Peer Group Choice and Chief Executive Officer Compensation

David F. Larcker

dlarcker@stanford.edu

Graduate School of Business

Rock Center for Corporate Governance

Stanford University

Charles McClure

charles.mcclure@chicagobooth.edu

Booth School of Business

University of Chicago

Christina Zhu

chrzhu@wharton.upenn.edu

The Wharton School

University of Pennsylvania

Draft: October 23, 2018

Abstract. We examine the peer group selection that boards of directors use when setting the level

of CEO compensation. This choice is controversial because it is difficult to ascertain whether peer

groups are selected to (i) attract and retain top executive talent or (ii) enable rent extraction by

inappropriately increasing CEO compensation. In contrast to prior research, our analysis utilizes

the degree to which the observed portfolio compensation level of peers is unusual relative to all

potential portfolios of peers the board of directors could have reasonably selected. Using a sample

of 12,894 firm-year observations covering the time period from 2008 to 2014, we estimate roughly

68% of firms appear to be engaging in rent extraction, while the remaining 32% seem to be

selecting peers to attract and retain CEO talent. Relative to firms which appear to select peers for

aspirational reasons, we find rent extraction firms have more realized negative governance

outcomes, but we find more structural governance differences between aspirational firms and the

smallest-sized rent extraction firms.

Keywords: CEO Compensation; Peer Groups; Agency Problems; CEO Labor Market

JEL Classification: M12, M52, G30, J33

We gratefully acknowledge the support of the Stanford Rock Center for Corporate Governance and the Stanford

Graduate School of Business Centers & Initiatives for Research, Curriculum and Learning Experiences (CIRCLE).

Charles McClure is grateful for the support from University of Chicago, Booth School of Business. Christina Zhu is

grateful for the support from The Wharton School, University of Pennsylvania. We thank Nathan Atkinson, Kurt

Gee, Ivan Marinovic, Venky Nagar, and workshop participants at Stanford University and Rice University for

helpful suggestions. We thank Christopher Wiley for assistance with the portfolio approach algorithm. We thank

several compensation consultants for sharing their institutional insight.

1

Peer Group Choice and Chief Executive Officer Compensation

1. Introduction

An important factor in setting executive compensation is the choice of peer firms used to

develop a benchmark for the CEO’s market wage. One controversial question in this process is

whether it is appropriate for firms to select highly paid peer firms that are larger than themselves.

Firms aspiring to invest in executive talent may select larger peers to attract and hire executives

from firms that command higher pay. Similarly, if firms lose their top executive talent to larger

firms, they may rationally change their peer selection to include those firms. For example, in

2015 American Axle & Manufacturing Holdings Inc. (AAM) had a peer group of 20 firms and

notes that this group “includes companies that compete with AAM for executive talent […] The

Committee believes that this approach reflects a generally accepted benchmark of external

competitiveness and supports our ability to attract and retain key executives”.1

In contrast to the rationale cited by board of directors for selecting larger peers, governance

activists and proxy advisory firms believe some firms select peers that are larger and/or have

higher compensation levels simply to justify a high level of CEO compensation. For example,

Glass Lewis criticized Omnicare for this practice:

We note the following concerns with the structure of the Company's

compensation programs:

Peer Group Concerns. A company's choice of a peer group can have a significant

impact on the size and structure of compensation. Shareholders need to be

satisfied that the peer group is appropriate and not cherry-picked for the purpose

of justifying or inflating pay. In general, we believe a peer group should range

from 0.5 to 2 times the market capitalization of the Company. In this case, Glass

Lewis has identified 23 peers outside this range, which represents approximately

79.4% of the peer group. (Meeting date May 24, 2011, quote from Ertimur et al.,

2013)

1 AAM DEF 14A filed on 03/24/2016, p. 19.

2

Similarly, Institutional Shareholder Services (ISS) includes an assessment of “Peer Group

Benchmarking” as a key evaluation focus in its recommendations for say-on-pay votes.2

Given the controversy surrounding the choice of peer groups, it is important to understand

whether peer group selection is either an appropriate labor market benchmarking analysis by the

board of directors or a mechanism for rent extraction by the CEO. Prior research provides mixed

insights into the determinants and consequences of peer group selection (Faulkender and Yang,

2010; Bizjak et al., 2011; Albuquerque et al., 2013; Cadman and Carter, 2013; Francis et al.,

2016). This observation mirrors the generally mixed scholarly views on executive compensation.

For example, some researchers view high executive compensation as rent extraction (Bebchuk

and Fried, 2005), whereas others interpret high wages as a natural outcome produced by labor

market competition for executive talent (Edmans and Gabaix, 2016). This study addresses a

variety of conceptual and methodological issues in prior research and provides new insights into

the choice of peer groups.

We make several contributions to the existing literature on peer group selection. First, we

show serious limitations of the matching methodology used in prior research. The standard

methodology essentially follows a procedure of propensity score matching each selected peer

with a similar non-peer firm, and then assessing whether the central tendency of CEO

compensation in the selected peer group is different (e.g., perhaps higher) than the central

tendency of CEO compensation for the matched group of peers or not. While this

methodological approach may seem reasonable at first glance, we show that a large fraction of

the matches produced by propensity score approaches lead to “comparable” peer firms that are

neither similar in terms of industry, size, nor other traditional selection criteria used by

2 See Institutional Shareholder Services (2015) for a general discussion of proxy advisory firms’ evaluation of peer

group selection by firms.

3

compensation committees. Thus, the resulting matched-pairs analyses are confounded and likely

to provide biased inferences about the board’s objectives when selecting peer groups.

Second, we shift the unit of analysis from the selection of individual peer firms to the

selection of a portfolio of peer firms. Although compensation committees clearly assess each

individual firm for inclusion in the peer group, they are ultimately selecting a portfolio of firms

that informs or justifies their choice for the level of CEO compensation.3 The CEO compensation

benchmark is often set at the median pay of the selected portfolio of peer firms. From the

compensation committee’s perspective, the desirability of each peer firm will depend on the

other firms already included in the peer group. Since matching methods such as propensity score

approaches ignore this dependence, the results produced by prior research are likely to be

misleading.

In order to address this concern, we develop a new measure for assessing peer groups,

denoted as Peer Portfolio Percentile (PPP), which mimics the board of director’s actual peer

group selection process. Specifically, we compare the median compensation for the selected peer

group to the distribution of median compensation for all alternative peer groups that could have

been reasonably selected by the board of directors using traditional selection benchmarks such as

firm size and industry. This measure enables us to assess whether the selected peer group

produces a CEO compensation benchmark that is at the 1st, 50th, 99th, or any other percentile of

the distribution of plausible peer groups. This distributional measure better captures whether the

peer group choice by the board of directors results in an unusually high or low compensation

benchmark.

3 To provide some confirmation of this assumption, we interviewed six senior compensation consultants at top

consulting firms. These conversations clearly support the idea that firms and consultants assess the applicability of

individual firms (i.e., some are clear inclusions and others are clear exclusions), but ultimately they are concerned

about the median compensation benchmark produced by a portfolio of peer firms.

4

Third, we assess whether peer group selection is associated with future firm performance. If

firms choose a highly paid peer group (i.e., a high PPP) to attract more talented executives who

require higher compensation, we should find that the high PPP selection is related to higher

future firm performance. Conversely, if higher peer group pay reflects rent extraction, we should

observe a negative relation between peer group pay and future operating performance. We expect

both aspirational and agency motivations to be observed in a large cross-section of firms.

Fourth, our methodological approach allows for the sample to contain a mixture of firms

where the estimated coefficient linking peer group choice with future firm performance can be

positive or negative for different subsets of firms. The typical pooled estimation approach used

in prior research does not easily allow for this type of heterogeneity. Thus, we use Latent Class

Analysis (LCA) to place firms into different homogeneous clusters depending on the sign and

statistical significance of the association between peer group choice and future firm

performance.4 Once these clusters are identified, we can uncover the distinguishing factors

associated with the observations in each cluster. For example, we expect firms with a negative

association between peer group pay and future operating performance to exhibit characteristics

associated with weak corporate governance. In contrast, we expect firms with a positive

association to have strong corporate governance.

Finally, we analyze a larger sample of firms and a longer time period than the research to

date. Prior literature typically analyzes only two or three years of data for a subset of firms

(Albuquerque et al., 2013; Faulkender and Yang, 2010, 2013; Bizjak et al., 2011). Specifically,

4 LCA has been used in empirical research in several disciplines, including biostatistics, medicine, psychology, and

marketing. For example, the marketing literature has used LCA for the purpose of consumer segmentation, and

researchers in the health sciences have used LCA to identify phenotypes of different diseases and disorders. For a

review of applications of LCA, see Hagennaars and McCutcheon (2002). For an accounting application, refer to

Larcker and Richardson (2004).

5

we examine a sample of 12,894 firm-year observations from 2008 to 2014. Our sample covers

approximately the Russell 3000 firms during this period and is much more comprehensive than

samples studied in prior research.

Across our sample of firms, the mean (median) PPP is 72.5 (87.3), which indicates that

boards of directors generally pick a peer group that supports higher compensation than the

median portfolio of firms that could have been reasonably selected. Using LCA, we find

consistent evidence that our sample has a mixture of three clusters, where roughly 68% of

observations have a negative relation between future performance and PPP (“rent extraction”

firm clusters) and roughly 32% of observations have a positive relation (“aspirational” firm

cluster). There are two distinct clusters that comprise the firms with a negative relation between

future performance and PPP. The smaller cluster of rent extraction firms, composed of 6% of

observations, exhibits lower CEO talent and weaker corporate governance structures relative to

the aspirational firm cluster. This cluster consists of smaller firms with lower operating

performance. The larger cluster of rent extraction firms (about 62% of all observations) is

comprised of larger firms and exhibits higher CEO talent with slightly weaker governance

structures relative to the aspirational cluster. Both rent extraction clusters have more realized

negative governance outcomes than the aspirational cluster.

Overall, our results are consistent with the claims of governance activists and proxy advisory

firms that peer group choice is related to rent extraction for a majority of firms. However, our

results differ from the prior conclusions of Faulkender and Yang (2010), Bizjak et al. (2011),

Albuquerque et al. (2013), and Francis et al. (2016) that conclude peer group choice is

characterized by the desire to attract and retain CEO talent. In addition, we offer an explanation

for prior studies’ conclusions that firms with governance issues do not select more highly paid

6

peers. While both rent extraction clusters have more realized negative governance outcomes than

the aspirational cluster, we find weaker structural governance systems primarily in the small-

sized rent extraction firms. Thus, prior research’s inconclusive findings may be due to the focus

on larger firms and ex-ante structural governance measures.

To help quantify the magnitude of peer group rent extraction, if we use the 50th percentile of

the distribution of compensation medians as a benchmark, the firms in the rent extraction clusters

earn approximately $12 billion in excess compensation from 2008 to 2014. This amount

corresponds to an overpayment of approximately 40% of CEO compensation. Thus, the

economic significance of peer group choice for the majority of firms is nontrivial.

The remainder of the paper proceeds as follows. Section 2 provides a review and assessment

of the prior literature examining peer group selection. Section 3 describes our sample selection

and provides descriptive statistics for the sample. Section 4 illustrates the limitations of the

typical matching approaches used in prior literature. Section 5 provides the conceptual

justification and computation steps for the PPP measure. The econometric approach used to

distinguish between rent extraction and labor market explanations for peer group selection is

developed in Section 6. The primary results are reported in Section 7 and associated sensitivity

analyses are discussed in Section 8. Section 9 provides a summary and concluding remarks.

2. Review of Prior Literature on Peer Group Selection

2.1 Institutional Background

Beginning in 2006, the Securities and Exchange Commission (SEC) required each firm to

disclose in its annual proxy statement “whether the company engaged in any benchmarking of

total compensation or any material element of compensation, identifying the benchmark and, if

7

applicable, its components (including component companies)”.5 As described in proxy statements,

most firms use a combination of industry and size along with other criteria, such as profitability,

talent flows, and geographic location, to create a peer set of 15-20 firms. Over 95% of the S&P

500 disclosed a peer group for fiscal year 2015 (Equilar, 2016) and approximately 80% of Russell

3000 firms benchmark their compensation against outside peers (Audit Analytics, 2015).

The typical disclosure is illustrated in the 2016 proxy (DEF14A filed on 04/21/2016, page

23) of Alliance Data Systems (ADS):

In 2013, the compensation committee, with the assistance of Meridian,

undertook a comprehensive review of the companies comprising the proxy peer

group. At that time, the compensation committee was presented an initial pool

of 100 possible peer companies based on a representative mix of our core

business competencies, including marketing, data, digital, card services and

specialty finance, whose general revenue size ranged from 0.3x to 3.0x of our

revenue and also sought to include high-performing companies that had

achieved a minimum 5% revenue growth and 8% EBITDA growth over the prior

year… From this analysis, the compensation committee selected a total of 16

proxy peer companies for 2016.

ADS’ primary screening criteria using industry and size are representative of most firms’.

Equilar (2016) reports that, of the 477 S&P 500 firms which disclosed peers in 2015, the two

most common criteria were similar industry (441 firms) and revenue (363 firms). Interestingly,

only 39 S&P 500 firms include profitability measures, such as EBITDA, in their selection

criteria. Compensation consultants confirm that firms should use size and industry criteria. For

example, Pay Governance recommends that firms choose peers that are within the same industry

and comparable in size, business operations, and geographic presence (Bout, 2011). To assess

the appropriateness of the selected peers, proxy advisory firms also create their own peer groups.

5 See SEC final rule 33-8732a, Item 402(b)(2)(xiv). The full rule can be accessed at

https://www.sec.gov/rules/final/2006/33-8732a.pdf.

8

ISS bases their selection of peer firms on industry (GICS) and size (revenue and market

capitalization) (Institutional Shareholder Services, 2015).

Firms typically calibrate their pay to the median of their selected peer group (Equilar, 2016).6

This practice of benchmarking to the median may be due to proxy advisory companies’

comparison of compensation to the median of a selected group of peers. For example, ISS

criticized Allegheny Technologies Inc. (meeting date May 11, 2012) in their proxy analysis and

vote recommendation (dated April 24, 2012):

As noted in the Pay for Performance discussion, CEO pay was 1.9 times the median

of ISS’ selected peers. The Company Selected Peer Group chart shows that three

companies –Alcoa, Nucor and United States Steel are significantly above two times

the company's revenue. (emphasis added)

ISS formed its own set of peers for Allegheny Technologies, and it found that the firm’s CEO

pay, benchmarked against a company-selected peer group, was much higher than the median of

the ISS-selected peer group. As a result, ISS recommended voting against the company’s

executive compensation.

2.2 Prior Literature

An extensive prior literature on executive compensation proposes two main views. The rent

extraction view is that managers seek to maximize their own compensation rather than

shareholder value (e.g., Bebchuk and Fried, 2005). This theory assumes that corporate

governance mechanisms are insufficient to mitigate agency problems between executives and

shareholders or even between the board of directors and shareholders. In contrast, the

shareholder value view is that boards of directors select compensation schemes to increase

6 For our sample of 12,894 firm years (described in Section 3), we confirm that firms do benchmark to the median

compensation of the selected peer group. Specifically, the median compensation paid to the CEO is $3,454,045

million and the median of the 50th percentile of compensation for the associated peer group is $3,814,396, or a

difference of $360,351. The cross-sectional Pearson (Spearman) correlation between CEO compensation and

median compensation for the peer group is 0.728 (0.764).

9

shareholder value (e.g., Gabaix and Landier, 2008). These two views represent the considerable

controversy over whether CEO compensation is best characterized as the result of agency

problems or optimal contracting.

Prior research provides mixed insights into the motivation of peer group selection for

establishing CEO compensation. This ambiguity mirrors the contrasting perspectives on

compensation more broadly, as described above. Faulkender and Yang (2010) find that firms

with governance concerns select more highly paid peers. Bizjak et al. (2011) find that firms seem

to manipulate peer group compensation upward, but they find no consistent evidence that

governance concerns influence peer group choice. Albuquerque et al. (2013) conclude that the

selection of highly paid peers is related to the desire to attract and retain CEO talent, rather than

the result of governance problems producing rent extraction. Finally, Cadman and Carter (2013)

find that the method used by researchers to select potential peers substantially influences whether

peer group selection is consistent with self-serving behavior or rational labor market

benchmarking.

3. Sample Selection

The sample used in prior research consists of only large firms and is limited to the two or

three years immediately following the 2006 disclosure requirement for peer groups. Specifically,

Bizjak et al. (2011) and Faulkender and Yang (2010, 2013) consider S&P 500 and S&P 400

firms and Albuquerque et al. (2013) only analyze firms with ExecuComp data, which are mostly

S&P 1500 firms. If larger firms face greater scrutiny when making compensation decisions, it

may be difficult to observe rent extraction activity in this restricted sample. To mitigate this

concern, our sample selection process begins with all firms in the Equilar database that disclose a

peer group in their proxy statement. This sample corresponds roughly to the Russell 3000 firms.

10

We also restrict our analysis to the time period from 2008 to 2014. Our sample period addresses

the timing concerns of Faulkender and Yang (2013) and SEC staff comments regarding the

initial confusion about which types of compensation benchmarks required disclosure as some

firms considered the identity of their peer group to be proprietary information.7

We require each firm in our sample to have strictly more firms in its industry and size

caliper than the number of firms it selected for its peer group. In order to construct PPP

(discussed below) we require firms to have enough potential peers from which to select multiple

portfolios of size k, where k is the number of actual peers they select. To eliminate “outliers” in

CEO compensation, we drop firms with CEO total compensation below $100,000. Finally, we

remove firm-years with one-year-ahead ROA less than -20% or greater than 20%. We remove

these firm-years because next year’s ROA may not be an appropriate performance measure for

firms with extreme ROA.8 Our final sample consists of 12,894 firm-year observations (2,888

unique firms) and covers 54.3% of the total market capitalization of NYSE/NASDAQ/Amex.

We compare each firm’s financial and compensation data in year t to its potential peer data in

year t-1. We make this adjustment to account for a well-known timing concern in the

compensation industry. In particular, when a board of directors decides compensation, it

typically knows the firm’s data for t; however, it only has publicly available data for potential

peers (i.e., t-1).

7 See https://www.sec.gov/divisions/corpfin/guidance/execcompdisclosure.htm. 8 For example, many of the firms with extreme future ROA are young firms in the pharmaceutical or technology

industries with little to no revenue and extreme losses, who are waiting for FDA approval or waiting for adoption of

the firm’s products or services. The market value of these companies is based on expectations of future growth well

beyond one year. For example, three observations with the lowest future ROA are Amarin Corporation in 2010,

Omeros Corporation in 2014, and Virnetx Holding Corporation. These are all development-stage technology

companies that had market values of $132.6, $550.7 and $44.7 million but revenue of $0, $0.523, and $0.026

million. Removing the 1,849 firm-years with absolute value of future ROA greater than 20% removes 633 unique

firms from the sample. Figure 1 presents a histogram of ROA, which shows the long left tail of future ROA values.

11

Table 1 reports descriptive statistics for our sample. Panel A compares the industry

composition of our sample to the industry composition of Compustat. We report the percentage

of our sample with each two-digit GICS industry classification, along with the corresponding

percentage with that classification in Compustat at both the beginning and end of our sample

(i.e., 2008 and 2014). In general, our sample composition closely approximates the industry

composition across all firms covered by Compustat.

Panel B of Table 1 reports sample summary statistics for selected financial and compensation

measures. All variables are defined in Appendix A. The mean (median) market capitalization is

$5,000 million ($1,361 million), total sales is $4,204 million ($1,047 million), and return on

assets (ROA) is 2.2% (2.9%). The mean (median) log of the standard deviation for the last 5

years’ ROA is 0.037 (0.023). We require at least two previous years of ROA data to compute this

measure, which results in 12,894 observations with available data. Panel B also reports

compensation measures and shows the mean (median) CEO in our sample earns $5.3 ($3.5)

million and has 15 (15) firms in the selected peer group.

4. Assessment of Matching Procedures Used in Prior Research

As discussed in Section 2, the methodology used in prior research involves matching each

individual firm in the selected peer group with a similar (non-peer) firm using propensity score

matching (PSM). These studies then test whether there is a statistically significant difference in

the central tendency of CEO compensation between the actual peer group and the matched peer

group. Although PSM can be a useful matching approach when there are many explanatory

covariates, it is necessary to demonstrate that the two groups exhibit covariate balance.

Unfortunately, the covariate balance across the variables used in the first stage probit model is

12

typically not reported (e.g., Faulkender and Yang, 2010, 2013; Albuquerque et al., 2013).9

Without covariate balance, it is problematic to attribute any observed differences in CEO

compensation between the selected peers and the matched peer group to the selection process of

the board of directors. Rather, the PSM process might have chosen firms with different economic

fundamentals than the firms selected by the board.

In order to assess the covariate balance in prior research, we first estimate the following

probit model:

Peerijt = ϴ1 + Σϴm Firm Characteristicsmijt + ,

(1)

where Peerijt is an indicator variable that is set to one if firm i uses firm j as a peer in year t. Firm

Characteristicsmijt is a vector of M firm attributes that capture important similarities between firm

i and firm j. We consider two specifications of equation (1). The first specification follows the

model of Faulkender and Yang (2010) and the second (“full”) specification combines aspects of

the specifications from Faulkender and Yang (2010) and Bizjak et al. (2011), as well as elements

found in practitioner guidelines discussed by Equilar and ISS (Institutional Shareholder Services,

2015; Equilar, 2016).10

The probit estimation results for both models are reported in Table 2. We find that

comparable firm size (i.e., sales, assets, and market capitalization), both firms being included in

popular market indices, similar industry, and both firms having CEO duality are primary

9 The one exception is Bizjak et al. (2011), which only reports the differences in size and performance across the

matched pairs. While they find balance along operating performance, they find both economically and statistically

significant differences in size between groups, so they “urge some caution in interpreting this result.” 10 We do not exactly follow the Faulkender and Yang (2010) specification. Specifically, we omit two variables – an

indicator for whether both firms are in the Dow Jones Industrial Average index and the number of peers selected by

the firm. We drop the Dow indicator because, in our sample with coverage comprising the Russell 3000, there are

very few instances when this variable is non-zero (which makes convergence difficult). We also omit the number of

peers, because including this variable would be equivalent to including a group fixed effect. Prior literature finds

bias in finite-sample nonlinear fixed effects models (Greene, 2004). All remaining variables are not fixed for all

firm-peer pairs in a given firm-year.

13

determinants of peer group selection. We also find that firms select peers based on whether they

are losing management talent to specific firms (i.e., firm j has managers previously employed by

firm i). In the full specification (column 3 of Table 2), we also find that comparable operating

performance (ROA), location in the same metropolitan statistical area (MSA), and similar

business complexity measured using geographical segments and business segments are related to

peer group choice. Finally, sharing the same compensation consultant and being in the peer

group for the firm being considered (a type of reciprocity where firm j uses firm i in their peer

group) increases the probability of firm i selecting firm j as a peer. These results are broadly

consistent with Bizjak et al. (2011), Faulkender and Yang (2010), and professional discussions

regarding compensation peer firms.

Based on these models, we can match each peer for firm i to another firm with the same

selection probability. The results of this PSM approach are presented in Table 3. Panel A

presents results for the Faulkender and Yang (2010) specification and Panel B presents results

for the full model specification. The first row of each panel reports the mean and median total

compensation for the selected peers and the matched sample of peers. The mean (median) peer

compensation is $6.08 ($4.00) million. The matched firms using the Faulkender and Yang (2010)

specification have a mean (median) compensation of $5.32 ($3.15) million, while the full model

matched firms have a mean (median) of $5.13 ($3.16) million. These matched pair differences

for both specifications are statistically significant at conventional levels. These results are

consistent with those of prior studies, which interpreted this statistically significant difference as

evidence that boards of directors strategically choose peers with higher CEO compensation.

However, the problem with the PSM approach in prior research is that there is little covariate

balance between the matched pairs, and this imbalance confounds the interpretation of the

14

compensation comparisons. This problem is clearly observed in Table 3. For example, we find

that the selected peers are larger and more profitable than the matched peers, so any difference in

compensation can easily be a result of differences in these firm characteristics that are known to

be correlated with the level of CEO compensation. One way to quantify the quality of the

matches is to use reasonable calipers around the observed measures for each peer chosen and

calculate the fraction of matched firms that fall within a particular caliper. Specifically, we

compute the fraction of matched firms within 50%-200% of the chosen peer’s revenue, and we

find that only 57% of matched firms satisfy this criterion. Using other proxies for size, we find

that 58% and 50% of matched firms are in the 50%-200% range of total assets and market

capitalization, respectively. Finally, we find that matched firms share the same two-digit (three-

digit) GICS code in only 81% (73%) of the matches.

One reason for the poor covariate balance in PSM is that the choice of potential peers is only

based on the overall probability score. For example, consider Alliance Data Systems (ADS,

which was discussed in Section 2) and one of its peers, Discover Financial Services (DFS). DFS

is within ADS’ market capitalization caliper of 50%-200% and ROA caliper of ±3%. Like ADS,

DFS is also in the S&P 500 and has multiple business segments. However, DFS is a financial

services company, and it does not share the same two-digit GICS as ADS, an information

technology company. While the companies are classified into different two-digit GICS codes,

they are comparable in their lines of business and end customers, a similarity which is not

captured by GICS.

Another firm with the exact same propensity score as DFS is a petroleum and natural gas

company, Chesapeake Energy. Like DFS, Chesapeake satisfies the size and ROA calipers and is

also in the S&P 500 with multiple business segments. While neither Chesapeake Energy nor

15

DFS has a two-digit GICS that matches that of ADS, Chesapeake Energy is a highly implausible

peer firm for ADS. Even if Chesapeake Energy satisfied all of the calipers, given that the two

businesses are vastly different, it is unlikely that ADS would have ever considered Chesapeake

Energy as a viable peer. The PSM approach used in prior research does not allow for the

important practical setting where a firm matches on many variables that are less important (e.g.,

geographical location), but fails to match on the most critical variables (e.g., size and industry).11

This example suggests that a sizable fraction of propensity score matched (non-selected) peer

firms are poor substitutes for the chosen peers. This notion is further reinforced by the absence of

covariate balance shown in Table 3. Overall, there are serious concerns about the appropriateness

of and the conclusions based on the matching procedure used in prior research.

5. Peer Portfolio Percentile (PPP) Measure

In order to address the problems with propensity score matching, we shift the unit of analysis

from the selection of individual firms to the portfolio of peer firms. This focus on the portfolio

also mimics the decision process by the board of directors. Although compensation committees

clearly assess individual firms for inclusion in the peer group, they are ultimately selecting a

portfolio of firms that informs—or justifies—their choice for the level of CEO compensation.12

11 This incongruity raises an important concern with PSM. This method implicitly assumes that certain values of

some variables can offset the poor matching of other variables. However, in this setting, certain variables are more

important than others and a significant difference along a critical dimension may cause the potential firm to never be

considered. The possibility that some firms are never considered is described in mathematics as non-Archimedean

geometry. Consider a discrete choice model with two covariates: 𝑃 = 𝑓(𝛽1𝑋1 + 𝛽2𝑋2) + 𝜖. Assume that 𝑋1 is a

measure of similarity in industry between the firm and the firm in question and 𝑋2 is a measure of the similarity in

size between the firm and the firm in question. If we assume that both 𝛽1 and 𝛽2 are positive, a low value of 𝑋1 can

be offset by a high value of 𝑋2 when computing 𝑓(𝛽1𝑋1 + 𝛽2𝑋2). However, if 𝑋1 is so small that the potential peer

is never considered similarity in 𝑋2 cannot overcome the deficiency. This practical hierarchical ordering is not

captured by traditional PSM, and unusual (and likely inappropriate) matches can be produced. 12 This portfolio perspective implies that the probability of a firm being selected as a peer is a function of the

characteristics of both the firm being evaluated (e.g., size, industry, CEO compensation level, etc.) and the

compensation levels for other firms already included in the peer group. In this setting, the estimated probabilities in

PSM from the first-stage probit that ignore role of other firms in the peer group will be biased and inefficient, which

raises additional concerns about using PSM (Arpino and Mealli, 2011; Arpino et al., 2016).

16

We implement this notion using a measure that compares the median compensation for the

selected peer group to the distribution of medians for all alternative peer groups that could have

been reasonably selected. We denote this measure by Peer Portfolio Percentile (PPP). PPP

enables us to assess whether the selected peer group produces a CEO compensation benchmark

that is at the 1st, 50th, 99th, or any other percentile of the distribution of plausible peer groups.

This distributional measure captures whether the peer group choice by the board of directors

results in an unusually high or low compensation benchmark for the CEO.

In our PPP computation, we use the total dollar amount of the peer firm CEO’s

compensation (Total Compensation) as reported in the SEC required summary compensation

table (DEF 14A). This measure includes cash compensation, bonus pay, payouts from long-term

incentive plans, and the valuation of option grants. This amount is frequently used by proxy

advisory firms and commonly reported in the financial press.

To construct PPP, we first identify the universe of plausible peers for a given firm. We apply

an industry filter based on two-digit GICS or the Hoberg-Phillips text-based industry

classification and a size caliper based on the restriction that a potential peer must have either

revenue or a market capitalization between 50% and 200% of the firm in question.13 We also

include all firms with any talent flows to or from the firm in question. We measure talent flows

using BoardEx data to determine whether any officer or senior manager at the potential peer or

the firm has ever been employed as an officer or senior manager at the other company. As

discussed in Section 2.1, we choose these calipers because industry, revenue, talent, and market

cap are cited as the most common peer selection criteria by companies, and 0.5x and 2.0x are

common cutoffs for the revenue criterion (Equilar, 2016). While the probit results reported in

13 We provide sensitivity analyses in Section 8 to assess whether our results are substantively affected by the choice

of filters to identify the universe of plausible peers.

17

Table 3 show that the sales caliper has a greater elasticity than our other two size proxies (i.e.,

market cap and assets), we also include a market cap caliper because, for firms such as growth

firms, market cap is a more suitable measure of size.14 The probit results also indicate that

common industry membership has a higher elasticity than most other firm characteristics.

We then calculate the empirical distribution of median CEO compensation for all possible

peer groups of the same size as the number of firms selected. Next, we determine the percentile

ranking of the actual selected median pay, relative to this empirical distribution. Figure 2

demonstrates the calculation of PPP for American Axle & Manufacturing Holdings (AAM),

which reported 20 compensation peers in fiscal year 2014. Based on the talent flows, industry,

and size calipers, AAM had 240 potential peers. Based on the combinatorics of selecting 20 from

a group of 240, there are 7.32×1028 possible sets of 20 peers. Panel A of Figure 2 presents the

histogram of these possible median CEO compensation amounts and their corresponding

probabilities. AAM’s chosen peer group had a median CEO compensation of $5.73 million,

which is shaded in red. Panel B of Figure 2 maps this chosen median to the empirical distribution

function of all potential medians and shows that it is at the 69.6th percentile of this distribution.

Because the chosen median is at the 69.6th percentile, AAM has a PPP of 69.6 in 2014.15

Naïvely calculating the medians of all possible combinations of firms is computationally

infeasible. We circumvent this limitation by observing that the upper bound on the number of

possible medians is far lower than the number of combinations of peers that produce these

medians. For example, if the number of peers is odd, the median must be one of the selected

14 The mean (median) number of potential peers generated by this approach is 194 (197) firms which is considerably

larger than the mean (median) peer group size of 15 (14). This caliper also selects a mean (median) of 65% (69%) of

the peers actually selected by a firm. 15 It may be the case that a firm selects peers that are not in the set of plausible peers (e.g., our example firm in

Section 4, ADS, selected Discover Financial Services, which is outside of its two-digit GICS code). Our PPP

measure can reasonably be interpreted as comparing the median of the selected peer group to medians of all

potential peer groups, where all potential peers satisfy the caliper restrictions.

18

firms (i.e., n) whereas the number of combinations to produce the median is much higher (i.e., n

choose k). Specifically, we develop an algorithm that is computationally feasible and identifies

the percentile of the median chosen relative to the distribution of all possible medians from the

firm’s potential peer set. We outline this algorithm in Appendix B.

The distribution of PPP for our sample of firm-years is presented in Panel A of Figure 3.

This measure ranges from 0 to 100, where 0 (100) indicates that the firm chose a median

compensation, based on their peer group of size k, that was the lowest (highest) compensation

relative to all possible combinations of k peers it could have selected from its plausible peer set.

Panel B of Figure 3 reports descriptive statistics for PPP. The mean (median) PPP is 72.7 (87.4).

Assuming our calipers accurately capture the set of potential peers, an average PPP that is

greater than 50 suggests firms systematically choose relatively more highly paid peers than

random selection from their potential peer set. Table 4 compares the decile of PPP in year t to

the decile of PPP in year t+1 and finds that PPP exhibits persistence over time. This persistence

is one validation for our measure because we would expect the board of director’s objectives

when selecting a peer group to be relatively stable over time.

Figure 3 shows that the distribution of PPP is relatively uniform over most of the range of

values. However, the frequency of firms substantially increases at larger values of PPP, with a

mass of firms having a PPP at, or just below, 100. Approximately 47% of our sample has a PPP

greater than 90, and it is important to understand whether this feature of the distribution is a

reasonable outcome or something that is induced by a weakness in our measurement approach.

The mass of firms with large values of PPP occurs because the combinatorics of selecting a

median for a subset of peers exacerbates any deviation from the median of all potential peers. To

make this combinatorics problem more concrete, suppose a firm has 100 potential peers and

19

selects 11 of these to be in its peer group. Assume the firm selects a peer group with peer P as its

chosen median, where P is the 20th highest paid peer of the 100. There are 1.41 × 1014 possible

peer groups of size 11 from this set of 100 (i.e., “100 choose 11”). However, there are only

1.07 × 1012 sets with a median larger than peer P. This substantial difference occurs because it

is far more likely that a random sample of 11 peers from this 100 will have at least 6 firms with

pay less than P (and hence, have a median less than P). Therefore, despite choosing a median

peer that is only at the 80th percentile of all potential peers (i.e., peer P is the 20th highest paid of

the 100), the PPP in this example would be close to the maximum possible PPP (i.e., PPP =

99.2).16

Due to this feature of the potential peer set, when firms choose a set of peers with higher

median compensation than a random draw from the potential peer set, the PPP is often very

high. Most of the potential medians will be below the median chosen, which explains why our

sample includes a large proportion of high PPP firms. Still, we want to ensure that our

distribution of PPP is not induced by our measurement approach. For example, PPP might

exhibit a predictable pattern with firm size or when the number of peers chosen by the firm (k) is

small relative to the number of potential peers (n).

To examine these concerns, we assess whether firm characteristics vary across the different

values of PPP (Table 5). Panel A reports mean values and Panel B reports medians. Column 2

(3) finds that observations with the largest market capitalization (revenue) are concentrated at

values of PPP=0, but there is a non-monotonic relation between this variable and PPP. This

grouping of large firms with small PPP values seems to occur because column 4 shows these

16 If firms choose peers outside of the potential set of peers with compensation that is higher than most (or all)

potential peers, PPP will be even higher than the resulting PPP from this example. In such a case, it is even less

likely that the selected median could be constructed from randomly drawing firms from the set of potential peers.

20

observations have 33 potential peers compared to the average of 194 for the entire sample.

Therefore, our caliper may be ill-suited for the largest firms which may be more likely to look

outside their industry for potential peers. Column 5 shows that the average (median) number of

peers selected by the firms, with the exception of low PPP firms, is relatively constant across

values of PPP with means (medians) in the range 13.7-17.1 (14-16). Except for observations

with PPP=0, over 50% of the selected peers are captured by our caliper. For observations with

PPP=0, only 31% are captured by our caliper. Again, we interpret this low figure as an

indication that our measure may be ill-suited for the largest firms, which select peers outside of

their industry. Although the mean and median descriptive statistics are not identical across PPP

groupings, the differences are substantively modest and do not suggest that the distribution of

PPP is induced by our computational approach.

6. Econometric Approach

Similar to prior research on compensation more broadly, we hypothesize that there are two

competing influences on peer group selection: (i) attraction and retention of top executive talent

and (ii) executive rent extraction (i.e., agency concerns). If firms choose a high paying peer

group to attract more talented executives who command higher compensation, we should find

that the selection of more highly paid peers is positively associated with future firm

performance.17 Conversely, if higher peer group pay reflects rent extraction, we should observe a

negative relation between peer group pay and future performance. Thus, similar to the approach

used by Albuquerque et al. (2013), we use the sign and statistical significance of the association

17 Although they do not examine the compensation benchmark produced by peer group selection, Francis et al.

(2016) find that firms that select peers with greater managerial ability exhibit larger stock returns and operating

performance. Their result is consistent with the talent attraction and retention story.

21

between our measure of peer group pay and future firm performance to distinguish between these

two hypotheses.

Both talent and agency motivations are likely to be observed in a large cross-section of

firms. Therefore, it is important for the econometric approach to allow for a mixture of firms

where the estimated coefficient linking PPP with future firm performance can be positive or

negative for different subsets of firms.18 We incorporate mixture features by employing Latent

Class Analysis (LCA). Specifically, we place firms into different homogeneous clusters

depending on the sign and statistical significance of the association between peer group choice

and future firm performance. Once these clusters are identified, we can uncover the

distinguishing factors associated with the observations in each cluster. For example, we would

expect firms with a negative association between peer group choice and future operating

performance to exhibit characteristics associated with poor corporate governance. In contrast, we

would expect firms with a positive association to have more talented CEOs.19

The LCA model assumes that the observed data can be characterized by 𝑃 clusters, each

with a different set of coefficient values. These subpopulations are called “latent” classes

18 The previous literature has also assumed that the same econometric model is applicable to all firms. However, if

different firms select peer groups for different reasons, namely talent attraction versus rent extraction, this

heterogeneity should be part of the econometric model. For example, consider a setting where there is a large cluster

of firms that chooses peers based on talent objectives and a smaller cluster of firms selects peers for rent extraction.

If one model is used to characterize all firms, it will be very difficult to detect the rent extraction motivation because

the results will be dominated by the large cluster of firms selecting peers based on talent objectives. Alternatively, if

the rent extraction and aspirational clusters are of a similar size, but have coefficients of different signs, a pooled

analysis is likely to find no association between PPP and future performance. 19 The typical pooled regression approach used in prior research does not easily allow for this type of heterogeneity.

For example, heterogeneity might be accommodated using interaction terms to see how the relation between PPP

and future performance varies with another variable. However, interactions are subject to multicollinearity concerns

because the main effects and associated interactions are included in the same regression model. Moreover, it is

necessary for the researcher to ex-ante know which variables should be used in the interactions. For example, if

corporate governance is assumed to be an important interactive effect, it is unclear which of the many corporate

governance variables should be used (e.g., board structure, ownership, compensation plan design, anti-takeover

provisions, etc.).

22

because each observation’s class membership is not directly observed. We assume the dependent

variable is distributed as a finite mixture of normal distributions so the likelihood expression is

𝐿 = Π𝑖=1𝑁 [Σ𝑝=1

𝑃 𝜆𝑘(2𝜋𝜎𝑘)−12𝑒𝑥𝑝 [

−(𝑦𝑖 − 𝑋𝑖𝐵𝑝)2

2𝜎𝑝2

]]

(2)

where 𝜆𝑝 is the unknown proportion of the sample that is contained in cluster 𝑘, 𝜎𝑝 is the

standard deviation of the error term in cluster 𝑝, and N is the sample size. 𝐵𝑝 represents the

coefficients of the linear model for cluster 𝑝. We assign firms to different clusters to maximize

this likelihood function in equation (2). We utilize hard clustering, where each observation is

assigned to the cluster with the greatest posterior probability.20

To determine the optimal number of clusters, we examine the fit statistic represented by the

Bayesian Information Criterion (BIC) (Nylund et al., 2007). The BIC is computed as

−2𝑙𝑜𝑔(𝐿) + 𝑃𝑙𝑜𝑔(𝑛), where 𝐿 is the likelihood, 𝑃 is the number of classes, and 𝑁 is the number

of observations. To determine the optimal number of classes that describes our data, we increase

P until it no longer leads to an appreciable improvement in the BIC. Once the number of clusters

is determined, we can probabilistically assign each observation to each of the P clusters.

The fundamental relationship of interest is the statistical association between peer group

choice (PPP) and future firm performance, after controlling for other variables shown in prior

literature to be related to future performance. Both accounting and stock return performance are

candidates for measuring firm performance. However, if the stock market fully incorporates

future cash flow implications of peer choice into price, we are unlikely to detect an effect on

20 In untabulated analyses, we also repeat our analyses using soft clustering, where each observation is weighted in a

weighted least squares regression based on the posterior probability that it belongs in a given cluster.

23

future stock returns. Thus, we focus on one-year-ahead annual return on assets (ROA) as our

measure of future performance.21 The basic regression equation of interest is:

ROAt+1 = β0 + β1PPPt + β2 LogSalest + β3Log(StdROAt) + βkIndustryk

+ βjYearj + υ

(3)

where ROAt+1 is Compustat IB divided by Compustat AT in year t+1, multiplied by 100.

Following Core et al. (1999) and Albuquerque et al. (2013), our control variables consist of

LogSales and Log(StdROA), where LogSales is the natural logarithm of one plus Compustat

REVT, and Log(StdROA) is natural logarithm of the standard deviation of the last five fiscal

years’ ROA. Industry fixed effects, where Industryk denotes two-digit GICS, eliminate the

common industry trends of ROAt+1. Year fixed effects (Yearj) eliminate the common time trend

across firms.22

Because of our focus on the β1 coefficient, we want the LCA to reveal whether there are

different clusters for this coefficient of interest, rather than clusters produced by differences in

the coefficients on the control variables. In order to adjust for the control variables, we first

estimate first-stage regressions to obtain residuals for ROA and PPP:

ROAt+1 = 0 + 1 LogSalest + 2Log(StdROAt) + kIndustryk + jYearj + ROA

(4a)

PPPt = 0 + 1 LogSalest + 2Log(StdROAt) + kIndustryk + jYearj + PPP

(4b)

We represent this procedure as a linear model equivalent to equation (3):

ROA = β0 + β1PPP + υ (5)

21 As we report in Section 8, our results are robust extending operating performance to the three-year-average return

on assets. 22 There may be industry-specific or year-specific components in the relation between ROA and PPP that should not

be removed using fixed effects (e.g., firms in a given industry might choose PPP that in a way that is systematically

related to future performance, and we want to capture this fixed component). Therefore, we repeat our analyses

excluding year and industry fixed effects. Removing these fixed effects results does not substantively change our

results.

24

The coefficient estimate β1 and its standard error are the same in equations (3) and (5). The

partial correlation coefficient, β1, measures the correlation between two variables after removing

the effect of a set of control variables. We use the univariate regression equation (5) as the

regression model for the LCA.

Once the number of clusters is determined using the BIC criterion, we probabilistically

assign each observation to a cluster by computing the estimated posterior probability from the

likelihood function associated with the finite mixture of normal distributions. After assigning

observations to clusters, model (3) can be estimated separately for each cluster, and we can test

for differences in estimated coefficients across clusters. For example, if there are two distinct

clusters (P = 2), we are interested in whether cluster one has a positive value for β1 (suggestive

of a rent extraction interpretation) and cluster two has a negative value for β1 (suggestive of an

aspirational interpretation).23 Similarly, if there are more than two distinct clusters, we might

expect that some clusters have a positive value and others have a negative value for β1 where the

magnitudes vary across clusters. To confirm these interpretations, we then determine whether

clusters with a negative coefficient have governance attributes commonly associated with weak

oversight and clusters with a positive coefficient have attributes commonly associated with the

search for executive talent.

23 In this example, one cluster consists of observations where increases in PPP are associated with increases in

future operating performance, and this raises the question of why all firms do not increase PPP to the maximum

value of 100. In the “aspirational” cluster, the endogenous choice of PPP is a function of the talent level desired by

each firm (which is a function of various “exogenous” variables such level of competition, capital stock, intellectual

property, and other value drivers of the firm). Each firm in this cluster would presumably select a talent level that

maximizes firm value given the relevant exogenous variables. Since these exogenous variables will vary across

firms, we would not expect all firms to select the same talent level, and thus we should observe different choices for

PPP in this cluster. A similar argument can be made for the second (“rent extraction”) cluster which consists of

observations where increases in PPP are associated with decreases in future operating performance. There are likely

to be “exogenous” differences in self-interest and corporate governance across firms, and thus we should observe a

range of PPP choices for this cluster of firms.

25

7. Results

7.1 Relation between PPP and Future Performance

The estimates for equation (3) and the equivalent equation (5) used in the LCA are reported

in Table 6, Columns 1 and 2. The coefficients β1 in both columns are -0.012 and statistically

significant. These findings contrast with those from prior research, suggesting that in the pooled

sample there is a negative association in higher paying peers and future operating performance.

When we apply LCA, we find that three clusters emerge when we use the BIC criterion.

Columns 3, 4 and 5 of Table 6 report results from estimating equation (3) for these three clusters.

In the smallest cluster (Column 3), the coefficient on PPP is negative and significant (-0.021, p <

0.01). In the largest cluster (Column 4), the coefficient on PPP is also negative and significant (-

0,019, p < 0.01). However, in the third cluster (Column 5), the coefficient is positive and

significant (0.010, p < 0.01).24

In Columns 6 and 7, we test whether 𝛽1 is significantly different across clusters by pooling

the sample and interacting cluster membership with our variable of interest, 𝜀𝑃𝑃𝑃. The

insignificance of the coefficients on 𝐶𝑙𝑢𝑠𝑡𝑒𝑟 2 × 𝜀𝑃𝑃𝑃 in Column 6 and 𝐶𝑙𝑢𝑠𝑡𝑒𝑟 1 × 𝜀𝑃𝑃𝑃 in

Column 7 imply that 𝛽1 in Column 3 is not statistically different from 𝛽1 in Column 4. However,

the coefficient on 𝐶𝑙𝑢𝑠𝑡𝑒𝑟 3 × 𝜀𝑃𝑃𝑃 is statistically significant (p < 0.01) in both Column 6

(comparing Cluster 1 to 3) and Column 7 (comparing Cluster 2 to 3), which suggests that the

positive coefficient is significantly different from both negative coefficients.

As we describe in further detail in Table 7, the first (second) cluster is comprised of smaller

(larger) firms, so we refer to it as the “small-sized rent extraction” (“large-sized rent extraction”)

24 The adjusted R2 in column 5 is 87%. Removing the industry and year fixed effects, the adjusted R2 drops to 53%.

Removing the control variables from the regression, in addition to removing fixed effects, decreases the adjusted R2

to 11%.

26

cluster. We label these two clusters as rent extraction clusters because of the negative association

between PPP and future operating performance. As reported by the summary statistic “Fraction

of Firms” in Table 6, the small-sized rent extraction cluster contains just 6% of firm-years,

whereas the large-sized rent extraction cluster contains 62% of firm-years. We refer to Cluster 3,

the cluster with a positive coefficient on PPP, as the “aspirational” cluster, as it has a positive

sign on PPP. The aspirational cluster contains 32% of firm-years.

As expected, we find that cluster membership is relatively stable within firm (untabulated). It

is unlikely many firms can substantially shift their compensation program from one year to the

next. Specifically, 66% of aspirational cluster firms remain in the aspirational cluster in the next

year, 50% of small-sized rent extraction firms remain in this cluster in the next year, and 78% of

large-sized rent extraction firms remain in this cluster in the next year. At the firm level, 12% of

the 2,888 unique firms are always in the aspirational cluster, and the remaining 88% of firms are

assigned into one of the two rent extraction clusters for at least one year. In addition, the

aspirational cluster percentages range from 25% (in 2008) to 34% (in 2011), which indicates that

cluster membership is not concentrated in any single year.

7.2 Validation of Measure and Cluster Characteristics

Despite labeling clusters as “rent extraction” and “aspirational” based on the sign of the

coefficient linking PPP to future operating performance, it is necessary to examine the attributes

of the firms in these clusters to validate our PPP measure. Furthermore, assessing cluster

differences provides new insights into attributes that distinguish “rent extraction” and

“aspirational” firms. In Table 7, we assess differences between the rent extraction and

aspirational clusters in five categories of attributes: (i) firm characteristics, (ii) compensation

characteristics, (iii) CEO talent measures, (iv) entrenchment and board structure measures, and

27

(v) realized governance measures. We report the mean values for the three clusters in Columns

1-3 and the pairwise differences between clusters in Columns 4-6.25

Category (i) provides summary measures of firm characteristics. Our three proxies for size

are market capitalization (Market Cap), assets (Assets), and sales (Sales). We also examine

differences in profitability (ROA). Smaller firms are less visible and are subject to less outside

scrutiny, making them more likely to extract rents. Similarly, we expect less profitable firms to

be extracting rents from shareholders. Consistent with these expectations, we find that Cluster 1

is comprised of smaller and less profitable firms than the aspirational cluster (Column 3).

Interestingly, the second rent extraction cluster (Column 2) is composed of the largest and most

profitable firms in our sample (Columns 4 - 6). This finding suggests that firms engaging in rent

extraction are not isolated to smaller, more obscure firms that are not performing well.

The second category of attributes we examine consists of CEO pay characteristics. We

examine total compensation across clusters (Total Compensation) and acknowledge that

compensation is affected by other firm characteristics such as firm size. Category (ii) also

includes the fraction of positive say-on-pay votes (ISS For SOP). Ertimur et al. (2013) show that

proxy advisory firms can act as information intermediaries for institutional shareholders by

gathering and processing information related to executive pay. Therefore, we expect ISS say-on-

pay support to be lower for rent extraction clusters. The first two rows of Category (ii) report the

comparison for compensation and ISS support for say-on-pay. The aspirational firms have

significantly higher total compensation than the small-sized rent extraction firms (difference of

25 All tests of significance for differences between clusters are derived from the empirical distribution of

bootstrapped samples. We determine significance with bootstrapped samples because firms appear multiple times in

our sample (i.e., they are present in multiple years). As many of our descriptive measures are autocorrelated,

traditional test statistics would tend to be inflated. To construct the empirical distribution, we randomly assign, with

replacement, observations into the three clusters, preserving the proportion of observations in each cluster. We then

compute the relevant statistic for the bootstrapped clusters and repeat this entire process 1,000 times. We then

compare the actual statistic to the empirical distribution of all bootstrap observations to determine significance.

28

$1.06, p<0.01) and significantly lower total compensation than the large-sized rent extraction

firms ($0.72, p<0.01). These differences are likely due to inherent differences in firm size as

reported in Category (i). The small-sized rent extraction cluster has 9% lower ISS support than

the aspirational cluster (p<0.01), but we find no difference in ISS support between the

aspirational cluster and the large-sized rent extraction firms. The results are consistent with ISS’s

focus on poorly performing firms (Ertimur et al. 2013).

In Category (ii) we also examine whether there are differences in the number of peers

(Number of Peers) across clusters. Because PPP is determined from a set of reasonable potential

peers, we predict rent extraction firms will choose fewer peers from this “reasonable” set

(Chosen in Caliper). In row 3 of Category (ii), we report that both rent extraction clusters have

significantly fewer peers, but the magnitudes of these differences are economically small as all

clusters have a mean of roughly 15. Both the small- and large-sized rent extraction clusters are

less likely to choose peers in their potential peer set caliper (-0.07, p<0.01; -0.07, p<0.01), which

validates that these firms are less likely to select “reasonable” peers. We also find that both rent

extraction clusters have a significantly higher PPP.26

Peer groups are often chosen with input from compensation consultants. Therefore, Category

(ii) also includes two measures related to compensation consultants. We report the fraction of

observations that use a top 10 compensation consultant (Top 10 Comp Consultant) and whether

management retains its own consultant (Separate Comp Consultants). Armstrong et al. (2012)

find that firms with weaker governance are more likely to use consultants to justify

26 A difference in PPP between clusters does not necessarily imply that some clusters are engaged in greater rent

extraction than others. Rather, it is the relation between PPP and future performance that identifies rent extraction,

Table 6 shows the rent extraction clusters have a negative association with future performance. This negative

relation, combined with the finding that the rent extraction clusters have larger PPP values, suggests these clusters

are engaged in more rent extraction than the aspirational cluster.

29

compensation choices. We also examine whether or not the board uses a different compensation

consultant than management, which might result in more independence and lower pay. Prior

literature finds mixed results when examining separate compensation consultants (Murphy and

Sandino, 2010). We find that both rent extraction clusters are more likely to use a large

compensation consultant. The fraction of firm-years with a Top 10 Comp Consultant in Clusters

1, 2, and 3 are 0.82, 0.75, and 0.72, respectively. We find the board and CEO of firms in the

small-sized rent extraction cluster are less likely to use separate compensation consultants and

this difference is marginally significant.

In Category (iii), our three proxies for CEO talent closely follow those in Albuquerque et al.

(2013). We include characteristics of firms at which the CEO was previously employed in any

position. These variables are abnormal ROA (CEO ROA), abnormal returns (CEO Returns), and

log of firm size (CEO Firm Size). More talented CEOs are those that were previously at larger or

better performing firms. Across all three CEO talent variables, the small-sized (large-sized) rent

extraction cluster is comprised of less (more) talented CEOs relative to the aspirational cluster.

Our results suggest that, while CEOs with low talent are extracting rents in small-sized firms, in

the majority of rent extraction firms, CEOs actually have higher talent than those in the

aspirational cluster.

Our fourth category includes ex-ante measures of entrenchment and board structure. We

include eleven different proxies for these structural governance characteristics, because it is

difficult to measure governance quality ex-ante (e.g., Daines et al., 2010). Prior literature

provides mixed evidence on the characteristics, and we make few predictions for these variables.

Prior literature has used CEO tenure (CEO Tenure) as a proxy for the power of the CEO (Baker

and Gompers, 2003; Coles et al., 2008). However, shorter CEO tenure could also suggest

30

increased firm turmoil (Gilson and Vetsuypens, 1993). Prior literature has also studied the

NEO/CEO pay ratio (Pay Ratio), CEO-Chairman duality (Is Chairman), and founder

involvement (Founder Involved), with mixed results on the impact of these measures (O’Reilly

et al., 1988; Willard et al., 1992; Main et al., 1993; Begley, 1995; Brickley et al., 1997; Goyal

and Park, 2002; Bebchuk et al., 2011).

We also examine whether the firm has a staggered board (Staggered Board). This

characteristic may be a manifestation of poor governance (Bebchuk and Cohen, 2005) or can

promote value creation by helping firms undertake long-term investments (Cremers et al., 2017).

We study the fraction of the board that is composed of outside directors (Percent Outside Dir)

and the fraction that is CEO-appointed (Fraction CEO Appoint). Prior literature has found mixed

evidence on whether director independence is optimal for shareholders (e.g., Adams and

Ferreira, 2007). Core et al. (1999) find a higher fraction of CEO-appointed directors as

systematic of poor governance. Busy boards (Busy Board) have been shown to be associated

with weak governance, but the tests in prior literature may have low power (Core et al, 1999;

Fich and Shivdasani, 2006; Ferris et al., 2003). We examine whether the firm is a dual class firm

with unequal voting rights across multiple share classes (Dual Class). Gompers et al. (2009) and

Masulis et al. (2009) find that dual class firms have agency problems related to the separation of

ownership and control. We include insider ownership (Insider Ownership) and note that prior

literature documents a non-monotonic relation with firm value (McConnell and Servaes, 1990).

We also examine whether proxy advisory support for directors (ISS For Directors) is

systematically different across clusters and expect lower support in rent extraction clusters.

Table 7, Category (iv) reports results for these structural governance measures. We find that

the small-sized rent extraction firms have lower CEO tenure than the aspirational firms (-1.53

31

years, p<0.01), which is consistent with shorter tenure being associated with increased turmoil.

The average NEO/CEO pay ratio for the small-sized rent extraction cluster is higher than the

aspirational cluster (0.04, p<0.01). Compared to the aspirational firms, the small-sized rent

extraction firms are less likely to have a CEO who is also the Chairman (-0.13, p<0.01), and

more likely to have a founder that is still involved in the firm (0.10, p<0.01). In addition, the

small-sized rent extraction firms are 5% more likely (p-value <0.01) to have a staggered board

than the aspirational firms. There are no significant differences in the percent outside directors

and fraction of the board that is CEO appointed. Small-sized rent extraction firms are more likely

to have a busy board (0.02, p<0.01), less likely to have a dual class structure (-0.01, p<0.1), and

tend to have higher insider ownership than the aspirational cluster (0.03, p<0.01). Relative to the

aspirational cluster, we find that small-sized rent extraction firms are less likely to have a

positive ISS recommendation for directors (-0.04, p<0.05). Overall, we find evidence that small-

sized rent extraction firms have weaker structural governance measures, compared to aspirational

firms.

Our results comparing the structural governance measures of the large-sized rent extraction

firms to those of the aspirational firms find fewer differences between these clusters. The large-

sized rent extraction firms have lower CEO tenure and a lower NEO/CEO pay ratio than the

aspirational cluster (-0.46, p<0.01; -0.02, p<0.01). We find no difference in CEO-chairman

duality but observe they are more likely to have a founder that is still involved in the firm (0.04,

p<0.01). We find no significant differences in the staggered board measure, percent outside

directors, and fraction CEO appointed between the large-sized rent extraction and aspirational

firms. While the large-sized firms are more likely to have a busy board (0.03, p<0.01) and have

higher insider ownership (0.01, p<0.01) than the aspirational firms, there are no significant

32

differences in dual class structure or ISS support. Overall, category (iv) shows significant

differences in governance structural between the small-sized rent extraction cluster and the

aspirational cluster but finds weaker evidence of differences with the large-sized rent extraction

cluster.

As poor governance is difficult to infer based on ex-ante measures, our final category in

Table 7 compares differences in realized negative firm outcomes across the three clusters. Prior

research finds that a higher probability of restatements (Accounting Restatements), internal

control weaknesses (ICW), SEC enforcement actions (SEC Enforcement), and shareholder

lawsuits (Shareholder Lawsuits) are associated with weak governance (e.g., Dechow et al, 1996;

Agrawal and Chadha, 2005; Zhang et al., 2007; Larcker et al., 2007). Thus, we expect these

realized measures to be higher in rent extraction clusters. We also include environmental, social,

and governance measures (ESG) as these ESG concerns are associated with bad corporate

behavior (e.g., Walls et al., 2012). Specifically, we include concerns related to products

(Product Concerns), diversity (Diversity Concerns), employee relations (Employee Relations

Concerns), and the environment (Environmental Concerns). We expect these concerns to be

more prevalent in the rent extraction clusters.

Our results reported in category (v) find that small-sized rent extraction firms have a 1.9%

and 3.5% higher (p< 0.1, p< 0.01) likelihood of an accounting restatement and an internal control

weakness, respectively. The small-sized rent extraction cluster has a significantly greater

likelihood of a shareholder lawsuit (0.088, p<0.01), but fewer product concerns (-0.49, p<0.01).

Finally, the small-sized rent extraction firms have more diversity (0.066, p<0.01) and employee

relations concerns (0.067, p<0.01) than the aspirational cluster.

33

The overall tenor of results in category (v) is similar for large-sized rent extraction firms.

While we observe no differences in the likelihood of an accounting restatement and internal

control weakness between large-sized rent extraction firms and aspirational firms, we do find

that large-sized rent extraction firms have nearly twice the likelihood of an SEC enforcement

action than the firms in the aspirational cluster (p-value < 0.01). They also have a significantly

greater likelihood of a shareholder lawsuit (0.068, p<0.01). With respect to ESG concerns, the

large-sized rent extraction firms have significant more product (0.042, p<0.01), diversity (0.024,

p<0.05), employee relations (0.063, p<0.01) and environmental concerns (0.015, p<0.05) than

the aspiration cluster.

In sum, we find the small-sized rent extraction firms can be distinguished along several

dimensions, while the large-sized rent extraction firms are more difficult to distinguish from the

aspirational firms. Our small-sized rent extraction firms have lower CEO talent, weaker

structural governance measures, and more negative realized outcomes than our aspirational

firms. Our large-sized rent extraction firms have higher CEO talent than our aspirational firms.

In contrast to our results comparing the small-sized rent extraction firms to the aspirational

cluster, we do not find substantial differences in structural governance measures between the

large-sized rent extraction firms and the aspirational firms. This weaker finding may be a

consequence of larger firms facing pressures to conform with structural governance measures.

However, when we examine realized governance measures, we find strong evidence that the

large-sized rent extraction firms exhibit significantly worse outcomes than the aspirational firms.

Our results cast doubt on the use of ex-ante structural governance measures as accurate

indicators of rent extraction, particularly for larger firms under more scrutiny. As the sample

used in prior studies consists of large firms, our results provide an explanation for prior studies’

34

findings that weak governance doesn’t seem related to higher peer group pay (e.g., Bizjak et al.,

2011; Albuquerque et al., 2013). Instead, we find that realized negative outcomes, including ESG

measures, are strongly associated with rent extraction.

7.3 Excess Pay from Rent Extraction

As we document in previous tables, rent extraction firms appear to select peers

opportunistically. To quantify the egregiousness of peer group selection for these firms, we

define excess peer pay as the difference between the chosen median compensation and the

counterfactual median at PPP=50. Row 8, Column 1 (4) of Table 8 reports that small-sized

(large-sized) rent extraction firms have an average of $1.44 ($1.49) million in excess peer pay.

Column 2 (5) reports this average as 53% (40%) of total compensation, to highlight the

magnitude of excess peer pay for small-sized (large-sized) rent extraction firms. Combining

these two clusters, Column 8 shows this excess accounts for 41% of the actual pay. Column 9

aggregates the excess pay and reports that aggregate 2008-2014 excess peer pay is over $12

billion for all rent extraction firms. Overall, Table 8 demonstrates the sizable magnitude of

expropriation by firms in our rent extraction clusters.

8. Sensitivity Analyses

In this section, we show that our results are robust to different specifications of the potential

peer set, definitions of future performance, and sample restrictions. The first sensitivity check

expands both our revenue and market capitalization calipers from [50%, 200%] to [30%, 300%].

In untabulated results, we again find that three clusters describe the data. Similarly, 7% (53%) of

observations are assigned into the small-sized (large-sized) rent extraction cluster with a 𝛽1 of

−0.015 (−0.016). The remaining 40% are assigned into an aspirational cluster with 𝛽1 = 0.008.

35

We also find qualitatively similar results when we compare firm and governance characteristics

across clusters. Consistent with our main results, the small-sized rent extraction cluster has less

talented CEOs and significantly weaker structural and ex-post governance measures than the

aspirational cluster. Relative to the small-sized rent extraction cluster, the large-sized rent

extraction cluster has fewer entrenchment and board structure differences with the aspirational

cluster but has similarly poor ex-post governance measures.

Our second robustness test uses average three-year future ROA as the dependent variable

instead of one-year ahead ROA. In untabulated results, we find three clusters best describe the

data, and 3% (70%) of the observations are in the small-sized (large-sized) rent extraction cluster

with 𝛽1 of -0.011 (-0.016).27 The remaining 27% are in an aspirational cluster with 𝛽1 = 0.011.

Comparing firm characteristics across clusters, we find qualitatively similar, but slightly

stronger, results than reported in Table 7. We find that both rent extraction clusters have more

structural governance variables that are significantly different from those of the aspirational

cluster. In particular, we find the large-sized rent extraction cluster now has fewer outside

directors, is less likely to have a staggered board, and is more likely to have a dual class

structure. Consistent with our main results, we find realized governance measures to be

significantly different between rent extraction and aspirational clusters.

As discussed in Section 5, our size caliper may be problematic for the largest and smallest

firms in our sample, because these firms may have an uneven distribution of potential peers

within their size caliper. For example, the largest firms will only be able to select peers out of a

potential peer set that includes firms mostly smaller than themselves. Therefore, we repeat our

analyses but remove the largest and smallest 5% of firms, where size is based on market

27 While the large-sized rent extraction cluster is significant at the 1% level, the small-sized rent extraction cluster is

marginally significant (p=0.12).

36

capitalization. In untabulated results, we again find that three clusters describe the data and 6%

(57%) of observations are assigned into the small-sized (large-sized) rent extraction cluster with

a significant coefficient of 𝛽1 = −0.024 (= −0.023). The remaining 37% are in an aspirational

cluster with 𝛽1 = 0.010. When we compare the sample characteristics, we find results largely

consistent with those reported in Table 7. These sensitivity checks assure us that the results are

not driven by our choice of caliper, future performance measure, or the largest and smallest firms

in our sample.

9. Summary and Conclusions

The vast majority of firms uses a peer group as an important factor in setting CEO

compensation. Although the CEO compensation of similar firms can provide the board of

directors with valuable information about the market wage, this benchmarking exercise is the

subject of considerable controversy. For example, governance activists and proxy advisory firms

claim that boards of directors select peers that are larger and have higher compensation levels to

justify a high level of CEO compensation. However, firms respond that their peer group reflects

a competitive labor market outcome. They assert their choice of peer groups reflect aspirations to

invest in higher executive talent and prevents them from losing executives to larger firms with

high pay levels. Given the important role played by peer groups in setting CEO compensation,

our study examines whether this choice by the board of directors is characterized as rational

attraction and retention of executive talent or a process enabling the CEO to engage in rent

extraction.

Prior research provides a mixed assessment of peer group choice but has several important

shortcomings related to its use of propensity score matching methods. We address the limitations

in prior research by developing a new measure for assessing peer groups, denoted as Peer

37

Portfolio Percentile (PPP), which mimics the actual peer group selection process used by boards

of directors. Specifically, we compare the median compensation for the selected peer group to

the distribution of medians for all alternative peer groups that could have been selected using

traditional selection benchmarks such as firm size and industry. In addition, because both

aspirational and rent extraction motivations are likely to be observed in a large cross-section of

firms, we use Latent Class Analysis (LCA) as our primary econometric approach. Using a

comprehensive sample of 12,894 firm-year observations from 2008 to 2014, we find that the peer

group rent extraction problems conjectured by governance activists and proxy advisory firms

appear to affect the majority of firms. On average, in our rent extraction clusters, the excess pay

resulting from egregious peer group selection accounts for 41% of actual pay or $12 billion in

aggregate. Our results find that only one-third of firms seem to use peer group choice to reflect

the competitive labor market for CEO talent.

Our analyses also provide insights into attributes that distinguish rent extraction firms from

aspirational firms. Specifically, our methodology results in two rent extraction clusters, one with

small-sized firms and one with large-sized firms. While the small-sized rent extraction firms

have lower CEO talent and weaker structural governance measures than aspirational firms, our

large-sized rent extraction firms have higher CEO talent and fewer structural governance

differences. Interestingly, both sets of rent extraction firms have weaker realized negative

outcomes, including SEC enforcement actions, shareholder lawsuits, and ESG concerns. By

allowing for a mixture of firms, our study suggests the majority of firms exhibits rent extraction

tendencies in selecting peer groups and shows that the largest of these firms, which may face

more scrutiny to conform to certain structural governance forms, still experience negative

realized governance outcomes.

38

References

Adams, R.B., Ferreira, D., 2007. A theory of friendly boards. Journal of Finance 62, 217-250.

Agrawal, A., Chadha, S., 2005. Corporate governance and accounting scandals. Journal of Law

and Economics 48, 371-406.

Albuquerque, A.M., De Franco, G., Verdi, R.S., 2013. Peer choice in CEO compensation.

Journal of Financial Economics 108, 160-181.

Arpino, B., Benedictis, L.D., Mattei, A., 2016. Implementing propensity score matching with

network data: the effect of the General Agreement on Tariffs and Trade on bilateral trade.

Journal of the Royal Statistical Society: Series C (Applied Statistics).

Arpino, B., Mealli, F., 2011. The specification of the propensity score in multilevel observational

studies. Computational Statistics & Data Analysis 55, 1770-1780.

Armstrong, C.S., Ittner, C.D., Larcker, D.F., 2012. "Corporate governance, compensation

consultants, and CEO pay levels." Review of Accounting Studies 17(2), 322-351.

Audit Analytics, 2015. Peer Benchmarking and Trends in Executive Compensation.

Baker, M. and Gompers, P.A., 2003. The determinants of board structure at the initial public

offering. Journal of Law and Economics, 46(2), 569-598.

Bebchuk, L.A., Cremers, K.M. U.C., Peyer, 2011. The CEO pay slice. Journal of Financial

Economics, 102(1), 199-221.

Bebchuk, L., Cohen, A., 2005. The costs of entrenched boards. Journal of Financial Economics

78, 409-433.

Bebchuk, L.A., Fried, J.M., 2005. Pay without performance: Overview of the issues. Journal of

Applied Corporate Finance 17, 8-23.

39

Begley, T.M., 1995. Using founder status, age of firm, and company growth rate as the basis for

distinguishing entrepreneurs from managers of smaller businesses. Journal of business venturing,

10(3), 249-263.

Bizjak, J., Lemmon, M., Nguyen, T., 2011. Are all CEOs above average? An empirical analysis

of compensation peer groups and pay design. Journal of Financial Economics 100, 538-555.

Bout, A.B.L., 2011. Smart Compensation: Developing and Using Peer Groups, Workspan.

Washington D.C.: Sheridan Press, 27-31.

Brickley, J.A., Coles, J.L. and Jarrell, G., 1997. Leadership structure: Separating the CEO and

chairman of the board. Journal of Corporate Finance, 3(3), 189-220.

Cadman, B., Carter, M.E., 2013. Compensation peer groups and their relation with CEO pay.

Journal of Management Accounting Research 26, 57-82.

Coles, J.L., Daniel, N.D. and Naveen, L., 2008. Boards: Does one size fit all?. Journal of

Financial Economics, 87(2), 329-356.

Core, J.E., Holthausen, R.W., Larcker, D.F., 1999. Corporate governance, chief executive officer

compensation, and firm performance. Journal of Financial Economics 51, 371-406.

Cremers, K.M., Litov, L.P. Sepe, S.M., 2017. Staggered boards and long-term firm value,

revisited. Journal of Financial Economics, 126(2), 422-444.

Daines, R.M., Gow, I.D., Larcker, D.F., 2010. Rating the ratings: How good are commercial

governance ratings?. Journal of Financial Economics, 98(3), 439-461.

Dechow, P.M., Sloan, R.G., Sweeney, A.P., 1996. Causes and consequences of earnings

manipulation: An analysis of firms subject to enforcement actions by the SEC. Contemporary

Accounting Research 13, 1-36.

Edmans, A., Gabaix, X., 2016. Executive compensation: A modern primer. Journal of Economic

Literature 54, 1232-1287.

Equilar, 2016. Peer Group Composition and Benchmarking.

40

Ertimur, Y., Ferri, F., Oesch, D., 2013. Shareholder votes and proxy advisors: Evidence from say

on pay. Journal of Accounting Research 51, 951-996.

Faulkender, M., Yang, J., 2010. Inside the black box: The role and composition of compensation

peer groups. Journal of Financial Economics 96, 257-270.

Faulkender, M., Yang, J., 2013. Is disclosure an effective cleansing mechanism? The dynamics

of compensation peer benchmarking. Review of Financial Studies 26, 806-839.

Ferris, S.P., Jagannathan, M., Pritchard, A.C., 2003. Too busy to mind the business? Monitoring

by directors with multiple board appointments. Journal of Finance 58, 1087-1111.

Fich, E.M., Shivdasani, A., 2006. Are busy boards effective monitors? Journal of Finance 61,

689-724.

Francis, B., Hasan, I., Mani, S., Ye, P., 2016. Relative peer quality and firm performance.

Journal of Financial Economics 122, 196-219.

Gabaix, X., Landier, A., 2008. Why has CEO pay increased so much? The Quarterly Journal of

Economics 123, 49-100.

Gilson, S.C. and Vetsuypens, M.R., 1993. CEO compensation in financially distressed firms: An

empirical analysis. The Journal of Finance 48(2), 425-458.

Gompers, P.A., Ishii, J. and Metrick, A., 2009. Extreme governance: An analysis of dual-class

firms in the United States. The Review of Financial Studies 23(3), 1051-1088.

Goyal, V.K. and Park, C.W., 2002. Board leadership structure and CEO turnover. Journal of

Corporate Finance 8(1), 49-66.

Greene, W., 2004. The behaviour of the maximum likelihood estimator of limited dependent

variable models in the presence of fixed effects. The Econometrics Journal 7(1), 98-119.

Hagenaars, J.A., McCutcheon, A.L., 2002. Applied latent class analysis: Cambridge University

Press.

41

Institutional Shareholder Services, 2015. U.S. Peer Group Selection Methodology and Issuer

Submission Process (Frequently Asked Questions).

Larcker, D.F., Richardson, S.A., 2004. Fees paid to audit firms, accrual choices, and corporate

governance. Journal of Accounting Research 42, 625-658.

Larcker, D.F., Richardson, S.A. and Tuna, I., 2007. Corporate governance, accounting outcomes,

and organizational performance. The Accounting Review 82(4), 963-1008.

Main, B.G., O'Reilly III, C.A. and Wade, J., 1993. Top executive pay: Tournament or

teamwork?. Journal of Labor Economics 11(4), 606-628.

Masulis, R.W., Wang, C. and Xie, F., 2009. Agency problems at dual‐class companies. Journal

of Finance 64(4), 1697-1727.

McConnell, J.J. and Servaes, H., 1990. Additional evidence on equity ownership and corporate

value. Journal of Financial economics, 27(2), 595-612.

Murphy, K.J. and Sandino, T., 2010. Executive pay and “independent” compensation

consultants. Journal of Accounting and Economics 49(3), 247-262.

Nylund, K.L., Asparouhov, T., Muthén, B.O., 2007. Deciding on the number of classes in latent

class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural

Equation Modeling 14, 535-569.

O'Reilly III, C.A., Main, B.G. and Crystal, G.S., 1988. CEO compensation as tournament and

social comparison: A tale of two theories. Administrative Science Quarterly, 257-274.

Walls, J.L., Berrone, P. and Phan, P.H., 2012. Corporate governance and environmental

performance: Is there really a link?. Strategic Management Journal 33(8), 885-913.

Willard, G.E., Krueger, D.A. and Feeser, H.R., 1992. In order to grow, must the founder go: A

comparison of performance between founder and non-founder managed high-growth

manufacturing firms. Journal of Business Venturing 7(3), 181-194.

42

Zhang, Y., Zhou, J., Zhou, N., 2007. Audit committee quality, auditor independence, and internal

control weaknesses. Journal of Accounting and Public Policy 26, 300-327.

43

Appendix A: Variable Descriptions

Variable Description Source

Accounting Restatements Indicator variable set to 1 if the firm has an accounting restatement (i.e.,

accounting rule application failure) in that fiscal year.

Audit Analytics

Assets Total assets, Compustat AT. Compustat

Assets Caliper Indicator variable set to 1if the potential peer has total assets within 50%-

200% of the firm’s

Compustat

Both are Chairmen Indicator variable set to 1 if both the firm’s CEO and the potential peer’s

CEO are also the chairmen

Equilar

Both are Not Chairmen Indicator variable set to 1 if neither the firm’s CEO nor the potential peer’s

CEO is a Chairman of the Board

Equilar

Both in SP 400 Indicator variable set to 1 if the firm and potential peer are both in the S&P

400

CRSP

Both in SP 500 Indicator variable set to 1 if the firm and potential peer are both in the S&P

500

CRSP

Busy Board Fraction of outside directors that have more than 2 board appointments in

the same fiscal year

Equilar

CEO Duality Indicator variable set to 1 when the CEO is also the Chairman of the Board. Equilar

CEO Firm Size Arithmetic mean of the log of Compustat PRCC_F*CSHO of all companies

at which the firm-year's CEO was a named executive officer in the last

three years, defined by fiscal year end between 1 and 36 months prior to the

firm-year’s fiscal year end.

Compustat, Equilar

CEO Return Arithmetic mean of the annual excess return of all companies at which the

firm-year's CEO was a named executive officer in the last three years,

defined by fiscal year end between 1 and 36 months prior to the firm-year’s

fiscal year end. Annual excess return is the sum of the 12 monthly excess

returns, calculated as the monthly return in excess of the size decile

monthly return, from CRSP.

CRSP, Equilar

CEO ROA Arithmetic mean of the industry-adjusted ROA of all companies at which

the firm-year's CEO was a named executive officer in the last three years,

defined by fiscal year end between 1 and 36 months prior to the firm-year’s

fiscal year end. Industry-adjusted ROA is calculated as the difference

between ROA and median ROA for the constituents of the same two-digit

SIC code in that fiscal year.

Compustat, Equilar

CEO Tenure Number of years the CEO has been at the firm. Equilar

Chosen / Potential Number of peers chosen divided by the number of potential peers satisfying

the size and industry caliper or talent flows condition.

Compustat,

Equilar, BoardEx

Diversity Concerns Number of diversity concerns. These concerns could include non-

representation of minorities in senior positions within the company and

major controversies on affirmative action issues.

KLD

Dual Class Indicator variable set to 1 if the firm has a dual class structure with unequal

voting rights across its multiple share classes. Potential dual class

companies were identified based on either: 1) identifying firms that disclose

they are relying on the controlled company exemption, which allows

controlled companies to avoid certain corporate governance listing

standards (controlled companies are firms where more than 50% of the

voting power for director elections is held by a single person, entity, or

group), or 2) identifying firms from SharkRepellent unequal voting rights

historical data. After identifying these potential dual class companies, we

confirm whether or not the firm has unequal voting rights by reading the

proxy statements and extracting voting and ownership information.

SharkRepellent,

SEC Form DEF

14A

Employee Relations Concerns Number of employee concerns. These concerns could include bad union

relations, a poor safety record, and a poorly funded pension plan.

KLD

44

Variable Description Source

Environmental Concerns Number of environmental concerns. These concerns could include

hazardous waste and environmentally unfriendly products.

KLD

Even Number of Peers Indicator variable set to 1 if the firm selected an even number of peers Equilar

Firm is a Peer Indicator variable set to 1 if the potential peer chose the firm in question as

a peer

Equilar

Founder Involved Indicator variable set to 1 if the company founder is a part of the firm’s

management

Equilar

Fraction CEO Appoint Fraction of outside directors that began their tenure after the CEO started at

the firm.

Equilar

Fraction of Peers in Caliper The fraction of selected peers that satisfy the size and industry caliper or

talent flows condition.

Compustat,

Equilar, BoardEx

Has Multiple Business

Segments

Indicator variable set to 1 if the firm reports multiple business segments Compustat

Has Multiple Geo. Segments Indicator variable set to 1 if the firm reports multiple geographic segments Compustat

ICW Indicator variable set to 1 if there is at least 1 internal control weakness for

that fiscal year.

Audit Analytics

Insider Ownership Fraction of common shares held by insiders, where total shares outstanding

is from CRSP. Insiders include officers, directors, members of advisory

committees, and beneficial owners. Insiders’ total common holdings

include direct and indirect holdings.

Thomson Reuters,

CRSP

Industry First two digits of Global Industry Classification Standard (GICS) industry

code.

Compustat

ISS For Directors Indicator variable set to 1 if ISS supported all of the proposed directors. ISS Voting

Analytics

ISS For SOP Indicator variable set to 1 if ISS recommended voting “For” the executive’s

compensation package

ISS Voting

Analytics

LogSales Log of (1 + Compustat REVT). Compustat

Log(StdROA) Log of the standard deviation of the last 5 fiscal years’ ROA. Compustat

Market Cap Fiscal year end market cap in millions, calculated as Compustat

PRCC_F*CSHO.

Compustat

Mkt Cap Caliper Indicates whether the potential peer has end-of-year market capitalization

within 50%-200% of the firm’s

Compustat

Multiple Business Segments Indicator variable set to 1 if the firm and the potential peer both report

multiple business segments

Compustat

Multiple Geographic

Segments

Indicator variable set to 1 if the firm and the potential peer both report

multiple geographic segments

Compustat

Number of Peers Number of peers selected by the firm for compensation benchmarking. Eqiular

Pay Ratio (Avg. NEO/CEO) Average compensation of the named executive officers, excluding the

CEO, divided by the CEO compensation

Equilar

Peer Portfolio Percentile

(PPP)

The percentile of the median peer compensation relative to empirical

distribution of all possible medians which could been chosen based on the

size and industry calipers. For details on variable construction, refer to

Section 5.

Equilar, Compustat

Percent Outside Dir Fraction of the directors on the board that are outside directors. Equilar

Potential Peers Number of firms satisfying the size and industry caliper or talent flows

condition. Specifically, we apply an industry filter based on two-digit GICS

or the Hoberg-Phillips text-based industry classification and a size caliper

based on the restriction that a potential peer must have either revenue or a

market capitalization between 50% and 200% of the firm in question. We

also include all firms with any talent flows to or from the firm in question.

Compustat,

BoardEx, Hoberg-

Phillips data library

Product Concerns The number of product concerns over the calendar year. These concerns

could include poor product safety, controversies over product advertising,

and other product-related community concerns.

KLD

ROA Compustat IB divided by Compustat AT, multiplied by 100. Compustat

45

Variable Description Source

ROA Caliper Indicator variable set to 1 if the potential peer has ROA within ±0.03 of the

firm

Compustat

Sales Compustat REVT Compustat

Sales Caliper Indicator variable set to 1 if the potential peer has sales within 50%-200%

of the firm’s sales.

Compustat

Same Consultant Indicator variable set to 1 if both firms engage the same compensation

consultant in a given year.

Equilar

Same MSA Indicates whether the firm and potential peer are headquartered in the same

Metropolitan Statistical Area (MSA)

Compustat,

Department of

Labor

S&P 400 Membership Indicator variable set to 1 if the firm is in the S&P 400. CRSP

S&P 500 Membership Indicator variable set to 1 if the firm is in the S&P 500. CRSP

SEC Inquiries Indicator variable set to 1 if the firm received any SEC inquiries in a given

year.

Capital IQ

Separate Comp Consultants Indicator variable set to 1 if the firm employed more than one

compensation consultant

Equilar

Share 2-digit GICS Indicator variable set to 1 if both firms have the same two-digit Global

Industry Classification Standard (GICS) industry code.

Compustat

Share 3-digit GICS Indicator variable set to 1 if both firms have the same three-digit Global

Industry Classification Standard (GICS) industry code.

Compustat

Share 2-digit SIC Indicator variable set to 1 if both firms have the same two-digit Standard

Industry Classification (SIC) industry code.

Compustat

Share 3-digit SIC Indicator variable set to 1 if both firms have the same three-digit Standard

Industry Classification (SIC) industry code.

Compustat

Shareholder Lawsuits Indicator variable set to 1 if the firm experienced a lawsuit during the fiscal

year

Capital IQ

Single Business Segment Indicator variable set to 1 if if the firm and the potential peer report only

one business segment

Compustat

Single Geographic Segment Indicates whether the firm and the potential peer report only one

geographic segment

Compustat

Staggered Board Indicator variable set to 1 if the firm has a staggered board. Equilar

Talent Flows Indicator variable set to 1 if any officer or senior manager at the potential

peer or the firm has ever been employed as an officer or senior manager at

the other company

BoardEx

Top 10 Comp Consultant Indicator variable set to 1 if a compensation consultant employed by the

company is a top ten consultant, as measured by number of engagements

for the fiscal year

Equilar

Total Compensation Total compensation of the CEO including: cash, bonus, stock, options,

long-term incentive plans, and all other compensation ($ millions).

Equilar

46

Appendix B: Algorithm to create the empirical distribution of medians

In this appendix, we describe our algorithm which makes the calculation of the distribution

of peers computationally feasible. The number of potential peers is denoted by n. The

implementation of this algorithm varies depending on whether the number of peers chosen by the

firm, k, is even or odd.

When the firm chooses an odd number of peers

Suppose we have 10 potential peers and the firm wants to choose 3 peers (i.e., 𝑘 = 3). To

implement our algorithm, we first sort the set of 10 potential peers by the total compensation of

their CEO. In this example, we will assume the potential peer compensation ranges from 1 to 10.

Since the firm chooses an odd number of peers, the median compensation for any peer group will

be the compensation for one of the selected firms. For instance, with 𝑘 = 3, the only way in

which the firm can pick a median pay of 2, is to have the median firm be the 2nd lowest paid

potential peer (which has a pay of 2). Thus, the firm must select the two peers with the 1st and 2nd

lowest pay in the potential peer set. The remaining peer can be any one of the eight peers with

pay 3 to 10. Therefore, there are 8 possible ways (8 choose 1) in which the firm can arrive at a

median pay of 2. We can perform this analysis for all 10 possible medians when 𝑘 = 3 and the

number of combinations are:

47

Median Pay Chosen # Below (𝑖) # Above (𝑗) Number of Ways to Obtain Median (𝑖 × 𝑗)

1 0 9 0

2 1 8 8

3 2 7 14

4 3 6 18

5 4 5 20

6 5 4 20

7 6 3 18

8 7 2 14

9 8 1 8

10 9 0 0

For 𝑘 > 1, it is not possible for the lowest or the highest potential peer to ever be selected as

the median. Therefore, for 𝑘 = 3, we only need to make 8 computations to construct the

distribution of medians. The general algorithm to calculate the frequencies is:

𝐹𝑟𝑒𝑞 𝑥 𝑖𝑠 𝑚𝑒𝑑𝑖𝑎𝑛 = (𝑖

𝑘 − 12

) (

𝑗𝑘 − 1

2 )

where 𝑥 = median of selected peer group, 𝑘 = number of peers chosen, 𝑖 = number of possible

peers with compensation < 𝑥, 𝑗 = number of possible peers with compensation > 𝑥. These

frequencies for each median provide the true distribution of medians for possible peer groups.

Using this distribution and the associated selected median peer CEO compensation, it is

straightforward to compute the percentile of peer CEO compensation by converting these

frequencies into an empirical distribution.

When the firm chooses an even number of peers

A similar process is done for instances when the firm selects an even number of peers. For an

even set of peers, as in the case of 𝑘 = 4, the median will be the average of the two middle

48

values in the group 𝑘. We denote these two values as x and y where 𝑥 < 𝑦. Since we calculate

the median as the average of 𝑥 and 𝑦, any potential peer with a value 𝑧, such that 𝑥 < 𝑧 < 𝑦,

cannot be selected.

For concreteness, we will use the same 𝑛 = 10 as above but now set 𝑘 = 4 (i.e., the firm

chooses 4 peers from a set of 10 potential peers). Suppose we want to know the frequency of

situations when 𝑥 = 2 and 𝑦 = 5 are averaged to form a median. In order for these potential

peers to form the two elements of the median, one firm with compensation less than 𝑥 must be

selected (i.e., potential peer 1) and one firm with compensation greater than 5 (i.e., 6, ..., 10).

This can occur 1 × 5 = 5 possible ways. The general formula for this algorithm is:

𝐹𝑟𝑒𝑞 𝑥 + 𝑦

2 𝑖𝑠 𝑚𝑒𝑑𝑖𝑎𝑛 = (

𝑖𝑘 − 2

2 ) (

𝑗𝑘 − 2

2 )

where 𝑘 = number of peers chosen, 𝑥 = smaller compensation number comprising median of

selected peer group, 𝑦 = larger compensation number comprising median of selected peer group,

𝑖 = number of possible peers with compensation < 𝑥, 𝑗 = number of possible peers with

compensation > 𝑦. This requires us to calculate less than (𝑛2) =

𝑛(𝑛−1)

2 frequencies and averages

of two numbers, which is again simple to implement.

49

FIGURE 1 – Distribution of Future ROA

This figure reports the distribution of ROA in t+1 for the sample prior to the requirement that observations must have

𝑅𝑂𝐴𝑡+1 between -20% and +20.

50

FIGURE 2 – PPP Example with American Axle Manufacturing

Panel A: Empirical Density of Median Compensation

Panel B: Empirical Distribution of Median Compensation

Panel A depicts the empirical density of medians from peer groups of size 20 that American Axle & Manufacturing

Holdings (AAM) could have selected from its potential peer set of 240 firms in fiscal year 2014. The potential peer

set of 240 firms is determined based on either: i) any talent flows between the two firms or ii) being in the same

industry and of a similar size as AAM. Talent flows are defined as any officer or senior manager at the potential peer

ever being employed as an officer or senior manager at AAM and vice versa. Being in the same industry is defined as

sharing a 2-digit GICs industry code with or being a TNIC-3 peer of AAM (Hoberg-Phillips text-based industry

classification system). Similar size is defined as revenue or market cap within [50%, 200%] of AAM's. The group of

20 peers had a median CEO compensation of $5.73 million. Panel B plots the empirical distribution of PPP (in

percentage points) and shows that the selected median CEO compensation is at the 69.6th percentile relative to all

potential peer groups of size 20 from the 240 firms in the plausible universe of peers.

51

FIGURE 3 – Distribution of PPP

Panel A: Distribution of PPP

Panel B: PPP Summary Statistics

N Mean

Std

Dev Min P25 Median P75 Max

PPP 12,894 72.5 31.6 0.0 52.4 87.3 98.8 100.0

Panel A presents a histogram for the distribution of chosen peer median pay relative to all other potential medians

which we label as the Peer Portfolio Percentile, PPP. As described in Section 5, the potential peer are those that i)

have any talent flows with the firm or ii) are in the same industry and of a similar size as the firm. Industry is based

on 2-digit GICs and size is determined by the potential peer either being within [50%, 200%] of the firm’s revenue

or market capitalization. Panel B reports summary statistics of PPP.

52

TABLE 1 – Summary Statistics

Panel A: Industry Breakdown

2008 Data 2014 Data

Sample Compustat Sample Compustat

(1) (2) (3) (4)

Energy 0.079 0.075 0.079 0.083

Materials 0.058 0.061 0.067 0.061

Industrials 0.121 0.123 0.134 0.114

Consumer Discretionary 0.134 0.135 0.116 0.127

Consumer Staples 0.040 0.041 0.038 0.043

Health Care 0.140 0.137 0.137 0.154

Financials 0.203 0.190 0.231 0.205

Information Technology 0.169 0.181 0.155 0.160

Telecommunication Services 0.019 0.020 0.012 0.015

Utilities 0.037 0.037 0.034 0.039

Panel B: Summary Statistics of Financial and Compensation Data

N Mean Std Dev P25 Median P75

(1) (2) (3) (4) (5) (6)

Market Cap 12,894 5,000 10,411 484 1,361 4,102

Assets 12,894 9,468 25,349 718 2,168 6,742

Sales 12,894 4,204 9,264 356 1,047 3,436

ROA (%) 12,894 2.2 8.0 0.5 2.9 6.2

Log(Std ROA) 12,894 0.037 0.044 0.011 0.023 0.045

Number of Peers 12,894 15.4 8.3 11.0 15.0 18.0

Total Compensation 12,894 5.3 6.3 1.7 3.5 6.7

This table reports the summary statistics for our sample. Panel A reports the two-digit GICS industry breakdown of the sample relative to Compustat for select

years. Column 1 reports the 2008 sample composition, column 2 reports the 2008 Compustat composition, column 3 reports the 2014 sample composition, and

column 4 reports the 2014 Compustat composition. Panel B reports financial and compensation metrics. All variables are defined in Appendix A. All unbounded

variables are winsorized at 1% and 99%.

53

TABLE 2 – Probit Results

FY Specification

Full Model

Estimate Marginal FX Estimate Marginal FX (1) (2)

(3) (4)

Intercept -3.358*** -3.731*** (375.27) (236.79)

Share 2-digit GICS 0.446*** 0.042

(24.11) Share 3-digit GICS 0.933*** 0.248

(56.15) Sales Caliper 0.545*** 0.206 0.496*** 0.051

(75.27) (51.82) Assets Caliper 0.396*** 0.113 0.33*** 0.024

(59.97) (43.58) Mkt Cap Caliper 0.22*** 0.046 0.199*** 0.011

(40.06) (29.47) Both in SP 500 0.697*** 0.350 0.693*** 0.110

(50.62) (36.14) Both in SP 400 0.307*** 0.075 0.283*** 0.019

(20.04) (13.72) ROA Caliper 0.08*** 0.004

(12.92) Same MSA 0.353*** 0.027

(25.80) Multiple Geographic Segments 0.119*** 0.006

(13.53) Single Geographic Segment -0.046*** -0.002

(4.32) Multiple Business Segments 0.034*** 0.001

(4.26) Single Business Segment -0.051*** -0.002

(4.70) Both are Chairmen 0.099*** 0.017 0.071*** 0.003

(13.29) (7.61) Both are Not Chairmen 0.02*** 0.003 0.007 0.000

(3.67) (1.05) Same Consultant 0.255*** 0.016

(16.13) Talent Flows 1.312*** 1.998 0.777*** 0.147

(61.41) (31.59) Firm is a Peer 1.348*** 0.850

(117.18) Share 2-digit SIC 0.865*** 0.594

(57.79) Share 3-digit SIC 0.474*** 0.157

(30.54)

Pseudo- R2 0.321 0.414 Observations 53,206,214 53,206,214

54

This table presents results from the probit model presented in equation (1). All variables are defined in Appendix A. Columns

1 (3) report the coefficient estimates from the Faulkender and Yang (2010) (Full Model) specification. Z-statistics are in

parentheses. Columns 2 (4) report the marginal effects of each variable, which is defined as the change in probability, in

percentage points, that the peer is selected when the variable changes from 0 to 1 holding all other variables fixed at 0. The

unconditional probability of being selected, when all variables are 0, is 0.05% for the Faulkender and Yang (2010)

specification and 0.01% for the Full Model specification. Analysis is clustered by firm. *** indicates significance at 1%; **

at 5%; and * at 10%.

55

TABLE 3 – Covariate Balance

Panel A: Faulkender and Yang (2010) Specification

Peer Matched Firm Difference

Mean Median Mean Median Mean Median

(1) (2) (3) (4) (5) (6)

Total Compensation 6.08 4.00 5.32 3.15 0.76*** 0.85***

Assets 20,006 2,877 17,255 2,329 2,750*** 549***

Market Cap 8,524 2,011 7,019 1,491 1,505*** 520***

Sales 6,232 1,428 5,384 1,046 848*** 382***

ROA 0.027 0.035 0.008 0.028 0.02*** 0.01***

S&P 500 Membership 0.288 0.000 0.249 0.000 0.040*** NA

S&P 400 Membership 0.187 0.000 0.148 0.000 0.040*** NA

CEO Duality 0.522 1.000 0.518 1.000 0.004*** NA

Panel B: Full Model Specification

Peer Matched Firm Difference

Mean Median Mean Median Mean Median

(1) (2) (3) (4) (5) (6)

Total Compensation 6.08 4.00 5.13 3.16 0.95*** 0.84***

Assets 20,006 2,877 13,707 2,172 6,299*** 706***

Market Cap 8,524 2,011 6,106 1,410 2,418*** 601***

Sales 6,232 1,428 5,048 1,041 1,184*** 387***

ROA 0.027 0.035 0.011 0.029 0.016*** 0.006***

S&P 500 Membership 0.288 0.000 0.231 0.000 0.057*** NA

S&P 400 Membership 0.187 0.000 0.152 0.000 0.036*** NA

Has Multiple Business

Segments

0.553 1.000 0.567 1.000 -0.014*** NA

Has Multiple Geo. Segments 0.597 1.000 0.603 1.000 -0.007*** NA

CEO Duality 0.522 1.000 0.509 1.000 0.013*** NA

This table presents covariate balance on selected financial and compensation metrics from PSM for the Faulkender

and Yang (2010) specification (Panel A) and the full model (Panel B). All variables are defined in Appendix A. In

each panel, Columns 1 and 2 report the mean and median statistics for the selected peers. Columns 3 and 4 report the

mean and median for the PSM firms. Columns 5 and 6 report the difference in means and medians as well as statistical

significance. Difference in means are based on t-tests and difference in medians are based on Mood’s median tests.

*** indicates significance at 1%; ** at 5%; and * at 10%.

56

TABLE 4 – Within-firm Persistence of PPP

Decile in year t+1

1 2 3 4 5 6 7 8 9 10

Dec

ile

in y

ear

t

1 0.550 0.199 0.103 0.061 0.030 0.023 0.013 0.009 0.005 0.007

2 0.195 0.271 0.199 0.107 0.096 0.042 0.041 0.025 0.015 0.010

3 0.100 0.187 0.210 0.176 0.113 0.093 0.046 0.037 0.022 0.016

4 0.053 0.118 0.165 0.183 0.164 0.106 0.094 0.062 0.035 0.021

5 0.022 0.075 0.139 0.138 0.163 0.170 0.122 0.094 0.050 0.027

6 0.017 0.052 0.079 0.118 0.147 0.192 0.143 0.117 0.101 0.033

7 0.015 0.026 0.058 0.099 0.120 0.137 0.169 0.170 0.148 0.058

8 0.011 0.021 0.035 0.063 0.070 0.113 0.172 0.217 0.204 0.095

9 0.004 0.012 0.024 0.041 0.048 0.096 0.131 0.199 0.261 0.184

10 0.011 0.015 0.006 0.030 0.031 0.038 0.054 0.086 0.170 0.558

This table presents the year-over-year persistence of PPP. Firms are split into deciles of PPP by year. The rows report

the within-sample decile of PPP in year t and columns report the within-decile PPP in year t+1. Each cell reports the

fraction of year t observations that are in the associated decile in year t+1.

57

TABLE 5 – Summary Statistics across PPP values

Panel A: Means

N Mkt Cap Sales

Potential

Peers

Number

of Peers

Chosen /

Potential

Fraction

of Peers

in Caliper

Even

Number

of Peers

PPP (1) (2) (3) (4) (5) (6) (7) (8)

0 34 36,190 25,067 33.50 23.65 0.72 0.31 0.65

0-10 553 3,332 3,140 213.44 16.25 0.11 0.68 0.54

10-20 614 3,167 2,667 216.41 14.83 0.10 0.72 0.49

20-30 524 4,215 3,090 208.24 14.26 0.11 0.71 0.52

30-40 516 3,669 3,064 208.19 14.20 0.11 0.71 0.44

40-50 536 3,827 3,051 212.05 13.77 0.10 0.71 0.49

50-60 618 3,601 2,346 212.45 14.11 0.11 0.71 0.52

60-70 694 3,834 3,026 201.82 14.05 0.11 0.71 0.52

70-80 889 4,031 3,545 202.13 14.36 0.12 0.71 0.49

80-90 1,159 4,181 3,288 199.67 13.89 0.11 0.71 0.48

90-100 1,877 4,926 3,735 189.11 14.43 0.12 0.68 0.50

100 4,880 6,298 5,659 177.33 17.15 0.17 0.58 0.50

All 12,894 5,000 4,204 192.73 15.45 0.14 0.65 0.50

Panel B: Medians

N Mkt Cap Sales

Potential

Peers

Number

of Peers

Chosen /

Potential

Fraction

of Peers

in Caliper

Even

Number

of Peers

PPP (1) (2) (3) (4) (5) (6) (7) (8)

0 34 38,291 22,336 24.00 17.00 0.72 0.29 1.00

0-10 553 1,061 624 220.00 15.00 0.07 0.75 1.00

10-20 614 973 539 221.50 14.00 0.06 0.77 0.00

20-30 524 1,172 678 217.00 14.00 0.06 0.78 1.00

30-40 516 1,014 736 219.00 14.00 0.07 0.76 0.00

40-50 536 988 710 223.00 14.00 0.06 0.75 0.00

50-60 618 1,036 646 233.00 14.00 0.06 0.75 1.00

60-70 694 1,239 830 213.00 14.00 0.07 0.77 1.00

70-80 889 1,235 872 220.00 14.00 0.07 0.75 0.00

80-90 1,159 1,314 887 203.00 14.00 0.07 0.75 0.00

90-100 1,877 1,490 1,087 187.00 14.00 0.08 0.72 0.00

100 4,880 1,651 1,641 167.00 16.00 0.10 0.60 1.00

All 12,894 1,361 1,047 196.00 15.00 0.08 0.69 1.00

This table reports the summary statistics across different values of PPP. Each row corresponds to a particular range

of PPP values. Panel A reports the mean values and Panel B reports the median values. All variables are defined in

Appendix A. All unbounded variables are winsorized at 1% and 99%.

58

TABLE 6 – Latent Class Analysis of future ROA and PPP

ROAt+1 εROA ROAt+1

Pooled Pooled Cluster 1 Cluster 2 Cluster 3 Pooled Pooled

(1) (2) (3) (4) (5) (6) (7)

PPP -0.012*** -0.020*** -0.019*** 0.010*** -0.020*** -0.019***

(0.003) (0.003) (0.004) (0.001) (0.003) (0.004)

LogSales 1.026*** 1.287*** 0.563*** 0.814*** 1.287*** 0.563***

(0.079) (0.163) (0.085) (0.015) (0.162) (0.085)

Log(StdROA) -0.493*** -0.881*** 0.320*** -0.544*** -0.881*** 0.320***

(0.115) (0.194) (0.111) (0.021) (0.192) (0.111)

εPPP -0.012***

(0.003)

Cluster 1 x εPPP -0.0004

(0.005)

Cluster 2 x εPPP 0.0004

(0.005)

Cluster 3 x εPPP 0.030*** 0.029***

(0.002) (0.004)

Year FE Yes Yes Yes Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes Yes Yes Yes

Observations 12,894 12,894 790 7,863 4,241 12,894 12,894

Fraction of firms 1.000 1.000 0.061 0.610 0.329 1.000 1.000

Adjusted R2 0.17 0.17 0.346 0.158 0.865 0.562 0.562

This table reports results from estimating models (4a) and (4b). Column 1 presents the results from (4a):

ROAt+1 =β0 + β1PPP + β2 LogSales + β3Log(StdROA) + βkIndustryk + βjYearj + υ

Column 2 reports results from estimating equation (4b):

ROA =β0 + β1PPP + υ

where ROA is the residual from estimating equation (2a) and PPP is the residual from estimating equation (2b):

ROAt+1 = 0 + 1 LogSales + 2Log(StdROA) + kIndustryk + jYearj + (2a)

PPP = 0 + 1 LogSales + 2Log(StdROA) + kIndustryk + jYearj +

(2b)

β1 are equivalent in columns 1 and 2. Columns 3, 4, and 5 estimate equation (4a), for the three classes of firms from LCA by assigning each observation to a cluster with

the highest posterior probability. Columns 6 and 7 estimate equation (4a) report the pooled results but include interactions for cluster membership. All variables are defined

in Appendix A and all dollar values are in millions. Standard errors (in parentheses) are clustered by firm and year. *** indicates significance at 1%; ** at 5%; and * at

10%.

59

TABLE 7 – Summary Statistics across Clusters

Rent Extraction Aspirational Difference

Cluster 1 Cluster 2 Cluster 3 1 – 2 1 – 3 2 – 3

(1) (2) (3) (4) (5) (6)

Fraction of Firms 6% 61% 33% (i) Firm Characteristics

Market Cap 2,101 5,490 4,632 -3,388*** -2,530*** 858***

Assets 3,363 9,848 9,900 -6,485*** -6,538*** -52

Sales 2,121 4,534 3,979 -2,413*** -1,857*** 555***

ROA (in Pct Points) -9.38 3.60 1.79 -12.99*** -11.17*** 1.81***

(ii) CEO Compensation

Total Compensation 3.85 5.63 4.93 -1.78*** -1.08*** 0.70***

ISS For SOP 0.80 0.88 0.89 -0.08*** -0.09*** -0.01

PPP 79.26 74.72 67.17 4.54*** 12.09*** 7.55***

Chosen in Caliper 0.63 0.63 0.70 0.00 -0.07*** -0.07***

Number of Peers 14.81 15.30 15.84 -0.49 -1.03*** -0.54***

Top 10 Comp Consultant 0.82 0.75 0.73 0.07*** 0.09*** 0.03***

Separate Comp Consultants 0.05 0.06 0.06 -0.02* -0.02 0.00

(iii) CEO Talent Measures

CEO ROA -0.08 0.03 0.01 -0.10*** -0.08*** 0.02***

CEO Return 0.02 0.07 0.04 -0.05*** -0.02* 0.04***

CEO Firm Size 6.54 7.29 7.16 -0.75*** -0.62*** 0.13***

(iv) Entrenchment and Board Structure

CEO Tenure 6.71 7.80 8.23 -1.09*** -1.51*** -0.42***

Pay Ratio (Avg. NEO/CEO) 0.50 0.44 0.46 0.06*** 0.04** -0.02**

CEO Duality 0.35 0.49 0.48 -0.13*** -0.13*** 0.01

Founder Involved 0.21 0.15 0.12 0.06*** 0.09*** 0.03***

Staggered Board 0.48 0.43 0.43 0.05** 0.06*** 0.00

Percent Outside Dir 0.80 0.80 0.80 0.00 -0.01 0.00

Fraction CEO Appoint 0.51 0.50 0.50 0.01 0.01 0.00

Busy Board 0.18 0.19 0.16 -0.01 0.02*** 0.03***

Dual Class 0.04 0.05 0.05 -0.01 -0.01 0.00

Insider Ownership 0.10 0.07 0.07 0.02*** 0.03*** 0.01***

ISS For Directors 0.78 0.83 0.82 -0.05*** -0.04*** 0.01

(v) Realized Governance Measures

Accounting Restatements 0.101 0.076 0.082 0.025** 0.019* -0.006

ICW 0.063 0.031 0.029 0.033*** 0.034*** 0.002

SEC Enforcement Actions 0.008 0.017 0.010 -0.009** -0.003 0.007***

Shareholder Lawsuits 0.287 0.268 0.201 0.019 0.086*** 0.067***

Product Concerns 0.080 0.169 0.130 -0.089*** -0.049*** 0.039***

Diversity Concerns 0.554 0.514 0.487 0.040** 0.066*** 0.026**

Employee Relations Concerns 0.254 0.250 0.190 0.003 0.064*** 0.061***

Environmental Concerns 0.123 0.131 0.115 -0.007 0.008 0.016**

This table reports summary statistics across clusters. All variables are defined in Appendix A. Columns 1 through 3 report the mean for

each cluster. Columns 4 through 6 report the mean difference as well as statistical significance of this difference between the two clusters.

All dollar values are in millions. All tests of statistical significance are based on the empirical distribution with 1,000 bootstrapped samples.

*** indicates significance at 1%; ** at 5%; and * at 10%.

60

TABLE 8 – Excess Peer Pay Relative to Pay at PPP=50 for Rent Extraction Observations

Small-sized Rent Extraction Firms Small-sized Rent Extraction Firms All Rent Extraction Firms

Excess Peer Pay

Average

Average

Percentage

Excess Peer Pay

Total

Excess Peer Pay

Average

Average

Percentage

Excess Peer Pay

Total

Excess Peer Pay

Average

Average

Percentage

Excess Peer Pay

Total

(1) (2) (3) (4) (5) (6) (7) (8) (9)

2008 2.26 0.76 267.2 2.20 0.73 2,163.2 2.21 0.73 2,430.4

2009 1.42 0.59 152.2 1.56 0.53 1,626.5 1.55 0.53 1,778.7

2010 1.03 0.32 114.4 1.23 0.37 1,363.4 1.21 0.37 1,477.8

2011 1.23 0.40 132.1 1.62 0.36 1,682.8 1.58 0.36 1,814.8

2012 1.40 0.71 143.2 1.43 0.30 1,534.3 1.43 0.33 1,677.5

2013 1.41 0.51 154.7 1.19 0.28 1,312.4 1.21 0.30 1,467.1

2014 1.26 0.44 163.0 1.30 0.27 1,253.1 1.30 0.29 1,416.2

Total 1.44 0.53 1,126.8 1.49 0.40 10,935.8 1.49 0.41 12,062.6

This table reports statistics related to excess pay for observations in either rent extraction cluster. We define excess peer pay defined as the difference between the realized pay

and the median peer pay at PPP=50. Excess Peer Pay Average is the average difference, in millions, between the realized pay and the median peer pay at PPP=50. Average

Percentage is the average ratio of excess peer pay to total compensation. Excess Peer Pay Total is the total difference, in millions, between realized pay and the median peer pay.

Columns 1-3 (4-6) report results for the small-sized (large-sized) rent extraction cluster. Columns 7-9 report results when the small- and large-sized rent extraction clusters are

combined. Rows 1-7 report statistics by year, Row 8 reports statistics over the entire sample period.