The Demand for Professional Team Sports: Traditional Finding s and New Developments Paul Downward and Alistair Dawson Working Paper No: 99. 7 Division of Economics Staffordshire University Business School Stoke on Trent st4 2DF

Download 107.99 Kb.

Page	3/3
Date	18.10.2016
Size	107.99 Kb.
	#2876

1 2 3

Bibliography

Conclusion

In this paper, the empirical literature on the demand for professional team sports has been discussed. Both the traditional short-run emphasis of demand studies and their main findings are outlined. Recent developments in demand estimates for the long run are then discussed. The main concern of the paper hinges on the appropriate interpretation of the diversity of findings in the literature. This paper argues that the more recent long-run studies should be emphasised because of their appropriate econometric methodology, but also because they reflect a changed emphasis from aggregating or averaging results across clubs over short-time periods. In this respect this paper argues that the determinants of demand are still not well understood, despite the many studies that have been conducted. In contrast a new research agenda is required that allows researchers to explore both the long-run and short run determinants of demand of both a sporting and economic nature simultaneously. Moreover, regressions should avoid averages over clubs. The cost of this approach is the time and expense of the researcher in constructing a data set. The benefit would be results that are more useful to the sporting commentator and regulator than is currently available.

Bibliography
Baimbridge, M., Cameron, S. and Dawson P., (1995) Satellite Broadcasting and Match Attendance: The case of Rugby League” in Applied Economics Letters, Vol 2, No 10, pp343-346.

Baimbridge, M., Cameron, S. and Dawson, P. Satellite Television and the demand for football: A whole new ball game? Scottish Journal of Political Economy, Vol 43, No 3, pp317-333

Baimbridge, M., (1997). ‘Match Attendance at Euro '96: Was the Crowd Waving or Drowning?’ Applied Economics Letters, 4, pp555-558.
Bird, P.J.W.N. (1982), ‘The Demand for League Football’, Applied Economics, Vol 14, No 6, pp637-649.
Borland, J. (1987) ‘The Demand for Australian Rules Football’, The Economic Record, 63, 182: 220—230.
Borland, J. and Lye, J. (1992) ‘Attendance at Australian Rules football: a panel study’, Applied Economics, 24: 1053--1058.
Cairns, J. (1988), Uncertainty of Outcome and the Demand for Football, University of Aberdeen Discussion Paper, 88-02.
Cairns, J. (1990) ‘The Demand for Professional Team Sports’, British Review of Economic Issues, 12, 28: 1--20.
Cairns, J., Jennett, N. and Sloane, P. J. (1986) ‘The Economics of Professional Team Sports: A Survey of Theory and Evidence,’ Journal of Economic Studies, 13, 1: 3--80.
Davies, B., Downward P.M., and Jackson, I. (1995), ‘The demand for rugby league: evidence from causality tests’ Applied Economics, Vol 27, pp1003-1007.
Davies, B., Downward P.M., and Jackson, I. (1995), ‘Cultural Determinants and the demand for Professional Team Sports: A Study of Rugby League Clubs’ Leisure Cultures: Values, Genders, Lifestyles, L.S.A.Publications.
De Marchi, N. and Gilbert, C. (eds) (1989), Oxford Economic Papers, Vol 41, Special Edition on Econometrics.
Demmert, H.G., (1973), ‘The Economics of Professional Team Sports,’ D.C. Heath and Co:Lexington Mass.
Dobson, S.M. and Goddard, J.A. (1995) ‘The demand for professional league football in England and Wales, 1925-92’, Discussion Paper, Department of Economics, University of Hull.
Dobson, S. and Goddard, J. (1988) ‘Performance, Revenue and Cross Subsidisation in the Football League 1927-1994’ Economic History Review, LI, 4, pp763-785.
Downward, P.M. and Dawson, A, (forthcoming), The Economics Of Professional Team Sports, Routledge:London.
Drever, P. and McDonald, J. (1981), ‘Attendances at South Australian Football Games’ International Review of Sports Sociology, Vol 16, No 2, pp103-113.
Fizel, J.L. and Bennett, R.W. (1989), ‘The impact of college football telecasts on college football attendance, Social Science Quarterly, Vol 70, No 4, pp980-988.
Fort, R. and Quirk, J. (1995) ‘Cross-subsidization, Incentives, and Outcomes in Professional Team Sports Leagues’, Journal of Economic Literature, 23,

1265--1299.

Gartner, M. and Pommerehene, W.W. (1978), ‘Der Fussballzuschauer – ein Homo Oeconomicus?’ Jahrbuch fur Sozial Wissenschaft, Vol 29, pp88-107.
Geddert, R.L. and Semple, R.K., (1985), Locating a Major Hockey Franchise: Regional Considerations, Regional Sciences Perspectives, 15, (1), 13-29
Hart, R.A., Hutton, J., and Sharot, T. (1975) ‘A Statistical Analysis of

Association Football Attendance’, Journal of the Royal Statistical Society; Series C (Applied Statistics), 24, 1: 17—27.

Hendry, D., Econometrics: Alchemy or Science, Blackwell:Oxford, 1992.
Hill, J.R., Madura, J. and Zuber, R.A. (1982), ‘The short run demand for major league baseball’, Atlantic Economic Journal, Vol 10, No 2, pp31-35.
Hynds, M. and Smith, I., (1994), ‘The Demand for Test Match Cricket’ University of St Andrews Working Paper, July.
Jennett, N. (1984) ‘Attendances, Uncertainty of Outcome and Policy in Scottish League Football’, Scottish Journal of Political Economy, 31, 2: 175—197.
Jones, J.C.H. and Ferguson, D.G. (1988), Location and Survival in the National Hockey League, Journal of Industrial Economics, Vol 36, No 4, pp443-457.
Kaempfer, W.H. and Pacey, P.L., (1986), ‘Televising college football: the complementarity of attendance and viewing’, Social Science Quarterly, Vol 67, No 1, pp176-185.
Kahn, L.M. and Sherer, P.O. (1988), ‘Racial Differences in professional basketball players’ compensation’, Journal of Labour Economics, Vol 6, No 1, pp40-61.
Kuypers, T. (1996) ‘The beautiful game? An econometric study of why people watch English football’, University College London Discussion Papers in Economics, 96-01.
Marburger, D.R., (1997), ‘Optimal Ticket pricing for Performance Goods’ Managerial and Decision Economics, Vol 18, pp375-381.
Noll, R.G., (1974) ‘Attendance and Price Setting’ in Noll, R.G. (ed)
Noll, R.G. (ed), (1974), ‘Government and the Sports Business, Brookings Institution, Washington D.C.

Peel, D. and Thomas, D. (1988) ‘Outcome Uncertainty and the Demand for Football: An Analysis of Match Attendances in the English Football League’,

Scottish Journal of Political Economy, 35, 3: 242—249.
Peel, D. and Thomas, D. (1996), ‘Attendance Demand: an investigation of repeat fixtures’ Applied Economics Letters, Vol 3, pp391-394.
Peel, D. and Thomas, D. (1997) ‘Handicaps, outcome uncertainty and attendance demand’, Applied Economics Letters, 4: 567--570.
Pesaran, M.H. and Smith, R. (1995), ‘Estimating long-run relationships from dynamic heterogeneous panels’, Journal of Econometrics , Vol 68, pp79-113.
Schollaert, P.T. and Smith, D. H., (1987) Team Racial Composition and Sports Attendance, Sociological Quarterly, Vol 28, No1, pp71-87.
Seigfried, J.Y. and Eisenberg, J.D., (1980) ‘The Demand for Minor League Baseball’, Atlantic Economic Journal, Vol 8, No 2, pp59-69.
Seigfried, J.Y. and Hinshaw, C.E. (1979), ‘The effect of lifting television blackouts in professional football no-shows’ Journal of the Economics of Business, Vol 32, No 1, pp1-13

Simmons, R. (1996) ‘The demand for English league football: a club-level analysis’, Applied Economics, 28, 2: 139—155.

Thomas, S. M. and Jolson, M.A., (1979), ‘Components of the demand for major league baseball’, University of Michigan Business Review, Vol 31, No 3, pp1-6.
Vrooman, J., A General Theory of Professional Sports Leagues, Southern Economic Journal, Vol 61, pp971-990, 1995.
Whitney, J.D. (1988), ‘Winning Games versus Winning Championships: The Economics of fan interest and team performance, Economic Inquiry, Vol 26, pp703-724.
Wilson, J., (1994), ‘Sport society and the state: Playing by the rules, Wayne State University Press.
Wilson, P. and Sim, B. (1995) ‘The demand for Semi-Pro League football in Malaysia 1989-91: a panel data approach’, Applied Economics, 27, 131--138.
Zhang, J.J., Pease, D.G.and Smith, D.W. (1998), ‘Relationship Between Broadcasting Media and Minor League Hockey Game Attendance’, Journal of Sports Management, Vol 12, pp103-122.
Zhang, J.J. and Smith, D.W. (1997), ‘Impact of Broadcasting on the Attendance of Professional Basketball Games’, Sports Marketing Quarterly, Vol 6, No 1, pp23-29.

1 If we take the demand model A_t=B1P_t^B2Y_t^B3where A refers to attendance, P is price and Y is income, 't' is the index of observation, i.e. either time period or subject, and B_i are coefficients then the demand model is non linear. This is because the coefficients are powers (this type of model is very common in empirical studies). This model cannot, therefore, be estimated by a linear regression. In contrast, we can estimate a linear regression model relating attendance to price and income if we use the logarithm of the values of the variables. Specifically, this implies that we have changed the model to LogA_t=B₁+B₂LogP_t+B₃LogY_t. The coefficient estimates from this model will be elasticities. This is because changes in logarithms are proportionate changes. Moreover, the interaction between the variables is removed. Consequently, for example, B₂ would measure the slope of the logarithmic model Log A_t/Log P_twhich will be the proportionate change in attendance resulting from the proportionate change in price - the price elasticity of demand. Note that the slope in the original non-linear model will not be ∂A_t/∂P_t=B₂but a more complicated function based on the interaction of the variables i.e. ∂A_t/∂P_t =B₂B₁P_t^B2-1Y_t^B3.

2 Interestingly this is not the case with time-series econometrics. As DeMarchi and Gilbert’s (1989) volume on the development of identification in econometrics notes, concern over the issue of multicollinearity as a form of model specification versus a problem of data, that dates back to the origins of the discipline, has been discussed in the context of the development of cointegration analysis.

3 As discussed in the text, time-series econometrics has produced a meaningful classification system based on the data. Of course this is in terms of establishing the shortrun and long run effects of variables. Factor analysis could play a similar role in cross-section studies where the number of independent variables is much larger and getting increasingly so.

4 In their 1996 study, the authors included distance in their regressions in a quadratic manner. The results suggest a minima which implies that local derbies are important but that committed fans do not let distance put them off attending fixtures.

5 Another Association football example is Stoke City one of the author’s own city teams!

6 In probability terms uncertainty of outcome could be measured, for example, by p_i(1-p_i) for the ‘ith’ team’s fixture where p refers to the probability of a win. Uncertainty of outcome will be at a maximum where p=0.5.

7 A slope dummy variable is constructed by assigning, for example, a value of ‘1’ to the variable if it is felt that the result of the fixture is increasingly uncertain, and ‘0’ otherwise. The dummy variable is multiplied by the betting-odds variable and this composite variable included in the regression. This implies that a selection of observations on betting odds associated with increasing uncertainty of outcome are entered into the regression analysis again. If the coefficient on this variable is positive and significant then one has identified uncertainty of outcome.

8 Note that the regression is still linear in the parameters and, as such, ordinary least squares can still be employed. This approach can thus be thought of as trying to fit a line of best fit to the relationship between match results and squared betting odds. Intuitively plotting this graph would appear linear unlike the graph drawn between attendance and betting odds.

9 These features of teams and players are usually measured with dummy variables. For example, the presence of an international player in a match or team would be scored ‘1’ or ‘0’ otherwise.

10 For those unfamiliar with cricket as a sport, rainfall very often stops play. Accordingly one would expect a negative association between rainfall and attendances

11 This is not because the authors feel that female attendance is unimportant per se but simply that the data reflects the historical nature of the data which focussed on collecting male statistics. In this respect the number of members of the armed forces are also included as the study covers the period of the second-world war, the Korean war, and periods of national service.

12 In statistical terms this means that the equations were estimated simultaneously because some of the influences on the variables were seen to be common. Estimating the equations simultaneously increases the (statistical) efficiency of the estimates.

13 The reason lagged ‘level’ terms are involved is because a regression of this periods attendance cannot be done on this period’s attendance because this would produce perfect multicollinearity and the regression would fail.

Directory: RePEc -> wuk -> stafwp

Download 107.99 Kb.

Share with your friends:

1 2 3