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Forecasting County-Level Economic Impacts and Competitive Impacts Among Casinos Douglas M. Walker, Ph.D. Casinonomics Consulting, LLC, Presentation to the Kansas Lottery Gaming Facility Review Board Topeka, June 10, 2015

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Page 1: Casinonomics Consulting (Doug Walker)

Forecasting County-Level Economic Impacts and Competitive Impacts Among Casinos Douglas M. Walker, Ph.D. Casinonomics Consulting, LLC,

Presentation to the Kansas Lottery Gaming Facility Review Board

Topeka, June 10, 2015

Page 2: Casinonomics Consulting (Doug Walker)

Background

• Professor of Economics, College of Charleston, SC

• Two books and over 50 academic articles/chapters published since 1996

• Focus on economic and social impacts of casinos & gambling

• Consultant for…

• Florida (employment & wage study, 2013)

• Iowa,

• Maryland,

• Massachusetts,

• Missouri (paper on casino competition 2014)

• Various industry groups

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Page 3: Casinonomics Consulting (Doug Walker)

Research strategy

• Review relevant academic evidence

• Adapt estimates of casino impacts to SE Kansas

• Discuss limitations

• Results can be used in consideration of other findings

• Consultants’ projections using different approaches

• Casinos’ own projections

• Two reports on Kansas submitted:

“Forecasting county-level economic impacts

of a new casino in southeast Kansas”

“Estimating the competitive impacts of casinos in northeast Oklahoma on the casinos proposed for southeast Kansas” 3

Page 4: Casinonomics Consulting (Doug Walker)

1- COUNTY-LEVEL ECONOMIC IMPACTS

• Academic literature suggests positive impacts from casinos

• Employment & wages

• State-level per capita income

• Tax revenues

• Substitution effect or “cannibalization”

• Common concern among casino critics, dating back to early 1990s

• Casinos as “factories” or “restaurants”

• Little rigorous or anecdotal evidence

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Page 5: Casinonomics Consulting (Doug Walker)

Cotti (2008) study

• Review of literature indicates Cotti provides the most comprehensive study of economic impacts

• Regression analysis, includes demographic variables, etc.

• 600,000 data points

• Data on all 3,000+ U.S. counties (excludes NV & NJ)

• 1990-96 (28 quarters)

• Variables: employment, average weekly wages

• Sectors: “All Industries” & “Leisure & Hospitality”

• From NAICS, North American Industrial Coding System

• Results can be interpreted as “average effects”

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Page 6: Casinonomics Consulting (Doug Walker)

Cotti results

• Isolates impacts of “casino existence” within a county

• Adjusts for county population

• Cherokee and Crawford are in “middle third” category

• Population between 15,000 and 46,000

Sector Employment Effect Earnings Effect

All Industries + 2.4% + 0.1%

Entertainment (NAICS 71) +22.5% + 7.7%

Hospitality (NAICS 72) + 2.9% + 2.1%

Weighted Average of Entertainment and Hospitality sectors

+ 6.3% + 3.1%

Table 1: Estimated middle third population county effects of casinos

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Page 7: Casinonomics Consulting (Doug Walker)

Limitations

• A comprehensive study, but…

• Data age (1990-96)

• Casino existence variable doesn’t control for:

• number of casinos in county

• casino size or revenues

• location within county

• customer base/location

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Page 8: Casinonomics Consulting (Doug Walker)

Peer county data

• Cotti results supplemented with peer county data

• Provides more recent experience/data

• 9 peer counties chosen similar to Cherokee & Crawford

• In “middle third” population category

• Casino opened between 2001 and 2011

• Excluded if casino opened during 2007-09 recession

• [Peer table next slide]

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Page 9: Casinonomics Consulting (Doug Walker)

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County, State 2013 County Population

Casino Location (City)

Casino Name

Opening Date

# Slot Machines

# Table Games

Casino Sq. Ft.

Proposed KS Casinos

Cherokee, KS 20,978 Galena Castle Rock n/a 1,400 35 65,000

Crawford, KS 39,278 Frontenac Camptown n/a 747 16 31,236

Crawford, KS 39,278 Pittsburg Kansas Crossing n/a 625 16 20,000

Peer Casinos

Orange County, IN

19,773 French Lick French Lick Nov. 2006 1,000 37 42,000

Black Hawk, IA 132,546 Waterloo Isle Casino June 2007 1,009 27 43,142

Lyon, IA 11,712 Riverside Riverside Aug. 2006 1,058 46 58,000

Palo Alto, IA 9,185 Emmettsburg Wild Rose May 2006 520 17 16,800

Worth, IA 7,541 Northwood Diamond Jo Apr. 2006 974 29 38,700

Ford, KS 34,819 Dodge City Boot Hill Dec. 2009 700 23 20,000‡

Sumner, KS 23,591 Mulvane Kansas Star Dec. 2011 1,825 55 49,746

Cattaraugus, NY 78,892 Salamanca Seneca Allegany Mar. 2007 2,000 33 68,300

Sequoyah, OK 41,218 Sallisaw Cherokee Sallisaw

June 2006 250 0 27,500

Table 2. Proposed Kansas casinos and peer casino counties

Page 10: Casinonomics Consulting (Doug Walker)

Data collection

• Counties

• Cherokee, Crawford – 2005.1 - 2014.2

• Peers (9 counties) – 1 yr before casino opening, 1 yr after

• Variables

• Number employed

• Average weekly wages

• Number of establishments • Cotti didn’t test this; will allow us to evaluate “cannibalization”

• Sectors

• All Industries

• Leisure & Hospitality • Arts, entertainment, and recreation (NAICS 71)

• Includes casinos

• Accommodation and food service (NAICS 72) • Includes hotels

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Page 11: Casinonomics Consulting (Doug Walker)

Estimated

Casino Effect

Employment Average Weekly Wages

No. of Establishments

All Industries

Leisure & Hospitality

All Industries

Leisure & Hospitality

All Industries

Leisure & Hospitality

Peer Counties

+17.3%

+145.2%

+6.7%

+87.9%

+1.7%

+7.6%

Cotti Study

+2.4%

+6.3%

+0.1%

+3.1%

n/a

n/a

Forecast

Effect

+9.9%

+75.8%

+3.4%

+45.5%

+1.7%

+7.6%

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Table 3. Estimated casino effects on economic variables

Page 12: Casinonomics Consulting (Doug Walker)

County-level forecasts

• Data shown from 2005.1 through 2016.2

• Historical data end 2014.3

• Casino assumed to open 2014.4

• Casino effect

• One-time effect

• Forecast shown from 2014.4 through 2016.2

• Based on trend from 2010-2014

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Page 13: Casinonomics Consulting (Doug Walker)

Percent vs absolute changes

• Forecasts assume same percent effect in both counties

• E.g., 9.9% employment effect in “All Industries”

• [From Table 3, slide 11]

• Absolute changes will vary across counties by their size

• Cherokee population = 20,100

• Crawford population = 39,300

• So predicted casino effect (absolute) in Crawford is larger

• Assumes similar casino size

• Higher population, more economic activity, bigger effect

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Page 14: Casinonomics Consulting (Doug Walker)

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Figure 2. Cherokee County employment – All Industries

Figure 3. Cherokee County employment – Leisure & Hospitality

Page 15: Casinonomics Consulting (Doug Walker)

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Figure 5. Cherokee County average weekly wages – Leisure & Hospitality

Figure 4. Cherokee County average weekly wages – All Industries

Page 16: Casinonomics Consulting (Doug Walker)

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Figure 6. Cherokee County number of establishments – All Industries

Figure 7. Cherokee County number of establishments – Leisure & Hospitality

Page 17: Casinonomics Consulting (Doug Walker)

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Figure 8. Crawford County employment – All Industries

Figure 9. Crawford County employment – Leisure & Hospitality

Page 18: Casinonomics Consulting (Doug Walker)

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Figure 10. Crawford County average weekly wages – All Industries

Figure 11. Crawford County average weekly wages – Leisure & Hospitality

Page 19: Casinonomics Consulting (Doug Walker)

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Figure 12. Crawford County number of establishments – All Industries

Figure 13. Crawford County number of establishments – Leisure & Hospitality

Page 20: Casinonomics Consulting (Doug Walker)

County

Employment Average Weekly Wages

No. of Establishments

All Industries

Leisure & Hospitality

All Industries Leisure & Hospitality

All Industries

Leisure & Hospitality

Cherokee

437

307

$24

$92

6

3

Crawford

1,247

1,461

$20

$92

15

7

20

Table 4. Forecasted casino effects, 2016

Page 21: Casinonomics Consulting (Doug Walker)

Conclusion – economic impacts

• Average county casino effect, using both peer and Cotti evidence:

• + 9.9% employment

• + 3.4% average weekly wags

• + 1.7% number of establishments

• Likely to be larger absolute effect in Crawford, given larger population and more economic activity

• But larger size of Castle Rock could offset this

• “Cannibalization” not expected overall

• + number of establishments

• But, change in empl. in Leisure & Hospitality > All Industries

• Likely substitution of jobs across industries 21

Page 22: Casinonomics Consulting (Doug Walker)

2- COMPETITIVE IMPACTS

• Literature on relationships among gambling sectors – aggregate level

• Casinos harm lotteries

• Casinos in neighboring states and within a state harm each other

• Limited evidence on property-level competitive effects

• This study examines likely competitive impacts of NE Oklahoma casinos on proposed SE Kansas casinos

• Regional competition could have a significant impact on each casino’s revenues

• Utilizes a model of competition among Missouri casinos

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Page 23: Casinonomics Consulting (Doug Walker)

Missouri model

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Page 24: Casinonomics Consulting (Doug Walker)

Regional market

• SE Kansas gaming zone faces no competition within 100 miles in KS, MO, or AR

• In NE Oklahoma there are 68 casinos

• Downstream Casino is the largest

• This study focuses on 14 within 25 miles of the state line

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Figure 1. Existing casinos in NE Oklahoma, spring 2015

Page 25: Casinonomics Consulting (Doug Walker)

Figure 2. Map of proposed Kansas casino sites and competing casinos in NE Oklahoma

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Page 26: Casinonomics Consulting (Doug Walker)

Map

#

Casino

Street Address

City

Square

Footage

Machine

Games

Table

Games

Dist.

from

Castle

Rock

Dist. from

Kansas

Crossing

Dist. from

Camptown

1 Downstream

Casino 69300 East Nee Rd. Quapaw 70,000 2,000 36 2.0 30.0 39.6

2 Eastern Shawnee

Travel Ctr 69721 East 100 Rd. Wyandotte 1,000^ 34 0 17.2 45.2 54.7

3 Bordertown Outpost

Casino 69701 East 100 Rd. Wyandotte 3,000 500 0 17.2 45.2 54.7

4 Bordertown Casino

and Arena 129 West Oneida St. Wyandotte 73,000 500 0 18.3 46.3 55.8

5 Indigo Sky Casino 70220 East Hwy 60 Wyandotte 45,000 1,270 6 19.6 47.6 57.1

6 High Winds Casino 61475 East 100 Rd. Miami 35,000 500 0 18.3 35.5 45.0

7 Quapaw Casino 58100 East 64th Rd. Miami 27,000 500 4 17.4 34.6 44.1

8 Buffalo Run Casino 1000 Buffalo Run Blvd. Miami 70,000 800 11 20.5 36.7 46.2

9 Lucky Turtle Casino 64499 East Hwy 60 Wyandotte 3,000 111 0 25.6 43.0 52.5

10 Wyandotte Nation

Casino 100 Jackpot Place Wyandotte 30,000 850 6 25.6 42.8 52.3

11 Prairie Moon Casino 202 South 8 Tribes Trail Miami 5,500^ 120 0 18.8 37.9 47.4

12 Prairie Sun Casino 3411 P St. NW Miami 11,000 252 0 19.9 37.1 46.6

13 The Stables Casino 530 H St. SE Miami 25,000 500 0 19.8 39.2 48.7

14 Grand Lake Casino 24701 S. 655th Rd. Grove 45,000 770 8 35.2 56.3 65.8

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Table 1. Oklahoma casinos’ data and driving distances to proposed Kansas casino sites

Page 27: Casinonomics Consulting (Doug Walker)

Theories of competition

• Location and competition

• Hotelling’s (1929) theory on distance and pricing

• People prefer the closest casino, everything else equal

• KS table games may attract patrons; KS novelty effect in short term

• Market “saturation”

• Small/no increase in regional revenue when supply increases

• Concern may be behind regional model (used in KS, MA, OK)

• But KS should only be concerned with KS, not regional, saturation

• KS wins, OK loses

• Agglomeration benefit

• Clustered casinos offer wider array of amenities that might stimulate demand more than same casinos in isolated locations

• Benefit would apply only to Castle Rock 27

Page 28: Casinonomics Consulting (Doug Walker)

Empirical model

• 2014 paper in Growth and Change

• Analyzed how revenue at 9 MO casinos were affected by competition

• 1997-2010

• Casinos open during entire sample period

• Estimated impacts from all casinos within 100 miles

• Includes some casinos in KS, IA, and IL

• Analyzed competing casinos that…

• opened

• closed

• changed size or proximity

• Controlled for demographic variables

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Page 29: Casinonomics Consulting (Doug Walker)

Competitive effects

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Page 30: Casinonomics Consulting (Doug Walker)

Changes in competition

• Examples using hypothetical casinos: Hollywood and its competitor Argosy

• Calculate DSCHollywood

• Initially Argosy has 500 machine games, and is located 20 miles from Hollywood: DSCmachines = 500/20, or 25

• 1 Argosy increases machine games to 550

• New DSC = 550/20, or 27.5

• A 10% increase from 25

• 2 Argosy moves down-river to be only 18.2 miles away

• New DSC = 500/18.2, or 27.5

• A 10% increase from 25

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Page 31: Casinonomics Consulting (Doug Walker)

Estimated effects from Missouri

• Hypothetical Hollywood Casino, and its competitor, the Argosy

• Results on Sq Ft and Machines are intuitive

• Table game result is interpreted as possible agglomeration

• Casinos with more tables may have more poker, non-gaming amenities, which might promote complementarity

• Or idiosyncrasies of Missouri market 31

Model Change by Argosy Effect on Hollywood

1 10% increase in DSC – Sq Ft Revenues decrease 4.5%

2 10% increase in DSC – Machines Revenues decrease 5.7%

10% increase in DSC – Tables Revenues increase 1.7%

Page 32: Casinonomics Consulting (Doug Walker)

Estimates for SE Kansas casinos

• Estimated effects in “% of revenues”

• Compared to if that individual casino didn’t exist

• Best interpretation is in comparing casinos’ relative impacts, rather than a particular $ amount

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Page 33: Casinonomics Consulting (Doug Walker)

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Casino

City

Square

Footage*

Impact on

Castle Rock

Impact on

Kansas

Crossing

Impact on

Camptown

Downstream Casino Quapaw 70,000 -48.4% -10.4% -9.8%

Eastern Shawnee Travel Ctr Wyandotte 1,000 -0.1% -0.1% -0.1%

Bordertown Outpost Casino Wyandotte 3,000 -0.2% -0.3% -0.3%

Bordertown Casino and Arena Wyandotte 73,000 -3.5% -6.8% -7.0%

Indigo Sky Casino Wyandotte 45,000 -2.0% -3.9% -4.1%

High Winds Casino Miami 35,000 -1.7% -4.1% -4.1%

Quapaw Casino Miami 27,000 -1.3% -3.2% -3.2%

Buffalo Run Casino Miami 70,000 -3.0% -8.3% -8.3%

Lucky Turtle Casino Wyandotte 3,000 -0.1% -0.3% -0.3%

Wyandotte Nation Casino Wyandotte 30,000 -1.0% -2.9% -3.0%

Prairie Moon Casino Miami 5,500 -0.3% -0.6% -0.6%

Prairie Sun Casino Miami 11,000 -0.5% -1.2% -1.2%

The Stables Casino Miami 25,000 -1.1% -2.6% -2.6%

Grand Lake Casino Grove 45,000 -1.1% -3.3% -3.5%

Table 3. Estimated NE Oklahoma casino impacts on SE Kansas casino revenues: Square footage model

Page 34: Casinonomics Consulting (Doug Walker)

Casino

City

Machine

Games

Table

Games*

Impact on

Castle Rock

Impact on

Kansas

Crossing

Impact on

Camptown

Downstream Casino Quapaw 2,000 36 -37.0% -4.7% -4.2%

Eastern Shawnee Travel Ctr Wyandotte 34 0 -0.1% -0.2% -0.2%

Bordertown Outpost Casino Wyandotte 500 0 -1.3% -2.9% -3.0%

Bordertown Casino and Arena Wyandotte 500 0 -1.2% -2.8% -2.9%

Indigo Sky Casino Wyandotte 1,270 6 -2.6% -6.1% -6.3%

High Winds Casino Miami 500 0 -1.2% -3.7% -3.6%

Quapaw Casino Miami 500 4 -1.1% -2.8% -2.7%

Buffalo Run Casino Miami 800 11 -1.2% -3.1% -3.0%

Lucky Turtle Casino Wyandotte 111 0 -0.2% -0.7% -0.7%

Wyandotte Nation Casino Wyandotte 850 6 -1.2% -4.0% -4.1%

Prairie Moon Casino Miami 120 0 -0.3% -0.8% -0.8%

Prairie Sun Casino Miami 252 0 -0.6% -1.7% -1.7%

The Stables Casino Miami 500 0 -1.1% -3.3% -3.4%

Grand Lake Casino Grove 770 8 -0.8% -2.3% -2.5%

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Table 4. Estimated NE Oklahoma casino impacts on SE Kansas casino revenues: Machine, table game count model

Page 35: Casinonomics Consulting (Doug Walker)

Limitations

• Does not provide revenue estimates, just an indication of relative threat from competing casinos

• At short differences, changes in DSC are dramatic

• Suggests Downstream is the most important competition

• Hotelling theory may suggest Downstream is the only competition

• Does not account for KS advantage of offering traditional table games

• Effect likely to be small

• SE Kansas is different from Missouri, which has two large markets

• Still, the model accounts for distance, size, demographics 35

Page 36: Casinonomics Consulting (Doug Walker)

Conclusion – Competitive impacts

• Downstream Casino poses greatest threat among casinos in NE Oklahoma

• It’s a much greater threat to Castle Rock than Camptown or Kansas Crossing

• Potential agglomeration benefits

• Minor relative to negative competitive impacts

• Apply only to Castle Rock

• Evidence here is consistent with wider divergence among among consultants’ and Castle Rock’s own projections

• Indicates greater competitive threat to Castle Rock

• May indicate Castle Rock underestimates its competition or otherwise overestimates revenue 36

Page 37: Casinonomics Consulting (Doug Walker)

Questions?

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