- Email: [email protected]
Does big-time success in football or basketball affect SAT scores?
Economrcs
of Educatton
Revmv, Vol 12, No 2, pp 177-181,1993
0272-7757/93 $6 00 + 0 00 PergamonPressLtd
Pnnted in Great Brltaln
Does Big-time
Success in Football Affect SAT Scores?
IRVIN B.
Department
Abstract influence scholastic academic presented average average campus
of Economics,
TUCKER
Belk College Charlotte,
III
of North
Carohna
at
-This paper presents tests of the argument that a high-quality athletic program has a positwe on the academic misslon of Its umverslty. The results are based on a model of average aptitude test (SAT) scores that are a function of pigskin success, hoop success and key variables for the unlversltles that engage in major intercollegiate athletics. The ewdence indicates that academic variables rather than athletic success variables determine the level of SAT scores of incoming freshmen students, but a highly ranked football team on campus boosts SAT scores over time. In contrast, there is no ewdence that a highly ranked basketball team on has an impact on either the level of or changes m average SAT scores.
THE MUCH PUBLICIZED
Commwlon
Report of the Knight Founon Intercollegiate Athletics
reached the principal conclusion that to prevent abuse college and university presidents should take charge of their athletic departments. The Knight Commission report came amidst growing concern over the role that intercollegiate athletic competition should play within our colleges and universitles. An important empirical question that is not addressed by the Knight Commission is whether there are positive spillovers to the academic components of colleges and universities arising either directly or Indirectly from the school’s participation in intercollegiate competition. Prior research is sparse and divided regarding the extent to which athletic competition enhances the academic mission of colleges and universities. For example, a paper by Tucker (1992) concludes that a highly ranked football team on campus decreases the graduation rate while a highly ranked basketball team has no Impact on the graduation rate. Shughart et al. (1986) present evidence that a successful athletic program reduces the academic performance of economics faculty. McCormick and Tmsley (1987), on the [Manuscript
AMATO
of Busmess Admmlstratlon, Umverslty Charlotte, NC 28223, U.S.A.
I. INTRODUCTION
datlon
and LOUIS
or Basketball
recewed
29 December
1991, revwon
other hand, find that athletics are important to the success of a university because football success has a positive influence upon the change in average scholastic aptitude test (SAT) score for incoming freshmen students. The purpose of our study is to investigate the question of whether the quality of football and basketball programs contributes to the academic mission of colleges and universities by improving the average SAT scores of incoming freshmen. This research improves upon earlier studies by using superior proxies for athletic success and considering success in basketball as well as football in determining incoming freshmen SAT.
accepted
177
II. MODEL AND DATA The basic model is based upon the premise that a umversity seeks to maximize the quality of incoming freshmen students. Colleges and universities attract students to their campus by offering a variety of academic and athletic variables that meet the consumption and investment needs of perspective students. A model for the average SAT score for colleges and universities is given below in equation (1): for publication
27 October
1992.1
178
Economics of Education Review
SAT,, =
010 +
GG + ~,t,
(1)
where SAT,, is the average incoming freshmen SAT score at the zth universtty m the tth year, a0 is a constant term, X,, is a vector of academic and athletic characteristics that reflect consumption and investment attributes of mcommg students, and ult is the disturbance term. For comparability, we used academic and athletic variables similar to those used by McCormick and Tinsley (1987) in seeking to explain variation in incoming average SAT. Academic characteristics deemed relevant to SAT scores include: volumes in the library (VOL), faculty salary (SAL), student faculty ratio (SFR), tuition (TUIT), enrollment (ENROLL), private university (PRIVATE), and age of the university (AGE). As stated above, our model also Included vartables measuring football success (FBS) and basketball success (BBS). To avoid differences in scheduling and quality of opposition, we rejected using the win/loss record used by McCormick and Tmsley to measure athletic success. Instead, we used athletic success variables based upon the final Associated Press (AP) rankings in football and basketball. The AP rankings depend on the opinions of selected sportswriters across the nation. In determining the ranking of football and basketball teams, these sportswriters account for strength of schedule as well as wm/loss record. The FBS variable was computed from the final football AP poll for each year of the study. Football success points are awarded for each team finishing in the top 20 during a particular year. The top rated team received 20 points, the number two team received 19 points, and so on down to the number 20 team which received one point. Each school has a football success score equal to the sum of the points for each year in the sample. The BBS variable was computed in exactly the same fashion using the AP final basketball poll for each year included in the sample. Over a IO-year period, therefore, FBS and BBS have a possible range of O-200 for each variable. Data between academic years 1980 and 1989 were used to estimate the relationship between incoming freshmen SAT scores and academic variables and athletic variables for the 63 big-ttme athlettc schools defined by McCormick and Tmsley (1987). Big-time schools include either members of big-time athlettc conferences or major indepen-
dents. The big-time athletic conferences consist of the Atlantic Coast Conference (ACC), Big Ten, Big Eight, Pacific Ten Athletic Conference (PAClO), Southeastern Conference (SEC), and Southwestern Conference (SWC). Major independents during the period of the study include: Boston College, Florida State, Miami, Syracuse, Penn State, Notre Dame, University of Pittsburgh, University of South Carolina, and West Virginia University.’ Table 1 gives means and standard deviations for each of the variables Included in the study All data were gathered from publicly available sources.* The a priori expectation is that each of the academic variables should exhibit a positive sign with the exception of SFR and ENROLL. More VOL and higher faculty compensation correspond to higher quality instruction that better students seek. Universities that charge more tuition have these funds to provide a superior education and attract freshmen with higher SAT scores. Lower SFR and ENROLL suggest that, cetens parabus, better students prefer smaller class size and college environments. Therefore, the expected signs for SFR89 and ENROLL89 IS negative. The expected sign for PRIVATE is positive based on the rationale that private-supported mstitutions have a greater incentive to admit higher quality students who will graduate. AGE is expected to be positive if older universities offer academic prestige and superior fundmg to educate students. Finally, if better quality students are attracted by the consumption benefits of intercollegiate athletics, we would expect the athletic success variables FBS and BBS to be positively related to incoming freshmen SAT scores. III. MODEL SPECIFICATIONS AND EMPIRICAL RESULTS Equation (2), which 1s similar to the spectftcation developed by McCormick and Tinsley (1987), contains the basic model: SAT = o. + a, VOL89 + o2 SAL89 + os SFR89 + (Y_,TUIT89 + (us ENROLL89 + u6 PRIVATE + CY,AGE89 + (us FBS + (us BBS + u. (2) The model represented by equation (2) was estimated for the end academic year of the sample, 1989-1990.3 This specification seeks to determine the extent to which academtc and athletic success variables can explain mtercollegiate variations in the
179
Effect of Athletic Success on SAT Scores Table 1. Variable Variable SAT80 SAT89 VOL80 voL89 SAL80 SAL89 SFRSO SFR89 TUT80 TUIT89 ENROLL80 ENROLL89 PRIVATE AGE89 FBS BBS
Description
descnptron
and range
1980 SAT score (855-1284) 1989 SAT score (875-1235) 1980 volumes m the hbrary (0 3-6 x lo6 volumes) 1989 volumes in the hbrary (0.4-7.2 x lo6 volumes) 1980 faculty salary ($20.7-$33:7 x lb”) 1989 facultv salarv ($36.4-$683 x l$) 1980 student faculty ratio (5.6-25) 1989 student faculty ratio (5-23) 1980 tuition ($lOO-$7140) 1989 tuition ($845-$14,230) 1980 enrollment (2300-51,000) 1989 enrollment (3600-51,000) Private school dummy
(0 = public; 1 = pnvate) University age in 1989 (63-205 years) Football success (O-140 points) Basketball success (O-144 points)
level of SAT scores. Empirical results generated from ordinary least squares estimation of equation (2) are given in Table 2. Examination of Table 2 suggests that academic variables as opposed to athletic variables are important in determining the quality of incoming freshmen students in 1989. The variables which appear significant m Table 2 are VOL89 and SAL89 which are both positively related to incoming SAT scores. The coefftcrent for ENROLL89 is significant and negatively related to incoming SAT scores. All other coefficients for explanatory variables in the model, including FBS and BBS, are insignificant. We interpret this to mean that, other factors held constant, a smaller university environment with higher paid professors and greater library volumes attracts an undergraduate with higher average SAT scores, while big-time athletic success has no significant impact. It is important to note that the
statistics Mean
SD
1010.22
108.75
1056.11
108.32
1 34
0 30
1.48
0.40
$26.23
$2.59
$46.29
$6 45
14.97
4.32
14.05
4 70
$1574.25
$1578.39
$3781.21
$4075.90
20173 43
11316.26
23235 03
11519.52
0.24
0 43
128 02
30.88
30.73
36 52
22.14
29.19
McCormick and Tinsley (1987) study did not report testing for the level of SAT scores. In additron to the model explaining SAT levels, we can develop a model that relates changes in SAT scores to changes in academic and athletic variables. Equation (3) contains the specification of a model that seeks to explain changes in SAT scores among big-trme athletic universities: ASAT = B. + pl AVOL + & ASAL + Bs ASFR + B4 ATUIT + Bs AENROLL + B6PRIVATE + B#BS + PsBBS + p,, (3) where the A notation indicates change in the variable between the academic years 1980 and 1989 Table 3 reports results from least squares estimation of equation (3). There are several noteworthy aspects of the empirical results presented m this table. First, consistent with the McCormick and
180
Economics of Education Review Table 2. Regression coefficients for the average SAT scores of Incoming freshmen in 1989
Variable Intercept
Table
3
of incommg freshmen,
Coefficient
Variable
VOL89
648.73* (89 56) 15 38*
AVOL
SAL89
(;.;;!
ASAL
SFR89 TUIT89 ENROLL90 PRIVATE AGE89 FBS BBS
Intercept
(1 74) -191 (1 84) 0.003 (0.005) -0.003* (0.001) 12.90 (42.43) -0.02 (0.27) 0.06 (; ;;)
ATUIT AENROLL PRIVATE FBS BBS
R2
N N
coefficients
0.7447 63
1980-1989 Coefficient 15 11 (28 86) -8 34 (;J;) (y$)
ASFR
(0 29) R2
RegressIon
for changes In average SAT scores
(1.25) -0.005 (0.004) -0.001 (0.001) 30.80 (28.33) 0.44* (0.16) 0.13 (0.19) 0.1969 63
Standard errors are m parenthesis, and asterisks denote coefficients that are slgmficant at the 5% level, two-tailed test.
Standard errors are in parenthesis, and asterisks denote coefflclents that are slgmflcant at the 5% level, two-talled test
in SAT scores. These conclusions must be tempered by the realization that the overall explanatory power of the model is quite low so that the majority of the variation in SAT changes remains unexplained. However, our results do support the previous finding by McCormick and Tinsley that FBS does indeed contribute to a positive change in the overall SAT score for mcoming freshmen. Our result that BBS does not contribute to changes in incoming freshmen SAT is an interesting extension of their study. changes
Tinsley study, FBS IS the only statistically significant variable in the model. Football success is positively
and significantly related to the change in SAT scores for entering freshmen. It is interesting to note that while FBS does exert a positive influence on changes in SAT scores, BBS does not.4 Using the estimated coefficient of 0.44 for FBS, and holding changes m the academic variables and BBS constant, a university with the average football success score in the sample of 31 points increased its SAT score by 14 points. This would imply that a university with the sample average SAT of 1010 in 1980 and the average football success of 31 points would experience a 3% increase in SAT score by 1989 as a result of pigskin success.’ Another interesting result concerns the VOL, SAL and ENROLL variables which were all slgnificant measures of SAT levels across universities in equation (2). Our results reported in Table 3, however, suggest that changes in these academic variables are not the important determinants for
IV. CONCLUSION The purpose of this study was to determine the effect that academics and athletics - including both football and basketball variables - have on the average SAT scores for mcoming freshmen at U.S. universities engaged in big-time collegiate sports. Our findings suggest that academic variables and not success in big-time sports is significant in determining levels of SAT scores. However, success
Effect of Athletic Success on SAT Scores in football is positive and significant in determining
changes in average SAT scores. This means the distribution of high-quality students shifts over time in favor of universities with a successful big-time football program. Therefore, football becomes important to the academic mission similar to the effect advertising can have on the market share of competing firms. The finding that basketball has no impact on SAT levels or changes is an interesting result and should be of particular interest to schools that participate m basketball only. The focus of recent discussions on intercollegiate sports has been on the negative aspects of athletic
181
competition. Our results suggest there is a potential for positive spillovers to the academic mission from a successful big-time football team which is consistent with the “athletics contributes to academics” argument. Since universities compete for quality students, it follows that they will continue the search for intercollegiate sports reform in order to minimize the negative aspects of athletics while preserving the positive spillover benefits that can accrue from success on the gridiron. The intriguing research question remains, are the benefits of a top-quality athletic program truly worth the costs? It is an analytical question begging for future study.
NOTES 1 Smce 1990 Florida State, Miami, South Carohna and Penn State have jomed maJor conferences. 2. Data on average SAT scores and other academic variables are from Peterson’s Guide fo Four-Year Colleges (1981-1990, various pages). For universities reporting only American College Test (ACT) scores, the ACT scores were converted to SAT scores usmg a conversion table from ACT. Data on professor salaries are from The Annual Report on the Economzc Status of the Profession (Academe, August, 1981, pp 235-290 and March-April, 1990, pp. 30-72). 3 An a priorz argument can be made that enrollment IS an endogenous variable Enrollment could be endogeneous if a umversity that IS experiencmg Increases m applications must choose between mcreasmg total enrollment, increasing the quality of students as reflected m average SAT scores, or some combmatron of Increased enrollment and Increased average SAT In order to allow for the presence of simultaneity, two-state least squares were estimated for the system of equations SAT and ENROLL and for the system ASAT and AENROLL. Neither simultaneous equation model offered evidence to support the presence of simultaneous equations bias. Since our focus IS on the determmants of SAT and ASAT, we report ordinary least squares results for these models 4 The model specification in Table 3 was also estimated using a dummy variable for FBS. The dummy variable was set to one for any university appearing m the final AP football poll during 1980-1989 and zero otherwise. Another test performed was to use a dummy variable for FBS which equals one for any university’s team ranked number one and zero otherwise In each equation, the football success dummy variable estimated coefficient was insignificant. Thus, weighted ranking is important rather than lust bemg ranked. 5 Based on the assumption that the impact of athletic success on the quahty of mcommg freshmen is not long-hved, recent AP poll scores in the lo-year period were given greater weight. We estimated the model with the weighted athletic success measures Instead of the unweighted athletic success variables. There were no sigmficant changes from the specifications in Tables 2 and 3.
REFERENCES KNIGHTFOUNDATIONCOMMISSION (1991) Keepzng Fazth with the Student-Athlete A New Model For Intercollegiate Athletics. Report on Intercollegiate Athletics, March MCCORMICK,R.E. and TINSLEY,M. (1987) Athletics versus academics? Evidence from SAT scores. J. Polzt. Econ. 95, 1103-1116. Peterson’s Guzde to Four-Year Colleges, 1990 (1991) 21st edition. Prmceton, NJ: Peterson’s Guides. SHUGHARTII, W.F., TOLLISON,R.D. and GOFF, B.L. (1986) Pigskms and publications At. Econ. J. 14, 46-50. TUCKERIII, LB. (1992) The impact of big-time athletics on graduation rates. At. Econ. J. 20, 45-72. The Annual Report on the Economic Status of the Profession, 1989-1990, (1990 March-April). Academe 76. 30-72.
of Educatton
Revmv, Vol 12, No 2, pp 177-181,1993
0272-7757/93 $6 00 + 0 00 PergamonPressLtd
Pnnted in Great Brltaln
Does Big-time
Success in Football Affect SAT Scores?
IRVIN B.
Department
Abstract influence scholastic academic presented average average campus
of Economics,
TUCKER
Belk College Charlotte,
III
of North
Carohna
at
-This paper presents tests of the argument that a high-quality athletic program has a positwe on the academic misslon of Its umverslty. The results are based on a model of average aptitude test (SAT) scores that are a function of pigskin success, hoop success and key variables for the unlversltles that engage in major intercollegiate athletics. The ewdence indicates that academic variables rather than athletic success variables determine the level of SAT scores of incoming freshmen students, but a highly ranked football team on campus boosts SAT scores over time. In contrast, there is no ewdence that a highly ranked basketball team on has an impact on either the level of or changes m average SAT scores.
THE MUCH PUBLICIZED
Commwlon
Report of the Knight Founon Intercollegiate Athletics
reached the principal conclusion that to prevent abuse college and university presidents should take charge of their athletic departments. The Knight Commission report came amidst growing concern over the role that intercollegiate athletic competition should play within our colleges and universitles. An important empirical question that is not addressed by the Knight Commission is whether there are positive spillovers to the academic components of colleges and universities arising either directly or Indirectly from the school’s participation in intercollegiate competition. Prior research is sparse and divided regarding the extent to which athletic competition enhances the academic mission of colleges and universities. For example, a paper by Tucker (1992) concludes that a highly ranked football team on campus decreases the graduation rate while a highly ranked basketball team has no Impact on the graduation rate. Shughart et al. (1986) present evidence that a successful athletic program reduces the academic performance of economics faculty. McCormick and Tmsley (1987), on the [Manuscript
AMATO
of Busmess Admmlstratlon, Umverslty Charlotte, NC 28223, U.S.A.
I. INTRODUCTION
datlon
and LOUIS
or Basketball
recewed
29 December
1991, revwon
other hand, find that athletics are important to the success of a university because football success has a positive influence upon the change in average scholastic aptitude test (SAT) score for incoming freshmen students. The purpose of our study is to investigate the question of whether the quality of football and basketball programs contributes to the academic mission of colleges and universities by improving the average SAT scores of incoming freshmen. This research improves upon earlier studies by using superior proxies for athletic success and considering success in basketball as well as football in determining incoming freshmen SAT.
accepted
177
II. MODEL AND DATA The basic model is based upon the premise that a umversity seeks to maximize the quality of incoming freshmen students. Colleges and universities attract students to their campus by offering a variety of academic and athletic variables that meet the consumption and investment needs of perspective students. A model for the average SAT score for colleges and universities is given below in equation (1): for publication
27 October
1992.1
178
Economics of Education Review
SAT,, =
010 +
GG + ~,t,
(1)
where SAT,, is the average incoming freshmen SAT score at the zth universtty m the tth year, a0 is a constant term, X,, is a vector of academic and athletic characteristics that reflect consumption and investment attributes of mcommg students, and ult is the disturbance term. For comparability, we used academic and athletic variables similar to those used by McCormick and Tinsley (1987) in seeking to explain variation in incoming average SAT. Academic characteristics deemed relevant to SAT scores include: volumes in the library (VOL), faculty salary (SAL), student faculty ratio (SFR), tuition (TUIT), enrollment (ENROLL), private university (PRIVATE), and age of the university (AGE). As stated above, our model also Included vartables measuring football success (FBS) and basketball success (BBS). To avoid differences in scheduling and quality of opposition, we rejected using the win/loss record used by McCormick and Tmsley to measure athletic success. Instead, we used athletic success variables based upon the final Associated Press (AP) rankings in football and basketball. The AP rankings depend on the opinions of selected sportswriters across the nation. In determining the ranking of football and basketball teams, these sportswriters account for strength of schedule as well as wm/loss record. The FBS variable was computed from the final football AP poll for each year of the study. Football success points are awarded for each team finishing in the top 20 during a particular year. The top rated team received 20 points, the number two team received 19 points, and so on down to the number 20 team which received one point. Each school has a football success score equal to the sum of the points for each year in the sample. The BBS variable was computed in exactly the same fashion using the AP final basketball poll for each year included in the sample. Over a IO-year period, therefore, FBS and BBS have a possible range of O-200 for each variable. Data between academic years 1980 and 1989 were used to estimate the relationship between incoming freshmen SAT scores and academic variables and athletic variables for the 63 big-ttme athlettc schools defined by McCormick and Tmsley (1987). Big-time schools include either members of big-time athlettc conferences or major indepen-
dents. The big-time athletic conferences consist of the Atlantic Coast Conference (ACC), Big Ten, Big Eight, Pacific Ten Athletic Conference (PAClO), Southeastern Conference (SEC), and Southwestern Conference (SWC). Major independents during the period of the study include: Boston College, Florida State, Miami, Syracuse, Penn State, Notre Dame, University of Pittsburgh, University of South Carolina, and West Virginia University.’ Table 1 gives means and standard deviations for each of the variables Included in the study All data were gathered from publicly available sources.* The a priori expectation is that each of the academic variables should exhibit a positive sign with the exception of SFR and ENROLL. More VOL and higher faculty compensation correspond to higher quality instruction that better students seek. Universities that charge more tuition have these funds to provide a superior education and attract freshmen with higher SAT scores. Lower SFR and ENROLL suggest that, cetens parabus, better students prefer smaller class size and college environments. Therefore, the expected signs for SFR89 and ENROLL89 IS negative. The expected sign for PRIVATE is positive based on the rationale that private-supported mstitutions have a greater incentive to admit higher quality students who will graduate. AGE is expected to be positive if older universities offer academic prestige and superior fundmg to educate students. Finally, if better quality students are attracted by the consumption benefits of intercollegiate athletics, we would expect the athletic success variables FBS and BBS to be positively related to incoming freshmen SAT scores. III. MODEL SPECIFICATIONS AND EMPIRICAL RESULTS Equation (2), which 1s similar to the spectftcation developed by McCormick and Tinsley (1987), contains the basic model: SAT = o. + a, VOL89 + o2 SAL89 + os SFR89 + (Y_,TUIT89 + (us ENROLL89 + u6 PRIVATE + CY,AGE89 + (us FBS + (us BBS + u. (2) The model represented by equation (2) was estimated for the end academic year of the sample, 1989-1990.3 This specification seeks to determine the extent to which academtc and athletic success variables can explain mtercollegiate variations in the
179
Effect of Athletic Success on SAT Scores Table 1. Variable Variable SAT80 SAT89 VOL80 voL89 SAL80 SAL89 SFRSO SFR89 TUT80 TUIT89 ENROLL80 ENROLL89 PRIVATE AGE89 FBS BBS
Description
descnptron
and range
1980 SAT score (855-1284) 1989 SAT score (875-1235) 1980 volumes m the hbrary (0 3-6 x lo6 volumes) 1989 volumes in the hbrary (0.4-7.2 x lo6 volumes) 1980 faculty salary ($20.7-$33:7 x lb”) 1989 facultv salarv ($36.4-$683 x l$) 1980 student faculty ratio (5.6-25) 1989 student faculty ratio (5-23) 1980 tuition ($lOO-$7140) 1989 tuition ($845-$14,230) 1980 enrollment (2300-51,000) 1989 enrollment (3600-51,000) Private school dummy
(0 = public; 1 = pnvate) University age in 1989 (63-205 years) Football success (O-140 points) Basketball success (O-144 points)
level of SAT scores. Empirical results generated from ordinary least squares estimation of equation (2) are given in Table 2. Examination of Table 2 suggests that academic variables as opposed to athletic variables are important in determining the quality of incoming freshmen students in 1989. The variables which appear significant m Table 2 are VOL89 and SAL89 which are both positively related to incoming SAT scores. The coefftcrent for ENROLL89 is significant and negatively related to incoming SAT scores. All other coefficients for explanatory variables in the model, including FBS and BBS, are insignificant. We interpret this to mean that, other factors held constant, a smaller university environment with higher paid professors and greater library volumes attracts an undergraduate with higher average SAT scores, while big-time athletic success has no significant impact. It is important to note that the
statistics Mean
SD
1010.22
108.75
1056.11
108.32
1 34
0 30
1.48
0.40
$26.23
$2.59
$46.29
$6 45
14.97
4.32
14.05
4 70
$1574.25
$1578.39
$3781.21
$4075.90
20173 43
11316.26
23235 03
11519.52
0.24
0 43
128 02
30.88
30.73
36 52
22.14
29.19
McCormick and Tinsley (1987) study did not report testing for the level of SAT scores. In additron to the model explaining SAT levels, we can develop a model that relates changes in SAT scores to changes in academic and athletic variables. Equation (3) contains the specification of a model that seeks to explain changes in SAT scores among big-trme athletic universities: ASAT = B. + pl AVOL + & ASAL + Bs ASFR + B4 ATUIT + Bs AENROLL + B6PRIVATE + B#BS + PsBBS + p,, (3) where the A notation indicates change in the variable between the academic years 1980 and 1989 Table 3 reports results from least squares estimation of equation (3). There are several noteworthy aspects of the empirical results presented m this table. First, consistent with the McCormick and
180
Economics of Education Review Table 2. Regression coefficients for the average SAT scores of Incoming freshmen in 1989
Variable Intercept
Table
3
of incommg freshmen,
Coefficient
Variable
VOL89
648.73* (89 56) 15 38*
AVOL
SAL89
(;.;;!
ASAL
SFR89 TUIT89 ENROLL90 PRIVATE AGE89 FBS BBS
Intercept
(1 74) -191 (1 84) 0.003 (0.005) -0.003* (0.001) 12.90 (42.43) -0.02 (0.27) 0.06 (; ;;)
ATUIT AENROLL PRIVATE FBS BBS
R2
N N
coefficients
0.7447 63
1980-1989 Coefficient 15 11 (28 86) -8 34 (;J;) (y$)
ASFR
(0 29) R2
RegressIon
for changes In average SAT scores
(1.25) -0.005 (0.004) -0.001 (0.001) 30.80 (28.33) 0.44* (0.16) 0.13 (0.19) 0.1969 63
Standard errors are m parenthesis, and asterisks denote coefficients that are slgmficant at the 5% level, two-tailed test.
Standard errors are in parenthesis, and asterisks denote coefflclents that are slgmflcant at the 5% level, two-talled test
in SAT scores. These conclusions must be tempered by the realization that the overall explanatory power of the model is quite low so that the majority of the variation in SAT changes remains unexplained. However, our results do support the previous finding by McCormick and Tinsley that FBS does indeed contribute to a positive change in the overall SAT score for mcoming freshmen. Our result that BBS does not contribute to changes in incoming freshmen SAT is an interesting extension of their study. changes
Tinsley study, FBS IS the only statistically significant variable in the model. Football success is positively
and significantly related to the change in SAT scores for entering freshmen. It is interesting to note that while FBS does exert a positive influence on changes in SAT scores, BBS does not.4 Using the estimated coefficient of 0.44 for FBS, and holding changes m the academic variables and BBS constant, a university with the average football success score in the sample of 31 points increased its SAT score by 14 points. This would imply that a university with the sample average SAT of 1010 in 1980 and the average football success of 31 points would experience a 3% increase in SAT score by 1989 as a result of pigskin success.’ Another interesting result concerns the VOL, SAL and ENROLL variables which were all slgnificant measures of SAT levels across universities in equation (2). Our results reported in Table 3, however, suggest that changes in these academic variables are not the important determinants for
IV. CONCLUSION The purpose of this study was to determine the effect that academics and athletics - including both football and basketball variables - have on the average SAT scores for mcoming freshmen at U.S. universities engaged in big-time collegiate sports. Our findings suggest that academic variables and not success in big-time sports is significant in determining levels of SAT scores. However, success
Effect of Athletic Success on SAT Scores in football is positive and significant in determining
changes in average SAT scores. This means the distribution of high-quality students shifts over time in favor of universities with a successful big-time football program. Therefore, football becomes important to the academic mission similar to the effect advertising can have on the market share of competing firms. The finding that basketball has no impact on SAT levels or changes is an interesting result and should be of particular interest to schools that participate m basketball only. The focus of recent discussions on intercollegiate sports has been on the negative aspects of athletic
181
competition. Our results suggest there is a potential for positive spillovers to the academic mission from a successful big-time football team which is consistent with the “athletics contributes to academics” argument. Since universities compete for quality students, it follows that they will continue the search for intercollegiate sports reform in order to minimize the negative aspects of athletics while preserving the positive spillover benefits that can accrue from success on the gridiron. The intriguing research question remains, are the benefits of a top-quality athletic program truly worth the costs? It is an analytical question begging for future study.
NOTES 1 Smce 1990 Florida State, Miami, South Carohna and Penn State have jomed maJor conferences. 2. Data on average SAT scores and other academic variables are from Peterson’s Guide fo Four-Year Colleges (1981-1990, various pages). For universities reporting only American College Test (ACT) scores, the ACT scores were converted to SAT scores usmg a conversion table from ACT. Data on professor salaries are from The Annual Report on the Economzc Status of the Profession (Academe, August, 1981, pp 235-290 and March-April, 1990, pp. 30-72). 3 An a priorz argument can be made that enrollment IS an endogenous variable Enrollment could be endogeneous if a umversity that IS experiencmg Increases m applications must choose between mcreasmg total enrollment, increasing the quality of students as reflected m average SAT scores, or some combmatron of Increased enrollment and Increased average SAT In order to allow for the presence of simultaneity, two-state least squares were estimated for the system of equations SAT and ENROLL and for the system ASAT and AENROLL. Neither simultaneous equation model offered evidence to support the presence of simultaneous equations bias. Since our focus IS on the determmants of SAT and ASAT, we report ordinary least squares results for these models 4 The model specification in Table 3 was also estimated using a dummy variable for FBS. The dummy variable was set to one for any university appearing m the final AP football poll during 1980-1989 and zero otherwise. Another test performed was to use a dummy variable for FBS which equals one for any university’s team ranked number one and zero otherwise In each equation, the football success dummy variable estimated coefficient was insignificant. Thus, weighted ranking is important rather than lust bemg ranked. 5 Based on the assumption that the impact of athletic success on the quahty of mcommg freshmen is not long-hved, recent AP poll scores in the lo-year period were given greater weight. We estimated the model with the weighted athletic success measures Instead of the unweighted athletic success variables. There were no sigmficant changes from the specifications in Tables 2 and 3.
REFERENCES KNIGHTFOUNDATIONCOMMISSION (1991) Keepzng Fazth with the Student-Athlete A New Model For Intercollegiate Athletics. Report on Intercollegiate Athletics, March MCCORMICK,R.E. and TINSLEY,M. (1987) Athletics versus academics? Evidence from SAT scores. J. Polzt. Econ. 95, 1103-1116. Peterson’s Guzde to Four-Year Colleges, 1990 (1991) 21st edition. Prmceton, NJ: Peterson’s Guides. SHUGHARTII, W.F., TOLLISON,R.D. and GOFF, B.L. (1986) Pigskms and publications At. Econ. J. 14, 46-50. TUCKERIII, LB. (1992) The impact of big-time athletics on graduation rates. At. Econ. J. 20, 45-72. The Annual Report on the Economic Status of the Profession, 1989-1990, (1990 March-April). Academe 76. 30-72.