How Do Conglomerates Structure Division
Manager Incentive Contracts?∗
Shashwat Alok †
and
Radhakrishnan Gopalan ‡
March 26, 2012
∗
The authors thank seminar participants at the Olin Business School for valuable comments. Any remaining errors are our own.
†
PhD Student in Finance, Olin Business School,Olin Business School, Washington University in St. Louis.
Corresponding author. e-mail: [email protected].
‡
Assistant Professor of Finance, Olin Business School, Washington University in St. Louis. Corresponding
author. e-mail: [email protected].
Abstract
While literature highlights how agency problems between division managers (DM) and
the CEO in multi-division firms can distort capital allocation, it has largely ignored the role
of DM incentive contacts in resolving these problems. For the first time in literature, we
use hand collected data to study the pay structure of division managers (DM). Using data
on 4,080 DM-year pay contracts, we relate DM pay to the performance of both her division
and other divisions in the firm. Consistent with the idea that the actions of a DM can
impose externalities on the rest of the firm, we find that DM pay loads positively on both
her division’s and the other divisions’ performance. Consistent with differences in their
spheres of influence we find that the pay for other divisions’ performance is significantly
greater for the firm’s CEO. While CEO pay is equally sensitive to the performance of all
divisions in the firm, DM pay is more sensitive to her division’s performance as compared
to the performance of other divisions. Further, pay for division performance is lower in
industries with less informative accounting earnings. This highlights an important cost of
conglomeration. The sensitivity of DM pay to other divisions’ performance is especially
greater for related divisions and for divisions with fewer growth opportunities as measured
by industry Tobin’s q and past division sales growth. Focusing on economic conditions,
we find that the sensitivity of DM pay to both the performance of her division and other
divisions is lower during economic downturns and periods of economic distress. Overall,
our evidence suggests that DM compensation is structured to solve agency problems in
multi-division firms and reflects the constraints in the contracting environment.
JEL Classification:G30, J31
2
Introduction
The costs and benefits of housing multiple divisions within the same firm (“conglomeration”)
has been of significant research interest (see Stein (2003) and Maksimovic and Phillips
(2007) for surveys). While single segment firms depend on the external financial markets
for capital, divisions within a conglomerate look to the headquarters or the firm’s CEO
for capital. Agency problems between the conglomerate’s CEO and the division manager
(DM from now) can distort capital allocation within conglomerates. Such problems can
arise due to asymmetric information (Stein (1997)), differences in risk attitudes (Holmstrom
(1979)), career concerns (Holmstrom and Costa (1986)) and private benefits of consumption
(Stein (2002)). While a properly designed incentive contract for the DM can mitigate such
problems, the lack of stock price for individual divisions can impede the design of such
contracts. The large literature on conglomerates has focussed on how the agency problems
between the DM and the firm’s CEO can distort capital allocation (e.g. Stein (2003),
Rajan et al. (2000a)). For the most part this literature has ignored the role of DM incentive
contracts in mitigating the problems. There is also very little empirical evidence on how
multi-division firms design DM incentive contracts. The objective of this paper is to fill this
gap. We analyze data on DM pay for over 4,000 division-years to document the structure
of DM pay and the extent to which it varies across firms.
The unique aspect of our paper is our ability to combine DM pay data with the performance of both her division and the other divisions in the firm. This allows us, for the
first time in literature, to measure the extent of pay-for-performance for DMs. We obtain
our data by hand matching two commonly used databases, ExecuComp and Compustat
business segment files. ExecuComp provides compensation data for the top five executives
of S&P 1500 firms. Along with their compensation, ExecuComp also provides the executive’s designation, which reveal some of them to be a high ranking official in a division.
For example, the designation of “Mr. Arun Sobti” in “ADC Telecommunications Inc” for
the year 2000 is “President-Broadband Access & Transport Group”. To obtain our data,
we hand match all such designations in ExecuComp with names of divisions from Compu-
1
stat business segment files. This provides us with a matched sample of DM compensation
(from ExecuComp) and division performance (from Compustat business segment files). Our
sample spans the period 1992-2009 and includes over 4,080 DM-year pay contracts. When
required, we complement our sample with compensation data from the firm’s proxy statements. As is clear, our sample is not a random sample of DMs. In Section 3.0.1 we discuss
the possible biases due to the non-random nature of the sample and the measures we take
to mitigate them.
In our empirical analysis, we relate DM pay to the performance of both her division and
the other divisions in the firm. We identify three hypothesis that have predictions relevant
for our setting. The Risk hypothesis as first formalized in Holmstrom (1979) predicts that
an agent’s pay should only be related to performance measures directly under her control.
To the extent the DM controls the performance of her division, she should obtain pay only
for her division’s performance. If the performance of other divisions informs the CEO about
common risk factors, optimal risk sharing would call for relative performance evaluation and
hence a negative relationship between DM pay and the performance of other divisions.
The Externality hypothesis on the other hand highlights the links between the divisions
of a conglomerate. These links can be real as in when divisions have a customer-supplier
relationship, or financial as in when divisions share a common pool of capital. In such
situations, DMs can take actions that benefit their division possibly at the expense of firm
value. This can be avoided by paying DMs for the performance of both her division and
the other divisions in the firm. The Externality hypothesis also predicts how the pay for
other division’s performance will vary with the extent of relatedness of the divisions and
their level of investment opportunities. The need for conglomerates to share capital may
vary with economic conditions (e.g., Gopalan and Xie (2008), Dimitrov and Tice (2006)).
This would imply time-variation in DM pay-for-performance. We group these predictions
under the Time series hypothesis and discuss them along with our results.
We begin our empirical analysis by relating DM pay to the performance of her division
and that of the other divisions in the firm. We use the return on assets (ROA), which we
calculate as the ratio of operating profits over book value of total assets as our measure of
2
performance. We find evidence for significant pay-for-performance for DMs. For the average
DM in our sample, a 1% increase in divisional ROA is associated with a 0.426% increase in
DM total pay. Given the mean division size and DM pay in our sample, this translates into
a $1.14 average increase in DM pay for every $1000 increase in annual divisional profits.
In comparison, for the same set of firms, CEO pay on average increases by $2.98 for every
$1000 increase in annual divisional profits. Thus DM pay-for-performance sensitivity is
almost 40% of that of the firm’s CEO.
Consistent with the Externality hypothesis, we find that DM pay is positively related
to the performance of the other divisions in the firm, especially when we employ firm fixed
effects. Further, consistent with the DM having a greater influence over her division’s
performance, DM-pay is more sensitive to her division’s performance as compared to the
performance of the other divisions. Specifically a 1% increase in the ROA of the other
divisions is associated with only a .22% increase in DM pay. Interestingly, CEO pay is
equally sensitive to the performance of all the divisions in the firm. This is consistent with
the CEO’s actions being perceived to have similar effects across the divisions of the firm.
The differences in DM and CEO pay contracts that we document is consistent with the
evidence in Aggarwal and Samwick (2003), who show that managerial compensation varies
with the level of authority.
The extent to which divisional ROA is informative about the true performance of the
division can vary across divisions. In our next set of tests we employ two industry-level
proxies for accounting informativeness to estimate its effect on DM pay-for-performance.
The first is the volatility of accounting profits of all stand alone firms in the industry,
with a higher volatility indicating less informative accounting performance (Lambert and
Larcker (1987) and Bushman et al. (1996), Ball et al. (2000) and Bushman et al. (2004)).
Our second proxy is the extent to which accounting profits are related to contemporaneous
stock returns. We measure this by regressing stock returns on accounting profits for all
stand alone firms within an industry (Kothari (2001)) and classifying industries with above
median regression coefficient as having more informative accounting profits. Consistent with
the Risk hypothesis, we find that DM pay for division’s performance in industries with more
3
informative accounting profits is twice that in industries with less informative accounting
profits. The lower pay-for-performance in divisions with less informative accounting profits
may be an important cost of conglomeration.
Consistent with the Externality hypothesis we find that DMs of divisions that are related
to the rest of the firm obtain greater pay for other division’s performance. In these tests
we classify divisions in firms with another division in the same three-digit SIC code as
being related to the rest of the firm. Rajan et al. (2000a) argue that distortions due to
agency conflicts between the DM and the firm’s CEO are likely to be especially severe
in conglomerates with divisions with very different investment opportunities. If DM pay is
designed to mitigate such distortions, then one would expect greater pay for other division’s
performance in firms with high diversity. Our results actually show greater pay for other
division’s performance in firms with lower levels of diversity.
Prior theoretical literature highlights that capital and pay can act as substitutes in
motivating DMs (Scharfstein and Stein (2000)). To specifically test this, we estimate the
effect of divisional capital expenditure on DM pay-for-performance and find some weak
evidence for higher pay for divisional performance among DMs of divisions with low capital
expenditure (see also Wulf (2002)). To test the effect of division growth opportunities on
DM pay-for-performance, we use industry Tobin’s Q and the past divisional sales growth
as our measures of growth opportunities. We find that DMs of divisions with low growth
opportunities obtain higher pay for other division’s performance. We do not find significant
differences in the pay for own division’s performance across divisions with different levels
of growth opportunities. To the extent conglomerates operate an internal capital market
(ICM) and redistribute capital, it would be optimal to take capital from the low growth
division and divert it to the high growth division. The low growth division’s DM will
truthfully report her profits and co-operate with such transfers if her pay is linked to the
performance of the high growth division.
When we jointly estimate the effect of growth opportunities and the extent of relatedness
on the DM’s pay for performance, we find greater pay for other division’s performance among
related divisions with more growth opportunities. This is consistent with the predictions in
4
Anctil and Dutta (1999) who argue that DMs of divisions that are investing and expanding
fast may have a greater ability to affect the performance of other divisions, especially if
they are related.
When we differentiate between economic upturns and downturns, we find that DM-pay
is more (less) sensitive to the performance of both her division and the other divisions in
the firm during economic upturns/industry boom (economics downturns/industry distress).
This evidence is consistent with performance measures being more noisy during downturns
and is also consistent with the asymmetric benchmarking for CEO pay as shown in Garvey
and Milbourn (2003).
To control for sample selection, we repeat all our tests using the Heckman two-step
procedure. We use the number of divisions reported by a firm in the Compustat business
segment files, Number of divisions and the relative size of the division, Relative size – the
ratio of the book value of total assets of the division to the book value of total assets
of the firm – as our instruments for sample selection. The identifying assumption in our
Heckman procedure is that while Number of divisions and Relative size should be related
to whether or not a DM’s pay is included in our sample, conditional on the control variables
employed, they should not be correlated with DM total compensation. We believe these two
requirements are satisfied in our setting. A DM will be included in our sample if she is among
the top five highest paid executives in the firm. Thus DMs from firms with fewer divisions
and those from larger divisions are more likely to be included in our sample. Consistent
with this conjecture, both Number of divisions and Relative size are significantly related to
the likelihood of a DM being included in our sample. We also do not think that conditional
on the control variables employed, Number of divisions and Relative size will be correlated
with DM total compensation. The specific control variables that we employ include the
division’s size, firm size, division’s past sales growth, capital expenditure, industry earnings
volatility and the extent of firm diversity. We find the results discussed above to be robust
to repeating all our tests using the Heckman two-step procedure.
Overall our evidence is consistent with DM compensation contracts being constrained
optimal. Consistent with the Risk hypothesis, we find that DMs have less pay-for-performance
5
in situations where the performance measures are noisy. The latter result highlights an important cost of conglomeration. Consistent with the Externality hypothesis we find that
DMs get pay for other division’s performance in situations where their actions can affect
the performance of the other divisions. Finally, consistent with the Time series hypothesis
we find pay for other division’s performance to be greater during economic upturns, when
the performance measures are likely to be less noisy.
The papers that are most related to our paper are Cichello et al. (2008) and Wulf (2002).
Cichello et al. (2008) study turnover and promotions of DMs and find divisional ROA to
be most closely related to job allocation decisions. While they find evidence of relative
performance evaluation (RPE) in promotion decisions they do not find a similar effect for
turnover decisions. In contrast, we focus on the structure of DM compensation contracts.
Wulf (2002) raises a question similar to ours, although there are important differences
between the two papers. Wulf (2002) uses data on 131 multi-division firms for the year
1993 to understand the effect of DM pay on divisional investment. Since she does not
have pay for individual DMs, she uses the average DM level pay to represent the pay of
the largest division’s manager. Focussing on how incentives and capital budgeting may
act as substitutes in overcoming agency problems, Wulf (2002) shows that the sensitivity
of divisional investment to divisions’ performance is lower (higher) when DMs have higher
(lower) pay for firm performance.
Unlike Wulf (2002), we employ a large panel of DM pay contracts from 708 firms to
understand its structure. Our data allows us to understand how DM pay varies in the
cross-section and through time and thus test a number of different hypothesis.
The rest of the paper is organized as follows. In the next section we develop the hypotheses. In Section 2 we describe our empirical specification, key variables and discuss
sample selection issues. Section 3 describes the data and provides the summary statistics.
In Section 4 we discuss our empirical results and Section 5 concludes.
6
1
Hypotheses
In this section we outline the hypothesis that have predictions relevant for our setting. We
group the hypothesis into three: Risk hypothesis, Externality hypothesis and Time series
hypothesis.
Holmstrom (1979) predicts that an agent’s pay should only depend on performance measures directly under her control and those that help learn about her actions with least noise.
We refer to this as the Risk hypothesis. To the extent a DM only controls the performance
of her division, the Risk hypothesis predicts that DM pay should only be related to her division’s performance. To the extent the accounting based measures of divisional performance
are noisy, the Risk hypothesis also predicts lower pay-for-divisional performance in industries with less informative accounting profits. A division’s performance may be affected by
both DM effort and exogenous factors outside her control. If a conglomerate’s divisions are
related in that their performance is subject to similar exogenous risk factors, the performance of other divisions will reveal information about the exogenous factors. Holmstrom
(1979) predicts that in such cases, it is optimal to reward the DM for divisional performance
relative to the performance of the other divisions. Summarizing, the predictions from the
Risk hypothesis are:
Prediction 1: DMs should only obtain pay for divisional performance.
Prediction 2: Pay for divisional performance should be lower in industries with less informative accounting profits.
Prediction 3: In conglomerates with related divisions, DMs should be paid for divisional
performance relative to performance of other divisions.
Divisions within a conglomerate are often linked. The links can be real as in when
divisions have a customer-supplier relationship, or be financial as in when divisions share
a common pool of capital. For example, one of the firms in our sample is Alcan Inc for
which we have pay and performance information for two DMs; the President and CEO of
the Primary metals group and the President and CEO of the Engineered products group.
7
From the names of these divisions, it is evident that some of the products of the primary
metals group could be bought by the engineered products group. Thus the actions of the
two DMs can affect the performance of both their divisions. We refer to the hypothesis that
highlights such links as the Externality hypothesis. With such linked divisions, paying DM
solely for divisional performance may induce actions that benefit the division possibly at
the expense of overall firm value. To avoid this, firms may link DM pay to the performance
of both her division and the other divisions in the firm. To the extent the DM has less
influence over the performance of other divisions, DM pay for other division’s performance
should be less than the pay for her division’s performance. DM pay for other division’s
performance should be greater in conglomerates with related divisions as such divisions are
more likely to share resources (Bernardo et al. (2004)).
Rajan et al. (2000a) model conflicts in conglomerates with divisions that vary in the
extent of investment opportunities and highlight how such conflicts may be resolved by
distorting the capital allocation. One can resolve such conflicts through incentive contracts
as well. If conflicts are greater in firms with more diverse divisions, then one would expect
greater pay for other division’s performance in such firms.
Conglomerate CEOs can provide incentives to the DM through pay and capital allocation. To the extent the DM has an incentive to manage larger divisions, CEOs can make
capital allocation contingent on prior performance and get the DM to take the right actions.
To the extent pay and capital allocation are substitute incentive mechanisms, we should
expect lower pay-for-division performance in divisions with more capital expenditure.
Focussing on capital allocation, conglomerates with divisions that vary in the extent
of investment opportunities may find it optimal to transfer capital from the low-growth
division to the high-growth division. The DM of the low growth division can be made to
go along with such transfers by linking her pay to the performance of the other divisions.
This would predict greater pay for other division’s performance for DMs of divisions with
less investment opportunities. On the other hand, Anctil and Dutta (1999) argue that DMs
of divisions that are investing and expanding fast may have greater ability to affect the
performance of other divisions. Thus Anctil and Dutta (1999) predict greater pay for other
8
division’s performance for DMs of high growth divisions especially if they are related to the
rest of the firm.
Summarizing, the Externality hypothesis predicts:
Prediction 4: DMs should be paid for performance of both her division and the other
divisions in the firm. Pay for other division’s performance should be greater for DMs in
firms that are related to the rest of the firm.
Prediction 5: Pay for other division’s performance will be greater for DMs in firms with
greater diversity.
Prediction 6: Pay for division’s performance will be lower for managers of divisions with
greater capital expenditure.
Prediction 7: Pay for other division’s performance will be greater for managers of divisions with less investment opportunities.
Prediction 7 - Alternative: Pay for other division’s performance will be greater for managers of divisions with more investment opportunities. This is especially so if the division
is related to the rest of the firm.
We now turn to the hypothesis that has predictions on how DM pay-for-performance
will vary through time. Gopalan and Xie (2008) show that conglomerates help divisions
overcome financial constraints during periods of industry distress. Dimitrov and Tice (2006)
highlight similar behavior among conglomerates during periods of economic downturns. To
facilitate such a transfer of resources across divisions during downturns, it is important for
DMs to care about overall firm performance. This can be achieved by linking their pay
to the performance of the other divisions. Note that downturns may also be periods of
greater aggregate uncertainty which in turn may increase the noise in observable measures
of performance. This would imply a lower overall pay-for-performance sensitivity during
such times.
In contrast, Bernardo et al. (2001) show that when divisions are related, DMs need to
9
be given high powered incentives to exert effort towards the success of the projects during
good times. This implies greater pay for other division’s performance in firms with related
divisions during economic upturns. Summarizing the predictions from the Time series
hypothesis, we have:
Prediction 8: Pay for other division’s performance will be greater during periods of industry distress and economic downturns.
Prediction 8 - Alternative: Overall pay-for-performance will be greater during periods
of industry booms and economic upturns.
We now discuss our empirical approach and construction of key variables.
2
Empirical design, key variables and sample selection
2.1
Empirical design and key variables
We are interested in understanding how DM pay is related to the performance of her division
and that of the other divisions in the firm. To achieve this, we estimate variants of the
following model.
Total compensationijt = α + β1 × Division ROAit + β2 × Other division ROAit + γ × Zijt + µt , (1)
where subscript i refers to the firm, subscript j refers to the division and subscript t refers
to time in years. The dependent variable Total compensation ijt is the sum of annual salary,
bonus, other annual compensation, long-term incentive payouts, and other cash payouts.
Division ROA (Other division ROA) is the return on assets of the division (other divisions).
We calculate ROA as the ratio of operating profits over total assets. In the case of other
divisions, we aggregate their operating profits and their total assets.
To test how DM pay-for-performance varies with the informativeness of accounting
earnings, we employ two industry-level measures of accounting informativeness. Our first
10
measure is the historic volatility of accounting earnings of firms within an industry (Lambert
and Larcker (1987) and Bushman et al. (1996), Ball et al. (2000) and Bushman et al.
(2004)). Our second measure is the value relevance of earnings, which we measure as the
extent to which accounting earnings are related to contemporaneous stock returns (Kothari
(2001)). We obtain this measure by regressing annual stock returns of all firms in an
industry on their annual earnings per share. The coefficient estimate is referred to as the
value relevance of accounting earnings. To test our prediction we divide our sample into
divisions with high and low levels of accounting informativeness and estimate (1) in the
subsamples and compare our estimates of β1 and β2 across the two subsamples. Note
that this is equivalent to estimating (1) with a full set of interaction terms between, High
information, a dummy variable that identifies industries with more informative accounting
profits and all the independent variables and testing for significance of the coefficients on
High information× Division ROA and High information× Other division ROA . We employ
a similar procedure to test all our cross-sectional predictions.
To test how DM pay-for-performance varies with the extent to which a division is related
to the rest of the firm, we identify divisions in firms with another division in the same three
digit SIC code industry as related to the rest of the firm.
To test Prediction 5, we follow Rajan et al. (2000a) and construct a diversity index
for each conglomerate in our sample. We measure diversity as the standard deviation
of divisional asset-weighted industry Tobin’s Q divided by the equally weighted average
industry Tobin’s Q of the divisions in the firm. We split our sample into firms with above
and below median diversity and estimate (1) in the two subsamples.
To test if actual capital allocation and DM pay are substitutes, we employ two proxies
based on the actual investment expenditures in the division. Our first proxy is the extent
of capital expenditure in the division which we measure as the ratio of division capital
expenditure over division total assets. Our second proxy is based on the extent to which
the division receives cash inflow from the other divisions in the firm (‘subsidy’). Our measure
is based on Billett and Mauer (2003) and estimates the divisional subsidy as the difference
between the division’s capital expenditure and the estimated after tax cash flow of the
11
division.
To test Prediction 7 we use two alternative measures of divisional investment opportunities. Our first measure is the industry Tobin’s Q which we calculate as the median
Tobin’s Q of all stand alone firms in the industry during the year. We classify industries
with above median Tobin’s Q as having more investment opportunities than industries with
below median Tobin’s Q.2 Our second measure of division investment opportunities is the
past sales growth of the division. We classify divisions with above median annual sales
growth during the previous year as having more investment opportunities.
To identify periods of industry distress, we use a dummy variable Distress, that takes
the value one for divisions in industries in distress. We classify an industry – at the threedigit SIC code level – to be in distress if the median two-year stock return of all single
segment firms is less than -30% (see Opler and Titman (1994) and Gopalan and Xie (2008)).
Along similar lines, we classify an industry to be in boom if the median two-year stock
return is greater than 30% and construct a dummy variable, Boom to identify divisions
in such industries. Our final variable, Recession, identifies recession years. We construct
this variable based on recession data from the NBER website to identify the years 1998,
2001-2002 and 2008-2009.
In all our regressions we control for firm size using, Log(Total assets), division size using
Log(Segment assets), and time fixed effects. The standard errors in all our regressions are
robust to heteroscedasticity and are clustered at the individual firm level.
2.2
Sample selection
We have pay data only for DMs who are among the top five highest paid executives in
the firm and whose designation is descriptive enough to enable us to match them with
the division name from Compustat business segment files. Thus our sample is likely to be
2
Finance literature often uses median Tobin’s Q of stand alone firms in the industry to proxy for investment opportunities of a division. The justification is based on Montgomery and Wernerfelt (1988) who
find that most of the cross-sectional variation in Tobin’s Q is explained by industry fixed effects. However,
Whited (1999) notes that the industry median is likely to overestimate the division’s true Q. Any noise in
the measurement of investment opportunity will bias our estimates downward.
12
a non-random subsample of the population of divisions in publicly listed conglomerates.
Specifically, the DMs in our sample are likely to control larger divisions – so as to be among
the top five highest paid executives in the firm – than a random DM. This can potentially
bias our conclusions. For example, the DMs in our sample may be more influential and
receive greater pay for the performance of other divisions because their actions are more
likely to have externalities.
To control for potential sample selection bias we use the two-step procedure outlined
in Heckman (1979). We use the number of divisions in the firm, Number of divisions and
the relative size of the division, Relative size – the ratio of the book value of total assets
of the division to the book value of total assets of the firm – as our instruments for sample
selection. Our identifying assumption is that while Number of divisions and Relative size
should be related to whether or not a DM is included in our sample, conditional on the
control variables employed, they should not be correlated with DM total compensation. We
believe these two requirements are satisfied in our setting. Number of divisions and Relative
size will affect the inclusion of the DM in our sample because DMs from firms with fewer
divisions and DMs of the larger divisions are more likely to be among the top five highest
paid executives in the firm. Further, there is no reason to expect the Number of divisions or
Relative size to affect DM total compensation especially after we control for both the size
of the division and firm size. To ensure the second requirement, in addition to the usual
set of control variables, we also include Division capital expenditure, Division sales growth,
Industry Tobin’s Q, Earnings volatility and Herfhindal index-3 digit as additional controls
in our two-step procedure. Detailed definition of all the variables we use in our analysis
is provided in Appendix A. In estimating the two-step procedure, we use boot strapped
standard errors in the second stage because the Inverse Mills ratio that we include in the
second stage is a generated regressor. Standard errors are also robust to heteroscedasticity
and clustered at the firm level.
13
3
Data and summary statistics
3.1
Data
The data for our paper is obtained from four standard sources. Data on stock returns and
firm financial data are obtained from CRSP-Compustat merged database. Financial data
for individual business divisions are from Compustat business segment files. The data on
division manager compensation and designation is collected from ExecuComp and Def 14A
proxy statement files. Data on CEO compensation is obtained from ExecuComp. Our
sample period extends from 1991-2009.
ExecuComp provides the annual compensation data of the top five executives for all
S&P 1500 firms. Along with their compensation, ExecuComp also provides the executive’s
designation, which sometimes reveal them to be a high ranking official in a division. For example the designation of “Mr. Arun Sobti” in “ADC Telecommunications Inc” for the year
2000 is “President-Broadband Access & Transport Group”. We hand match the division
name as indicated in the executive’s designation with names of divisions from Compustat
business segment files to obtain a matched sample of DM compensation and division performance. Using this procedure we are able to identify over 6,747 individual division-year
observations. From this overall sample, we retain divisions that have positive and nonmissing values for division sales and are in firms with a minimum of two divisions. This
leaves us with 4,080 division-year observations. The drop in the number of observations
from 6,747 to 4,080 is mainly because of the requirement of positive sales. All the service
divisions such as “Maintenance” have zero or missing values for division sales. In Section
3.2, we compare the average compensation characteristics of all divisions managers that we
identify (the sample of 6,747) to the subsample with non-missing division sales.
3.2
Summary statistics
In Panel A of Table 1, we provide the summary pay characteristics of DMs and CEOs in
our sample. In the first panel we provide the pay characteristics of the DMs of divisions
14
with positive sales while in the second panel we provide the pay characteristics of DMs
with missing sales. Comparing the first panel to the second panel we find that managers of
divisions with positive sales have slightly lower compensation as compared to managers of
divisions with missing sales and this lower compensation is reflected in all four components
of pay. We also find that the DMs of divisions with positive sales are slightly older than
DMs of divisions with missing sales.
In the third panel we provide the average pay characteristics of the CEOs in our sample.
The CEOs are for the divisions with positive sales. We find that total compensation of CEOs
is significantly higher than that of division managers ($4,747.31 thousand as compared to
$1677.32 thousand). The higher pay is reflected in all the four components of pay. The
average CEO is of similar age as the average DM.
In Panel B of Table 1 we provide the summary financial characteristics of the divisions
in our sample. The mean Log(Division assets) of our sample is 6.44, which translates into
a book value of division assets of $627.7 million. Thus the divisions in our sample are
fairly large. The median value of Log(Division assets) is comparable to the mean value.
In comparison, the mean value of Log(Other division assets) – the natural logarithm of
the book value of total assets of all the other divisions in the firm – is 6.88. Thus the
size of the division for which we have DM pay is comparable to the size of the rest of the
firm. Since our sample firms have three divisions on average, we are more likely to have
pay characteristics of the DM of the largest division. This is not surprising because the
DM of the largest division is more likely to be among the top five highest paid executives
in the firm. Our sample comprises of the larger firms in Compustat. The mean value of
Log(Total assets) of our sample is 7.73 as compared to 5.66 for all firms in Compustat. We
find that the divisions in our sample are on average profitable as seen from the mean value
of Division ROA of 0.14. Our divisions also tend to be more profitable than the rest of
the firm as can be seen by comparing the mean value of Division ROA to that of Other
division ROA. The divisions for which we have pay data are also growing fast (mean value
of Division sales growth of 0.137), but the rest of the firm appears to be growing at a faster
rate (mean value of Other division sales growth of 0.201). The divisions in our sample
15
have growth opportunities as seen from the mean value of Industry Tobin’s Q of 1.62. The
average Diversity index of the conglomerates in our sample is .26. We classify about 22.4%
of the division-years in our sample as distressed as can be seen from the mean value of
Distress and classify about 21.5% of the division-years in our sample as being in a boom
period as can be seen from the mean value of Boom. Almost one-third of the division-years
in our sample are in recession, which is mainly because of the recent financial crisis. We
now present the results of the univariate tests of our predictions.
Panel C reports the correlation between the variables we use in our analysis. Consistent with externality hypothesis, we find that DM pay is positively correlated with the
performance of both her division and the other divisions in the firm. Not surprisingly, DM
compensation is higher in larger firms and for DMs of larger divisions. We find that DM
compensation is higher for divisions in industries with more volatile performance. Consistent with conglomerates using pay and capital allocation as substitutes, we find that DM
pay is lower in divisions with higher capital expenditure and is higher in divisions with high
past sales growth.
From the table we find that the correlation between Division ROA and Other division’s
ROA is modest at 26.3%. This ensures our ability to separately identify DM pay for
both division performance and other division’s performance. In Section 4.1 we discuss
the tests we do to ensure that multi-collinearity is not a serious problem for us. Not
surprisingly, the more profitable divisions in our sample tend to be larger, receive greater
capital (positive correlation with capital expenditure) and have greater growth opportunities
(positive correlation with industry Tobin’s Q).
The correlation between firm size and division size in our sample is very high (0.827),
mainly due to the inclusion of a disproportionate number of divisions that are the largest
in the firm. Larger divisions appear to have fewer growth opportunities (as seen from the
negative correlation between Log(Division assets and Industry Tobin’s Q & Division sales
growth) and they also do less capital expenditure as a proportion of division size.
We now proceed to multivariate tests of our predictions.
16
4
Empirical results
4.1
DM pay-for-performance
We begin our empirical analysis with tests of Prediction 1 and present the results in Table 2.
The sample in Column (1) includes all executives who manage divisions with positive sales.
The dependent variable is Log(Total compensation). Focussing on Column 1, the positive
and significant coefficient on Division ROA indicates that DMs obtain pay for division
performance. We also find that the coefficient on Other division ROA is also positive but
not significant. This is consistent with the Risk hypothesis. From the coefficients on the
control variables we find that managers of larger divisions in bigger firms obtain higher total
compensation. The coefficient on Division ROA indicates that a 1% increase in divisional
ROA is associated with a .426% increase in DM pay. Given that the average value of
division total assets in our sample is $627.7 million and the mean total compensation for a
DM is $1.677 million, our estimate in Column 1 translates into a $1.14 increase in DM pay
for every $1000 increase in annual divisional profits.
In Column (2) we repeat our test after including firm fixed effects and find that the
coefficient on both Division ROA and Other division ROA is positive and significant. Comparing Column (2) to Column (1) we find that the coefficient on Other division ROA is
higher and its standard error is lower in Column (2). Thus once we control for firm fixed
effects, our results are actually consistent with the Externality hypothesis. Consistent with
DMs having a greater influence on division performance, we find that the pay for division
performance is greater than the pay for other division performance. The coefficient on
Other division ROA in Column (2) indicates that a 1% increase in other division’s ROA is
associated with a .22% increase in DM pay. In dollar terms a $1000 increase in the profits
of the other divisions is associated with a $0.39 increase in DM pay.
Given that we have pay characteristics of the manager of the largest division, one concern would be the extent to which Division ROA and Other division ROA are correlated
and hence our ability to independently estimate pay for division and other division perfor-
17
mance. As mentioned before, in the full sample, the correlation between Division ROA and
Other division ROA is moderate at 0.263, Further to ensure that multi-collinearity between
Division ROA and Other division ROA is not clouding our inferences, we estimate the variance inflation factor (VIF) which measures the increase in variance of estimated coefficient
due to collinearity. The VIF in our sample is 1.07 which is considered small. According to
the standard rule of thumb (Obrien (2007)) a VIF greater than ten is considered a cause
for concern. Further in our sample, the lowest Eigen value is 0.422 and the condition number based on Eigen values is 2.074. An Eigen value close to zero or a very high condition
number, say greater than thirty, implies a high degree of linear-dependency between the
two variables. These tests help us allay any concerns of multi-collinearity.
In Column (3) & (4) we present the results from the Heckman two-step procedure.
In Column (3) we present the results of the first-stage regression. The sample for this
estimation includes all divisions with non-missing values of sales and total assets in the
Compustat business segment files and which belong to the S&P 1500 index (the ExecuComp sample). The results indicate that more profitable divisions (positive coefficient on
Division ROA), larger divisions (positive coefficient on Log(Division assets)), from smaller
firms (negative coefficient on Log(Total assets)), from firms with few divisions (negative
coefficient on Number of divisions), and divisions whose size is a smaller proportion of firm
size (negative coefficient on Relative size), are more likely to be included in our sample.
Note that the coefficient on Relative size is opposite to what one would expect mainly because of the inclusion of division size and firm size in our specification. Our results indicate
that conditional on firm and division size, divisions that are a smaller proportion of firm
size are more likely to be included in our sample. We further find that the divisions in
our sample have lower past sales growth, higher industry Tobin’s Q, are from industries
with higher earnings volatility and from firms with lower Herfindahl index. In Column (4)
we present the results of the second stage estimation with Log(Total compensation) as the
dependent variable and include the inverse Mill’s ratio estimated from the first stage as an
additional regressor. Note that we include additional control variables in the Heckman twostep procedure to ensure that Number of divisions and Relative size are truly exogenous.
18
From Column (4) we see that the estimate of DM pay-for-performance after we control for
sample selection is similar to what we obtain when we do not control for sample selection
(Column (1)). This indicates that sample selection is not biasing our coefficient estimates.
From the control variables we find that DMs of divisions that have high past sales growth,
those from industries with high Tobin’s Q have higher total compensation. The number
of observations in Column (4) is less than that in Column (1) because of missing values of
division capital expenditure and sales growth.
A firm’s division manager and its CEO are likely to have different spheres of influence.
While the DM’s actions are likely to have a direct effect on her division’s performance,
her actions are likely to affect other division’s performance only in an indirect manner. In
contrast, a CEO’s actions are likely to affect the performance of all the divisions in the
firm. If firms design incentive contracts taking these differences into account, then the
pay-for-performance sensitivity of CEOs should be different from that of DMs. To see if
this is the case, in Column (5), we repeat our estimation with CEO compensation for the
firms in our sample as the dependent variable. Consistent with our conjecture, we find that
pay-for-performance sensitivity of the CEO is different from that of the DM. Unlike DM
pay, we find CEO pay to be equally sensitive to both Division ROA and Other division
ROA. The estimates from Column (5) indicate that a 1% increase in divisional ROA is
associated with a .394% increase in CEO pay. This translates into an average increase in
CEO compensation of $2.98 for every $1000 increase in annual divisional profits. Thus
although the coefficients on Division ROA is similar across Column (1) and Column (5),
due to differences in the level of DM and CEO pay, the pay-for-performance sensitivity for
the DM and the CEO are very different in terms of dollar amounts. In unreported tests we
find that the loading on Other division ROA is significantly different between Column (1)
and Column (5).
In Column (6) we repeat our estimates from Column (5) after including firm fixed effects
and in Column (7) we present the results of the second stage regression after controlling
for sample selection using the Heckman two-step procedure. Note that the first stage for
this estimation is the same as in Column (3). We find the results in both Columns (6) &
19
(7) to be similar to the ones in Column (5). In unreported tests, we repeat our analysis
with Bonus and Short-term pay (salary + bonus) as measures of compensation and obtain
qualitatively similar results.
4.2
Pay for division and firm performance and accounting informativeness
In Table 3, we test Prediction 2. We use the volatility of earnings of firms in an industry
as our first measure of accounting informativeness with a higher volatility indicating an
industry with less informative profits. We divide our sample into divisions in industries
with above and below median earnings volatility and estimate (1) in the two subsamples
and present the results in Column (1) and Column (2) respectively. The results indicate
that consistent with Prediction 2, DM pay for division performance is indeed lower (higher)
for divisions in industries with high (low) earnings volatility. The DM pay for division
performance in industries with more informative accounting profits is approximately twice
that in industries with less informative accounting profits. Interestingly, we do not find
any significant difference in pay for other division’s performance across the two subsamples.
From the row titled ∆ Division ROA we find that the coefficients on Division ROA are
significantly different across Columns (1) and (2). Note that our tests in the row titled ∆
Division ROA is equivalent to estimating (1) with a full set of interaction terms between High
information, a dummy variable that identifies industries with more informative accounting
profits and all the independent variables and testing for significance of the coefficient on
High information × Division ROA.
In Columns (3) & (4) we repeat our tests after controlling for the Inverse Mills ratio
and obtain similar results. In these tests, due to smaller sample size and the greater noise
in our estimates, we find that the coefficient on Division ROA is not significantly different
between the two columns although the magnitude of the difference is large.
In the next two columns we use the value relevance of accounting earnings as our measure
of earnings informativeness. As before, we divide our sample into divisions in industries with
20
above and below median earnings value relevance and estimate (1) within the two subsamples. Our results indicate that DM pay for divisional performance is greater for divisions in
industries with more informative earnings. Here again the coefficient in Column (5) is more
than twice as large as that in Column (6), and the difference is significant at less than five
percent level. In Columns (7) and (8) we repeat the tests after controlling for the Inverse
Mills ratio and obtain qualitatively similar results. These results offer significant support for
Prediction 2 and highlight one important cost of conglomeration. Conglomerates with divisions in industries with less informative accounting profits offer lower pay-for-performance
to the DM. To the extent pay-for-performance is useful in aligning the interests of the DM
with that of the firm’s shareholders, this will prove costly.
4.2.1
DM pay-for-performance and relatedness of divisions
In Table 4 we estimate how DM pay-for-performance varies with the degree of relatedness
of the divisions in the firm. According to the Externality hypothesis pay for other division’s
performance should be higher for divisions that are related to the rest of the firm as such
divisions are more likely to share resources with the other parts of the firm. On the other
hand, according to the Risk hypothesis, relative performance evaluation (RPE) should call
for a negative relationship between DM pay and the performance of the other divisions
when the divisions in the firm are related. To test these contrasting predictions, we classify
divisions in firms with another division in the same 3-digit SIC code as being related to
the rest of the firm and repeat our tests in the subsamples of related and un-related divisions. The evidence in Columns (1) and (2) indicate that consistent with Prediction 4 and
inconsistent with RPE, DM pay is positively related to the other division’s performance
in divisions that are related to the rest of the firm. The loading on Other division ROA
in Column 1 is twice that in Column 2 and the coefficient in Column 2 is not statistically
different from zero. In unreported tests, we repeat these test with relatedness defined at
2-digit SIC code industry level and obtain qualitatively similar results. In Columns (3) and
(4) we repeat our tests after controlling for sample selection using the Heckman two-step
procedure and obtain results similar to those in Columns (1) and (2).
21
In Columns (5)–(8) we test Prediction 5 and estimate the effect of firm diversity on DM
pay-for-performance. Specifically in Columns (5) and (6) we divide our sample into conglomerates with high and low levels of diversity and repeat our tests in the two subsamples.
We use the measure of diversity first proposed in (Rajan et al. (2000a)) to do these tests.
According to Prediction 5, pay for other division’s performance should be greater for DMs
in firms with high diversity. Our evidence in Columns (5) and (6) is not consistent with
Prediction 5. We find evidence for greater pay for other division’s performance in firms with
low diversity. The selection corrected coefficient estimates from Columns (7) & (8) confirm
that the results are not due to sample selection.
4.3
DM pay-for-performance and division capital expenditure
In Panel A of Table 5, we test Prediction 6 and estimate the effect of division capital
expenditure on DM pay-for-performance. These tests will inform us as to whether DM
pay and capital allocation are used as substitute incentive mechanisms. To do this, in
Column (1) and (2) we divide our sample into divisions with above and below median
capital expenditure and repeat our tests. We measure division capital expenditure as the
ratio of capital expenditure over division total assets. The results indicate that pay for
division performance is actually greater for DMs of divisions with high capital expenditure.
This is inconsistent with pay and capital allocation acting as substitutes. We do not find
the coefficient on Division ROA to be significantly different across the two Columns. The
coefficient on Other division ROA indicates greater pay for other division performance for
divisions with low capital expenditure. If divisions with lower capital expenditure have lower
investment opportunities then these results are consistent with Prediction 7. In Columns
(3) and (4) we repeat the tests after controlling for self selection and obtain similar results.
In Columns (5) and (6) we use the extent of subsidy for a division as a measure of the
capital allocation to the division and repeat our tests. The idea is that the division with
a higher subsidy is being rewarded with greater capital allocation and if pay and capital
are substitutes, then such divisions should have a lower pay-for-performance sensitivity.
The results do not indicate any significant difference in pay for division performance across
22
divisions with high and low subsidy. On the other hand, we find that there is significant pay
for other segment performance only for divisions with low subsidy. This again is consistent
with Prediction 7 and indicates that conglomerates link the pay of DMs of divisions with
low subsidy to the performance of the other divisions so as to align their incentives with the
whole firm. Due to noise in our estimates, we find that pay for other segment performance
is not significantly different across Column (5) & (6). In Columns (7) and (8) we repeat
our estimates after controlling for sample selection. Here we find some evidence consistent
with Prediction 6 of greater pay-for-performance in divisions with less subsidy. However,
the difference in the coefficient on Division ROA is not significantly different across the two
Columns. This offers weak support for Prediction 6.
4.4
DM pay-for-performance and division investment opportunities
In Panel B of Table 5 we test Prediction 7 and estimate the effect of division investment
opportunities on DM pay-for-performance. In Columns (1) and (2) we split our sample into
divisions in industries with above and below median industry Tobin’s Q and estimate DM
pay-for-performance within the two subsamples. Prediction 7 (Prediction 7 - Alternate)
would indicate greater DM pay for other division’s performance in divisions in industries
with low (high) industry Tobin’s Q.
Our results from Column (1) and (2) show that while pay for divisional performance
is greater for DMs of divisions in low Q industries, due to noise in our estimation, the
coefficient on Division ROA is not significantly different across the two Columns. Comparing
the coefficient on Other division ROA we find that DMs of divisions in industries with low
Tobin’s Q do get significantly greater pay for other division’s performance as compared to
DMs of divisions in industries with high Tobin’s Q. We also find that the coefficient on
Other division ROA is significantly different across the two subsamples. In fact we do not
find any evidence of pay for other division’s performance among divisions in industries with
high Tobin’s Q. In unreported tests we repeat our estimates after including firm fixed effects
and get results consistent with the ones reported here. As mentioned, conglomerates that
want to transfer resources from the division in high-Q industries to the division in low-Q
23
industries can get the former division’s manager to co-operate with such transfers by linking
her pay to the performance of the high-Q division.
In the next two columns we repeat our estimates after controlling for sample selection
using the Heckman two-step procedure and again find greater pay for other division’s performance among divisions in industries with low Tobin’s Q. Interestingly, once we control
for sample selection, we actually find greater pay for divisional performance among divisions
in high-Q industries. Again the coefficient on Division ROA is not significantly different
across the two columns.
In Columns (5) – (8) we repeat our tests using past division sales growth as a measure
of investment opportunities. We divide the divisions in our sample into those with above
and below median sales growth and repeat our tests. Here again we find that while the
pay for divisional performance is higher in the high growth divisions, it is not significantly
higher. On the other hand, pay for other division’s performance is significantly higher in
divisions with low past sales growth rate. This offers further support for Prediction 7.
Our tests in Panel B of Table 5 do not provide evidence consistent with Prediction
- 7 Alternative of greater pay-for-other division’s performance among divisions with low
investment opportunities. One concern with these tests is that we do not condition on the
extent to which the division is related to the rest of the firm. Hence in Panel C of Table 5 we
design a sharper test of Prediction - 7 Alternative by estimating the effect of both the extent
of relatedness of a division with the rest of the firm and its investment opportunities on the
DM’s pay-for-performance. According to Prediction - 7 Alternative, pay for other division’s
performance should be greater for DMs of divisions with greater investment opportunities
especially if the division is related to the rest of the firm. In Columns (1) – (4) we use the
Tobin’s Q of the division’s industry as a measure of investment opportunity. In Columns
(1) – (2) we look at divisions that are related to the rest of the firm, i.e., the firm has
another division in the same three-digit SIC code, and subdivide the sample into high and
low-Tobin’s Q divisions and repeat our tests. The results indicate that among divisions
related to the rest of the firm, pay for other division’s performance is significantly greater
for divisions with high investment opportunities. This is consistent with Prediction - 7
24
Alternative. We find that the coefficient on Other division ROA is significantly different
across Columns (1) and (2). On the other hand, among divisions not related to the rest of the
firm (Columns (3) – (4)), we find evidence for greater pay for other division’s performance
among divisions in low-Q industries.
In Columns (5) – (8) we repeat the tests with division sales growth as the measure of
investment opportunity and again find evidence for greater pay for other division’s performance among high growth divisions that are related to the rest of the firm. Due to noise
in our estimates, we find that the coefficient on Other division ROA is not significantly
different across Columns (5) and (6). Finally the results in Columns (7) & (8), among divisions not related to the rest of the firm, we find evidence for greater pay for other division’s
performance among divisions with lower sales growth. Overall the evidence in this Panel is
consistent with Prediction - 7 Alternative.
4.5
DM pay-for-performance and industry conditions
In Table 6, we estimate how DM pay-for-performance varies with industry and economic
conditions. In Columns (1) & (2) we estimate how industry distress affects DM pay-forperformance. As mentioned before, we classify a industry-year as in distress if the two-year
stock return of the median firm is below 30%. Prediction 8 (Prediction 8 - Alternative)
indicates greater (lesser) pay for other division’s performance during periods of industry distress. We estimate (1) for divisions in distressed and non-distressed industries and present
the results in Columns (1) and (2) respectively. Our results indicate greater pay for divisional performance during non-distress periods as compared to during distress periods. This
is consistent with performance measures being noisy during periods of industry distress and
is also consistent with the asymmetric benchmarking of CEO compensation as documented
in (Garvey and Milbourn (2003)). We also find greater pay for other division’s performance
during non-distress periods as compared to during distress periods. This is inconsistent
with Prediction 8 but is consistent with Prediction 8 - Alternative.
In Columns (3) and (4) we divide our sample divisions into those in boom and normal
25
time periods and repeat our tests. We classify a industry year as in boom if the median
two year stock return of firms in that industry is greater than 30%. Here again we find
greater pay for division’s performance during periods of industry boom. Due to noise in
our estimates, we find that that coefficient estimates are not significantly different across
the two subsamples. We also find that pay for other division’s performance is only present
during periods of industry boom. This again is consistent with Prediction 8 - Alternative
In the next two Columns we repeat our tests differentiating between economic recessions
and expansions. We classify the years 1998, 2001-02 and 2008-09 as recessionary. Our results
indicate that pay for divisional performance is greater during non-recessionary period as
compared to during recessions. The coefficient on Division ROA is significantly different
across the two Columns. Interestingly when we look at a economy wide downturn, we no
more find evidence of greater pay for other division’s performance during downturns. We
actually find pay for other division’s performance to be significant only during recessions.
It is reasonable to expect that recessions will have an adverse affect on all the divisions of a
conglomerate. We find that during such times, a conglomerate tends to link DM pay to the
overall performance of the firm as opposed to the performance of an individual division.
In Panel B of Table 6 we repeat these tests after controlling for potential selection bias
using the Heckman two-step procedure and find results consistent with those in Panel A.
5
Conclusion
We use a large sample of division manager incentive contracts to document the structure
of DM pay and the extent to which it varies across firms. The unique aspect of our paper
is our ability to combine DM pay data with the performance of both her division and the
other divisions in the firm. This allows us, for the first time in literature, to measure the
extent of pay-for-performance for DMs.
DMs obtain significant pay for performance. A $1000 increase in the profits of her division is associated with $1.14 increase in DM pay. There is significant difference between
26
the structure of CEO and DM pay. While CEOs obtain similar pay for performance across
all the divisions of the firm, DM pay is more sensitive to her division’s performance as compared to the performance of the other divisions. The sensitivity of DM-pay to divisional
performance is decreasing in the precision of the performance measure. This highlights
an important cost of conglomeration. DMs of divisions with less investment opportunities
obtain greater pay for other division’s performance. This is likely to align their interest
with the rest of the firm and enable the firm to shift capital towards the other divisions.
Finally, we find that DM pay is more(less) sensitive to both the performance of her division and that of the other divisions during economic upturns/industry booms (economic
downturns/industry distress.)
Overall, our analysis sheds light on the role of compensation contracts in mitigating
internal agency conflicts in conglomerates and highlights some of the constraints on the
contracting environment.
27
Appendix A: Variable definitions
• Total compensation: It is the sum of annual salary, bonus, other annual compensation, longterm incentive payouts, and other cash payouts
• Division ROA: The ratio of division’s operating profits over total assets of the division.
• Other division ROA:For each division of the firm we calculate the Division-asset weighted
average of the Division ROA of all other divisions in the firm.
• Log(Division assets): Natural log of the value of total assets of the division
• Log(Total assets): Natural log of the value of total assets of the conglomerate.
• Earnings Volatility: The standard deviation of operating income after depreciation of all stand
alone firms in the division’s 3-digit SIC industry.
• Earnings Coefficient: The coefficient estimate obtained from linear regression of stock returns
on change in ROA for all stand alone firms in the division’s 3-digit SIC industry.
• High earning vol : Dummy variable that takes the value one for divisions in industries with
above median Earnings volatility
• High earnings coeff : Dummy variable that takes the value one for divisions in industries with
above median Earnings Coefficient
• Tobin’s Q: The median Tobin’s Q of all stand alone firms in the division’s 3-digit SIC code
industry during the year.
• Segment Sales growth: Percentage annual change in division sales.
• High Q: Dummy variable that takes the value one for divisions in industries with above median
Tobin’s Q.
• High sales growth: Dummy variable that takes the value one for divisions with above median
Segment Sales growth.
• Segment capex : The ratio of segment capital expenditure to value of segment total assets.
• High capex : A dummy variable that identifies divisions with above median Segment capex.
• High Subsidy: A dummy variable that identifies divisions that receive subsidy (computed
using the procedure outlined in Billett and Mauer (2003)) above the sample median.
• Related : A dummy variable that takes the value one if the number of other divisions in the
same 3-digit SIC code industry is greater than one.
• High diversity: A dummy variable that identifies conglomerates that have a diversity index
(computed using the procedure outlined in Rajan et al. (2000b)) above the sample median.
• Herfindahl index-3 digitHerfindahl index of the conglomerate’s sales across industries defined
at the three-digit SIC code level
• Distress: A dummy variable that takes the value one during a year if the median two-year
stock return of all single segment firms in that division’s 3-digit SIC industry is less than -30%.
• Boom: A dummy variable that takes the value one during a year if the median two-year stock
return of all single segment firms in that division’s 3-digit SIC industry is greater than 30%.
• Recession: A variable that counts the number years the NBER classifies as recessionary.
28
References
Aggarwal, R. K. and A. A. Samwick (2003). Performance incentives within firms: The effect
of managerial responsibility. The Journal of Finance 58 (4), 1613–1650.
Anctil, R. M. and S. Dutta (1999). Negotiated transfer pricing and divisional vs. firm-wide
performance evaluation. The Accounting Review 74 (1), pp. 87–104.
Ball, R., S. P. Kothari, and A. Robin (2000). The effect of international institutional factors
on properties of accounting earnings. Journal of Accounting and Economics 29 (1), 1 –
51.
Bernardo, A. E., H. Cai, and J. Luo (2001). Capital budgeting and compensation with
asymmetric information and moral hazard. Journal of Financial Economics 61 (3), 311
– 344.
Bernardo, A. E., H. Cai, and J. Luo (2004). Capital budgeting in multidivision firms:
Information, agency, and incentives. Review of Financial Studies 17 (3), 739–767.
Billett, M. T. and D. C. Mauer (2003). Cross-subsidies, external financing constraints,
and the contribution of the internal capital market to firm value. Review of Financial
Studies 16 (4), 1167–1201.
Bushman, R., Q. Chen, E. Engel, and A. Smith (2004). Financial accounting information,
organizational complexity and corporate governance systems. Journal of Accounting and
Economics 37 (2), 167 – 201.
Bushman, R. M., R. J. Indjejikian, and A. Smith (1996). Ceo compensation: The role of
individual performance evaluation. Journal of Accounting and Economics 21 (2), 161 –
193.
Cichello, M., C. E. Fee, C. J. Hadlock, and R. Sonti (2008). Promotions, Turnover, and
Performance Evaluation: Evidence from the Careers of Division Managers. Accounting
Review, Vol. 84, No. 4, pp. 1119-1143, July 2009 .
Dimitrov, V. and S. Tice (2006). Corporate diversification and credit constraints: Real
effects across the business cycle. Review of Financial Studies 19 (4), 1465–1498.
Garvey, G. and T. Milbourn (2003). Incentive compensation when executives can hedge
the market: Evidence of relative performance evaluation in the cross section. jf 58 (4),
1557–1582.
Gopalan, R. and K. Xie (2008). Conglomerates and Industry Distress. SSRN eLibrary.
Heckman, J. J. (1979, January). Sample selection bias as a specification error. Econometrica 47 (1), 153–61.
Holmstrom, B. (1979). Moral hazard and observability. Bell Journal .
Holmstrom, B. and R. Costa (1986). Managerial incentives and capital management. Quarterly Journal of Economics.
Kothari, S. (2001). Capital markets research in accounting. Journal of Accounting &
Economics 31, 105–231.
29
Lambert, R. A. and D. F. Larcker (1987). An analysis of the use of accounting and market
measures of performance in executive compensation contracts. Journal of Accounting
Research 25, pp. 85–125.
Maksimovic, V. and G. Phillips (2007). Conglomerate firms and internal capital markets. In
B. E. Eckbo (Ed.), Handbook of Corporate Finance:Empirical Corporate Finance. North
Holland.
Montgomery, C. A. and B. Wernerfelt (1988). Diversification, ricardian rents, and tobin’s
q. The RAND Journal of Economics 19 (4), pp. 623–632.
Obrien, R. (2007). A caution regarding rules of thumb for variance inflation factors. Quality
& Quantity 41, 673–690. 10.1007/s11135-006-9018-6.
Opler, T. C. and S. Titman (1994). Financial distress and corporate performance. Journal
of Finance 49 (3), 1015–40.
Rajan, R., H. Servaes, and L. Zingales (2000a). The cost of diversity: The diversification
discount and inefficient investment. The Journal of Finance 55 (1), pp. 35–80.
Rajan, R., H. Servaes, and L. Zingales (2000b). The cost of diversity: The diversification
discount and inefficient investment. Journal of Finance 55 (1), 35–80.
Scharfstein, D. S. and J. C. Stein (2000). The dark side of internal capital markets: Divisional rent-seeking and inefficient investment. Journal of Finance 55 (6), 2537–2564.
Stein, J. (2002). Information production and capital allocation: Decentralized versus hierarchial firms. Journal of Finance 57 (5), 1891–1921.
Stein, J. C. (1997). Internal capital markets and the competition for corporate resources.
Journal of Finance 52 (1), 111–33.
Stein, J. C. (2003). Agency, information and corporate investment. In G. Constantinides,
M. Harris, and R. M. Stulz (Eds.), Handbook of the Economics of Finance, Volume 1,
Chapter 2, pp. 111–165. Elsevier.
Wulf, J. (2007). Authority, risk, and performance incentives: Evidence from division manager positions inside firms*. The Journal of Industrial Economics 55 (1), 169–196.
Wulf, J. M. (2002, April). Internal capital markets and firm-level compensation incentives
for division managers. Journal of Labor Economics.
30
Table 1: Summary statistics
Panel A: Division manager and CEO compensation
Variable
Total compensation
Salary
Bonus
Stock grants
Stock option grants
Age
Total compensation
Salary
Bonus
Stock grants
Stock option grants
Age
Total compensation
Salary
Bonus
Stock grants
Stock option grants
Age
Matched subsample
N
Mean
Median
4236 1677.032 1000.286
4236
359.89
325
4236
239.794
116.159
4236
112.764
0
4236
415.654
65.41
1704
51.835
52
Sample - Missing sales
2154 1987.546 1190.553
2154
379.475
338.589
2154
260.818
97.499
2154
127.043
0
2154
494.838
32.284
977
50.573
50
CEO compensation
4184 4747.306 2948.155
4184
361.825
325.687
4184
567.803
287.123
4184
113.702
0
4184
416.332
65.737
1702
51.832
52
Std. Dev.
3225.585
179.428
881.878
608.509
1916.544
6.562
2536.913
177.212
564.466
574.98
1186.861
6.655
6743.895
179.412
832.34
612.064
1923.779
6.563
Panel B: Division and industry characteristics
Variables
Log(Division assets)
Log(Other divisions assets)
Log(Total assets)
Segment ROA
Other divisions ROA
Segment capital expenditure
Other divisions capital expenditure
Segment sales growth
Other divisions sales growth
Ind. Market to book
Earnings volatility
Diversity
Distress
Boom
Recession
N
4236
4236
4204
4226
4121
3880
3775
3685
3533
4003
3861
3961
4121
4121
4236
31
Mean
6.442
6.881
7.732
0.139
0.112
0.059
0.056
0.137
0.201
1.618
477.659
0.227
0.224
0.215
0.292
Median
6.41
6.954
7.599
0.122
0.106
0.042
0.04
0.071
0.074
1.453
142.703
0.197
0
0
0
Std. Deviation
1.579
1.979
1.442
0.201
0.179
0.065
0.054
0.53
0.724
0.641
1252.334
0.145
0.417
0.411
0.455
Panel C: Univariate correlations
Log(Total Compensation)
Division ROA
Other divisions RoA
Log (Total assets)
Log(Division Assets)
Earnings volatility
Accounting beta
Division Capital Expenditure
Industry Tobin’s Q
Division sales growth
Log
Division Other divisions
Log
Log
(Total Comp) ROA
ROA
(Total assets) (Division Assets)
1.000
0.098∗∗∗ 0.075∗∗∗
0.653∗∗∗
0.564∗∗∗
1.000
0.263∗∗∗
−0.009
−0.079∗∗∗
1.000
0.027∗
0.002
1.000
0.827∗∗∗
1.000
Earnings
volatility
0.128∗∗∗
0.034∗∗
0.019
0.089∗∗∗
0.081∗∗∗
1.000
Accounting
beta
0.019
−0.049∗∗∗
−0.043∗∗
0.135∗∗∗
0.147∗∗∗
0.047∗∗∗
1.000
Division
Capex
−0.086∗∗∗
0.098∗∗∗
−0.007
−0.105∗∗∗
−0.139∗∗∗
0.034∗∗
−0.070∗∗∗
1.000
Industry
Tobin’s Q
0.007
0.103∗∗∗
0.076∗∗∗
−0.062∗∗∗
−0.048∗∗∗
0.122∗∗∗
−0.182∗∗∗
−0.018
1.000
Division
sales growth
0.081∗∗∗
0.007
0.052∗∗∗
−0.040∗∗
−0.006
−0.017
−0.031∗
0.036∗∗
0.024
1.000
This table reports the summary statistics and correlation matrix of the key variables used in our analysis. Panel A reports pay characteristics of DMs and CEOs in are sample.
Panel B reports firm and division financial characteristics for our sample. Panel C reports correlations between key variables used on our analysis.The data covers the period
1992-2009. The compensation data is from Execucomp, segment level financial data is from the Compustat Business Segment files and firm-level data is from the Compustat
Industrial Annual files. (∗∗∗ ), (∗∗ ), (∗ ) denote statistical significance at 1%, 5%, and 10% levels respectively.
32
Table 2: DM pay-for-performance
Division ROA
Other division ROA
Log(Division assets)
Log(Total assets)
Log(Total comp.)
(1)
(2)
.426
.311
(.086)∗∗∗
(.086)∗∗∗
.170
.220
(.110)
(.087)∗∗
.073
.102
(.019)∗∗∗
(.014)∗∗∗
.317
.236
(.022)∗∗∗
(.049)∗∗∗
Number of div.
Relative size
DM
Pr(Selection)
(3)
.534
(.082)∗∗∗
.100
(.121)
.649
(.064)∗∗∗
-.512
(.065)∗∗∗
-.156
(.021)∗∗∗
-1.975
(.180)∗∗∗
Inv. Mills
Division capital expenditure
33
Division sales growth
Ind. Tobin’s Q
Earnings volatility
Herfindal
Const.
Firm FE
Obs.
R2
3.384
(.211)∗∗∗
NO
4080
.507
3.902
(.21)∗∗∗
YES
4080
.789
-.188
(.285)
-.045
(.018)∗∗
.083
(.028)∗∗∗
.00004
(.00002)∗
-.834
(.126)∗∗∗
.322
(.211)∗∗∗
NO
118471
0.207
Log(Total comp.)
(4)
.384
(.118)∗∗∗
.130
(.153)
.040
(.028)
.348
(.026)∗∗∗
-.196
(.099)∗∗
.218
(.299)
.061
(.025)∗∗
.176
(.034)∗∗∗
.00001
(.00002)
.144
(.096)
3.516
(.361)∗∗∗
NO
3013
.525
CEO
Log(Total comp.)
(5)
(6)
(7)
.394
.342
.240
(.098)∗∗∗
(.081)∗∗∗
(.123)∗
.370
.411
.365
(.114)∗∗∗
(.106)∗∗∗
(.187)∗
.028
.019
-.035
(.020)
(.014)
(.023)
.415
.306
.472
(.024)∗∗∗
(.066)∗∗∗
(.029)∗∗∗
3.568
(.226)∗∗∗
NO
4028
.508
4.741
(.448)∗∗∗
YES
4028
.802
-.283
(.088)∗∗∗
.235
(.342)
.032
(.054)
.161
(.037)∗∗∗
-.00002
(.00003)
.077
(.124)
3.862
(.342)∗∗∗
NO
2975
.548
This table reports the results of a panel data regression of DM and CEO compensation on segment and other division’s ROA. Specifically, we estimate the following panel
regression model:
yijt = α + β1 × Division ROAit + β2 × Other division ROAit + β3 × Log(T otal assets)it + β4 × Log(Division assets)jt + Time FE + Firm FE + λ × M ills,
where the dependent variable y is DM compensation in Columns (1), (2) & (3), CEO compensation in Columns (4), (5) & (6). Fixed effects are included in Columns (3) &
(4). The inverse mills ratio for Heckman correction are included only in columns (3) & (6). The data covers the period 1992-2009. The compensation data is from Execucomp,
segment level financial data is from the Compustat Business Segment files and firm-level data is from the Compustat Industrial Annual files. The standard errors are robust to
heteroskedasticity and clustered at the firm level. (∗∗∗ ), (∗∗ ), (∗ ) denote statistical significance at 1%, 5%, and 10% levels respectively.
Table 3: DM pay-for-performance and accounting informativeness
Division ROA
Other division ROA
Log(Division assets)
Log(Total assets)
High earning
vol
(1)
.359
(.105)∗∗∗
.157
(.130)
.042
(.023)∗
.342
(.026)∗∗∗
Low earning
vol
(2)
.715
(.128)∗∗∗
.098
(.120)
.131
(.026)∗∗∗
.286
(.030)∗∗∗
3.205
(.229)∗∗∗
2534
.498
3.461
(.274)∗∗∗
1327
.531
High earning
Low earning
vol
vol
(3)
(4)
.242
.665
(.113)∗∗
(.159)∗∗∗
.146
.065
(.213)
(.151)
.027
.095
(.029)
(.044)∗∗
.359
.325
(.027)∗∗∗
(.041)∗∗∗
-.198
-.111
(.083)∗∗
(.116)
3.214
3.474
(.520)∗∗∗
(.479)∗∗∗
1898
1115
.555
.539
-.423
(.207)∗∗
.081
(.266)
Inv. Mills
Const.
Obs.
R2
∆ Division ROA
34
∆ Other division ROA
-.355
(.157)∗∗
.059
(.166)
High earnings
coef.
(5)
.562
(.109)∗∗∗
.133
(.129)
.077
(.024)∗∗∗
.290
(.024)∗∗∗
Low earnings
coef.
(6)
.245
(.124)∗∗
.208
(.155)
.079
(.026)∗∗∗
.350
(.033)∗∗∗
3.658
(.228)∗∗∗
2398
.508
2.724
(.277)∗∗∗
1682
.528
.317
(.163)∗
-.075
(.195)
High earnings
Low earnings
coef.
coef.
(7)
(8)
.448
.340
∗∗∗
(.161)
(.136)∗∗
.058
.353
(.155)
(.166)∗∗
.046
.047
(.028)
(.035)
.317
.388
(.025)∗∗∗
(.043)∗∗∗
-.141
-.206
(.094)
(.098)∗∗
3.612
2.864
(.453)∗∗∗
(.429)∗∗∗
1701
1312
.529
.587
.108
(.206)
-.296
(.158)∗
This table reports the results of a panel data regression of DM and CEO compensation on segment and other division’s ROA. Specifically, we estimate the following panel
regression model:
yijt
=
α + β1 × Division ROAit + β2 × Other division ROAit
+β3 × Log(T otal assets)it + β4 × Log(Division assets)jt
+Time FE + λ × M ills,
where the dependent variable y is DM compensation. In Column (1) , Column (2) (Column (3),Column (4)), we report the results for subsample of divisions in industries with
earnings volatility above (below) the median, and in Column (5),Column (7) (Column (7),Column (8)), we report the results for subsample of divisions in industries with earnings
coefficient above (below) the median. ∆ Division ROA and ∆ Other division ROA are the difference in coefficient estimates for the subsamples. The inverse mills ratio for
Heckman correction are included only in columns (3), (4), (7) & (8). The data covers the period 1992-2009. The compensation data is from Execucomp, segment level financial
data is from the Compustat Business Segment files and firm-level data is from the Compustat Industrial Annual files. The standard errors are robust to heteroskedasticity and
clustered at the firm level. (∗∗∗ ), (∗∗ ), (∗ ) denote statistical significance at 1%, 5%, and 10% levels respectively.
Table 4: DM pay-for-performance and division relatedness
Division ROA
Other division ROA
Log(Division assets)
Log(Total assets)
Related
(1)
.265
(.135)∗
.604
(.168)∗∗∗
.042
(.037)
.384
(.041)∗∗∗
Not related
(2)
.458
(.100)∗∗∗
.087
(.110)
.081
(.021)∗∗∗
.301
(.025)∗∗∗
2.818
(.206)∗∗∗
671
.613
3.460
(.222)∗∗∗
3409
.492
Inv. Mills
Const.
Obs.
R2
∆ Division ROA
35
∆ Other division ROA
-.192
(.164)
.516
(.197)∗∗∗
Related
(3)
.198
(.303)
.745
(.242)∗∗∗
.014
(.042)
.386
(.045)∗∗∗
-.254
(.144)∗
3.340
(.584)∗∗∗
441
.649
Not related
(4)
.427
(.101)∗∗∗
.072
(.156)
.050
(.033)
.338
(.030)∗∗∗
-.164
(.124)
3.443
(.427)∗∗∗
2572
.531
-.229
(.240)
.674
(.279)∗∗
High diversity
(5)
.365
(.101)∗∗∗
-.011
(.106)
.090
(.019)∗∗∗
.313
(.026)∗∗∗
Low diversity
(6)
.413
(.130)∗∗∗
.639
(.181)∗∗∗
.070
(.033)∗∗
.317
(.032)∗∗∗
3.286
(.260)∗∗∗
1896
.509
3.386
(.326)∗∗∗
2024
.517
-.047
(.158)
-.649
(.202)∗∗∗
High diversity
(7)
.386
(.139)∗∗∗
-.030
(.156)
.083
(.031)∗∗∗
.320
(.034)∗∗∗
-.120
(.108)
3.170
(.487)∗∗∗
1435
.547
Low diversity
(8)
.332
(.185)∗
.623
(.220)∗∗∗
-.013
(.051)
.387
(.046)∗∗∗
-.358
(.142)∗∗
3.979
(.525)∗∗∗
1534
.56
.054
(.204)
-.653
(.276)∗∗
This table reports the results of a panel data regression of DM compensation on segment and other division’s ROA for the subsamples based on segment’s relatedness to other
divisions and firm diversity. The specification is the same as that in Table 3. y is DM compensation.In Column (1) , Column (2) (Column (5),Column (5)), we report the results
for subsample of divisions with number of other divisions in the firm in the same 2-digit SIC code industry > (≤) 1, and in Column (5),Column (7) (Column (7),Column (8)),
we report the results for subsample of divisions in firms with the value of diversity measure above (below) the median. ∆ Division ROA and ∆ Other division ROA are the
difference in coefficient estimates for the subsamples. The inverse mills ratio for Heckman correction are included only in columns (3), (4), (7) & (8). The data covers the period
1992-2009. The compensation data is from Execucomp, segment level financial data is from the Compustat Business Segment files and firm-level data is from the Compustat
Industrial Annual files. The standard errors are robust to heteroskedasticity and clustered at the firm level. (∗∗∗ ), (∗∗ ), (∗ ) denote statistical significance at 1%, 5%, and 10%
levels respectively.
Table 5: DM pay-for-performance and division growth
Division ROA
Other division ROA
Log(Division assets)
Log(Total assets)
High capital
expenditure
(1)
.534
(.137)∗∗∗
.035
(.165)
.088
(.031)∗∗∗
.324
(.033)∗∗∗
Low capital
expenditure
(2)
.379
(.096)∗∗∗
.239
(.095)∗∗
.067
(.021)∗∗∗
.318
(.024)∗∗∗
3.211
(.272)∗∗∗
1166
.556
3.450
(.228)∗∗∗
2914
.49
Inv. Mills
Const.
36
Obs.
R2
∆ Division ROA
∆ Other division ROA
.156
(.152)
-.204
(.150)
Panel A: DM pay-for-performance and divisional investments
High capital
Low capital
High
Low
expenditure
expenditure
subsidy
subsidy
(3)
(4)
(5)
(6)
.373
.403
.416
.441
∗
∗∗∗
∗
(.192)
(.099)
(.218)
(.118)∗∗∗
.105
.203
-.047
.252
(.201)
(.118)∗∗
(.201)
(.104)∗
.066
.028
.105
.088
(.052)
(.036)
(.033)∗∗∗
(.026)∗∗∗
.326
.360
.295
.312
(.033)∗∗∗
(.029)∗∗∗
(.045)∗∗∗
(.030)∗∗∗
-.239
-.171
(.137)∗
(.102)∗
3.236
3.483
3.710
3.454
(.519)∗∗∗
(.439)∗∗∗
(.281)∗∗∗
(.260)∗∗∗
942
2450
1055
1958
.558
.539
.537
.512
-.029
-.024
(.192)
(.251)
-.299
-.098
(.226)
(.176)∗
High
subsidy
(7)
.271
(.233)
.006
(.245)
.109
(.033)∗∗∗
.286
(.034)∗∗∗
-.226
(.129)∗
3.481
(.527)∗∗∗
904
.562
Low
subsidy
(8)
.599
(.187)∗∗∗
.275
(.176)
.063
(.040)
.339
(.044)∗∗∗
-.160
(.125)
3.643
(.448)∗∗∗
1634
.541
-.275
(.257)
-.269
(.324)
This table reports the results of a panel data regression of DM compensation on segment and other division’s ROA for the subsamples based on segment investments. The
specification is the same as that in Table 3. y is DM compensation.In Column (1) , Column (3) (Column (2),Column (4)), we report the results for subsample of divisions
with capital expenditures above (below) the median, and in Column (5),Column (7) (Column (6),Column (8)), we report the results for subsample of divisions with divisional
subsidies above (below) the median. ∆ Division ROA and ∆ Other division ROA are the difference in coefficient estimates for the subsamples. The inverse mills ratio for
Heckman correction are included only in columns (3), (4), (7) & (8). The data covers the period 1992-2009. The compensation data is from Execucomp, segment level financial
data is from the Compustat Business Segment files and firm-level data is from the Compustat Industrial Annual files. The standard errors are robust to heteroskedasticity and
clustered at the firm level. (∗∗∗ ), (∗∗ ), (∗ ) denote statistical significance at 1%, 5%, and 10% levels respectively.
Division ROA
Other division ROA
Log(Division assets)
Log(Total assets)
High
Tobin’s Q
(1)
.330
(.117)∗∗∗
-.041
(.102)
.086
(.024)∗∗∗
.348
(.032)∗∗∗
Low
Tobin’s Q
(2)
.472
(.117)∗∗∗
.275
(.131)∗∗
.057
(.024)∗∗
.311
(.026)∗∗∗
2.968
(.263)∗∗∗
1842
.533
3.562
(.276)∗∗∗
2019
.522
Inv. Mills
Const.
Obs.
R2
∆ Division ROA
∆ Other division ROA
-.142
(.156)
-.316
(.12)∗∗∗
Panel B: DM pay-for-performance and divisional investment opportunities
High
Low
High sales
Low sales
Tobin’s Q
Tobin’s Q
growth
growth
(3)
(4)
(5)
(6)
.437
.310
.620
.352
∗∗∗
∗∗
∗∗∗
(.131)
(.152)
(.154)
(.096)∗∗∗
-.047
.288
.008
.221
(.203)
(.178)
(.142)
(.114)∗
.074
.033
.103
.058
(.037)∗∗
(.036)
(.030)∗∗∗
(.021)∗∗∗
.366
.326
.305
.324
(.042)∗∗∗
(.030)∗∗∗
(.036)∗∗∗
(.024)∗∗∗
-.096
-.246
(.127)
(.102)∗∗
2.786
3.751
3.587
3.250
(.487)∗∗∗
(.420)∗∗∗
(.311)∗∗∗
(.198)∗∗∗
1250
2830
1444
1569
.572
.538
.518
.511
.127
.268
(.185)
(.169)
-.335
-.212
(.158)∗∗
(.126)∗
High sales
growth
(7)
.521
(.174)∗∗∗
-.073
(.164)
.079
(.040)∗∗
.331
(.041)∗∗∗
-.162
(.139)
3.602
(.681)∗∗∗
1051
.549
Low sales
growth
(8)
.339
(.101)∗∗∗
.221
(.171)
.023
(.028)
.353
(.028)∗∗∗
-.209
(.079)∗∗∗
3.433
(.318)∗∗∗
1962
.55
.182
(.222)
-.294
(.156)∗
37
This table reports the results of a panel data regression of DM compensation on segment and other division’s ROA for the subsamples based on segment’s growth potential.
The specification is the same as that in Table 3. y is DM compensation.In Column (1) , Column (3) (Column (2),Column (4)), we report the results for subsample of divisions
in industries with Tobin’s Q above (below) the median, and in Column (5),Column (7) (Column (6),Column (8)), we report the results for subsample of divisions with sales
growth above (below) the median. ∆ Division ROA and ∆ Other division ROA are the difference in coefficient estimates for the subsamples. The inverse mills ratio for Heckman
correction are included only in columns (3), (4), (7) & (8). The data covers the period 1992-2009. The compensation data is from Execucomp, segment level financial data is
from the Compustat Business Segment files and firm-level data is from the Compustat Industrial Annual files. The standard errors are robust to heteroskedasticity and clustered
at the firm level. (∗∗∗ ), (∗∗ ), (∗ ) denote statistical significance at 1%, 5%, and 10% levels respectively.
Related
Division ROA
Other division ROA
Log(Division assets)
Log(Total assets)
Const.
Obs.
R2
∆ Division ROA
∆ Other division ROA
High q
Low q
(1)
.102
(.195)
.928
(.252)∗∗∗
.070
(.049)
.405
(.064)∗∗∗
2.694
(.232)∗∗∗
283
.706
(2)
.333
(.145)∗∗
.435
(.175)∗∗
.007
(.047)
.377
(.058)∗∗∗
3.117
(.273)∗∗∗
374
.587
-.231
(.353)
.493
(.582)
Panel C: DM pay-for-performance, division relatedness and division growth
Not related
Related
Not related
High q
Low q
High sales
Low sales
High sales
Low sales
growth
growth
growth
growth
(3)
(4)
(5)
(6)
(7)
(8)
.339
.523
.589
.238
.656
.376
∗∗
∗∗∗
∗∗∗
∗∗∗
(.139)
(.152)
(.125)
(.178)
(.219)
(.111)∗∗∗
-.115
.197
.261
.638
-.051
.131
(.098)
(.166)
(.191)
(.179)∗∗∗
(.136)
(.126)
.093
.070
.145
.012
.090
.073
(.071)∗∗
(.051)
(.039)∗∗
(.022)∗∗∗
(.031)∗∗∗
(.027)∗∗∗
.331
.293
.304
.406
.311
.301
(.042)∗∗∗
(.029)∗∗∗
(.050)∗∗∗
(.049)∗∗∗
(.043)∗∗∗
(.027)∗∗∗
2.791
2.825
3.628
3.338
3.050
3.623
(.324)∗∗∗
(.276)∗∗∗
(.078)∗∗∗
(.193)∗∗∗
(.292)∗∗∗
(.241)∗∗∗
184
487
1066
2343
1559
1645
.509
.515
.658
.609
.506
.495
-.184
.351
.279
(.194)
(.279)
(.212)
-.312
-.377
-.182
(.167)∗
(.468)
(.124)
38
This table reports the results of a panel data regression of DM compensation on segment and other division’s ROA for the subsamples based on interaction between segment’s
growth potential and relatedness. The specification is the same as that in Table 3. y is DM compensation.In Column (1) , Column (2) (Column (3),Column (4)), we report the
results for subsample of divisions in industries with Tobin’s Q above (below) the median, and in Column (1),Column (3) (Column (2),Column (4)), we report the results for
subsample of divisions with sales growth above (below) the median. ∆ Division ROA and ∆ Other division ROA are the difference in coefficient estimates for the subsamples.
The inverse mills ratio for Heckman correction are included only in columns (3), (4), (7) & (8). The data covers the period 1992-2009. The compensation data is from
Execucomp, segment level financial data is from the Compustat Business Segment files and firm-level data is from the Compustat Industrial Annual files. The standard errors
are robust to heteroskedasticity and clustered at the firm level. (∗∗∗ ), (∗∗ ), (∗ ) denote statistical significance at 1%, 5%, and 10% levels respectively.
Table 6: DM pay-for-performance and industry/economic conditions
Division ROA
Other division ROA
Log(Division assets)
Log(Total assets)
Const.
Obs.
R2
∆ Division ROA
∆ Other division ROA
39
Panel A: DM pay-for-performance and industry/economic conditions
Non-distress
Boom
Non boom
Recession
Non Recession
(2)
(3)
(4)
(5)
(6)
.648
.591
.390
.241
.551
∗∗∗
∗∗∗
∗∗∗
∗∗
(.101)
(.131)
(.095)
(.119)
(.109)∗∗∗
.460
.139
.196
.154
.293
(.140)∗∗
(.202)∗∗
(.116)
(.119)∗
(.128)
.079
.112
.061
.067
.075
(.020)∗∗∗
(.027)∗∗∗
(.022)∗∗∗
(.031)∗∗
(.021)∗∗∗
.311
.293
.330
.340
.309
(.023)∗∗∗
(.030)∗∗∗
(.025)∗∗∗
(.032)∗∗∗
(.024)∗∗∗
3.374
3.617
3.097
3.986
3.421
(.230)∗∗∗
(.317)∗∗∗
(.214)∗∗∗
(.150)∗∗∗
(.218)∗∗∗
3100
857
3140
1192
2888
.502
.55
.505
.552
.477
-.553
.201
-.310
(.130)∗∗∗
(.149)
(.150)∗∗
-.279
.321
.042
(.224)
(.112)
(.123)∗∗
Distress
(1)
.094
(.107)
.014
(.130)
.064
(.029)∗∗
.351
(.034)∗∗∗
3.088
(.155)∗∗∗
897
.576
This table reports the results of a panel data regression of DM compensation on segment and other division’s ROA for the subsamples based on industry and macroeconomic
conditions. The specification is the same as that in Table 3. y is DM compensation. In Column (1) (Column (2)), we report the results for subsample of divisions in industries
with Distress dummy equal to 1 (0), in Column (3) (Column (4)), we report the results for subsample of divisions in industries with Boom dummy equal to 1 (0), and in Colimn
(5) (Column (6)), we report the results for subsample of divisions in industries with Recession dummy equal to 1 (0). ∆ Division ROA and∆ Other division ROA are the
difference in coefficient estimates for the subsamples. The data covers the period 1992-2009. The compensation data is from Execucomp, segment level financial data is from
the Compustat Business Segment files and firm-level data is from the Compustat Industrial Annual files. The standard errors are robust to heteroskedasticity and clustered at
the firm level.(∗∗∗ ), (∗∗ ), (∗ ) denote statistical significance at 1%, 5%, and 10% levels respectively.
Division ROA
Other division ROA
Log(Division assets)
Log(Total assets)
Inv. Mills
Const.
Obs.
R2
∆ Division ROA
∆ Other division ROA
Panel B: DM pay-for-performance and industry/economic conditions - Heckman two-step procedure
Distress
Non-distress
Boom
Non boom
Recession
Non Recession
(1)
(2)
(3)
(4)
(5)
(6)
.072
.590
.329
.377
-.011
.687
(.119)
(.141)∗∗∗
(.214)
(.114)∗∗∗
(.102)
(.137)∗∗∗
.002
.245
.423
.085
.129
.103
(.128)
(.184)
(.229)∗
(.177)
(.181)
(.176)
.095
.021
.005
.063
.063
.035
(.025)∗∗
(.026)
(.035)∗∗∗
(.024)
(.031)
(.033)∗
.345
.354
.306
.368
.389
.326
(.032)∗∗∗
(.030)∗∗∗
(.036)∗∗∗
(.034)∗∗∗
(.028)∗∗∗
(.028)∗∗∗
-.179
-.171
-.212
-.202
-.366
-.098
(.112)
(.089)∗
(.112)∗
(.078)∗∗∗
(.097)∗∗∗
(.110)
4.085
3.083
4.234
3.280
3.307
3.425
(.378)∗∗∗
(.401)∗∗∗
(.429)∗∗∗
(.338)∗∗∗
(.282)∗∗∗
(.461)∗∗∗
644
2337
897
2116
658
2323
.626
.53
.573
.545
.597
.509
-.518
-.047
-.698
(.151)∗∗∗
(.249)
(.222)∗∗∗
-.243
.337
.026
(.228)
(.132)
(.159)
40
This table reports the results of a panel data regression of DM compensation on segment and other division’s ROA for the subsamples based on industry and macroeconomic
conditions . The specification is similar that in Table 6 except that we control for sample selection using heckman correction (include inverse mills ratio). In Column (1)
(Column (2)), we report the results for subsample of divisions in industries with Distress dummy equal to 1 (0), in Column (3) (Column (4)), we report the results for subsample
of divisions in industries with Boom dummy equal to 1 (0), and in Colimn (5) (Column (6)), we report the results for subsample of divisions in industries with Recession
dummy equal to 1 (0). ∆ Division ROA and∆ Other division ROA are the difference in coefficient estimates for the subsamples. The data covers the period 1992-2009. The
compensation data is from Execucomp, segment level financial data is from the Compustat Business Segment files and firm-level data is from the Compustat Industrial Annual
files. The standard errors are robust to heteroskedasticity and clustered at the firm level.(∗∗∗ ), (∗∗ ), (∗ ) denote statistical significance at 1%, 5%, and 10% levels respectively.
Related
Division ROA
Other division ROA
Log(Division assets)
Log(Total assets)
High q
Low q
(1)
.102
(.195)
.928
(.252)∗∗∗
.070
(.049)
.405
(.064)∗∗∗
(2)
.333
(.145)∗∗
.435
(.175)∗∗
.007
(.047)
.377
(.058)∗∗∗
2.694
(.232)∗∗∗
283
.706
3.117
(.273)∗∗∗
374
.587
Panel C: DM pay-for-performance, division relatedness and division growth
Not related
Related
Not related
High q
Low q
High sales
Low sales
High sales
Low sales
growth
growth
growth
growth
(3)
(4)
(5)
(6)
(7)
(8)
.339
.523
.589
.238
.656
.376
∗∗
∗∗∗
∗∗∗
∗∗∗
(.139)
(.152)
(.125)
(.178)
(.219)
(.111)∗∗∗
-.115
.197
.261
.638
-.051
.131
(.098)
(.166)
(.191)
(.179)∗∗∗
(.136)
(.126)
.093
.070
.145
.012
.090
.073
(.071)∗∗
(.051)
(.039)∗∗
(.022)∗∗∗
(.031)∗∗∗
(.027)∗∗∗
.331
.293
.304
.406
.311
.301
(.042)∗∗∗
(.029)∗∗∗
(.050)∗∗∗
(.049)∗∗∗
(.043)∗∗∗
(.027)∗∗∗
mills
Const.
Obs.
R2
∆ Division ROA
∆ Other division ROA
41
-.231
(.353)
.493
(.582)
3.050
(.324)∗∗∗
1559
.509
3.623
(.276)∗∗∗
1645
.515
-.184
(.194)
-.312
(.167)∗
2.791
(.078)∗∗∗
184
.658
2.825
(.193)∗∗∗
487
.609
.351
(.279)
-.377
(.468)
3.628
(.292)∗∗∗
1066
.506
3.338
(.241)∗∗∗
2343
.495
.279
(.212)
-.182
(.124)
This table reports the results of a panel data regression of DM compensation on segment and other division’s ROA for the subsamples based on interaction between segment’s
growth potential and relatedness. The specification is the same as that in Table 3. y is DM compensation.In Column (1) , Column (2) (Column (3),Column (4)), we report the
results for subsample of divisions in industries with Tobin’s Q above (below) the median, and in Column (1),Column (3) (Column (2),Column (4)), we report the results for
subsample of divisions with sales growth above (below) the median. ∆ Division ROA and ∆ Other division ROA are the difference in coefficient estimates for the subsamples.
The inverse mills ratio for Heckman correction are included only in columns (3), (4), (7) & (8). The data covers the period 1992-2009. The compensation data is from
Execucomp, segment level financial data is from the Compustat Business Segment files and firm-level data is from the Compustat Industrial Annual files. The standard errors
are robust to heteroskedasticity and clustered at the firm level. (∗∗∗ ), (∗∗ ), (∗ ) denote statistical significance at 1%, 5%, and 10% levels respectively.
Table 6: DM pay-for-performance and industry/economic conditions
Division ROA
Other division ROA
Log(Division assets)
Log(Total assets)
Const.
Obs.
R2
∆ Division ROA
∆ Other division ROA
42
Panel A: DM pay-for-performance and industry/economic conditions
Non-distress
Boom
Non boom
Recession
Non Recession
(2)
(3)
(4)
(5)
(6)
.648
.591
.390
.241
.551
∗∗∗
∗∗∗
∗∗∗
∗∗
(.101)
(.131)
(.095)
(.119)
(.109)∗∗∗
.460
.139
.196
.154
.293
(.140)∗∗
(.202)∗∗
(.116)
(.119)∗
(.128)
.079
.112
.061
.067
.075
(.020)∗∗∗
(.027)∗∗∗
(.022)∗∗∗
(.031)∗∗
(.021)∗∗∗
.311
.293
.330
.340
.309
(.023)∗∗∗
(.030)∗∗∗
(.025)∗∗∗
(.032)∗∗∗
(.024)∗∗∗
3.374
3.617
3.097
3.986
3.421
(.230)∗∗∗
(.317)∗∗∗
(.214)∗∗∗
(.150)∗∗∗
(.218)∗∗∗
3100
857
3140
1192
2888
.502
.55
.505
.552
.477
-.553
.201
-.310
(.130)∗∗∗
(.149)
(.150)∗∗
-.279
.321
.042
(.224)
(.112)
(.123)∗∗
Distress
(1)
.094
(.107)
.014
(.130)
.064
(.029)∗∗
.351
(.034)∗∗∗
3.088
(.155)∗∗∗
897
.576
This table reports the results of a panel data regression of DM compensation on segment and other division’s ROA for the subsamples based on industry and macroeconomic
conditions. The specification is the same as that in Table 3. y is DM compensation. In Column (1) (Column (2)), we report the results for subsample of divisions in industries
with Distress dummy equal to 1 (0), in Column (3) (Column (4)), we report the results for subsample of divisions in industries with Boom dummy equal to 1 (0), and in Colimn
(5) (Column (6)), we report the results for subsample of divisions in industries with Recession dummy equal to 1 (0). ∆ Division ROA and∆ Other division ROA are the
difference in coefficient estimates for the subsamples. The data covers the period 1992-2009. The compensation data is from Execucomp, segment level financial data is from
the Compustat Business Segment files and firm-level data is from the Compustat Industrial Annual files. The standard errors are robust to heteroskedasticity and clustered at
the firm level.(∗∗∗ ), (∗∗ ), (∗ ) denote statistical significance at 1%, 5%, and 10% levels respectively.
Division ROA
Other division ROA
Log(Division assets)
Log(Total assets)
Inv. Mills
Const.
Obs.
R2
∆ Division ROA
∆ Other division ROA
Panel B: DM pay-for-performance and industry/economic conditions - Heckman two-step procedure
Distress
Non-distress
Boom
Non boom
Recession
Non Recession
(1)
(2)
(3)
(4)
(5)
(6)
.072
.590
.329
.377
-.011
.687
(.119)
(.141)∗∗∗
(.214)
(.114)∗∗∗
(.102)
(.137)∗∗∗
.002
.245
.423
.085
.129
.103
(.128)
(.184)
(.229)∗
(.177)
(.181)
(.176)
.095
.021
.005
.063
.063
.035
(.025)∗∗
(.026)
(.035)∗∗∗
(.024)
(.031)
(.033)∗
.345
.354
.306
.368
.389
.326
(.032)∗∗∗
(.030)∗∗∗
(.036)∗∗∗
(.034)∗∗∗
(.028)∗∗∗
(.028)∗∗∗
-.179
-.171
-.212
-.202
-.366
-.098
(.112)
(.089)∗
(.112)∗
(.078)∗∗∗
(.097)∗∗∗
(.110)
4.085
3.083
4.234
3.280
3.307
3.425
(.378)∗∗∗
(.401)∗∗∗
(.429)∗∗∗
(.338)∗∗∗
(.282)∗∗∗
(.461)∗∗∗
644
2337
897
2116
658
2323
.626
.53
.573
.545
.597
.509
-.518
-.047
-.698
(.151)∗∗∗
(.249)
(.222)∗∗∗
-.243
.337
.026
(.228)
(.132)
(.159)
43
This table reports the results of a panel data regression of DM compensation on segment and other division’s ROA for the subsamples based on industry and macroeconomic
conditions . The specification is similar that in Table 6 except that we control for sample selection using heckman correction (include inverse mills ratio). In Column (1)
(Column (2)), we report the results for subsample of divisions in industries with Distress dummy equal to 1 (0), in Column (3) (Column (4)), we report the results for subsample
of divisions in industries with Boom dummy equal to 1 (0), and in Colimn (5) (Column (6)), we report the results for subsample of divisions in industries with Recession
dummy equal to 1 (0). ∆ Division ROA and∆ Other division ROA are the difference in coefficient estimates for the subsamples. The data covers the period 1992-2009. The
compensation data is from Execucomp, segment level financial data is from the Compustat Business Segment files and firm-level data is from the Compustat Industrial Annual
files. The standard errors are robust to heteroskedasticity and clustered at the firm level.(∗∗∗ ), (∗∗ ), (∗ ) denote statistical significance at 1%, 5%, and 10% levels respectively.