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Appraising the Impact Of Casino Dollars On Local Government Assessment Values in the Magnolia State




Manuscript prepared for the symposium on Globalization and Urbanization: Challenges and Opportunities, sponsored by the Journal of Developing Areas, May 6-8, Nashville, TN.





Rodney E. Stanley

Institute of Government

Tennessee State University





















Objective: The purpose of this research is to explain the impact of the casino industry on assessment values in Mississippi.  Methodology: Various socio-demographic variables are measured through the use of pooled time series cross-sectional regression analysis for determining if the casino industry has impacted assessment values in Mississippi.  Results: The data suggests that the casino industry has failed to impact assessment values in local communities that chose to adopt the gaming industry.  Limitations:  The results of this research may be limited due to the nature of this study focusing only on 26 school districts over an eleven-year period. Implications: Local government policy makers should be cautious in banking on additional revenues associated with increased property values as a result of the casino industry.


The revitalization of Mississippi’s economy occurred in 1990 with the passage of the Mississippi Gaming Control Act, which authorized casino gaming in local communities that chose to adopt this revenue-generating device (Rivenbark, 1997).  During the 1980s, Mississippi was faced with severe budgetary hardships, due to the collapse of the oil industry.  Operating a mere budget $2 billion, policy makers in Mississippi were forced to slash many governmental programs and reduce the number of many other services.  However, since the adoption of casino gaming in Mississippi, governmental revenues have increased dramatically as a result of increased values on personal property (Clynch and Kaatz, 1999; Stanley, 2001).  Despite the existence of such perceptions in some of the research on casino gaming in Mississippi, none of the studies incorporate formal models testing the data.  This research fills a gap in the existing literature because no other quantitative empirical study deals specifically with the impact of the casino industry on personal property assessment values.  This study addresses the following research question: are casino school districts in Mississippi witnessing an increase in total assessment personal property values compared to matching noncasino school districts? 

Literature Review

Casino Gaming In America

Public administrators and political functionaries, in Mississippi and other American states, experienced a most intense and challenging decade during the 1990s.  State governments witnessed a tremendous increase in demands on their governmental services, and an unprecedented number of un-funded mandates from the federal government, along with a tax- payer revolt (Ryen, 1992).  As the demand for social intervention programs increased, and the amount of available resources for funding these programs decreased, governmental officials used their ingenuity in generating revenue to offset the cost of running government. “Games of chance,” in one variation or another were the revenue generating mechanisms chosen by many state governments as their “economic savior” (Rivenbark and Rounsaville, 1995: p.3). 

            According to Franckiewicz (1993), ten states have supported casino gaming as a supplemental revenue-generating device.  They are: Colorado, Illinois, Iowa, Louisiana, Mississippi, Missouri, Montana, Nevada, New Jersey, and South Dakota.  Recently, the states of Michigan and Indiana have also adopted casino gambling bringing the total of twelve states utilizing this revenue generating device to pay the expense of operating government (National Gambling Impact Study, 2000).  Recent scholarly endeavors measuring the economic impacts of casino gaming include: economic development (Oliver, 1995; Perniciaro, 1995) marketing and tourism (Denise von Herrman, Ingram, and Smith, 2000) municipal revenues (Clynch and Rivenbark, 1995; Clynch and Kaatz, 1999) taxation (Rivenbark and Rounsville, 1996; Rivenbark, 1998) and education (Stanley, 2001). 

The Gaming Industry’s Impact On Mississippi

            The idea of the casino as a revenue-generating source for state and local government in Mississippi is beginning to receive an enormous amount of attention in academic literature. Recent scholarly endeavors have included the impact of casino dollars on economic development (Oliver, 1995; Perniciaro, 1995) marketing and tourism (Denise von Herrman, Ingram, and Smith, 2000) municipal revenues (Clynch and Rivenbark, 1995; Clynch and Kaatz, 1999) taxation (Rivenbark and Rounsville, 1996; Rivenbark, 1998) and education (Stanley, 2001). 

The Casino Industry and Economic Development In Mississippi

Oliver (1995) provides the academic community with a detailed description of how casinos were adopted in Harrison County, Mississippi for enhancing economic revitalization. The revitalization of Mississippi occurred in 1990 with the passage of the Mississippi Gaming Control Act, which authorized casino gambling in local communities that chose to adopt this revenue- generating device.  During the 1980s, Mississippi was faced with severe budgetary hardships.  Mississippi was operating on a budget of around $2 billion, which was not sufficient enough to cover all the expenses that the state was incurring, and they were forced to slash governmental programs.  The one program that received the largest cut was education.

            Initially the casino was rejected by voters in Harrison County, but the author contributes the adoption of the casino to educating the populace on the social benefits, primarily economic development aspects, that casino revenue would bring to their community.  The Harrison County Development Commission hired an independent agency from Reno, Nevada to conduct an economic impact assessment on the effects that dockside gambling would have on their community.  The independent assessment agency performed the impact analysis by identifying the number and size potential of dockside gambling operations.  They determined that each facility would accommodate a certain number of gambling devices due to limited space.  Since gambling was new in Mississippi there were no counties to study.  Therefore, they selected two comparison groups in Nevada to conduct their study.  The two groups used in the analysis were North Shore of Lake Tahoe and Laughlin.  Both comparison groups are municipalities in Nevada.  The analysis compared anticipated tax revenue to estimated increased costs, and identified deficit or surplus positions to local and state governments.  The analysis estimated that revenues from dockside gaming should be around $37 million dollars the first year of operation.  Once the impact assessment was completed, the Harrison County Development Commission began arguing that their community would benefit economically through casino revenues. 

            Residents in Harrison County were eventually convinced that casinos might generate valuable resources for their community.  In 1990, the constituency voted on the adoption of dockside gaming.  To the surprise of many local governmental leaders, the casino, in its first year, exceeded the amount of revenue that the impact analysis had initially stated.  In 1993, the US News & World Report cited Mississippi as the number one state in economic recovery.  The magazine credited the casino industry with this success story (Oliver, 1995) (Harrison County does not have any casinos, except in the municipalities of Biloxi and Gulfport.  However, D’Iberville has passed legislation that will allow casinos in their community).

            Additionally, Perniciaro (1995) reports similar findings to Oliver.  However, his work deals specifically with the impact that casinos have had on economic development in Atlantic City, New Jersey.  Atlantic City adopted the casino industry in 1976 after several years of population decline.  The city was facing bankruptcy due to industry relocation.  There were hardly any jobs for it’s residents and the city was gradually losing it’s tax base because people were relocating to find work.  Therefore, the city desperately needed to find new avenues for economic development in order to continue to exist.  This is when local policy makers decided to try the casino industry.  By the end of 1994, some $5 billion had been invested, and revenues of over $3 billion were being collected annually.  The unemployment rate dropped to its lowest level ever in 1994, to 3.4 percent. 

Casinos, especially in Mississippi, have been credited with generating enormous amounts of economic growth, which resulted in larger tax revenues.  This economic growth is important to education funding both directly and indirectly.  For instance, when businesses decide to build in a casino district, the millage rates of the school district are affected.  These taxes are then placed into a general fund account or directly funneled into a specific program such as education.  This depends on local legislation passed by school boards, city councils, etc.  Clynch and Kaatz (1999) postulated that millage rates would decrease as more businesses decided to relocate in casino districts.  They argued that casino districts could afford to lower their millage rates in order to attract business.  In theory, attracting more business would increase the amount of revenue local governments would receive from property taxes, even if the rate were decreased.  The authors found that millage rates have remained the same in Mississippi, but contend that casinos in Mississippi are still relatively new, and as time passes they claim that millage rates will decrease, resulting in more economic development from future private investment in casino districts.

Mississippi Casinos, Marketing and Tourism

            Denise von Herrman, Robert Ingram, and William C. Smith (2000), in a gaming impact report on marketing, tourism, and economic development, funded by the Mississippi Legislature, argue that casino gaming in Mississippi has dramatically impacted the areas of tourism.  The authors contend that hotels, air services, dining, retail, leisure attractions, convention facilities, and entertainment attractions have all witnessed an increase in the number of individuals served, because of casinos.  With this increase in tourism, Mississippi has witnessed an increase in tax revenues from these industries.  They recommend that Mississippi should pursue a stronger effort at marketing these ventures in order to increase the amount of taxation from tourism in the future.  However, the authors are against any increases in the tax rate on casinos because Mississippi already taxes its casinos at a higher rate than most other casino states.  Furthermore, many of the casinos in Mississippi are operating below the profit margins of similar casinos in other states because of Mississippi’s tax rate.  They contend that an increase in the tax rate may cause some casinos to go bankrupt, and cause others to find Mississippi unprofitable and completely close their gaming establishment.  In their overall assessment of casinos on tourism, marketing, and economic development in Mississippi, Denise von Herrman, Robert Ingram, and William C. Smith posit that casino gaming has made a positive impact on these areas in society.

Mississippi Casinos and Municipal Revenues

            Another issue addressed in the academic literature regarding casinos is the impact that casino generated revenue is having on the fiscal health of municipalities.  Clynch and Rivenbark (1995) stated that the casino industry in Mississippi impacted the general fund revenues significantly.  The initial projections of this impact were far exceeded by the actual amount of revenue generated by casinos.  In turn, Mississippi witnessed a large increase in the general fund revenues.  Furthermore, Mississippi began witnessing an increase in revenues from other sources as well.  For instance, more jobs resulted in an increase in the amount of individual income taxes received by the state.   Clynch and Kaatz (1999) concluded that municipalities in Mississippi have benefited tremendously from casino revenues.  The authors argued that assessment values increased dramatically in municipalities housing casinos. Furthermore, the authors found that general revenues increased and expenditures on public works and public safety increased as well, to meet the growing needs of population growth.  Basically, many of the municipalities in Mississippi housing casinos have witnessed population increases that have put a strain on their infrastructures.  The casino revenue has made it possible for these municipalities to keep up with these growing demands.  The authors conclude that despite millage rates remaining the same, which they predicted to decrease, the overall year-end balances for operating budgets have increased significantly since the adoption of the casino industry in Mississippi.

Overall, governmental employees throughout Mississippi attribute Mississippi’s recent economic success to the gaming industry (Stanley, 2001).  For example, from 1992 to 1997, the assessed value of property in Tunica County rose from $16.1 million to $566.1 million.  As a result, the school millage rate declined from 11.4 cents per $1,000 assessed value to 4.2 cents per $1,000.  In other words, the tax bill on an $80,000 home dropped from $912.08 to $338.40 in five years (Mississippi Gaming Commission, 2000). 

Mississippi Casinos and Taxation

            A third issue regarding casino gaming in the academic literature deals with taxation.  Rivenbark and Roundsville (1995) and Rivenbark (1997) postulate that casino taxation in Mississippi is regressive.  Rivenbark and Roundsville stipulate that since policy makers in Mississippi have allowed casino gaming in only specified areas, the tax incidence is placed on Mississippi residents.  The authors contend that accessibility to casinos plays a major role in the issue of tax incidence.  They argue that casinos are experiencing more play from local residents than people on vacation.  The authors attribute this to location.  The residents of Mississippi are paying most of the taxes received from casinos.  Rivenbark (1998), through telephone interviews, and the use of log-linear regression analysis, demonstrates that the poor in Mississippi have more access to casinos and are paying more of the taxes.  From this data he concludes that casino revenues are regressive because Mississippi residents, compared to the rest of the country, are much poorer, and cannot afford to pay these taxes.  However, the allure of “get rich quick schemes,” such as the casino, attracts those who reside close to them.  Therefore, the location of casinos in low-income communities is having a regressive effect on the economy because of the immense play they are receiving from those individuals residing where casinos are located. 

Despite the regressive nature of this revenue-generating device, Mississippi has chosen to continue the operation of its casino industry.  Currently there are 31 casinos operating in Mississippi today (Mississippi Gaming Commission, 2000).  Thirty are state regulated and the Mississippi Band of Choctaw Indians operates one in Philadelphia, Mississippi.  The gaming industry produces 10 percent of the state’s annual budget.  Nearly $160 million is derived from tax collections on gross gaming revenues in the state-regulated casinos, and $140 million is generated by new sales taxes and income taxes (Mississippi Gaming Commission, 2000).  The casino industry currently employs approximately 38,000 people.  The payroll of all the casinos in the state is more than $600 million dollars a year.  From August 1992 through the present, the casino industry has contributed nearly $3 billion of capital investment in Mississippi.  The casino industry has also paid more that $1.2 billion in gaming taxes since July of 1992, and more than $841 million in state taxes, along with nearly $400 million to the local governments where casino gaming is legal (Mississippi Gaming Commission, 2000).

Mississippi Casinos and Education

In regards to impacting education, Stanley (2002) found that only four school districts in Mississippi were currently benefiting from casino proceeds.  Utilizing a pooled time series cross-sectional regression model, comparing thirteen casino school districts to thirteen noncasino school districts, the author concluded that due to the presence of twenty-four casinos in only four school districts, increases in per pupil expenditures for education are minimal in Mississippi.  Additional, through the use of personal interviews, the author found that many local government practitioners perceived the casino industry as an economic savior for school districts because the supplemental revenue generated by taxing the casinos has provided school supplies, updated technology and in some cases school renovations and new school buildings.  The author recognizes the limitations of qualitative research in making hasty generalizations about such social phenomenon and suggests that future studies should focus on the casino industry’s impact on assessment values since taxation of personal property is the primary source of local government revenue for education.

The Rationale For Studying The Casino Industry’s Impact On Local Government Assessment Values


            The funding formulas used by many local governments and special districts in Mississippi rely predominantly on property taxes.  The property tax is stable, easy to administer, and it generally taxes wealth.  Everyone pays property taxes in some form or another.  For the farmer it may serve as a tax on his capital investment; for the renter it is included within the rent payment; for the merchant it is part of the overhead that is ultimately passed on to the consumer in the price of goods or services (Wood and Honeyman, 1998).    

            Once the local governmental entity determines the local dollar amount to be raised through taxes, they issue what is called a millage rate to raise the funds.  A millage rate is administered by taxing the total assessed property value within the boundaries of the school district.  The assessed valuation is a percentage of the assessed value of the property. The millage rate is used in conjunction with assessed value to raise local money for education. The rate may be expressed as a mill or hundred rates.  Once the rate is established, the rate is applied to the individual assessed value of each parcel of real estate, and improvements therein to determine the individual tax each property owner must pay.  An example of this formula is as follows:  A school district determines that its local levy is to be $900,000 pursuant to state regulations and local need.  The assessed valuation is $60,000,0000.  The tax rate would be 15 mills or $.15 per $1000.  Thus, a home with an assessed value of $50,000 would pay $750 per tax year.  Therefore, the literature suggests that the assessment value, which determines the amount of property tax each individual will pay, is a major factor in the amount of revenue each school district will receive for education across America (Wood and Honeyman, 1998).

            Since the assessed value of personal property is the primary source of local government revenues for education, an empirical assessment of the casino industry’s impact on this source of governmental revenue is needed.  Are casino school districts in Mississippi witnessing an increase in total assessment personal property values compared to matching noncasino school districts?  The current research on the Mississippi gaming industry fails to address this issue leaving a gap in the literature.  To fill the current literature gap this study will test the following hypothesis:



H1: School districts in Mississippi with casinos have witnessed an increase in total assessed property values, compared to matching school districts in Mississippi without casinos.


Data & Methodology


Assessment value (Dependent Variable) Average Assessment value of personal property (measured in $100 thousand)[1]


Number of Students - the number of students in each Mississippi school district.


Millage Rates – the percentage of taxable income levied on real and personal property in each Mississippi school district.


Casino Presence – Dummy variable coded 0 = casino school districts; 1 = Non-casino school districts.


Unemployment Rates – Unemployment rates in school districts used as a proximity variable to test casino tax revenue’s impact on assessment value.  It is measured in terms of county data.


The model tested in this research project for empirical results using pooled time series analysis are: Total spending on per pupil expenditures for education. The time frame used in this analysis is eleven years: 1989 – 90 to 1999 – 2000 school year. 

Assessment values – Mississippi Report Card on Education, Mississippi

Department of Education


Number of Students – Mississippi Statistical Abstracts, Mississippi State University


Millage Rates - Mississippi State Superintendent’s Report on Education


Casino Presence – Dummy variable coded 0 = casino school districts; 1 = Non-casino school districts.


Unemployment Rates – Mississippi Statistical Abstracts, Mississippi State University


The following regression equations were used to test the regression model in this study:

Table One

1) Y (Assessed Value)  = a +  (B1) Casino PRESENCE t-1  + (B2) Unemployment rates t-1    + (B3) Number of Students t-1  + (B4) Millage Rates t-1  + E



Model: 1989-2000


Casino Presence                                                           

Unemployment Rate                                                              Assessment Value of Personal

Number of Students                                                                              Property

Millage Rates



Since the revenue for assessed value of personal property is used to fund education, the comparison groups were chosen premised on previous studies conducted by the Mississippi Department of Education.[2]  These studies utilized a process for choosing comparison groups based on approximation ranges in the number of students in each school district, spending on education per pupil, and per pupil assessment values by each school district.  The range categories used in selecting the comparison groups were as follows: 1000 – 15,000 for number of students, millage rate 15.00 - 90.00, and $10,000 – $50,000 for the assessment value of personal property in each school district. [3]  

Table Two



Mississippi School Districts Used In the Study


With Casinos                                                   Without Casinos (comparison groups)

Natchez-Adams County School District            Benton County School District

Coahoma County School District                      Carrol County School District

Clarksdale School District                                 Ocean Springs School District

Hancock County School District                       Lee County School District

Bay St. Louis School District                            Hattiesburg Municipal School District

Harrison County School District                        Jackson County School District

Biloxi City School District                                 Long Beach City School District

Gulfport City School District                             Pascagoula City School District

Tunica County School District               Ranking County School District

Vicksburg City School District              Tupelo City School District

Leland  School District                          Oxford School District

Western Line School District                             Webster County School District

Greenville City School District               Yazoo City School District



Table Three


casino gaming VERSUS NON-Casino School Districts BEFORE casino gaming 1989 – 1994


Group Statistics


                                     Dummy          Mean              St.D.                T – Score        p.>


(0 = Casino School Districts Before Casino Gaming)

(1 = Non-Casino School Districts Before Casino Gaming)



Assessment Value         .00                  3651                396.08             .905                 .367

                                    1.00                 3588                465.31            

Number of Students      .00                   5117                3120.43           .016                 .987

                                    1.00                 5110                3106.38

Millage Rate                 .00                   43.47               9.2616             -.709                .378

                                    1.00                 44.68               10.3307

Unemployment Rate     .00                   24355              8727.492         -.307                .760

                                    1.00                 24753              7469.109


            In Table Three, an independent samples t-test was conducted on the data set for 1994 (year before casino proceeds impacted per pupil spending on education in Mississippi), to measure the difference in per pupil spending between the casino school districts and matching non-casino school districts.  The independent samples means test analysis demonstrates that the differences between the casino school districts and comparison groups used in the study were virtually the same before casino gaming proceeds were spent on education by Mississippi.  This statistical report is important because it lays the foundation of the study by suggesting that the casino school districts and matching non-casino school districts reported no statistically significant differences in per pupil spending on education before casino gaming came to Mississippi, suggesting that the units of analysis used in the data set were comparable.


This research project uses “pooled time series cross-sectional data analysis” as the measuring device for the previously stated hypothesis (Beck and Katz, 1996: 1).  One of the most promising advantages of using pooled time series cross sectional analysis is its ability in offering explanations of the past, while simultaneously predicting the future behavior of exogenous variables in relation to endogenous variables.  Pooled time series cross-sectional regression analysis allows the researcher to focus on more than one case in predicting social phenomenon, whereas simple time series analysis strictly deals with specific cases at different time points causing data management complications, while also being costly and time consuming.  Furthermore, ARIMA time-series methods of data analysis place an overwhelming emphasis on the burden of controlling for autocorrelation and heteroskedasticity to ensure data dependability.  Autocorrelation and heteroskedasticity do pose threats to data analysis, however, according to Beck and Katz (1996) they are more of a “nuisance” than a real threat (p. 3). 

Despite the numerous advantages of pooled time series analysis using N (number of cases) at T (time points) for predicting the future of a particular social intervention program, a number of methodological disadvantages limit the usage of this data measuring device.  The basic assumptions underlying traditional Ordinary Least Squares (OLS) regressions are violated in a pooled model, and such departures may exhibit severe consequences for the reliability of the estimators (Stimson, 1985).  For instance, the following assumptions are usually made in regards to the error term in pooled time series regression.

1) The error term has a mean of zero,

2) The error term has a constant variance over all observations,

3) The error terms corresponding to different points in time are not correlated (Ostrom, 1978).


The accuracy of the regression model is inevitably measured by the error term.  Hence, if the standard error is small, then all of the sample estimates based on the sample size tend to be similar and considered representative of the population parameters.  The exact opposite is true if the error term is large, then the statistics fail to represent the population parameters.  Of the previously mentioned assumptions, the error term corresponding to different points in time failing to correlate is the most important assumption violation.  When the observations from different points in time are correlated, one of the assumptions is violated, usually the latter one.  When this violation occurs autocorrelation is present, creating estimators that negate true representation of social phenomenon. 

            Autocorrelation violates an assumption of the regression model that the residuals are independent of one another.  Its presence affects the accuracy of the error term, which biases the model’s t-ratios and the confidence limit.  Autocorrelation may be eliminated from a research project by identifying and including an independent dummy variable[4] that explains part of the unexplained variance.  Beck and Katz (1996) address the issue of autocorrelation by calling it more of a nuisance than a real problem.  They contend that lagging the endogenous variable(s) will assist in controlling for serial correlation.  A lagged regression model relates a current endogenous variable to past values of the exogenous and endogenous variables reducing the risk of autocorrelation. The Durbin-Watson M was reviewed to ensure that autocorrelation was not a problem in the data set (Durbin, 1970).

A second major methodological problem with pooled time series cross-sectional data analysis is heteroskedasticity.  In pooled data, some units, for a variety of reasons, are inherently more various than others at all times.  Such differential variability is usually of modest concern in un-pooled data because it affects only a single case at a time.  In pooled data, however, it is likely to inflict a larger amount of harm to data sets.  For instance, basic size differences between units are one such endemic source of heterogeneity.  To account for the differences among states, intercepts for the cross-sectional unit are employed.  On the reasonable assumption that variation is roughly a fixed proportion of size, analysis of units of substantially different sizes induces heteroskedasticity in any regression.  But the problem can take on considerable proportion that causes concern when each cross section consists of T cases in time.  Therefore, the size problem of the sample can be reduced by standardizing the data set (Beck and Katz, 1995).  The emphasis of this study is concerned more with changes across time rather than across school districts because as the t-test will suggest (later in the study) that virtually no difference exists between the experimental and control groups used in this study.  White’s test for heteroskedasticity was consulted, and the statistic suggested that this methodological nuisance was not a problem in the data set.

The variance inflationary factor (VIF) checks for multicollinearity among the variables (a situation in the data set where two or more variables are highly correlated) in the regression equation.  Instead of, however, accepting the validity of this statistic on the assumption that SPSS is right, measures were taken to test for this statistical problem.  All the variables in the equation were regressed against one another to ensure that, according to Fox (1991), no variables indicated a VIF of 5.6 or more.  Furthermore, the tolerance levels were reviewed and no variables reported levels below .9.  Therefore, multicollinearity was not considered a problem in the data set.

Findings & Discussion



                                         b                    st.e               beta                   t                       p.


(Constant)                             -305.147                  1377.295                                 -.222                        .825

Unemployment Rate            101.691                   115.112                   .021                         .883                         .378

Number of Students            2.571                       4.676                       .013                         .550                         .583

Millage Rates                        -.00489                    .006                         -.016                        -.802                        .423

Casino Presence                   -349.173                  5.497                       -.013                        -.577                        .565

(0 = Casino Districts)

(1 = Non-Casino Districts)


R                                             .941

R2                                                                  .886

Adjusted R2                                         .884

Df                                            4

F                                             495.246

Sig. Of   F                              .001

N =                                         286


            Table four is presenting the findings of the pooled time series cross-sectional regression analysis.  The adjusted R2 of the model reports an impressive variance of 88 percent, but nonetheless the model is identifying no statistically significant differences among the four exogenous variables and the one endogenous variable.  Inferring that casinos have impacted assessment values is an assumption that cannot be made from the data reported in this study, therefore, the null hypothesis failed to be rejected.  Other factors such as economic development and demographic locations of casinos need to be incorporated into a formal model to account for the location of casinos adjacent to and in school districts with casinos.  

            Due to the absence of a law stipulating the timing within which school districts must re-assess land the results of these statistical tests may be skewed.  Timing means that after 1992 school districts must re-assess 25 percent of their land every four years according to Mississippi law. Prior to the passage of this law, school districts were not required to re-assess 25 percent of their land every four years.  Despite this possible problem with the data, the analyses still serves an important purpose by indicating that the assessment value of casino school districts have changed since the adoption of casino gaming in Mississippi.


            Although many practitioners in Mississippi attribute the casino industry with economic revitalization of their economy, the statistical information in this study fails to support the notion that personal property assessment values have increased as a result of the gaming industry.  This research serves as empirical evidence to suggest that policy makers should think twice about banking on additional revenues that will be generating from increasing the value of personal property in casino school districts.  Relying on supplemental revenues for paying the cost of government may be noting more than an enormous hoax!



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[1] However, due to the absence of a law stipulating the timing that school districts must re-assess land, the results of these statistical tests may be skewed.  Timing means that after 1992 school districts must re-assess 25 percent of their land every four years according to Mississippi law. Prior to the passage of this law, reassessment was not a requirement for local governments in Mississippi.

[2] Charles Shivers, Director of Financial Accountability, Mississippi Department of Education and Dr. Gary Johnson professor of Educational Leadership at Mississippi State University stipulate that the Mississippi Department of Education has used the following indicators in the past to determine comparative school districts in various educational finance studies: average daily attendance, 1st month enrollment, property per pupil assessment values, whether the districts have 16th section trust lands, whether they are municipal or county districts, or rural or urban, per pupil spending, and total federal spending.  Mr. Shivers endorses the indicators (population, per pupil assessment value and spending per pupil) utilized in this study for generating the comparative school districts that were studied (Charles L. Shivers, CPA, Tuesday, January 9, 2001, 3:28 p.m.; Dr. Gary Johnson, January 8, 2001, 2:08 p.m.).  See the Mississippi Department of Education’s official Homepage for such studies.  Available at: (



[4] The dummy variable incorporated in this study is casino presence coded 0 = casino school districts and 1 = noncasino school districts.