Choosing To Be "Good": How
Managers Determine Their Impact on
Financial and Social Performance
Bryan Hong
Dylan Minor
Working Paper 16-011
Choosing To Be "Good": How Managers
Determine Their Impact on Financial
and Social Performance
Bryan Hong
Ivey Business School
Dylan Minor
Harvard Business School
Working Paper 16-011
Copyright © 2015 by Bryan Hong and Dylan Minor
Working papers are in draft form. This working paper is distributed for purposes of comment and discussion only. It may
not be reproduced without permission of the copyright holder. Copies of working papers are available from the author.
Choosing to be "Good": How managers determine their
impact on …nancial and social performance
Bryan Hong
Ivey Business School, Western University
Dylan Minor
Kellogg School of Management, Northwestern University
This version: June 2015
Abstract
We investigate the relationship between a manager’s in‡uence on …rm …nancial and
social performance. To examine the mechanism governing the relationship between a
manager’s investment decisions along both dimensions of performance, we use a formal
agency theory model to develop testable implications. In our empirical results, we …nd
that a manager’s in‡uence on …rm CSR activities is negatively related to their in‡uence
on …nancial performance. Also, as suggested by the implications of the model, we …nd
that managers who operate in industries with more volatile …nancial performance and
receive a lower share of compensation from incentive-based pay are more likely to have
a positive in‡uence on …rm social performance.
1
1
Introduction
Corporate social responsibility (CSR) has become an increasingly mainstream business activity for many …rms. In 2012, U.S. …rms spent more than $40 billion1 on CSR-related
initiatives and an additional $14.6 billion2 on charitable donations. A substantial segment
of institutional investors also now consider the social performance of …rms when making
their investment decisions, with over 10 percent of professionally managed U.S. investment classi…ed as socially responsible (Kitzmueller and Shimshack 2012). The increasing
prevalence of CSR activities has sparked considerable debate over whether such activities
actually improve …rm …nancial performance, and the literature continues to remain divided
(see Gri¢ n and Mahon 1997, Orlitsky et al. 2003, and Margolis et. al 2009 for reviews).
However, empirical studies conducted thus far have primarily focused on investigating the
relationship between CSR and …nancial performance at the organization level. In this
study, we examine the relationship between CSR and …nancial performance at the level of
individual top managers, and investigate the mechanisms that determine how an individual
manager’s in‡uence on CSR is related to their impact on …nancial performance.
Our study has three primary motivations. First, individual managers are those who
actually strategize, make decisions, and execute CSR initiatives, and therefore their individual preferences play a critical role in the process of determining …rm social performance
(Waldman and Siegel 2008, Aguinis and Glavas 2012). Prior studies that examine the
relationship between CSR and …nancial performance at the organization level may have
important potential implications for managers, but as we show formally in this study, this
analysis does not necessarily clarify the actual relationship between social and …nancial
performance for individual executives. Other factors, such as a …rm’s culture or operating environment, can also play a signi…cant role in determining both CSR and …nancial
outcomes at the organization level, and could explain many of the …ndings of these studies (Wood 1991, McWilliams et al. 2006). Second, a growing number of scholars have
noted that relatively few empirical CSR studies exist at the individual level of analysis,
which has contributed to a lack of progress in understanding the underlying mechanisms
that determine CSR (Aguilera et. al 2007, Margolis and Walsh 2003, Wood 2010, Aguinis
2011, Orlitzky et al. 2011). And …nally, while prior literature has found evidence that the
1
See survey report by Verdantix, available at:
http://www.verdantix.com/index.cfm/papers/Products.Details/product_id/107/us-sustainablebusiness-spending-2009-14/
2
See NPS report summary available at: http://www.nps.gov/partnerships/fundraising_individuals_statistics.htm
individual preferences of top managers play an important role in determining both social
and …nancial performance outcomes (Bertrand and Schoar 2003, Chin et al. 2013), little
is known about how a manager’s in‡uence on both dimensions might be related. We argue that in practice, managers are unlikely to make decisions a¤ecting …nancial and social
performance in isolation, and propose a mechanism which considers how managers choose
to invest across both performance outcomes without ruling out possible e¤ects of CSR on
…nancial performance.
To illustrate the relevance of understanding this relationship, consider the outdoor
clothing company Patagonia, which donates 1% of its revenues to environmental organizations and produces high-quality products that command a price premium in the market.
How are social and …nancial performance related when Patagonia’s managers make their
decisions? In a case study by Casadesus-Masanell et. al (2009, p.209), a former senior
manager at the company provides potentially con‡icting views, declaring at …rst that they
seek to "dispel the myth that in order to have a high quality product you have to have
something damaging to the environment," and then later stating "There is some tension
between the environment and product quality...the reality is that they don’t always go
hand in hand." Taken together, these seemingly opposing observations provide an ambiguous conclusion as to whether managers who wish to invest in improving social performance
need to be concerned with tradeo¤s with …nancial performance, as well as the underlying
mechanism governing their decisions.
Top managers within …rms are tasked with having to make decisions across a variety of
functions within the organization which contribute to organizational outcomes (Bertrand
and Schoar 2003, Adams et al. 2005). When considering the relationship between an
executive’s investments in …nancial and social performance, managers must decide the
allocation of costly resources along each dimension of performance subject to a set of
external incentives, their own personal interests, and the interests of the …rm (Reinhardt
et. al 2008). Exposing the rich interplay between these di¤erent forces has important
implications for understanding how managers in‡uence …nancial and social performance,
as well as normative implications for improving the socially responsible (or irresponsible)
practices of …rms. To theoretically examine the relationship between a manager’s in‡uence
on social and …nancial performance, we show formally that the relationship between CSR
and …nancial performance at the organization level may not necessarily be consistent at
the individual manager level, and develop a formal model that describes how managers
decide to in‡uence both outcomes. The model follows a multitasking framework, where
managers are able to choose investments in CSR and other projects that only yield …nancial
performance returns, and explores the tension between these forces when investments along
either dimension of performance are costly to make.
We test the predictions of our model by combining KLD data measuring CSR activities
for a number of publicly-listed U.S. …rms from 1992-2009, …nancial data from Compustat,
and data on individual managers obtained from Execucomp. In order to estimate the relationship between a manager’s in‡uence on …nancial and social performance, we follow a
two-stage approach and begin by …rst estimating manager …xed e¤ects on each dimension
and isolating a manager’s individual contribution to …nancial and social performance from
other factors that also a¤ect …rm-level performance. Then, in our second stage of analysis
we estimate the relationship between manager …xed e¤ects for each dimension of performance. In our results, we …nd a negative relationship between a manager’s in‡uence on
…nancial performance and their impact on social performance. Also, as suggested by additional implications from the model, we …nd that managers have a more positive in‡uence
on …rm CSR activities the lower their share of total compensation from incentive pay, and
the more volatile the …nancial performance outcomes of their industry.
In the remainder of the paper, we develop our argument in more detail. In the following section, we consider how managers determine their in‡uence on …nancial and social
performance, and derive testable implications. In Section 3, we describe the data, develop
our empirical speci…cation and discuss our identi…cation strategy. In Section 4, we present
and discuss the empirical results. In Section 5, we o¤er conclusions.
2
Theoretical Considerations
Agency theory has been used as early as Jensen and Meckling (1976) to explain how a principal and agent forge a working relationship and carry out their respective commitments.
Since then, it has been recognized that agents often need to carry out multiple tasks. In
their seminal paper, Holmstrom and Milgrom (1991) created a framework to understand
the tradeo¤s encountered in such a multiple task world. In this spirit, and to develop
testable predictions, we build a multi-tasking model where a manager must work on tasks
that can increase the pro…ts and social performance of her …rm.
We do not assume that tasks have only …nancial or social performance e¤ects. Clearly,
some tasks may have both …nancial and social payo¤s. Instead, we begin by recognizing
that any “bundled”task can be projected onto two orthogonal dimensions of …nancial and
social performance. As long as the manager has su¢ cient task variety, she can combine
them to create the desired level of investment on each dimension. Thus, we are able
to consider …nancial and social investment of e¤ort as separate inputs, even though any
particular task will often entail some of each type of investment.
We assume that output for …nancial performance, which we will operationalize as pro…t,
is yf = xf +"f and output for social performance is ys = xs +"s ; where xi is the investment
choice for task i and "i is noise distributed as N (0;
the manager with a linear contract p (yf ) =
2 );
i
where i 2 ff; sg: The …rm pays
+ yf ; where
is her salary and
2 [0; 1]
is a share of …nancial pro…t. To simplify the exposition, we consider the case where the
…rm cannot contract on social performance output ys , although we note that this does not
fundamentally change our predictions or mechanism.3 Finally, the cost of investment e¤ort
C (xf ; xs ) is increasing and convex in both of its arguments: C1 > 0; C2 > 0; C11 > 0, and
C22 > 0: A straightforward parametrization that meets our above conditions and provides
closed form solutions is to set C (xf ; xs )
1 2
2 xf
+ 12 x2s + xs xf : This parameterization also
assures that we are not forcing one task to be favored over the other due to simply assuming
di¤erent task cost e¢ ciencies. Although it is standard in the literature to assume
2 (0; 1)
(e.g., see Bolton and Dewatripont (2005)), we will instead allow greater possibilities with
the set f j 2 ( 1; 1);
6= 0g: If
> 0; 4 this means that increasing investment on one
dimension increases the marginal cost of increased investment on the other dimension.
This negative spillover can be thought of as opportunity costs across inputs. By contrast,
if
< 0; increased input on one task decreases the marginal cost of increased investment on
the other task. This positive spillover can be thought as synergies across inputs. Finally, if
= 0; there is no multi-tasking issue. Assuming away any multi-tasking issue means that
it is as if there are two workers: one performing CSR-related tasks and the other …nancial
tasks and having one invest on one dimension of performance has no e¤ect on the other.
Thus, we do not analyze
= 0: Finally, if j j
1; this means that the spillover costs across
tasks are weakly more severe than the direct costs themselves, which we do not consider
since it is ulikely to apply to the kinds of environments that we are studying.
Similar to Holmstrom and Milgrom (1991), the manager is assumed to be risk averse
3
When the model allows for contractibility on social performance, however, the analysis is signi…cantly
more complicated. Thus, for simplicity we present this version of the model to describe all of the main
insights and mechanism of our formal theory.
4
Here forward, to ease exposition, we will use the notation < 0 and > 0 to mean the more cumbersome
2 ( 1; 0) and 2 (0; 1); respectively.
with utility u(w) =
e
rw ;
where e is the number e and r measures the manager’s degree of
risk aversion. It can be shown then that the manager’s certainty equivalent compensation
is
CE = E fu [p (yf ) +
=
where
and
M
2
i
+ xf +
M ys
M xs
1
r
2
C (xf ; xs )]g
1 2
2 2
r
f
2 M
2
s
C (xf ; xs ) ;
is the variance of …nancial performance (i.e., pro…t) and social performance
is the agent’s valuation of social output. We only assume that
M
2 R: That
is, managers may prefer positive or negative social performance. For example,
M
could
capture the degree to which a manager personally values the welfare of the community
where they operate.
The …rm is risk neutral and also is allowed to have utility over both …nancial and social
performance.5 The …rm’s payo¤ is
E[
Thus,
F
F]
= E [yf +
F xs
p (xf )] :
measures the degree the …rm values social performance. This problem di¤ers
fundamentally from the canonical multi-tasking problem in that the second task, social
performance, is a public good: both the manager and …rm bene…t from its production but
only the manager pays for its production through her e¤ort investment cost.
We can …rst characterize the manager’s choices of social and …nancial investments in
the following Lemma.
Lemma 1 The manager provides …nancial performance investment level xf =
social performance investment level xs =
M
1
1
M
2
and
2
Proof: see appendix.
5
In the following analysis, it will be apparent that …rms do not need to value social performance, although
we will assume in our discussion that …rms have heterogeneous valuations of CSR, and at least some …rms
value CSR. Patagonia, Ben and Jerry’s, and The Body Shop are three examples of …rms that seem to "care"
about CSR in a way that transcends any particular manager’s tenure at the …rm.
Since the …rm does not adjust optimal compensation based on the manager’s valuation
of social performance (CSR), we note that if a manager has greater personal utility for
CSR, then from Lemma 1 we have
@
@
xs =
M
1
2
1
> 0:
Therefore, in equilibrium, a manager that places greater value on CSR will personally
a¤ect an increase in her …rm’s CSR, independent of the …rm’s own valuation of social
performance and regardless of . This is despite the fact that the …rm is aware of how
much the manager personally values CSR.
From Lemma 1 we can also see how a manager responds to increased incentive pay
@
x =
@ s
1
2
:
< 0:
Thus, when > 0 ( < 0); increasing performance pay decreases (increases) CSR output
and increase …nancial performance, since
1
@
xf =
@
1
Consequently, when
2
> 0:
> 0 ( < 0) and a …rm increases its manager’s performance-based
pay, the executive will provide more …nancial performance investment and less (more) social
performance investment. Prior literature examining the e¤ect of incentive compensation
on social performance has thus far been unable to produce consistent evidence of a clear
relationship (see Berrone and Gomez-Mejia (2009) for a review). However, past studies
have focused on the relationship between compensation policy and organization-level social
performance. In practice, incentive contracts a¤ect the individual-speci…c in‡uence that
managers have on …rm social and …nancial performance, which is only a component of
the …rm’s overall performance outcome. The contribution of other factors outside of the
individual-speci…c in‡uence of managers may be one potential explanation for why previous
studies have been unable to …nd consistent evidence. Here, we address this potential issue
by directly investigating the relationship between incentives given to managers and the
individual-speci…c in‡uence of managers on …rm social performance, and consider explicitly
the mechanism relating a manager’s investments in social and …nancial performance.
We now characterize the optimal linear performance sharing contract :
Lemma 2 The optimal performance contract is
=
1
1+r
F
2
f
(1
2
)
:
Proof: see appendix.
We can see from Lemma 2 that as a …rm places greater weight on the value of CSR
(i.e., has greater
F)
and
> 0; it will induce a higher level of CSR investment through
lowering the power of its incentives (i.e., reducing
@
@
=
F
1+r
2
f
1
):
2
< 0:
and
@
x =
@ s
1
2
< 0:
In contrast, if there are positive spillovers between engaging in both social and …nancial
activities
< 0, the …rm o¤ers higher powered incentives as it cares more about increasing
CSR output.
Based on these derivations we reach our primary empirical prediction that we take to
the data:
Proposition 3 When
> 0 ( < 0), a manager’s …nancial performance yf and social
performance ys are negatively (positively) related
Proof: see appendix.
When
> 0; this Proposition says that after controlling for the degree of economies of
scope , a manager faces a tradeo¤ in a¤ecting social and …nancial performance. Intuitively,
managers have bounded resources and, therefore, increasing their exertion on one task must
mean at least some reduced e¤ort on the other. Technically, any primitive of the model
that increases the equilibrium level of investment on one dimension has the opposite e¤ect
on the other. The sole exception is the economies of scope parameter. In fact, improved
economies of scope induces both greater …nancial and social investment on the part of the
manager. When
< 0 the opposite prediction holds. Hence, the extent to which a manager
faces a tradeo¤ between personally in‡uencing social and …nancial performance must be
resolved empirically, which motivates our analysis in Section 3.2.
Our analysis also provides two additional predictions that we can take to the data:
Proposition 4 When
> 0 ( < 0) ; manager social output ys is
1. increasing (decreasing) in …nancial performance volatility
2
f
2. decreasing (increasing) in incentive share
Proof: see Appendix.
The …rst prediction stems from the fact that as the returns to …nancial e¤orts become
less certain, a manager’s incentives for …nancial e¤ort compared to social performance
e¤ort are dampened, thus encouraging him to contribute more to social performance when
> 0, all things equal. Hence, as a manager must operate in an environment with increased
uncertainty over pro…ts, we expect her CSR output to increase. The opposite holds when
< 0:
The second prediction follows from Lemma 1 and analysis that follows showing
1
2
. Intuitively, when
@
@
xs =
> 0; increased …nancial performance sharing entices the man-
ager to invest relatively more in …nancial tasks over social tasks. However, when
< 0
investment increases for both tasks.
Prior literature investigating the relationship between risk and CSR has generally found
a negative correlation between risk and …rm social performance (see Orlitzky and Benjamin
(2001) and Margolis Elfenbein Walsh (2009) for reviews). However, previous studies have
only considered this relationship at the organization level. As a complement to this literature, we examine here the underlying mechanism governing the relationship between risk
and an individual manager’s in‡uence on …rm social performance.
In principle, the predictions from our propositions are empirically testable. However, we
note that the model’s predictions address how an individual manager’s in‡uence on …nancial
and social performance are related to each other, as well as other variables. This requires
that an empirical test of the model …rst isolate an individual manager’s in‡uence from
other organization-level e¤ects on performance. In the next section, guided by the notion
of the ecological fallacy, we discuss the importance of separating a manager’s in‡uence
from other …rm-speci…c factors.
2.1
The Ecological Fallacy
First recognized by Robinson (1950), the term ecological fallacy refers to a fallacy in the
interpretation of statistical relationships where the relationship between variables demonstrated at a higher level of analysis is also assumed to hold true at the individual level.
While scholars have recognized the importance of multilevel analysis (Staw et al. 1981,
Glick 1985, Klein et al. 1999, Hitt et al. 2007), these studies have primarily focused on how
variables at di¤erent levels of analysis a¤ect outcomes at a single level, but not on the relationship between distinct organizational outcome variables at di¤erent levels of analysis.
Consequently, a di¤erent methodological approach from standard multilevel tools, such as
hierarchical linear modeling, is required to test for the ecological fallacy (Ho¤man 1997).
As we describe later in the empirical section, we instead utilize a methodology originally
used in the economics literature by Bertrand and Schoar (2003) to estimate relationships
between di¤erent …rm outcome variables at the individual manager level.
Problems of statistical inference resulting from the ecological fallacy have been empirically demonstrated across a number of contexts and scienti…c …elds, including political
science (Kramer 1983, Inglehart and Welzel 2003), sociology (Hammond 1973, Hannan
and Burstein 1974, Firebaugh 1978,), epidemiology (Morgenstern 1982,Piantadosi et al.
1988, Diez-Roux 1998), and organizational behavior (Glick and Roberts 1984, Klein et
al. 1994). While numerous speci…c causes can exist for the ecological fallacy, the general
problem is that presuming consistency of relationships across di¤erent levels of analysis
makes assumptions that are often too strong regarding the structure of interactions between the independent and dependent variables across levels. In many empirical contexts,
these assumptions are likely to be violated.
To illustrate the general problem of making inferences across levels, consider two levels
of analysis, the organization and the individual manager, and a variable X that is hypothesized to be related to …rm performance variable P ERF at the organization level. Both
X and P ERF are …rm-level variables that are determined by both the individual manager
(subscript denoted by mgr) and a set of factors independent of the manager (subscript
denoted by nonmgr), which we refer to here as a “non-manager component.”The relationship between variable X and P ERF at the organization level (subscript denoted by org)
can then be formally shown to be the sum of covariances between the individual manager
and non-manager components of X and P ERF :6
cov (P ERForg ; Xorg ) = cov (P ERFnonmgr ; Xnonmgr ) + cov (P ERFnonmgr ; Xmgr )
+cov (P ERFmgr ; Xnonmgr ) + cov (P ERFmgr ; Xmgr )
6
The full derivation can be found in the Appendix.
In this case, the organization level relationship actually depends upon four separate relationships between the individual manager and non-manager components of X and P ERF .
When implications for individual managers are stated using results from a regression of
P ERF on variable X at the organization level, the following assumption is made:
cov (P ERForg ; Xorg ) = cov (P ERFmgr ; Xmgr )
This is equivalent to the assumption that:
cov (P ERFnonmgr ; Xnonmgr ) + cov (P ERFnonmgr ; Xmgr ) + cov (P ERFmgr ; Xnonmgr ) = 0
The validity of this assumption depends upon the true underlying mechanisms determining the relationship between X and P ERF at the organization level. However, we argue
that in practice, there is typically no reason, theoretical or otherwise, to believe that this
assumption should necessarily be true. For example, consider the covariance of the nonmanager components of X and P ERF . If, for example, organizations with higher quality
employees are likely to have higher levels of X, and are also likely to exhibit higher levels of
P ERF , then the non-manager components of X and P ERF are likely to have a positive
covariance. If this is true, then in order for the assumption to hold, the sum of the remaining two covariance terms cov (P ERFnonmgr ; Xmgr ) and cov (P ERFmgr ; Xnonmgr ) must be
negative and exactly o¤set cov (P ERFnonmgr ; Xnonmgr ). However, in many empirical contexts this type of assumption is unlikely to be valid, and may result in incorrect inferences.
Hence, our empirical strategy, as outlined below, will explicitly separate manager and …rm
…xed e¤ects.
3
3.1
Data and Empirical Approach
Sample and Measures
The data used for this study consists of a sample of managers, …rms, and measures of
…nancial and social performance linked in a panel structure. To measure …rm CSR activities, social performance ratings from Kinder, Lydenberg, Domini Research & Analytics
(KLD) from 1992 to 2009 are used. KLD ratings are among the most in‡uential and
widely studied measures of CSR used in prior literature (Berman et al. 1999, Chatterji et
al. 2009), covering a sample of U.S. publicly listed …rms. Following Servaes and Tamayo
(2013), we de…ne our primary CSR measure to include the KLD categories of community,
diversity, employee relations, environment, and human rights. By choosing this de…nition, we take a conservative approach that more closely matches the types of managerial
decisions addressed by our model, and also exclude categories that individual executives
are unlikely to have control over (e.g. a …rm’s industry).7 To test the robustness of our
results across di¤erent de…nitions of CSR, we also consider measures where the product
and corporate governance categories are added to our primary CSR measure. To construct
our measures of …rm-level CSR, we subtract the total number of concerns from the total
number of strengths over the selected KLD categories, following the convention used by
previous empirical studies (Gri¢ n and Mahon 1997, Waddock and Graves 1997, Johnson
and Greening 1999, Chatterji et. al 2009). To measure …nancial performance, we use Return on Assets as our primary measure, calculated by dividing operating income by the
total assets of the …rm, winsorized at the 2.5% level in each tail to reduce the in‡uence
of outliers.8 As secondary measures of …nancial performance, we use Return on Equity
(calculated as net income divided by shareholder’s equity) and Return on Sales (calculated
as operating income divided by sales), winsorized at the same level.
To link our measure of CSR to the identities of individual managers, KLD ratings data is
merged with ExecuComp, which tracks the top …ve highest paid executives for a wide range
of U.S. publicly listed …rms from 1992 to the present. These data typically include the …rm’s
CEO, CFO, COO, and subdivision CEOs. After the ExecuComp, Compustat, and KLD
data are initially merged, all unmatched observations are then dropped from the sample.
Following the approach by Bertrand and Schoar (2003), the sample is then restricted to
…rms that contain a manager who switches to at least one other …rm (which must also be
visible in the data). By considering the set of managers who switch …rms over time for
our analysis, both manager and …rm …xed e¤ects can be estimated simultaneously in our
empirical speci…cation. In the formal model described earlier, the decisions of managers
to invest in …nancial and social performance ultimately result in an individual-speci…c
contribution to overall …rm social and …nancial performance. By estimating manager …xed
7
KLD measures CSR activities across thirteen categories: community, diversity, employee relations,
environment, human rights, product, alcohol, gaming, …rearms, military, nuclear, tobacco, and corporate
governance.
8
Winsorizing at lower percentiles produces similar results, although the in‡uence of extreme outliers
reduces statistical signi…cance. In the unwinsorized data, the minimum value is over 5 standard deviations
below the mean, and the maximum is over 6 standard deviations above the mean.
e¤ects for both social and …nancial performance, we are able to measure the in‡uence of
an individual manager on both …rm-level performance measures.
To ensure that managers have su¢ cient opportunity to in‡uence …rm outcomes, the
sample includes only those managers who have at least three years of observable tenure
at each …rm where they are employed. The …nal sample using KLD measures consists of
149 managers who switched organizations, spanning the years 1992 to 2009. Of the 149
managers, 64 are CEOs, 17 are CFOs, and 68 occupy other top executive positions in their
last position in the data.
Table 1 displays descriptive statistics for the sample used for the study. Table 2 shows
the types of changes in position and industry for the managers in the sample. The majority
of managers were originally in top executive positions other than CEO or CFO. Similar
to Bertrand and Schoar (2003), we note that a substantial percentage of executives (37%)
change industries, where we de…ne an industry as a …rm’s two-digit North American Industry Classi…cation System (NAICS) code. Also, our sample does not contain any managers
that are originally CFOs, although some eventually take CFO positions after switching
…rms.
3.2
Empirical Speci…cation
To empirically examine the relationship between a manager’s in‡uence on …nancial and
social performance, we begin by …rst estimating the individual manager’s contribution to
each …rm outcome. In principle, one straightforward method of estimating the in‡uence
of individual managers on …rm outcomes is to include a …xed e¤ect for each relevant manager, and add a set of control variables measuring all other relevant factors. However, in
practice, obtaining a comprehensive set of control variables using this type of regression approach is di¢ cult, especially if some relevant factors, such as …rm culture or organizational
structure, might a¤ect performance but lack good or even available measures. Without
a comprehensive set of control variables, any quantitative estimation using this type of
regression approach may overstate (or understate) the e¤ect of individual managers on
…rm outcomes, thereby leading to incorrect inferences. To illustrate how this might occur,
consider the example of executives who donate to environmentally focused charities, and
their in‡uence on social performance. If executives who donate more to environmentally
focused charities are also more likely to work in …rms where the organization is already
very committed to environmental sustainability, then the regression analysis will overstate
the degree to which those executives in‡uence CSR activities because of the confounding
e¤ect of the …rm’s culture, assuming a good measure of …rm culture cannot be included as
a control variable.
To address this empirical challenge, we follow the sampling methodology described
earlier and estimate both manager and …rm …xed e¤ects in the same speci…cation. This
approach follows prior work by Bertrand and Schoar (2003) and Pierce and Snyder (2008)
that separates organization-level and individual-level e¤ects to identify the in‡uence of
individuals on organizational outcomes. The inclusion of …rm …xed e¤ects is particularly
useful in this context, since many factors in‡uencing performance that are di¢ cult to
measure or are not measured in existing data can still be estimated and controlled for in this
approach. Recent empirical work on CSR by Servaes and Tamayo (2013) provides striking
evidence that failing to control for unobservable time-invariant characteristics through the
inclusion of …rm …xed e¤ects can lead to spurious …ndings. By including …rm …xed e¤ects
in our baseline speci…cation, we address many of these concerns.
In our …rst stage of analysis, we are able to estimate the degree to which …nancial and
social performance changes with the identity of individual top managers. The baseline
regression speci…cation utilizing this approach is presented here:
P ERFit =
(M AN AGERj ) + F IRMi + Y EARt + CON T ROLSit + "it ;
where each performance measure P ERFit for …rm i at time t is regressed on …xed e¤ects
for each manager j, …rm i; year t; as well as a set of control variables. The CON T ROLSit
include logged total assets (measuring …rm size), the ratio of debt to total assets (…rm
risk), the ratio of R&D to sales (R&D intensity), and the ratio of advertising to sales
(advertising intensity), following prior empirical studies that recognize these factors as
being related to …rm CSR (McWiliams and Siegel 2000, Servaes and Tamayo 2013).9 To
address heteroscedasticity and lack of independence of observations for the same …rm,
standard errors are clustered at the …rm level.
In our analysis, we note that since managers are unlikely to switch …rms randomly,
their selection may explain a …nding of statistical signi…cance of manager …xed e¤ects. For
example, a manager may switch …rms during a period when the hiring …rm independently
9
We note that industry dummies are already included implicitly through the inclusion of …rm …xed
e¤ects.
decides to increase its investments in CSR, creating a correlation between a manager’s
arrival to a new …rm and increases in CSR. Later, when we discuss the robustness of our
…ndings, we consider whether these alternative selection mechanisms may be a¤ecting our
results.
In the second stage of our analysis, we are now able to test our empirical predictions
developed in Section 2. Proposition 3 states our primary prediction that a manager’s individual e¤ects on …rm …nancial and social performance are negatively related. To test this
proposition, following the methodology of Bertrand and Schoar (2003), we estimate the relationship between an individual manager’s …xed e¤ects on …nancial and social performance
for the managers in our relevant sample. The speci…cation we use is as follows:
F EjS =
+
F EjF + "j ;
where each …xed e¤ect F EjS for social performance for manager j is regressed on that
manager’s …xed e¤ect for …nancial performance F EjF . To account for the di¤erent levels of
precision in each manager …xed e¤ect estimate, we use a weighted least squares regression
approach, where we weight each observation by the inverse variance of the error of each
…xed e¤ect estimate in the independent variable.10
4
Results and Discussion
Table 3 summarizes nine regressions where we regress each of the three CSR categorizations
of manager …xed e¤ects on the respective manager …xed e¤ect for …nancial performance.11
Column (1) shows that the e¤ect a manager has on our primary CSR measure is strongly
and negatively related to the e¤ect that same manager has on all three of their …rm’s
…nancial performance measures, as precited when
> 0: Column (2) displays the results
for the primary CSR measure with the KLD “Product”category included. This additional
category is less clearly a measure of CSR activities; the Product category includes measures
of product quality, safety, R&D, and marketing or contracting controversies. Nevertheless,
10
This is to address the measurement error in the independent variable. We assume the error structure
of a particular observation to be E[ 2 jX] = s2 e2 , where s2 is the variance of the observation if …xed e¤ect
estimates were equally precise and e2 is the variance of the …xed e¤ect estimate. Hence, our weights are
e 2 . This follows Bertrand and Schoar (2003).
11
To test whether the manager …xed e¤ects were statistically signi…cant, we conducted a Wald test, and
found that manager …xed e¤ects were signi…cant for all social performance measures at the 1% level and
all …nancial performance measures at the 5% level.
adding this category yields similar results to our baseline results in column (1). In column
(3), we add KLD’s category of corporate governance. Using this third de…nition of CSR,
we also …nd similar results to our primary measure.
One potential concern with these results is that because our estimation strategy tracks
managers that change …rms, managers who change industries may be creating a bias in our
estimates. For example, consider a CEO that works initially at an engineering company
and then switches to an oil company. At the engineering …rm, she personally contributes
to increasing the …rm’s pro…tability and social performance. However, when she joins the
oil …rm, due to the nature of the industry, it is more di¢ cult to increase social performance
compared to the engineering …rm, although she is still able to similarly in‡uence …nancial
performance. Hence, a negative correlation between performance e¤ects would arise as a
result of her switching industries, and not as a result of her individual decision making. This
would generate our observed results, but be an alternative mechanism to that proposed
by the model.12 To address this concern, we add a dummy control variable for managers
that change industries. We also add controls for manager age, the volatility of earnings for
their …rm’s industry and the manager’s share of pay that is incentive pay.13 The former
variable could be related to risk aversion, whereas the latter two variables are predicted via
the model to mediate a manager’s investment level in CSR. Results for this speci…cation
of controls are reported in Table 4. The …ndings are very similar to those shown in Table
3 where we used no such controls.
Another concern is that, similar to changing industries, there is a potential selection
issue in managers that change positions. For example, a manager may be a CEO for one
of their …rm spells and a di¤erent position for their next spell; this could potentially create
some type of correlation between their impact on …nancial and social performance across
spells as their span and breadth of control and duties potentially change. To address this
concern, we control for each of the observed position changes in our data. Table 5 reports
results of adding these controls to all of the controls used in Table 4. The …ndings are
similar to the …ndings reported in Table 4.
Next, we consider our secondary empirical predictions found in Proposition 5. In particular, since we found a negative relationship between managerial social and …nancial
12
This selection story could also go in the opposite direction as well, though we explain only one side of
it here for explication purposes.
13
We did not add a control for gender. Our sample contains only 4 female managers. However, controlling
for the gender of these four managers did not materially change the estimates of any of our regressions.
impact, suggesting
> 0; we expect volatility of earnings to lead to increased levels of
manager-induced CSR and also for increased incentive pay to result in reduced contributions to CSR. To capture the volatility of earnings in a manager’s operating environment,
we use a manager’s industry average standard deviation of operating income at the 4 digit
NAICS code level.14 Note that since standard deviation is strictly increasing in variance,
Proposition 5 also predicts that increased standard deviation of earnings results in greater
manager investment in CSR. We use industry-level rather than …rm-level earnings since
…rm-level earnings are potentially endogenous to the manager’s task choices. Table 6 reports the relationship of operating income volatility and our primary measure of CSR.
Columns (1) through columns (3) suggest, as predicted by the model, increased volatility
results in increased CSR investment by the manager. Columns (4) through (6) add controls for manager position changes. Adding this additional set of controls produces similar
results to the previous estimates.
The other variable of interest, incentive pay, is calculated as total variable pay (i.e.,
total manager compensation minus salary) divided by total compensation. For our primary
measure of CSR, we …nd a modest statistical relationship at the 10% level; increased
incentive pay results in a manager having a smaller e¤ect on their …rm’s CSR. When
including all controls, as shown in column (4), we …nd a relationship at the 5% level.
When adding the KLD Product category to our primary CSR measure we introduce
some categories that could theoretically operate in the opposite direction of pure social
performance: product quality, safety, and R&D are all characteristics that should improve
overall …nancial performance. This means that incentive pay would have a positive relationship with these factors. However, being involved in marketing controversies is arguably
more closely related to social performance. Thus, the relationship of incentive pay to KLD’s
product category is ambiguous.
In our data, the only components of KLD’s corporate governance category that were
reported consistently prior to 2006 are total level of compensation to top executives and
board members, minority ownership of other KLD rated …rms, and "other" issues. After
2006, KLD added …ve categories to the corporate governance measure, substantially broadening the de…nition of corporate governance. To the extent the narrower measurer is less
precisely capturing corporate governance, we would expect that adding it to our baseline
measure would create less precise results.
14
For managers that switch 4 digit NAICS codes in the data, the average of all of their annual 4 digit
NAICS level observations is used.
A …nal issue we address is the possibility that there exists some trend in corporate
social and …nancial performance that is systematically related to the departure and hiring
of managers across …rms.
For example, perhaps …rms that hire new managers tend to
be experiencing improving social performance while …nancial performance simultaneously
deteriorates. This type of trend would mean a new manager inherits a …rm with negatively
correlated social and …nancial performance. Of course, there is no reason to believe that the
trend could not be just the opposite: those …rms receiving new managers have both deteriorating …nancial and social performance, which would mean a manager inherits positively
correlated social and …nancial performance. This would go against our …nding a negative
relationship of manager in‡uence on social and …nancial performance. Nonetheless, we
recreated our entire analysis with the addition of a time trend model. Speci…cally, we
added a linear time trend that begins three years before a manager departs and continues
…ve years after the new manager takes over. This was estimated by adding a regressor that
takes on the value of 1 three years prior to a managers departure and increases each year
by 1, ending with a value of 8 …ve years after a manager change. All other periods take on
the value of 0: Table 7 reports the analysis shown in Table 5— relating social and …nancial
manager …xed e¤ects with all controls— with the addition of this time trend. As can be
seen, estimates are qualitatively similar and obtain all of the same respective signi…cance
levels as before reported in Table 5. Regressions with fewer controls (i.e., recreating Table
3 and Table 4 with a linear time trend) have similar results but were omitted for brevity.
We also perform the same analysis for Table 6, our analysis of the relationship of …nancial
output volatility and incentive pay, with the addition of a time trend. As reported in Table
8, we …nd similar results to before.15
5
Conclusion
We studied a sample of top managers from U.S. publicly listed …rms to determine their
individual e¤ects on CSR outcomes across a broad range of types of CSR. We began
our analysis by developing an agency theory model where managers must allocate costly
resources across the dimensions of …nancial and social performance. To test the predictions
of the model, we …rst empirically isolated a manager’s e¤ect on CSR from their …rm’s e¤ect.
15
We also test statistically whether or not …rms that hire a new manager in our sample exhibit di¤erent
trends in social performance compared to …rms that do not hire a switching manager in our sample, and
…nd no evidence of di¤erences in social performance trends.
After separating …rm and manager …xed e¤ects, we were then able to test the model, and
found evidence of a tradeo¤ between …nancial and social performance for managers. We
do not claim that …rms in the aggregate face such a tradeo¤; rather, top managers at …rms
personally face this tradeo¤ in how they individually a¤ect …rm outcomes.
We also tested the model’s secondary prediction that those managers working in an environment of greater …rm pro…t volatility might choose to invest more in CSR. Intuitively,
those managers in more volatile …nancial performance environments receive a relatively
better return to allocating more investment to social versus …nancial performance e¤orts.
Finally, we examined if managers with greater incentive pay are more likely to negatively
in‡uence …rm-level CSR. In our results, we …nd consistent evidence of our …nancial performance volatility prediction and partial evidence for our incentive prediction.
Overall, our …ndings suggest that for those …rms desiring to increase their CSR performance, hiring a manager with a strong and positive CSR record could prove helpful.
However, …rm-speci…c factors independent of our sample of managers appear to play a critical role in explaining variation in CSR outcomes. In particular, if a …rm is able to mitigate
pro…t volatility or decrease its level of incentive pay, our results suggest that this would
result in increased individual manager investment in CSR. Finally, boards of directors need
to be realistic in that if a particular manager is charged with increasing CSR output, it
is likely she will not simultaneously increase …nancial output; in fact, there will likely be
a reduction in personal contribution to …nancial output. However, one potential solution
is to have top managers focus on di¤erent tasks. Whereas one manager (e.g., CFO) could
produce more …nancial output, another manager (e.g., CEO) could be charged with greater
social output. Job and task design for bringing about the optimal …rm …nancial and social
peformance could prove a promising avenue for future research.
The results of this study also highlight the opportunity for empirical approaches that
are better able to separate the many di¤erent factors that may in‡uence CSR, including
disentangling a manager’s e¤ect from their …rm’s e¤ects. Empirical work in CSR is frequently challenged by unavailable or too few good measures, making omitted variable bias
a particularly di¢ cult problem. Approaches like the one used in this study (i.e., collecting
panel data and implementing both …rm and manager …xed e¤ects) provide some remedy
to many of these issues and help us understand the actual mechanism(s) at work. Further
empirical work should ideally make greater use of these types of approaches and will, whenever possible, consider the actual magnitudes of their e¤ects on CSR and other important
…rm outcomes.
Table 1. Descriptive Statistics
1.
2.
3.
4.
5.
6.
5.
6.
7.
8.
7.
8.
Variable
Mean
σ
CSR (Primary Measure)
0.83
2.91
CSR (including Product)
0.46
3.10
CSR (including Product and Corp. Governance)
-0.02
3.19
ROA
0.10
0.07
ROE
0.12
0.20
ROS
0.13
0.11
Firm Total Assets (in millions of USD)
30,108.4 107,110.5
Debt / Total Assets
0.24
0.16
R&D Intensity
0.03
0.07
Advertising Intensity
0.01
0.03
Manager Age
53.9
6.9
Average length of firm spell (in years)
6.6
3.6
Sample size (N)
Min
-8
-9
-10
-0.08
-0.67
-0.13
103.5
0
0
0
32
1
Max
15
15
14
0.29
0.59
0.47
2,223,299
1.39
1.56
0.23
86
18
14,535
Age was missing for a significant number of observations. There were 6,152 nonmissing
observations. Since R&D and Advertising expenditures are only reported when material,
missing values for both were set to zero.
Table 2. Manager Movements across Firms and Industries
CEO
CFO
Other
CEO
13
[5]
0
7
[1]
CFO
0
0
0
51
[19]
17
[9]
61
[21]
To:
From:
Other
This table provides summary counts of managers’ transitions across positions and
industries in the final sample used for estimation. Each transition is across firms.
Numbers shown in brackets denote the number of transitions of each change in
position that are also a change in industry for a switching manager.
Table 3. Relationship Between Financial and CSR Manager Fixed Effects
Dependent Variable: Manager CSR Fixed Effect (FE) Estimates
CSR Measure
Manager FE
Estimates
Return on Assets
Primary
(1)
-21.1111***
(7.7229)
Including Product
Category
(2)
-17.5200**
(7.1377)
Return on Equity
-4.7779***
(1.7881)
-4.4046**
(1.8218)
-3.6378**
(1.6756)
Return on Sales
-10.3444***
(3.8825)
-10.1514***
(3.6859)
-8.8518**
(3.7727)
149
149
149
N
Including Product and
Corp Gov Category
(3)
-18.7648***
(6.7647)
Each table entry in this table corresponds to a different regression. Each entry reports the coefficient
from a weighted regression of the fixed effects from the column variable on the fixed effects from the
row variable. Observations in these regressions are weighted by the inverse of the standard error
of the independent variable. Standard errors of the coefficient estimates are in parentheses.
Robust standard errors are reported in parentheses.
* p<0.10, ** p<0.05, *** p<.01
Table 4: Relationship between Financial and CSR Manager Fixed Effects with Added
Controls
Dependent Variable: Manager Social Performance Fixed Effect (FE) Estimates
CSR Measure
Manager FE
Estimates
Return on Assets
Primary
(1)
-23.1164***
(6.9708)
Including Product
Category
(2)
-19.6203***
(6.5759)
Including Product and
Corp Gov Category
(3)
-21.2333***
(6.4206)
Return on Equity
-4.8121**
(1.9802)
-4.5140**
(2.0742)
-4.3712**
(2.0112)
Return on Sales
-10.7045***
(3.9312)
-10.4385***
(3.7682)
-10.4076***
(3.8426)
149
149
149
N
Each table entry in this table corresponds to a different regression. Each entry reports the coefficient
from a weighted regression of the fixed effects from the column variable on the fixed effects from the
row variable. Observations in these regressions are weighted by the inverse of the standard error
of the independent variable. Standard errors of the coefficient estimates are in parentheses.
Controls include age, incentive share, manager industry change, and standard deviation
of industry operating income
Robust standard errors are reported in parentheses.
* p<0.10, ** p<0.05, *** p<.01
Table 5: Relationship of Financial and CSR Manager Fixed Effects with Full Controls
Dependent Variable: Manager Social Performance Fixed Effect (FE) Estimates
CSR Measure
Manager FE
Estimates
Return on Assets
Primary
(1)
-23.8721***
(6.7774)
Including Product
Category
(2)
-19.9064***
(6.3163)
Return on Equity
-4.0457**
(2.0138)
-4.0093*
(2.1256)
-3.8961*
(2.1781)
Return on Sales
-10.9361**
(4.2424)
-10.7124**
(4.1830)
-10.6731**
(4.1486)
149
149
149
N
Including Product and
Corp Gov Category
(3)
-21.0687***
(6.3298)
Each table entry in this table corresponds to a different regression. Each entry reports the coefficient
from a weighted regression of the fixed effects from the column variable on the fixed effects from the
row variable. Observations in these regressions are weighted by the inverse of the standard error
of the independent variable. Standard errors of the coefficient estimates are in parentheses.
Controls include age, incentive share, manager industry change, manager position change,
and standard deviation of industry operating income
Robust standard errors are reported in parentheses.
* p<0.10, ** p<0.05, *** p<.01
0.0001***
(0.0000)
-1.1607*
(0.6478)
-0.0114*
(0.0068)
0.2632***
(0.0749)
No
149
0.212
Volatility of Industry
Operating Income
Incentive Pay (β)
Age
Manager Industry Change
Position Change Controls
Observations
R Squared
* p<0.10, ** p<0.05, *** p<.01
Robust standard errors are reported in parentheses.
Primary
(1)
VARIABLES
149
0.188
No
0.2140**
(0.0830)
-0.0080
(0.0081)
-0.5045
(0.6076)
0.0001***
(0.0000)
Including Product
Category
(2)
CSR Measure
Dependent Variable: Manager CSR Fixed Effect (FE) Estimates
149
0.102
No
0.2076*
(0.1244)
-0.0060
(0.0076)
0.1953
(0.6862)
0.0001***
(0.0000)
Including Product
and Corp Gov
Category
(3)
149
0.236
Yes
0.2220***
(0.0739)
-0.0146*
(0.0075)
-1.2504**
(0.6134)
0.0002***
(0.0000)
Primary
(4)
149
0.197
Yes
0.2247**
(0.0889)
-0.0112
(0.0072)
-0.6327
(0.6081)
0.0001***
(0.0000)
Including Product
Category
(5)
CSR Measure
149
0.129
Yes
0.2334**
(0.1128)
-0.0110
(0.0090)
-0.0361
(0.7332)
0.0001***
(0.0000)
Including Product
and Corp Gov
Category
(6)
Table 6: The Relationship between CSR Manager Fixed Effects and Volatility of Industry Income and
Manager Incentive Pay
Table 7: Relationship of Financial and CSR Manager Fixed Effects with Full Controls with
Time Trends
Dependent Variable: Manager Social Performance Fixed Effect (FE) Estimates
CSR Measure
Including Product and
Corp Gov Category
(3)
-20.3788***
(6.0215)
Manager FE
Estimates
Return on Assets
Primary
(1)
-19.6992***
(6.0966)
Including Product
Category
(2)
-18.3364***
(6.3663)
Return on Equity
-3.6812**
(1.8607)
-3.6991*
(2.0075)
-3.6061*
(2.0916)
Return on Sales
-9.8664**
(4.4066)
-10.8876**
(4.2088)
-10.3561**
(4.2003)
149
149
149
N
Each table entry in this table corresponds to a different regression. Each entry reports the coefficient
from a weighted regression of the fixed effects from the column variable on the fixed effects from the
row variable. Observations in these regressions are weighted by the inverse of the standard error
of the independent variable. Standard errors of the coefficient estimates are in parentheses.
Controls include age, incentive share, manager industry change, manager position change,
and standard deviation of industry operating income
Time trend is a linear time trend beginning 3 years before and continuing 5 years after a manager change.
Robust standard errors are reported in parentheses.
* p<0.10, ** p<0.05, *** p<.01
0.1957**
(0.0894)
No
-1.4136**
(0.6624)
-0.0121
(0.0079)
0.2471***
(0.0896)
No
149
0.171
Volatility of Industry
Operating Income
Incentive Pay (β)
Age
Manager Industry Change
Position Change Controls
Observations
R Squared
* p<0.10, ** p<0.05, *** p<.01
Robust standard errors are reported in parentheses.
0.0001***
(0.0000)
0.0001***
(0.0000)
VARIABLES
149
0.159
-0.0096
(0.0083)
-0.6541
(0.6304)
Including Product
Category
(2)
Primary
(1)
CSR Measure
Dependent Variable: Manager CSR Fixed Effect (FE) Estimates
-0.0123
(0.0075)
0.1988**
(0.0927)
Yes
149
0.165
-0.0148
(0.0089)
0.2123**
(0.0858)
Yes
149
0.198
-0.0059
(0.0075)
0.1850
(0.1142)
149
0.094
No
149
0.115
Yes
0.2173**
(0.1074)
-0.0102
(0.0089)
-0.1197
(0.6964)
-0.7647
(0.6484)
0.0850
(0.6398)
-1.4434**
(0.6014)
Including Product
Category
(5)
Including Product
and Corp Gov
Category
(6)
0.0001***
(0.0000)
0.0002***
(0.0000)
Primary
(4)
CSR Measure
0.0001***
(0.0000)
0.0001***
(0.0000)
Including Product
and Corp Gov
Category
(3)
Table 8: The Relationship between CSR Manager Fixed Effects and Volatility of Industry Income and
Manager Incentive Pay with Time Trends
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6
Appendix
6.1
Proofs
Lemma 1: The manager provides …nancial performance investment level xf =
social performance investment level xs =
M
1
M
2
and
2
1
Proof: We …rst solve the manager’s problem. The manager’s problem is equivalent to
maximizing her certainty equivalent since
rw
e
is strictly increasing w and she cannot
e¤ect the variance of output. Thus, we have
max
+ xf +
M xs
max
+ xf +
M xs
xf ;xs
xf ;xs
1
r
2
1
r
2
1
r
2
1
r
2
2 2
f
2 2
f
2
M
2
s
2
M
2
s
C (xf ; xs )
1 2 1 2
x + x + xs xf
2 f 2 s
This yields
F OCf
:
(xf + xs ) = 0
F OCs :
Now solving for e¤orts, we get xs =
M
M
(xs + xf ) = 0
M
xf
; which we substitute to yield
(xs + xf ) = 0
xf
+ xf
= 0
xf
=
which means
xs =
Q.E.D.
M
1
2
1
M
2
Lemma 2 The optimal performance sharing contract is
=
1
1+r
F
2
f
(1
2
)
:
Proof: The …rm’s problem becomes
max (1
) xf +
;
subject to xf =
C (xf ; xs )
1
M
2
and xs =
M
and
2
1
F xs
+ xf +
M xs
2 2
1
f
2r
1
2
2r M
2
s
0;
where we have normalized the outside option of the manager to zero.
Substituting in the above IR and IC constraints, yields the unconstrained problem
max
(1
) xf +
1
r
2
F OC
:
2
M
x0f + (
F
F xs
+
0
M ) xs
2
x0f
=
x0f (1
) + x0s
=
x0f (1
) + x0s (
()
r
()
=
M xs
1
r
2
2 2
f
1 2 1 2
x + x + xs xf
2 f 2 s
2
s
=
1
+ xf +
+ x0s (
2
1
0
2
f + xf =
x0f + x0s F
r 2f + x0f
2
f
r
F
+
xs
M
M
2
1
1+r
1
0
2
f
r
x0f + x0s
=
xs x0s
M
+
F)
F
xf x0f
2
M
x0s xf
xf )
r
r
2
f
xs x0f
2
f
F
F
2
f
2
1
This yields the optimal output sharing contract as
=
1
1+r
F
2
f
1
2
Q.E.D.
Proposition 3. When
> 0 ( < 0), a manager’s …nancial performance yf and social
performance ys are negatively (positively) related
Proof: Recall that
xf
=
xs =
=
M
2
1
M
2
1
1
1+r
F
2
f
2
1
To identify the negative relationship we …rst consider how xf and xs change as a
function of each primitive of the model, holding
then show how
…xed. First assume that
> 0: We
changes with each primitive of the model to identify the e¤ects of all
primitives (except ) , which completes the proof.
@
@
:
xf
=
M
@
@
M
xs =
M
@
@
1
M
@
@
M
2
=
2
=
M
1
M
1
2
<0
2
>0
1
1
:
@
x =
@ f
@
x =
@ s
Hence, changes in either
M
or
@
@
@
@
:
=
2
=
M
1
1
1
2
>0
1
2
<0
create opposite changes in xf and xs , inducing a negative
correlation. Now we analyze changes in
xs through
1
M
2
; which only indirectly a¤ect changes in xf and
@
@
=
@
F
@
@r
@
@
@
2
f
F
1
1+r
@
@r
=
@
=
2
f
@
1
1+r
2
f
2
1
F
2
f
1
1+r
!
F
2
1
!
2
f
2
1
2
f
1+r
2
f
=
2
2
f
2
f
1+r
(1
2
1
2
r 1
=
<0
2
1
1
1+r
!
F
=
(1
2
1
F)
2
<0
F)
2
<0
Hence, conditional on ; outcomes of xf and xs are negatively related. As can readily
be shown, the opposite holds with
< 0:
Q.E.D
Proposition 4 When
> 0 ( < 0) ; manager social output ys is
1. increasing (decreasing) in …nancial performance volatility
2
f
2. decreasing (increasing) in incentive share
Proof:
First assume that
> 0:
2;
f
1. We take the derivative of
@
@
2
f
xs =
=
=
yielding
@
M
2
f
@
1
1+r
M
@
@
2
1
2
f
1
2
1+r
2
(1
)
2
1
r 1
F
2
f
2
(1
2
f
1
2
F)
2
>0
Since ys is strictly increasing in xs ; the result follows.
2. Lemma 1 and Lemma 2 and the accompanying analysis in Section 2 show manager
social investment is increasing in : Since ys is strictly increasing in xs ; the result follows.
The opposite results holds for both 1. and 2. when
< 0:
Q.E.D.
6.2
Formal Model of the Ecological Fallcy
We present a formal model of the ecological fallacy inspired by Robinson (1950), with an
application to our paper’s empirical setting of comparing social and …nancial performance.
Assume …rm …nancial performance is given by
Yf = fc + fm ;
where fm is the manager in‡uence and fc is non-manager in‡uence (i.e., we refer to
this as company). Social performance is similarly
Ys = sc + sm :
Now consider the covariance1 of …nancial and social performance across a population
of …rms whose performance is observed at the overall …rm-level:2
cov (Yf ; Ys )
= cov (fc + fm ; sc + sm )
= E [(fc + fm
E [fc + fm ]) (sc + sm
= E [(fc
E [fc ] + fm
= E[(fc
E [fc ]) (sc
+E[(fm
E [fm ]) (sc
E [sc ])] + E[(fc
E [fm ]) (sc
E [sc + sm ])]
E [sc ] + sm
E [fc ]) (sm
E [sc ])] + E[(fm
E [sm ])]
E [sm ])]
E [fm ]) (sm
E [sm ])]
= cov (fc ; sc ) + cov (fc ; sm ) + cov (fm ; sc ) + cov (fm ; sm )
From the …nal line, the …rst covariance term is the relationship between a company’s
in‡uence (outside of the manager’s in‡uence) on its social and …nancial performance. The
second covariance term is the relationship between a company’s in‡uence on …nancial
1
Recall that the covariance and the correlation coe¢ cient have identical signs. The latter is essentially a
normalization of the former by means of the product of the variables’standard deviations. However, using
covariance rather than the correlation coe¢ cient greatly simpli…es the mathematical analysis.
2
Note that we do not index a given …rm output by i: Instead, consider the variables Yf and Ys as random
variables; that is, we want to identify the expected covariance between any two di¤erent …rm-level outputs,
across the population of …rms. To estimate these covariances in practice, we of course then use a …nite
sample N …rms. However, the logic is identical.
performance and the manager’s in‡uence on social performance. The third covariance term
is the relationship between a company’s in‡uence on social performance and the manager’s
in‡uence on …nancial performance. The fourth covariance term is the relationship between
the manager’s in‡uence on social and …nancial performance.
Note that if the company …xed-e¤ects do not vary across …rms (i.e., aside from a
…rm’s manager, all …rms are identical), then the …rms’ in‡uence on social and …nancial
performance are the same across …rms and we simply have
cov (Yf ; Ys )
= cov (fc ; sc ) + cov (fc ; sm ) + cov (fm ; sc ) + cov (fm ; sm )
= cov (fm ; sm ) :
That is, the relationship between manager-level social and …nancial performance is the
same as overall …rm-level outcomes. This shows how strong of an assumption it is to
assume that overall …rm-level outcomes are related in the same way as manager outcomes:
for such an assumption to hold it must be that the …rm’s manager is the only force from
within the …rm that a¤ects …rm-level outcomes.3
6.3
First Stage Regressions Estimating Manager Fixed E¤ects
Table A.1. Regressions for Return on Assets, Return on Equity
3
We exclude the knife edge case where the …rst three covariance terms perfectly sum to zero.
Table A.2. Regressions for Return on Sales, Primary CSR Measure
Table A.3. Regressions for Primary CSR Measure including Product Category, Primary
CSR Measure including both Product and Corporate Governance Categories