The Role of Proxy Advisory Firms: Evidence from a Regression+

Transcription

The Role of Proxy Advisory Firms:
Evidence from a Regression-Discontinuity Design
Nadya Malenko and Yao Shen
April 2015
Abstract
Proxy advisory …rms have become important players in corporate governance. We use
exogenous variation in Institutional Shareholder Services (ISS) recommendations generated by a cuto¤ rule in ISS guidelines to answer two questions. First, what e¤ect do
ISS recommendations have on voting outcomes? Second, what are the sources of ISS
in‡uence? Using a regression discontinuity design, we …nd that a negative ISS recommendation on a say-on-pay proposal leads to a 25 percentage point reduction in voting
support. We next examine potential channels of ISS in‡uence and do not …nd evidence
that investors follow ISS recommendations for their information content. Thus, our
evidence suggests that the more likely channel of ISS in‡uence is the certi…cation role
of ISS recommendations.
Keywords: proxy advisors, ISS, proxy voting, shareholder activism, regression discontinuity, say-on-pay, executive compensation
JEL Classi…cation Numbers: G34, D72, J33
The authors are from the Carroll School of Management at Boston College, Finance Department. We
are grateful to Bernard Black, Sudheer Chava, Mariassunta Giannetti, Dirk Jenter, Oguzhan Karakas,
Darren Kisgen, Camelia Kuhnen, Andrey Malenko, Jordan Nickerson, Je¤rey Ponti¤, Jonathan Reuter,
Miriam Schwartz-Ziv, Philip Strahan, Jerome Taillard, and seminar participants at Boston College, McGill
University, and University of California, Davis for helpful comments and discussions. We also thank
Jun Crystal Yang and Pouyan Foroughi for excellent research assistance. Email: [email protected],
[email protected].
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1
Introduction
Shareholder voting plays a key role in corporate governance. Each year, investors cast billions
of votes that a¤ect the structure of boards, mergers and acquisitions, and …rms’corporate
governance policies. Shareholder voting has become increasingly important due to a rise
in shareholder activism, the increase in institutional ownership and regulations requiring
institutions to disclose their votes, the introduction of mandatory say-on-pay, and the shift
to majority voting for director elections. A signi…cant development in recent years has been
the growing in‡uence of proxy advisory …rms, which counsel institutional investors on how
to vote their shares. The largest proxy advisor, Institutional Shareholder Services (ISS),
issues recommendations on about 39,000 companies in 115 countries. According to the SEC
commissioner Michael Piwowar, “proxy advisory …rms may exercise outsized in‡uence on
shareholder voting” and the “Dodd-Frank provisions, such as mandatory say-on-pay votes,
make proxy advisory …rms potentially even more in‡uential.”1 Multiple commentators have
raised concerns about proxy advisors’one-size-…ts-all approach to governance matters, lack
of transparency, and potential con‡icts of interests.2 These concerns have prompted the SEC
and regulatory bodies in other countries to take steps aimed at increasing the transparency
and accountability of proxy advisors and investment advisors using their services.3
Given the role of shareholder voting in corporate governance and the ongoing calls for
regulation of the proxy advisory industry, it is important to answer two questions. First,
do proxy advisors have a strong impact on voting outcomes? And second, if they do, what
is the source of their in‡uence? In particular, do proxy advisors’ recommendations a¤ect
votes because they convey new information to investors, or because they play a certi…cation
role, protecting institutions from potential criticism and legal liability for their voting practices? Answering these questions is challenging because of an identi…cation problem. While
a number of papers have shown a positive correlation between ISS recommendations and
1
See the transcript of the December 5, 2013 SEC roundtable. Say-on-pay is an advisory vote expressing
shareholder approval or disapproval of the …rm’s executive compensation practices. The Dodd-Frank Act
required almost all US public …rms to provide shareholders with such a vote.
2
E.g., “Outsized Power & In‡uence: The Role of Proxy Advisers” by the SEC commissioner Daniel
Gallagher (Gallagher, 2014) and the SEC Concept Release on the U.S. Proxy System (July, 2010).
3
The SEC sought public comment on the role of proxy advisors in July 2010, held a public roundtable
in December 2013, and released guidance on investment advisors’responsibilities in using proxy advisors in
June 2014 (SLB No. 20). See also the “Final Report and Feedback Statement on the Consultation Regarding
the Role of the Proxy Advisory Industry” by the European Securities and Markets Authority (ESMA) and
“Guidance for Proxy Advisory Firms” by the Canadian Securities Administrators.
2
voting outcomes,4 this relation is not necessarily causal: the same unobservable …rm and
management characteristics that lead ISS to give a negative recommendation can also lead
shareholders to withdraw their support for the proposal. Accordingly, there is disagreement
about whether ISS is truly as in‡uential as many commentators claim.5 Even ISS itself,
trying to alleviate regulators’concerns about its oversized in‡uence, emphasizes:
“As many investors can be expected to share a general approach to assessing corporate governance practices, it is perhaps not surprising that correlations can be found
between ISS recommendations and shareholder voting outcomes. However, this does
not prove causality, nor are those correlations consistent. In our view, it is more logical
to interpret broad correlation as indicating that ISS policies, analyses and recommendations are based on principles and approaches which are shared by many investors.”
(ISS Responses to ESMA Discussion Paper, Jun 21, 2012)
In this paper, we address this empirical challenge in a regression discontinuity (RD) setting
by using exogenous variation in ISS recommendations due to a cuto¤ rule employed by ISS in
its guidelines on say-on-pay proposals. Speci…cally, ISS used to conduct an initial screen of
companies focusing on their one- and three-year total shareholder returns (TSRs) relative to
certain cuto¤s and only performed a deeper analysis of the company’s executive compensation
practices for companies below the cuto¤. For example, the ISS 2012 white paper “Evaluating
Pay for Performance Alignment” notes: “In the last few years, the approach has utilized a
quantitative methodology to identify underperforming companies – i.e., those with both 1and 3-year total shareholder return (TSR) below the median of peers in their 4-digit Global
Industry Classi…cation Standard (GICS) group. Underperforming companies then received
an in-depth qualitative review, focused primarily on factors such as the year-over-year change
in the CEO’s total pay, the 5-year trend in CEO pay versus company TSR, and the strength
of performance-based pay elements.”
More speci…cally, as we discuss in Section 2, the cuto¤ for a given company is calculated
as the median TSR of all …rms that are both in the company’s four-digit GICS industry
4
They include Alexander et al. (2010), Bethel and Gillan (2002), Cai, Garner, and Walking (2009),
Ertimur, Ferri, and Oesch (2013), Iliev and Lowry (2015), Larcker, McCall, and Ormazabal (2014), and
Morgan et al. (2011), among others. A more detailed review of the literature is provided below.
5
For example, BlackRock, in its comment to the SEC, states: “We believe that the in‡uence of proxy
advisory …rms in general, and ISS in particular, have been overstated.” (www.sec.gov/comments/s7-1410/s71410-254.pdf). See also Choi, Fisch, and Kahan (2010).
3
group and in Russell 3000, where the TSRs are computed on the last day of the calendar
quarter closest to the company’s …scal-year-end. In the rest of the paper, we refer to this
rule as the ISS “cuto¤ rule.”
The ISS rule implies that …rms below the cuto¤ undergo more scrutiny to get a positive
recommendation from ISS than …rms above the cuto¤, and hence the probability of a negative
ISS recommendation should increase discontinuously just below the cuto¤. Indeed, we show
that relative to …rms just above the cuto¤, there is a 15% increase (from 10% to 25%) in the
probability of a negative recommendation for …rms just below the cuto¤. This jump is large
given that the average probability of a negative ISS recommendation in our sample is 12.7%.
At the same time, the somewhat arbitrary nature of the ISS cuto¤ suggests that being just
above or below the cuto¤ is locally random, i.e., …rms just above and just below the cuto¤
are similar across all characteristics except, potentially, the ISS recommendation. We verify
this conjecture by showing that …rm characteristics are smooth around the cuto¤, that …rms
do not manipulate their TSRs to move above the cuto¤, and by conducting falsi…cation tests
on samples where the cuto¤ rule is not used. Thus, any discontinuous decrease in voting
support below the cuto¤ can be attributed to the causal e¤ect of ISS recommendations.
We therefore implement a fuzzy RD design by focusing on a narrow bandwidth around
the cuto¤ and instrumenting a negative ISS recommendation with an indicator variable that
equals one for …rms below the cuto¤ (Imbens and Lemieux (2008), Roberts and Whited
(2012)). Importantly, because the cuto¤ is based on industry medians, the sample of …rms
in a bandwidth around the cuto¤ is likely to be representative of the sample as a whole.
Our results are as follows. First, we …nd that ISS recommendations have a strong causal
e¤ect on voting outcomes: a negative ISS recommendation leads to a 25 percentage point
decrease in voting support for say-on-pay proposals. This e¤ect is economically large: dissent above 20% is generally viewed as an indication of substantial dissatisfaction (e.g., Del
Guercio, Seery, and Woidtke, 2008; Ferri and Maber, 2013) and leads companies to change
their compensation practices (Ferri and Maber, 2013; Ertimur, Ferri, and Oesch, 2013).6 Our
main speci…cation is a local linear regression estimated on a 5% bandwidth, and our results
are robust to multiple bandwidths, using ‡exible polynomial functions, and controlling for
6
See also “At Annual Meetings, 70 Is the New 50, Study Shows,” the Wall Street Journal, March 2015.
According to proxy advisors’ policies, support of less than 70% (ISS) or 75% (Glass Lewis) warrants an
explicit board response, and investor respondents to the ISS 2011-2012 policy survey share the same view.
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various …rm characteristics. We also …nd some evidence that the e¤ect of ISS is stronger
in …rms with less concentrated institutional ownership, consistent with the hypothesis that
larger shareholders have more incentives to invest in information acquisition and are therefore
less likely to rely on proxy advisors’recommendations (e.g., Iliev and Lowry, 2015).
Given the signi…cant e¤ect of ISS recommendations on voting outcomes, it is important
to understand the sources of ISS in‡uence. There are two channels that can explain why
investors follow ISS recommendations. One is the informational channel: investors follow
ISS primarily because its recommendations and reports convey new valuable information to
them. Indeed, the rationale for the existence of proxy advisors is that, given institutional
investors’diversi…ed holdings, it may be more cost-e¤ective for them to outsource research to
a third party than to do their own research for each proposal of each …rm in their portfolios.
This is especially so given that most investors believe that proxy statements are too long,
obscure, and di¢ cult to understand (see Section 5 for details). The second potential channel
of ISS in‡uence is the certi…cation role of its recommendations. Because institutions have a
…duciary duty to vote in their clients’best interests,7 they may have incentives to follow ISS
recommendations regardless of their actual information content as it may protect them from
criticism and litigation. For example, the 2003 SEC rule explicitly states that an institution
“could demonstrate that the vote was not a product of a con‡ict of interest if it voted client
securities in accordance with a pre-determined policy, based upon the recommendations of an
independent third party.”The certi…cation role of ISS is enhanced by its coordination e¤ect:
by following ISS, institutions ensure that they vote similarly to many other shareholders,
which may further decrease their chances of being criticized for their voting practices.8
Distinguishing between the informational and certi…cation channels is important to understand the e¤ect of ISS on voting outcomes, especially in light of the frequent calls to
regulate the proxy advisory industry. To disentangle the two channels, we again rely on
the ISS cuto¤ rule, which states that …rms below the cuto¤ receive “an in-depth qualitative
review”and only get a positive recommendation if the review shows that their compensation
practices are appropriate. Consider two …rms that both receive a positive ISS recommenda7
The 2003 SEC rule “Proxy Voting by Investment Advisers”(Release No. IA-2106) requires mutual fund
managers to vote proxies in their clients’ best interests, and the Department of Labor’s 1994 Interpretive
Bulletin 2509.94-2 imposes a similar requirement on pension funds.
8
Relatedly, Matvos and Ostrovsky (2010) point out that management’s ability to punish a given fund for
its voting behavior is lower if it has to retaliate against a larger number of funds. This may also motivate
funds to follow ISS recommendations even if these recommendations do not have any information content.
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tion, but one falls just below and the other just above the cuto¤. If the in-depth review by
ISS provides new valuable information to investors, then a positive recommendation for the
…rm below the cuto¤ is a stronger positive signal about the …rm’s compensation practices
than a positive recommendation for the …rm above the cuto¤ (which was given without an
in-depth review). Hence, if the informational channel is important, voting support should
be higher for the …rm below the cuto¤. As we discuss in Section 5, this argument does not
require investors to be aware of the ISS cuto¤ rule because the research report distributed
by ISS to its clients contains the summary of the analysis underlying each recommendation.
Focusing on the subsample of …rms with positive ISS recommendations, we show that the
average di¤erence in voting support between …rms just above and just below the cuto¤ is
about 2% with a standard error of 1.4%, which is statistically and economically insigni…cant.
For comparison, the standard deviation of voting support in the subsample with positive ISS
recommendations is 7%. Moreover, the di¤erence has the opposite sign from that predicted
by the informational channel: voting support above the cuto¤ is higher. This suggests that
either the ISS in-depth review does not convey new information to investors or, if it does, this
new information does not a¤ect investors’voting decisions. In other words, we do not …nd
evidence that investors follow ISS say-on-pay recommendations because of their information
content. Hence, at least in our sample of say-on-pay proposals, the more likely channel of
ISS in‡uence is the certi…cation role of ISS recommendations.
The key assumption of our RD design is that whether a …rm falls just above or below the
cuto¤ is locally random. We perform several tests to verify this assumption and to show that
our results are not driven by di¤erences in …rm characteristics around the cuto¤. First, we
redo our analysis on two samples for which the cuto¤ rule does not apply: say-on-pay voting
in 2012 (the year in which ISS stopped using this rule) and voting for director elections
during our main sample period. In both cases, we do not …nd any discontinuity in voting
support around the cuto¤. Second, we show that the distribution of various elements of CEO
compensation and other …rm characteristics is smooth around the cuto¤. Third, we alleviate
the concern that …rms manipulate their TSRs to move above the cuto¤ by performing the
McCrary (2008) test and showing that the density of the forcing variable is smooth around
the cuto¤. Such manipulation is indeed unlikely given that the cuto¤ depends on the TSRs
of all …rms in the industry and the TSRs are determined by stock price movements. Finally,
we consider several placebo cuto¤s and show no evidence of discontinuity around them.
6
Our paper contributes to the growing literature on shareholder activism9 and the role
of institutional investors in …rms’ corporate governance.10 In particular, it contributes to
the literature on shareholder voting. Prior research shows that shareholder voting has a
signi…cant impact on …rms’policies and value, even when votes are nonbinding.11 In an RD
setting, Cuñat, Gine, and Guadalupe (2012, 2013) …nd that relative to proposals that fail
by a small margin, proposals that pass by a small margin yield an abnormal return between
1.3% and 2.4%, depending on the proposal type.12 In the context of say-on-pay voting,
Ferri and Maber (2013) show that about 80% of UK …rms with substantial voting dissent
respond by removing controversial compensation practices, and Ertimur, Ferri, and Oesch
(2013) …nd similar evidence for US …rms.13 Given the signi…cance of shareholder voting, it
is important to understand what factors a¤ect investors’voting decisions. Our paper shows
that a major determinant of shareholder votes are the recommendations of proxy advisory
…rms, but that the information content of these recommendations might not be the main
reason why investors follow them.
There is a growing literature on proxy advisory …rms. A number of papers document a
signi…cant relation between ISS recommendations and shareholder support on various voting
issues. They include Alexander et al. (2010), Bethel and Gillan (2002), Cai, Garner, and
Walking (2009), Iliev and Lowry (2015), and Morgan et al. (2011), among others. In
the context of say-on-pay, Ertimur, Ferri, and Oesch (2013) show that the sensitivity of
votes to proxy advisors’recommendations varies with the institutional ownership structure
and the rationale behind the recommendation, and Larcker, McCall, and Ormazabal (2014)
document a negative market reaction to compensation program changes made to avoid a
negative recommendation. Our contribution to this literature is twofold. Our paper is the
…rst to identify the causal e¤ect of ISS recommendations on voting outcomes. Several prior
9
See Karpo¤ (2001), Gillan and Starks (2007), and Brav, Jiang, and Kim (2009) for reviews.
E.g., Hartzell and Starks (2003), Almazan, Hartzell, and Starks (2005), Aghion, Van Reenen, and Zingales
(2013), Mullins (2014), Crane, Michenaud, and Weston (2014), Appel, Gormley, and Keim (2015), and Boone
and White (2015).
11
See Ferri (2012) for a survey of nonbinding voting and Levit and Malenko (2011) for a theoretical analysis.
12
While these papers also use RD in the context of shareholder voting, the approach in our paper is di¤erent
since we compare …rms that fall just below and just above the ISS cuto¤. Some other …nance studies that use
the RD design include Bakke and Whited (2012), Beshears (2013), Chava and Roberts (2008), Iliev (2010),
Keys et al. (2010), Liberman (2013), Mullins (2014), Rauh (2006), and Reuter and Zitzewitz (2013).
13
Relatedly, Schwartz-Ziv and Wermers (2014) show that low say-on-pay support is followed by a decrease
in excess compensation and better selection of peer …rms when ownership is relatively concentrated. See also
Ertimur, Ferri, and Muslu (2010) and Cai and Walkling (2011).
10
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studies acknowledge that the positive association between recommendations and votes might
not be causal and suggest that the causal e¤ect of ISS can range between 6% and 35%
(Ertimur, Ferri, and Oesch (2013), Choi, Fisch, and Kahan (2010)). By using exogenous
variation in ISS recommendations, we estimate the causal e¤ect of ISS to be about 25%.
Second, our setting allows us to distinguish between di¤erent channels of ISS in‡uence and
show that the more likely channel is the certi…cation e¤ect. This …nding is broadly in line with
Ertimur, Ferri, and Oesch (2013) and Iliev and Lowry (2015), who …nd evidence consistent
with certain institutions conducting their own independent research, and with Aggarwal,
Erel, and Starks (2014), who show that investors’ reliance on ISS has declined over time,
partly due to information received through media.
The remainder of the paper is organized as follows. Section 2 outlines our empirical
identi…cation strategy. Section 3 describes the data and variable construction. Section 4
analyzes the e¤ect of ISS recommendations on voting outcomes, and Section 5 studies the
channels through which this e¤ect occurs. Section 6 presents tests of the validity of the RD
approach and discusses the robustness of the results. Section 7 shows how the e¤ect of ISS
on voting outcomes varies across …rms. Finally, Section 8 concludes.
2
Methodology
A positive correlation between shareholder support and ISS recommendations does not imply
that ISS has a causal e¤ect on voting outcomes because of omitted variables that could a¤ect
both ISS recommendations and shareholder votes. In this section, we discuss how we address
this identi…cation issue through a regression discontinuity design.
The source of exogenous variation in ISS recommendations is the following cuto¤ rule used
by ISS in 2010 and 2011. When giving recommendations on say-on-pay proposals, ISS used
to …rst screen companies based on their TSRs and focused its e¤orts on “underperforming”
…rms. Speci…cally, ISS identi…ed a company as “underperforming” if both its one- and
three-year TSRs were below the respective median TSRs of other …rms in the company’s
four-digit GICS group, where the TSRs were calculated on the last day of the calendar
quarter closest to the company’s …scal-year-end. Note also that ISS only included Russell
3000 …rms in its de…nition of the company’s four-digit GICS group.14 After identifying the
14
See, e.g., the “2011 U.S. Proxy Voting Guidelines Summary”white paper and the “Financial Highlights
8
“underperforming” companies using these cuto¤s, ISS conducted an in-depth qualitative
review of their compensation practices before giving a say-on-pay recommendation.15
This rule suggests that ISS is likely to give a positive say-on-pay recommendation without
conducting deep analysis if the …rm is not identi…ed as “underperforming,”but will carefully
scrutinize the …rm’s compensation practices (pay-performance alignment, the strength of
performance-based elements, pay relative to peers, quality of disclosures, and other issues)
before giving it a positive recommendation if the …rm is “underperforming.”16 As we show
in Section 4.1, this leads to a discrete jump in the probability of a negative recommendation
for “underperforming” …rms. We therefore implement a fuzzy RD design by instrumenting
a negative ISS recommendation with an indicator variable BelowCuto¤, which equals one if
the …rm’s one- and three-year TSRs both fall below their respective industry medians, and
zero otherwise (see Imbens and Lemieux (2008) and Roberts and Whited (2012) for reviews
of the fuzzy RD methodology.) Formally, for …rm i in year t, BelowCuto¤
BelowCuto¤
it
it
is given by
8
< 1 if M axT SR < 0;
it
=
: 0 otherwise;
where M axT SR is the forcing variable, measured in percentage points and de…ned as
(1)
M axT SRit = max(T SRit
(1)
(3)
M edianT SRit ; T SRit
(3)
M edianT SRit );
(n)
(n)
T SRit is the n-year TSR of …rm i computed in year t, and M edianT SRit is the median
n-year TSR in year t computed across all Russell 3000 …rms in the same four-digit GICS
group as …rm i. We describe the calculation of the TSRs in Section 3.
Data Overview,” which says “The GICS Sector TSR displayed for US companies is the median TSR for
companies in the same 4-digit GICS group and Russell 3000 index membership — i.e., for a company
included in the Russell 3000 index, sector peers will be drawn only from the Russell 3000 index.”
15
See “Evaluating Pay for Performance Alignment ISS’Quantitative and Qualitative Approach,” Feb 17,
2012. See also page 38 of the “2011 U.S. Proxy Voting Guidelines Summary” and pages 37-38 of the “2010
SRI U.S. Proxy Voting Guidelines” for descriptions of the same rule.
16
The rule does not imply that ISS always gives a positive recommendation to a …rm above the cuto¤.
Indeed, in addition to the above methodology, ISS checks for three key “problematic pay practices” that,
according to its guidelines, can lead to a negative recommendation on a stand-alone basis: “single-trigger”
or “modi…ed single-trigger”provisions in severance contracts, “tax gross-ups,”and repricing of stock options
without shareholder approval (e.g., the ISS “2011 U.S. Proxy Voting Guidelines Summary”and Pearl Meyer
& Partners “Client Alert: ISS Issues Policy Updates and FAQs for 2011 Proxy Season”.) This policy is
consistent with our …ndings: to understand the ISS methodology better, we examine the corresponding
(2010 or 2011) proxy statements of the 78 …rms that received a negative ISS recommendation despite being
above the cuto¤ and …nd that more than 90% of these …rms had at least one of these three pay practices.
9
The identi…cation assumption is that the relation between voting support and the variable
M axT SR would be smooth around the cuto¤ in the absence of di¤erential ISS recommendations around the cuto¤. This assumption is plausible because the cuto¤ used by ISS is
somewhat arbitrary. First, it is based on the TSRs of a speci…c group of …rms (those that
are both in Russell 3000 and in the …rm’s four-digit GICS group),17 and second, the TSRs
are calculated on a speci…c date (the last day of the quarter closest to the …rm’s …scal-year
end). We formally test this identi…cation assumption in Sections 6.1-6.3.
To identify the causal e¤ect of ISS recommendations on voting outcomes, we conduct the
two-stage least squares (2SLS) procedure by estimating the following two-equation system.
The …rst (second) equation corresponds to the …rst (second) stage:
NegRec =
0
+
1 BelowCuto¤
Votes =
0
+
1 NegRec
+ f1 (MaxTSR) + BelowCuto¤
+ g1 (MaxTSR) + BelowCuto¤
f2 (MaxTSR) + X + u,
g2 (MaxTSR) + X + ";
where Votes is the percentage of votes in favor of a say-on-pay proposal, NegRec is an
indicator variable equal to one if the ISS recommendation is negative and zero if positive,
f1 ; f2 ; g1 ; and g2 are continuous functions of the forcing variable M axT SR, and X is a vector
of control variables. As is standard in the literature (e.g., Imbens and Lemieux, 2008), we
estimate a linear probability model for the …rst stage, and show the robustness to the probit
formulation in Table 6. The variable BelowCuto¤ serves as the excluded instrument for the
endogenous variable NegRec in the second-stage regression. Our main coe¢ cient of interest
is
1,
which captures the local average treatment e¤ect of a negative ISS recommendation
on voting support. Standard errors are computed as the usual 2SLS standard errors.
Our main speci…cation, presented in Tables 2 and 3, is a local linear regression, i.e., fi
and gi are linear functions estimated on a small bandwidth around the cuto¤ (we show the
robustness to including higher-order polynomials in Table 6). In particular, we estimate
NegRec =
Votes =
0
0
+
+
1 BelowCuto¤
1 NegRec
+
+
2 MaxTSR
2 MaxTSR
+
+
3 BelowCuto¤
3 BelowCuto¤
17
MaxTSR + X + u
MaxTSR + X + ":
According to the investor survey by Bew and Fields (2012), investors use di¤erent peer groups from
those used by proxy advisors. The ISS peer group selection has been criticized by companies for including
…rms engaged in a di¤erent business than the company and not including the company’s publicly recognized
competitors (see, e.g., the proxy statement amendment of Allegheny Technologies on April 29, 2011).
10
Following the suggestion in Imbens and Lemieux (2008), we focus on the rectangular kernel
and verify the robustness of the results to di¤erent bandwidths in Table 6. In model 1 of
Tables 2 and 3, we set
3
and
3
to zero, thus restricting the slope of the linear regression to
be the same on the two sides of the cuto¤. In models 2-5, we allow
3
and
3
to be di¤erent
from zero, thus allowing for di¤erent slopes on the two sides of the cuto¤.
Note that as long as the covariates are continuous around the cuto¤ (the assumption
that we verify for various …rm characteristics in Section 6.2), fuzzy RD design does not
require the inclusion of control variables other than the forcing variable to produce consistent
estimates (Imbens and Lemieux, 2008). Nevertheless, we include several …rm characteristics
that may a¤ect voting support for say-on-pay proposals, such as characteristics of the CEO
compensation package, the …rm’s ownership structure, and other …rm characteristics, as well
as year and industry …xed e¤ects, and show the robustness of our results.
We estimate the above equations on a narrow bandwidth around the cuto¤, i.e., we only
include observations that satisfy
h < M axT SR < h, where h is the bandwidth. Following
the practical considerations in Imbens and Lemieux (2008), we use the same bandwidth for
the …rst- and second-stage regressions. The tradeo¤ in choosing the bandwidth is that a
larger bandwidth increases precision by including more observations, but also introduces an
additional bias. In our main analysis in Tables 2-5, we use a bandwidth of 5%. In Table
6, we repeat the analysis on many alternative bandwidths and show that our estimates are
robust to the choice of the bandwidth. In addition, we apply the cross-validation procedure
that is commonly used in the literature to determine the optimal bandwidth. The details of
this procedure are outlined in Appendix C. The cross-validation procedure yields the optimal
bandwidth between 4% and 5%, consistent with our baseline bandwidth of 5%.
3
Data and variable construction
The data on ISS recommendations and voting outcomes come from the ISS Voting Analytics
database, which covers Russell 3000 …rms. For each …rm and each proposal on the agenda,
the database provides the percentage of votes for, votes against, and abstentions, whether the
proposal passed or failed, and the ISS recommendation on the proposal. There is variation
across …rms in the way voting support is calculated. While the numerator is always the
number of votes in favor of the proposal, the denominator, captured by the variable “base,”
11
is di¤erent across …rms. Base can be the sum of the votes in favor and against the proposal
(51.83% of the sample), the sum of the votes in favor and against the proposal plus the number
of abstentions (47.77% of the sample), or the total number of shares outstanding (0.40% of
the sample). We use the appropriate denominator to calculate Votes, the percentage voting
support for each company. We focus on observations for which both the voting support and
the ISS recommendation are observed.
We obtain the data on TSRs and …rm characteristics from Compustat, the data on institutional ownership from Thomson Reuters 13F, and the data on executive compensation
and insider ownership from GMI Ratings (formerly Corporate Library).18 We …rst match
the ISS sample to Compustat and CRSP by ticker and company name.19 We then merge the
sample with GMI Ratings by ticker and name, and with Thomson Reuters 13F by CUSIP,
name, and ticker. Appendix A contains the de…nitions of all the variables used in the paper.
For most of the analysis, except the falsi…cation tests in Section 6.3, we focus on sayon-pay proposals voted on in 2010 and 2011 – the years in which ISS applied its cuto¤
methodology. Even though the cuto¤ rule might have been used prior to 2010, we did not
…nd a formal mentioning of this rule in the 2007-2009 ISS guidelines. We do not lose many
observations by not including prior years because say-on-pay proposals were relatively rare
before 2011, when they were made mandatory by the Dodd-Frank Act.20 We also do not
include 2012 because, as discussed in Section 6.3, ISS stopped using its cuto¤ rule that year.
Instead, we use the observations from 2012 (as well as the sample of director elections) as a
falsi…cation test to show the validity of our RD setting.
Constructing the TSR cuto¤s
According to the ISS guidelines, “ISS utilizes S&P’s Compustat database for TSR calculated
values. The Total Return concepts are annualized rates of return re‡ecting price appreciation
plus reinvestment of dividends (calculated monthly) and the compounding e¤ect of dividends
18
We use GMI Ratings and not Execucomp because Execucomp only tracks executive compensation in
S&P 1500 …rms, while our sample covers Russell 3000 …rms.
19
We conduct two rounds of matching to ensure that the match is correct. The …rst approach is to merge
the data with Compustat to obtain GVKEY (and CUSIP) and then merge with the CRSP/COMPUSTAT
Merged link table to get PERMNO (and CUSIP). The second approach is to …rst merge the data with the
stock names …le from CRSP to get PERMNO, and then merge with the link table to obtain GVKEY. We
cross-check the observations after the two rounds of matching.
20
There were only 404 say-on-pay proposals during the 2005-2009 period, compared to 2,020 say-on-pay
proposals in 2010-2011.
12
paid on reinvested dividends.” We therefore also use Compustat’s Total Return concept to
calculate TSRs. In particular, we multiply the current month’s adjusted close price by
the current month’s Total Return Factor provided by Compustat, divide the result by the
product of the adjusted close price multiplied by the Total Return Factor from the prior
period (one or three prior years for T SR(1) and T SR(3) , respectively), and annualize the
three-year return. The Total Return Factor is a multiplication factor that re‡ects monthly
price appreciation and reinvestment of monthly dividends and cash equivalent distributions
and the compounding e¤ect of dividends paid on reinvested dividends.
The TSR cuto¤ for each …rm is calculated as the median TSR of Russell 3000 …rms in the
same four-digit GICS industry.21 ISS guidelines specify the following dates to compute TSRs.
TSRs are downloaded at the end of each calendar quarter, i.e., on the last day of March, June,
September, and December. For a given …rm and year, the relevant download date is the last
day of the calendar quarter closest to the …scal-year end of the subject …rm. For example, if
a …rm’s …scal-year-end is March 31, TSRs for …rms in the same industry group are calculated
on March 31.22 Besides manually calculating the median industry TSRs, we obtain the list
of median TSRs from the ISS website. For example, Appendix B presents a screenshot of the
webpage with median TSRs for each four-digit GICS industry downloaded at the end of the
four most recent calendar quarters. We obtain similar tables for most periods in our sample
and …nd that they mostly match our manually calculated cuto¤s. Because the medians from
the ISS website capture the cuto¤s used by ISS more precisely, we use these medians for all
quarters for which they are available on the ISS website and use our manually-calculated
medians for the remaining quarters. For robustness, we have repeated the analysis using the
manually-calculated medians for all quarters and obtained similar results.
ISS guidelines do not specify whether a given …rm’s TSR, which is compared to the
industry median cuto¤, is downloaded on the …rm’s …scal-year-end date or, similarly to
the corresponding industry median, at the end of the calendar quarter closest to its …scalyear-end.23 The two approaches coincide for …rms whose …scal-year-end falls on the end of
21
The list of Russell 3000 …rms was obtained from Bloomberg.
See, e.g., the guidelines on the ISS website, which say “ISS downloads TSR performance data at the end
of March, June, September, and December for each of the four-digit industry GICS groups to determine the
median applicable for Russell 3000 companies. Given that executive compensation is reported on a …scal
year basis, and that …scal year ends vary by company, the applicable list will depend on the closest TSR
performance download where a company’s …scal year falls.”This methodology was also con…rmed in authors’
communication with ISS representatives.
23
ISS uses TSRs downloaded on the …rm’s …scal-year-end date for some of its other recommendations, such
22
13
the calendar quarter, which constitute 90% of the sample. To follow the ISS methodology
precisely, we restrict our sample to these 90% of …rms. We also note that regardless of the
company’s TSRs, ISS always gives it a positive say-on-pay recommendation if the total dollar
value of CEO compensation is su¢ ciently small, in particular, below the …fth percentile of
…rms in that year. This implies that the fuzzy RD setting is not directly applicable for
this group of …rms, so we restrict our sample to observations with the total value of CEO
compensation above the …fth percentile in that year. Our results are very close if we keep
these observations, and the only di¤erence is that at the …rst stage, the discontinuity in the
probability of a negative ISS recommendation around the cuto¤ is slightly smaller.
Descriptive statistics
Our …nal sample covers 1,932 …rms and 2,020 say-on-pay proposals in 2010 and 2011: 106 in
2010, and 1,914 in 2011, when say-on-pay became mandatory for a large number of …rms.24
Panel A of Table 1 presents descriptive statistics of the sample …rms. The average one- and
three-year TSR is 29.86% and -0.8%, respectively. The number of observations below the ISS
cuto¤ (i.e., with M axT SR < 0) is 613, which constitutes 30% of the sample. The average
market capitalization is $4.35 billion, the average total value of executive compensation
is $4.76 million, and the average say-on-pay voting support is 90.13%. Panel B presents
voting outcomes depending on the ISS recommendation. The probability that ISS gives
a negative recommendation is 12.7% (256 observations out of 2,020). The average voting
support is 93.2% if ISS gives a positive recommendation, and 68.9% if ISS gives a negative
recommendation. While all 1,764 proposals with a positive ISS recommendation received
more than 50% voting support and thus passed, 29 out of 256 proposals with a negative
recommendation failed. These statistics are consistent with the strong positive association
between ISS recommendations and voting outcomes documented in prior studies.
[INSERT TABLE 1 HERE]
For illustrative purposes, Figure 1 plots the distribution of ISS recommendations (on
the left) and voting support for say-on-pay proposals (on the right) on a relatively wide
as evaluating pay-for-performance alignment. See, e.g., “Evaluating Pay for Performance Alignment.”
24
The fact that say-on-pay proposals were not mandatory in 2010 does not a¤ect our methodology: the
factors that led a say-on-pay proposal to be included in the agenda do not invalidate the RD design as long
as they are continuous around the cuto¤.
14
interval around the cuto¤. The x-axis presents the forcing variable M axT SR, measured in
percentage points. Each dot in the left …gure represents the average probability of a negative
ISS recommendation measured in absolute values (from 0 to 1) in bins of 1%. Similarly, each
dot in the right …gure represents the average voting support, Votes, measured in percentage
points, in bins of 1%. The solid lines in both …gures represent the …tted values of a cubic
polynomial. Visual inspection of the graphs reveals a discontinuity in both variables around
the cuto¤. We formally verify the presence of these discontinuities and quantify their exact
magnitudes in Sections 4.1 and 4.2, by estimating local linear regressions in a more narrow
bandwidth.
[INSERT FIGURE 1 HERE]
4
E¤ect of ISS on voting outcomes
In this section, we answer the …rst question: What is the e¤ect of ISS recommendations on
voting outcomes? Section 4.1 documents a discontinuity in the probability of a negative ISS
recommendation around the cuto¤, and Section 4.2 uses this discontinuity to estimate the
causal e¤ect of ISS recommendations on voting support.
4.1
Discontinuity of ISS recommendations around the cuto¤
We start by showing that the probability of a negative ISS recommendation increases discontinuously when a …rm’s one- and three-year TSRs fall below the industry median TSRs.
We present the graphical analysis in Figure 2 and the results of the …rst-stage estimation in
Table 2. The x-axis of Figure 2 presents the forcing variable M axT SR in a 5% bandwidth
around the cuto¤. Each dot shows the average probability of a negative ISS recommendation, measured in absolute values (from 0 to 1), in bins of 1%. The solid and dashed lines
represent, respectively, the …tted values and 95% con…dence intervals of the …rst-stage regression, estimated on the interval
5% < M axT SR < 5%. This subsample includes 403
observations, with 175 of them corresponding to …rms below the cuto¤.
Both visual inspection of the averages across bins and the local linear regression show that
the probability of a negative recommendation is discontinuous and jumps from about 10% to
about 25% once the company falls below the cuto¤. This increase is economically large given
15
that the sample average probability of a negative recommendation is 12.7%. Importantly,
although the con…dence intervals slightly overlap, the estimates on the left and right are
signi…cantly di¤erent at the 5% level as is formally shown in Table 2.25
Table 2 reports the results of the …rst-stage regression for several speci…cations estimated
on a 5% bandwidth around the cuto¤. Model 1 restricts the slope of the linear control function to be the same on both sides of the cuto¤, while models 2-5 allow for di¤erent slopes
around the cuto¤ by including the interaction term BelowCuto¤ M axT SR. Model 3 extends
model 2 by adding year and industry …xed e¤ects.26 In models 4 and 5, we add additional
control variables that have been used in the literature on say-on-pay and proxy advisors (e.g.,
Ertimur, Ferri, and Oesch, 2013). Speci…cally, in model 4, we control for corporate governance characteristics that are likely to a¤ect ISS recommendations and voting support on
say-on-pay. They include various characteristics of the executive compensation package (the
total dollar value of executive compensation, the percentage change in executive compensation from the previous year, and the proportion of stock-based compensation de…ned as the
proportion of total value coming from stock and option awards), as well as the percentage
of institutional and insider ownership.27 In addition, in model 5, we control for other …rm
characteristics such as …rm size (measured as the logarithm of the market value of equity),
return on assets (ROA), and market-to-book ratio. In all speci…cations, the coe¢ cient on
BelowCuto¤ is about 0.15, i.e., the probability of a negative ISS recommendation for …rms
just below the cuto¤ is by 15 percentage points higher than for …rms just above the cuto¤.
This e¤ect is consistent with the visual estimate from Figure 2. The test for comparing
coe¢ cients between the models (Clogg, 1995) con…rms that the coe¢ cients on BelowCuto¤
across all speci…cations are not statistically signi…cantly di¤erent from each other.
25
It is generally known in statistics that the test of non-overlapping 95% con…dence intervals is more
conservative and corresponds to a lower p-value than 0.05 (e.g., Goldstein and Healy (1995), Schenker and
Gentleman (2001)).
26
The industry dummies are based on four-digit GICS classi…cation to make the de…nition of the industry
consistent with that used by ISS.
27
Since the say-on-pay vote is to approve executive compensation over the preceding …scal year, we use
compensation variables from the …scal year preceding the …scal year of the shareholder meeting.
16
4.2
E¤ect of ISS recommendations on voting support
Using the discontinuity in ISS recommendations as an instrument, we next analyze the e¤ect
of ISS on voting outcomes. We start by presenting the graphical analysis in Figure 3. This
…gure is similar to Figure 2, but the y-axis now corresponds to Votes, voting support for
the say-on-pay proposal, measured in percentage points. The …gure shows a discontinuity
in voting support around the cuto¤: Votes is by about four percentage points lower for
…rms just below the cuto¤ relative to …rms just above the cuto¤. Given the identi…cation
assumption, we attribute this discontinuity to the causal e¤ect of ISS recommendations. For
comparison, we …nd no similar discontinuity in votes for samples where the ISS cuto¤ rule
is not used (see Section 6.3 and Figure 5).
Table 3 presents the results of the second-stage regression. The outcome variable is Votes,
measured in percentage points, and the main variable of interest is NegRec, the indicator of
a negative ISS recommendation. We estimate several speci…cations corresponding to those
in Table 2. In particular, we control for year and industry …xed e¤ects in models 3-5 and for
governance and other …rm characteristics in models 4-5. Importantly, our estimates are not
biased by any omitted variables as long as these variables are continuous around the cuto¤.
In models 1 and 2, the coe¢ cient on NegRec is about -25 and is signi…cant at the 5%
level, suggesting that a negative ISS recommendation reduces the percentage of votes in
favor of a say-on-pay proposal by 25 percentage points. As expected under the identi…cation
assumption, the e¤ect is quantitatively similar and remains signi…cant once we include year
and industry …xed e¤ects and other control variables: the coe¢ cient on NegRec in model 5 is
-26.6 and is signi…cant at the 5% level. In Section 6.4, we verify the robustness of this e¤ect
to the choice of the bandwidth and the inclusion of higher-order polynomial controls.
The 25% e¤ect is economically important. Indeed, practitioners and prior academic studies consider voting dissent of above 20% to be an indication of strong shareholder dissatisfaction (e.g., Del Guercio, Seery, and Woidtke (2008), Ferri and Maber (2013)). According to a
2011 investor survey, 72% of investor respondents believe that voting dissent above 30% warrants an explicit response from the board regarding improvements in pay practices, and 20%
17
is the most commonly cited dissent level that should trigger such a response (see “2011-2012
Policy Survey Summary of Results”by ISS). Accordingly, say-on-pay dissent above 20%-30%
prompts most …rms to change their compensation policies. Ferri and Maber (2013) examine
UK …rms’responses to say-on-pay votes and show that about 80% of …rms with more than
20% voting dissent remove controversial pay practices, such as generous severance contracts
and problematic performance-based vesting conditions in equity grants. Similarly, Ertimur,
Ferri, and Oesch (2013) …nd that more than 70% of US …rms with at least 30% voting dissent
change their compensation policies following the vote. Schwartz-Ziv and Wermers (2014) …nd
that companies with low say-on-pay support are likely to decrease excessive compensation
and pick peer …rms that more closely resemble their own company when ownership is concentrated. Why do …rms respond to low say-on-pay support? In the past, low support levels,
and especially failed say-on-pay votes, have led to shareholder lawsuits, negative media attention, and damage to …rms’ reputation. In its guidelines, ISS notes that if a company
fails to “adequately respond to the company’s previous say-on-pay proposal that received
the support of less than 70 percent of votes cast,” ISS may recommend to vote against the
members of the compensation committee and potentially the full board, and Glass Lewis
places extra scrutiny on …rms with less than 75% approval. These consequences, together
with the in‡uence of ISS on voting outcomes that we document in this paper, suggest that
proxy advisors play an important role in …rms’governance practices.
5
Channels of ISS in‡uence
In this section, we examine potential channels through which ISS may a¤ect voting outcomes.
The …rst is the informational channel: shareholders follow ISS because its recommendations
and research reports provide new information to them. Because many institutions hold stock
in thousands of …rms, they may not be able to do the necessary research for all …rms in their
portfolios and may prefer to outsource this research to proxy advisors. According to Michelle
Edkins, Managing Director at BlackRock, who represented the …rm at the SEC roundtable
on proxy advisors, “There are days when we are voting 25, 30 meetings across our team. And
so having that information synthesized and accessible is hugely important to us being able to
take an informed decision.”The informational role of ISS may be particularly important given
that most investors are dissatis…ed with the quality of information they receive through proxy
18
statements. According to a 2015 investor survey, only 38% of institutional investors believe
that corporate disclosure about executive compensation is clear and easy to understand.
Investors also complain that proxy statements are too long and claim to read only 32% of a
typical proxy.28 Proxy advisors can therefore help because they “produce extremely detailed
analyses of things that in many cases have been essentially rendered intentionally obscure in
the proxy process”(Damon Silvers, AFL-CIO representative at the SEC roundtable.)
The other potential channel for the in‡uence of ISS is the certi…cation e¤ect: following
ISS recommendations may help an institutional investor prove that it voted in its clients’
best interests and thereby prevent criticism and litigation. The SEC commissioner Daniel
Gallagher notes that “relying on the advice from the proxy advisory …rm became a cheap
litigation insurance policy: for the price of purchasing the proxy advisory …rm’s recommendations, an investment adviser could ward o¤ potential litigation over its con‡icts of interest”
(Gallagher, 2014). For example, some institutional investors may be reluctant to vote against
management because they care about current or future business ties with the …rm and the
possibility of managerial retaliation (e.g., Davis and Kim (2007), Matvos and Ostrovsky
(2010)). A positive say-on-pay recommendation from ISS could then play a certi…cation
role, allowing these investors to vote for management without the risk of being accused of
con‡icts of interest. The certi…cation role of ISS is further strengthened by its coordination role: following ISS helps institutions coordinate their votes with other shareholders and
thereby decreases potential criticism –both from their clients if they vote with management,
and from management if they vote against it (Matvos and Ostrovsky, 2010). The certi…cation
and coordination e¤ects are closely related, so we refer to both as the “certi…cation channel”
and try to distinguish it from the informational channel.
To understand which channel is more important, recall that if a …rm falls above the cuto¤,
ISS is likely to give a positive recommendation without conducting “an in-depth qualitative
review”of the …rm’s compensation practices. In contrast, …rms below the cuto¤ are subject
to “an in-depth qualitative review” and will only receive a positive recommendation if the
review shows that their pay policies are appropriate. Consider two …rms with a positive ISS
recommendation and suppose that …rm A is just below the cuto¤ and …rm B is just above
28
See the “2015 Investor Survey: Deconstructing Proxy Statements — What Matters to Investors” by
Stanford University, RR Donnelley, and Equilar. See also the 2012 investor survey by Bew and Fields (2012),
which further emphasizes proxy advisors’role as data aggregators for asset managers.
19
the cuto¤. The ISS rule implies that the positive recommendation for …rm A is based on
an in-depth review, but the positive recommendation for …rm B is not (or less likely to be).
Under the assumption that the ISS in-depth review provides new valuable information to
shareholders, the positive recommendation for …rm A is a stronger positive signal about its
pay practices than the positive recommendation for …rm B. Thus, if the informational channel
is important, i.e., investors change their voting decisions based on the information they receive
from ISS, then voting support for …rm A’s say-on-pay proposal should be higher. In contrast,
if investors primarily follow ISS recommendations for certi…cation and not because of their
actual information content, then voting support for …rms A and B should be similar because
investors do not care that the positive recommendation for …rm A is more informative.
Note that this argument does not rely on investors knowing about the ISS cuto¤ methodology. Indeed, for every company in its portfolio, the client of ISS receives a research report
that contains both ISS recommendations on each proposal and, importantly, the summary
of the analysis underlying each recommendation. As long as the summary for a …rm that
received an in-depth review (i.e., a …rm below the cuto¤) is longer and more substantial than
the summary for a …rm that did not receive such a review (i.e., a …rm above the cuto¤), the
informational channel would predict higher voting support for the …rm below the cuto¤. Finally, it is quite likely that investors were aware of the ISS methodology and knew whether a
given …rm was above or below the cuto¤: ISS guidelines are widely publicized, and moreover,
at the beginning of its research report for investors, ISS includes a table summarizing the
…rm’s one- and three-year TSRs and the relevant cuto¤s, i.e., the median four-digit GICS
group one- and three-year TSRs.
We therefore focus on the subsample of observations with a positive ISS recommendation
and compare voting support for …rms just above and just below the cuto¤. Speci…cally, we
estimate the regression
Votes =
0
+
1 BelowCuto¤
+
2 M axT SR
+
3 BelowCuto¤
M axT SR + X + "
on a 5% bandwidth around the cuto¤. A positive and signi…cant coe¢ cient on BelowCuto¤
would be evidence of the informational channel. Table 4 presents the results, with speci…cations 1-5 corresponding to those in Tables 2 and 3. In all speci…cations, the coe¢ cient
on BelowCuto¤ is insigni…cant and has the opposite (negative) sign from the positive sign
20
predicted by the informational channel. The point estimate of
1
is about -2 (indicating
that voting support just above the cuto¤ is by 2 percentage points higher than just below
the cuto¤), with a standard error of 1.4. For comparison, the standard deviation of Votes in
the subsample of …rms with a positive ISS recommendation is 7.0 percentage points (and is
6.6 percentage points in the 5% bandwidth within this subsample). Thus, the coe¢ cient on
BelowCuto¤ is statistically and economically insigni…cant.
The negative and insigni…cant coe¢ cient on BelowCuto¤ is inconsistent with the predictions of the informational channel. This suggests two possibilities. One is that the in-depth
review conducted by ISS does not provide new information to investors. The other is that
the ISS review provides new information, but this information does not impact investors’
voting decisions. For example, if investors are reluctant to vote against management but do
not want to be accused of con‡icts of interest, they are likely to follow a positive ISS recommendation regardless of what they think about its actual information content. Overall,
our evidence suggests that, at least in our sample of say-on-pay proposals, the more likely
channel of ISS in‡uence is the certi…cation role of ISS recommendations.29
6
Validity of the RD design and robustness
In this section, we perform several tests aimed to show the validity of our RD setting and
the robustness of the results.
6.1
No manipulation of the forcing variable
Our approach relies on the assumption that …rms are randomly assigned to treatment, i.e.,
being just above or just below the cuto¤ is random. In particular, we assume that …rms cannot manipulate their TSRs in a way that pushes them just above the cuto¤. This assumption
is plausible: …rst, it is not that easy for a company to manipulate its TSR on a speci…c date
29
Another potential way to test the informational channel would be to check whether the stock price
reaction to a positive ISS recommendation is higher for …rms just above than for …rms just below the cuto¤.
We cannot perform this test because ISS research reports are proprietary, and hence the date when they are
sent to investors is not known to us. The only paper we know that has access to ISS proprietary reports and
hence can look at the price reaction to the release of these reports is Ertimur, Ferri, and Oesch (2013).
21
given that it depends on stock price movements. Moreover, the TSR cuto¤ for a given …rm
is a function of TSRs of all other …rms in the industry, which is di¢ cult to predict.
To verify the assumption of no manipulation formally, we perform the procedure proposed
by McCrary (2008), which tests for a discontinuity in the density of the forcing variable
M axT SR around the cuto¤. Figure 4 plots the estimated density of M axT SR in a 5%
bandwidth around the cuto¤ and shows that the distribution is smooth. The absolute value of
the McCrary test statistic for the 5% bandwidth is 0.84, which is not statistically signi…cantly
di¤erent from zero at any conventional level.30 Thus, we cannot reject the null hypothesis
that the density of the forcing variable is smooth around the cuto¤, suggesting that …rms do
not manipulate their TSRs to move above the cuto¤.
6.2
Continuity of covariates
To further test the assumption of random assignment to treatment, we compare the distribution of various …rm characteristics just above and just below the cuto¤. Under the
null hypothesis of random assignment, the distribution of characteristics una¤ected by ISS
recommendations should be smooth around the cuto¤. We perform two sets of tests.
First, we perform the RD analysis using each …rm characteristic as the outcome variable. Speci…cally, we regress each characteristic on BelowCuto¤, M axT SR, BelowCuto¤ M axT SR, and year and industry …xed e¤ects using a 5% bandwidth around the cuto¤.
The estimated coe¢ cient and the standard error on the variable BelowCuto¤ are reported in
the second column of Table 5. The table shows that BelowCuto¤ is not statistically signi…cant for any characteristic, consistent with the distribution being smooth around the cuto¤.
We also repeat the graphical analysis similar to Figures 2-3 for each of these characteristics
and do not …nd any evidence of discontinuity either (these …gures are omitted for brevity).
Second, we compare the average value of each …rm characteristic in two narrow intervals
around the cuto¤:
5% < M axT SR < 0 and 0 < M axT SR < 5%. The results are
summarized in Table 5. The p-values for the di¤erence in means test, presented in the last
column of the table, con…rm that the means of each …rm characteristic on the two sides of
30
Both the …gure and the McCrary test statistic were generated using the code provided by J. McCrary
on his website: http://eml.berkeley.edu/~jmccrary/DCdensity/.
22
the cuto¤ are not statistically signi…cantly di¤erent from each other.
6.3
Tests on alternative samples and placebo cuto¤s
We further con…rm the validity of our RD setting by repeating the analysis on two di¤erent
samples. In particular, instead of looking at say-on-pay proposals in 2010-2011, which is
our main sample, we study say-on-pay proposals in 2012 and director elections. (Other
types of proposals are much less common and hence do not provide enough observations in
a narrow bandwidth around the cuto¤.) The rationale for these falsi…cation tests is that
the ISS cuto¤ rule does not apply to these two samples. Indeed, in 2012, ISS signi…cantly
changed its say-on-pay guidelines and, among other things, stopped using its 2010-2011
cuto¤ methodology.31 We formally verify this by estimating the …rst-stage regression on the
2012 sample and showing that the coe¢ cient on BelowCuto¤ is insigni…cant (see Table D.1
in Appendix D). Similarly, the …rst-stage regression on the sample of director elections in
Table D.2 of Appendix D shows that the cuto¤ rule does not a¤ect ISS recommendations on
director elections either.32
Thus, if our identi…cation is valid and being above or below the cuto¤ only a¤ects 20102011 say-on-pay voting outcomes through ISS recommendations, then voting outcomes for
say-on-pay proposals in 2012 and director elections should not exhibit any discontinuity
around the cuto¤. Moreover, when examining director elections, we restrict attention to
2010-2011 and to those …rms in each year that had a say-on-pay proposal in that year and
hence are included in the main sample. By restricting the sample this way, we ensure that
our main sample of say-on-pay proposals in 2010-2011 and the sample of director elections
31
For example, after discussing the cuto¤ rule used in 2010-2011, the ISS 2012 white paper “Evaluating
Pay for Performance Alignment” notes: “This year, ... ISS decided to re…ne our approach to pay-forperformance evaluations and develop a more sophisticated methodology to drive the quantitative component
of the analysis.”The 2012 methodology is also aimed to screen …rms before conducting an in-depth qualitative
review, but the new screening rule does not allow us to perform the RD analysis for two reasons. First, the
comparison group now consists of 14-24 …rms separately chosen by ISS for each company, which are not
known to us. Second, the new screening is based on several characteristics of the compensation package
(CEO’s pay rank within a peer group, the multiple of the CEO’s pay relative to the peer group, and the
…ve-year trend in CEO pay), which are easily manipulable.
32
Because each …rm has many directors but only one say-on-pay proposal, we aggregate the director
elections observations by …rm-year-recommendation to make the sample of director elections consistent with
our main sample. In particular, for each …rm and year, we take all directors who received a positive (negative)
recommendation from ISS and calculate the average proportion of votes in favor of these directors.
23
contain the same …rms at the same points in time. Hence, continuity of voting outcomes in
the second sample provides strong evidence that our RD design is valid.
Figures 5a and 5b present the results of the analysis. Speci…cally, we repeat Figure 3
on these two samples and show that in both samples, voting support is smooth around the
cuto¤. The results of the regression analysis con…rm the absence of discontinuity in votes at
any conventional level of signi…cance and are omitted for brevity.
Finally, we repeat the analysis on our original sample of say-on-pay proposals in 20102011, but using several placebo cuto¤s: M axT SR = c for c 2 f 3%; 3%; 5%; 5%g. For
each c, we …rst verify that ISS recommendations exhibit no discontinuity around the cuto¤
M axT SR = c (these results are omitted for brevity) and then repeat the graphical analysis
of Figure 3 but on the interval c
5% < M axT SR < c + 5%. The results, presented in
Figure 6, show no discontinuity in voting support around these placebo cuto¤s.
6.4
Robustness tests
In this section, we show the robustness of our main results in Tables 2 and 3 to alternative
bandwidths and speci…cations. Table 6 presents the summary of these results. The …rst two
rows in each panel present the estimate and standard error of the coe¢ cient on BelowCuto¤
in the …rst-stage regression, and the third and fourth rows present the estimate and standard
error of the coe¢ cient on NegRec in the second-stage regression.
Columns 1-8 of the table present the analysis of model 3 in Tables 2 and 3 on bandwidths
ranging between 3% and 10%.33 The results for both the …rst and second stages are quantitatively similar across bandwidths. In addition, we calculate the optimal bandwidth using
the cross-validation procedure described in Appendix C. We …nd that the optimal bandwidth
is between 4% and 5%, supporting our choice of 5% for the baseline analysis.
An alternative to estimating a local linear regression on a narrow bandwidth is to use a
larger sample but include higher-order polynomials of the forcing variable (e.g., Roberts and
33
The results for other speci…cations are similar and are omitted for brevity.
24
Whited, 2012). In columns 9-11 of Table 6, we estimate regressions with higher-order polynomials of M axT SR on a 20% bandwidth, allowing for di¤erent functional forms on the two
sides of the cuto¤. For example, the regressors in column 9 include BelowCuto¤, M axT SR,
M axT SR2 , BelowCuto¤ M axT SR, BelowCuto¤ M axT SR2 , and year and industry …xed
e¤ects. The table shows that using higher-order polynomials does not a¤ect our estimates.
Finally, in column 12, we estimate the probit model for the …rst-stage regression. The
marginal e¤ect of the coe¢ cient on BelowCuto¤ is reported in square brackets to the right
of the coe¢ cient and equals 0.131, which is consistent with the estimates from the linear
probability model. The coe¢ cient on NegRec in the second-stage regression is strongly
statistically signi…cant and has a similar magnitude to that in other speci…cations.
7
Variation in the e¤ect of ISS across …rms
The 25% estimate captures the impact of ISS on aggregate shareholder support and does
not distinguish between di¤erent types of shareholders. ISS recommendations are likely to
have a stronger impact on institutional investors (who are the main clients of ISS), and especially on those institutions who subscribe to ISS rather than to other proxy advisors.34 In
addition, ISS recommendations are likely to have a stronger impact on smaller institutions,
which do not hold a concentrated stake in the …rm. This is because institutions with a
concentrated stake have stronger incentives to do their own independent research, and their
research allows them both to vote in a value-increasing way and to rationalize their voting
decisions, alleviating concerns about con‡icts of interest. Thus, institutions with a concentrated stake are less likely to follow ISS recommendations, regardless of whether they rely
on these recommendations for certi…cation or for information.
In this section, we present evidence consistent with these hypotheses by studying how the
impact of ISS on voting outcomes varies across …rms. In particular, we examine the impact of
ISS depending on the level of institutional ownership and the degree of institutional ownership
34
ISS controls about 61% of the market in terms of its clients’ equity assets. The second largest proxy
advisor, Glass Lewis, controls 36% of the market, and all other proxy advisory …rms have a much smaller
market share (Government Accountability O¢ ce report “Corporate Shareholder Meetings: Issues Relating
to Firms That Advise Institutional Investors on Proxy Voting”).
25
concentration (the data on ISS subscribers are not publicly available). Of course, because
the …rm’s ownership structure is determined endogenously, we should be cautious about the
interpretation of the cross-sectional results in this section.
To study the e¤ect of institutional ownership, we restrict the sample to observations in a
bandwidth around the cuto¤ and calculate the median institutional ownership in the resulting
sample. We next divide this sample into two subsamples, based on whether institutional
ownership falls below or above the median, and refer to the …rst (second) subsample as the
subsample of …rms with “low (high) institutional ownership.”We then repeat the RD analysis
in Sections 4.1-4.2 on each of the two subsamples. Because the sample size drops twice when
we cut the sample into these subsamples, we focus on a 10% bandwidth to avoid losing power
and to keep the size of each subsample similar to the sample size in our main tests. The
results of the estimation are presented in model 1 of Table 7. The coe¢ cient on NegRec in
the second stage is -24.1 and -32.3 for the low and high institutional ownership subsamples,
respectively. Thus, the magnitude of the coe¢ cients is consistent with the hypothesis that
the e¤ect of ISS is stronger in …rms with higher institutional ownership, although the formal
test cannot reject the hypothesis that the two coe¢ cients are equal.35
We next examine institutional ownership concentration by looking at the institutional
ownership Her…ndahl-Hirschman index, de…ned as the sum of squared share ownership over
all institutional investors (the variable instown_hhi from Thomson Reuters 13F). As before,
we divide the sample into subsamples depending on whether ownership concentration is above
or below the sample median and conduct the analysis separately on the two subsamples.
Models 2 and 3 of Table 7 present results for two speci…cations, not controlling and controlling
for the level of institutional ownership, respectively. As expected, the impact of ISS on
voting outcomes is stronger when institutional ownership is less concentrated, although the
di¤erence in coe¢ cients is not signi…cant. These results are in line with the …ndings of
Ertimur, Ferri, and Oesch (2013) and Iliev and Lowry (2015).
35
To test for the di¤erence in coe¢ cients, we use the Stata command cmp, which estimates multiequation
systems and can be applied in the 2SLS context.
26
8
Conclusion
Proxy voting is a key channel of shareholder activism and has been playing an increasingly
in‡uential role in corporate governance. It is therefore important to understand what factors
a¤ect investors’voting decisions. Proxy advisory …rms, and ISS in particular, have emerged
as prominent players in the proxy voting process. In this paper, we examine the role of ISS
by analyzing the e¤ect of its recommendations on voting outcomes and the channels of its
in‡uence. To study these questions, we use exogenous variation in ISS recommendations due
to a cuto¤ rule in the ISS voting guidelines. Speci…cally, when giving recommendations on
say-on-pay proposals, ISS used to conduct an initial screen of …rms based on their one- and
three-year TSRs and performed a deeper analysis of a …rm’s compensation policies if both
TSRs fell below certain industry-related cuto¤s. This rule suggests that the probability of a
negative ISS recommendation increases discontinuously for …rms just below the cuto¤, the
implication that we verify in the data. We therefore use a regression discontinuity design to
identify the causal e¤ect of ISS recommendations on voting outcomes. An advantage of this
approach is that because the TSR cuto¤s are based on industry medians, the sample of …rms
in a bandwidth around the cuto¤ is likely to be representative of the sample as a whole.
Our results are twofold. First, we …nd that ISS has a strong e¤ect on voting outcomes:
a negative ISS recommendation reduces the percentage of votes in favor of a say-on-pay
proposal by about 25 percentage points. This e¤ect is economically large, con…rming many
commentators’concerns about the in‡uence of proxy advisors. Second, we distinguish between two potential channels of ISS in‡uence: the informational role of ISS recommendations
and their certi…cation role. By comparing voting support for companies with positive ISS
recommendations that received and did not receive in-depth review from ISS, we do not …nd
evidence that investors rely on ISS recommendations because of their information content.
Hence, at least in our sample, the evidence is more consistent with the certi…cation channel,
whereby following ISS recommendations helps institutions protect themselves from criticism
and legal liability. Our …ndings contribute to the ongoing debate on the role and economic
impact of proxy advisory …rms.
27
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30
Figures and tables
Figure 1: Probability of a negative ISS recommendation and voting support
The …gure illustrates the fuzzy RD design by plotting the distribution of negative ISS recommendations and voting support around the cuto¤. The x-axis presents the forcing variable M axT SR,
measured in percentage points, in a 10% bandwidth centered around the cuto¤. The y-axis of the
left …gure corresponds to the probability of a negative ISS recommendation, measured in absolute
values (from 0 to 1). The y-axis of the right …gure corresponds to Votes, the percentage of votes in
favor of the say-on-pay proposal, measured in percentage points. The dashed vertical line represents
the cuto¤ M axT SR = 0. Each dot in the left (right) …gure represents the average probability of a
negative ISS recommendation (percentage of votes in favor of the say-on-pay proposal) in bins of
1%. The solid lines represent the …tted values of a third-degree polynomial of M axT SR. Variable
de…nitions are provided in Appendix A.
31
Figure 2: Probability of a negative ISS recommendation around the cuto¤
0
.1
.2
.3
.4
Probability of a Negative ISS Recommendation
-5
-4
-3
-2
-1
0
MaxTSR
1
2
3
4
5
The …gure shows that the probability of a negative ISS recommendation increases discontinuously for
…rms that fall just below the ISS cuto¤. The x-axis presents the forcing variable M axT SR, measured
in percentage points, in a 5% bandwidth centered around the cuto¤. The y-axis corresponds to
the probability of a negative ISS recommendation, measured in absolute values (from 0 to 1).
The dashed vertical line represents the cuto¤ M axT SR = 0. Each dot represents the average
probability of a negative ISS recommendation in bins of 1%. The solid and dashed lines represent,
respectively, the …tted values and 95% con…dence intervals of the …rst-stage regression NegRec
= 0 + 1 BelowCuto¤ + 2 M axT SR + 3 BelowCuto¤ M axT SR + u, estimated on a 5% bandwidth
around the cuto¤, i.e., on the interval 5% < M axT SR < 5%. This subsample includes 403
observations. Variables de…nitions are provided in Appendix A.
32
Figure 3: Voting support around the cuto¤
84
86
88
90
92
94
Percent of Votes in Favor
-5
-4
-3
-2
-1
0
MaxTSR
1
2
3
4
5
The …gure shows that voting support for say-on-pay proposals decreases discontinuously for …rms
that fall just below the ISS cuto¤. The x-axis presents the forcing variable M axT SR, measured
in percentage points, in a 5% bandwidth centered around the cuto¤. The y-axis corresponds to
Votes, the percentage of votes in favor of the say-on-pay proposal, measured in percentage points.
The dashed vertical line represents the cuto¤ M axT SR = 0. Each dot represents the average
percentage of votes in favor of the say-on-pay proposal in bins of 1%. The solid and dashed lines
represent, respectively, the …tted values and 95% con…dence intervals of the regression of Votes
on BelowCuto¤ , M axT SR, and the interaction term BelowCuto¤ M axT SR, estimated on a 5%
bandwidth around the cuto¤, i.e., on the interval 5% < M axT SR < 5%. This subsample includes
403 observations. Variable de…nitions are provided in Appendix A.
33
Figure 4: Density of the forcing variable
0
.05
.1
.15
.2
Density of MaxTSR
-5
-4
-3
-2
-1
0
1
MaxTSR
2
3
4
5
The …gure con…rms that companies do not manipulate their TSRs to push themselves above the
ISS cuto¤ by showing that the density of the forcing variable is smooth around the cuto¤. The
x-axis presents the forcing variable M axT SR, measured in percentage points, in a 5% bandwidth
centered around the cuto¤. The y-axis corresponds to the density of M axT SR, measured in absolute
values. The solid vertical line represents the cuto¤ M axT SR = 0. The …gure shows the histogram,
estimated density, and 95% con…dence intervals of M axT SR in a 5% bandwidth around the cuto¤,
generated using the code provided by J. McCrary on his website and based on McCrary (2008).
This subsample includes 403 observations. The absolute value of the corresponding test statistic is
0.84, which is not statistically signi…cantly di¤erent from zero at any conventional level.
34
Figure 5: Falsi…cation tests on alternative samples
(a) Say-on-pay proposals in 2012
(b) Director elections in 2010-2011
80
90
85
92
90
94
95
96
98
100
-5
-4
-3
-2
-1
0
MaxTSR
1
2
3
4
5
-5
-4
-3
-2
-1
0
MaxTSR
1
2
3
4
5
The …gures show that voting support exhibits no discontinuity around the cuto¤ M axT SR = 0 for
two samples where the ISS cuto¤ rule does not apply. Figure (a) considers the sample of say-on-pay
proposals in 2012. Figure (b) considers the sample of director elections, where to match our main
sample, we restrict attention to 2010 and 2011 and to those …rms in each year that had a say-on-pay
proposal in that year, and aggregate the observations by …rm-year-recommendation. The x-axis
presents the forcing variable M axT SR, measured in percentage points, in a 5% bandwidth centered
around the cuto¤. The y-axis corresponds to Votes, the percentage of votes in favor of the say-onpay proposal (directors), measured in percentage points. The dashed vertical line represents the
cuto¤ M axT SR = 0. Each dot represents the average percentage of votes in favor of the say-on-pay
proposal (directors) in bins of 1%. The solid and dashed lines represent, respectively, the …tted
values and 95% con…dence intervals of the regression of Votes on BelowCuto¤ , M axT SR, and the
interaction term BelowCuto¤ M axT SR, estimated on a 5% bandwidth around the cuto¤, i.e., on
the interval 5% < M axT SR < 5%. This subsample includes 289 and 470 observations for 2012
and director elections, respectively. Variable de…nitions are provided in Appendix A.
35
Figure 6: Placebo tests using alternative cuto¤s
(a) Cuto¤ -3%
(b) Cuto¤ 3%
80
80
85
85
90
90
95
95
-8
-7
-6
-5
-4
-3
MaxTSR
-2
-1
0
1
2
-2
-1
0
(c) Cuto¤ -5%
1
2
3
MaxTSR
4
5
6
7
8
8
9
10
(d) Cuto¤ 5%
88
82
84
90
86
92
88
94
90
96
-10
-9
-8
-7
-6
-5
MaxTSR
-4
-3
-2
-1
0
0
1
2
3
4
5
MaxTSR
6
7
The …gure shows that voting support on the sample of say-on-pay proposals in 2010-2011 exhibits
no discontinuity around placebo cuto¤s M axT SR = c for c 2 f 3%; 3%; 5%; 5%g. For each c, the
x-axis presents the forcing variable M axT SR, measured in percentage points, in a 5% bandwidth
centered around the cuto¤ M axT SR = c. The y-axis corresponds to Votes, the percentage of
votes in favor of the say-on-pay proposal, measured in percentage points. The dashed vertical line
represents the cuto¤ M axT SR = c. Each dot represents the average percentage of votes in favor
of the say-on-pay proposal in bins of 1%. The solid and dashed lines represent, respectively, the
…tted values and 95% con…dence intervals of the regression of Votes on BelowCuto¤ c , M axT SR,
and BelowCuto¤ c M axT SR, estimated on c 5% < M axT SR < c + 5%, where BelowCuto¤ c
is the indicator variable that equals one if M axT SR < c and zero otherwise. This subsample
includes 364, 453, 327, and 458 observations for c = 3%, 3%, 5%, and 5%, respectively. Variable
de…nitions are provided in Appendix A.
36
Table 1: Descriptive statistics
Panel A
Mean
29.86
-0.80
18.99
4.35
1.89
0.02
4.76
0.40
0.72
0.12
90.13
TSR(1) (%)
TSR(3) (%)
MaxTSR (%)
Market Value of Equity (in $ billion)
Market-to-Book
ROA
CEO Total Compensation (in $ million)
Proportion of Stock-Based Compensation
Institutional Ownership
Insider Ownership
Voting support (in percentage points)
25th
4.11
-11.57
-3.21
0.42
1.04
0.00
1.46
0.23
0.58
0.02
86.97
50th
21.30
0.16
8.50
1.10
1.35
0.03
3.00
0.42
0.76
0.05
94.88
75th
47.36
9.42
28.50
3.43
2.06
0.07
6.20
0.57
0.88
0.13
97.74
Std Dev
49.87
19.22
42.85
8.59
1.68
0.15
4.96
0.27
0.23
0.18
11.72
Panel B
ISS recommendation
Against
For
Total (Against and For)
Mean
68.9%
93.2%
90.1%
Voting Support
10th
50th
90th
48.5% 68.9% 88.7%
83.3% 95.6% 99.1%
73.5% 94.9% 99.0%
Std Dev
15.2%
7.0%
11.7%
Number of observations
by voting outcome
Fail Pass
Total
29
227
256
0
1,764
1,764
29 1,991
2,020
The table presents descriptive statistics for the sample of 1,932 companies and 2,020 say-on-pay
proposals in 2010 and 2011. Panel A presents summary statistics (average, percentiles, and standard
deviation) for the variables used in the study. Panel B presents voting outcomes depending on the
ISS recommendation. Columns 2-6 of Panel B present summary statistics of the voting support
for the subsample of “Against” recommendations, the subsample of “For” recommendations, and
the whole sample. The next two columns present the number of observations with a given voting
outcome (fail or pass) for a given ISS recommendation, where a proposal fails if it receives less
than 50% support. The last column presents the total number of observations with a given ISS
recommendation. Variable de…nitions are provided in Appendix A.
37
Table 2: Probability of a negative ISS recommendation (…rst stage)
BelowCuto¤
MaxTSR
(1)
NegRec
(2)
NegRec
(3)
NegRec
(4)
NegRec
(5)
NegRec
0.145**
(0.068)
-0.008
(0.012)
0.146**
(0.069)
-0.010
(0.016)
0.004
(0.024)
0.146**
(0.070)
-0.017
(0.016)
0.013
(0.024)
0.148**
(0.067)
-0.012
(0.016)
0.009
(0.023)
0.007*
(0.004)
0.062***
(0.012)
-0.064
(0.093)
-0.007
(0.096)
0.412***
(0.125)
No
No
403
0.062
No
No
403
0.062
Yes
Yes
403
0.076
Yes
Yes
394
0.184
0.142**
(0.067)
-0.014
(0.016)
0.010
(0.023)
0.018***
(0.006)
0.061***
(0.012)
-0.060
(0.095)
0.004
(0.096)
0.360***
(0.127)
-0.043**
(0.020)
-0.133
(0.172)
0.024
(0.023)
Yes
Yes
393
0.198
BelowCuto¤ MaxTSR
CEO Total Compensation
Change in CEO Total Compensation
Insider Ownership
Log(Market Value of Equity)
ROA
M/B
Year FE
Industry FE
Observations
R2
The table shows that the probability of a negative ISS recommendation increases discontinuously
if the company falls just below the ISS cuto¤ and corresponds to the …rst stage of the 2SLS
procedure. All speci…cations are estimated on a 5% bandwidth around the cuto¤, i.e., on the
interval 5% < M axT SR < 5%. The outcome variable is NegRec, the indicator variable that
equals one if ISS gives a negative recommendation, and zero otherwise. The main variable of
interest is BelowCuto¤ , which is an indicator variable that equals one if the …rm is below the ISS
cuto¤ (M axT SR < 0), and zero otherwise. We estimate a linear probability model, and hence
the coe¢ cient on BelowCuto¤ can be interpreted as the di¤erence in the probability of a negative
ISS recommendation between …rms just below and just above the cuto¤. Variable de…nitions are
provided in Appendix A. Standard errors are reported in parentheses. *, **, and *** represent
signi…cance at the 10%, 5%, and 1% levels, respectively.
38
Table 3: Causal e¤ect of ISS recommendations on voting outcomes (second stage)
NegRec
MaxTSR
(1)
(2)
(3)
(4)
(5)
Votes
Votes
Votes
Votes
Votes
-24.620**
(11.210)
0.045
(0.358)
-25.111**
(11.160)
0.175
(0.450)
-0.294
(0.560)
-29.001**
(11.588)
0.129
(0.531)
-0.302
(0.585)
-28.717***
(10.655)
0.125
(0.455)
-0.198
(0.544)
-0.273**
(0.120)
0.261
(0.720)
-0.047
(2.228)
-5.597**
(2.246)
13.023**
(5.292)
No
No
403
0.547
No
No
403
0.547
Yes
Yes
403
0.536
Yes
Yes
394
0.605
-26.555**
(10.853)
0.226
(0.463)
-0.277
(0.534)
-0.677***
(0.241)
0.167
(0.717)
-0.210
(2.218)
-6.009***
(2.186)
14.370***
(4.861)
1.658**
(0.665)
5.750
(4.247)
0.353
(0.585)
Yes
Yes
393
0.628
BelowCuto¤ MaxTSR
Insider Ownership
ROA
M/B
Year FE
Industry FE
Observations
R2
The table shows that a negative ISS recommendation causes a signi…cant decline in the proportion
of votes in favor of a say-on-pay proposal and corresponds to the second stage of the 2SLS procedure.
The outcome variable is Votes, the percentage of votes in favor of a say-on-pay proposal, measured
in percentage points. The instrumented variable is NegRec, the indicator variable that equals one if
ISS gives a negative recommendation, and zero otherwise. Estimation is conducted via 2SLS, where
NegRec = 0 + 1 BelowCuto¤ + 2 MaxTSR+ 3 BelowCuto¤ MaxTSR+ X + u is the …rst stage,
estimated in Table 2, and Votes = 0 + 1 NegRec+ 2 MaxTSR + 3 BelowCuto¤ MaxTSR+ X + "
is the second stage, estimated in this table. The variable BelowCuto¤ is an indicator variable that
equals one if the …rm is below the ISS cuto¤ (M axT SR < 0), and zero otherwise. All speci…cations
are estimated on a 5% bandwidth around the cuto¤, i.e., on the interval 5% < M axT SR < 5%.
Variable de…nitions are provided in Appendix A. Standard errors are reported in parentheses. *,
**, and *** represent signi…cance at the 10%, 5%, and 1% levels, respectively.
39
Table 4: Voting outcomes for …rms with a positive ISS recommendation
BelowCuto¤
MaxTSR
(1)
Votes
(2)
Votes
(3)
Votes
(4)
Votes
(5)
Votes
-2.201
(1.386)
-0.166
(0.238)
-2.167
(1.415)
-0.189
(0.305)
0.060
(0.490)
-2.301
(1.450)
-0.219
(0.309)
0.128
(0.504)
-1.756
(1.431)
-0.228
(0.304)
0.342
(0.494)
-0.230***
(0.082)
-0.792
(0.567)
0.586
(1.951)
-1.925
(2.027)
7.976***
(2.916)
No
No
338
0.012
No
No
338
0.012
Yes
Yes
338
0.015
Yes
Yes
329
0.101
-1.605
(1.396)
-0.124
(0.298)
0.174
(0.486)
-0.584***
(0.124)
-0.498
(0.568)
0.230
(1.972)
-2.418
(1.981)
9.179***
(2.853)
1.428***
(0.425)
7.896**
(3.348)
0.072
(0.499)
Yes
Yes
328
0.159
BelowCuto¤ MaxTSR
Insider Ownership
ROA
M/B
Year FE
Industry FE
Observations
R2
The table shows that voting outcomes for say-on-pay proposals with a positive ISS recommendation
for companies just below and just above the ISS cuto¤ are not statistically signi…cantly di¤erent
from each other. We …rst restrict the sample to observations with a positive ISS recommendation
and then estimate all speci…cations on a 5% bandwidth around the cuto¤, i.e., on the interval
5% < M axT SR < 5%. The outcome variable is Votes, the percentage of votes in favor of a
say-on-pay proposal, measured in percentage points. The main variable of interest is BelowCuto¤ ,
which is an indicator variable that equals one if the …rm is below the ISS cuto¤ (M axT SR < 0),
and zero otherwise. The coe¢ cient on BelowCuto¤ can be interpreted as the di¤erence between
voting support for …rms just below the cuto¤ and voting support for …rms just above the cuto¤,
conditional on a positive ISS recommendation. Variable de…nitions are provided in Appendix A.
Standard errors are reported in parentheses. *, **, and *** represent signi…cance at the 10%, 5%,
and 1% levels, respectively.
40
Table 5: Distribution of …rm characteristics around the cuto¤
M/B
ROA
Change in CEO Total
Compensation
Proportion of Stock-Based
Compensation
Insider Ownership
RD coe¤. on
BelowCuto¤
-0.02
(0.29)
-0.10
(0.15)
-0.03
(0.02)
0.06
(0.96)
6.33
(29.38)
0.06
(0.04)
-0.03
(0.04)
0.01
(0.03)
-5%<MaxTSR<0
Mean
# Obs
0<MaxTSR<5%
Mean
# Obs
p-value
(di¤. in means)
7.62
173
7.49
230
0.39
1.56
173
1.62
229
0.49
0.03
173
0.03
230
0.82
5.81
173
5.17
230
0.22
0.44
172
0.41
227
0.85
0.42
173
0.42
230
0.91
0.72
173
0.71
230
0.62
0.12
169
0.09
226
0.12
The table shows that the distribution of …rm characteristics is smooth around the cuto¤. For each
…rm characteristic in the …rst column, column “RD coe¤. on BelowCuto¤”presents the results of a
local linear regression of this characteristic on BelowCuto¤ , M axT SR, BelowCuto¤ M axT SR, and
year and industry …xed e¤ects using a 5% bandwidth around the cuto¤. The estimated coe¢ cients
on BelowCuto¤ are reported in the …rst row, and standard errors are reported in parentheses.
Subsequent columns present the means of each …rm characteristic in two narrow intervals around
the cuto¤: 5% < M axT SR < 0 and 0 < M axT SR < 5%, as well as the corresponding number of
observations in these intervals. The last column presents the p-values for the di¤erence in means
test. Variable de…nitions are provided in Appendix A.
41
Table 6: Robustness
(1)
3%
(2)
4%
(3)
5%
(4)
6%
(5)
7%
(6)
8%
BelowCuto¤
(First Stage)
0.176**
(0.087)
0.170**
(0.075)
0.146**
(0.070)
0.186***
(0.063)
0.186***
(0.057)
0.190***
(0.053)
NegRec
(Second Stage)
-20.329*
(12.011)
-24.680**
(10.261)
-29.001**
(11.588)
-27.992***
(8.330)
-21.089***
(7.807)
-23.465***
(7.084)
238
313
403
490
574
651
(7)
9%
(8)
10%
(9)
20%
Quadratic
(10)
20%
Cubic
(11)
20%
Quartic
(12)
5%
Probit
0.199***
(0.050)
0.178***
(0.047)
0.194***
(0.051)
0.190***
(0.065)
0.205***
(0.078)
0.641** [0.131]
(0.324)
-23.292***
(6.367)
-28.562***
(6.931)
-22.384***
(7.073)
-23.616***
(9.164)
-23.905**
(10.181)
-29.340***
(11.565)
717
785
1,244
1,244
1,244
403
Observations
BelowCuto¤
(First Stage)
NegRec
(Second Stage)
Observations
The table shows that the choice of the bandwidth and the degree of the fuzzy RD polynomial do
not have a material e¤ect on the results of the paper. The …rst two rows in each panel present
the estimate and standard error of the coe¢ cient on BelowCuto¤ in the …rst-stage regression, and
the third and fourth rows present the estimate and standard error of the coe¢ cient on NegRec in
the second-stage regression of the 2SLS procedure. Columns 1-8 present the results for the linear speci…cation corresponding to model 3 in Tables 2 and 3 (i.e., with regressors BelowCuto¤ ,
M axT SR, BelowCuto¤ M axT SR, and year and industry …xed e¤ects) on alternative bandwidths,
ranging between 3% and 10%. In columns 9, 10, and 11, respectively, we estimate second, third,
and fourth-order polynomial functions of the forcing variable M axT SR, allowing for di¤erent functional forms on the two sides of the cuto¤, on a 20% bandwidth around the cuto¤. For example, the
regressors in column 9 include BelowCuto¤ , M axT SR, M axT SR2 , BelowCuto¤ M axT SR, BelowCuto¤ M axT SR2 , and year and industry …xed e¤ects. In column 12, we estimate the probit model
for the …rst-stage regression. The marginal e¤ect of the coe¢ cient on BelowCuto¤ is reported in
square brackets to the right of the coe¢ cient. Variable de…nitions are provided in Appendix A. *,
**, and *** represent signi…cance at the 10%, 5%, and 1% levels, respectively.
42
Table 7: Variation in the e¤ect of ISS across …rms
(1)
(2)
(3)
BelowCuto¤
(First Stage)
Low
High
0.176**
0.187***
(0.068)
(0.069)
Ownership Concentration
Low
High
0.164**
0.158**
(0.071)
(0.066)
Ownership Concentration
Low
High
0.158**
0.156**
(0.071)
(0.066)
NegRec
(Second Stage)
-24.080***
(8.649)
-32.303***
(10.190)
-41.117***
(12.066)
-21.269*
(11.044)
-43.024***
(12.884)
-19.946*
(10.799)
Yes
Yes
Yes
389
No
Yes
Yes
Yes
386
No
Yes
Yes
Yes
386
Yes
Yes
Yes
Yes
386
Yes
Yes
Yes
Yes
386
IO control
MaxTSR controls
Year FE
Industry FE
Observations
Yes
Yes
Yes
388
The table shows how the e¤ect of ISS on voting outcomes varies with the …rm’s ownership structure.
Model 1 considers the e¤ect of institutional ownership, and models 2 and 3 consider the e¤ect
of institutional ownership concentration. Institutional ownership is de…ned as total institutional
ownership in fraction of shares outstanding (instown_perc from Thomson Reuters 13F). Ownership
concentration is measured by the institutional ownership Her…ndahl-Hirschman index, de…ned as
the sum of squared share ownership over all institutional investors (the variable instown_hhi from
Thomson Reuters 13F). We …rst restrict the sample to observations with M axT SR within a 10%
bandwidth around the cuto¤ and calculate the median value of institutional ownership (ownership
concentration) in the resulting sample. Next, we divide this sample into two subsamples, based
on whether the …rm’s institutional ownership (ownership concentration) falls below or above the
median, and refer to the …rst and second subsample as the subsample of …rms with low and high
institutional ownership (ownership concentration), respectively. We then repeat the 2SLS procedure
on each of the two subsamples. Models 1 and 2 present the results for the linear speci…cation with
regressors BelowCuto¤ , M axT SR, BelowCuto¤ M axT SR, and year and industry …xed e¤ects, and
in model 3, we also control for the level of institutional ownership. Variable de…nitions are provided
in Appendix A. *, **, and *** represent signi…cance at the 10%, 5%, and 1% levels, respectively.
43
Appendix
A. Variable de…nitions
Main variables
Variable
De…nition
Source
T SR(1) (%)
One-year TSR, de…ned as the one year percentage change
in the adjusted close price multiplied by the total return factor:
T SR(1) =100 [(PRCCMt TRFMt /AJEXMt ) /
(PRCCMt 1 TRFMt 1 /AJEXMt 1 ) - 1].
CSRP/Compustat
Merged
T SR(3) (%)
Three-year TSR, de…ned as the annualized three year percentage change in the adjusted close price multiplied by the total return factor: T SR(3) =100 [[(PRCCMt TRFMt /AJEXMt ) /
(PRCCMt 3 TRFMt 3 /AJEXMt 3 )]1=3 - 1].
CSRP/Compustat
Merged
MaxTSR (%)
De…ned as max(T SRit M edianT SRit ; T SRit M edianT SRit ),
(n)
where T SRit is the n-year TSR of …rm i in year t, and
(n)
M edianT SRit is the median n-year TSR in year t computed across
all Russell 3000 …rms in the same four-digit GICS group as …rm i.
CSRP/Compustat
Merged
BelowCuto¤
Indicator variable that takes the value of one if MaxTSR is negative,
and zero otherwise.
CSRP/Compustat
Merged
Votes (%)
The percentage of votes in favor of a say-on-pay proposal.
ISS Voting Analytics
NegRec
The indicator variable that takes the value of one if ISS gives a negative ("Against") recommendation, and zero otherwise.
ISS Voting Analytics
(1)
(1)
44
(3)
(3)
Control variables
Variable
De…nition
Source
Natural logarithm of the market value of equity, which is
calculated by multiplying the company’s stock price by its
number of outstanding shares as of its …scal-year-end.
Compustat
M/B
The ratio of the market value of assets (equity market capitalization plus the book value of other liabilities) to the
book value of assets.
Compustat
ROA
Net income divided by total assets: ROA=NI/AT.
Compustat
($ million)
The total compensation of the CEO (variable CEOTotSumComp from GMI Ratings) as reported in the company’s
proxy statement. It equals the aggregate total dollar value
of each form of compensation quanti…ed in the summary
compensation table, including base salary, bonus, stock
awards, option awards, non-equity incentive plan, change
in pension value and nonquali…ed deferred compensation,
and all other compensation.
GMI Ratings
Proportion of Stock-Based
Compensation
The sum of stock and option awards divided by CEO
total compensation: (CEOOptionAwards + CEOStockAwards)/CEOTotSumComp from GMI Ratings.
GMI Ratings
Change in CEO Total
Compensation
De…ned as (CEO Total Compensation in year t - CEO Total
Compensation in year t-1)/CEO Total Compensation in
year t-1.
GMI Ratings
Total institutional ownership in fraction of shares outstanding (INSTOWN_PERC from Thomson Reuters Institutional (13f) Holdings - Stock Ownership Summary).
Thomson Reuters 13F
Insider Ownership
The estimated fraction of outstanding shares held by top
management and directors, as reported in the company’s
most recent proxy statement (InsidersPctg from GMI Ratings).
GMI Ratings
45
B. TSR cuto¤s from the ISS website
The following picture presents a screenshot from the ISS website: http://www.issgovernance.com/policygateway/industry-group-us-tsr-medians-performance-related-policy. It contains the list of ISS cuto¤s, i.e.,
one- and three-year median TSRs for each four-digit GICS group, downloaded at the end of the four most
recent calendar quarters. (ISS provides these cuto¤s because it still uses them for some issues, e.g., CEOchairman separation.) We downloaded similar lists from the ISS website for most periods in our sample.
The following picture presents an expanded view of the last table above, with the TSRs downloaded on
March 31, 2015.
46
C. Cross-validation procedure
To …nd the optimal bandwidth, we perform the following cross-validation procedure, as outlined in
Imbens and Lemieux (2008).
1. First, we discard 50% of observations from the left tale on the left side of the cuto¤ and 50%
of observations from the right tale on the right side of the cuto¤.
2. Then, for a given bandwidth h, we take the following steps.
(a) For each observation i to the right of the cuto¤ (M axT SRi > 0), we estimate a linear regression at M axT SRi , using observations within bandwidth h to the right of
M axT SRi . In particular, let (^ R (M axT SRi ) ; ^ R (M axT SRi )) solve
8
9
<
=
X
min
(N egRecj
(M axT SRj M axT SRi ))2 :
;
; :
j:M axT SRi <M axT SRj <M axT SRi +h
(b) Similarly, for each observation i to the left of the cuto¤ (M axT SRi < 0), we estimate
a linear regression at M axT SRi , using observations within bandwidth h to the left of
M axT SRi . In particular, let (^ L (M axT SRi ) ; ^ L (M axT SRi )) solve
8
9
<
=
X
min
(N egRecj
(M axT SRj M axT SRi ))2 :
;
; :
j:M axT SRi h<M axT SRj <M axT SRi
(c) We then calculate the …tted value ^ (M axT SRi ) as ^ R (M axT SRi ) for observations
to the right of the cuto¤ (i.e., those with M axT SRi > 0), and as ^ L (M axT SRi ) for
observations to the left of the cuto¤ (i.e., those with M axT SRi < 0).
(d) For each observation i, we calculate the deviation of the …tted value ^ (M axT SRi )
from the actual value N egReci , and calculate the sum of squared deviations across all
observations:
N
1 X
(N egReci ^ (M axT SRi ))2
CVN egRec (h)
N
i=1
3. The optimal bandwidth for the …rst-stage regression is then calculated as
hopt
1 = arg min CVN egRec (h)
h
4. We repeat steps 2-3 above, but for the outcome variable Votes. In particular, we replace
N egRec by Votes in all equations of step 2 and …nd the corresponding optimal bandwidth as
hopt
2 = arg minh CVV otes (h).
5. The optimal bandwidth, which should be used in the estimation of both the …rst and the
second stage, is then given by
opt
hopt = min(hopt
1 ; h2 ):
47
D. Additional tables
Table D.1: First-stage regression on the 2012 say-on-pay sample
BelowCuto¤
MaxTSR
(1)
NegRec
0.044
(0.072)
-0.002
(0.014)
(2)
NegRec
0.046
(0.072)
-0.010
(0.019)
0.016
(0.027)
(3)
NegRec
0.070
(0.078)
-0.004
(0.021)
0.017
(0.029)
(4)
NegRec
0.111
(0.080)
0.003
(0.021)
0.011
(0.029)
0.021***
(0.005)
0.001
(0.000)
-0.046
(0.104)
-0.022
(0.119)
0.051
(0.291)
No
No
289
0.007
No
No
289
0.008
Yes
Yes
289
0.007
Yes
Yes
274
0.093
BelowCuto¤ MaxTSR
Insider Ownership
ROA
M/B
Year FE
Industry FE
Observations
R2
(5)
NegRec
0.083
(0.079)
-0.009
(0.020)
0.021
(0.029)
0.032***
(0.008)
0.000
(0.000)
-0.072
(0.104)
-0.034
(0.118)
-0.124
(0.290)
-0.046*
(0.025)
-0.236
(0.339)
0.011
(0.033)
Yes
Yes
272
0.115
The table presents the …rst-stage regression for the sample of say-on-pay proposals in 2012. All
speci…cations are estimated on a 5% bandwidth around the cuto¤. The outcome variable is NegRec,
the indicator variable that equals one if ISS gives a negative recommendation, and zero otherwise.
BelowCuto¤ is an indicator variable that equals one if the …rm is below the ISS cuto¤, and zero
otherwise. Variable de…nitions are provided in Appendix A. Standard errors are reported in parentheses. *, **, and *** represent signi…cance at the 10%, 5%, and 1% levels, respectively.
48
Table D.2: First-stage regression on the sample of director elections
BelowCuto¤
MaxTSR
(1)
NegRec
0.101
(0.062)
0.014
(0.011)
(2)
NegRec
0.096
(0.062)
0.030**
(0.014)
-0.033
(0.021)
(3)
NegRec
0.080
(0.065)
0.029*
(0.015)
-0.035
(0.022)
(4)
NegRec
0.062
(0.066)
0.030**
(0.015)
-0.036
(0.022)
0.002
(0.004)
-0.000
(0.000)
-0.006
(0.089)
-0.164*
(0.093)
0.358***
(0.113)
No
No
470
0.006
No
No
470
0.011
Yes
Yes
470
0.010
Yes
Yes
448
0.060
BelowCuto¤ MaxTSR
Insider Ownership
ROA
M/B
Year FE
Industry FE
Observations
R2
(5)
NegRec
0.059
(0.066)
0.029*
(0.015)
-0.033
(0.022)
-0.002
(0.006)
-0.000
(0.000)
-0.031
(0.091)
-0.154
(0.094)
0.374***
(0.115)
0.020
(0.019)
-0.248
(0.160)
-0.008
(0.023)
Yes
Yes
447
0.067
The table presents the …rst-stage regression for the sample of director elections. To match our
main sample, we restrict attention to 2010 and 2011 and to those …rms in each year that had a
say-on-pay proposal in that year, and aggregate the observations by …rm-year-recommendation. All
speci…cations are estimated on a 5% bandwidth around the cuto¤. The outcome variable is NegRec,
the indicator variable that equals one if ISS gives a negative recommendation, and zero otherwise.
The main variable of interest is BelowCuto¤ , which is an indicator variable that equals one if the
…rm is below the ISS cuto¤ (M axT SR < 0), and zero otherwise. Variable de…nitions are provided
in Appendix A. Standard errors are reported in parentheses. *, **, and *** represent signi…cance
at the 10%, 5%, and 1% levels, respectively.
49

The Role of Proxy Advisory Firms: Evidence from a Regression+

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