What a Difference a Day Makes (In Executive …...1 What a Difference a Day Makes (In Executive Compensation) “The salary amount for Mr. Shore for the 2013 fiscal year is more than

What a Difference a Day Makes

(In Executive Compensation)

Bjorn N. Jorgensen*

Steve K. Rock**

Ana S. Simpson*

Abstract

We document curious regularities in executive compensation related to the calendar. Firms

using 52/53 week financial reporting conventions increase CEO pay in 53-week years,

consistent with rewarding the CEO for working an additional week relative to the previous

(52-week) year. However, firms do not appear to revert CEO compensation in the subsequent

year when the CEO works 52 weeks again. We do not, however, detect an increase in

executive pay in leap years for firms using a calendar-year reporting period. The 53-week

findings are robust to controlling for the firms’ financial performance which prior literature

documents is affected by longer reporting periods. We investigate whether corporate-

governance-related initiatives (Say-On-Pay) and other corporate governance mechanisms

moderate the additional week effect. We also consider whether CFO pay is similarly

impacted by fiscal year length changes and find no evidence thereof.

This version: October 2016. Please do not quote without permission. We thank seminar participants

at ESSEC-Paris, Exeter and Nottingham business schools.

* Department of Accounting, London School of Economics and Political Science, Houghton

Street, London WC2A 2AE, United Kingdom. E-mails: [email protected] and

[email protected].

** University of Colorado at Boulder. Leeds School of Business. 419 UCB. Boulder, CO 80309-

0419, USA. Phone: +1 (303) 735-5009 E-mail: [email protected].

1

What a Difference a Day Makes

(In Executive Compensation)

“The salary amount for Mr. Shore for the 2013 fiscal year is more than the salary amounts for

the 2014 and 2012 fiscal years, not because of any salary increase, but because the 2013 fiscal

year consisted of 53 weeks while the 2014 and 2012 fiscal years each consisted of 52 weeks.

Mr. Shore has declined to accept the Compensation Committee’s offer of a salary increase and

a bonus each year since the Company’s 2001 fiscal year, except for the bonuses for the 2008

through 2013 fiscal years, which he donated in their entirety to charity.”

2014 DEF 14A - Park Electrochemical Corp

1. Introduction

In this paper, we document curious predictable patterns in CEO compensation that are

not easily reconcilable with efficient compensation. First, while the majority of US firms report

financial performance and pay executives on a calendar annual basis, about 14% of firms in

our samples report financial performance on a 52/53 week annual basis. We investigate

whether these firms accordingly compensate their executives on a 52/53 week basis. Some

firms report that CEO salary increases by 1/52 because the firm’s 53 week fiscal year has an

additional week relative to prior year. Our evidence corroborates the idea that total

compensation is higher for the longer fiscal year. However, we also test whether CEOs are paid

less in a 52-week fiscal year following a 53 week year. Our evidence suggests that executive

compensation is inefficient in this regard. These findings have implications for our

understanding of contracting between the CEO and the board.

To illustrate, CocaCola reports its annual financial statements on a calendar-year basis

with every fiscal year ending on December 31. In contrast, PepsiCo ends its fiscal reporting

year on the last Saturday of the month of December. As a result, PepsiCo typically reports

financial performance via income statements for a 52-week interval, but occasionally needs a

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53rd week to maintain its last Saturday of the month of December fiscal-year reporting

convention.1

“In 2011, we had an additional week of results (53rd week). Our fiscal year ends on the last

Saturday of each December, resulting in an additional week of results every five or six years.

The 53rd week increased 2011 net revenue by $623 million and operating profit by $109

million ($64 million after- tax or $0.04 per share).”

PepsiCo, Inc. Fiscal 2011 Annual Report, ending 12/31/11, p. 38:

We conduct our study using 8,893 fiscal firm-year observations in the time period 1993-

2015 and our sample includes firms using annual fiscal year-end (365/366 day) and 52/53 fiscal

year-end reporting conventions. Our current sample includes just ExecuComp firms, though

our core result holds using firms on Morningstar and SEC Filings, providing a more

generalizable sample. With the main ExecuComp sample, we incorporate a first-stage selection

model to control for firms’ selection decisions with respect to their reporting period

conventions.

Consistent with efficient contracting, we document that CEOs on average are

compensated for working the additional 53rd week. Surprisingly, and seemingly inconsistent

with efficient compensation contracting, CEO compensation does not appear to decrease/revert

in the 52-week fiscal year immediately following a 53-week fiscal year. These findings extend

Johnston et al. (2012) who find that both analysts and investors appear to be inefficient in

processing earnings news for firms with additional weeks in fiscal quarters (“14 weeks”).

Johnston et al. (2012) attribute the analyst inefficiency to lack of effort and/or lack of incentive

to adjust for the additional week and suggest that investor inefficiency may be due to limited

attention (Hirshleifer and Teoh, 2003), though they note the latter explanation is less satisfying.

None of these explanations for contracting inefficiency seem plausible in our context. Rent

extraction by managers/agents is a more likely explanation. Therefore, we explore cross-

1 In a similar vein, rental properties in London are quoted based on either per calendar month or per calendar week

(pcw). With pcw, rent payments due exhibit predictable patterns over time since rent due will be for four weeks

in most months but five weeks in some months.

3

sectional variation for firms that fail to revert to a lower salary for a 52-week annual reporting

period following a 53-week year. As such, our findings should further inform the debate on

corporate governance and the efficiency of executive compensation.

The remainder of our paper is organized as follows. Section 2 develops our research

hypotheses, Section 3 presents our research methods, Section 4 provides details of our data and

sample, Section 5 presents our results and Section 6 concludes.

2. Hypothesis Development

Our first hypothesis is straightforward – it seems likely that firms’ compensation

committees will consider the additional week of work in a 53-week fiscal year in determining

executive compensation. Specifically, we focus on firms that use a 52/53 week reporting

convention, such that the typical reporting period has 52 weeks (or 364 days). Since every year,

the 52 week reporting period falls short of a calendar annual period by one day, or two days in

a leap year, these firms will have an additional reporting week (a 53rd week) every five or six

years. We hypothesize that executive compensation is higher for such 53-week fiscal years

relative to 52-week fiscal years, controlling for performance which also impacts compensation.

This seems reasonable because, as we note above, some annual reports comment that the CEO

received a raise because s/he worked an additional week in a 53-week fiscal year. Appendix 2

provides examples of CEOs who have their base salaries reduced following 53-week fiscal

reporting years (Park Electrochemical Corp and Home Depot). Some firms explicitly mention

the 53rd week impact on CEO salary, e.g., in Park Electrochemical Corp’s 2014 Def 14A

disclosure, “[t]he salary amount for Mr. Shore for the 2013 fiscal year is more than the salary

amounts for the 2014 and 2012 fiscal years, not because of any salary increase, but because the

2013 fiscal year consisted of 53 weeks while the 2014 and 2012 fiscal years each consisted of

52 weeks.” To provide some tension, it could be the case that some firms’ compensation

4

committees may not fully incorporate mechanical calendar effects when setting executive

compensation. Our first hypothesis in null form is:

HO1: CEO compensation does not change for 53-week fiscal years following a 52-week

fiscal year after controlling for company performance.

Our second hypothesis investigates whether compensation reverts in the first 52-week

fiscal year following a 53-week fiscal year. In the absence of contracting frictions,

compensation committees should adjust the non-performance-based salary portion of executive

compensation downward following a 52-week fiscal year. If compensation committees are not

boundedly rational and perfectly recall prior period interval length, then even performance

compensation based on income statement measures, like sales and earnings (Curtis et al. 2014;

Johnston et al. 2012), and derived ratios, like ROE, should be adjusted for the mechanical

effects attributable to the additional week.

There are at least two potential explanations for why compensation committees may

not make such adjustments to compensation. First, as noted by Johnston et al. (2012), agents,

in this case compensation committee members, may be boundedly rational or pay limited

attention to detail. Complete adjustment for the one week reduction in performance interval

seems like a strong assumption and related research explores alternative assumptions such as

forgetfulness, or limited cognitive abilities across economic agents (e.g., see, among many

others, Hand (1990), Schrand and Walther (2000), Bernheim and Thomadsen (2005),

Thomadsen and Bhardwaj (2011)). Second, depending on how strong governance mechanisms

work in the company, CEOs may have the ability to extract rents from shareholders in the form

of excessive compensation and periodic 53-week reporting intervals may present opportunities

for such rent extraction.

When compensation committees experience contracting frictions of either type, then

base salary might not be adjusted downwards following a 53-week year when the CEOs receive

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a salary raise of 2%=1/52. Similarly, compensation committees may not adjust accounting-

based performance measures for the reduced reporting interval following a 53-week fiscal year.

Put differently, the 53-week period creates a ratcheting effect in accounting-based performance

measures where the compensation committee may not “undo” the effect of the additional week.

Formally, we test the null hypothesis that the effect of the additional week in a 53-week year

completely reverses in the subsequent (52-week) reporting year against the alternate that firms

do not adjust their CEO compensation levels to reflect the decreased time period:

HO2: CEO compensation decreases in 52-week fiscal years following 53-week fiscal

years to reflect the one-week reduction in reporting interval after controlling for company

performance.

Our third hypothesis explores the moderating impact that strong governance may have

in reducing rent extraction by managers. Specifically, if company compensation committees

behave rationally, then CEO compensation in 52-week fiscal years following 53-week fiscal

years should decrease, both with respect to fixed salary and performance-based compensation.

One reason compensation may not decrease is because CEOs are able to extract rents from

shareholders by exerting influence over compensation committees (or nominating incompetent

compensation committee members). The amount of rent extraction should be mitigated in firms

with stronger corporate governance mechanisms protecting the interests of shareholders (Core,

Holthausen, and Larcker, 1999). Hence our third hypothesis, stated in null form, is:

HO3: The extent of CEO compensation reduction in 52-week fiscal years following 53-

week fiscal years is unrelated to corporate governance, after controlling for company

performance.

We acknowledge that a company’s interval reporting convention (i.e., fiscal calendar

reporting or 52/53-week fiscal year reporting) is arguably a choice. As such, we include fiscal

calendar reporting firms in our sample and run a first stage logit model to document what firm

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characteristics determine this reporting choice. Details related to motivating the first stage

regression and results of that estimation are described in section 4 below.

Research Models

Core, Holthausen and Larcker (1999) use performance and corporate governance

characteristics to explain cross-sectional variation in executive compensation and interpret the

residual from their regression as abnormal compensation. To test our first two hypotheses, HO1,

and HO2, we consider the following cross-sectional regression:

Compit = β0 + β1 Compit-1 + β2 v53wit + β3 Post_53weekit + Σ δ * Controlsit

+ industry fixed effects + year fixed effects +εit (1)

Where the dependent variable is the log of compensation, and the independent variables

include lagged log of compensation and two categorical variables: one for a 53-week year

(v53w), and the other for the year following a 53-week year (Post_53week).2 Following prior

research (Core et al., 1999), we control for changes in return on equity (∆ROE), annual stock

returns (Ann_ret), and corporate governance variables from Core et al. (1999). We control for

industry and year fixed effects.3 We also control for the potential concern that firms that

incorporate a 52/53 week reporting convention are systematically different than other firms

using the following first-stage logit model to predict companies’ fiscal financial reporting

convention:

CFIRM53it = β0 + β1 Sizeit + β2 Salit + β3 CEO_firstyrit + β4 CEO_Chairit

+ β5 CEO_stock_ownit + β6 Insider_ratioit + β7 Over69_ratioit + β8 Interlock_ratioit

+ β9 Busydir_ratioit + β10 Board_sizeit + industry fixed effects + year fixed effects + εit (2)

Where CFIRMS53 is an indicator variable = 1 if the firm follow a 52/53 week reporting

2 We obtain qualitatively similar results with compensation regressions in changes as in Cadman et al. 2010. We

chose to conduct the analysis in compensation levels to be consistent with Core et al. 1999 who also consider

corporate governance impacts in levels. 3 Gormley and Matsa (2014) and Amir et al. (2016) discuss the common use of industry fixed effects. We find

qualitatively similar results when instead using firm fixed effects.

7

convention and 0 otherwise. Size is measured as natural log of market capitalization, Sal is

natural log of sales. We include corporate governance variables CEO_firstyr = 1 if the CEO is

in her/his first year, zero otherwise, CEO_Chair = 1 if the CEO is the Chairman of the board,

zero otherwise, CEO_stock_own = percentage of common shares owned by the CEO,

Insider_ratio = percentage of directors on the board who are non-independent, Over69_ratio =

percentage of outside directors who are over the age of 69, Interlock_ratio = percentage of

outside directors who are interlocked, Busydir_ratio = percentage of outside directors who

serve on 3 or more other boards, Board_size = total number of directors on the board, and

industry fixed effects and year fixed effects, where industry fixed effects are at the four-digit

SIC code level. From regression (2), we capture the Inverse Mills’ ratio and include it in

regression (1). We find support for our first alternative hypothesis if β1 is greater than zero,

thereby rejecting HO1. Similarly, we test our second hypothesis based on the sign and

significance of β2.

We augment model (1) to test our third hypothesis. Specifically, in addition to including

the level of corporate governance variables as determinants of compensation changes, we

include interaction variables between Post_53week and our corporate governance proxies:

Compit = β0 + β1 Compit-1 + β2 v53wit + β3 Post_53weekit

+ Σ β4 * Post_53weekit * Corp_Govit + Σ δ * Controlsit

+ industry fixed effects + year fixed effects + εit (3)

We use model (3) to test whether corporate governance has a mitigating impact on the

relation between compensation and the possible reversion in CEO compensation in years

following 53-week reporting years.

3. Data and Sample

We obtain compensation data from ExecuComp for S&P 1500 firms from 1992

onwards. To construct our sample of 52/53 week firms we initially identify firms with actual

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period end date (Compustat data item ‘apdedate’) different from calendar year end date

(Compustat data item ‘datadate’) in any year of the sample period. For those firms we hand

collect the actual fiscal year end dates for firm years where ‘apdedate’ is unavailable

(‘apdedate’ is more widely available in Compustat from 2005 onwards). The data item

‘apdedate’ is sometimes miscoded on Compustat for firms that use calendar year end dates for

presentation purposes and disclose their 52/53 fiscal year ends only in the notes to the financial

statements. We recognize such errors when firms appear to switch from calendar to 52/53 week

reporting too often. For those firms we hand collect the fiscal year ends from the financial

statements for the entire sample period.

Table 1, Panel A shows the formation of the sample across data sources, though tables

4 – 9 reflect just the ExecuComp sample. The initial non-overlapping samples from

ExecuComp, Morningstar and SEC Filings (searched on Compustat CIK codes) consist of

34,464, 17,433 and 6,882 firm-year observations, respectively. After imposing restrictions for

available Compustat and CRSP data, the final sample includes 31,824 ExecuComp firm-years,

12,493 Morningstar firm-years, and 4,299 SEC Filings firm-years. Of the total 48,616

observations, 6,999 (14.4%) are 52/53 week fiscal reporting firm-years, and 41,617 (85.6%)

are calendar fiscal reporting firm-years. Table 1, Panel B shows the break-down of firm-year

observations by year and firm type. The number of observations increases gradually over time.

The significant increase in the number of observations from 2007 onwards is due to the addition

of data from SEC Filings for firms outside the Russell 3000. The percentage of 52/53 week

firms increases modestly over time to about 2005, then with the addition of Morningstar and

SEC Filing firms decreases to the end of the sample period. The decrease reflects that firms

from these data sources tend to be smaller and size is negatively correlated with the 52/53

annual reporting convention.

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Table 2 shows the industry breakdown of both groups of firms. In total, eleven industries

are represented ranging from agriculture, forestry and fishing to public administration. The

majority of calendar firms are concentrated in three industries: manufacturing, finance,

insurance and real estate, and services. The 52/53 week firms are primarily represented in

manufacturing and retail trade. Since the industry compositions of calendar and 52/53 week

firms are different, we consider industry as a determinant of the choice 52//53 week versus

calendar firm.

Table 3 includes descriptive statistics for dependent and independent variables across the

various sample sources. As expected, the size of firms, measured using either market

capitalization or sales is greater for the ExecuComp firms, then the MorningStar sample, and

finally the SEC Filings sample. E.g., the mean natural log of market capitalization is 7.48 for

ExecuComp firms versus 5.60 and 4.76 for MorningStar and SEC filings samples, respectively.

Returns follow a similar pattern, as does change in compensation. Performance metrics (∆ROE

and Ann_ret) are more volatile as we move across sample origin from larger firms

(ExecuComp) to smaller firms (SEC filings).

Table 4 reports results of a logit regression estimation used to predict reporting

convention (52/53 week fiscal reporting firms versus calendar fiscal reporting firms) – equation

(2). The purpose of this regression is to generate an inverse Mills ratio to control for the

endogeneity associated with firms choosing their reporting convention. We find that the best

predictor of 52/53 week reporting firms is four-digit SIC industry membership. Note that

compensation impacts driven by industry membership are addressed in our subsequent analysis

by using industry fixed effects in our compensation change models. Sal, defined as the natural

log of sales, is also positively correlated with the choice to report using a 52/53 week

convention. We find that among the corporate governance variables, CEO_stock_own is

consistently negatively related to firms’ choice to have a 53/52-week reporting cycle. Overall

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the pseudo-r-squared is rather high, approaching 30%, so we have a reasonable selection

model.

Table 5 panel A reports descriptive statistics related to our corporate governance control

variables, which are subsequently included in our models (1) and (3), consistent with Core et

al. 1999. The distributions of these variables differ substantially from Core et al., but their

sample period is 1982-84, and corporate governance mechanisms have changed substantially

since that time. Panel B reports correlations across corporate governance variables and the

inverse Mills ratio generated from table 4. Some corporate governance measures are highly

correlated. We note that Board_size is relatively highly negatively correlated with both

CEO_stock_own and Insider_ratio. Some of the corporate governance variables are highly

correlated with the inverse Mills ratio, which could raise some concern regarding

multicollinearity in our models which include the ratio.

4. Results

Table 6 reports our core results. Column 1 presents results excluding corporate

governance variables. We find support to reject HO1, that compensation is no different in 53-

week fiscal years relative to prior and subsequent 52-week years, controlling for performance,

in favor of our alternative hypothesis that compensation increases. This is a robust result.

Consistent with prior research, both (∆ROE and Ann_ret) are positively and significantly

related to compensation – i.e., performance impacts pay. Lagged compensation is also highly

positively correlated with compensation. We do not find support to reject HO2 – the coefficient

on Post_53week is not statistically significantly different from zero, implying that we cannot

conclude that CEO compensation decreases for 52-week fiscal reporting periods following 53-

week fiscal reporting periods, controlling for performance. This is a curious result, which

indicates that it does not appear that while compensation is increased for a 53-week fiscal

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reporting year, on average it is ratcheted upward to that level and not correspondingly

decreased in the 52-week fiscal reporting year following a 53-week reporting year.

Column 2 of table 6 includes corporate governance variables from Core et al. as well

as other control variables. Lagged compensation is highly correlated with current

compensation and first-year CEOs are, on average, less compensated than their predecessors.

Similar to Core et al., when the CEO is also the chair, we find that CEOs are compensated

more. Our results are also consistent with Core et al. for Board_size (positive) and

Over69_ratio (positive). Our results are opposite to Core et al. for Insider_ratio, though they

do not hypothesize a sign. Finally, our results indicate that Interlock_ratio is positively and

significantly related to CEO compensation which Core et al. hypothesize. Importantly,

controlling for all of these factors, we note that the core result that v53w is positive and

significant indicating that controlling for other factors, CEOs’ total compensation is greater in

53-week reporting years. Similar to our results in column 1, we are unable to find support for

the story that CEO compensation levels revert to pre-53-week year levels following a 53

week reporting year.

We make an initial attempt to explore the determinants of the apparent, on average,

compensation gain by CEOs in subsequent 52-week reporting years in column 3 of table 6. In

model (3) we interact the Post_53week variable with corporate governance variables to see if

corporate governance moderates the apparent rent extraction. We find that the v53w variable

continues to be positive and significant after the inclusion of corporate governance variables

and interactions with Post_53week. This implies that controlling for corporate governance

impacts, CEO’s are compensated for working an additional 53rd week. We do not find

support for any of the corporate governance variables mitigating the reversion of

compensation following a 53-week year. As such, we are unable to provide evidence as to

12

whether corporate governance plays any role with respect to the apparent inefficient

compensation we note regarding compensation in years following 53-week reporting years.

Table 7 considers the separate impact of an additional day in leap-years (calendar

years that have February 29th and hence 366 days) along with the 52/53 reporting year

variables, similar to the first column of table 6. The results indicate that leap years with

additional days do not appear to significantly impact CEO compensation in the year of an

additional day or the year following an additional day. However, the 52/53 week year impacts

noted in table 6 remain.

Table 8 reports the analysis similar to the first column of table 6 (absent the impact of

corporate governance variables) separating the sample period at 2011. We consider the

separate sample periods to assess the impact of ‘say-on-pay’ which impacted US reports in

2011. Consequently, the result noted before related to increased compensation in 53 week

reporting years is evident only in the post ‘say on pay’ regime era (in column 1). We do not

provide evidence in either time period that compensation decreases in 52-week reporting

years following 53-week reporting years.

Finally, we wanted to investigate whether in addition to CEOs, other executives

receive higher pay in 53 week fiscal years.4 We provide preliminary results related to whether

the effects we observe relate only to CEOs or if CFOs experience similar pay patterns across

4 These tests are partly motivated by firm disclosures, such as the following from Park Electrochemical Corp ‘s

2003 DEF 14A (Filing Date 2003-06-12):

“The salary amounts for Messrs. Shore and Spooner and Ms. Groehl for the 2002 fiscal year are more than the

salary amounts for the 2001 fiscal year not because of any salary increases, but because the 2002 fiscal year

consisted of 53 weeks while the 2001 fiscal year consisted of 52 weeks. The 2003 fiscal year consisted of 52

weeks; accordingly, the salary amounts for Messrs. Shore and Spooner and Ms. Groehl for the 2003 fiscal year

are the same as their salary amounts for the 2001 fiscal year. The salary amount for Mr. Watson is more for the

2002 fiscal year than for the 2001 fiscal year because he was employed by the Company for only part of the

2001 fiscal year. None of the named executive officers has received any salary increase since February 28,

2000, other than Mr. Watson (see note (f) below:”

13

reporting years of different lengths. Table 9 reports regressions of differences between CEO

and CFO compensation (CEO – CFO) on its lagged value as well as control variables and our

variables of interest v53w and Post_53week. Of note, we find that the coefficient on

CFO_firstyr is positive and significant. This indicates that CFOs tend to be paid less than

their predecessors in their first year of employment. More importantly, we find that

controlling for other factors, incremental pay for 53-week employment periods appears

confined to CEOs as the coefficient on v53w is positive and significant. In other words, it

does not appear that CFO compensation follows the same pattern.

5. Conclusion

We explore efficient contracting in CEO pay using the setting of 52/53 week-fiscal

reporting years. We provide some evidence consistent with efficient contracting in that CEOs

on average are compensated for working the additional 53rd week, controlling for other factors.

Perhaps surprisingly, and seemingly inconsistent with efficient compensation contracting, we

find that following a 53-week fiscal year, CEO compensation does not appear to revert to trends

based on prior 52-week fiscal year levels. We make an initial attempt to explore what may

influence the second result in cross section. We do not find much evidence consistent with

corporate governance mechanisms impacting the non-reversion in compensation trends

following a 53-week reporting year. We find that the increased compensation in 53-week

reporting years is most prominent after ‘say on pay’ was implemented in US compensation.

We also find evidence that our results on increased 53-week pay pertain to CEOs, but

apparently not for CFOs.

We emphasize that our core results are tentative and future research might explore these

relations more carefully. Specifically, we caution the reader that while we treat the timing of

the 53rd week reporting period to be independent of the executive and firm performance, we do

find some evidence of strategic timing (reported in Appendix 3). These findings suggest that

14

managers appear likely to choose the timing of the 53rd week to immediately precede the firm’s

Initial Public Offering (IPO). We cannot rule out that other undiscovered determinants of

timing of the additional week might affect our inferences. Nevertheless, our findings should

further inform the debate on corporate governance and efficiency in executive compensation.

13

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Appendix 1 Variable definitions

v53w Indicator variable equal to 1 for 53-week fiscal years following 52-

week fiscal years for 52/53 week firms; 0 otherwise.

Ann_ret Twelve month cumulative stock return from the end of year t-1 to the

end of year t.

Board_size Total number of directors on the board.

Busydir_ratio Percentage of outside directors who serve on three or more other boards.

Cal An indicator variable equal to 1 for calendar firms; 0 otherwise.

CEO_firstyr An indicator variable equal to 1 if it is the CEO’s first year in office; 0

otherwise.

CEO_chair An indicator variable equal to 1 if the CEO is also chairman of the board.

CEO_stock_own Percentage of outstanding shares owned by the CEO.

CFIRM53 An indicator variable equal to 1 for firms that report by 52/53 week

year; 0 for calendar year firms

CFO_firstyr An indicator variable equal to 1 if it is the CFO’s first year in office; 0

otherwise.

Comp ExecuComp:

The natural logarithm of Total Executive Compensation

defined as: Salary + Bonus + Other Annual + Restricted Stock

Grants + LTIP Payouts + All Other + Value of Option Grants

(Exec Comp data item TDC1)

Morningstar:

The natural logarithm of Total Executive Compensation from

SEC Filings:

The natural logarithm of Total Executive Compensation from

companies SEC Filings

∆Comp Change in Comp from year t-1 to year t

Insider_ratio Percentage of board members who are managers, retired managers, or

relatives of current managers.

Interlock_ratio Percentage of outside directors who are interlocked.

Inverse Mills’ ratio Estimated from the logit regression:

CFIRM53it = β0 + β1Sizeit + β2Salit + industry fixed effects + year fixed

effects + εit.

Leap An indicator variable equal to 1 for leap years; 0 otherwise;

Sal Natural logarithm of Sales (Compustat item Sal).

Over69_ratio Percentage of outside directors who are over 69 years of age.

Post_53week Indicator variable equal to 1 for 52-week years following 53-week

years for 52/53 week firms; 0 otherwise.

Post_Leap An indicator variable equal to 1 for years following leap years; 0

otherwise.

ROE Income before extraordinary items (Compustat item IB) scaled by total

shareholders’ equity (Compustat item SEQ)

∆ROE Change in ROE.

Size Natural logarithm of fiscal year-end market capitalization.

15

Appendix 2 – Examples of Companies that increase base salary for 53-week reporting

periods and decrease base salary for CEOs for 52-week fiscal periods following 53-week

fiscal reporting periods. 53-week fiscal reporting years are in bold.

Park Electrochemical Corp.:

Year Salary ($)

Brian E. Shore, President and CEO 2016 357,760















The Home Depot, Inc.:

Year Salary ($)

Francis S. Blake, Chief Executive Officer &

Chairman 2013 1,066,000


Chairman 2012 1,086,500


Chairman 2011 1,066,000


Chairman 2010 1,056,538


Chairman 2009 1,025,000

16

Appendix 3 – Application to IPO setting.

We wanted to further investigate the source of the timing of the 53 week. We conjecture that

the choice of 53 week fiscal year might be driven by the timing of each firm’s Initial Public

Offering (IPO). To investigate this issue, we identify 52/53 week Compustat firms that have

actual period end date (Compustat dataitem ‘apdedate’) for two consecutive periods greater

than 366 days. For these firms we download the IPO date from SDC Platinum and, where

unavailable, supplement with hand collection of the IPO date from registration statements

filed on Edgar from 1996 onwards and Mergent WebReports and Mergent Archives prior to

1996. We start with an initial sample of 1,074 52/53 week firms with available IPO dates. We

then remove firms that have switched to 52/53 week reporting after the IPO (527 firms) and

firms with unavailable data on their latest 53 week year preceding the IPO (162 firms). Our

final IPO sample consists of 385 firms. For the 162 firms with unavailable data on 53-week

pre-IPO year we predict the missing 53 week year by subtracting 5 or 6 years form the first

53 week year post IPO after verifying that the firm existed in the predicted 53 week pre IPO

year. This results in addition of 61 firms to the initial sample and an extended sample of 446

firms.

Figures 1A through 5A below offer statistical histogram-based tests to support the

hypothesis that firms’ choice of 53 week reporting period was initially timed to precede the

IPO.

17

Figure 1A: Distribution of 53-week year relative to IPO year

The test statistic for a discontinuity at -1 is calculated as follows:

If pi = 120/385 and pi-1 = pi+1 =1/6,

t-stat for discontinuity =120−(84+83)/2

√385∗120

385 ∗(1−

120

385)+

1

4∗385∗(

1

6+

1

6 )∗(2−

1

6−

1

6)

=3.13

0

20

40

60

80

100

120

140

-5 -4 -3 -2 -1 0

c

o

u

n

t

Distribution of 53-week year relative to IPO year

Bin Frequency

-5 13

-4 27

-3 58

-2 83

-1 120

0 84

Total 385

18

Figure 2A: Distribution of actual and predicted 53-week year relative to IPO year

Missing 53-week year preceding IPO is predicted as the first 53-week year after the IPO

minus 6

Bin Frequency

-5 33

-4 51

-3 74

-2 83

-1 121

0 84

Total 446

If pi = 121/446 and pi-1 = pi+1 =1/6,

t-stat for discontinuity = 121−(84+83)/2

√446∗121

446 ∗(1−

121

446)+

1

4∗446∗(

1

6+

1

6 )∗(2−

1

6−

1

6)

=3.06

0

20

40

60

80

100

120

140

-5 -4 -3 -2 -1 0

c

o

u

n

t


19

Figure 3A: Distribution of actual and predicted 53-week year relative to IPO year

Missing 53-week year preceding IPO is predicted as the first 53-week year after the IPO

minus 5

Bin Frequency

-5 13

-4 47

-3 82

-2 99

-1 120

0 85

Total 446

If pi 120/446 and pi-1 = pi+1 =1/6,


√446∗120

446 ∗(1−

120

446)+

1

4∗446∗(

1

6+

1

6 )∗(2−

1

6−

1

6)

= 2.29

0

20

40

60

80

100

120

140

-5 -4 -3 -2 -1 0

c

o

u

n

t


20

Figure 4A: Distribution of 53-week year relative to IPO year for firms with positive earnings

in the year before the IPO

Bin Frequency

-5 11

-4 22

-3 46

-2 66

-1 95

0 68

Total 308

If pi 95/308 and pi-1 = pi+1 =1/6,


√308∗95

308 ∗(1−

95

308)+

1

4∗308∗(

1

6+

1

6 )∗(2−

1

6−

1

6)

= 2.69

0

10

20

30

40

50

60

70

80

90

100

-5 -4 -3 -2 -1 0

c

o

u

n

t


21

Figure 5A: Distribution of 53-week year relative to IPO year for firms with negative (non-

positive) earnings in the year before the IPO

Bin Frequency

-5 2

-4 5

-3 12

-2 17

-1 25

0 16

Total 77

If pi 25/77 and pi-1 = pi+1 =1/6,


√77∗25

77 ∗(1−

25

77)+

1

4∗77∗(

1

6+

1

6 )∗(2−

1

6−

1

6)

= 1.62

0

10

20

30

40

50

60

70

80

90

100

-5 -4 -3 -2 -1 0

c

o

u

n

t


22

Table 1

Panel A: Sample formation

ExecuComp Morningstar SEC Filings Total

Initial sample 34,464 17,433 6,882 58,779

Missing Compustat data (2,382) (4,827) (2,470) (9,679)

Subtotal 32,082 12,606 4,412 49,100

Missing CRSP data (258) (113) (113) (484)

Total 31,824 12,493 4,299 48,616

Panel B: Sample break-down by year

Year Calendar

firms

% Calendar

firms

52/53 week

firms

% 52/53

week firms Total

1993 315 91% 30 9% 345

1994 776 88% 109 12% 885

1995 1,080 86% 182 14% 1,262

1996 1,151 85% 211 15% 1,362

1997 1,100 83% 230 17% 1,330

1998 1,133 82% 246 18% 1,379

1999 1,129 82% 249 18% 1,378

2000 1,172 82% 266 18% 1,438

2001 1,204 81% 283 19% 1,487

2002 1,146 80% 280 20% 1,426

2003 1,181 80% 297 20% 1,478

2004 1,222 81% 295 19% 1,517

2005 1,307 81% 306 19% 1,613

2006 1,391 82% 315 18% 1,706

2007 2,807 88% 389 12% 3,196

2008 3,160 88% 445 12% 3,605

2009 3,214 87% 470 13% 3,684

2010 3,057 87% 442 13% 3,499

2011 2,985 87% 431 13% 3,416

2012 2,968 88% 417 12% 3,385

2013 2,865 88% 379 12% 3,244

2014 2,963 88% 389 12% 3,352

2015 2,291 87% 338 13% 2,629

41,617 86% 6,999 14% 48,616

23

Table 2 Sample composition by industry

Industry

SIC codes Calendar

firms

%

Calendar

firms

52/53

week

firms

% 52/53

week

firms Total

Agriculture, Forestry and

Fishing

0100 – 0999 52 74% 18 26% 70

Mining 1000 – 1499 2,394 100% 3 0% 2,397

Construction 1500 – 1799 516 95% 25 5% 541

Manufacturing 2000 – 3999 15,209 81% 3,588 19% 18,797

Transportation,

Communications, Electric,

Gas and Sanitary service

4000 – 4999

4,538 98% 72 2% 4,610

Wholesale Trade 5000 – 5199 1,209 75% 396 25% 1,605

Retail Trade 5200 – 5999 920 29% 2,205 71% 3,125

Finance, Insurance and Real

Estate

6000 – 6799

9,588 99% 96 1% 9,684

Services 7000 – 8999 6,587 92% 554 8% 7,141

Public Administration 9100 – 9729 10 83% 2 17% 12

Other (non-classifiable) 9900 – 9999 594 94% 40 6% 634

41,617 6,999 48,616

24

Table 3 Descriptive statistics

Panel A: Descriptive statistics – ExecuComp sample (n = 31,824)

1st Pctl 25th Pctl Mean Median 75th Pctl 99th Pctl

ROE -1.36 0.05 0.09 0.12 0.18 1.11

Size 3.69 6.38 7.48 7.39 8.51 11.66

Sal 3.48 6.21 7.28 7.19 8.34 11.21

Ann_ret -0.78 -0.12 0.16 0.10 0.34 1.98

∆ROE -1.38 -0.05 -0.01 0.00 0.04 1.41

∆Comp -2.52 -0.28 0.08 0.08 0.47 2.48

Panel B: Descriptive statistics – MorningStar sample (n = 12,493)


ROE -2.21 -0.08 -0.04 0.06 0.12 1.42

Size 1.86 4.19 5.60 5.44 6.87 10.53

Sal -0.97 3.84 5.16 5.05 6.50 10.29

Ann_ret -0.83 -0.22 0.12 0.04 0.31 2.61

∆ROE -2.27 -0.08 -0.02 0.00 0.05 2.59

∆Comp -2.26 -0.18 0.05 0.04 0.28 2.19

Panel C: Descriptive statistics – SEC Filings sample (n = 4,299)


ROE -2.21 -0.22 -0.14 0.02 0.10 1.42

Size 0.96 3.56 4.76 4.71 5.86 9.33

Sal -1.33 3.61 4.64 4.62 5.77 9.39

Ann_ret -0.90 -0.41 0.02 -0.08 0.24 2.73

∆ROE -2.27 -0.15 -0.06 -0.02 0.05 2.59

∆Comp -2.07 -0.18 0.05 0.04 0.29 2.35

Panel D: Descriptive statistics – Combined sample (n = 48,616)


ROE -2.21 0.01 0.04 0.10 0.17 1.42

Size 2.11 5.45 6.75 6.80 8.09 11.40

Sal 0.66 5.25 6.50 6.60 7.88 11.00

Ann_ret -0.82 -0.17 0.14 0.08 0.32 2.20

∆ROE -2.27 -0.06 -0.02 0.00 0.04 2.59

∆Comp -2.43 -0.24 0.07 0.06 0.40 2.43

25

Table 4 Determinants of the choice of calendar vs 52/53 week year reporting

Dependent variable CFIRM53 CFIRM53 CFIRM 53 CFIRM 53 CFIRM 53

Size -0.397*** 0.025 -0.019 -0.055 -0.155

(-5.30) (0.29) (-0.19) (-0.43) (-0.98)

Sal 0.551*** 0.019 0.129 0.214 0.341**

(7.82) (0.22) (1.19) (1.55) (2.05)

CEO_firstyr 0.118 0.138 0.157 0.176 0.221

(1.32) (1.09) (1.30) (1.29) (1.38)

CEO_chair -0.202* -0.213 -0.205 -0.070 -0.101

(-1.70) (-1.62) (-1.48) (-0.44) (-0.54)

CEO_stock_own 0.002 -0.007 -0.039** -0.055*** -0.043*

(0.13) (-0.44) (-2.14) (-2.78) (-1.88)

Insider_ratio -0.001 -0.004 -0.013 -0.018* -0.030***

(-0.17) (-0.61) (-1.64) (-1.93) (-2.78)

Over69_ratio -0.008*** -0.007** -0.005 -0.004 -0.004

(-2.60) (-2.08) (-1.33) (-0.97) (-0.68)

Interlock_ratio -0.085 -0.103 -0.083 -0.070 -0.101

(-0.91) (-1.38) (-1.47) (-1.18) (-1.47)

Busydir_ratio 0.008 0.007 0.010 0.009 0.020**

(1.58) (1.22) (1.56) (1.30) (2.56)

Board_size -0.133*** -0.023 -0.036 -0.075 -0.105

(-4.27) (-0.55) (-0.76) (-1.41) (-1.64)

Year_FE Yes Yes Yes Yes Yes

Industry FE (1-digit SIC) No Yes




Observations 8,893 8,893 8,893 8,893 8,893

Pseudo R-squared (%) 5.84 25.93 31.06 28.07 29.45

This table presents estimates from the following regression:

CFIRM53it = β0 + β1Sizeit + β2Salit + β3CEO_firstyrit + β4CEO_Chairit + β5CEO_stock_ownit +

β6Insider_ratioit + β7Over69_ratioit + β8Interlock_ratioit + β9Busydir_ratioit + β10Board_sizeit +

industry fixed effects + year fixed effects + εit. (2)

The industry indicator variables are based on 1, 2, 3, and 4-digit (CRSP) SIC codes. Detailed

variable definitions are provided in Appendix 1. Standard errors are clustered by firm and year.

*, ** and *** indicate significance at the 0.10, 0.05 and 0.01 levels, respectively.

Table 5 Panel A – Descriptive Statistics - Corporate Governance Variables (n = 8,893)

26

Variable Mean 25th Pctl Median 75th Pctl Std Dev

Board_size 9.27 8.00 9.00 11.00 2.25

Busydir_ratio 8.03 0.00 0.00 14.29 11.09

CEO_chair 0.53 0.00 1.00 1.00 0.50

CEO_firstyr 0.09 0.00 0.00 0.00 0.29

CEO_stock_own 2.26 0.24 0.79 2.06 4.26

Insider_ratio 21.15 11.11 20.00 28.57 10.73

Interlock_ratio 0.05 0.00 0.00 0.00 0.74

Over69_ratio 22.33 8.33 20.00 33.33 19.00

Corporate governance variable definitions:

Board_size Total number of directors on the board

Busydir_ratio Number of outside directors who serve on 3 or more

other boards as a percentage of the total number of

outside directors

CEO_chair Indicator variable equal to 1 if CEO is also chairman of

the board; 0 otherwise

CEO_firstyr An indicator variable equal to 1 if it is the CEO’s first

year with a given firm; 0 otherwise

CEO_stock_own Percentage of CEO stock ownership

Insider_ratio Percentage of directors on the board who are non-

independent

Interlock_ratio Number of outside directors who are interlocked as a

percentage of the board size

Over69_ratio Number of outside directors who are over the age of 69

as a percentage of the total number of outside directors

27

Panel B – Pearson Correlations between Inverse Mill’s ratio and Corporate Governance

Variables

Inverse

Mills’

ratio

Board-

size

Busydir

ratio

CEO

chair

CEO

firstyr

CEO

stock

own

Insider

ratio

Interlock

ratio

Over69

ratio

Board_size 0.083 1.000

p-value 0.000

Busydir_ratio -0.2704 0.116 1.000

p-value 0.000 0.000

CEO_chair 0.048 0.072 0.021 1.000

p-value 0.000 0.000 0.0015

CEO_firstyr -0.0998 0.041 0.020 -0.074 1.000

p-value 0.000 0.306 0.003 0.000

CEO_stock _own 0.199 -0.179 -0.063 0.149 -0.083 1.000

p-value 0.000 0.0000 0.0000 0.0000 0.000

Insider_ratio 0.327 -0.190 -0.118 -0.1161 -0.037 0.1998 1.000

p-value 0.000 0.0499 0.0000 0.0000 0.000 0.034

Interlock_ratio 0.075 -0.020 -0.026 0.024 -0.008 0.011 0.016 1.000

p-value 0.000 0.0021 0.0001 0.0003 0.2101 0.133 0.016

Over69_ratio 0.131 0.002 -0.038 -0.019 -0.037 0.044 0.159 0.006 1.000

p-value 0.078 0.788 0.000 0.003 0.000 0.258 0.014 0.366

28

Table 6 - Executive compensation and 53-week years

Compit Compit Compit

Compit-1 0.588*** 0.573*** 0.573***

(9.65) (8.86) (8.80)

∆ROE 0.092** 0.080** 0.080**

(3.21) (3.03) (2.99)

Ann_ret 0.262*** 0.275*** 0.275***

(3.51) (3.52) (3.47)

v53w 0.087** 0.082* 0.083*

(2.49) (2.22) (2.21)

Post_53week -0.184 -0.094 -0.105

(-0.41) (-0.21) (-0.21)

Compit-1* Post_53week 0.023 0.013 -0.010

(0.48) (0.28) (-0.18)

Compit-1*CEO_firstyr -0.198* -0.197*

(-2.06) (-2.04)

CEO_firstyr 1.341 1.335

(1.65) (1.64)

CEO_chair 0.142*** 0.142***

(4.51) (4.29)

CEO_stock_own -0.002 -0.003

(-0.32) (-0.38)

Insider_ratio 0.008** 0.008**

(2.57) (2.58)

Over69_ratio 0.001* 0.001*

(2.14) (1.80)

Interlock_ratio 0.031* 0.031*

(1.95) (1.87)

Busydir_ratio -0.005* -0.004*

(-1.94) (-1.81)

Board_size 0.076*** 0.075***

(8.33) (7.77)

CEO _chair* Post_53week 0.023

29

(0.18)

CEO _firstyr* Post_53week -0.017

(-0.12)

CEO_stock_own* Post_53week 0.008

(0.33)

Insider_ratio* Post_53week -0.005

(-1.01)

Over69_ratio* Post_53week -0.002

(-0.41)

Busydir_ratio* Post_53week -0.003

(-0.64)

Board_size* Post_53week 0.037

(1.25)

Inverse mills’ ratio -0.262*** -0.484*** -0.484***

(-8.97) (-4.88) (-4.91)

Observations 8,798 8,798 8,798

Ind_FE Yes Yes Yes

Year_FE Yes Yes Yes

Adj. R-squared *(%) 53.3 55.3 55.3

This table presents estimates from the following regressions:

Column 1: Compit = β0 + β1 * v53wit + β2 * Post_53weekit + Σ δ * Controlsit + industry fixed

effects + year fixed effects +εit (1)

Columns 2 and 3: Compit = β0 + β1 * v53wit + β2 * Post_53weekit + Σ β3 * Post_53weekit *

Corp_Govit + Σ δ * Controlsit + industry fixed effects + year fixed effects + εit (3)

Where: v53w = 1 for 53-week years for 52/53 week firms following a 52-week year; 0 otherwise;

Post_53week = 1 for 52-week years following 53-week years for 52/53 week firms; 0 otherwise;

Corp_Govit = Particular corporate governance variable.

The Inverse Mills’ ratio is estimated from the logit regression:



industry fixed effects + year fixed effects + εit.. (2)

The industry indicator variables are based on 4-digit (CRSP) SIC codes. Standard errors are

clustered by firm and year. Detailed definitions of the variables are provided in Appendix 1.


30

Table 7 Executive compensation, 53-week years and leap years

Compit Compit Compit

Compit-1 0.588*** 0.588*** 0.573***

(9.73) (9.62) (8.84)

∆ROE 0.092** 0.092** 0.080**

(3.17) (3.20) (3.03)

Ann_ret 0.263*** 0.263*** 0.276***

(3.51) (3.50) (3.53)

v53w 0.083** 0.077*

(2.36) (1.98)

Post_53week -0.190 -0.101

(-0.42) (-0.23)

Compit-1* Post_53week 0.023 0.014

(0.48) (0.29)

Compit-1*CEO_firstyr -0.198*

(-2.06)

Cal*Leap -0.017 -0.008 -0.002

(-0.78) (-0.33) (-0.08)

Cal*Post_leap -0.032 -0.019 -0.026

(-0.94) (-0.72) (-1.18)

CEO_firstyr 1.343

(1.65)

CEO_chair 0.142***

(4.48)

CEO_stock_own -0.002

(-0.32)

Insider_ratio 0.008**

(2.55)

Over69_ratio 0.001*

(2.01)

Interlock_ratio 0.031*

(1.93)

Busydir_ratio -0.005*

31

(-1.95)

Board_size 0.076***

(8.18)

Inverse Mills’ ratio -0.262*** -0.261*** -0.483***

(-9.03) (-8.91) (-4.85)

Observations 8,798 8,798 8,798

Ind_FE yes yes yes

Year_FE yes yes yes

Adj. R-squared(%) 53.3 53.3 55.3


Column 1: Compit = b0 + b1 Cal*Leapit + b2* Cal*Post_Leapit + + Σ δ * Controlsit + industry

fixed effects + year fixed effects + eit

Columns 2 and 3: Compit = b0 + b1 Cal*Leapit + b2* Cal*Post_Leapit + b3*v53wit + b4*

Post_53weekit + Σ δ * Controlsit + industry fixed effects + year fixed effects + eit

Where: v53w = 1 for 53-week years of 52/53 week firms; 0 otherwise; Post_53week = 1 for

years following 53-week years of 52/53 week firms; 0 otherwise; Leap = 1 for leap years; 0

otherwise; Cal = 1 for calendar firms; 0 otherwise. Post_Leap = 1 for years following leap years;

0 otherwise.






clustered by firm and year. Detailed definitions of the variables are provided in Appendix A.


32

Table 8 Executive compensation and 53-week years pre- and post-2011 ‘Say on Pay’

Regime

Compit

(Year>=2011)

Compit

(Year<2011)

Compit-1 0.601*** 0.491***

(6.37) (7.72)

∆ROE 0.083* 0.083

(2.16) (1.40)

Ann_ret 0.436*** 0.175*

(5.59) (2.63)

v53w 0.126* 0.038

(2.55) (0.65)

Post_53week -0.699 0.484

(-1.40) (0.68)

Compit-1* Post_53week 0.076 -0.048

(1.36) (-0.53)

Compit-1*CEO_firstyr -0.185* -0.234

(-2.65) (-0.90)

CEO_firstyr 1.271* 1.560

(2.06) (0.74)

CEO_chair 0.148** 0.150**

(3.34) (3.65)

CEO_stock_own -0.012 0.008

(-1.08) (0.89)

Insider_ratio 0.008* 0.009*

(1.75) (2.11)

Over69_ratio 0.001* 0.001

(1.88) (0.85)

Interlock_ratio 0.035* 0.034

(2.26) (1.01)

Busydir_ratio -0.005 -0.004

(-1.64) (-1.19)

Board_size 0.074*** 0.086**

(5.09) (5.61)

33

Inverse Mills’ ratio -0.485* -0.540**

(-2.75) (-3.88)

Observations 5,336 3,419

Ind_FE yes yes

Year_FE yes yes

Adj. R-squared (%) 62.4 42.9


Compit = β0 + β1 * v53wit + β2 * Post_53weekit + Σ δ * Controlsit + industry fixed effects + year

fixed effects +εit (1)

Column 1: post-SOP period, Column 2: pre-SOP period

Where: v53w = 1 for 53-week years for 52/53 week firms following a 52-week year; 0 otherwise;

Post_53week = 1 for 52-week years following 53-week years for 52/53 week firms; 0 otherwise;

Corp_Govit = Particular corporate governance variable.






clustered by firm and year. Detailed definitions of the variables are provided in Appendix 1.


34

Table 9 Differences between CEO Pay and CFO Pay and 53 week reporting years

CEO_payit - CFO_payit

CEO_payit-1 – CFO_payit-1 0.443***

(4.69)

∆ROE 0.032*

(1.90)

Ann_ret 0.032*

(1.72)

v53w 0.115*

(1.90)

Post_53week 0.258

(1.27)

(CEO_payit-1 – CFO_payit-1)* Post_53week -0.193

(-1.61)

(CEO_payit-1 – CFO_payit-1)*CEO_firstyr -0.224

(-1.53)

CEO_firstyr 0.053

(0.33)

CFO_firstyr 0.161***

(5.43)

CEO_chair 0.073**

(3.27)

CEO_stock_own -0.017**

(-2.43)

Insider_ratio -0.003

(-1.54)

Over69_ratio 0.001

(1.44)

Interlock_ratio -0.017

(-1.66)

Busydir_ratio 0.001

(0.50)

Board_size 0.000

35

(0.01)

Inverse Mills' ratio 0.008

(0.11)

Observations 8,404

Ind_FE yes

Year_FE yes

Adj. R-squared (%) 24.0


CEO_PAYit – CFO_PAYit = a0 + a1(CEO_PAYit-1 – CFO_PAYit-1)it + a2* v53wit + a3*

Post_53weekit + Σ δ * Controlsit + industry fixed effects + year fixed effects + eit

Where: v53w = 1 for 53-week years of 52/53 week firms; 0 otherwise; Post_53week = 1 for

years following 53-week years of 52/53 week firms; 0 otherwise




firm fixed effects + year fixed effects + εit.. (2)

Standard errors are clustered by firm and year. Detailed definitions of the variables are provided

in Appendix A.


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