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I need a presentation in POWERPOINT form about 10 minutes worth about discrimination. My topic highlights the discrimination of NFL head coaches and/or management. I would first like to talk about what discrimination is in law, what the rooney rule is in-depth, why it has come about, and what it has changed, and what Brian Flores and others have sued the NFL over. Lastly, bring up recent instances of potential discrimination by the NFL. For example, Lovie Smith being fired after one season with Texans. Or Eric Bienemy, Chiefs offensive coordinator not being hired as a head coach despite interviewing for multiple vacancies and being well qualified. Examples of black NFL coaches being fired with winning records. Conclude with 2 critical thinking questions about what can be done about the issue, and or how the lawsuit will/won’t shape the future of the profession.
https://www.nbcsports.com/philadelphia/eagles/what-nfls-rooney-rule#:~:text=What%20is%20the%20origin%20of,being%20fired%20in%20early%202002
.
https://apnews.com/article/brian-flores-miami-dolp…
https://www.si.com/nfl/texans/news/houston-texans-…
https://www.forbes.com/sites/davidberri/2018/01/02/black-head-coaches-in-the-nfl-are-much-more-likely-to-be-fired-with-a-winning-record/?sh=5d8afffd1cb8
Sport Management Review 23 (2020) 978–991
Contents lists available at ScienceDirect
Sport Management Review
journal homepage: www.elsevier.com/locate/smr
National Football League head coach race, performance,
retention, and dismissal
Steven Salagaa,* , Matthew Juravichb
a
b
University of Georgia, 361 Ramsey, 330 River Road, Athens, GA 30602, United States
University of Akron, 302 Buchtel Common, Akron, OH 44325-5103, United States
A R T I C L E I N F O
A B S T R A C T
Article history:
Received 8 April 2019
Received in revised form 26 December 2019
Accepted 26 December 2019
Available online 17 February 2020
The authors tested for evidence of racial discrimination in the employment retention of
National Football League head coaches. A robust data set spanning the modern history of
the sport (1985–2018) was generated to examine managerial employment tenure,
dismissal, and subsequent organizational performance. After controlling for performance
differences and heterogeneity between head coaches, the authors uncover statistically
significant evidence that Non-White head coaches experience longer employment spells
relative to White head coaches in the Rooney Rule era. No statistically significant evidence
of racial differences in the rate at which head coaches are fired is found. Both employment
tenure and dismissal are largely driven by raw performance, and to a lesser degree, relative
performance. Finally, the relationship between head coach race and organizational
performance is examined, but no statistically significant differences by race are uncovered.
© 2020 Sport Management Association of Australia and New Zealand. Published by Elsevier
Ltd. All rights reserved.
Keywords:
Race
Performance
Employment retention
Dismissal
1. Introduction
Researchers have spent considerable effort investigating the employment dynamics of managerial and executive
leadership in sport organizations (e.g., Allen & Chadwick, 2012; Fort, Lee, & Berri, 2008; Holmes, 2011; Kahn, 2006). This
attention is warranted given theory and evidence suggesting organizational performance is closely aligned with the personal
characteristics (Hambrick, 1995; Hambrick, 2007; Hambrick & Mason, 1984; Henderson, Miller, & Hambrick, 2006) and
productivity (e.g., Bandiera, Hansen, Prat, & Sadun, 2017; Grusky, 1963) of individuals occupying key leadership positions
within the firm. The literature therefore has used the investigation of managerial employment outcomes to better
understand variation in organizational performance and industry behavior.
Extant research has typically investigated managerial employment dynamics through firm decision making with respect
to entry, compensation, retention, and dismissal. In team sports, the head coach (HC) is often the focus of analysis given the
perceived importance of the position in influencing success, and in related studies the HC is considered analogous to the
chief executive officer (e.g., Foreman & Soebbing, 2015; Humphreys, Paul, & Weinbach, 2016; Ndofor, Priem, Rathburn, &
Dhir, 2009). In line with theory, researchers have established a strong link between performance and managerial labor
market outcomes (Grusky, 1963) as raw performance has been shown to directly impact HC employment retention and
dismissal in a variety of settings including National Collegiate Athletic Association (NCAA) football (Holmes, 2011; Mixon &
Treviño, 2004), the National Football League (NFL; Allen & Chadwick, 2012; Lefgren, Platt, Price, & Higbee, 2019), and the
* Corresponding author.
E-mail addresses: salaga@uga.edu (S. Salaga), mjuravich@uakron.edu (M. Juravich).
https://doi.org/10.1016/j.smr.2019.12.005
1441-3523/© 2020 Sport Management Association of Australia and New Zealand. Published by Elsevier Ltd. All rights reserved.
S. Salaga, M. Juravich / Sport Management Review 23 (2020) 978–991
979
National Basketball Association (NBA; Kahn, 2006; Wangrow, Schepker, & Barker, 2018). Performance relative to
expectations has also been shown to influence managerial turnover (e.g., Allen & Chadwick, 2012; Humphreys et al., 2016).
Researchers have also assessed whether candidate availability (Foreman & Soebbing, 2015) and organizational deviance
(Foreman, Soebbing, & Seifried, 2019) are associated with HC dismissal.
An important but relatively small subset of this literature examines managerial employment dynamics through the lens
of potential racial discrimination. Goff and Tollison (2009) used NFL coaching data to explain that racial integration in the
coaching industry proceeds similarly to innovation in other markets. Braddock, Smith, and Dawkins (2012) demonstrated
that race is associated with the ability to obtain positions of managerial authority in the NFL. Rider, Wade, Swaminathan, and
Schwab (2016) illustrated that performance-reward bias limits minority coaches from advancing in the NFL coaching ranks.
Similarly, Foreman, Soebbing, Seifried, and Agyemang (2018) showed that Black NFL coaches are less likely to be promoted.
Missing from the literature is empirical analysis directly examining potential racial discrimination of incumbent
managers in positions of power in the NFL. In other words, while existing research produces evidence illustrating that access
and upward mobility can be problematic for minority coaches in the sport, there is a lack of research which directly examines
whether minority managers are held to higher standards once they obtain HC status. Therefore, the current manuscript
bridges the gap between race and managerial employment retention and dismissal in context of the NFL.
Racial discrimination in sports labor markets is an important topic socially, as well as in the management and economics
literatures. Broadly, labor market discrimination has been traditionally defined by economists as the unequal treatment of
workers, holding constant the productivity of those workers (Becker, 1971). In North American professional sport, labor
market discrimination at the managerial level is a particularly contentious topic given that Black athlete participation rates
in professional football and basketball far exceed the percentage of Black individuals employed in front office or coaching
positions (Kahn, 2006). To illustrate, The Institute for Diversity and Ethics in Sport reports that 69.7 % of NFL players are Black,
while only 21.9 % of head coaches, 9.5 % of league-level managers, and zero franchise CEOs or Presidents are Black (Sonnad,
2018).
Empirically testing for labor market discrimination is challenging, largely due to the difficulty in adequately controlling
for differences in worker productivity (Kahn, 1991). Unlike many other industries, an advantage of analyzing sports labor
markets is the public availability of necessary data such as performance outcomes, employment status, and personal
characteristics (Kahn, 2000). The NFL serves as an important setting to analyze potential labor market discrimination, not
only because of the lack of managerial positions held by Blacks and other minorities, but also because of league-level efforts
to increase diversity. In December 2002, the NFL initiated the “Rooney Rule” which required franchises to interview at least
one minority candidate prior to hiring a new HC. However, this has been considered a “soft” affirmative action policy aimed
at increasing the diversity of the candidate pool rather than a policy aimed at directly determining hiring policy (DuBois,
2016).
The contribution of this manuscript is valuable to the sport management literature because the hiring decision represents
only a fraction of the employment relationship between the firm and worker (Conlin & Emerson, 2006). In other words,
assessing potential discrimination is important not only in the areas of access and entry, but also in understanding the
dynamics of how hired workers retain their jobs and the manner in which employment relationships end. Industry concerns
regarding inequality also extend beyond labor market entry to retention and dismissal as five of the eight NFL HCs fired at the
conclusion of the 2018 season were Black.
Accordingly, we investigate potential labor market discrimination in the NFL head coaching market by assessing whether
minority HCs are held to higher standards in retaining their positions. We analyze the length of HC employment spells and
the rate at which HCs are fired using a robust data set spanning from 1985 to 2018. The data controls for HC performance, HC
functional and technical experience, HC personal demographics, as well as league, franchise, and market characteristics. Our
empirical results provide insight into the intersection of racial discrimination, managerial employment, and organizational
performance.
2. Literature review
2.1. Diversity and discrimination in sport
The literature examining diversity and discrimination in sport is robust (e.g., Cunningham, 2019; Cunningham & Sagas,
2008; Kahn, 1991). This research commonly evaluates representation with respect to race, sex, gender, and ethnicity and
most frequently investigates potential discrimination with respect to access and treatment (Cunningham, 2019). Reasons for
potential inequalities include the persistence of stereotypes (e.g., Heilman, 2012), prejudice (e.g., Brewer, 2007), the
dynamics of organizational cultures (e.g., Burton, 2015), and institutional bias (e.g., Walker & Sartore-Baldwin, 2013).
Diversity research has frequently focused on the HC and chief administrator (i.e., athletic director) levels in NCAA and
professional sport. In professional and intercollegiate women’s basketball, scholars have noted access inequality as diversity
at the athlete-level has not translated to equivalent representation at the HC level (Darvin, Pegoraro, & Berri, 2018). Relatedly,
underrepresentation of women in both head and assistant coaching positions has also been found throughout NCAA
athletics (Kennedy, 2009). In addition, among NCAA athletic directors, women have been found to lag far behind men in
terms of representation (Peachey & Burton, 2011).
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S. Salaga, M. Juravich / Sport Management Review 23 (2020) 978–991
With respect to treatment discrimination, some evidence exists for gender differences in compensation among head
coaches of women’s teams at the NCAA Division 1 level (Brook & Foster, 2010; Humphreys, 2000). At the intersection of
access and treatment discrimination is evidence from NCAA women’s soccer illustrating that women are more likely to be
hired into more difficult head coaching positions (Wicker, Cunningham, & Fields, 2019). Thus, in comparison to the NFL,
similar phenomena exist with respect to female HCs and athletic directors being underrepresented and facing potential
barriers to success. Within the NFL HC labor market, policy has been instituted in an attempt to address the dearth of
minority HCs where, despite the majority of the playing talent pool being comprised of Black athletes, head coaches are
decidedly White.
In professional sport, research investigating racial and ethnic discrimination are most common as outlined in Kahn (1991)
and Kahn (2000). Kahn (1991) categorizes this literature into four sectors: salary discrimination, positional discrimination,
customer discrimination, and hiring discrimination. Due to the breadth of the discrimination literature in sport, we limit our
focus to work in the context of the NFL.
2.2. Discrimination in the National Football League
The NFL discrimination literature can be categorized based on whether the analysis is at the player or managerial level.
Analysis of discrimination at the player level is largely focused on salary inequality. This work dates back to Mogull (1973),
who examined a limited sample of NFL and American Football League salaries and uncovered no significant differences
between races. Kahn (1992) found that Non-White players were paid a premium in areas with a larger percentage of NonWhite residents, while the same held for White players employed in primarily White areas. Kahn (1992) also found salaries
for White players were slightly larger but were not statistically different in the majority of estimations.
More recent literature has focused on positional salary inequality with substantial support for discrimination against
Black players. Berri and Simmons (2009) found evidence of performance-based salary discrimination against Black
quarterbacks in the upper half of the salary distribution. Keefer (2013) uncovered salary discrimination against Black NFL
linebackers across the salary distribution which was robust to variation in the statistical technique used. Ducking, Groothuis,
and Hill (2017) also discovered evidence of salary discrimination against Black linebackers and suggested these results were
evidence of discrimination against Black players who had decision making responsibility. Alternatively, there is limited
evidence of reverse discrimination as Gius and Johnson (2000) estimated that White players earned 10 % less than Black
players, though they analyzed only one season of data. Ducking et al. (2017) also demonstrated Non-Black defensive lineman
were paid less, holding all else equal.
There is also empirical work which fails to uncover evidence of salary discrimination. Ducking, Groothuis, and Hill (2014)
analyzed data from six positional groups and found player performance, not race, determined salary. Burnett and Van Scyoc
(2013, 2015) examined salaries for rookie wide receivers, linebackers, and offensive lineman and did not find compensation
differences by race.
The NFL hiring discrimination literature is less developed and includes work analyzing inequality in market entry, open
market hiring, and employment retention. Conlin and Emerson (2006) demonstrated that the costs and benefits of
discrimination differ at the point of market entry relative to retention or promotion decisions. They analyzed NFL draftees
and showed that Non-White players faced discrimination at the point of market entry, but not in job retention. Ducking,
Groothuis, and Hill (2015) analyzed the career length of NFL players at six positions and after controlling for variation in
player performance, did not find significant differences in exit discrimination by race. Berri and Simmons (2009) also failed
to find differences by race when evaluating entry of quarterbacks into the league via the amateur draft. However, Volz (2017)
established that Black quarterbacks were significantly more likely to be benched relative to White quarterbacks, suggesting
inequality in starting position retention.
At the managerial level, Madden’s (2004) work used a relatively small sample (1990–2002) and uncovered Black HCs
were held to higher hiring standards given they had superior levels of performance but were hired less frequently. Malone,
Couch, and Barrett (2008) conducted a follow-up study which included additional data, and subsequently found no
differences in HC performance by race. Fee, Hadlock, and Pierce (2006) investigated the drivers of internal promotion and
external hiring in the market for HCs and found little evidence that race was associated with either. Goff and Tollison (2009)
investigated the drivers of racial integration in the NFL head coaching market and found slight evidence that winning
organizations were more likely to hire a Black HC. Braddock et al. (2012) demonstrated that access to managerial positions in
the NFL were linked to both race and position centrality.
Foreman et al. (2018) illustrated that Black NFL coaches were less likely to be promoted to higher-level coaching positions.
Four recent studies investigated NFL HC dismissal as Foreman and Soebbing (2015) failed to uncover a statistically significant
effect of race on dismissal when investigating the relationship between candidate availability and HC turnover. Foreman
et al. (2019) likewise found no racial differences in HC dismissal in work estimating the relationship between personal
conduct violations and managerial turnover. Allen and Chadwick (2012) and Lefgren et al. (2019) did not account for racial
variation in their dismissal modeling of NFL HCs.
A related line of research also examines whether the Rooney Rule had a positive impact on minority hiring. Solow, Solow,
and Walker (2011) concluded race was not influential in NFL head coaching hiring decisions and that the Rooney Rule did not
increase the number of minority head coaches. Alternatively, Madden and Ruther (2011) suggested the policy reduced the
S. Salaga, M. Juravich / Sport Management Review 23 (2020) 978–991
981
hiring disadvantage faced by Black head coaching candidates. DuBois (2016) used a difference-in-differences approach,
which produced a positive and statistically significant impact on minority hiring following the Rooney Rule.
This leaves the literature on employment discrimination in the NFL void of empirical work which directly investigates
how race is linked to employment outcomes for incumbent managers. Thus, we contribute to the literature on treatment
discrimination by assessing whether race is a statistically significant and practically relevant driver of HC employment
retention (i.e., tenure) and termination.
3. Data and empirical approach
Our analyses are conducted at the level of the individual HC employment spell. The data includes all permanent HC
employment spells beginning in 1985 or later and follows all spells through the conclusion of the 2018 season. Interim HC
spells that do not directly transfer into a permanent HC position are excluded.1 There are 208 total observations. All coaching
and franchise level data was collected from pro-football-reference.com. The remainder of the data is publicly available and
was collected at various websites.
In line with the employment discrimination literature, we specify the employment outcome of interest as a function of
several factors as follows:
HC Employment Outcome = f(HC performance, HC employment (functional) experience, HC playing (technical) experience, HC
personal characteristics, league factors, franchise & market characteristics)
(1)
Our primary outcome of interest is the length of a HC employment spell which is measured by the number of regular
season games coached in a continuous spell. We use Cox proportional hazard modeling which has been used in a variety of
contexts to measure time to failure. It allows for the ability to properly account for right-censored observations as not all HCs
in the sample completed their employment spell by the conclusion of the 2018 season (e.g., Hoang & Rascher, 1999; Staw &
Hoang, 1995). In hazard modeling, the dependent variable is the hazard rate and in the current study represents the
conditional probability of a HC exiting an employment spell, either voluntarily or involuntarily, at a given point in time. An
advantage of the Cox (1972) model is that it does not impose a functional form to the shape of the hazard function, allowing
the covariates the ability to shift the baseline hazard function.
The conditional hazard function is expressed as follows:
l(t | X1, X2) = lo (t) exp(β1 X1 + β2 X2) + e,
(2)
where l is the hazard rate, t is the time variable, lo (t) is the baseline hazard function, X1 is a vector of variables specific to the
HC and X2 is a vector of all other variables as noted above. β1 and β2 are the coefficient vectors to be estimated and e is the
error term. The hazard rate is exponentiated so that it remains greater than zero. The time variable, t, used to calculate the
hazard rate, is GamesCoached, which is an integer equal to the number of regular season games coached by the HC during the
employment spell. Standard errors robust to heteroskedasticity are specified in all models.
Our first independent variable of interest is NonWhite, which is an indicator denoting the race of the HC as Black or
Hispanic. The only three ethnicities represented in the data set are Black, Hispanic, and White and there are only three
Hispanic coaches – Ron Rivera, Wayne Fontes, and Tom Flores. Given this, we categorize all HCs as NonWhite or White to
measure differences between White and minority coaches. The choice also allows us to avoid interpreting a separate effect
for Hispanic coaches generated from only three observations.2 Our other independent variable of interest is
NonWhite*Rooney, which is an interaction between NonWhite and the Rooney Rule indicator which is described below.
The inclusion of this interaction allows for the ability to estimate whether outcomes for minority coaches differ after the
implementation of the Rooney Rule.
HC performance variables account for the performance of the HC and the team he manages over the duration of the
employment spell as performance and spell length are expected to be positively related. Win%Tenure is the regular season
winning percentage of the HC during the employment spell and accounts for raw performance. ATS%Tenure is the regular
season against the spread (ATS) winning percentage of the HC during the spell. Its inclusion accounts for the performance of
the HC and team relative to the expectations set by the wagering market (Foreman et al., 2019). As noted by Humphreys et al.
(2016), both measures are appropriate for inclusion as they can have an independent impact on managerial tenure.3
CoachGM is an indicator identifying the HC simultaneously served as General Manager (GM) during at least a portion of the
employment spell. A HC holding both responsibilities indicates the HC managed player personnel and subsequently had
elevated influence on franchise decision making which could be associated with a lower probability of dismissal (Foreman &
Soebbing, 2015; Wangrow et al., 2018).
Fired/Resigned is an indicator equal to one when the HC was directly terminated by the employer or when the HC
voluntarily removed himself from the position via resignation. We treat these conditions the same as resignations in this
1
These are exclusively short spells (usually one or two games) at the end of a season following an in-season HC firing.
We note that we use the terms “Non-White” and “minority” interchangeably moving forward.
3
Win%Tenure and ATS%Tenure are positively correlated at 0.6691. This value is at an acceptable level with respect to collinearity (Kutner, Nachtsheim, &
Neter, 2004).
2
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S. Salaga, M. Juravich / Sport Management Review 23 (2020) 978–991
context traditionally represent forced removals by the franchise. This variable is expected to be negatively associated with
spell length given the joint incentive to maintain successful employment relationships.
Functional experience variables capture the degree of relevant coaching experience possessed by each HC at the start of
the employment spell. We operationalize functional experience by accounting for both the type and length of past coaching
experience at the NFL and NCAA levels – an approach used by Rider et al. (2016) and Foreman et al. (2018). NFLHCExp,
NFLCoordExp, and NFLAsstExp represent the number of seasons of previous NFL head coaching, coordinator, and assistant
level experience, respectively. Similarly, NCAAHCExp, NCAACoordExp, and NCAAAsstExp represent the number of seasons of
previous NCAA head coaching, coordinator, and assistant level experience.4
Technical experience variables measure the type and quality of the playing experience possessed by each HC.
YearsNFLPlayer represents the number of seasons the HC played at the NFL level. It is included given the positive relationship
uncovered between the accumulated technical experience of NBA GMs and the performance of the teams they constructed
(Juravich, Salaga, & Babiak, 2017).5 AVPlayer represents the production of the HC as a player at the NFL level and tests the
expert leadership hypothesis outlined in Goodall, Kahn, and Oswald (2011). We utilize the approximate value (AV) metric
calculated by pro-football-reference.com which represents the annual contribution of an individual player to team success.
We sum the annual values for each player to generate their career value which serves as a measure of player quality across
the career lifespan.6
We account for the type of technical experience by first utilizing position indicators, which identify the primary position
of the HC as a player.7 Former quarterbacks comprise 28.85 % of the spells in the sample, indicating owners collectively prefer
hiring quarterbacks as HCs. These data are in agreement with Grusky (1963) who showed baseball players in highinteraction positions were more likely to be awarded future managerial positions. Foreman et al. (2018) also showed NFL
players at central positions had a higher probability of promotion in coaching careers. However, in the current study, the
position indicators are largely non-significant in the empirical modeling. Therefore, we instead classify each HC as an
offensive (OffensiveCoach) or defensive coach (DefensiveCoach) based where the majority of their coaching experience was
accumulated.
HC personal characteristics account for demographic differences between HCs in the sample. Age represents the age in
years of the HC at the start of the employment spell as managerial age has been shown to be negatively correlated with
willingness to adapt (Chuang, Nakatani, & Zhou, 2009). QSUnivRanking is the QS World University ranking of the highest
ranked university or college from which the HC graduated or attended. Its inclusion accounts for the possibility that
educational background is positively associated with performance (Juravich et al., 2017; Wally & Baum, 1994).
We control for league-level factors which may be associated with employment outcomes. SalaryCap is an indicator
denoting the employment spell began following the start of the salary cap in 1993. Allen and Chadwick (2012) argue that
franchise expectations of their HCs may have increased following the introduction of the salary cap, and if accurate, this
institutional change could be associated with duration or dismissal. Rooney is an indicator identifying whether the
employment spell began following the introduction of the Rooney Rule in 2003. Though the Rooney Rule was aimed at
promoting diversity in hiring, we measure its effect as there could be carryover effects in employment tenure or related
outcomes. Previous research including Fee et al. (2006), Holmes (2011), and Foreman and Soebbing (2015) suggest the
availability of alternative candidates could influence executive turnover. To account for this, we take a similar approach to
Wangrow et al. (2018) and calculate the number of experienced HCs available for hire in the labor market at the time a HC
exits a spell. Specifically, LaborPool is equal to the number of HCs exiting an employment spell in the previous three seasons
who did not subsequently accept another HC position within that time period.
Franchise characteristics could impact the ability of the head coach to influence success and are included as controls.
Payroll is the normalized team payroll during the duration of the HC employment spell and accounts for team spending on
playing talent relative to the industry average. Win%5 is the franchise regular season winning percentage in the five seasons
prior to the start of the HC employment spell. It is possible that the relative short-run success level of the franchise could be
related to HC tenure as more successful teams may be less likely to retain a HC who is not leading his team to success.8
TeamTenure is the number of years the franchise had been located in its media market at the start of the HC spell. Tainsky
(2010) demonstrated a positive relationship between franchise tenure and market-level consumer demand and in this case,
there may be variation between markets in sensitivity to team performance, which may have carryover effects on HC tenure.
Previous research also illustrates the importance of franchise-level interaction between the HC and GM as well as the HC
and ownership (e.g., Foreman & Soebbing, 2015; Foreman et al., 2019; Juravich et al., 2017; Wangrow et al., 2018). Owner/HC
4
We assess the sensitivity of this approach by alternatively operationalizing functional experience using a) the number of seasons of NFL and NCAA
coaching experience at hire and b) the combined number of seasons of coaching experience at both levels at hire. There are no substantive changes in the
results when using these alternative measures.
5
We do not account for NCAA playing experience as only two HCs in the sample did not play at the college level.
6
Staw and Hoang (1995) demonstrated that draft position is related to player employment duration in the NBA. We attempted to include an indicator
denoting whether the HC was selected in the NFL Draft as a player. However, it is collinear with YearsNFLPlayer (r=0.7420). We tested this indicator in place of
YearsNFLPlayer, and the results are not substantially different.
7
These include: quarterback (QB), running back (RB), wide receiver (WR), tight end (TE), offensive line (OL), defensive line (DL), linebacker (LB), defensive
back (DB) and those not playing at the college or NFL level (NoPosition).
8
We also test a ten-year measure of team success and find no substantial differences in results.
S. Salaga, M. Juravich / Sport Management Review 23 (2020) 978–991
983
is a continuous variable identifying the number of years the majority owner was in place prior to the beginning of the HC
employment spell. GM/HC is an equivalent variable equal to the number of years the GM was in place prior to the start of the
HC spell. Their inclusion accounts for the degree of executive entrenchment in the organization and measures whether
ownership and GM tenure are associated with HC tenure. NewOwner and NewGM are indicators identifying whether a new
majority owner and new GM, respectively, began control during the HC spell. It is expected both will be negatively associated
with tenure given the organizational hierarchy in the NFL.
Market characteristics control for differences in the metropolitan statistical area (MSA) of the franchise employing the
HC, which could be associated with labor market outcomes (e.g., Kahn & Shah, 2005; Volz, 2017). Income is the 2016 median
household income and %BlackResidents is the percentage of black residents in the franchise MSA.
4. Results
4.1. Descriptive results
Table 1 displays summary statistics for the full sample. On average, the length of a HC employment spell is almost 67
games (SD = 51.51) or 4.18 seasons. Minority HCs represent 13.9 % of the sample or 29 total spells. The average spell winning
Table 1
Summary Statistics.
Variable
Description
Mean
Std. Dev.
Min
Max
GamesCoached
NonWhite
White
NonWhite*Rooney
Win%Tenure
ATS%Tenure
Fired/Resigned
Fired
FiredInSeason
CoachGM
AllExperience
AllNFLExp
NFLHCExp
NFLCoordExp
NFLAsstExp
AllNCAAExp
NCAAHCExp
NCAACoordExp
NCAAAsstExp
YearsNFLPlayer
AVPlayer
OffensiveCoach
DefensiveCoach
QB
RB
WR
TE
OL
DL
LB
DB
No Position
Age
QSUnivRanking
SalaryCap
Rooney
LaborPool
Payroll
Win%5
TeamTenure
Owner/HC
NewOwner
GM/HC
NewGM
Income
%BlackResidents
Number of regular season games coached during spell
HC is Black or Hispanic = 1
HC is Non-Black = 1
Interaction between NonWhite and Rooney
HC regular season winning % during spell
HC against the spread (ATS) winning % during spell
HC is terminated by franchise or resigns to end spell = 1
HC is terminated by franchise to end spell = 1
HC is terminated within season by franchise to end spell = 1
HC simultaneously served as GM during spell = 1
# seasons of NFL & NCAA coaching experience at start of HC spell
# seasons of NFL coaching experience at start of HC spell
# seasons of NFL head coaching experience at start of HC spell
# seasons of NFL coordinator experience at start of HC spell
# seasons of NFL assistant coach experience at start of HC spell
# seasons of NCAA coaching experience at start of HC spell
# seasons of NCAA head coaching experience at start of HC spell
# seasons of NCAA coordinator experience at start of HC spell
# seasons of NCAA assistant coach experience at start of HC spell
# of seasons the HC played as a player in the NFL
Career approximate value (production) of HC as a NFL player
Majority of HC coaching experience is on offense = 1
Majority of HC coaching experience is on defense = 1
HC primary position played as player was quarterback = 1
HC primary position played as player was running back = 1
HC primary position played as player was wide receiver = 1
HC primary position played as player was tight end = 1
HC primary position played as player was offensive line = 1
HC primary position played as player was defensive line = 1
HC primary position played as player was linebacker = 1
HC primary position played as player was defensive back = 1
HC did not play at NCAA or NFL level = 1
HC age in years at start of spell
QS World University ranking of highest ranked college attended
HC employment spell began in salary cap (> = 1993) era = 1
HC employment spell began in Rooney Rule (> = 2003) era = 1
# of experienced NFL HCs available for hire in candidate pool
Normalized team payroll over duration of HC spell
Team regular season win % in 5 seasons prior to start of HC spell
# of seasons franchise located in media market at start of HC spell
# years majority owner in place prior to start of HC spell
New majority owner takes power during HC spell = 1
# years GM in place prior to start of HC spell
New GM takes power during HC spell = 1
Median household income in franchise MSA (2016 dollars)
% of Black residents in franchise MSA
66.889
0.139
0.861
0.101
0.440
0.481
0.846
0.721
0.183
0.149
20.480
12.057
2.358
4.019
5.681
8.423
1.885
2.087
4.452
2.298
9.918
0.606
0.394
0.288
0.038
0.038
0.135
0.087
0.072
0.115
0.207
0.019
49.341
609.067
0.832
0.514
15.986
0.060
0.452
42.091
15.043
0.168
4.298
0.514
64869
14.776
51.510
0.347
0.347
0.302
0.150
0.068
0.362
0.450
0.387
0.357
7.438
6.226
4.020
3.684
3.776
7.487
3.939
3.014
4.132
4.108
25.664
0.490
0.490
0.454
0.193
0.193
0.342
0.282
0.259
0.320
0.406
0.138
7.135
365.942
0.375
0.501
1.917
0.681
0.116
21.779
14.282
0.375
6.808
0.501
12804
7.867
13
0
0
0
0.063
0.250
0
0
0
0
5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
31
2
0
0
12
3.177
0.188
1
0
0
0
0
48804
1.9
304
1
1
1
0.766
0.750
1
1
1
1
40
30
17
20
19
28
18
13
20
15
159
1
1
1
1
1
1
1
1
1
1
1
65
1000
1
1
20
2.244
0.788
96
56
1
36
1
94430
34
N = 208.
984
S. Salaga, M. Juravich / Sport Management Review 23 (2020) 978–991
percentage is 0.440 (SD = 0.150), which lies below 0.500 as lower performing HCs are replaced more often, meaning they
comprise a larger portion of the sample. The HC employment spell ends in firing or resignation 84.6 % of time. The average HC
has almost 20.5 years of combined NFL and NCAA coaching experience (SD = 7.44) and is 49 years old (SD = 7.14) at the time
they are hired. Just over half of the spells in the sample began following the introduction of the Rooney Rule. Relative
franchise payroll is negative and short-run win percentage is below 0.500 as franchises with less success replace their HCs
more often and represent more of the sample.
Table 2 takes the data from Table 1 and separates it by HC race in order to determine whether any systematic differences
exist between Non-White and White HCs. On average, minority HCs have slightly lower spell winning percentages but have
employment spells which last over seven games longer. Non-White HCs are fired at a substantially higher rate relative to
White HCs and given that none in the sample resigned either suggests ownership has not provided them with the
opportunity to do so or they have selected not to do so. On average, Non-White HCs have over 1.5 seasons more NFL coaching
experience at the time of hire. Non-White HCs are also more likely to be previous NFL players and higher quality NFL players.
Over 72 % of minority HCs in the sample were hired after the introduction of the Rooney Rule. Relative franchise payroll is
lower in Non-White HC spells, but on average, higher performing franchises have hired minority HCs. Franchises located in
markets with a lower median household income and a lower percentage of Black residents have been more likely to hire a
minority HC.
Table 2
Summary Statistics Comparing Non-White against White NFL Head Coaches at Hire.
All Non-White HCs
All White HCs
Variable
Mean
Std. Dev.
Min
Max
Mean
Std. Dev.
Min
Max
GamesCoached
Win%Tenure
ATS%Tenure
Fired/Resigned
Fired
FiredInSeason
CoachGM
AllExperience
AllNFLExp
NFLHCExp
NFLCoordExp
NFLAsstExp
AllNCAAExp
NCAAHCExp
NCAACoordExp
NCAAAsstExp
YearsNFLPlayer
AVPlayer
OffensiveCoach
DefensiveCoach
QB
RB
WR
TE
OL
DL
LB
DB
No Position
Age
QSUnivRanking
SalaryCap
Rooney
LaborPool
Payroll
Win%5
TeamTenure
Owner/HC
NewOwner
GM/HC
NewGM
Income
%BlackResidents
73.276
0.432
0.485
0.828
0.828
0.103
0.138
20.412
13.515
1.966
3.084
8.466
6.897
1.448
1.034
4.414
3.690
23.069
0.345
0.655
0.172
0.103
0.034
0.034
0
0.103
0.138
0.414
0
49.448
657.035
0.897
0.724
16.552
0.111
0.463
45.966
13.966
0.138
5.862
0.552
63123
12.411
58.233
0.165
0.070
0.384
0.384
0.310
0.351
8.117
5.648
3.196
3.042
4.114
6.909
3.146
1.936
4.363
4.560
39.227
0.484
0.484
0.384
0.310
0.186
0.186
0
0.310
0.351
0.501
0
6.869
303.701
0.310
0.455
1.617
0.682
0.142
20.300
15.681
0.351
8.079
0.506
11694
6.351
16
0.088
0.325
0
0
0
0
5.5
4
0
0
4
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
33
27
0
0
13
2.182
0.238
16
0
0
0
0
49402
1.9
256
0.759
0.625
1
1
1
1
39.813
29.813
10
9
19
23
9
5
13
15
159
1
1
1
1
1
1
0
1
1
1
0
64
1000
1
1
20
1.471
0.788
82
56
1
33
1
89469
24.0
65.855
0.441
0.480
0.849
0.704
0.196
0.151
20.491
11.821
2.421
4.170
5.229
8.670
1.955
2.257
4.458
2.073
7.788
0.648
0.352
0.307
0.028
0.039
0.151
0.101
0.067
0.112
0.173
0.022
49.324
601.296
0.821
0.480
15.894
0.052
0.450
41.464
15.218
0.173
4.045
0.508
65152
15.160
50.442
0.148
0.068
0.359
0.458
0.398
0.359
7.346
6.297
4.142
3.764
3.528
7.565
4.056
3.125
4.106
3.999
22.163
0.479
0.479
0.463
0.165
0.194
0.359
0.302
0.251
0.316
0.379
0.148
7.196
375.215
0.384
0.501
1.950
0.682
0.111
21.998
14.083
0.379
6.571
0.501
12984
8.035
13
0.063
0.250
0
0
0
0
5
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
31
2
0
0
12
3.177
0.188
1
0
0
0
0
48804
1.9
304
0.766
0.750
1
1
1
1
35
30
17
20
17
28
18
13
20
15
116
1
1
1
1
1
1
1
1
1
1
1
65
1000
1
1
20
2.244
0.750
96
53
1
36
1
94430
34.0
NonWhite N = 29; White N = 179.
S. Salaga, M. Juravich / Sport Management Review 23 (2020) 978–991
985
4.2. Do the length of NFL head coach employment spells differ by race?
Table 3 provides Cox hazard rate estimates to assess whether the length of HC employment spells differ by race. A hazard
ratio above one indicates the variable has a positive effect on the conclusion of an employment spell, or that it is associated
with decreased employment spell length. Alternatively, a hazard ratio below one is interpreted such that the covariate has a
negative effect on the conclusion of a spell, or that it is tied to increased employment spell length.
The estimates in the first column of Table 3 utilize the full sample. The hazard rate for NonWhite is not statistically
significant and indicates over the full sample period no systematic differences in spell length exist between White and
minority HCs. However, the hazard rate of NonWhite*Rooney is 0.418 and is statistically significant at the five percent level.
This demonstrates that all else equal, following the introduction of the Rooney Rule, minority HCs had a 58.20 % (1 – 0.418)
lower hazard of exiting an employment spell (increased spell length) at a given point in time.
Both Win%Tenure and ATS%Tenure display sizeable positive effects on spell length, indicating that HCs who have higher
levels of raw and relative performance experience longer employment tenures. The finding that relative performance is
positively associated with spell length supports the work of Humphreys et al. (2016) in college football. As expected, HCs
who are fired or resign experience shorter spells. Spell duration does not vary at a statistically significant level when a HC
also holds the responsibility of GM.
The amount of functional experience accumulated by a HC at the start of a spell is not strongly associated with spell
length. Given that NFL coaching experience is not a significant predictor of spell length suggests franchise owners do not
systematically vary in retaining HCs based on experience level. The hazard rate on NCAAHCExp is statistically significant and
above one which implies that NFL HCs have a 5.80 % decrease in spell length for each additional year they served as a NCAA
HC. Given that college HC experience is negatively correlated with all three levels of NFL experience, the effect could signal
that owners are less patient with coaches from the college ranks. The type and quality of functional experience
(YearsNFLPlayer, AVPlayer and OffensiveCoach) also fails to produce statistically significant effects on spell length.
The age and the quality of the educational background possessed by the HC do not significantly influence spell length.
There is also little evidence that the introduction of the salary cap or implementation of the Rooney Rule had substantial
carryover effects on employment spell length for HCs collectively. The number of experienced HCs available for hire is also
not significantly linked to spell length.
Table 3
Do the Length of NFL HC Employment Spells Differ by Race?
All HCs
Low Performing HCs
High Performing HCs
NonWhite
NonWhite*Rooney
Win%Tenure
ATS%Tenure
Fired/Resigned
CoachGM
NFLHCExp
NFLCoordExp
NFLAsstExp
NCAAHCExp
NCAACoordExp
NCAAAsstExp
YearsNFLPlayer
AVPlayer
OffensiveCoach
Age
QSUnivRanking
SalaryCap
Rooney
LaborPool
Payroll
Win%5
TeamTenure
Owner/HC
NewOwner
GM/HC
NewGM
Income
%BlackResidents
1.207 (0.579)
0.418** ( 2.040)
0.002*** ( 7.104)
0.036* ( 1.878)
4.309*** (4.100)
0.959 ( 0.211)
0.998 ( 0.055)
0.992 ( 0.295)
1.028 (0.957)
1.058** (2.546)
0.995 ( 0.150)
1.013 (0.483)
0.986 ( 0.324)
1.007 (1.113)
0.857 ( 1.060)
1.027 (1.342)
1.000 ( 0.607)
1.046 (0.276)
1.340 (1.435)
0.957 ( 0.947)
1.077 (0.518)
5.727** (2.303)
0.998 ( 0.535)
1.001 (0.235)
0.740* ( 1.749)
1.008 (0.673)
0.687*** ( 2.635)
1.000 (0.064)
0.984* ( 1.948)
0.619 ( 0.875)
0.683 ( 0.649)
0.004*** ( 3.637)
0.007** ( 2.005)
