Transcript
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
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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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
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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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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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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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
7
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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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
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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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
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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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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Figure 2. Cherokee County employment – All Industries
Figure 3. Cherokee County employment – Leisure & Hospitality
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Figure 5. Cherokee County average weekly wages – Leisure & Hospitality
Figure 4. Cherokee County average weekly wages – All Industries
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Figure 6. Cherokee County number of establishments – All Industries
Figure 7. Cherokee County number of establishments – Leisure & Hospitality
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Figure 8. Crawford County employment – All Industries
Figure 9. Crawford County employment – Leisure & Hospitality
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Figure 10. Crawford County average weekly wages – All Industries
Figure 11. Crawford County average weekly wages – Leisure & Hospitality
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Figure 12. Crawford County number of establishments – All Industries
Figure 13. Crawford County number of establishments – Leisure & Hospitality
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
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
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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Missouri model
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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
Figure 2. Map of proposed Kansas casino sites and competing casinos in NE Oklahoma
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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
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
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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Competitive effects
•
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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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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%
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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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
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
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
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
Questions?
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