What a Difference a Day Makes (In Executive …...1 What a Difference a Day Makes (In Executive...
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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 Please do not quote without permission.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
** 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].
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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.
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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
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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.
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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
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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:”
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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
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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.
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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.
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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
Brian E. Shore, President and CEO 2015 336,368
Brian E. Shore, President and CEO 2014 357,760
Brian E. Shore, President and CEO 2013 364,640
Brian E. Shore, President and CEO 2012 357,760
Brian E. Shore, President and CEO 2011 357,760
Brian E. Shore, President and CEO 2010 357,760
Brian E. Shore, President and CEO 2009 357,760
Brian E. Shore, President and CEO 2008 364,640
Brian E. Shore, President and CEO 2007 357,760
Brian E. Shore, President and CEO 2006 357,760
Brian E. Shore, President and CEO 2005 357,760
Brian E. Shore, President and CEO 2004 357,760
Brian E. Shore, President and CEO 2003 357,760
Brian E. Shore, President and CEO 2002 364,640
The Home Depot, Inc.:
Year Salary ($)
Francis S. Blake, Chief Executive Officer &
Chairman 2013 1,066,000
Francis S. Blake, Chief Executive Officer &
Chairman 2012 1,086,500
Francis S. Blake, Chief Executive Officer &
Chairman 2011 1,066,000
Francis S. Blake, Chief Executive Officer &
Chairman 2010 1,056,538
Francis S. Blake, Chief Executive Officer &
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
Distribution of 53-week year relative to IPO year
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,
t-stat for discontinuity =120−(99+85)/2
√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
Distribution of 53-week year relative to IPO year
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,
t-stat for discontinuity =95−(68+66)/2
√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
Distribution of 53-week year relative to IPO year
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,
t-stat for discontinuity =25−(16+17)/2
√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
Distribution of 53-week year relative to IPO year
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)
1st Pctl 25th Pctl Mean Median 75th Pctl 99th Pctl
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)
1st Pctl 25th Pctl Mean Median 75th Pctl 99th Pctl
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)
1st Pctl 25th Pctl Mean Median 75th Pctl 99th Pctl
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
Industry FE (2-digit SIC) No Yes
Industry FE (3-digit SIC) No Yes
Industry FE (4-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:
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 4-digit (CRSP) SIC codes. Standard errors are
clustered by firm and year. Detailed definitions of the variables are provided in Appendix 1.
*, ** and *** indicate significance at the 0.10, 0.05 and 0.01 levels, respectively.
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
This table presents estimates from the following regressions:
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.
The Inverse Mills’ ratio is estimated from the logit 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 4-digit (CRSP) SIC codes. Standard errors are
clustered by firm and year. Detailed definitions of the variables are provided in Appendix A.
*, ** and *** indicate significance at the 0.10, 0.05 and 0.01 levels, respectively.
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
This table presents estimates from the following regressions:
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.
The Inverse Mills’ ratio is estimated from the logit 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 4-digit (CRSP) SIC codes. Standard errors are
clustered by firm and year. Detailed definitions of the variables are provided in Appendix 1.
*, ** and *** indicate significance at the 0.10, 0.05 and 0.01 levels, respectively.
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
This table presents estimates from the following regressions:
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
The Inverse Mills’ ratio is estimated from the logit regression:
CFIRM53it = β0 + β1Sizeit + β2Salit + β3CEO_firstyrit + β4CEO_Chairit + β5CEO_stock_ownit +
β6Insider_ratioit + β7Over69_ratioit + β8Interlock_ratioit + β9Busydir_ratioit + β10Board_sizeit +
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.
*, ** and *** indicate significance at the 0.10, 0.05 and 0.01 levels, respectively.