Breeding Incenive Programs and Demand for California horoughbred Racing: he radeoff Beween Quaniy and Qualiy by Marin D. Smih Deparmen of Agriculural and Resource Economics Universiy of California, Davis Davis, CA 95616-8512 Phone: (530) 754-8172 Fax: (530) 752-5614 e-mail: smih@primal.ucdavis.edu Prepared for 1999 American Agriculural Economics Associaion Annual Meeing Nashville, ennessee Augus 8-11, 1999 Subjec Code: 15 Absrac: Boh quaniy of horses and qualiy simulae demand for horse race gambling. his paper addresses he poenial for a quaniy/qualiy radeoff due o breeding incenives for California horoughbreds. Economeric analysis is used o assess he demand for qualiy and quaniy of horses, and resuls sugges he likely ne benefi of breeding incenives on he indusry a large. Copyrigh 1999 by Marin D. Smih. All righs reserved. Readers may make verbaim copies of his documen for non-commercial purposes by any means, provided ha his copyrigh noice appears on all such copies.
Breeding Incenive Programs and Demand for California horoughbred Racing: he radeoff Beween Quaniy and Qualiy * he horoughbred racing indusry in California is currenly embroiled in a policy debae abou breeding incenive programs. Some paries hink ha special awards o breeders are incenives ha improve he long-run healh of he indusry. Ohers see hem as subsidies ha undermine he qualiy of he breed and ulimaely harm he indusry a large. Indusry officials agree ha increased qualiy of horoughbreds simulaes demand for he indusry s end produc, horse race gambling. I may no be obvious, however, ha a higher quaniy of horses may also simulae he demand for horse race gambling. How do breeding incenives affec qualiy and quaniy of horoughbred race horses? How do quaniy and qualiy, in urn, affec demand for horse race gambling? his paper addresses hese quesions wih an analyical model of breeder decisions and an economeric analysis of demand for horoughbred racing in California. In recen years, ineres has grown in agriculural economics abou qualiy effecs of agriculural policies ha do no aim a changing produc qualiy. For insance, subsidies and quoas aimed a ransferring income o farmers may have he uninended consequence of alering produc qualiy. Breeding incenives in he California horoughbred horse racing indusry provide an ineresing wis on his debae; hough quaniy and qualiy migh boh simulae * I would like o hank Jim Chalfan, Jim Wilen, Jeffrey Williams, Jenni James, Eidan Apelbaum, Dawn Parker, and paricipans in a deparmen seminar for helpful commens. I am especially graeful o Kim Jacobs a California Horse Racing Informaion Managemen Sysems for assisance in consrucing he aendance and handle daabases. Finally, I wish o hank Dan Smih, Craig Fravel, Doug Reed, Wilson Shirley, and Rollin Baugh for providing background racing indusry and policy informaion. 1
demand, he ne effec of quaniy a he expense of qualiy is unknown. his issue has no ye been addressed in he economics lieraure abou horse rading. here are, however, numerous sudies abou horse racing ha deal wih similar issues. Aricles ha have focused on horse breeding are mainly hedonic analyses of pedigree characerisics such as Buzby and Jessup (1994) and Lansford, Jr., Freeman, opliff, and Walker (1998). Kraf (1996) provides a dynamic analysis of breeding markes bu does no consider breeding incenive policy variables. Church and Bohara (1992) sudy he regulaory environmen of horse racing in New Mexico and find ha oo many racing days are graned for join indusry profi maximizaion, bu he number of days is consisen wih sae revenue maximizaion. he mos relevan opic in he lieraure is demand for parimuuel wagering, e.g. halheimer and Ali (1992,1995), Pescarice (1980), Morgan and Vasche (1982), and Ali and halheimer (1997). his las paper finds a posiive and saisically significan relaionship beween qualiy and wagering. Perhaps he mos imporan policy-relevan finding of hese sudies is ha demand for wagering wih respec o a reducion in he akeou rae (i.e. he share of parimuuel handle ha is no reurned o he beors) is elasic. Unlike hese papers, my economeric analysis uses individual race daa o esimae he effecs of racing characerisics on handle. Previous sudies ha have looked a individual race being aciviy have focused on marke efficiency hypoheses and quesions abou risk preferences of beors, e.g. Ali (1977, 1979) and Golec and amarkin (1998). he breeder incenives program in California consiss of four componens: breeder awards, owner awards, sallion awards, and resriced purses. he firs hree provide funds in addiion o he regular purse (award money for winning horses) o California breeders, owners of California-bred horses, and owners of California sallions. As such, breeders and sallion owners mainain an open ineres in he offspring of heir horses even if hey do no own hese offspring. California horse racing 2
also provides incenives o owners of California-bred horses by creaing resriced purse races in which only Cal-breds are eligible o run. Figure 1 repors he magniude of each componen of he program over ime. o pu hese figures in conex, purse awards in California during 1997 oaled $136 million and 1997 oal parimuuel handle in California was roughly $3 billion. Revenues ha suppor he breeding incenive program come from he parimuuel handle akeou. For 1998, he oal akeou for convenional wagers was 15.63%, wih 4.446% going o he Sae of California, 5.545% o he race racks, 4.646% o sakes and purses,.365% o breeder incenive awards,.33% o local governmens, and.1% o equine research. hus, possible alernaive uses of breeder incenives include qualiy enhancemen hrough invesmen in breeding research, increasing purses for open company (i.e. nonresriced purse races), increasing sae or race rack revenues, or reducing he akeou rae. he concepual analysis of breeding incenives focuses on breeder and owner awards. Consider a breeder who owns a mare of given qualiy. he breeder mus decide o breed or no, and if breed, wha sallion qualiy. Acual qualiy of he offspring is a random variable (q). One can hink of he sallion choice and he fixed mare qualiy as joinly generaing he deerminisic componen of say q. If a live foal is born, he breeder chooses o sell he foal as a yearling or keep he foal for racing. Finally, he owner chooses when o reire he horse (). 1 Denoe he discoun facor as δ, profis as π, and he probabiliy ha a horse buyer races he horse in California as φ. C(q ) is he sud fee plus he fixed cos of horse care for firs year, P(q) is he sale price of he yearling, NR() are racing revenues ne of variable coss (as a funcion of ime and qualiy), S() is he scrap value (breeding value a reiremen), b() denoes breeder awards, and o() represen owner awards. 1 Of course, some horses may never make i o he race rack. his does no modify he analysis because a he decision imes, breeders and owners choose based on expecaions of funcions of he random variable q. 3
For a given foal, he breeder sells if E π Eπ. When here is no breeding incenives Sell Race policies, his condiion is: P( q) E{ [ δ NR( )] +δ = 1 S( )} (1) Similarly, a buyer will purchase he yearling if Eπ Race price, or: P( q) E{ [ δ NR( )] +δ = 1 S( )} (2) Assuming he same expecaions and informaion, hese wo condiions imply ha a sale will ake place if and only if he sale price equals he discouned expeced ne racing reurns plus he discouned expeced scrap value. he breeder chooses o breed if Eπ 0, which implies: breed δ E{ = 1 [ δ NR( )] + δ S( )} C( q) 0 (3) Owner and breeder awards modify he decision rules. he breeder will sell if: E{ φ δ b( )} + P( q) E{ δ [ NR( ) + o( ) + b( )] + δ = 1 = 1 S( )} (4) he buyer is willing o purchase he horse if: P( q) E{ = 1 δ [ NR( ) + o( )] +δ S( )} (5) If φ=1, hen expeced breeder awards cancel from boh sides of (4), and (4) and (5) ogeher give rise o a selling environmen like in (1) and (2). For φ<1, however, sales would never ake place because boh inequaliies (4) and (5) could never hold wih posiive breeder awards. Aleraion of he breeding may also lead o hin markes for yearlings. he breeder will breed he horse if: 4
δ E{ = 1 δ [ NR( ) + o( ) + b( )] + δ S( )} C( q) 0 (6) Bu, his implies ha if expeced breeder awards are large enough, some horses will be bred ha will never be sold. o see his, consider he case where (6) holds bu E{ = 1 δ [ NR( ) + o( )] + δ S( )} 0 (7) Clearly, given (7), no buyer would pay a posiive price for he yearling. hus, breeder awards may give rise o hin yearling markes. In conras, owner awards do no disor yearling markes because expeced value of he policy is capialized fully ino he sale price of he horse. 2 Inequaliies (1)-(7) no only quesion he viabiliy of a yearling marke wih breeding incenives, bu hey also highligh he poenial for breeding sub-marginal horses. Comparing (3) o (6), even in he absence of resriced purse races and sallion awards, some horses may be bred under an incenives program ha would oherwise no be bred. his would sugges ha quaniy increases and average qualiy decreases. Wha hese inequaliies do no address is he poenial for breeding incenives o draw breeding ino he sae of California. If horses are drawn ino he sae from elsewhere, quaniy increases even more. he key unknowns are wheher ou-of-sae horses drawn ino California increases average qualiy, and if so, wheher his increase is enough o offse qualiy declines due o submarginal in-sae breeding. Alhough he magniudes of breeding incenive qualiy and quaniy effecs are empirical quesions lef unanswered, i is useful o analyze empirically how qualiy and quaniy affec horse racing demand. Demand has wo componens: race rack aendance and parimuuel handle (being aciviy), 5
he laer generaing he lion s share of revenues. Aendance on a given day is a funcion of prices, income, adverising, and race characerisics for ha day. Handle for a given race is a funcion of prices, income, aendance, and race characerisics. So he following equaions describe he demand sysem: A = A H O f ( P, P, P, I, ADV, Z ) (8) H i H O = g( P, P, I, A, Zi ) (9) where A is aendance; H is handle; indexes he day; i indexes he race; P A, P H, and P O are prices of aendance, handle, and oher goods; I is income; ADV is adverising; and Z is a vecor of race characerisics. 3 Noe ha all prices are exogenous, and he demand sysem is recursive. he daa se conains aendance, handle, and race characerisics for all races run in California during 1997 and 1998. 4 Race days are allocaed such ha on any given day here is never more han one major rack racing in Norhern California and one in Souhern California. hus, in Souhern California, i will be Del Mar, Hollywood Park, or Sana Ania. In Norhern California, he major rack will be eiher Golden Gae or Bay Meadows. Racing a sae fairs akes place beween major race rack mees and only overlaps some wih oher fair mees a any given ime. 5 On-rack aendance (ONA) is aendance a he acual live racing. Off-rack (OFFA) is oal aendance a oher race racks and saellie wagering faciliies in he same geographical region as he rack wih live racing. oal handle (REGHANDL) is he sum of on-rack and off-rack handle a racks in he same region. As an illusraive example, off-rack aendance and handle a Del Mar, which is in Souhern California, 2 I is worh noing ha Kenucky, which has he mos prosperous breeding indusry and yearling marke in he counry, has large owner awards bu very limied breeder awards. 3 Noe ha he vecor of characerisics ha affecs aendance is a summary of all of he races ha day. 4 Due o some missing aendance figures, abou 15% of he observaions are excluded from he regression analyses. 5 he fair mees ha overlap ake place in Fresno and Humbold, boh of which are geographically isolaed from oher Norhern and Souhern California racing faciliies. 6
include aendance and handle on Del Mar races a Sana Ania and Hollywood Park bu does no include aendance and handle on Del Mar races a Golden Gae or Bay Meadows. Because prices, income, and adverising largely do no vary wihin a season a a paricular race rack, demand analysis of aendance and handle admis a hedonic specificaion. Fixed effecs (i.e. race rack/season dummy variables) capure differences across race racks and years in admission prices, adverising campaigns, and incomes of he local populaions. he akeou rae beween 1997 and 1998 did no change. 6 his leaves a rich se of regressors o explain aendance and handle. PURSE is he award money ha is disribued o owners of he op finishers in a race. I is boh a policy variable and a proxy for qualiy (i.e. higher purses, ceeris paribus, arac beer horses). As an indicaor of qualiy, he coefficien on purse in aendance and handle regression should be posiive if racing fans demand qualiy. RUNNERS indicaes he number of horses in he being even. his is a measure of quaniy of horses, so he expeced coefficien is posiive if beors really do prefer more horses o less in a being even. CAL-BRED is a dummy variable for wheher he race is a resriced purse for California-bred horses only. If Cal-breds are of lower qualiy on average, I expec his variable o be negaive. SPRIN, CLAIM, DIR, MAID, SAKE, WEEKEND, and LAS are all dummy variables as well. SPRIN indicaes a race less han one mile, CLAIM indicaes a claiming race (generally a lower qualiy race), DIR indicaes a race on dir (as opposed o urf or grass), MAID sands for maiden race (a race resriced o non-winners), SAKE is a sake race (generally a high qualiy race), WEEKEND indicaes he race day is Saurday or Sunday, and LAS denoes he las race of he day. Averages of 6 echnically, here is a differen akeou rae for convenional (Win, Place, and Show) and exoic (e.g. exaca, rifeca) wagering. As such, he aggregae akeou rae is endogenous. he race-by-race daa, unforunaely, do no break down handle beween convenional and exoic wagers. o ge around his problem, one can hink of convenional and exoic wagers as differen goods wih differen prices, bu he relaive prices remain consan over he sample period. 7
RUNNERS, CAL-BRED, SPRIN, CLAIM, DIR, MAID, SAKE, and WEEKEND appear in he aendance regressions along wih a variable for number of races (RACES) and an addiional dummy, (FRIDAY). able 1 repors resuls from on and off rack aendance regressions (using ordinary leas squares). he adjused R-squared values of.72 and.81 are exremely high considering he amoun of variaion in aendance wihin race rack seasons. Posiive and significan coefficiens on AVPURSE and SSAKE as well as he negaive and significan coefficiens on SCLAIM and SMAID (in he on rack regression only) sugges ha racing fans demand qualiy. RACES is negaive in boh regressions and significan a he 10% level in he off rack aendance. hough his appears srange, i may be due o mulicollineariy involving he weekend variable and he fixed effecs for fair racing, since more races are offered on weekends a some race racks and ypically fair racing offers more evens bu is lower qualiy. he number of horses running (AVRUN) does no have a saisically significan effec on aendance. able 2 repors he OLS handle regression resuls. Again, he adjused R-square of.81 is exremely high; race and race day characerisics explain mos of he variaion in wagering aciviy. On and off rack aendance as well as number of runners in a race are all imporan deerminans of handle. he posiive and significan PURSE variable along wih he negaive and significan CAL-BRED, CLAIM, and MAID variables all highligh he imporance of qualiy in handle demand. Curiously, he SAKE variable is negaive (bu no significan). his is likely due o mulicollineariy problems wih oher variables involving qualiy (paricularly PURSE). One furher noe abou his regression is ha 8
LAS is posiive, significan, and quie large in magniude. 7 For losers on he day, his may reflec a las desperae aemp o recover losses. For winners, on he oher hand, his effec may reflec risk-aking wih ransiory income, namely he day s winnings. I also calculaed elasiciies for variables ha are significan a he 5% level. Elasiciies include boh direc and indirec effecs if he variables are significan in he aendance regressions as well. So, for insance, he elasiciy of handle wih respec o purse is: ε H Aon H = { + Purse Purse A Aoff H + Purse A handle, purse } on off Purse H (10) he purse elasiciy of handle is highly inelasic. Given ha previous sudies have found elasic demands wih respec o akeou, adding o purse size o increase handle is no sensible in isolaion. However, he effec of purse increases on aendance revenues, he abiliy o draw qualiy horses, and an increased quaniy of enrans are poenial jusificaions for his policy. In conras o he highly inelasic purse elasiciy of handle, he number of runners elasiciy of handle is only slighly inelasic. here is some evidence ha breeding incenives successfully generae quaniy, bu he ne effec on handle demand appears negaive. he average number of runners for Cal-bred races was 9.2 and 8.7 in 1997 and 1998 respecively, compared o averages for non-cal-bred races of 7.9 and 7.6. hese numbers also sugges a downward rend in number of runners indusry-wide. By applying he regression coefficiens on RUNNERS and CAL-BRED only, he handle increase from.5 exra runners in 1997 is more han offse by he decrease from being a Cal-bred race (.5*25392-17351 = -4655). Wih only.3 exra runners in 1998, he ne effec is even more negaive. 7 If only he rack could have more han one las race each day! 9
Empirical analysis of demand for horse racing highlighs he poenial radeoff beween qualiy and quaniy of horses. Convenional wisdom suggess ha, ceeris paribus, racing fans prefer higher qualiy races. his asserion is suppored by saisical evidence of higher aendance levels on days wih high grade sakes races and more parimuuel handle on beer races. Mos racing fans would also indicae ha hey prefer races wih more horses in hem, e.g. a welve horse field, ceeris paribus, is preferred o a five horse field. his asserion is also suppored by saisical evidence, hough quaniy appears o affec handle only (no aendance). A plausible explanaion is ha more horses in a field on average improve he qualiy of a race as a being even. he ne effec of breeding incenive programs Figure 1 California Breeding Incenive Awards $30,000,000 $25,000,000 $20,000,000 $15,000,000 Resriced Purses Sallion Awards Breeder Awards Owner Awards $10,000,000 $5,000,000 $0 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 is difficul o assess. If more runners are produced for a low cos, i may be worh poenial qualiy slippage. 10
able 1 Fixed Effecs Aendance Regressions On rack Variable Coefficien S. Error -saisic P-value Coefficien S. Error -saisic P-value RACES -148.99 121.55-1.23 0.221-144.54 80.54-1.80 0.073 AVPURSE 0.06 0.01 6.87 0.000 0.04 0.01 6.23 0.000 AVRUN 201.16 145.23 1.39 0.166-82.25 96.23-0.86 0.393 SDIR -860.13 1,112.52-0.77 0.440-609.02 737.16-0.83 0.409 SMAID -1,958.51 912.14-2.15 0.032 35.69 604.38 0.06 0.953 SCAL -1,208.46 1,025.16-1.18 0.239-237.19 679.27-0.35 0.727 SCLAIM -4,401.92 892.24-4.93 0.000-2,488.13 591.20-4.21 0.000 SSAKE 16,316.00 1,858.54 8.78 0.000 5,601.95 1,231.46 4.55 0.000 D97NF 6,656.91 1,880.98 3.54 0.000 9,128.70 1,246.33 7.32 0.000 D98NF 7,152.78 1,865.02 3.84 0.000 8,848.07 1,235.75 7.16 0.000 D97DM 13,327.00 1,864.45 7.15 0.000 16,741.00 1,235.38 13.55 0.000 D98DM 12,920.00 1,839.13 7.03 0.000 15,758.00 1,218.60 12.93 0.000 D97FP 5,188.43 2,134.74 2.43 0.015 10,729.00 1,414.47 7.59 0.000 D98FP 4,879.45 2,113.13 2.31 0.021 12,013.00 1,400.15 8.58 0.000 D97O 10,299.00 1,874.32 5.50 0.000 12,435.00 1,241.92 10.01 0.000 D98O 11,618.00 1,855.75 6.26 0.000 12,757.00 1,229.62 10.38 0.000 D97SA 10,765.00 1,845.87 5.83 0.000 14,474.00 1,223.07 11.83 0.000 D97HS 9,780.82 1,803.26 5.42 0.000 14,527.00 1,194.83 12.16 0.000 D97HF 7,020.32 1,787.66 3.93 0.000 11,540.00 1,184.50 9.74 0.000 D98HF 6,571.36 1,760.32 3.73 0.000 11,276.00 1,166.38 9.67 0.000 D97BM 6,405.06 1,807.87 3.54 0.000 7,386.79 1,197.89 6.17 0.000 D98BM 6,502.13 1,831.42 3.55 0.000 7,410.50 1,213.49 6.11 0.000 D97GG 5,575.50 1,853.13 3.01 0.003 8,154.41 1,227.88 6.64 0.000 D98GG 5,448.28 1,826.94 2.98 0.003 7,865.56 1,210.53 6.50 0.000 WEEKEND 2,035.97 293.79 6.93 0.000 2,791.91 194.67 14.34 0.000 FRIDAY 1,479.85 276.91 5.34 0.000 681.28 183.48 3.71 0.000 Observaions 1,004 1,004 Adj. R-squared 0.7155 0.8143 Mean of Dependen Variable 6,749 8,464 Noe: Consan was dropped o include all rack/season dummies. Off rack
able 2 Fixed Effecs Parimuuel Handle Regression Variable Coefficien Sd. Error -saisic P-value X-bar Elasiciy ON A 7.82 0.35 22.39 0.000 6,749.10 0.1967 OFF A 11.18 0.57 19.71 0.000 8,464.01 0.3524 PURSE 0.41 0.03 12.44 0.000 29,042.11 0.0558 RUNNERS 25,392.00 610.06 41.62 0.000 7.92 0.7495 CAL-BRED -17,351.00 3,632.43-4.78 0.000 0.11-0.0076 SPRIN 1,807.24 2,353.58 0.77 0.443 0.61 CLAIM -18,749.00 2,664.11-7.04 0.000 0.68-0.0661 DIR -4,273.18 3,665.79-1.17 0.244 0.86 MAID -10,916.00 2,432.14-4.49 0.000 0.29-0.0051 SAKE -2,849.96 5,969.29-0.48 0.633 0.05 WEEKEND 1,493.68 2,653.60 0.56 0.574 0.18 LAS 166,365.00 3,336.57 49.86 0.000 0.12 0.0732 D97NF -220,239.00 7,370.93-29.88 0.000 D98NF -214,429.00 7,205.06-29.76 0.000 D97DM 14,289.00 10,194.49 1.40 0.161 D98DM 19,809.00 9,882.62 2.00 0.045 D97FP -148,683.00 9,967.41-14.92 0.000 D98FP -132,954.00 10,289.47-12.92 0.000 D97O -60,400.00 9,823.20-6.15 0.000 D98O -74,951.00 9,503.68-7.89 0.000 D97SA 1,050.10 8,968.26 0.12 0.907 D97HS -28,702.00 9,017.46-3.18 0.002 D97HF -60,906.00 8,825.40-6.90 0.000 D98HF -57,374.00 9,100.91-6.30 0.000 D97BM -168,622.00 6,579.76-25.63 0.000 D98BM -165,952.00 6,453.86-25.71 0.000 D97GG -166,569.00 6,895.51-24.16 0.000 D98GG -166,021.00 6,764.43-24.54 0.000 Observaions 8,560 Adj. R-squared 0.8121 Mean of Dependen Variable 268,430 Noes: Consan was dropped o include all rack/season dummies. Mean of ONA and OFFA differ from aendance regression dependen variable means because here hey are race weighed averages. Elasiciies are calculaed if he variable is significan a he 5% level in he handle regression. Elasiciies include boh direc and indirec effecs if he variables are significan in he aendance regressions.
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