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The Impact of Local Labor Market Conditions on the Demand for Education: Evidence from
Indian Casinos
Bill Evans and WooYoung Kim
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The research in this presentation was conducted while the authors were Special Sworn Status researchers of the U.S. Census Bureau at the Center for Economic Studies. Research results and conclusions expressed are those of the authors and do not necessarily reflect the views of the Census Bureau. These results has been screened to insure that no confidential data are revealed.
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Costs of College, 2003/4
• Direct: Annual tuition and fees, 4 year schools– Public: $4,650– Private: $18,950
• Indirect: forgone earnings– Full-time/full year workers, aged 18-21, earned
$18,144 – 50 weeks/40 hours per week, $9/hour
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Puzzle
• Foregone earnings are such a large cost of education
• Yet it is difficult to find evidence about how changing opportunities impact education
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• Number of papers that examine impact of local labor markets on attainment
• Many studies– Define “local” as the state level– Use unemployment rate as the measure of the local
market• At best, weak evidence that local labor market
conditions matter
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• Manski and Wise (1983) – “weakly support the presumption that there is
some interaction between local labor market opportunities and the continuation of schooling…”
• Card and Lemieux (2000)– coefficient on the local unemployment rate
variable was typically small and routinely statistically insignificant.
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However….
• Once local is defined at the sub-state level, results are more pronounced
• County-level papers– Rivkin (1995) HSB– Rees and Mocan (1997)
• Similar to results in Hoynes (2000) who examines local economy and welfare take-up
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Black, McKinnish, Sanders, 2005
• Coal boom and bust in KY and PA• Coal mining requires low skilled worker• When boom
– Wages increased more for high school dropouts than for high school graduates
– High school enrollment decreased
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This paper
• Use the rise of Indian gaming as shock to local markets to examine impact on demand for educ.
• Since late 1980s, over 400 Indian casinos have opened– ½ of people in tribes belong to one w/a casino– Generate $26 billion in net revenues today– Have lowered unemployment, increased earnings, lowered
poverty– Especially true for low skilled workers– Most “local” operations
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How casinos impact education?
• Positive:– Increase family income– Improve quality K-12 schools– Most tribes now heavily subsidize educ.– College scholarships
• Negative:– Increase employment of low skilled workers which
the benefit of more education
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Why this is a good quasi-experiment?
• Casinos permanent shocks• Most casinos are in rural areas so impacted
population is easy to define• Casinos are prohibited in some places so you
have a natural control group– Rise of gaming happened in the go-go 1990s– Need to control for general economic trends
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Preview of results
• Among people 25-40, find increases in employment and wages, esp. for – low skilled workers– Indians (compared to non-Indians)
• Most of the new jobs were in entertainment industry/local government
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• Among teens– Lower HS completion rate– Lower college entrance rate– Only among Indians
• Among young adults (20-24)– Similar results
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Implications
• High pockets of poverty in the US• Many of the residents have low skill• Thought that housing/job mismatch leads to
such concentration• One proposed reform is to introduce
employment into these areas• If employment growth is in low skilled sector,
may discourage human capital investment
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Some facts about American Indians
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Couple of Facts about Indians
• Indians in US– 2.5 million as Indian only– 1.6 million Indian & some other race
• ½ live on Federally recognized Reservations• Of residents on reservations, ½ are non-Indians• 567 tribes, about 350 in lower 48• Most tribes are small
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• Largest number of Indians live in Oklahoma where there are no Reservations
• Largest reservation is the Navajo Nation– Three states (UT, AZ, NM)– 27,000 sq miles– Larger than 10 states in area– 300K people– DOES NOT Have a casino
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Characteristics of AA/AN 1990 Census
Variable US AA/ANAA/AN on
trust land
Blacks in rural
areas
Med HH inc. $30,056 $20,025 $12,459 $11,642
% in poverty 10.0% 20.9% 47.3% 42.6%
% unemploy. 6.3% 9.3% 25.6% 14.2%
%< HS degree 24.5% 34.5% 46.2% 56.4%
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Characteristics of AA/AN, 1990 Census
HouseholdLacks: US AA/AN
AA/ANOn trust
landBlacks in
rural areas
Plumbing 1.1 6.0 20.2 6.6
Complete kitchens
1.1 5.4 17.5 4.3
A vehicle 11.5 17.1 22.4 15.4
A phone 5.2 23.2 53.4 27.8
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How did we get so many casinos?
Because of state excise taxes on cigarettes
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States role on reservations• Indian tribes are sovereign nations• States have limited authority on reservations• PL-280
– Passed in 1953– Gave 6 states complete criminal and limited civic
jurisdiction in reservations – Replaced tribal and federal courts for criminal matters on
reservations– Eventually expanded to 10 other states
• State criminal laws (but not civil) are enforceable on reservations
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Cabazon Indians• 25 member tribe near Palm Springs, CA• Cigarette mail-order business and smoke shop
– Did not pay state cigarette excise tax– Profits of $3000/day
• 1980 Supreme Court decision– Sales to tribal members not taxable– Sales to non-tribal members are– Ended the smoke shop
• Which lead to…….
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Cabazons (continued)
• Cabazons opened a “card room”
• Card rooms legal in CA but illegal in the county where the Cabazons were located
• Series of cases which lead to a Supreme Court decision
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California v. Cabazon and Morongo Bands of Mission Indians (1987)
• Decided in favor of Indians• Reasoning similar to earlier Seminole case
about bingo– CA allowed card rooms – subject to local laws– The law was civil rather than criminal in nature– Tribes not subject to state/local civil laws
• Tribes retain “attributes of sovereignty” over members and territory that is only subordinate to the US
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Cabazon Decision (continued)
• Impact of Cabazon decision– If states allow gaming, Tribes can engage in the
activity free of state regulation– Only Federal government can expressly ban
gaming on reservation• Throws the issue back to the Feds
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Cabazon Decision (continued)
• States wanted the Feds to ban gaming
• NJ/AC lobbied heavily for a Federal ban
• Tribes wanted no federal action – thinking the Cabazon decision gave them the most latitude
• Which lead to…………
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Indian Regulatory Gaming Act (IGRA)
• Swiftly passed in 1988• Formed the Nat. Indian Gaming Commission
(NIGC)• Established 3 classes of games
– Class I: social games; ceremonial; small prizes.– Class II: bingo– Class III: casinos, pari-mutuel, lotteries;
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IGRA (continued)
• Class I -- Essentially unregulated• Class II
– Must be run by tribes– Regulated by NIGC
• Class III– Legal in states that allow casino-style gaming– Must agree to scope of operation with state via
“compact”
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IGRA (continued)
• Nobody liked the IGRA– Tribes saw as restrictive– States realized they now made the tough decisions– NV/Atlantic City interests thought they would be
hurt• Each state dealt with Indian gaming differently• Courts have broadly interpreted law to allow
many types of gaming
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Indian Casinos Today
• 240 tribes run 370 class III casinos
• Generate $26 billion in net revenues
• Half of Indians in service areas were in tribes with casinos
• Dramatic increase in expenditures on gaming
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Rise in Native American Gaming Operations
020406080
100120140160180200
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
Tot
al N
umbe
r of
Cas
inos
O
pen
0%
10%
20%
30%
40%
50%
60%
Perc
enta
ge o
f Pop
ulat
ion
Ope
ratin
g C
asin
o
Total casinos open Percentage of 1989 population
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Legal Gaming, 1982-2003(2003$)
1982 2003Industry Bil. $ Mkt. shr Bil. $ Mkt. shr.Nevada/AC $8.0 40.3% $15.1 20.7%Lotteries $4.2 20.8% $19.9 27.3%Parimutual $4.3 21.6% $3.8 5.2%Ind. gaming ----- ----- $16.8 23.0%Riverboats ----- ----- $10.2 14.0%Other $3.4 17.2% $7.1 12.7%Total $19.9 $72.9
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What is a “median” Indian casino?
• 450 slot machines• Small number of table games• 800 member tribe• $10 - $25 million in net revenues• 500K within 50 miles of the casino• Not a “destination”
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Gold Country Casino, Oroville CA(160 slot machines)
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Ute Mountain Casino – CO(500 slots)
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Camel Rock Casino, Santa Fe(675 slots)
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Grand Casino Mille Lac, MN(1,500 slots)
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Soaring Eagle, Mt Pleasant, MI(4,800 Slots)
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Foxwoods, CT(5,700 slots)
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Data
• Restricted use long-form data from 1990/200 census
• Sent to 1/6 households
• Detailed demographic/housing/economic data about household and each individual
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Public use versions
• 1% and 5% PUMS – Does not identify areas smaller than PUMA– We cannot place people on or near reservations
• Summary files– Provide aggregate data at detailed level of
aggregation (track, city, reservation, county, congressional district)
– But does not allow statistics across detailed subgroups (like Indian vs. Non-Indian)
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Restricted use data
• Full 1/6 sample• Detailed geographic data
– Allows us to place people on or near reservations– Allows us to examine important heterogeneity
across groups• Our sample
– People from 265 reservations– 142 with casinos by 1999– About 103K people aged 35-40
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Two data sets
• What groups are teens looking at to obtain data bout their future?– Assume looking at people slightly older, 25-40
year olds– Has the casino changed job prospects for this
group?• The teens themselves
– Are they responding to the economic opportunities of the older group
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Population Counts, 25-40
With a CasinoBy 1999
Without a CasinoBy 1999
1990 2000 1990 2000
Indians 12,073(37.9%)
14,867(41.6%)
10,230(59.6%)
12,699(66.1%)
Non-Indians
19,775(62.1%)
20,896(58.4%)
6,925(40.4%)
6,499(33.9%)
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Econometric Model
• Compare growth in economic outcomes for those tribes with and without a casino
• Yijt =Xijtβ + Casinoj*Y2000t*α + vj + ut + eijt
• Control for – Demographics (age, race/ethnicity, sex)– Tribe and Year effects– Casino =1 if a tribe opens a casinos before 1999
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• Arbitrary correlation in errors within tribe/year• Personal weight used• Majority of casinos opened during 1990s, but
some (17 casinos) opened as early as 1988/1989– Casinos opened in the 1990s: data before and after– Casinos opened in the late 1980s: still ‘treated’ because the
casinos have grown in size
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Basic Model
• Called difference in difference
• Compare outcomes on reservation before and after casinos
• Compare to changes in reservations without casinos
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49time
Y (Unemployment)
tb ta
Y1
Y2
Yb
Ya
True effect = Y1-Y2
Estimated effect = Yb-Ya
t1
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• Intervention occurs at time period t1
• True effect of law– Y1 – Y2
• Only have data at tb and ta– If using time series, estimate Yb – Ya
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51time
Y
t1tb ta
Yb
Ya
treatment
control
Yd
Yc
Treatment effect=(Yb-Ya) – (Yd-Yc)
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Difference in Difference
BeforeChange
AfterChange Difference
Group 1(Treatment)
Mt1 Mt2 ΔMt =Mt2 – Mt1
Group 2(Control)
Mc1 Mc2 ΔMc =Mc2 – Mc1
Difference ΔΔMΔMt – ΔMc
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Key Assumption
• Control group identifies the time path of outcomes that would have happened in the absence of the treatment
• In this example, unemployment would have fallen by Yd-Yc even without a casino
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54time
Y
t1tb ta
Ya
Yb
treatment
Control
Yc
Yd
Treatment effect=(Ya-Yb) – (Yc-Yd)
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Adequacy of controls?
• Run model to predict who opens a casino– Not correlated with emp/pop or poverty rates– Only correlated with size of tribe and proximity to
urban area
• Use SF3a data from 1980/1990 – Assume casinos were adopted in the 1980s– Find no correlated with trends and casino adoption
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Multiple race codes
• 2000 for the 1st time allowed multi race codes• Nationwide, # of Indians jumps considerably
– 1.9 million AA/AN in 1990– In 2000
• 2.5 million report sole race in 2000• Another 1.6 million report it in combo w/ another
• On reservations, only 3% use multiple race code
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Sample Statistics
Variable SampleWith a Casino Without a Casino
1990 2000 1990 2000
In LF 25-40 .7763 .7719 .7228 .7044
Empl. 25-40 in LF
.8685 .8918 .8355 .8399
Full Em. 25-40 in LF
.6210 .6869 .5545 .5977
Hourly Wages
25-40 FT/FY
13.69 14.14 12.69 12.76
In H.S. 15-18 .8674 .8738 .8609 .8756
H.S.G. 20-24 .7234 .7218 .6627 .7064
Col. 20-24 .3588 .3990 .2925 .3426
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Labor Force ParticipationAll
Indian<HSD .0202 (.0174)HSD .0321 (.0126)
>HSD .0273 (.0124)Non-Indian
<HSD -.0160 (.0261)HSD .0091 (.0117)
>HSD .0041 (.0118)# of Obs. 103,923R2 .1064
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Labor Force ParticipationAll Male
Indian<HSD .0202 (.0174) -.0227 (.0167)HSD .0321 (.0126) -.0014 (.0139)
>HSD .0273 (.0124) .0204 (.0151)Non-Indian
<HSD -.0160 (.0261) -.0189 (.0301)HSD .0091 (.0117) .0219 (.0145)
>HSD .0041 (.0118) .0044 (.0137)# of Obs. 103,923 50,771R2 .1064 .0980
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Labor Force ParticipationAll Male Female
Indian<HSD .0202 (.0174) -.0227 (.0167) .0664 (.0275)HSD .0321 (.0126) -.0014 (.0139) .0629 (.0189)
>HSD .0273 (.0124) .0204 (.0151) .0344 (.0158)Non-Indian
<HSD -.0160 (.0261) -.0189 (.0301) -.0073 (.0419)HSD .0091 (.0117) .0219 (.0145) -.0034 (.0171)
>HSD .0041 (.0118) .0044 (.0137) .0028 (.0183)# of Obs. 103,923 50,771 53,152R2 .1064 .0980 .0931
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Employment RateAll
Indian<HSD .0597 (.0178)HSD .0551 (.0129)
>HSD .0221 (.0106)Non-Indian
<HSD .0544 (.0154)HSD .0196 (.0132)
>HSD .0046 (.0125)# of Obs. 78,117R2 .1064
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Employment RateAll Male
Indian<HSD .0597 (.0178) .0568 (.0230)HSD .0551 (.0129) .0674 (.0175)
>HSD .0221 (.0106) .0209 (.0157)Non-Indian
<HSD .0544 (.0154) .0820 (.0191)HSD .0196 (.0132) .0229 (.0142)
>HSD .0046 (.0125) .0000 (.0165)# of Obs. 78,117 41,888R2 .1064 .1198
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Employment RateAll Male Female
Indian<HSD .0597 (.0178) .0568 (.0230) .0689 (.0258)HSD .0551 (.0129) .0674 (.0175) .0401 (.0141)
>HSD .0221 (.0106) .0209 (.0157) .0211 (.0125)Non-Indian
<HSD .0544 (.0154) .0820 (.0191) -.0091 (.0370)HSD .0196 (.0132) .0229 (.0142) .0111 (.0170)
>HSD .0046 (.0125) .0000 (.0165) .0097 (.0117)# of Obs. 78,117 41,888 36,229R2 .1064 .1198 .0971
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Full-Time/Full-Year Employment RateAll
Indian<HSD .0317 (.0174)HSD .0601 (.0163)
>HSD .0487 (.0125)Non-Indian
<HSD .0333 (.0259)HSD .0241 (.0203)
>HSD -.0097 (.0132)# of Obs. 78,117R2 .1052
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Full-Time/Full-Year Employment RateAll Male
Indian<HSD .0317 (.0174) .0319 (.0220)HSD .0601 (.0163) .0436 (.0212)
>HSD .0487 (.0125) .0015 (.0173)Non-Indian
<HSD .0333 (.0259) .0272 (.0227)HSD .0241 (.0203) .0338 (.0224)
>HSD -.0097 (.0132) -.0004 (.0200)# of Obs. 78,117 41,888R2 .1052 .1598
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Full-Time/Full-Year Employment RateAll Male Female
Indian<HSD .0317 (.0174) .0319 (.0220) .0194 (.0258)HSD .0601 (.0163) .0436 (.0212) .0736 (.0205)
>HSD .0487 (.0125) .0015 (.0173) .0860 (.0152)Non-Indian
<HSD .0333 (.0259) .0272 (.0227) .0456 (.0533)HSD .0241 (.0203) .0338 (.0224) .0075 (.0362)
>HSD -.0097 (.0132) -.0004 (.0200) -.0166 (.0215)# of Obs. 78,117 41,888 36,229R2 .1052 .1598 .0672
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Hourly Wage RateAll
Indian<HSD 1.19 (.78)HSD 1.48 (.36)
>HSD 1.33 (.43)Non-Indian
<HSD .45 (.59)HSD .47 (.37)
>HSD .19 (.73)# of Obs. 45,382R2 .1922
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Hourly Wage RateAll Male
Indian<HSD 1.19 (.78) 1.78 (.88)HSD 1.48 (.36) 1.68 (.39)
>HSD 1.33 (.43) 1.38 (.49)Non-Indian
<HSD .45 (.59) .78 (.67)HSD .47 (.37) .55 (.49)
>HSD .19 (.73) .93 (.63)# of Obs. 45,382 25,473R2 .1922 .1783
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Hourly Wage RateAll Male Female
Indian<HSD 1.19 (.78) 1.78 (.88) .51 (.87)HSD 1.48 (.36) 1.68 (.39) 1.22 (.52)
>HSD 1.33 (.43) 1.38 (.49) .98 (.40)Non-Indian
<HSD .45 (.59) .78 (.67) .02 (.87)HSD .47 (.37) .55 (.49) .36 (.37)
>HSD .19 (.73) .93 (.63) -.83 (1.07)# of Obs. 45,382 25,473 19,909R2 .1922 .1783 .1702
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Where are jobs created?
• Ran series of linear probability models for those in workforce
• Outcome is whether employed in particular industry
• Five subsamples– All– Non Indians– Indians, then males and females
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Where were Jobs created?
• Indian Males– Construction (-2.6)– Manufacturing (-3.7)– Agriculture (+1.8)– Arts, entertainment,
accom.,food (+7.8)– Other services (+0.9)
• Indian Females– Manufacturing (-3.0)– Public services (-3.8)– Transport.,warehousin
g, communication (+1.3)
– Arts, entertainment, accom.,food (+6.8)
– Other services (+1.3)
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Where were jobs created?
• Non-Indians– Construction (+1.2)– Arts, entertainment, accom. food (+2.0 for
females only)
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What about schooling
• 2 samples– 15-18– 20-40
• Allow treatment effect to vary– By age– By Indian/non-Indian
• Implicit tests of model– Should find little impact of the casino on older
people, non-Indians
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High School Enrollment RateAll
Indian 15 -.0002 (.0110)16 -.0051 (.0137)17 -.0361 (.0209)18 -.0661 (.0204)
Non-In. 15 -.0190 (.0244)16 -.0147 (.0126)17 .0195 (.0281)18 .0478 (.0358)
# of Obs. 33,315R2 .1101
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High School Enrollment RateAll Male
Indian 15
-.0002 (.0110) .0208 (.0138)
16 -.0051 (.0137) -.0032 (.0171)17 -.0361 (.0209) -.0243 (.0210)18 -.0661 (.0204) -.0476 (.0260)
Non-Ind. 15 -.0190 (.0244) -.0157 (.0264)16 -.0147 (.0126) -.0159 (.0184)17 .0195 (.0281) .0100 (.0284)18 .0478 (.0358) .0664 (.0355)
# of Obs. 33,315 17,078R2 .1101 .1186
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High School Enrollment RateAll Male Female
Indian 15 -.0002 (.0110) .0208 (.0138) -.0206 (.0161)16 -.0051 (.0137) -.0032 (.0171) -.0032 (.0208)17 -.0361 (.0209) -.0243 (.0210) -.0509 (.0299)18 -.0661 (.0204) -.0476 (.0260) -.0872 (.0259)
Non-Ind. 15 -.0190 (.0244) -.0157 (.0264) -.0222 (.0258)16 -.0147 (.0126) -.0159 (.0184) -.0136 (.0176)17 .0195 (.0281) .0100 (.0284) .0289 (.0362)18 .0478 (.0358) .0664 (.0355) .0368 (.0481)
# of Obs. 33,315 17,078 16,237R2 .1101 .1186 .1173
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High School Graduation RateAge Group Male
Indian 20-24 -.0955 (.0310)25-29 -.0396 (.0196)30-34 -.0252 (.0166)35-40 .0251 (.0189)
Non-Indian 20-24 -.0308 (.0264)25-29 -.0239 (.0371)30-34 -.0313 (.0326)35-40 -.0204 (.0261)
# of Obs. 64,656R2 .0943
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High School Graduation RateAge Group Male Female
Indian 20-24 -.0955 (.0310) -.1147 (.0202)25-29 -.0396 (.0196) -.0931 (.0194)30-34 -.0252 (.0166) .0085 (.0177)35-40 .0251 (.0189) -.0177 (.0187)
Non-Indian 20-24 -.0308 (.0264) -.0338 (.0256)25-29 -.0239 (.0371) -.0572 (.0302)30-34 -.0313 (.0326) .0079 (.0332)35-40 -.0204 (.0261) -.0404 (.0263)
# of Obs. 64,656 67,391R2 .0943 .0836
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Any College EducationAge Group Male
Indian 20-24 -.0530 (.0175)25-29 -.0277 (.0175)30-34 -.0447 (.0156)35-40 .0144 (.0188)
Non-Indian 20-24 .0339 (.0433)25-29 -.0169 (.0533)30-34 -.0736 (.0531)35-40 -.0318 (.0350)
# of Obs. 64,656R2 .0861
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Any College EducationAge Group Male Female
Indian 20-24 -.0530 (.0175) -.0876 (.0196)25-29 -.0277 (.0175) -.0636 (.0185)30-34 -.0447 (.0156) .0201 (.0189)35-40 .0144 (.0188) .0028 (.0184)
Non-Indian 20-24 .0339 (.0433) .0207 (.0444)25-29 -.0169 (.0533) -.0653 (.0456)30-34 -.0736 (.0531) .0401 (.0483)35-40 -.0318 (.0350) -.0475 (.0378)
# of Obs. 64,656 67,391R2 .0861 .0711
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Migration
• Census: Household-Based Survey– Away at school: NOT in dataset
• If high school graduates like to attend college off reservations– Understate the demand for education
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New Sample for Education
• Only Indians due to data limitation• Age: 20-29• ON the reservations 5 years prior to the Census
and OFF the reservations at the time of Census• Include Indians: on and near reservations
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Three groupsGroup # 5 years prior
to censusAt time of census
1 On On
2 Off On
3 On Off
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High School GraduationPeople on
reservations at census
Group 1,2
Male Female
20-24 -.0697(.0321)
-.1052(.0166)
25-29 -.0156(.0202)
-.0892(.0143)
# of Obs.
15,382 16,361
R2 .0558 .0619
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High School GraduationPeople on
reservations at census
People on reservations 5yrs before
censusGroup 1,2 Group 1,3
Male Female Male Female
20-24 -.0697(.0321)
-.1052(.0166)
-.0715(.0201)
-.0647(.0168)
25-29 -.0156(.0202)
-.0892(.0143)
-.0085(.0176)
-.0407(.0174)
# of Obs.
15,382 16,361 18,760 20,019
R2 .0558 .0619 .0452 .0414
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High School GraduationPeople on
reservations at census
People on reservations 5yrs before
census
People on reservations
5yrs bef. and at cen.
Group 1,2 Group 1,3 Group 1
Male Female Male Female Male Female
20-24 -.0697(.0321)
-.1052(.0166)
-.0715(.0201)
-.0647(.0168)
-.0739(.0336)
-.1074(.0178)
25-29 -.0156(.0202)
-.0892(.0143)
-.0085(.0176)
-.0407(.0174)
-.0132(.0214)
-.0953(.0148)
# of Obs.
15,382 16,361 18,760 20,019 13,606 14,345
R2 .0558 .0619 .0452 .0414 .0552 .0592
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Any College EducationPeople on
reservations at census
Group 1,2
Male Female
20-24 -.0603(.0153)
-.0969(.0189)
25-29 -.0358(.0145)
-.0759(.0182)
# of Obs.
15,382 16,361
R2 .0456 .0573
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Any College EducationPeople on
reservations at census
People on reservations 5yrs before
censusGroup 1,2 Group 1,3
Male Female Male Female
20-24 -.0603(.0153)
-.0969(.0189)
-.0450(.0149)
-.0504(.0228)
25-29 -.0358(.0145)
-.0759(.0182)
-.0091(.0166)
-.0178(.0199)
# of Obs.
15,382 16,361 18,760 20,019
R2 .0456 .0573 .0462 .0485
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Any College EducationPeople on
reservations at census
People on reservations 5yrs before
census
People on reservations
5yrs bef. and at cen.
Group 1,2 Group 1,3 Group 1
Male Female Male Female Male Female
20-24 -.0603(.0153)
-.0969(.0189)
-.0450(.0149)
-.0504(.0228)
-.0559(.0162)
-.1002.0185)
25-29 -.0358(.0145)
-.0759(.0182)
-.0091(.0166)
-.0178(.0199)
-.0324(.0156)
-.0804.0181)
# of Obs.
15,382 16,361 18,760 20,019 13,606 14,345
R2 .0456 .0573 .0462 .0485 .0425 .0570
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Conclusion
• Indian Gaming Regulatory Act (1988): successful
• Casino operation: favorable changes in local labor market for Indians
• Primary beneficiaries (Indians): dropping out of high school and not going to college
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• Greater availability of high-paying, low skill job– some unintended negative consequences
• Suggest– Caution for economic renewal policies
• In college enrollment decision, other factors are more important than tuition costs.