How Race Affects Dismissals of College Football Coaches

FRANKLIN G. MIXON, JR.* University of Southern Mississippi, Hattiesburg, MS 39406 LEN J. TREVINO Washington State University, Pullman, WA 99164

We employ a discrete-time hazard model and a Blinder-decomposition approach to explore the possibility of racial discrimination in the dismissal and retention of college football coaches by university administrations.A rich data set consisting of 81 institutions over an 11-year period (1990-2000) that contains, in addition to a coach 's race, variableson cumulative winning records, annual on-the-field improvements, and pre-/post-seasongame participationby institutionsand their coaches is employed. Our study finds that black coaches, on average, face a dismissal probability that is 9.6 percentagepoints below that of their nonblack counterparts, ceterisparibus, suggesting that black head coaches may be the beneficiaries offavorable treatment by university administrators. Such a result likely stems from universities' approach to social concerns involving race and gender issues. This finding also fits a construct that considers workplace discriminationas multi-dimensional. For example, black representation in the college football coaching ranks may be disproportionatelylow, possibly as a result of discrimination. However once hired, black coaches are given more time to succeed than nonblacks, other things constant. "You have almost as good a chance of seeing an African-American on the cast of Friends as one wearing a head coach's headset on a football sideline" (Caple, 2001).

I. Introduction The underrepresentation by blacks among head football coaches employed by Divi- sion IA colleges and universities in the National Collegiate Athletics Association (NCAA) has become a topic of considerable debate in recent years. ESPN.com, cnnsi.com, and The Sporting News have produced investigative journalistic reports on the subject, and many in the media have followed the recent hiring of a black head football coach - Tyrone Willingham - by the University of Notre Dame, an institution often thought of as the symbol of college football (Blaudschun, 2000; Caple, 2001; Sporting News Online, 2001). During the 1990-2000 period, the 81 college football teams representing the major conferences of the NCAA's Division IA list of institutions (plus Notre Dame) played 886 individual football seasons. Of these, about 3.8 percent were coached by blacks. Some reports claim that this figure represents racially

JOURNAL OF LABOR RESEARCH Volume XXV, Number 4 Fall 2004 646 JOURNAL OF LABOR RESEARCH motivated hiring practices (Hubbard, 2001). This number compares unfavorably with the current percentage of black NFL coaches (6.25 percent), and, more importantly, the 1995 percentage of college graduates who are black (7.00 percent), the midpoint of our sample (StatisticalAbstract of the U.S., 1996). Such numerical comparisons, however, do not constitute an empirical test, and furthermore, fail to recognize that university administrations' treatment of blacks in the college football coaching ranks entails more than their numerical representation. While these facts and figures are interesting, it is also important to examine how race affects the institutional decision to dismiss or retain coaches. Such an examination might reveal (im)partial treatment by race that extends well beyond the hiring stage. Workplace discrimination may be a multi-faceted phenomenon. To undertake such an exploration, we collected a large, rich data set consisting of 81 institutions over an 11 -year period (1990-2000) that reveals information on race, cumulative winning records, annual on-the-field improvements, and pre-/post-season game participation by institutions and their coaches. Many of these variables relate to the increasing monetary considerations in collegiate sports - as Leeds and von All- men (2002, p. 391) report, the average NCAA Division IA football programs earns an annual "profit" of about $3.2 million. These data are employed in a discrete-time hazard model, and our results are then used in a Blinder-decomposition approach to flesh out the possibility of racial discrimination in the dismissal and retention practices of university administrations. Below, we review some of the recent literature on racial discrimination in sports, contrasting its focus with the aim of the present study.

II. Racial Discriminationin Sports: A Brief Review of Recent Literature The economics of discrimination has played a prominent role in the field of labor economics, providing part of the foundation for Gary Becker's Nobel Prize in Economic Science. His work (Becker, 1957) examined discrimination by race, religion, and gender, among other considerations such as personality, and became the seminal bench- mark for other important studies. Many subsequent studies have examined both black-white wage differentials and male-female wage differentials; among the extensions to Becker's work are Blinder (1973), Oaxaca (1973), Flanagan (1974), and Hirsch and Leppel (1982), to name a few. Recently, economists have extended Becker's work by examining forms of employment discrimination in sports. Among these are Kahn and Sherer (1988), Hoang and Rascher (1999), Bodvarsson and Partridge (2001), and McCormick and Tollison (2001). For a more extensive list of recent work, see Leeds and von Allmen (2002).1 Most of these studies share two common threads: They analyze pay discrimination (or other employment discrimination) in professional basketball, and they usually attrib- ute forms of pay discrimination to customer (fan) discrimination. For example, Kahn and Sherer (1988) report that black compensation in the NBA, ceteris paribus, is about 20 percent below nonblacks' compensation. Furthermore, they report the prediction that, ceteris paribus, replacement by an NBA franchise of one black player with an FRANKLIN G. MIXON, JR. and LEN J. TREVINO 647 identical white player raises home attendance by 8,000-13,000 per season; their evidence is also consistent with customer or fan discrimination. Hoang and Rascher (1999) report evidence that white players in the NBA have a 36 percent lower risk of being cut than black players, ceteris paribus, translating to an additional two years of expected career length for whites. Perhaps most important is their finding that the career earnings effect of exit discrimination (in the 1980s) was about $500,000 larger than the career earnings effect due to wage discrimination. All of these results are attributed to customer discrimination (Hoang and Rascher, 1999). Of course, customer discrimination studies are not without critics. For instance, McCormick and Tollison (2001) found that NBA salaries are lower for blacks than whites, but the differential is not likely due to customer discrimination. Their data point out that black players actually play more than comparable white players, and they offer a price discrimination theory of the observed pay differential based, in part, on relative supply elasticity differences. This study differs from those listed above in various ways. First, we examine employment discrimination in collegiate football instead of the professional ranks. Sec- ond, our study of discrimination does not look at wages or hiring in the employment process. Instead, we examine the probability that a college football coach is dismissed (or retained) at the end of a given season (or year) as a function of race and other control variables. We do this by way of a unique estimation method, a discrete-time hazard model described in more detail in the following section.

III. A Frameworkfor Analyzing the Race Effect in College Football The statistical framework for the dismissal decision in college football coaching is based on the lottery-adoption model developed by Caudill et al. (1995). Herein, we assume that the tendency of university i to dismiss coach m at the end of any given football season is given by an unobservable variable, Y*. What is observed, at the end of any given season, is the outcome of the dismissal/retention decision process, Y(Mad- dala, 1983). If Y*>l, the coach is dismissed at the end of the season, and Y=l. If Y*S0, the coach is not dismissed, and Y=0. Again, following Caudill et al. (1995), a reduced- form model of the dismissal tendency can be written:

Yi- = xip + Fi'

where Xi is a vector of exogenous variables representing institutional/market pressures affecting dismissal; ,Bis a vector of parameters to be estimated; and Ei is a random error term. If Ei follows the standard logistic distribution, the probability of the dismissal of the coach, P, is given by the familiar formula: P(Dismissal)=exp(Xif)/[ I +exp(Xift)]. (2)

The probability that a coach is not dismissed in a given year is 1-P. At the end of each year (football season) in the sample, each school either dismisses its coach or does not, with probabilities P and I-P, respectively. 648 JOURNAL OF LABOR RESEARCH

Following Caudill et al. (1995), we obtain probabilities like those in equation (2) from a discrete-time hazard model of the dismissal decision. What is actually mod- eled is the timing of the dismissal decision. A university that dismisses its coach at the end of any given season contributes information on the determinants of dismissal, while a school that does not dismiss its coach at the end of that same season provides information about the determinants of retention.

IV. A Hazard Model of the Dismissal of College Football Coaches In a hazard model, data on the independent variables in a given year (football season) are used to determine the probability of a coach's dismissal in that year. Although hazard models are widely used in economics, discrete-time hazard models are less common. An example of the latter is provided by Caudill et al. (1995). When possible, discrete-time hazard models are estimated by maximum likelihood. The likelihood function for estimating this model is composed of probabilities of two types (Caudill et al., 1995, p. 557). The first expression describes the probability that the coach is dismissed at the end of the season, or:

P(Ti = ti ) = Pi, (=1n G - Pii) (3

Equation (3) shows that the probability of dismissal is the product of the probability that the coach is not dismissed after seasons (periods) I through t -1 multiplied by the probability that the coach is dismissed at the end of season (period) t. For a university not yet dismissing its coach, the expression is the probability that dismissal has not occurred in the sample after seasons (periods) 1 through T, where T is the number of seasons (periods) in the sample, or: T P(Ti > T)=fn (- Pii). 4 j=1 If the probability that the university dismisses its coach after season t is expressed as in the logit model given in equation (2) above, the model can be estimated by maximum likelihood (logit) employing individual data (Caudill et al., 1995, p. 557).

V. The Data To study the possibility of racial discrimination in the dismissal decision in college football, we employed a pooled data set - a cross section of universities (81) over an 11-year period of time (1990-2000).2 Schools were chosen from the major football participating conferences in the National Collegiate Athletic Association's Division IA, with the addition of Notre Dame (major independent). These 81 universities are listed, by conference, in Table 1. For most of our variable series, the data set contains 886 observations. A description of our variables and their summary statistics are provided in Table 2. The dependent variable in our model, FIRED, is a dichotomous variable equal to one when the school dismissed its football coach at the end of the particular season. FRANKLIN G. MIXON, JR. and LEN J. TREVNO 649

Table 1 Universities Sampled: Football Conference Affiliation

Football Conference Affiliation Schools Sampled Atlantic Coast Clemson, Duke, Florida State, Georgia Tech, Maryland, North Carolina, North Carolina State, Virginia, Wake Forest Big 10 Illinois, Indiana, Iowa, Michigan, Michigan State, Minnesota, Northwestern, Ohio State, Penn State, Purdue, Wisconsin

Big 12 Baylor, Colorado, Iowa State, Kansas, Kansas State, Missouri, Nebraska, Oklahoma, Oklahoma State, Texas, Texas A&M, Texas Tech Big East Boston College, Miami, Pittsburgh, Rutgers, Syracuse, Temple, Virginia Tech, West Virginia Conference USA Alabama-Birmingham, Army, Cincinnati, East Carolina, Houston, Louisville, Memphis, Southern Mississippi, Texas Christian, Tulane

Independent Notre Dame Mountain West Air Force, Brigham Young, Colorado State, New Mexico, San Diego State, UNLV, Utah, Wyoming

Pacific 10 Arizona, Arizona State, California, Oregon, Oregon State, Stanford, USC, UCLA, Washington, Washington State Southeastern Alabama, Arkansas, Auburn, Florida, Georgia, Kentucky, LSU, Mississippi, Mississippi State, South Carolina, Tennessee, Vanderbilt

For instances where the coach was retained or resigned (i.e., not dismissed), FIRED is equal to zero.3 In our discrete-time hazard construct, the dependent variable retains the value of zero until the school removes its coach - then its value is 1. Beyond that point, no additional observations for that coach (at that school) are included in our sample.4 The first regressor listed in Table 2 is RACE, our variable of interest. RACE is a dummy variable equal to one for observations on black head football coaches, and zero otherwise. The sign and significance of RACE indicates whether, ceteris paribus, the likelihood that black coaches are dismissed by universities differs from that of their nonblack counterparts. We treat the relationship between RACE and FIRED as an empirical exploration. We do, however, employ a number of other control variables so that any racial component in the dismissal and retention practices of universities can be correctly understood. Among these is CUWIN, which is equal to the coach's cumu- 650 JOURNAL OF LABOR RESEARCH

Table 2 Variable Names, Definitions, and Summary Statistics

Std. Variable Definition n Mean Dev.

FIRED A dummy variable equal to one if coach m is fired/ 886 0.096 0.295 dismissed by university i and the end of year 1, and zero otherwise. RACE A dummy variable equal to one if coach m is black, 886 0.038 0.192 and zero otherwise. CUWIN Coach m's cumulative winning ratio at university 886 0.519 0.184 i through the end of each year, t. GAMES The number of games played in each year t. 886 0.630 0.693 beyond 11, by each university's (i's) football team. YR The year (t) of each observation, minus 1989. 886 6.017 3.163 WINCHNG Coach m's winning ratio at university i in year t 759 0.006 0.206 minus coach m's winning ratio at university i in year t-l. FIRST A dummy variable equal to I if coach m is in his 886 0.141 0.348 first year/season at university i in year t, and zero otherwise.

lative winning ratio (wins to total games) through the end of each year. It is only in very rare circumstances that winning performers are dismissed, and usually winning coaches are rewarded with contract extensions, higher salaries, bonuses, or revenue- sharing options with television contracts and other ancillary activities associated with the school's football programs (e.g., summer football camp revenues). We expect, therefore, that CUWIN will be inversely related to the likelihood that coach m is dismissed, ceteris paribus. Among other performance measures on the right hand side is GAMES, or the total number of games played each season minus 11. The traditional Division IA regular season is 11 games. Participation beyond 11 games usually requires participation in a preseason Classic game, such as the Pigskin Classic, the Eddie Robinson Classic, or the Kickoff Classic, or participation in a post-season bowl game. In each case, the participating university may get a significant payday. In the former example, the game receives a large payoff and the spotlight as a season-opening contest, and is played (in most cases) before a national television audience and with little other game com- petition (if any). In the latter case, not only are national television audiences at stake, but also significant bowl payoffs. Currently, the minimum payoff for bowl participation is $750,000 per school, with some bowl games - such as the Orange Bowl or the Fiesta Bowl - designated as Bowl Championship Series games that provide payoffs of over $12 million per team (see USA Today college football website; Leeds and von Allmen, 2002, p. 392). Head coaches earning invitations to these "extra" games (i.e., games beyond the normal 11) are earning more dollars for their respective insti- FRANKLIN G. MIXON, JR. and LEN J. TREVINO6 651 tutions, and should not face dismissal, ceteris paribus. GAMES is expected to be negatively related to FIRED.5 The coach's winning ratio at the end of season t minus his winning ratio at the end of season t -l is included as WINCHNG. This variable tracks season-to-season improvement (or deterioration) in a coach's performance, and should be negatively related to the likelihood of dismissal as head coach (FIRED).6 The year of each observation, minus 1989, is included as YR. Because the importance of money in collegiate sports is a growing phenomenon in modem times, one would expect that the likelihood of dismissal, for any level of performance, would be greater in 2000 than in 1990, ceteris paribus. Therefore, we expect that YR will be positively related to FIRED. Finally, in versions of our model not employing WINCHNG, we include a dummy variable (FIRST) equal to one if the coach is in his first year, and zero otherwise. Univer- sities usually allow head coaches time to hire assistants and implement their program or philosophy. As such, we expect FIRST to obtain a negative sign.7

VI. Results Logit estimation results for four different versions of our model are presented in Table 3. The odd-numbered specifications contain WINCHNG with 759 observations. The even-numbered specifications contain FIRST and employ 886 observations. The final two versions also include a conference dummy variable series, with the Southeastern Conference as the omitted category. 8 In version (1) of Table 3, all of the variables retain their expected signs. Both CUWIN and GAMES are negative and significant at the 0.01 level. WINCHNG is also negative, and significant at the 0.05 level. Though positive, YR falls marginally outside of usual significance levels in version (1). Our variable of interest, RACE, retains a negative coefficient. Its parameter is useful in estimating the total effect of race on the dismissal decision. That effect compares the probability of dismissal across black and nonblack coaches with other regressors held at their means, and is presented at the bottom of the version (1) results. Ceteris paribus, black coaches face a dismissal probability of 0.083, compared to a dismissal probability of 0.088 for their nonblack counterparts. Black coaches face a dismissal probability that is approximately 0.005 (one-half percentage point) lower; the RACE parameter is, however, insignificant. This insignificant result may simply be an artifact of the paucity of black coaching observations. There are eight black coaches, covering 34 years in our 11-year sample of 81 institutions. As Jackson and Lindley (1989) explain, however, even a significant finding on a race or gender dummy coefficient is incomplete evidence of race or gender discrimination in various labor market settings. Therefore, our insignificant result regarding RACE in version (1) will be explored in greater detail below. In other versions of the model, including FIRST or the conference dummy variable series, RACE retains a negative coefficient that is similar in size to that of version (1). It also remains insignificant in all three additional versions. Both CUWIN and GAMES continue to retain their expected negative signs across the three additional 652 JOURNAL OF LABOR RESEARCH 652 JOURNAL OF LABOR RESEARCH

Table 3 Maximum Likelihood (logit) Regression Estimates, Dependent Variable: FIRED (t-values in parentheses)

(1) (2) (3) (4) constant -0.9321 -1.01I1** 0.0884 0.0541 (-2.15) (-2.38) (0.17) (0.10) RA CE -0.0620 -0.0987 -0.0781 -0.1239 (-0.12) (-0.19) (-0.14) (-0.23) CUWIN -2.3097*** -2.0053*** -2.7470*** -2.4879*** (-2.71) (-2.47) (-3.13) (-2.98) GAMES -0.7990*** -0.8520*** -0.8402*** -0.8800*** (-2.85) (-3.24) (-3.00) (-3.36) YR 0.0502 0.0487 0.0562 0.0538 (1.29) (1.28) (1.42) (1.39) WINCHNG -1.1855** -1.1108* (-1.97) (-1.81) FIRST -2.4267*** -2.5874*** (-3.31) (-3.50) Conference Dummies NO NO YESa YESb n 759 886 759 886 Model X2 48.0*** 55.9*** 62.0*** 71.6*** Pseudo R2 0.092 0.100 0.118 0.128

RESET F-statistic( 2, 1.988 2.895* 0.928 0.541 Prob(FIRED=I ):c Black Coach 0.083 0.061 0.077 0.055 Non-black Coach 0.088 0.067 0.083 0.061

Notes: ***(**)[*] denotes the 0.01(0.05)[0. 10] level of significance. aFor the conference dummies, SEC is the omitted category. The coefficients for all of the conference dummies included are negative, with those for C-USA, Big 10, Big 12, Pac-10, Big East, and Mountain West conferences significant at the 0.05 level or better. The coefficient for the ACC is significant at the 0.16 level. bConference dummies included the same is in footnote 1, with similar results as those listed in footnote 1. 'All variables except RACE are held at their respective means to calculate Prob(FIRED=I). versions, and are both significant at the 0.01 level. The new variable, FIRST, retains the expected negative sign and is also significant at the 0.01 level. Lastly, the coefficient on YR remains relatively stable and continues to be statistically insignificant, while WINCHNG falls just outside the 0.05 level of significance in version (3).9 Each of the specifications is jointly significant and fails to reject the Regression Specifica- tion Error Test's (Ramsey, 1969) null hypothesis of "no specification error (at the 0.05 level)". Neither substitution of the variable FIRST nor inclusion of the conference dummy variable series changes the empirical finding of -0.005 (approximately) on the total effect of race on the dismissal decision (see black and nonblack probabilities at bottom of table). This finding also remains statistically insignificant in the final three versions of Table 3. Using specification (1) of Table 3, some additional predictions are possible. For instance, holding all variables at their mean values with the exception of CUWIN, black FRANKLIN G. MIXON, JR. and LEN J. TREVINO 653 and nonblack coaches would predictably need to fall to CUWIN of 0.519 and 0.547, respectively, to face the same dismissal probability of 0.083. For WINCHNG, the figures for black and nonblack coaches are 0.006 and 0.075, respectively, to face this dismissal probability of 0.083 (holding all other regressors at their mean levels). These two comparisons reveal that a cumulative winning ratio for a black coach and a black coach's year-to-year improvement can each be less than those for a nonblack coach in order for each to face the same predicted dismissal probability (i.e., 0.083). Lastly, if all regressors are held at their respective means and YR is set to 6.017 for black coaches and to 4.717 for nonblack coaches, the dismissal probability for each is 0.077. Though interesting, these results are based on the coefficient for RACE in version (1), and thus the total effect of race on dismissal probabilities, which is incomplete evidence on any racially motivated dismissal/retention practices evident in our data on football coaches.1I Therefore, to provide more complete and accurate information on a possible racial component to this process, the estimates from version (1) of Table 3 are decomposed. The absence of interaction effects in the models above limits their ability to pre- dict a racial component to the dismissal of head football coaches. As Jackson and Lind- ley (1989) describe, the constant effect measures the direct effect that race plays (i.e., the intercept component of racial discrimination) in the dismissal/retention process in the college football coaching profession. As Jackson and Lindley (1989) state, a significant constant effect unambiguously indicates racial discrimination. The constant effect is the estimated difference in the dismissal probabilities of nonblack and black head coaches implied by the dummy variable coefficient from a logit model using the pooled data, the four control variables (CUWIN, GAMES, YR, and WINCHNG) and the four interaction effects (i.e., RACE multiplied by the four controls).II The estimated probability difference taken from RACE in this unrestricted model is -0.096, suggesting that black coaches, on average, face a dismissal probability that is 9.6 percentage points lower than that faced by their nonblack counterparts; this difference is significant at the 0.097 level.12 This finding provides evidence that race plays a significant role in the dismissal/retention process and that black head coaches may be the beneficiaries of favorable treatment relative to their nonblack counterparts by university administrators. Such a result likely stems from universities' approach to social concerns involving race and gender issues. Equal access to admissions by race and gender has been a popular issue for media scrutiny. Also, the recent firing of University of Arkansas head basketball coach Nolan Richardson has brought with it allegations of racial motivation and racial maltreatment during his tenure there. Other popular, well-known black coaches in college basketball, such as John Chaney and John Thompson, and political commen- tators such as Jesse Jackson, have been vocal critics of universities' hiring and retention practices (Sporting News Online, 2001). Such negative media attention may be mitigated by institutions granting favorable treatment to black coaches in college football (once hired). 654 JOURNAL OF LABOR RESEARCH

Future work in this area could employ the discrete-time hazard construct used here to examine institutional decisions made at the hiring stage, provided that an adequate data source is developed. Our model could also be easily extended to examine how race affects the likelihood of dismissal in professional sports. The model is also amenable to a statistical analysis of CEO dismissal probabilities, by race and gender, within corporations and other entities in the private and public sectors. Of course, additional empirical work on dismissal likelihoods in college football coaching would be a useful avenue for future research, given the array of findings presented in this study.

VII. Concluding Comments There are currently only two blacks among all head coaches within the major conferences (plus Notre Dame) in the NCAA, representing only about 2.5 percent of college head coaches. One of these, Tyrone Willingham, became (in 2002) the first black coach in Notre Dame history. Our results herein suggest that, other things constant, black coaches like Willingham, on average, face a significantly lower dismissal probability than their nonblack counterparts. Application of this group finding to Notre Dame's case suggests that Willingham may receive more institutional support than his nonblack predecessor, Bob Davie. Given that a decomposition of our discrete- time hazard results indicate that this lower dismissal probability is attributable to race, workplace discrimination may, therefore, encompass more than numerical representation within the industry.

NOTES

*The authors thank an anonymous referee, Kamal Upadhyaya, and Troy Gibson for helpful comments, and Taisa Minto for tireless work on data collection and other administration of this project. We also thank the University Research Council for financial support through a URC02 Grant. IFor an excellent review of earlier studies, see Kahn (1991). His review considers discrimination studies that examine professional baseball, basketball, football, hockey, and tennis. 2Five observations (out of a possible 891 ) were lost due to incomplete information on one or more of our regressors. 31n almost all cases, classification of FIRED was not problematic. In cases where there was some uncer- tainty, we attempted to draw conclusions from contemporaneous news sources, etc. 4For example, throughout the length of our data series, the value of FIRED for all Florida State observations is zero, because Bobby Bowden has retained his head coaching position there since before 1990, and through the end of 2000 (our cutoff year). Butch Davis, the head coach at the University of Miami, resigned to accept the more lucrative head coach position of the Cleveland Browns in the National Football League after the 2000 season. Consequently, FIRED also takes the value of zero for Miami (Davis) in 2000. Jim Donnan, however, was dismissed as head coach at the end of the 2000 season by the University of Geor- gia. Thus, FIRED takes the value of one for the Georgia (Donnan) observation in the year 2000. 5 Financial payoffs from bowl game appearances was considered as a substitute for GAMES in our specification. In doing so, however, we would lose information that GAMES provides on participation in preseason Classic games, which are also very lucrative. Financial payoffs to these games are not published. Another FRANKLIN G. MIXON, JR. and LEN J. TREVINO 655

consideration we encountered is that a school's bowl receipts are a function of tickets sold by the institution, among other participation factors. While baseline bowl receipts figures are published, they often dif- fer from a school's actual payoff from participating in a particular bowl. Actual payoffs are not available. Lastly, published data on baseline bowl payoffs are only available for the current year, or 2002-2003, and not previous years such as 1990-2000. We. therefore, chose to include GAMES as a proxy in our analysis. 6 Given the construct of WINCHNG, observations on the coach's first year or season at any school i fall out. Thus, use of WINCHNG in our regression specification reduces the usable sample from 886 to 759 observations. 7 We note that our specification does not include a measure of customer (fan) discrimination, such as the percentage of a university's alumni who are black, or the percentage of a school's graduates who are black and who contribute financially to the institution. Neither of these measures is available from the expansive U.S. News Colleges & Universities database; in addition, our search of other sources for an adequate measure of customer discrimination of this sort failed to yield a positive result. Our results do. however, include Regression Specification Error Test statistics (Ramsey, 1969), indicating that the absence of this measure did not influence our parameter estimates. 8The conference dummy variables are included to capture different institutional views toward football suc- cess. For instance, C-USA schools such as Army and Tulane likely weight performance measures for coaches (i.e., wins and losses) differently than Southeastern Conference schools, such as Tennessee and LSU. 9YR was also interacted with the performance measures (CUWIN and WINCHNG) in Version (I) of Table 3 to test whether the likelihood of dismissal for poor performance is greater in later years, given the growing importance of financial considerations in collegiate sports. Neither of the interaction terms is significant, and their exclusion from the model is acceptable given the results of a likelihood ratio test. 0Again, even a significant finding on RACE, and thus the total effect, would be inconclusive in this regard. Illn using the interaction terms, we ran into some difficulty obtaining a model-likelihood statistic, a common occurrence in logit/probit estimations (Caudill, 1987, 1988; Oskanen, 1986; Anderson, 1987). In our case, parameter estimates were obtained, and, following Caudill (1987, 1988), we compared our logit results with those from a linear probability model specification (LPM). The LPM estimation produced parameters with the same signs and approximate significance levels as the logit model. It also produced a similarly sized R2 statistic and an F-statistic with a comparable significance level as the model X2 statistic from the logit specification. Further use of our logit results, such as in calculating the subcomponents of the total effect (and their subcomponents), therefore, is appropnate. 12See Jackson and Lindley (1989) for details on the decomposition technique/measures and all of the relevant formulae. The structural, residual, and coefficient effects for this study, along with relevant explana- tion and statistical significance, are available from the authors on request.

REFERENCES

Anderson, Gordon J. "Prediction Tests in Limited Dependent Variable Models." Journal of Econometrics 34 (January-February 1987): 253-61. Becker. Gary S. The Economics of Discrimination. Chicago, Ill.: University of Chicago Press, 1957. Blaudschun, Mark. "Minority Hiring Record Getting Worse." Sporting News Online (June 12, 2000): 1-2. Blinder, Alan S. "Wage Discrimination: Reduced Form and Structural Estimates." Journal of Human Resources 8 (Fall 1973): 436-55. Caple, Jim. "Someone Has to Make a Stand on Minority Coaching Issue." (May 21, 2001): 1-3. Caudill, Steven B. "An Advantage of the Linear Probability Model over Probit or Logit." Oxford Bulletin of Economics and Statistics 50 (November 1988): 425-27. 656 JOURNAL OF LABOR RESEARCH

. "Dichotomous Choice Models and Dummy Variables." The Statistician 36 (Fall 1987): 381-83. Jon M. Ford, Franklin G. Mixon, Jr., and Ter Chao Peng. "A Discrete-Time Hazard Model of Lottery Adoption." Applied Economics 27 (June 1995): 555-61. Flanagan, Robert J. "Labor Force Experience, Job Turnover, and Racial Wage Differences." Review of Eco- nomics and Statistics 56 (November 1974): 521-29. Hirsch, Barry T. and Karen Leppel. "Sex Discrimination in Faculty Salaries: Evidence from a Historically Women's University." American Economic Review 72 (September 1982): 829-35. Hoang, Ha and Dan Rascher. "The NBA, Exit Discrimination, and Career Earnings." Industrial Relations 38 (January 1999): 69-91. Hubbard, Lee. "Black Coaches: Still Not in the Game." Black World Today (October 11, 2001): 1-2. Jackson, John D. and James T. Lindley. "Measuring the Extent of Wage Discrimination: A Statistical Test and a Caveat." Applied Economics 21 (April 1989): 515-40. Kahn, Lawrence M. "Discrimination in Professional Sports: A Survey of the Literature." Industrial and Labor Relations Review 44 (April 1991): 395-418. Kahn. Lawrence M. and Peter D. Sherer. "Racial Differences in Professional Basketball Players' Compen- sation." Journalof Labor Economics 6 (January 1988): 40-61. Leeds, Michael and Peter von Allmen. The Economics of Sports. Boston, Mass.: Addison Wesley, 2002. Maddala, G.S. Limited Dependent and Qualitative Variables in Econometrics. New York: Cambridge Uni- versity Press, 1983. McCormick, Robert E. and Robert D. Tollison. "Why Do Black Basketball Players Work More for Less Money?" Journalof Economic Behavior and Organization44 (February 2001): 201-19. Oaxaca, Robert. "Male-Female Differentials in Urban Labor Markets." InternationalEconomic Review 14 (October 1973): 693-709. Oskanen, E.H. "A Note on Observation-Specific Dummies and Logit Analysis." The Statistician 35 (Fall 1986): 413-16. Polachek, Solomon W. "Potential Biases in Measuring Male-Female Discrimination." Journal of Human Resources 10 (Spring 1975): 205-29. Ramsey, James B. "Tests for Specification Errors in Classical Linear Least-Squares Regression Analysis." Journalof the Royal Statistical Society 31 (October 1969): 350-71. Sporting Newvs Online. "Jesse Jackson to Meet with NCAA about Minority Coaches." (July 25, 2001): 1-2. StatisticalAbstractof the United States. Washington, D.C.: Bureau of the Census, 1996. COPYRIGHT INFORMATION

TITLE: How Race Affects Dismissals of College Football Coaches SOURCE: J Labor Res 25 no4 Fall 2004 WN: 0429702296006

The magazine publisher is the copyright holder of this article and it is reproduced with permission. Further reproduction of this article in violation of the copyright is prohibited. To contact the publisher: http://www.gmu.edu/