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Farm Team Shuffle: The Effects of Major League Affiliations in

Minor League Baseball

Nola Agha, University of San FranciscoJoe Cobbs, Northern Kentucky University

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Minor League Baseball (MiLB)

• 19 leagues• 6-16 teams per

league• Attendance gains

24 of last 29 seasons

• 40+ million attendees (2010)

• Shifting geographic trend in parent affiliation

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Club Affiliation Decision

• Major League Administratorso Cannibalize attendance?o Player/Administrator travel timeo Administrative costso Managerial oversight/ownership

• Minor League Administratorso Attendance +/-o Fan identificationo Brand association/equity

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Research Questions

1. Does geographical proximity benefit the minor league team?

2. Do quality features of the major league club benefit the minor league team?

3. Does switching to a better affiliation benefit the minor league team?

4. Is there a switchingcost?

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Demand Theory in Baseball

• Attendance = f[price, quality, substitutes, income]

• MiLB: classifications not homogeneous(Agha, 2012; Branvold, Pan, & Gabert, 1997; Gitter & Rhoads, 2010)

o Win percentage non-significant at AAA; significant at AA

• New MiLB stadium• MLB team within 100 miles (-)• New MLB stadium

H1

H2

H3

H4

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Organizational Alliance Theory

• Smaller firms align with larger firms to establish marketplace legitimacy (Sarkar, Echambadi, & Harrison, 2001)

o Alliance strategy entails switching costs

• Alliance partner characteristics(Castellucci & Ertug, 2010; Dyer & Singh, 1998)

o Status: enhanced endorsement (Sarkar et al., 2001)

o Proximity: knowledge sharing, relational assets (Dyer & Singh, 1998)

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Alliance-based Hypothesis

• Alliance partner characteristics o Geographic distance (miles)o Status of MLB affiliate

o Market sizeo Popularity (attendance)o Win percentage H6c

H6b

H6a

H5

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Switching-based Hypothesis

• Switching costo Negative effect on MiLB team demand

• Attenuated by new partner characteristicso Geographic distance (miles)o Status of MLB affiliate

o Market sizeo Popularity (attendance)o Win percentage

H7

H9c

H9b

H9a

H8

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Data

• 15 years: 1992-2006o AAA: American Association, International

League, Pacific Coast Leagueo AA: Eastern League, Southern League,

Texas League

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Model

yjt = β1Xjt + β2Zjt + υj + εjt

yjt = natural log annual attendanceβ1 = vector of demand parametersXjt = vector of demand variablesβ2 = vector of MLB club parametersZjt = vector of MLB club variablesυj = PMSA specific fixed-effect εjt = random disturbance

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Results• Analysis 1: Do quality and distance to

alliance partner matter? (yes)

Variable AAA AA

H1. 36% Win percent 0.216 ***0.364

H2. 24% New MiLB Stadium ***0.215 0.075

H3. -53%, -13% Number of MLB in PMSA ***-0.749 **-0.141

H4. 6% New MLB Stadium **0.059 0.029

Strike 94/95 0.006 0.057

H5. 0.024% Affiliate Distance -0.00023258 ***0.0002

H5. -0.00001% Affiliate Distance Squared 0.0000001 ***-0.0000001

H6a. -0.000001% Affiliate Population **-0.00000001 0.00000001

H6b. 43% Affiliate Win Percent **0.434 0.343

H6c. -0.00001% Affiliate Attendance **-0.00000005 -0.00000002

***p<0.01, **p<0.05

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Results• Analysis 2: Does switching to a better or

closer affiliate matter? (no) • Is there a switching cost? (yes)

Variable AAA AAH1. 42% Win percent 0.280 ***0.424H2. 22% New MiLB Stadium ***0.200 0.081H3. -51%, -13% Number of MLB in PMSA ***-0.711 **-0.134H4. 6% New MLB Stadium **0.060 0.031

Strike 94/95 0.035 **0.075H7. -25% Affiliate Change Dummy -0.024 ***-0.293H8. Change to Closer Affiliate -0.129 0.059

H9a. Change to Affiliate with Higher Population -0.096 0.027

H9b.Change to Affiliate with Higher Win Percent 0.002 0.132

H9c.Change to Affiliate with Higher Attendance -0.144 0.134

***p<0.01, **p<0.05

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Discussion

• Consistent with demand theoryo AAA fans more concerned with MLB

affiliate successo MLB is substitute for MiLB

• Alliance implicationso AAA status as decision criteria for

affiliate decisionso AA switching costs, proximity as

decision criteria for affiliate decisions