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The Income and Health Effects of Tribal Casino Gaming on American Indians

Demography

Abstract

The legalization of American Indian casino gaming in the late 1980s allows examination of the relationship between income and health in a quasi-experimental way. Revenue from gaming accrues to individual tribes and has been used both to supplement tribe members’ income and to finance tribal infrastructure. We assembled annual data from 1988–2003 on tribal gaming, health care access (from the Area Resource File), and individual health and socioeconomic characteristics data (from the Behavioral Risk Factors Surveillance System). We use this information within a structural, difference-in-differences framework to study the effect of casino gaming on tribal members’ income, health status, access to health care, and health-related behaviors. Our difference-in-differences framework relies on before-after comparisons among American Indians whose tribe has at some time operated a casino and with-without comparisons between American Indians whose tribe has and those whose tribe has not initiated gaming. Our results provide identified estimates of the positive effect of gaming on American Indian income and on several indicators of American Indian health, health-related behaviors, and access to health care.

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Fig. 1

Notes

  1. Ruhm (2005) suggested that it is not only income that matters, but also the additional time due to being unemployed that enables one to conduct health-producing activities; under certain conditions, the latter effect may exceed the negative effect of the income change.

  2. Statistics from the National Indian Gaming Commission (NICG) (see http://www.nigc.gov/).

  3. When Congress passed the Indian Gaming Regulatory Act (IGRA) in 1988, state regulatory rights were recognized over Class III gaming. Class III excludes Class II gaming (primarily bingo, which tribes themselves regulate) and traditional Indian games (Class I).

  4. Gaming has also benefitted states, both directly through payments agreed upon in state compacts and through lowering of payments tied to poverty. For example, in Minnesota, which established 22 compacts in 1989 and has casinos on all 11 reservations, welfare expenditures were reduced in counties with a casino by about 16% within two years of the casino’s establishment. See American Indian Policy Center (2005).

  5. However, because BRFSS uses telephone sampling, results may be biased by omitting households without phones. As of 1990, 23% of American Indian households did not have telephones (U.S. Census Bureau 1994). We expect that telephone usage in this population has increased over the period of our analysis.

  6. We find a negative and statistically insignificant relationship between employment and Class III tribal gaming, controlling for gender, age, marital status, survey year, and the full set of county contextual variables. Results are available from the authors upon request.

  7. Specifically, this sample is defined as American Indian respondents whose county of residence has a tribe with Class III gaming for two or more years as of the year the respondent was interviewed. Summary statistics for respondents whose tribe did not have a gaming facility at the time that they were interviewed are not shown in the table but are available from the authors. We omit observations whose affiliated tribe established a gaming facility during the year of interview or the subsequent year. This lag allows time for the casino to become a going enterprise with the opportunity to generate income.

  8. There are no counties with a casino for two or more years that then subsequently lose the casino.

  9. BRFSS gathers information on household income levels for all years of our analysis. Income is recorded by class interval, with an open category at the top. We adjust all class intervals for inflation (year 2000 dollars) and use the midpoint of all categories. Respondents with income in the open-ended top income category are assigned an income equal to the lower limit (inflation-adjusted income) × 1.5. In an alternative analysis reported below, we also estimate interval regressions in which we make no assumption regarding the nature of the underlying income distribution within each category or for the top-coded interval.

  10. Although not shown, the Restricted Sample is likely to have fewer American Indians living in metropolitan areas because individuals must live in the same county as a reservation in order to be included in the Restricted Sample.

  11. An interesting question concerns the pattern of income changes across the income distribution associated with the presence of casino gaming. In the Restricted Sample, the largest income increase is recorded for the second quartile, suggesting that the income increases occur primarily in the lower-middle- to middle-income groups. Moreover, the lower bound of the third quartile increases more than the upper bound. A two-sample Wilcoxon rank sum (Mann-Whitney) test of differences across the entire income distribution indicates statistical significance. This is consistent with calculated income changes at same quantiles of the distributions. We find an increase in income of $947 at the 25th percentiles of the with-without distributions (6%), $4,946 at the medians (21%), and $1,819 at the 75th percentiles (4.4%).

  12. One reader suggested that we estimate the following year and county fixed-effects model:

    figure a

    In this equation, Z t are year dummy variables, and D j are county dummy variables. Individual characteristics are also included. We are unable to estimate county fixed effects because of lack of within-county variation for some counties (1,173 counties, of a total of 2,130 counties, have three or fewer observations, and 619 counties contain only one American Indian in the data set).

  13. We use this value because the Akee study better identified members of eligible tribes than we are able to do. Alternatively, it is an estimate based on only one tribe.

  14. We conjecture that part of the reason for this difference is the greater difficulty in associating those residing in metropolitan areas with whether their tribe has one or more casinos, or whether they are official members of a tribe.

  15. The intervals used in this analysis, based on the data collected in BRFSS, are <$10,000; $10,000–$15,000; $15,000–$20,000; $20,000–$25,000; $25,000–$35,000; $35,000–$50,000; and >$50,000 for those first observed in the 1998–1993 period. For those first observed in the 1994–2003 period, the highest interval is broken in two: $50,000–$75,000 and >$75,000. All these intervals are converted to year 2000 dollars in the interval regression analysis.

  16. For example, during the 1989–2003 period, the number of medical providers per capita increased in counties that established a casino compared with the counties with American Indians in our sample that had no casino.

  17. The Evans-Topoleski data consisted of a complete list of nongaming tribes and gaming tribes both with and without compacts (including dates of gaming compacts, opening dates of tribes’ first casinos, number of slots in tribes’ first casinos, and square footage of tribes’ first casinos), compiled from several Internet sources (i.e., Bureau of Indian Affairs website, National Indian Gaming Commission website, Gamblinganswers.com, and Casinocity.com), as well as popular press articles, phone correspondence, and tribal casino websites. The U.S. Census Bureau’s publication General Population Characteristics: American Indian and Alaska Native Areas (based on the 1990 census; U.S. Census Bureau 1990) was used to determine the state and county location(s) of federally recognized tribal land; county data (rather than individual data) were used in analyzing the economic impacts of legalizing gambling among American Indians. Evans and Topoleski found that four years after tribes opened casinos, employment had increased by 26%, and the fraction of adults who worked but were poor declined by 14%. They were able to study only limited health effects.

  18. Evans and Topoleski (2002) collected information only on tribes’ first casinos. Although we include this information in our data set, we also included information on additional casinos that may be associated with gaming tribes. We excluded extremely small gaming operations, such as those at laundromats and trading posts.

  19. Information for this data set was collected from the National Indian Gaming Commission (NIGC) and the National Indian Gaming Association (NIGA) (for tribal affiliation).

  20. BRFSS is a source of timely cross-sectional prevalence data for common health status indicators, health care utilization, health care insurance coverage, health-related behaviors, and health risk factors for adults in the United States. Because the BRFSS is designed to collect prevalence data for individual states, each state conducts its own monthly random-digit-dial telephone survey. These state-by-month data are then aggregated yearly by the Centers for Disease Control and Prevention.

  21. We use the restricted-access BRFSS data in order to include respondents living in rural or sparsely populated counties. Due to confidentiality concerns, BRFSS does not allow public access to data from respondents living in a county where the annual sample is small.

  22. The Area Resource File is a “national county-level health resources information system designed to be used by planners, policymakers, researchers, and other analysts interested in the nation’s health care delivery system and factors that may impact health status and health care in the United States. The ARF database contains statistics on the following categories of health resources: health professions, health training programs, health facilities, measures of resource scarcity, and health status. The system contains information on more than 6,000 variables for each of the nation’s counties.” See the Health Resources and Services Administration website (http://www.arfsys.com) for details on this data source.

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Acknowledgments

The authors wish to thank the Robert Wood Johnson Foundation for their support for this project and the Guggenheim Foundation for their support for Barbara Wolfe. The authors also thank the following persons for helpful comments: two referees for this journal, attendees at the 2009 summer research workshop on health economics in Sydney (AU) including in particular Michael Grossman, colleagues at the Research School for the Social Sciences (now the Research School of Economics) at the Australian National University, those in the economics department of Queensland University of Technology, and Ari Kapteyn. We also wish to thank Gary Sandefur for his insights, William Evans for sharing the Evans-Topolesky data, and Hannah Goble for her work with the county data. All responsibility for errors remains with the authors.

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Correspondence to Jessica Jakubowski.

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Names of authors are in reverse alphabetical order; all contributed equally though uniquely to the paper.

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Appendix: Construction of the Data Set

Appendix: Construction of the Data Set

Gaming and American Indian Tribes

To identify American Indian tribes with Class III gaming, we begin with the extensive tribal gaming data collected by Evans and Topoleski (2002).Footnote 17 We then supplement this information with the following:

  1. 1.

    Exhaustive casino-by-casino data with specific information on the locations and characteristics of nearly all American Indian gaming facilities in the contiguous 48 states that have survived to 2005, providing gaming facility–specific information on tribal affiliation, county of location, the presence of a Class III gaming compact or casino-style gaming, and date of facility opening.Footnote 18

  2. 2.

    A tribal-level data set containing summary gaming data for each tribe, including the opening year of the first tribal gaming facility, constructed from the detailed gaming facility data plus additional information on the geographic location of all tribal reservation land to the tribal level data. By matching tribes to BRFSS respondents’ county of residence, we obtain the county of residence of each individual observation.Footnote 19

Individual-Level Health, Income, and Socioeconomic Data

We use the Behavioral Risk Factors Surveillance System (BRFSS), sponsored by the U.S. Centers for Disease Control and Prevention, to obtain information on income and health-related variables of both American Indian and non–American Indian individuals.Footnote 20 In addition to health information, BRFSS respondents report basic sociodemographic and socioeconomic characteristics, as well as the county of residence.Footnote 21 BRFSS gathers information on household income levels for all years of our analysis. Income is recorded by class interval, with an open category at the top. We adjust all class intervals for inflation (year 2000 dollars) and use the midpoint of all categories. Respondents with income in the open-ended top income category are assigned an income equal to the lower limit (inflation-adjusted income) × 1.5. We compiled cross-sectional BRFSS data for the 16 years from 1988 to 2003. When aggregated, these data provide us with a large sample of American Indians (N = 24,029).

Because BRFSS does not identify the specific tribe to which an American Indian respondent belongs, we linked individual BRFSS respondents to tribes and tribal gaming by assuming tribal affiliation based on county of residence. Using this information, we match tribal information on the existence and nature of gaming to the county in which the tribal reservation is located and then to the BRFSS data containing information on American Indian status; county of residence; and individual income, health status, utilization, and behavior information. Through this procedure, we establish a geographic link between individual observations in the BRFSS sample and county-specific federally recognized tribal reservations and/or American Indian casinos.

County Data on Population, Health, and Economic Characteristics

We also use county-level data using the Area Resource File (ARF), available from the U.S. Department of Health and Human Services.Footnote 22 ARF contains information on the availability and aggregate utilization of health resources and facilities, population, and economic data for each county. We employ nearly all available data on economic/employment conditions for each county as of 1990, which is prior to the beginning of casino gaming for most tribes. We link these data to individual BRFSS respondents based on their county of residence. These indicators allow us to control for the environmental and economic conditions, including health care availability, in the counties in which American Indians live, and thus to avoid issues of selectivity in which tribes establish casinos.

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Wolfe, B., Jakubowski, J., Haveman, R. et al. The Income and Health Effects of Tribal Casino Gaming on American Indians. Demography 49, 499–524 (2012). https://doi.org/10.1007/s13524-012-0098-8

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