Site hosted by Build your free website today!

A Logistic Regression Model Explaining Casino Adoption Among Counties In Mississippi









Rodney E. Stanley

Institute of Government

Tennessee State University

330 10th Avenue North

Nashville, TN 37203
















Objective: The purpose of this research is to explain the adoption of casino policy among counties in Mississippi.  Methodology: Various socio-demographic variables are measured through the use of logistic regression analysis for determining casino adoption in Mississippi.  Results: The data suggests that the fiscal health of a specific county is the best predictor among Mississippi counties voting on the proposed casino legislation.  Limitations:  The results of this research may be limited due to the nature of this study focusing only on counties in Mississippi, a small data set (n=42), and absence of political variables such as interest group strength and political ideology.  Implications: State sponsored gaming policy will most likely be adopted if the constituents perceive such policy endeavors as possible sources of supplemental revenue for local government.


            Issues related to the adoption of state supported lotteries and casino gaming among the American states have recently gained much attention from academic scholars (Berry and Berry, 1990; Rivenbark and Rounsaville, 1995; Rivenbark, 1997; Brown and Kubasek, 1997; Furlong, 1998; and Pierce and Miller, 1999).  Although research pertaining to state supported gaming has permeated the literature for thirty-five years (Mikesell, 2001), only recently have attempts been made to explain why some governmental entities chose to adopt games of chance, while others chose to by-pass this revenue generating device.   This analysis will trace the development of attempts to explain morality policies such as state supported lotteries and casino gaming in an effort to identify a gap in the current literature regarding the issue.  Numerous studies are found in the literature explaining policy adoption variation across the American states in both state-sponsored lotteries and state supported casino gaming.  However, the literature fails to address intra state variations in policy adoption among local counties in states adopting casino gaming.  Why is it that some counties in Mississippi have adopted casino gaming while others have chosen to by-pass this revenue-generating device? This research fills the current literature gap by explaining intra county differences that lead to the adoption of casino gaming in Mississippi.  Logistic regression analysis is chosen as the preferred statistical procedure for measuring the data in this study.


Legalized Gambling

            In this current era of devolution in government, state and local governments are examining and implementing various alternative methods and referendums to increase their revenues.  Many, as a means to increase the government’s treasury without having to raise taxes and unduly burden the lower class, have legalized gambling in the form of casinos, riverboats, and lotteries.  The utilization of legalized gambling for this purpose has been debated among researchers, scholars, legislators, and citizens.  Legalized gambling is less efficient than taxes on labor income (Rodgers and Stuart, 1998).  In addition, low income interest groups either carry more of the tax burden, or they receive fewer benefits from their implementation than citizens in other income groups (Borg, Mason, and Shapiro, 1971; Livernois, 1997).  Also, governments and citizens may experience unwanted problems and consequences of legalized gambling including increased unemployment, decreased retail competition, increased public debt, and increased crime (Pable, 1996; Gross, 1998).

            Legalized gambling, however, has provided benefits to state and local residents that may not have been realized through any other means.  Lottery profits in Georgia, Florida, and Kentucky have been earmarked for education allowing younger residents in these states to attend state universities or colleges on full or partial scholarships (Barry, 1995).  Also, the educational systems of these states have utilized profits from lotteries to enhance the support network of computers, satellite dishes, and media technology in state schools.  However, debate still arises as to whether or not legalized gambling is an appropriate answer for increasing revenues for state and local governments.

            Many residents realize the benefits of lottery sales and casino action as supplements to state funded programs. However, if these profits are utilized to replace original funding from the states, citizens may not reap any benefits, and their present circumstances may actually worsen.  In many states, lottery profits are earmarked for education, economic development, distressed cities and towns, and senior citizen programs.  In others, these profits fall into the general fund and may be directed to various programs as prescribed by the state legislature.  However, any benefit derived from legalized gambling requires current and future programs to provide more to the state’s citizens than what they currently receive.

            Despite the appeal of state operated lotteries as a supplemental revenue source for social programs, some states have repeatedly rejected legislation that would bring gaming to their respective states.  For instance, in 1999 the state of Alabama, in a referendum brought to the people, rejected casino gaming almost 2 to 1.  In 1990, Mississippi rejected a state operated lottery even though this state had previously endorsed casino gambling (National Gambling Impact Study, 2000).  Why do some states choose to operate state lotteries while others consistently oppose any legislation that supports state supported gaming?  Part of this discrepancy can be explained by a state’s political culture.

            Numerous studies have attempted to explain the differences seen between the public policies of state governments.  Many have evaluated the conditions and characteristics that propel certain states to adopt specific expenditures or innovations regarding education, welfare, and transportation.  Researchers have found that political party competition, interest groups, gubernatorial power, public opinion, and political culture may help explain many of the dissimilarities seen in state government policies.

            One of the most noted essays concerning state political systems is the analysis of Southern politics presented by V. O. Key.  Key (1949) points out that the nature of the South’s political system has been shaped by this region’s problems with racial relations, its agrarian economy, and the tendency of its residents to remain non-participatory in political matters.  He also notes that “custom, the organization of the economic system, and now and then, private violence have a role in determining who govern and who gets what” (Key, 1949, p.4).

            Daniel Elazar (1984) also describes the influence of political culture on both state political systems and the legislation that these systems promote.  He identifies three major subcultures existing across the United States which were brought to this country by the early American settlers. Elazar notes that these tendencies are often found among politicians and the general public, and they serve to shape each individual’s understanding of government and, ultimately, its purposes and outcomes.

            This author points out that the individualistic subculture emphasizes the market place and a limited role of government (Elazar, 1984).  The moralistic subculture promotes the commonwealth and expects government to advance the interest of the public.  The third subculture identified by Elazar is the traditionalistic political culture.  Traditionalists expect government to maintain the existing social and economic hierarchy, and governance remains an obligation of the elite rather than the ordinary citizen.  Elazar utilizes the early American settlement patterns to identify the dominant political subcultures existing in each of the fifty American states.  States in the extreme North, the Northeast, and those located on the Pacific Coast are dominated by the moralistic subculture.  States across the mid-section of the United States are classified as individualistic, and those states in the South are generally seen as traditionalistic.

            Ira Sharkansky (1969) utilizes Elazar’s classifications to create a nine point linear scale which allows for comparative state analysis in empirical terms.  The creation of these nine points allows for both the primary and secondary cultures that may exist in a state.  Sharkansky (1969) hypothesizes that certain political traits are associated with each type of culture.  Each of the fifty states is given a political culture score, and these scores are correlated with twenty-three variables reflecting political participation, government size, government prerequisites, and government program implementation.  Sharkansky (1969) notes that two thirds of these dependent variables demonstrate the expected relationship with Elazar’s scale of political cultures.  He concludes that political culture can be related to several state traits regarding politics and public service.

            Other authors in the political field have been critical of Elazar’s topography due to its lack of empirical evidence.  Clynch (1972) argues that the interval scale developed by Sharkansky does not exhibit the same relationships with the dependent variables when regionalism is included.  He notes that the impact of political culture can be seen intra-regionally rather than nationally.  Schiltz and Rainey (1978) conduct a secondary analysis of data from thirteen states in 1968 originally conducted by the Comparative State Elections Project in order to determine whether or not Elazar’s assumptions of the existing political subcultures in the fifty states are statistically substantiated.  These two authors conclude that very little statistical evidence exists to support Elazar’s classifications.

            Robert Savage (1981) however, points out several flaws in the research analysis conducted by Schiltz and Rainey.  He notes that these two authors fail to address several ambiguities found in the survey.   He also argues that they form hypotheses which are loosely construed from Elazar’s work.  Savage questions their statistical analyses and the “rummage sale approach” that Schiltz and Rainey utilize in their political culture study (Savage, 1981, p. 331).  He argues that Elazar’s theory has been proven valuable to political research.  Savage (1981) points out that this normative scale is “the one political measure that compares favorably with traditional socioeconomic indicators in explaining policy variations among the states” (Savage, 1981, p. 336).

            Nardulli (1990) utilizes data from a 1986 telephone survey to also examine the utility of Elazar’s typology.  The intent of this analysis is to determine whether or not Elazar’s assumptions concerning citizens and politics are correct.  Also, Nardulli questions whether the citizens categorized in Elazar’s political subcultures exhibit the characteristics required in these classifications.  This author finds that many individuals in the survey did not adhere to the belief systems in each subculture identified by Elazar (Nardulli, 1990).  Nardulli concludes that failure of Elazar to operationalize his scheme for categorizing geographic locales “makes it difficult to rebut the implication that his classifications measure little more than sectional differences” (Nardulli, 1990, p. 304).

            Other research concerning political culture and policy adoption has focused on social class, regional diffusion, and economic disparity.  Black and Black (1987) note that even when power and influence within the South shifted as the population of a new middle class Southerners composed of professional, technical, managerial, clerical workers began to outnumber the agrarian middle class, the political agenda of these states remained virtually unchanged because most members of this new middle class shared the same agrarian beliefs, values, and interests.  Both the traditionalistic and entrepreneurial individualistic cultures that emerged placed great importance on financial self-reliance and minimal government intervention (Black and Black, 1987, p.60).  The authors point out that the transformation of social order within the South from 1940-1980 has not resulted in a political culture shift.  A majority of white residents in this region still believe in individual responsibility for economic well-being. Therefore, the policies adopted in these states reflect these fundamental values.

            Virginia Gray (1973) examines the adoptions of certain education, welfare, and civil rights policies by states from a “have – have not” perspective.  She points out that the differences exhibited in state policy innovations in these areas are often explained by political differences and economic disparities.  Innovative states tend to be wealthier and exhibit greater political party competitiveness than the less innovative states (Gray, 1973, p. 1182).  Also, states with higher mean percentages of Progressive party strength (prior to 1913) are more innovative in adopting particular legislation examined by Gray (Gray, 1973, p. 1183).  Gray confirms that both political and economic explanations can be successfully utilized to explain state policy innovations.

            Berry and Berry (1990) demonstrate that regional diffusion, as well as, political, economic, and social characteristics serve as plausible explanations of state government policy innovations.  These two authors examine state lottery adoptions utilizing cross-sectional time series analysis to reveal that the internal political and economic characteristics of a state will influence the probability that the state adopts a lottery.  Previous adoption of the lottery by neighboring states is also directly related to the utilization of this innovation.  In addition, states with declining fiscal health exhibit a higher probability of lottery adoption.  Berry and Berry (1990) note that the probability of state lottery adoption increases with the number of neighboring states that have adopted lotteries. Lottery adoption is most likely to occur during an election year, and least likely to occur in the years immediately following the election (Berry and Berry, 1990).  In addition, states with lower per capita incomes and states with higher percentages of religious fundamentalists are least likely to adopt lotteries.  Berry and Berry (1990) conclude that regional diffusion and internal determinants are valid explanations of state lottery adoptions.

            Pierce and Miller (1999) explain the variations in the diffusion of state lottery adoptions.  Through the use of historical analysis (measuring lottery adoptions across space and time), these authors argue that the politics of lottery adoption vary with the purposes for which the lottery’s revenue will be used.  According to the authors, education and general fund politics are used to sell lottery adoption in the states.  They found that states which adopt lotteries for curing the education “crisis” in America instead of generating revenue for general fund “needs” receive less opposition from fundamentalist because these opponents realize that their children’s educations are at stake (p. 698).  Therefore, state operated lotteries become less sinful.  Pierce and Miller indicate that measuring the amount of fundamentalism in morality policy issues such as gaming will assist scholars in understanding policy adoption trends in America.  The authors find that anti-gaming sentiments are strongest among those citizens who classify themselves as conservative fundamentalists. Ellison and Nybroten (1999) reported similar findings among conservative fundamentalists in Texas as well.

Since the lottery is often the precursor to casino gaming, Furlong (1998) imports variables from various lottery studies to launch a research project explaining the adoption of casino gaming in America. Furlong applies logistic regression analysis to the adoption of casino gaming and finds that, although casino gaming in America is isolated to only twelve states, similarities seen in the socio-demographic indicators of lottery states are also discovered in states adopting casino gaming.  Ideological preferences, per capita tax rankings, per capita tax rates, and job growth reported as predictors in the statistical model employed by Furlong. These variables indicate that motives related to political feasibility and economic development provide the best explanation of recent state casino gaming adoptions. 

            In theory, the literature suggests that certain states are more likely than others to adopt lottery or casino gaming due to various socio-demographic variables.  Do the arguments suggested by the previous scholar apply to intra state differences among counties in a particular state? The literature does not attempt to explain the adoption of casino gaming at the county level of government.  Although political culture as defined by Elazar and Sharkansky are beyond the scope of this study, Clynch’s reference to the failure of current literature attempts to explain intra state variations in political culture lends support for the argument that intra state variations among counties in Mississippi may serve as a determinant to why some counties in Mississippi adopt casino gaming while others choose to by-pass this revenue-generating device. Before the data in this analysis is examined, a brief discussion of the adoption of casino gaming in Mississippi will follow.

Casino Gaming In Mississippi

            The Mississippi Gaming Control Act of 1990 (Mississippi Code Sections 75-76-100; 75-76-195) sanctioned casino gaming to Mississippi.  Due to the collapse of the oil industry in this state, concerned citizens in Vicksburg consulted their state Senator (Bob Dearing) about the possible utilization of casino gaming in the Magnolia state for revision of the economy.  Despite rejection of lottery legislation just a few months earlier, casino legislation was authored by Representative Montgomery in the House of Representatives, while Senators Dearing and Gallot co-authored a similar bill in the Senate.  The House bill passed with limited resistance, and after heated debate among policymakers in the Senate, the upper house ratified the legislation by a vote of 22 – 20 (eight Senators reframed from voting on the legislative bill).  Casino gaming in Mississippi was passed with the stipulation that only counties bordering waters ways (the Gulf Coast and Mississippi River) were allowed to vote on the adoption of this revenue-generating device.  Once the county had adopted casino legislation, each municipality located in the county would have the opportunity to vote on the casino bill.  Originally, fourteen counties voted on the bill, and eight of those counties chose to bring casino gaming to their respected communities.  Currently, 30 casinos operate in eight counties throughout the Magnolia state (the Choctaw casino in Philadelphia, MS is excluded from this figure because it is on federal land and cannot be taxed by the state) (Mississippi Gaming Commission, 2002).

Data and Methodology

            The exogenous variables chosen for this study are derived from studies by Berry and Berry (1990), Furlong (1998), and Pierce and Miller (1999).  This analysis is distinguished from previous studies explaining policy adoption because the unit of analysis is county level data. The endogenous variable used in the study is the adoption of casino gaming. The conceptual and operational definitions of these variables are as follows:

Conceptual & Operational Definitions

Casino Adoption (Dependent Variable)

Furlong (1998) measured the diffusion of casino adoption across the American states.  This study applies Furlong’s argument to the measurement of the adoption of casino gaming across counties in a particular state (Mississippi).  The endogenous variable is measured and collected in the following manner: Casino legislation adopted by counties in Mississippi is coded 0 = the year casino gaming was adopted by the county and 1 = noncasino counties. The data was collected from the Mississippi Gaming Commission.

Fiscal Health

Pierce and Miller (1999) utilized state tax revenue as an economic indicator for determining lottery diffusion among the states.  This study adopts a similar economic indicator, tax revenue and tax expenditures received by each county, measured in adjusted inflationary dollars.  As in the case of Berry & Berry (1990), total expenditures are subtracted from total revenues and a variable is created and designated as the fiscal health of each county.  The data is collected from the Mississippi Statistical Abstracts published by the Business and Administration Department at Mississippi State University.

Poverty Level

            Much of the lottery literature (Mikesell, 2001; Mikesell, 1989, Livernois, 1987; Mikesell and Zorn, 1986) and casino gaming literature (Oliver, 1995; Perniciaro, 1995 Mason and Stranahan, 1996; Denise von Herrman, Ingram, and Smith, 2000) has included studies measuring the impact of casino dollars on economic development, marketing and tourism and education among the states or in a given state such as Mississippi (Spindler, 1995; Rogers and Stuart, 1995; Miller and Pierce, 1997; French and Stanley, 2001; Stanley, 2001)  A common theme found in many of these studies suggests that additional revenue generated from state supported gaming will assist states in financial costs associated with various social policies and programs.  In theory, if gaming is supported by a state, then the poverty level in that state will decrease.  This argument applies to Mississippi as well.  Many of the counties located on Mississippi waterways are located in the delta which is one of the poorest regions in the United States (Shaffer, 2001).  Policy makers in Mississippi felt a need to bring casino gaming to the delta in order to revitalize the state’s economy.  The counties located in the Mississippi delta region maintain some of the largest poverty rates in the country (Shaffer, 2001).  These large poverty rates have established a need for job growth in this region.  Therefore, the percentage of families at or below the poverty line is a variable created as a possible predictor of casino adoption among counties in Mississippi.  This data is collected from the Mississippi Statistical Abstracts published by the Business and Administration Department at Mississippi State University.

Religious Affiliation

Berry & Berry (1990) and Pierce and Miller (1999) suggest that the religious affiliation of the population in a state impacts the adoption of state lotteries.  A similar variable is incorporated in this study to determine if a person’s religious affiliation impacts the adoption of casino gaming within a particular county.  This study defines religious affiliation as the percentage of residents by county who classify themselves as Baptists.  The number of Baptists in each county is used as a social indicator because, according to Ellison and Nybroten (1999), Baptists tend to consider themselves conservative and morally opposed to state supported gaming. Furthermore, Baptists make up the largest portions of religious groups fundamentally opposed to any type of state supported gaming.  This data is collected from the Mississippi Chapter of the Southern Baptist Convention in Jackson, Mississippi.

Political Culture

Political culture has received much attention as a possible social indicator in gaming policy diffusion across the states (Elazar, 1984; Sharkansky, 1970, Clynch, 1971).  This study incorporates a dichotomous variable measuring Mississippi’s political culture.  According to Shaffer (2001), Mississippi is divided culturally between the north, south, and delta regions.  The northern sphere of Mississippi encompasses everything from north of Hattiesburg to the Tennessee border, excluding those counties located in the Mississippi delta.  The southern part of Mississippi includes the counties located adjacent to and south of Hattiesburg.  This cultural divide exists because of the large number of Louisiana immigrants located in the southern realm of Mississippi who adhere to Roman Catholicism and French-Cajun heritage (Shaffer and Horne, 1998).  North of Hattiesburg, the culture is predominantly “traditionalistic” as discussed by Elazar.  However, the Mississippi delta is unique due to the high levels of poverty, unemployment, and predominantly African-American populations that reside in this region.  Since religious affiliation is a separate variable, and Mississippi’s overall political culture is defined as traditionalistic, a dichotomous cultural variable is created to determine if differences exist between counties the Mississippi delta and other counties in Mississippi (Shaffer and Horne, 1998).  The variable is coded as follows: 0 = Mississippi delta and 1 = all other counties in Mississippi).

Party Control

            Party control of the state was a predictor found in the studies by Berry and Berry (1990), Furlong (1998), and Pierce and Miller (1999) regarding state sponsored gaming.  These studies measured party control by the dominance of a specific party at the state and local level of governance.  For instance, if the governor and state legislature were dominated by the Democratic Party, then the state was coded as Democratic.  Since Mississippi’s local and state politics are dominated by the Democratic Party (Shaffer, 2001), this study measures party control by the number of votes cast in each county during the 1988 presidential election.  The 1988 and 1992 presidential elections provided the county level data available measuring voting behavior in Mississippi during the time span in which this study is concerned (1989 - 1991).  The 1992 presidential election witnessed a slight increase in the number of counties controlled by the Democratic Party. Therefore, 1992 presidential election results are used for the party control variable in 1990 and 1991, to account for these changes in voting behavior.  Furthermore, in order to measure party control at the county level in a similar manner as the studies previously mentioned, differences are determined according to party identification of the mayor and city council.  In Mississippi, there is hardly any differentiation at the local level of governance because the Democratic Party controls politics (Shaffer, 2001). Therefore, this study utilizes presidential vote by county in an effort to explain party control in Mississippi.  If the Democratic presidential candidate received the largest portion of the votes during the 1988 election, then the county was coded as if the Democratic Party controlled the county.  The data was collected from the National Association of Counties (NACO), which publishes socio-demographic variables recorded by the US Census Bureau. This variable is coded as follows: 0 = Democrat and 1 = Republican.

Education Level

            The level of education found among residents in the counties studied replicates a similar variable used by prior adoption studies at the state level unit of analysis (Berry and Berry, 1991).  The data was collected from the Mississippi Superintendents Report on Education.  The coding scheme utilized for this independent variable represents the percentage of residents in casino, and matching non-casino school districts in Mississippi, with a college degree.


            The year is a counter variable used to measure the two prior years leading to the adoption of casino gaming in Mississippi, along with accounting for the year of adoption.  This counter variables assists in accounting for factors that may have attributed to the adoption of casino gaming in this three year period.  Furthermore, accounting for three years of time the data set is substantially increased in order to receive a more reliable analysis in the regression equation (Sayer, 1991).


            The number of residents in Mississippi casino and matching noncasino school districts represents the final variable used in the equation.  This variable was utilized as an additional exogenous variable to account for possible impacts of population levels on casino adoption in Mississippi.  The variable was retrieved from the Mississippi Statistical Abstracts published by Mississippi State University.  The variable in measured in terms of number of residents living in casino and matching noncasino school districts.

Units of Analysis

            The units of analysis in the study are counties that were given the opportunity to adopt casino gaming according to the Mississippi Gaming Control Act of 1990, “counties bordering waterways” (Mississippi Code Sections 75-76-100; 75-76-195).  The following counties were given this opportunity: Adams, Bolivar, Claiborne, Coahoma, Desoto, Hancock, Harrison, Issaquena, Jackson, Jefferson, Tunica, Warren, Washington, and Wilkinson.  Since the state of Mississippi adopted casino gaming in 1990, 1989 data is used to account for political and financial conditions prior to the adoption of casino gaming. Data from 1990 and 1991 is used to account for the years in which each of the counties in the regression model adopted casino gaming.

[Insert Table One Here]


Binary Logistic Regression Equation


Y (Casino Adoption)  = a + (B1) Fiscal Health + (B2) Religious Affiliation + (B3) Political Culture + (B4) Poverty Level + (B5) Party Control + (B6) Education+ (B7) Year + (B8) Population + E


The statistical method employed in this study to measure the data is logistic regression (Mertler & Vannatta, 2001).  This statistical methodology is employed because the endogenous variable is dichotomous (0 = yes and 1 = no) in regards to adopting casino gaming.  Other studies such as Furlong (1998) and Ellison & Nybroten (1999) utilize logistic regression in a similar manner as employed by this study.    Several problems are associated with using logistic regression, and the most notable is multicollinearity.  To check for high correlations among predictor variables, a preliminary multivariate regression analysis is conducted on the data set, and the collinearity statistics are reviewed.  The tolerance for all variables exceeds 0.1; therefore multicollinearity in the data set is not a problem. [1]

Null Hypotheses:


H1: Counties in good fiscal health are likely to adopt casino gaming in a similar manner as those counties with poor fiscal health.


H2: Counties with higher levels of Baptists are more likely to adopt casino gaming in a similar manner as counties with lower levels of Baptists.


H3: Counties located in the Mississippi delta tend to adopt casino gaming in a similar manner as counties located in other parts of Mississippi.


H4: Counties with higher levels of poverty tend to adopt casino gaming in a similar manner as counties with lower levels of poverty.


H5: Counties controlled by the Democratic Party tend to adopt casino gaming in a similar manner as counties controlled by the Republican Party.


H6: Counties with higher education levels tend to adopt casino gaming in a similar manner as counties with lower education levels.


H7: Counties with higher populations tend to adopt casino gaming in a similar manner as counties with smaller populations.



[Insert Table Two Here]



The Hosmer and Lemeshow goodness-of-fit p-value of 12.091, with 8 df, suggests that the model fits the observed distribution of counties.  The estimates for R2, Cox & Snell R ^ 2 = .189 and Nagelkerke R ^ 2 = .263 indicate that a small amount of variance is being explained in the regression model.  In the overall regression model, 71.43% of the cases are classified correctly.  Despite the acceptance of only one statistically significant variable among the predictors in the regression model, the direction of the constant (B) offers valuable information that a negative relationship is occurring in the regression model.  The data suggests that as a county’s fiscal health increases its likelihood of adopting casino gaming decreases.

The unstandardized regression coefficient (B) for fiscal health suggests that for every unit increase in a county’s fiscal health, a decrease will occur in the likelihood of casino adoption among counties in Mississippi.  The odds ratio Exp (B) represents a decrease of .9999 when a 1 percent increase in fiscal health occurs in the model.  The significance level of fiscal health reported a statistical significant p <.05; therefore, the findings suggest that this variable is the best predictor in the model. 

The religious affiliation of Mississippi counties fails to report an acceptable statistical significance of p <.05 or p<.1.  However, for every 1 percent increase in the odds ratio, the likelihood of casino adoption increases 1.000 among counties in Mississippi.  The direction of the B suggests that for every unit increase in the number of Baptists in a particular county, the likelihood of that county adopting casino gaming increases.  Although this inference is indicated by the direction of the B, the regression model fails to report any statistical support for such an accusation that is acceptable at 95% ratio or better.  The null hypothesis fails to be rejected.

The political culture of residents in Mississippi counties also fails to report an acceptable p<.05 or p<.1. The value of the odds ratio suggests that a 1 percent increase in the level of political culture increases the probability of casino adoption by 1.562.  Again, failure of the data to demonstrate an acceptable significance level does not allow for the rejection of the null hypothesis regarding this variable’s impact on casino adoption in Mississippi.

The poverty level of Mississippi counties also fails to report an acceptable statistical of p <.05 or p<.1.  However, for each 1 percent increase in the odds ratio, the likelihood of casino adoption decreases 10280.043 among counties in Mississippi.  Despite the immense need suggested by the high poverty level in many of the counties in this regression model, the null hypothesis cannot be rejected in regards to political culture’s impact on casino adoption in Mississippi.

Finally, party control also fails to report any statistical findings that allow for the rejection of the null hypothesis in this study.  Party control demonstrates that for each 1 percent increase in the odds ratio, the likelihood of casino adoption increases 2.0026 among counties in Mississippi.  In other words, those counties considered more Democratic tend to support casino adoption compared to counties considered more Republican.  Although this inference is suggested by the data, the insignificance of the p <.05 or p<.1 does not allow for the rejection of the null hypothesis.

Despite the lack of statistical support for many of the socio-demographic variables in the regression model, this study lends support to previous lottery and casino studies (Berry & Berry, 1990) by suggesting that the fiscal health of a county is the primary indicator of casino adoption in Mississippi.  Therefore, the null hypothesis (H1) Counties with better fiscal health are likely to adopt casino gaming in a similar manner as those counties with poor fiscal health, is rejected in this study.  The other four null hypotheses cannot be rejected, however, suggesting that these variables display little impact on the regression model.  [2]

This study lends empirical support to Stanley’s (2001) case study research by suggesting that the adoption of casino gaming in Mississippi is primarily a financial issue rather than a moral issue.  Stanley (2001), notes that Biloxi residents voted on casino gaming twice.  In the first vote, casino gaming was defeated easily because policy makers sold the issue as a supplemental source of revenue for the general fund, an issue that Pierce and Miller (1999) addressed as well.  Stanley (2001) found that various local interest groups, including governmental employees representing public safety, public education and public transportation, lobbied local policymakers and suggested that earmarking funds for specific governmental programs would lead to the passage of the casino bill in Biloxi, Mississippi. After policy makers designated 20 percent of the casino proceeds received through the 3.2 percent tax levied on the gaming industry into these social programs, Biloxi, adopted casino gaming by fourteen votes (Stanley, 2001).  However, the absence of such variables as interest group strength and political ideology in the regression model limits the findings of this study.  Future studies should include such variables in order to establish a better understanding of local political factors that may impact the diffusion of gaming policy among counties in a similar manner as reported in the literature of Berry and Berry (1990), Furlong (1998), and Pierce and Miller (1999). 


The purpose of this research is to explain the adoption of casino policy among counties in Mississippi. Various socio-demographic variables are measured through the use of logistic regression analysis to determine casino adoption in Mississippi.  The data suggests that the fiscal health of a county is the best predictor of casino adoption among Mississippi counties voting on the proposed legislation.  Due to the limited size of the data set (n=42) and the absence of political variables explaining variations among counties, the results of this research may be limited.  The inferences of this study suggest that counties experiencing fiscal problems, if given the opportunity, may choose a gaming device as a supplemental source of revenue.


Barry, Tom (1995). “The Indiana Queen Who Made a Georgia Governor’s Gamble Pay Off For Education”, Georgia Trend, December, pp. 20-67.


Berry, Frances Stokes, and William D. Berry (1990). “State Lottery Adoptions as Policy Innovations: An Event History Analysis”, American Political Science Review, Vol. 84, pp. 395-416.


Black, Earl and Merle Black. (1987).  Politics and Society in the South.  Cambridge: Harvard University Press.


Borg, Mary O., Paul M. Mason, and Stephen L. Shapiro (1991). “The Incidence of Taxes on Casino Gambling “, The American Journal of Economics and Sociology, Vol. 50, No. 3, July, pp. 323-332.


Brown, Neil M.; Kubasek, Nancy K. (1997).  “Should We Encourage Expansion of the Casino Gaming Industry?”  Review of Business.  Vol. 18, # 3, p. 9.


Clynch, Edward J. (1972). “A Critique of Ira Sharkansky’s The Utility of Elazar’s Political Culture”, Polity, Vol. 5, pp. 139-141.


Elazar, Daniel J. (1984).  American Federalism: A View From the States.  3rd ed. New York: Harper and Row.


Ellison, Christopher G., Nybroten, Kathleen A. (1999). “Conservative Protestantism and Opposition to State-Sponsored Lotteries: Evidence from the 1997 Texas Poll.” Social Science Quarterly, Volume 80, No. 2, June, p. 356-370.


Fox, John. 1991.  Regression Diagnostics:  Quantitative Applications In The Social Sciences.  California:  Sage Publication.


French, P. Edward,; Stanley Rodney E. (2001). “An Empirical Assessment Measuring the Impact of Lottery Proceeds On Per Pupil Expenditures In America.”  Forthcoming Publication In The Social Science Journal.


Furlong, Edward J. (1998).  “A Logistic Regression Model Explaining Recent State Casino Gaming Adoptions.”  Policy Studies Journal, Vol. 26, No. 3, p. 371-383.


Gray, Virginia (1973) “Innovation in the States: A Diffusion Study”, American Political Science Review, Vol. 67, pp. 1174-1193.


Gross, Meir  (1998).  “Legal Gambling as a Strategy for Economic Development.”  Economic Development Quarterly.  12: 203-211.


Key, V.O. Jr. (1949).  Southern Politics in State and Nation.  New York: Random House.


Livernois, John R. (1987) “The Redistributive Effects of Lotteries”, Public Finance Quarterly, Vol. 15, No. 3, July pp. 339-351.


Mason, Paul M.; Stranahan, Harriet (1996).  “The Effects of Casino Gambling on State Tax Revenue.”  Atlantic Economic Journal.  Dec., Vol. 24, p. 336.


Mikesell, John L (2001). “Lotteries in State Revenue Systems:  Gauging A Popular Revenue Source After 35 Years.”  State & Local Government Review. Vol. 33, # 2.


Mikesell, John L (1989).  “A Note on the Changing Incidence of State Lottery Finance.”  Social Science Quarterly.  70: 513-521.


Mikesell, John L.; Zorn, Kurt C.  (1986). “State Lotteries as Fiscal Savior or Fiscal Fraud:  A Look at the Evidence.”  Public Administration Review.  July/August, p. 311-320.


Miller, Donald E.; Pierce, Patrick A. (1997). “Lotteries for Education:  Windfall or Hoax?”  State and Local Government Review.  29: 34-42.


Mississippi Gaming Commission (2002).  Official Homepage. Available at: (


Mississippi Gaming Control Act (1990).  Mississippi Code Sections 75-76-100; 75-76-195.


Nardulli, Peter F. (1990) “Political Subcultures in the American States”, American Politics Quarterly, Vol. 18, pp. 287-315.


National Gambling Impact Study (2000).  Official Website. Available at: (


Oliver, Michael J. (1995).  “Casino Gaming on the Mississippi Gulf Coast.”  Economic Development Review.  Vol. 13, #4, p. 34-42.


Ostrom, Charles W. Jr.  1978.  Time Series Analysis: Regression Techniques.  California: Sage Publication.


Pable, William (1996). “Gambling on the Future”, Public Management, December, pp. 8-10.


Perniciaro, Richard C. (1995). “Casino Gambling in Atlantic City:  Lessons for Economic Developers”.  Economic Development Review.   


Pierce, Patrick A., Miller, Donald E. (1999). “Variations in the Diffusion of State Lottery adoptions: How Revenue Dedication Changes Morality Politics.”  Policy Studies Journal, Volume 27, No. 4, p. 696-706.


Rivenbark, William C.  (1997). “Taxation and Revenue Generation”.  Public Administration Quarterly.  Vol. 24, Issue, 2, p. 267.


Rivenbark, William C.; Rounsaville, Bradley B. (1995). “The Incidence of Casino Gaming Taxes in Mississippi:  Setting the stage”.  Public Administration Quarterly.


Rodgers, William M. and Charles Stuart (1995). “The Efficiency of a Lottery as a Source of Public Revenue”, Public Finance Quarterly, Vol. 23, No.2, April, pp. 242-254.


Savage, Robert L. (1981). “Looking for Political Subcultures: A Critique of the Rummage – Sale Approach”, Vol. 43,  pp. 331-336.


Shaffer, Stephen D. (2001). “Political Parties in Modern Mississippi,” in Politics in Mississippi, 2nd edition, edited by Joseph B. Parker, Sheffield Publishing Company.


Shaffer, Stephen D. (2001). Entries on “Mississippi Democratic Party,” “Mississippi Republican Party,” and “Trent Lott” in the Encyclopedia of the Republican Party and the Encyclopedia of the Democratic Party, produced by the International Encyclopedia Society, M.E. Sharpe publisher, forthcoming.


Shaffer, Stephen D. (1997). “The End of Regionalism? Revisiting V.O. Key’s Delta Versus Hills Sectionalism in Mississippi Politics,” with Latarsha Horne, American Review of Politics, vol. 19, summer,  97-114.


Sharkansky, Ira (1969). “The Utility of Elazar’s Political Culture: A Research Note”, Polity, Fall Vol. 2,  pp. 67-83.


Spindler, Charles J. (1995).  “The Lottery and Education: Robbing Peter to Pay Paul?”  Public Budgeting and Finance.  3: 54-62.


Stanley, Rodney.  (2001). “The Effect of Casino Gaming On Financing Education In Mississippi.”  Dissertation.  Mississippi State University.


Von Herrman, Denise; Ingram, Robert; Smith William C. (2000). “Gaming In the Mississippi Economy.”  Published by the University of Southern Mississippi. 



Table One:

Formal Model



Fiscal Health

Religious Affiliation                                                               

Political Culture                                                                                 Casino Adoption        

Poverty Level

Party Control

Education Level                       




Table Two:



Variables                         B                  SE                 Wald               Sig.               Exp (B) 

Fiscal Health                 -.0001              .0006               .0352               .0352               .9999  

Religious Affiliation       .0009              .0003               .0604               .8059               1.000  

Political Culture            .4462               .8532               .2735               .6010               1.562  

Poverty Level               9.238               11.05               .6978               .4035             10280.0

Party Control                .6944               .8142               .7274               .3937               2.002  

Education Level            12.77               16.04               .6373               .4247               353.62

Year                             -2.375              .9012               6.945               .0084               .0936

Population                    .0228               .0159               2.06                 .1512               1.023

Constant                     -.5635              1.128               .2494               .6175                          



-2 Log Likelihood         44.662

Goodness of Fit            39.443

Cox & Snell R ^ 2        .189

Nagelkerke R ^ 2         .263

N = 42


[1] See Mertler & Vannatta (2001) for other possible problems associated with the use of logistic regression analysis (p. 317).

[2] Preliminary analysis was conducted on the data set to check for multicollinearity.  In the original analysis the variable debt was included in the study.  However, due to multicollinearity this variable was deleted from the study and a second preliminary analysis was conducted on the data set and multicollinearity was not considered a problem (VIF and tolerance levels did not exceed the levels discussed by Fox, 1991).