0.02). We further examine the correlation between ∆Ve / Ve and
∆We /We as well as the correlation between ∆Vc / Vc and ∆Wc /Wc . Both these
correlations are significantly positive, once again, supporting our main hypotheses.
V.
Conclusions and Implications
Accounting performance measures such as earnings and cash flows seek to serve
multiple purposes in organizations. Prior studies have investigated their usefulness for
valuation and incentive contracting purposes separately. However, except for a recent
study by Bushman, Engel and Smith (2006) which examines the association between the
valuation and incentive-contracting roles of accounting earnings, little evidence exists on
the linkage between these two roles. In this study, we examine the association between
pay-sensitivities and value-relevance of earnings and cash flows. We derive measures of
pay-sensitivities and value-relevance using a stylized principal-agent setting
characterized by two performance measures. Application of the insights from the model
to earnings and cash flows data of a large sample of Compustat firms leads to the
conjecture that a positive association exists between value-relevance and payperformance sensitivity of earnings and cash flows.
Using CEO compensation and accounting data for a large number of U.S. firms
over an eleven year period from 1993 to 2003, we find that pay-sensitivity of earnings is
higher for firms that exhibit high value-relevance of earnings. We also find that marginal
pay-sensitivity of cash flows is positively associated with the incremental value-relevance
of cash flows. As the value-relevance of cash flows increases relative to that of earnings
in the later years during our sample period, we find that the compensation weight on cash
29
flows relative to that on earnings increases as well. Overall, our results suggest that
value–relevance of performance measures plays an important role in the choice of
accounting performance measures for incentive contracting purposes.
30
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34
Appendix I
A two-action, two-signal principal-agent model to investigate the association
between valuation-relevance and compensation weight on signals
We consider a special case of the basic model described in Feltham and Xie (1994)
to investigate the association between compensation weights and value-relevance
measures. The principal is risk neutral and the outcome x of value to her takes the form:
x  a1  a2   1
1 ~ N (0,  12 )
While x is not contractible, the two actions a1 and a2 also generate two performance
measures, y and z which are observable and available for contracting7:
y  a1   1   2
 2 ~ N (0,  22 )
z  a2   1   3
 3 ~ N (0,  32 )
All noise terms are independently distributed. We denote  12 as the common variance
stemming from the stochastic term  1 in the expressions for x, y and z,  22 as the specific
variance of  2 in the expression for y and  32 as the specific variance of  3 in the
expression for z. The total variance of y is  12 +  22 and that of z is  12 +  32 . The
covariance between y and z is  12 .
The agent is risk averse and incurs direct personal costs C (a) 
1 2
(a1  a 22 ) . The
2
agent’s preferences are represented by a negative exponential utility function
characterized by unit absolute risk aversion, i.e., U ( Z )  e Z where Z  W  C (a ) is his
net wealth. Following Feltham and Xie (1994), we consider a compensation contract that
7
We normalize the marginal product of effort as well as the sensitivities of the two performance measures
to the respective effort components to unity. This is done for notational simplicity and helps us focus on a
parsimonious set of exogenous parameters.
is linear in the signals, i.e., W    w y y  wz z . We refer to wy and wz as the
compensation weights on y and z, respectively, in the discussion that follows. The
optimal compensation weights for this agency problem can be completely characterized
by the three-parameter vector  12 ,  22 ,  32  . Applying Feltham and Xie’s solution (1994,
p. 433) to our setting, we can derive the optimal compensation weights
wy  (1   32 ) D and wz  (1   22 ) D where D  1   12   22 1   12   32    14
Our main objective is to analyze how these compensation weights are associated
with the informativeness of these performance measures about the outcome to the
principal. We designate y as the primary signal and z as the supplementary signal. We
follow Collins et al. (1997) and define value-relevance of a signal in terms of its
informativeness. Accordingly, we specify R1 as the informativeness (value-relevance) of
signal y, and R2 as the incremental informativeness (incremental value-relevance) of
signal z over and above y.
The conditional variance of the outcome x given the
realization of y is var  x y    12 22 ( 12   22 ) and the conditional variance given the
realizations of both y and z is var  x y, z    12 22 32 ( 12 22   22 32   12 32 ) .
These
expressions for the conditional variances imply that,
R1  1  var( x y ) 
2
1
  1  
2
2
1  22
(   ) 
1  12  1  22
2
1
2
2
and
 2 2 2 ( 2 2   2 2   12 32 ) 
R2  1  var( x y, z ) var  x y   1   1 2 3 2 12 2 2 2 32

 1  2 ( 1   2 )



(A1)


1
 32
1
1
1 
 2  2  2
 1  2  3 
(A2)
36
The value-relevance measures R1 and R2 are scaled measures of the precisions of specific
performance measures.
The association between compensation weight and value-relevance measures
Since agencies are completely characterized by the three-parameter vector {  12 ,
 22 ,  32 }, we describe below the distinct and opposing effects the variance of the payoffrelevant term (  12 ) and the variance of the idiosyncratic noise terms (  22 ,  32 ) have on
the association between valuation and incentive-contracting roles.
We first derive the partial derivatives that capture the change in compensation
weights with respect to changes in the variances of the noise terms.
The partial
derivatives w y1 , w y 2 , w y 3 , wz1 , wz 2 , w31 (where wy1 = wy 12 and so on) and their
respective signs are:
wy1 = - (1+  32 )(2+  22   32 )/D2 < 0
(A3)
wy2 = - (1+  32 )(1+  12   32 )/D2 < 0
(A4)
wy3 =  12 (1+  22 )/D2 > 0
(A5)
wz1 = - (1+  22 )(2+  22   32 )/D2 < 0
(A6)
wz2 =  12 (1+  32 )/D2 > 0
(A7)
wz3 = - (1+  22 )(1+ 12   22 )/D2 < 0
(A8)
Similarly, we also derive the partial derivatives that capture the change in valuerelevance measures with respect to changes in the variances of noise terms. The partial
derivatives R11, R12, R13, R21, R22, R23 (where R11 = R1 12 and so on) and their
respective signs are:
37
R11 = (1- R1)/( 12   22 ) > 0
(A9)
R12 = -R1/( 12   22 ) < 0
(A10)
R13 = 0
(A11)
R21 = R22  32 /  14 > 0
(A12)
R22 = R22  32 /  24 > 0
(A13)
R23 = - R22( 12   22 )/  12 22 < 0
(A14)
The own derivatives of compensation weights with respect to the corresponding
idiosyncratic noise variances (wy2 and wz3) and the own derivatives of value-relevance
measures with respect to the idiosyncratic noise variances (R12 and R23) are both negative.
This confirms that both valuation and compensation weights decrease when performance
measure noise increases due to an increase in the volatility of the non-value relevant
stochastic term. However, the derivatives of compensation weights with respect to the
payoff-relevant noise variance (wy1 and wz1) are both negative while the derivatives of
value-relevance measures with respect to the payoff-relevant noise variance (R11 and R21)
are both positive. This indicates that an increase in the variance of the value-relevant
noise term makes the performance measures more informative for valuation purposes but
less informative for contracting purposes8.
More generally, the changes in compensation weights ( wy and wz ) and valuation
weights (R1 and R2) with respect to small changes in the common variance (  12 ) and
8
We present the results for the more general case of a two-action, two-signal model to highlight different
managerial actions that may have different implications for earnings and cash flows. The sensitivities of
compensation and value-relevance measures to underlying agency parameters and the insights derived from
the model in understanding the context-specific nature of association between the valuation and incentivecontracting roles of accounting performance measures are qualitatively similar if we use a single-action,
two-signal setup.
38
idiosyncratic variances (  22 and  32 ) in the neighborhood of an agency characterized by
the triple {  12 ,  22 ,  32 } are:
Wy   12 wy1   22 wy 2   32 wy 3
(A15)
W z   12 wz1   22 wz 2   32 wz 3
(A16)
R1   12 R11   22 R12   32 R13
(A17)
R2   12 R21   22 R22   32 R23
(A18)
The sign of
W y
R1
as well as that of
 Wz
can be positive or negative depending
R2
on the relative magnitudes of the various variances  12 ,  22 and  32 and the relative
magnitudes of the change in these variances i.e.,  12 ,  22 and  32 . In other words, the
association between the valuation and compensation weights is dependent on the
distribution of  12 ,  22 and  32 in the observed cross-section of firms. To understand
how cross-sections with different  12 ,  22 and  32 distributional characteristics can
exhibit different associations between the valuation and contracting roles consider the
following scenarios.
Assume that, for example, the cross-section of firms is characterized by equal
changes in the firm characteristics in the neighborhood of any firm i.e.,  12   22   32 .
Further assume that the idiosyncratic variances of the performance measures are equal i.e.,
 22   32 for every firm in this cross-section. For this group of firms, the sign of the
association between valuation and contracting is dependent entirely on whether the payoff variance  12 is greater or less than the idiosyncratic variance  22 (or  32 ) . If it is
39
greater, then the association is always positive and negative otherwise. This is because
the reduction in valuation weight triggered by an increase in idiosyncratic variance is
greater in magnitude than the increase in valuation weight triggered by an increase in the
pay-off variance for the firms with  12 >  22 . As a consequence, both valuation and
compensation weights decline for these firms when there is an increase in the variance
vector. The opposite scenario happens and valuation weight increases and compensation
weight decreases when  12 <  22 .
Next consider a different cross-section of firms with the property that all firms in
this group have identical pay-off variance i.e., these agencies are characterized by no
variation in  12 (i.e.,  12  0 ). Further assume that the variances of the idiosyncratic
error terms of the two signals are equal in magnitude and variation (i.e.,  22   32 and
 22   32 ). This particular set of agencies exhibits the property that the changes in
valuation and compensation weights triggered by changes in the idiosyncratic error term
variances always have the same sign in the neighborhood of any agency in the set.
Finally, consider the case when the cross-section of the agencies is characterized by no
variation in  22 and  32 (  22  0 ,  32  0 ). The difference between two agencies is
entirely due to the difference in  12 . For this particular group of agencies
as
W y
R1
as well
 Wz
are always negative in the neighborhood of any agency in the set.
R2
The various cases described above highlight the context-specific association
between valuation and compensation weights for a correlated two performance measure
setup. The interesting question is whether the actual cross-sectional distribution of the
40
performance measure variance characteristics is conducive to a predominantly positive or
negative association between the valuation and contracting roles for the sample firms
under consideration. Our stylized two performance measure setup provides a simple and
effective way to answer this question through the estimation of firm-specific values of the
triple {  12 ,  22 ,  32 } from the variance-covariance structure of the two performance
measures .9 Further the change in compensation weight as well as the change in
valuation weight namely can be estimated in the neighborhood of any representative firm
in the cross-sectional distribution.
We apply the above insights to estimate compensation weights and valuation
weights when the accounting performance measures under consideration are y =
earnings-per-share (EPS) and z = cash-flows-per-share (CFPS). To obtain stable
measures of variance and covariance matrix, we require each firm to have a minimum of
10 years of EPS and CFPS data during the period 1980-2004. We also remove firms for
which any of the estimates of  12 ,  22 , and  32 is negative. For a final sample of 1,351
firms, we estimate the triple {  12 ,  22 ,  32 } and use the decile values of the estimated
variances to construct nine representative firms that characterize the cross-sectional
distribution of the firms. The table below provides the values of  12 ,  22 , and  32 , the
theoretical compensation and valuation weights Wy, R1, Wz and R2 as well as the sign of
9
Note that var(y) =  1 +  2 , var(z) =  1 +  3 and cov(y,z) =  1 . This implies that  1 = cov(y,z),
2
2
2
2
2
2
 22 = Var(y) - cov(y,z) and  32 = var(z) - cov(y,z) can be estimated from the sample variance-covariance
matrix of y and z.
41
the ratios of change in compensation weight and the change in valuation weight
( Wy / R1 and Wz / R2 ) in the neighborhood of these representative firms.10
Table A1
Estimated Variances, Compensation Weights and Valuation Weights for
Representative Firms
Decile
1
2
3
4
5
6
7
8
9
12
 22
0.036
0.070
0.115
0.171
0.237
0.345
0.510
0.894
1.973
0.027
0.058
0.102
0.170
0.256
0.410
0.658
1.141
2.610
 32
0.055
0.124
0.208
0.329
0.510
0.839
1.381
2.655
5.413
Wy
0.910
0.837
0.757
0.670
0.592
0.495
0.396
0.281
0.149
R1
0.571
0.547
0.528
0.501
0.481
0.458
0.437
0.439
0.431
Wz
0.886
0.788
0.690
0.590
0.492
0.379
0.276
0.165
0.084
R2
0.221
0.204
0.206
0.206
0.194
0.182
0.172
0.159
0.172
Wy / R1
Wz / R2
+
+
+
+
+
+
+
-
+
+
+
+
+
+
+
+
The cross-sectional analysis based on the performance-measure characteristics
reveals that the compensation weights and valuation weights are high (low) at low (high)
levels of variances of the pay-off relevant stochastic term and the idiosyncratic error
terms. Further, an increase in the variance vector {  12 ,  22 ,  32 }in the neighborhood of
representative agencies results in a decline in both valuation as well as compensation
weights for 7 out of 9 agencies for earnings and 8 out of 9 agencies for cash flows
suggesting that, in general, the association between the two is positive.
10
The change in the various variance measures i.e.,  1 ,  2 or  3 ,as the case may be, is estimated as
2
2
2
1% of the inter-quartile range of the corresponding variance measures in the estimation of Wy , Wz , R1
and R2 .
42
Figure I
Relative weights on earnings and cash flows for valuation and
compensation purposes
Relative weight of cash flows vs earnings
Relative weight
0.8
0.6
0.4
0.2
0
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Calendar year
Relative valuation weight of cash flows vs earnings
Relative compensation weight of cash flows vs earnings
Notes:
The above figure shows relative valuation and compensation weights of earnings and cash flows over the
year 1993 to 2003. We obtain valuation weights on earnings (  1 ) and cash flows (  2 ) from the year-byyear cross-sectional regression of the following model:
Pi ,t   0   1 EPS i ,t   2 CPS i ,t   3 BPS i ,t   it
Pit is price per share of firm i at the end of the third month after fiscal year-end t. EPSit is earnings per share
of firm i during year t, defined as earnings before extraordinary items (Compustat annual #18) scaled by
number of common shares outstanding adjusted for stock splits and stock dividends. CPS it is cash flows per
share of firm i during year t, defined as cash flows from operation (#308 if the firm-year observation is
after 1988, and #110 - #4 + #1 + #5 – #34 if the firm-year observation is before 1988) scaled by number of
common shares outstanding adjusted for stock splits and stock dividends. BPS it is book value per share of
firm i during year t, defined as book value of common equity (#60) scaled by number of common shares
outstanding adjusted for stock splits and stock dividends.
We obtain compensation weights on earnings (  1 ) and cash flows (  2 ) from the year-by-year crosssectional regression of the following model:
log( CASHCOMP ) i ,t   0   1 EPS i ,t   2 CPS i ,t   it
CASHCOMPit is CEO’s total cash compensation (salary + bonus) for firm i in year t.
The relative valuation weight of cash flows versus earnings is defined as
compensation weight of cash flows versus earnings is defined as
 2 /  1 . The relative
 2 / 1 .
43
Table 1
Descriptive statistics of sample characteristics
MEAN
STD
Q1
MEDIAN
Q3
log( CASHCOMPit )
6.617
0.736
6.096
6.576
7.099
 log( CASHCOMPit )
-1.630
1.332
-2.371
-1.567
-0.794
log( TOTALCOMPit )
7.356
1.054
6.590
7.284
8.049
 log( TOTALCOMPit )
-1.126
1.469
-1.931
-1.058
-0.182
EPSit
1.201
1.929
0.508
1.096
1.794
ROAit
0.052
0.067
0.025
0.053
0.086
ROA it
-0.016
0.427
-0.038
0.000
0.029
CPSit
2.720
3.184
1.018
2.052
3.667
CFOAit
0.104
0.071
0.061
0.100
0.145
CFOAit
-0.006
0.477
-0.070
0.000
0.067
BPS it
11.486
10.991
5.603
9.133
14.419
IOSi,t
-0.056
0.244
-0.201
-0.130
-0.002
Leveragei,t
0.377
0.658
0.045
0.182
0.462
Trade_Cyclei,t
79.559
115.285
30.932
67.727
111.813
EARN_Noisei,t
0.089
0.132
0.026
0.048
0.095
EARN_Persistencei,t
0.756
0.459
0.441
0.786
1.075
CFO_Noisei,t
0.137
0.146
0.057
0.092
0.166
CFO_Persistencei,t
0.505
0.384
0.177
0.505
0.748
Variable definition:
CASHCOMPit is total cash compensation (salary + bonus) in year t.
TOTALCOMPit is CEO’s total compensation in year t, comprised of salary, bonus, other annual, total value
of restricted stock granted, total value of stock options granted (using Black-Scholes), long-term incentive
payouts, and all other total.
EPSit is earnings per share defined as earnings before extraordinary items (Compustat annual #18) scaled
by number of common shares outstanding adjusted for stock splits and stock dividends.
CPSit is cash flows per share defined as cash flows from operation (#308 if the firm-year observation is
after 1988, and #110-#4 + #1 + #5 – #34 if the firm-year observation is before 1988) scaled by number of
common shares outstanding adjusted for stock splits and stock dividends.
BPS it is book value of common equity (#60) scaled by number of common shares outstanding adjusted for
stock splits and stock dividends.
ROAit is earnings before extraordinary items (#18) scaled by average book value of assets (#6).
CFOAit is cash flows from operation (#308 if the firm-year observation is after 1988, and #110-#4 + #1 +
#5 – #34 if the firm-year observation is before 1988) scaled by average book value of assets (#6).
44
Table 1
Continued
IOSi,t is a proxy for investment opportunity set from factor analysis of the following: the ratio of market to
book value of equity, the ratio of the market value of equity plus book value of debt to the book value of
assets, the ratio of market value of equity plus book value of debt to gross plant, property and equipment.
Leveragei,t is the ratio of long-term debt to year-end market value of equity.
Trade_Cyclei,t =  ARi ,t  ARi ,t 1  / 2    INVi ,t  INVi ,t 1  / 2    APi ,t  APi ,t 1  / 2 

 
  Purchases / 360 
Sales / 360
COGS / 360

 
 

EARN_Noisei,t is the time-series standard deviation of ROAit for each firm starting from 1980.
CFO_Noisei, is the time-series standard deviation of CFOAit for each firm starting from 1980.
EARN_Persistencei,t is the estimate of (1  ), computed from an IMA (1,1) earnings process starting
from 1980:
EARN i ,t  EARN i ,t 1  UE( EARN i ,t )  UE( EARN i ,t 1 )
CFO_Persistencei,t is the estimate of (1  ), computed from an IMA (1,1) cash from operations process
starting from 1980:
CFOi ,t  CFOi ,t 1  UE(CFOi ,t )  UE(CFOi ,t 1 )
Δ denotes change from year t-1 to year t.
45
Table 2
Value-relevance of primary performance measure and incremental value-relevance
of supplementary performance measure
Panel A
Value-relevance of earnings and incremental value-relevance of cash flows
We obtain the value-relevance of earnings and of cash flows from firm-specific regression of the following
equations using a 10-year rolling window estimation method. We require each firm to have at least 8 years
of data available starting from 1980. We designate earnings as the primary performance measure and cash
flows as the supplementary performance measure.
R2bv is obtained from:
Pi ,t   0   1 BPS i ,t  eit
(1)
R2earnbv is obtained from:
Pi ,t   0  1 EPS i ,t   2 BPS i ,t  u it
(2)
R2total is obtained from:
Pi ,t   0   1 EPS i ,t   2 CPS i ,t   3 BPS i ,t   it
(3)
2
Value-relevance of earnings ( ve ) is ( Rearnbv
 Rbv2 ) /(1  Rbv2 ) , and incremental value-relevance of cash
2
2
2
flows ( vc) is ( Rtotal
 Rearnbv
) /(1  Rearnbv
).
Mean
STD
Q1
Median
Q3
1
1.824
3.394
0.343
1.321
2.684
1
4.265
14.783
0.175
2.107
5.877
2
1.162
4.219
-0.132
0.800
2.087
1
3.872
16.389
-0.087
1.917
5.895
2
0.713
10.185
-1.187
0.201
2.095
3
1.088
4.321
-0.251
0.730
2.046
R2bv
0.402
0.305
0.109
0.368
0.671
R2earnbv
0.568
0.265
0.360
0.598
0.794
R2 total
0.652
0.239
0.486
0.698
0.852
2
 Rbv2 ) /(1  Rbv2 ) ]
ve =[ ( Rearnbv
0.262
0.237
0.053
0.198
0.423
2
2
2
 Rearnbv
) /(1  Rearnbv
)]
vc =[ ( Rtotal
0.195
0.210
0.027
0.115
0.301
Table 2
Panel B
Continued
Value-relevance of cash flows and incremental value-relevance of earnings
We obtain the value-relevance of earnings and of cash flows from firm-specific regression of the following
equations using a 10-year rolling window estimation method. We require each firm to have at least 8 years
of data available starting from 1980. We designate cash flows as the primary performance measure and
earnings as the supplementary performance measure.
R2bv is obtained from:
Pi ,t   0   1 BPS i ,t  eit
(1)
R2cfobv is obtained from:
Pi ,t  0  1CPSi ,t  2 BPS i ,t  uit
(4)
R2total is obtained from:
Pi ,t  0  1CPSi ,t  2 EPSi ,t  3 BPS i ,t   it
(3)
2
Value-relevance of cash flows ( vc ) is ( Rcfobv
 Rbv2 ) /(1  Rbv2 ) , and incremental value-relevance of earnings
2
2
2
( ve) is ( Rtotal
 Rcfobv
) /(1  Rcfobv
).
Mean
STD
Q1
Median
Q3
1
1.824
3.394
0.343
1.321
2.684
1
1.498
8.522
-0.690
0.515
2.652
2
1.535
3.655
0.120
1.098
2.437
1
0.713
10.185
-1.187
0.201
2.095
2
3.872
16.389
-0.087
1.917
5.895
3
1.088
4.321
-0.251
0.730
2.046
R2bv
0.402
0.305
0.109
0.368
0.671
R2cfobv
0.521
0.282
0.279
0.538
0.768
R2total
0.652
0.239
0.486
0.698
0.852
0.195
0.208
0.028
0.117
0.302
0.258
0.242
0.047
0.186
0.419
2
vc =[ ( Rcfobv
 Rbv2 ) /(1  Rbv2 ) ]
2
total
ve =[ ( R
R
2
cfobv
) /(1  R
2
cfobv
)]
Variables are defined in table 1.
47
Table 3
Association between pay-sensitivity and value-relevance of primary performance
measure and incremental value-relevance of supplementary performance measure
We estimate year-by-year regressions of the following equation from 1993 to 2003. We obtain valuerelevance of primary performance measure and incremental value-relevance of supplementary performance
measure from the estimated equations in table 2.
log( COMPi )   0   e ( ROA i )   c (CFOAi )  we (v ei * ROA i )  wc (v ci * CFOAi )
  e Controli * ROA i   c Controli * CFOAi  u i
Panel A
Variable
Cash compensation and value-relevance of performance measures
Earnings as the primary Cash flows as the primary
performance measure
performance measure
Mean
(FamaMean
(FamaPredict Coefficient MacBeth Coefficient MacBeth
t-statistic)
t-statistic)
Intercept
5.714
(126.71)
6.063
(154.85)
ROAit
2.190
(4.72)
1.955
(5.28)
CFOAit
-0.981
(-2.39)
-0.841
(-2.62)
ve ROAit
+
2.146
(2.79)
2.347
(5.09)
vc CFOAit
+
1.533
(2.31)
1.655
(1.97)
ve
-0.037
(-0.47)
-0.082
(-2.19)
vc
-0.118
(-1.90)
-0.152
(-1.03)
IOSi,t* ROAit
-0.543
(-0.61)
-0.794
(-1.22)
Leveragei,t* ROAit
0.325
(0.55)
-0.085
(-0.15)
EARN_Noisei,t* ROAit
-0.759
(-2.50)
-0.659
(-2.17)
EARN_Persistencei,t* ROAit
-0.627
(-1.41)
-0.290
(-0.79)
Trade_Cyclei,t* ROAit
-0.903
(-2.33)
-0.127
(-0.27)
IOSi,t* CFOAit
1.041
(1.78)
0.946
(2.01)
Leveragei,t* CFOAit
-0.606
(-1.43)
0.189
(0.67)
CFO_Noisei,t* CFOAit
1.422
(6.53)
1.230
(5.66)
CFO_Persistencei,t* CFOAit
-0.988
(-4.58)
-0.799
(-3.43)
Trade_Cyclei,t* CFOAit
0.210
(1.13)
-0.733
(-3.25)
Returnit
0.268
(7.76)
0.263
(10.58)
Log(Total Assets)
1.461
(48.19)
1.569
(106.28)
Mean Adj. R2
41.8%
40.7%
N
6,976
6,955
48
Table 3
Panel B
Continued
Total compensation and value-relevance of performance measures
Variable
Earnings as the primary Cash flows as the primary
performance measure
performance measure
Mean
(FamaMean
(FamaPredict Coefficient MacBeth Coefficient
MacBeth
t-statistic)
t-statistic)
Intercept
5.990
(63.96)
6.111
(49.27)
ROAit
-0.679
(-0.59)
-0.974
(-1.09)
CFOAit
-1.956
(-2.59)
-0.820
(-1.43)
ve ROA it
+
1.772
(2.13)
1.785
(2.46)
vc CFOAit
+
2.699
(2.16)
1.984
(2.34)
ve
0.062
(0.67)
-0.008
(-0.14)
vc
-0.155
(-1.01)
-0.075
(-0.66)
IOSi,t* ROAit
-0.229
(-0.19)
-0.854
(-0.85)
Leveragei,t* ROAit
1.890
(1.59)
1.546
(1.92)
EARN_Noisei,t* ROAit
0.705
(1.03)
0.355
(0.57)
EARN_Persistencei,t* ROAit
-2.558
(-3.94)
-1.661
(-3.34)
Trade_Cyclei,t* ROAit
0.446
(0.61)
1.597
(1.86)
IOSi,t* CFOAit
3.407
(5.56)
3.265
(4.87)
Leveragei,t* CFOAit
-2.211
(-4.89)
-1.662
(-4.92)
CFO_Noisei,t* CFOAit
1.779
(4.20)
0.937
(2.80)
CFO_Persistencei,t* CFOAit
-0.519
(-2.47)
-0.583
(-2.61)
Trade_Cyclei,t* CFOAit
0.315
(0.87)
-1.456
(-3.26)
Cashflow Shortfalli,t
-0.165
(-2.23)
-0.093
(-1.58)
Net Operating Lossi,t
-0.174
(-2.09)
-0.048
(-0.65)
Dividend ConstraintI,t
0.406
(6.64)
0.446
(9.43)
Stock Returni,t-1
0.276
(2.99)
0.361
(4.30)
Stock Returni,t
0.355
(3.62)
0.369
(4.57)
Log(Total Assets)
2.098
(30.10)
2.276
(26.97)
Mean Adj. R2
42.5%
42.8%
N
6,891
6,950
49
Table 3
Continued
Variable definition:
Cash flow shortfall is the three-year average of [(common and preferred dividends + cash flow from
investing-cash flow from operations)/total assets].
Net operating loss is an indicator variable equal to one if firm has net operating loss carry-forwards in any
of the three previous years.
Dividend constraint is an indicator equal to one if the firm is dividend constrained in any of the three
previous years. We categorize a firm as dividend constrained if [(retained earnings at year-end + cash
dividends and stock repurchases during the year)/the prior year's cash dividends and stock repurchases], is
less than two. If the denominator is zero for all three years, we also categorize the firm as dividend
constrained.
Stock return is the cumulative return for firm i over the 12 month period of the fiscal year.
Other variables are defined in table 1.
Industry dummies based on 2-digit SIC code are included.
50
Table 4
Value-relevance of change in primary performance measure and incremental
value-relevance of change in supplementary performance measure
Panel A
Value-relevance of change in earnings and incremental value-relevance of
change in cash flows
We obtain the value-relevance of earnings and of cash flows from firm-specific regression of the following
equations using a 10-year rolling window estimation method. We require each firm to have at least 8 years
of data available starting from 1980. We designate earnings as the primary performance measure and cash
flows as the supplementary performance measure.
R2earn is obtained from: Ri ,t   0   1 EARN i ,t  eit
R2total is obtained from: Ri ,t   0  1 EARN i ,t   2 CFOi ,t   it
(5)
(6)
2
Value-relevance of change in earnings ( ve ) is Rearn
, and incremental value-relevance of change in cash
2
2
2
flows ( vc) is ( Rtotal
 Rearn
) /(1  Rearn
).
Mean
STD
Q1
Median
Q3
1
2.885
8.255
0.204
1.388
3.818
1
2.820
9.550
0.087
1.369
4.037
2
0.432
6.742
-0.610
0.121
1.118
R2earn
0.232
0.226
0.041
0.158
0.370
R2 total
0.361
0.242
0.155
0.328
0.541
2
ve [ Rearn
]
0.232
0.226
0.041
0.158
0.370
2
2
2
 Rearn
) /(1  Rearn
)]
vc [ ( Rtotal
0.168
0.190
0.022
0.095
0.251
51
Table 4
Continued
Panel B
Value-relevance of change in cash flows and incremental value-relevance of
change in earnings
We obtain the value-relevance of earnings and of cash flows from firm-specific regression of the following
equations using a 10-year rolling window estimation method. We require each firm to have at least 8 years
of data available starting from 1980. We designate cash flows as the primary performance measure and
earnings as the supplementary performance measure.
R2cfo is obtained from:
Ri ,t   0  1CFOi ,t  eit
(7)
R2 total is obtained from:
Ri ,t  0  1CFOi ,t  2EARN i ,t   it
(6)
2
Value-relevance of change in cash flows ( vc ) is Rcfo
, and incremental value-relevance of change in
2
2
2
earnings ( ve) is ( Rtotal
 Rcfo
) /(1  Rcfo
).
Mean
STD
Q1
Median
Q3
1
0.843
5.035
-0.318
0.289
1.395
1
0.432
6.742
-0.610
0.121
1.118
2
2.820
9.550
0.087
1.369
4.037
R2cfo
0.166
0.189
0.022
0.094
0.248
R2 total
0.361
0.242
0.155
0.328
0.541
0.166
0.189
0.022
0.094
0.248
0.233
0.228
0.040
0.158
0.373
2
vc [ Rcfo
]
2
total
ve [ ( R
 R ) /(1  R ) ]
2
cfo
2
cfo
Variable definition:
Ri,t is the cumulative market-adjusted return for firm i over the 12 month period of the fiscal year.
EARN it is change in earnings before extraordinary items (#18) from year t-1 to year t, scaled by beginningof-year market value of equity.
CFOit is change in cash flows from operation (#308 if the firm-year observation is after 1988, and #110-#4
+ #1 + #5 – #34 if the firm-year observation is before 1988), scaled by beginning-of-year market value of
equity.
52
Table 5
Association between pay-sensitivity and value-relevance of change in primary
performance measure and incremental value-relevance of change in supplementary
performance measure
We estimate year-by-year regressions of the following equation from 1993 to 2003. We obtain valuerelevance of primary performance measure and incremental value-relevance of supplementary performance
measure from the estimated equations in table 4.
 log( COMPi )   0   e (ROA i )   c (CFOAi )  we (v ei * ROA i )  wc (v ci * CFOAi )
  e Controli * ROA i   c Controli * CFOAi  u i
Panel A
Variable
Cash compensation and value-relevance of change in performance measures
Earnings as the primary Cash flows as the primary
performance measure
performance measure
Mean
(FamaMean
(FamaPredict Coefficient MacBeth Coefficient MacBeth
t-statistic)
t-statistic)
Intercept
0.100
(1.60)
0.095
(1.49)
ROAit
1.284
(2.74)
1.255
(3.27)
CFOAit
0.229
(0.38)
0.135
(0.29)
ve ROAit
+
2.157
(2.43)
2.816
(2.25)
vc CFOAit
+
1.667
(2.20)
1.433
(2.93)
ve
-0.044
(-2.72)
-0.043
(-1.97)
vc
-0.002
(-0.05)
-0.011
(-0.25)
IOSi,t* ROAit
-0.982
(-3.51)
-1.148
(-3.78)
Leveragei,t* ROAit
0.523
(1.33)
0.559
(1.43)
EARN_Noisei,t* ROAit
-0.623
(-1.98)
-0.868
(-2.68)
EARN_Persistencei,t* ROAit
1.071
(2.38)
0.973
(2.57)
Trade_Cyclei,t* ROAit
-0.144
(-0.52)
-0.002
(-0.01)
IOSi,t* CFOAit
-0.097
(-0.36)
-0.013
(-0.06)
Leveragei,t* CFOAit
0.204
(0.94)
0.072
(0.34)
CFO_Noisei,t* CFOAit
-0.730
(-3.47)
-0.489
(-3.08)
CFO_Persistencei,t* CFOAit
0.195
(0.87)
0.269
(1.31)
Trade_Cyclei,t* CFOAit
3.595
(1.12)
3.455
(1.41)
Returnit
0.140
(8.25)
0.142
(8.49)
Adj. R2
16.6%
16.2%
N
5,764
5,766
53
Table 5
Panel B
Continued
Total compensation and value-relevance of change in performance measures
Variable
Earnings as the primary Cash flows as the primary
performance measure
performance measure
Mean
(FamaMean
(FamaPredict Coefficient MacBeth Coefficient
MacBeth
t-statistic)
t-statistic)
Intercept
0.167
(1.55)
0.231
(1.20)
ROAit
0.427
(0.90)
0.454
(1.22)
CFOAit
0.184
(0.60)
0.307
(0.97)
ve ROAit
+
3.865
(1.97)
4.531
(1.95)
vc CFOAit
+
1.621
(1.77)
0.147
(0.09)
ve
-0.145
(-1.69)
-0.084
(-1.14)
vc
-0.013
(-0.15)
-0.106
(-0.94)
IOSi,t* ROAit
-0.009
(-0.01)
0.083
(0.08)
Leveragei,t* ROAit
0.260
(0.98)
0.355
(1.14)
EARN_Noisei,t* ROAit
-1.442
(-1.14)
-1.302
(-0.89)
EARN_Persistencei,t* ROAit
0.610
(1.47)
0.677
(1.71)
Trade_Cyclei,t* ROAit
-0.004
(-2.02)
-0.005
(-2.27)
IOSi,t* CFOAit
-0.897
(-0.89)
-0.646
(-0.76)
Leveragei,t* CFOAit
0.390
(1.08)
0.434
(1.25)
CFO_Noisei,t* CFOAit
-1.272
(-1.27)
-1.131
(-1.17)
CFO_Persistencei,t* CFOAit
-0.788
(-1.81)
-0.879
(-2.08)
Trade_Cyclei,t* CFOAit
0.001
(0.48)
0.001
(0.33)
Cashflow Shortfalli,t
0.294
(3.84)
0.309
(4.51)
Net Operating Lossi,t
-0.030
(-1.04)
-0.030
(-1.05)
Dividend ConstraintI,t
-0.010
(-1.04)
-0.004
(-0.34)
Stock Returni,t-1
-0.002
(-0.05)
-0.001
(-0.02)
Stock Returni,t
0.110
(2.29)
0.117
(2.53)
Included
Included
Mean Adj. R2
9.2%
9.2%
N
5,054
5,064
Industry Dummies
Variable definitions:
EARN_Noisei,t is the time-series standard deviation of
ROAit for each firm starting from 1980.
CFO_Noisei, is the time-series standard deviation of CFOAit for each firm starting from 1980.
54
Table 6
Industry-specific estimation of value-relevance and pay-sensitivity for change in
earnings and change in cash flows over 1993-1997 and 1998-2003
Panel A
Industry-by-industry coefficient estimates from regressing market-adjusted
return on change in earnings and change in cash flows in two subperiods 1993-1997 and
1998-2003
Ri ,t    Ve  EARN i ,t  Ve  EARN 2 i ,t  Vc  CFOi ,t  Vc  CFO2 i ,t   i ,t
Ve
ΔVe
Vc
ΔVc
Mean
0.544
-0.297
0.009
0.156
Median
0.365
-0.207
-0.029
0.148
Std Dev.
0.515
0.512
0.367
0.358
Z-stat
5.91
-2.83
0.33
2.71
(p-value)
(<0.01)
(<0.01)
(0.76)
(<0.01)
Mean Adj. R2
32.1%
N
29
Panel B
Industry-by-industry coefficient estimates from regressing change in cash
compensation on change in earnings and change in cash flows in two subperiods 1993-1997
and 1998-2003
 log( CASHCOMPi ,t )    We  EARN i ,t  We  EARN 2 i ,t  Wc  CFOi ,t  Wc  CFO2 i ,t
 Wr  RETi ,t  Wr  RET 2 i ,t   i ,t
We
ΔWe
Wc
ΔWc
Wr
ΔWr
Mean
0.592
-0.349
-0.015
0.146
0.039
-0.039
Median
0.494
-0.231
-0.033
0.062
0.044
-0.036
Std Dev.
0.520
0.521
0.272
0.308
0.134
0.177
Z-stat
6.40
-3.88
0.17
2.30
1.89
-2.21
(p-value)
(<0.01)
(<0.01)
(0.87)
(0.02)
(0.06)
(0.03)
Mean Adj. R2
16.7%
N
29
55
Table 6
Continued
Variable definitions:
EARN i ,t
EARN 2 i ,t  
0
if t  1997
CFOi ,t
CFO2 i ,t  
0
RETi ,t
RET 2 i ,t  
0
if t  1997
otherwise
otherwise
if t  1997
otherwise
where RET is cumulative return for firm i over the 12 month period of the fiscal year.
We define industries at 2-digit SIC level. We require at least 20 observations for each industry-subperiod
combination. Aggregate z-statistics are computed from t-statistics in the industry-subperiod regressions,
assuming cross-sectional independence among industries:
Z
1
N
tj
N

j 1
kj
(k j  2)
where t j is the t-statistic for industry j, k j is the degree of freedom in regression for industry j, and N is
the number of industries in the sample. The Z-statistic is distributed asymptotically as standard normal. Pvalue is based on two-tailed Z-statistics.
56