1

The Impact of Local Labor Market Conditions on the Demand for Education: Evidence from

Indian Casinos

Bill Evans and WooYoung Kim

2

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.

3

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

4

Puzzle

• Foregone earnings are such a large cost of education

• Yet it is difficult to find evidence about how changing opportunities impact education

5

• 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

6

• 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.

7

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

8

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

9

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

10

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

11

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

12

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

13

• Among teens– Lower HS completion rate– Lower college entrance rate– Only among Indians

• Among young adults (20-24)– Similar results

14

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

15

Some facts about American Indians

16

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

17

• 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

18

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%

19

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

20

How did we get so many casinos?

Because of state excise taxes on cigarettes

21

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

22

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…….

23

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

24

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

25

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

26

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…………

27

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;

28

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”

29

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

30

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

31

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

32

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

33

34

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”

35

Gold Country Casino, Oroville CA(160 slot machines)

36

Ute Mountain Casino – CO(500 slots)

37

Camel Rock Casino, Santa Fe(675 slots)

38

Grand Casino Mille Lac, MN(1,500 slots)

39

Soaring Eagle, Mt Pleasant, MI(4,800 Slots)

40

Foxwoods, CT(5,700 slots)

41

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

42

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)

43

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

44

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

45

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%)

46

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

47

• 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

48

Basic Model

• Called difference in difference

• Compare outcomes on reservation before and after casinos

• Compare to changes in reservations without casinos

49time

Y (Unemployment)

tb ta

Y1

Y2

Yb

Ya

True effect = Y1-Y2

Estimated effect = Yb-Ya

t1

50

• 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

51time

Y

t1tb ta

Yb

Ya

treatment

control

Yd

Yc

Treatment effect=(Yb-Ya) – (Yd-Yc)

52

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

53

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

54time

Y

t1tb ta

Ya

Yb

treatment

Control

Yc

Yd

Treatment effect=(Ya-Yb) – (Yc-Yd)

55

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

56

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

57

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

58

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

59

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

60

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

61

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

62

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

63

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

64

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

65

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

66

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

67

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

68

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

69

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

70

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

71

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)

72

Where were jobs created?

• Non-Indians– Construction (+1.2)– Arts, entertainment, accom. food (+2.0 for

females only)

73

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

74

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

75

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

76

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

77

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

78

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

79

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

80

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

81

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

82

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

83

Three groupsGroup # 5 years prior

to censusAt time of census

1 On On

2 Off On

3 On Off

84

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

85

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

86

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

87

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

88

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

89

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

90

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

91

• 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.