1. *I would like to acknowledge Professor Bob Slagter of Birmingham-Southern College for his
help throughout all stages of this paper.
Education Funding, a State Lottery, and Morality Policy:
Can Time Heal All Wounds in Alabama?
By: Brett Snider
Birmingham-Southern College
This paper examines Alabama’s current methods and levels of education funding in terms of the
contribution a lottery could make to provide an adequate and consistent source of revenue.
Resistance to a lottery is assessed in terms of the extent and type of religious belief in the state
and its impact through morality policy. The paper proposes a lottery as the solution to Alabama’s
education funding problem. It assesses the dynamic of public support for a lottery through
analysis of statewide surveys conducted in 2003 and 2010. A major finding is that support for a
lottery in Alabama increased from 2003 to 2010, showing that Alabama may be ready to enact a
lottery in order to improve its education funding.

2. Snider 2
The education system in Alabama has been neglected due to Alabama’s history with an
exploitation economy. However, as Alabama has moved away from industries that rely on
unskilled labor, the need for a better education system has come to the forefront (this will be
expounded upon in further detail later). The solution to Alabama’s education funding is a state
lottery. State lotteries have been beneficial for many states, including others located in the
Southeast such as Georgia, Tennessee, and South Carolina. In fact, Alabama brought the lottery
to a statewide vote in 1999. However, a coalition of religious leaders came together in opposition
of the lottery on the grounds of morality, believing lotteries take advantage of the poor by
instituting a regressive tax (Powell and Self, 2001). The morality-based argument eventually
prevailed and the lottery was rejected in Alabama by a 54-46 margin. Following the failed lottery
vote, Alabama continued to fall further behind in education through the 2000s, as the state
legislature was consistently unable to provide adequate funds or find an alternative method to
funding education (Leachman and Mai, 2014). Because of this ineptitude, it is time for Alabama
to once again examine the possibility of a lottery, especially considering Governor Robert
Bentley’s announcement during his 2015 inauguration speech that “We [Alabama] face a budget
shortfall that rises into the hundreds of million dollars” (Cason, 2015). This paper will attempt to
show support for the hypothesis that Alabama’s religious base has shifted its support in favor of
enacting a lottery in order to fund education.
Alabama’s Educational Woes
Alabama’s education system has been notoriously underfunded throughout all of recent
history. The 1999 lottery referendum vote would have helped to ease the burden, yet it was
denied. Instead, Alabama relies on the Education Trust Fund, or ETF, to fund public schools.
The ETF, whose budget held $5.85 billion in 2007, generates revenue from a statewide income

3. Snider 3
tax and a 4% sales tax (Key, 2010). This does not produce enough revenue to adequately fund
Alabama’s education, but it has to suffice due to Alabama’s inability to raise taxes. The Lid Bill,
a constitutional amendment from 1978, inhibits the increasing of property taxes by setting
assessment ratios of fair market values at 10% for residential and agricultural property, 20% for
commercial property, and 30% for utilities properties (Key, 2010). As a result, more than 80% of
Alabama’s educational funding comes from income taxes, even though the state’s median
household income of $54,045 in 2013 was the fourth lowest in the nation (“State Median
Income”, 2014). This poor funding is especially noticeable in Alabama’s rural schools. Rural
schools are more important to Alabama than most other states, as Alabama ranked 11th nationally
in terms of amount of rural students in 2008-2009 (Lindahl, 2011). However, Alabama spends
over $1,000 less than the national average on rural per pupil expenditures (Lindahl, 2011).
Unfortunately, the poor funding has translated to poor results. Alabama ranks near the bottom
among states in most major educational categories, including 47th in high school graduation and
48th in math proficiency in 2008 (Key, 2010).
Education funding in Alabama took an even larger hit after the economic recession of the
late 2000s. Over the six year period from 2008 to 2014, Alabama cut its per pupil expenditures
by $1,144 (Leachman and Mai, 2014). This cutback translates to a 12.2% decrease in spending
per student in Alabama (Leachman and Mai, 2014). Alabama has been unable to replace the lost
educational revenue caused by the recession, either. Six years after the 2008 recession began,
Alabama boasts the largest negative change in spending per student in the nation (Leachman and
Mai, 2014). The state level cuts are extremely detrimental in Alabama because of the previously
mentioned Lid Bill. Local school districts are expected to generate the lost revenue from state
cuts through raising local taxes, a near impossible task in Alabama. The reduced funding has

4. Snider 4
consequences beyond education as well. The economy has taken a hit because the lost jobs
related to education funding cuts have lessened spending and lowered tax revenues. A viable
solution to counteracting these educational woes lies with a state lottery. A state lottery has the
potential to increase education funding in Alabama and help get the economy back on track.
Lotteries in the South
Lotteries are now commonplace throughout the United States, as 43 states and
Washington DC all allow the form of gambling to take place. However, while lotteries have a
large presence in the United States today, they are a relatively new policy ideal. States enacted
lotteries in a sequence known as regional diffusion, which is the process of state-level policy
makers borrowing policy solutions from other states (Ingle et. al, 2007). Jensen (2003) argues
that such policy diffusion is a result of increasing legitimacy. As lotteries expanded from region-
to-region and state-to-state, they gained legitimacy and opposition on moral grounds diminished.
Jensen (2003) conducted a study from 1964-1996 in which he found support for his hypothesis
that moral legitimation regarding lotteries had occurred. The first lottery votes took place in New
England during the 1960s. These votes were followed by Midwest and Western states in the
1970s and 1980s, while Southern states began considering lotteries in 1986 (Bobbitt, 2003). This
regional policy diffusion coincided with states assuming larger roles in educational funding, as
lottery revenue was seen as the optimal funding option (Moon and Shin, 2005). Moon and Shin
(2005) found that per pupil expenditures were increasing due to the influx of lottery revenues, as
a one-dollar increase in lottery revenue added $1.524 to the education spending per pupil.
Typically, there are two types of lotteries in the United States: general fund lotteries and
education lotteries. Pierce and Miller (1999) found that it is important to distinguish what lottery
revenues will be funding, as education lotteries do not incur the same type of opposition as

5. Snider 5
general fund lotteries. In their model, Pierce and Miller (1999) noted that marking a lottery as
educational helped to counteract the religious fundamentalism opposition based on sinful
gambling. These findings were reasserted by Ferralio (2013) who saw that lottery revenues
targeting education could invoke moral legitimation. As of 2005, 41 lotteries existed in the
United States. Eleven of these lotteries were general fund lotteries, while eighteen were fully or
partially earmarked for education (Kearney, 2005). Tennessee, Georgia, Florida, and South
Carolina are all among the Southern states that earmark their lottery revenue for education.
Arguing Against a Lottery: Fungibility and Morality
While it may seem earmarking funds for education is a surefire way for states to enact a
lottery, that is not so. Keating (1996) found that states with lotteries intended to fund education
spend less on education than states without lotteries. One of the biggest critiques of state lotteries
is a lack of transparency and a history of misused funds. This concept revolves around
fungibility; once lottery funds meant to enhance the educational funds are received, they often
supplant rather than supplement the current means of funding (Stanley and French, 2004).
Campbell (2003) discovered that Florida is a prime example of fungibility, as lottery revenues
have taken the place of non-lottery revenues intended for education, and the non-lottery revenues
originally intended for education have been dispersed to other areas. Lottery opponents often use
fungibility in an attempt to show that lotteries are unsuccessful in funding education. However,
fungibility can be prevented through careful implementation of lottery legislation, shown by
Georgia, which will be examined in more depth later.
Morality and religion play a large role in lottery opposition, as alluded to earlier. There is
a deeply entrenched tradition of religion in the United States, stemming from the colonial era and
continuing still today. For example, Vincent et. al, (2006) noted that only seven percent of

6. Snider 6
United States citizens identified themselves as atheist or agnostic (Vincent et. al, 2006). Religion
plays an even larger role in the South, where it is especially prominent. Vincent et. al (2006)
found that 75% of Southerners compared to 57% of Easterners think that religion can solve the
world’s problems. Oftentimes, religion will be used to shape political views, a relationship
addressed by Campbell (2004), Mooney (2008), Knill (2013), and Satterwaithe (2013). This use
of religious values to frame political ideals has become known as morality policy. However, not
all morality policy is identical; thus, Knill (2013) divided morality policy into two categories:
manifest and latent. According to Knill (2013), manifest refers to when issue positions are based
on value conflicts, while latent means the value conflicts are not inherent but they can be framed
as such. Some issues, such as abortion, more easily invoke the moral dimension; these are
manifest morality policies. Oftentimes, though, politicians and community leaders will use
morality to shape an issue in an attempt to sway the outcome; these are latent morality policies.
Gambling, and lotteries by definition, falls under the latent morality category. In Alabama’s case,
the religious coalitions that formed to oppose the lottery shaped the issue as morality policy. The
importance of this cannot be understated, as the religious presence in the South boosts the impact
of such an action.
When considering that one of the biggest opposition groups to any form of gambling is
Evangelical Protestants (Campbell 2004; Satterhwaite 2005), the moral framing of the Alabama
lottery as detrimental to the poor had a key impact on the referendum’s failure. As Campbell
(2004) noted, church mobilization in Alabama for the 1999 referendum reached unprecedented
levels, which resulted in Evangelical Protestants turning out at 56% of the total voters. Campbell
(2004) used EI software that estimated 81% of the Evangelical Protestant voters who turned out

7. Snider 7
for the lottery referendum opposed it. Thus, the ability of Evangelical Protestants to affect the
outcome of the lottery illustrates the heavy presence of morality policy in Alabama.
Alabama and Mississippi are the only states to holdout on enacting a lottery in the South,
though Mississippi does have a large presence of casinos. This inability to enact a lottery is
typically attributed to the failure of the moral legitimation in Alabama. However, Vincent et. al
(2006) found Alabama’s religious composition to be very highly correlated with Georgia (0.988),
Tennessee (0.997), and South Carolina (0.988), all of which were able to overcome issues with
morality, adopt state lotteries, and receive supplemental revenue. In Georgia, Tennessee, and
South Carolina, lottery proponents faced heavy opposition from religious groups similar to the
coalition that arose in Alabama. Pro-lottery groups in Tennessee faced off against the Gambling
Free Tennessee Alliance, which was made up of the major Baptist, Catholic, and Methodist
leaders, yet Tennessee passed the lottery by a 58-42 margin (Bobbitt 2003). In South Carolina,
Olson et. al (2003) noted that the South Carolina Christian Action Council was the most
prominent opposition group, but it could not sway the public enough as the South Carolina
lottery passed with a 56% majority. These examples indicate that, while religion may be a large
factor in the lottery votes, opposition based in morality can be overcome.
Interestingly, while fungibility and morality may prove difficult to circumvent when
attempting to enact a lottery, they do nothing to stop people from playing the lottery. In fact,
lottery gambling is a booming industry. Borg et. al (2005) noted that 38 states generated $11.62
billion in lottery revenue in 1997, while Kearney (2005) found a $19.9 billion revenue in 2003.
Ghent and Grant (2007) found that even religion, which had a significant, negative effect on the
lottery vote, did not hinder ticket sales in South Carolina. Naturally, with a multi-billion dollar
business such as lotteries, citizens from states without lotteries are crossing state lines and

8. Snider 8
buying lottery tickets. Cross-border lottery shopping provides a large amount of revenue for
lottery states bordering non-lottery states. Data from both South Carolina (Ghent and Grant,
2012) and Tennessee (Giacopassi et. al, 2006) show the impact of cross-border lottery shopping.
Ghent and Grant (2012) estimated that North Carolina residents contributed approximately $1.1
billion to the South Carolina Education Lottery over the first fifty months of its enactment, of
which South Carolina received $423 million in revenue. Giacopassi et. al (2006) found that
Tennessee counties bordering Alabama had higher expenditures than other counties due to cross-
border lottery shopping (This concept is why outside gambling interests are so keen to deprive
Alabama of a lottery, which will be discussed later). Pro-lottery groups see cross-border lottery
shopping data as an example of revenue escaping the state and supplementing the budgets of
other states.
Implementing an Education Lottery: The Georgia Example
The Georgia lottery is an excellent example of how to overcome different forms of
opposition and enact a lottery in the heavily religious South. Georgia avoids fungibility by
keeping its state lottery as transparent as possible. Instead of supplanting old education funds, the
Georgia lottery enactment created four programs that only receive funding from lottery revenue.
The four created programs include the HOPE Scholarship Program, the Pre-Kindergarten
Program, the Technology Program, and the Construction Program (Mccrary, 2003). During the
first seven years of the Georgia lottery’s existence, 488,000 Georgia high school graduates
received college scholarships, 308,000 pre-schoolers had their attendance fully funded, $1.6
billion was spent on technology purchases, and $600 million went to education construction
(Mccrary, 2003). This highly successful means of funding has been met with excellent approval
ratings as well. Campbell (2003) found that 93 percent of respondents approved of the HOPE

9. Snider 9
Scholarship, while 85 percent approved of the pre-kindergarten programs. If Alabama wants to
overcome opposition and increase its education funding, the Georgia lottery is a great example to
follow.
Alabama’s 1999 Lottery Referendum
The lottery vote in Alabama came on the heels of Governor Don Siegelman’s election
after Siegelman built his campaign on the idea of a lottery to fund education. In order for
Alabama to enact a lottery, a public referendum is needed to amend the Constitution (Carlton,
2000). The Alabama lottery referendum was held on October 12, 1999, and many of the pre-vote
polls showed majorities in favor of the referendum. According to Powell and Self (2002), the
pro-lottery voters viewed the lottery purely from a financial standpoint, hoping to build a better
financial base that would help fund Alabama’s underperforming education system. These voters
were opposed by a coalition of religious leaders in Alabama. As mentioned earlier, the anti-
lottery coalition successfully painted the lottery as morally wrong, which resonated heavily with
the religious base of Alabama. Powell and Self’s 2002 study found that 60.7% of people who
attended church weekly were anti-lottery voters. While the religious coalition certainly seems to
have had the largest effect on Alabama’s lottery vote, they were not the only opponents. Ingle et.
al (2007) found that a contingent of outside gambling interests, including the Choctaw Indians of
Mississippi, also funneled money into the opposition movement. Alabama’s lottery proponents,
unable to overcome the combination of the religious coalition and outside business’ interests,
watched as the Alabama Education Lottery was vetoed by a 54% majority.
Methods
This study attempts to examine whether support for a lottery in Alabama has shifted,
particularly in terms of the religious base, since the lottery referendum in 1999. In order to

10. Snider 10
examine the changing attitudes, this study includes two separate surveys conducted by
Birmingham-Southern College: one from 2003 and one from 2010. The surveys, conducted both
over the telephone and on the Internet, addressed a multitude of areas, though the questions
regarding a state lottery and religion are the focus of this study. The participants were at least
eighteen years old and representative of Alabama’s electorate. The 2003 survey had 551
respondents, while the 2010 survey had 642 respondents.
In order to best evaluate the changing opinions between 2003 and 2010, I combined the
data from the two surveys and created a new dataset. In order to do this, I picked relevant
variables from both years and renamed each variable from the 2010 dataset so that it matched the
naming of the same variable in the 2003 dataset. Within this new dataset, I created a variable
called “Survey Year.” This method allows me to better interpret both the correlations and the
regression because I can analyze the relationship between my variables and the change in time.
Respondents
Referring to Table 1, 64% of the respondents in 2003 were females. Of the 549
respondents who gave their ethnicity, 78.5% were white. 481 people responded to the question
regarding income, of which 42.61% stated their income to be over $50,000. Furthermore,
42.37% of the respondents were college graduates. The mean age of the respondents was 46.78
years old. The respondents were generally moderately conservative with Republican leanings.
Table 1 also shows that 59.56% of the 2010 respondents were female. Similarly to 2003,
the 2010 respondents were mainly white at 81.28%. The 2010 respondents’ income and
education aligned closely to the 2003 respondents as well. 43.23% of 2010 respondents reported
incomes of over $50,000, while 39.06% said they were college graduates. In 2010, the mean age

11. Snider 11
of the respondents was 53.03. The 2010 respondents were slightly more conservative and
Republican than the 2003 respondents.
Table 1 about here
Measures
This study relies on single questions regarding approval of lotteries from both the 2003
and 2010 questionnaires. In 2003, the lottery question solicited an agree/ disagree response from
the statement, “Alabama should establish a lottery for education.” The 2010 lottery question
opted for a more general approach by using a support/ oppose format but connecting the lottery
to tax revenue and state governments as a whole. The 2003 question was coded on a scale of
agreement in which 1 represented strongly disagree, 2 represented disagree, 3 represented
neutral, 4 represented agree, and 5 represented strongly agree. In 2010, the scale was reversed as
1 represented strongly support, 2 represented support, 3 represented oppose, and 4 represented
strongly oppose. Thus, I recoded the 2010 scale to mirror that of the 2003 coding so that strongly
oppose was the same value as strongly disagree (1), oppose was the same value as disagree (2),
support was the same value as agree (4), and strongly support was the same value as strongly
agree (5). In the recoded scale, lower values meant less approval while higher values meant
higher approval.
The two variables I chose to evaluate the religiosity of the respondents were church
attendance and the view of the Bible as the literal word of God. My reasoning for choosing these
two variables was twofold. For one, the questions measuring these two variables were similar
across both surveys. Secondly, church attendance and the literal view of the Bible have often
been used to evaluate religiosity (Campbell, 2004; Mckeown and Carlson, 1987; Ellison and
Nybroten, 1999; Jensen, 2003). I used these two variables to examine whether religion has had a

12. Snider 12
changing effect on support for a lottery over time while also controlling for the effects of age,
ethnicity, education, income, gender, ideology, and party identification. The question regarding
church attendance referred to the average amount of times one attends church a month. For both
the 2003 and 2010 data, I recoded the church attendance variable with a cutoff of twenty times
per month in order to remove extreme outliers. In 2003, there was a mean church attendance of
4.5551 times a month, while the 2010 data showed a mean of 4.1140 times a month. The literal
view of the Bible question was a nominal variable originally. I recoded this variable for both
datasets into two dichotomous variables: BibleLiteral and BibleNot. For the BibleLiteral
variable, one equaled “The Bible is the literal word of God” while zero equaled all other
responses. For the BibleNot variable, one equaled “The Bible is not the literal word of God”
while zero equaled all other responses. In 2003, 47.56% of respondents believed the Bible to be
the literal word of God, while 31.26% believed the Bible to be the word of God but not
everything in it should be taken literally. In 2010, 51.83% of the respondents believed the Bible
to be the literal word of God, while 30.46% saw the Bible as not literal.
Table 2 about here
Table 3 about here
Results
Table 4 presents a crosstabulation between lottery support and survey year. This table shows
that, in 2003, 40.1% of respondents disagreed or strongly disagreed with the lottery. In
comparison, 60.0% of the respondents showed support for a lottery, while 28.4% strongly
agreed. In 2010, only 30.0% of the respondents somewhat or strongly opposed the lottery, an
11.1% decrease from 2003. In contrast, 70.0% of the respondents from 2010 supported the

13. Snider 13
lottery. 40.5% of the 2010 respondents strongly agreed with a lottery, an increase of 12.1% from
2003. Overall, Table 4 shows a 10.0% increase in support for the lottery from 2003 to 2010.
Table 4 about here
I chose to use the Mann-Whitney U test to determine if the distribution of values of the
Lottery variable were statistically different in 2010 compared to 2003. The Mann-Whitney U test
makes the assumptions that the dependent variable is either ordinal or continuous and the
independent variable has two separate categories. Furthermore, the test requires the two groups
of the independent variable to have different participants. My dependent variable, Lottery, is
ordinal and my independent variable for this test, Year, has two separate categories with different
participants. When I ran the Mann-Whitney U test, I found that I needed to reject the null
hypothesis that the distribution of Lottery is the same across the Year categories, meaning that
Lottery was statistically different in 2010 compared to 2003; this finding was significant at a p-
level of .01.
Table 5 holds a Pearson’s Correlation Matrix for the combined dataset. There are strong,
negative correlations between both Bible Literal and Lottery (r=-.222, p<.01) and Church and
Lottery (r=-.406, p<.01). Interestingly, BibleNot has a positive correlation with Lottery, though it
is weak (r=.088, p<.01). Age (r=-.170, p<.01) and Ethnicity (r=-.112, p<.01) are also negatively
correlated to Lottery, though not as strongly as the religious variables. The variable with the
strongest positive correlation to Lottery is Ideology (r=.321, p<.01), which is mirrored by a
negative correlation from PartyID (r=-.216, p<.01).
The Pearson’s Correlation Matrix is ideal for examining the relationships between
variables and Year (survey year). These correlations show if the time change from 2003 to 2010
has corresponded with positive or negative growth in other variables. For example, Table 4

14. Snider 14
showed an increase in support for the lottery from 2003 to 2010; in the Pearson’s Correlation
Matrix, Lottery is positively correlated to Year (r=.111, p<.01). BibleLiteral (r=.054, p<.10) has
a weak, positive correlation with Year, suggesting a small increase in view of the Bible as literal
over the time period. On the other hand, Church (r=-.055, p<.10) has a weak, negative
correlation to Year, showing a decrease in church attendance. Age (r=.169, p<.01) has a
moderately strong correlation to Year, demonstrating an increase in the age of the 2010
respondents in comparison to the respondents from 2003.
Table 5 about here
Table 6 presents a multiple regression analysis of the combined dataset. The regression
contains two models, with Model 1 analyzing a select few demographic and political variables
that I decided to use as my control variables. These variables include Age, Ethnicity,
CollegeGrad, Income, Gender, Ideology, PartyID, and Year (Survey Year). Of these variables,
Age (b=-.012, p<.01), CollegeGrad (b=-.186, p<.10) Gender (b=.198, p<.05), Ideology (b=.374,
p<.01), PartyID (b=-.099, p<.01), and Year (b=.480, p<.01) were all significant. Ideology was
the strongest positive predictor with a Beta of .259, meaning the more liberal respondents were
more likely to support a lottery. Year (Beta=.155) and Gender (Beta=.096) were also positive,
meaning respondents in 2010 showed more lottery support than respondents in 2003 and males
were more likely than females to show support. Age (Beta=-.133) was the strongest negative
predictor of support for a lottery, as older respondents were less likely to support the lottery than
younger respondents. PartyID (Beta=-.103) was a negative predictor as well, as those who
identified themselves as Republican were less likely to support the lottery. CollegeGrad (Beta=-
.060) was also a negative predictor, though relatively weak, meaning people who graduated from

15. Snider 15
college were less likely to support a lottery. Model 1 had an R-squared of 0.142, meaning this
model explains 14.2% of the variance in support for a lottery.
In Model 2, I added my three religious variables: BibleLiteral, BibleNot, and Church.
The r-squared increased to 0.241, meaning Model 2 explains 24.1% of the variance in support for
a lottery. Furthermore, when I analyzed the change in r-squared from Model 1 to Model 2, I
found the change to be significant. This finding suggests that my religious independent variables
are important indicators of support for a lottery. Of my control variables in Model 2, only
Gender became insignificant. Age (b=-.008, p<.01), CollegeGrad (b=-.240, p<.10), Ideology
(b=.263, p<.01), Party ID (b=-.070, p<.05), and Year (b=.337, p<.01) all remained significant
with the inclusion of my independent variables. BibleLiteral (b=-.226, p<.10) and Church (b=-
.114, p<.01) were both significant, negative predictors of support for a lottery; BibleNot was
insignificant. Ideology (Beta=.181) was the strongest positive predictor of support for a lottery,
followed by Year (Beta= .109), meaning the more liberal respondents and the 2010 respondents
were more likely to support a lottery than the more conservative respondents and the 2003
respondents. Church attendance (Beta=-.304) was by far the strongest negative predictor, as
those who attended church more were less likely to support a lottery. Older respondents (Age
Beta=-.094), college graduates (CollegeGrad Beta=-.077), Bible Literalists (BibleLiteral Beta=-
.073), and Republicans (PartyID Beta=-.072) were all more likely to oppose the lottery.
Table 6 about here
Discussion
Lotteries as a means to fund education have been vehemently debated across the United
States. As a result of policy diffusion, this progressive ideal has caught hold in the majority of
states. However, a select few holdouts, including Alabama, have managed to either avoid voting

16. Snider 16
on lotteries or have defeated a lottery referendum when one has arisen. In Alabama’s case, this
resentment towards enacting a lottery was rooted in morality, with a coalition of religious leaders
leading the charge. I hypothesized that, as Alabama’s educational system has continued to
deteriorate since the lottery referendum vote in 1999, I would find that the religious base had
shifted its support in favor of a lottery. I did not find support for my hypothesis, as both church
attendance and the view of the Bible as literal were negative predictors of support for a lottery in
my model.
However, while the religious variables were negative predictors, there was an overall
increase in support for the lottery from 2003 to 2010, shown by the Year variable in both Model
1 and Model 2. Thus, my data supports the notion that, in time, Alabama can overcome
opposition from the religious base and enact a lottery. The most telling data to try and explain
this phenomenon revolves around the Age and Ideology variables. Age (r=-.196) is negatively
correlated to Ideology, suggesting that the older respondents are more conservative. I speculate
that generational replacement is occurring and that the younger generations are more accepting
of liberal ideals than the generation that is being replaced. On top of the generational
replacement, I speculate that the younger generations are seeing the need for a greater level of
funding as they have more recently progressed through the educational system and may even
have children in the system currently.
Interestingly, Church Attendance was the strongest predictor, positive or negative, in my
model by far with a Beta of -.304 (In comparison, the second strongest predictor was Ideology
with a Beta of .181). My other significant religious variable, Bible Literal, had a weak Beta of
-.073. I believe there is a key explanation as to why church attendance is such a strong predictor.
First, as the old saying goes, actions speak louder than words. While church attendance and the

17. Snider 17
literal view of the Bible are both intended to measure religiosity, church attendance is the only
variable that shows a commitment to religion. The literal view of the Bible merely shows a belief
in a religious ideal; church attendance, on the other hand, reflects how often people actually go to
a place of worship and interact with religion. Furthermore, I speculate that churches condition
their attendants to believe lotteries are immoral. The more someone attends church, the more
they are subjected to this form of conditioning. As a result, people who attend church more often
are more likely to become cemented in an anti-lottery stance. This explanation makes sense
when considering the unprecedented levels of church mobilization for the 1999 lottery
referendum; the voters most likely to be mobilized in opposition of the lottery were the ones who
had been attending church the most.
There are two significant limitations to my study. The first revolves around the fact that
all of my variables, except for one, were based on the use of similarly worded questions in 2003
and 2010. The exception was my lottery variable. In 2003, the survey asked respondents to agree
or disagree with the following statement: “Alabama should establish a lottery for education.” In
comparison, the 2010 survey asked respondents if they supported or opposed a lottery that would
increase tax revenue and state government services. While both questions were coded on a scale
measuring approval for a lottery, prior research has indicated that connecting a lottery to
education as opposed to general funds can produce more support (Ferralio, 2013). Despite the
differentiation in question wording, though, I still found more support for a lottery in 2010 than
2003. I speculate that if the 2010 survey had connected the lottery to education as well, then I
would have found even more support for a lottery in Alabama. Furthermore, the most recent data
used in this study stems from five years ago. The opinions and political positioning of
Alabamians may have changed significantly over the past five years. Therefore, I would

18. Snider 18
encourage a future study to continue building upon my work by using my same variables with
new data. This future study could potentially show a continued trend in increased support for a
lottery that would benefit lottery proponents.
My study is interesting because it shows that Alabamians may be ready to enact a lottery.
Education in Alabama has been notoriously underfunded and inadequate. A lottery could
potentially quell this problem and raise the standards of an Alabama education. This research
also provides a blueprint on how to enact a lottery in a highly religious state. Alabama can
overcome opposition if it is willing to follow the model set forth by the Georgia lottery; this
includes connecting the lottery directly to education and avoiding fungibility by remaining as
transparent with the funds as possible. In conclusion, my study finds support for a lottery in
Alabama has increased significantly since the referendum in 1999, despite the evidence that
religious variables remain correlated to opposition.

19. Snider 19
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20. Snider 20
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21. Snider 21
Index
Table 1: Descriptive Statistics
Variable N Mean SD
2003 2010 Combined 2003 2010 Combined 2003 2010 Combined
Dependent Variable
Alabama needs a lottery (Range: 1-5; 1=
Strongly disagree, 5= Strongly agree)
548 637 1166 3.29 3.62 3.46 1.516 1.548 1.56
Independent Variables
The Bible is the literal word of God (1=
literal word of God, 0=else)
549 627 1178 0.4756 0.5183 0.4932 0.49986 0.50006 0.50017
The Bible is not the literal word of God
(1=Not Literal word of God, 0=else)
537 627 1167 0.3126 0.3046 0.3119 0.46398 0.46062 0.46347
Church attendance per month (Range 0-
20)
534 623 1159 4.5551 4.114 4.3244 4.11046 4.02822 4.09579
Control Variables
Age (in years) 546 636 1185 46.78 53.03 50.3 17.581 16.798 17.456
Ethnicity (1 = white 0 = else) 549 641 1190 0.785 0.8128 0.8269 0.41119 0.39038 0.3785
College Grad (1 = college grad+ 0 = else) 545 640 1188 0.4237 0.3906 0.4091 0.4946 0.48827 0.49187
Income (1 = 50,000+ 0 = else) 481 606 1088 0.4261 0.4323 0.4375 0.49502 0.49581 0.49361
Gender (1=male 0=female) 547 638 1189 0.3622 0.4044 0.388 0.48107 0.49116 0.48751
Ideology (Higher= More Liberal) 542 634 1178 2.48 2.37 2.42 1.11 1.031 1.067
Party ID (Higher= More Republican) 525 585 1178 3.12 3.27 3.25 1.69 1.525 1.598
Survey Year (1= 2010 0=2003) 1193 0.5381 0.49875

22. Snider 22
Table 2: Variable Names, Questions, and Coding for 2003 Data
Names Questions Coding
Dependent Variables
Alabama Lottery (lottery1) Alabama should establish a lottery for
education.
1=SD; 2=D; 4=A; 5=SA
(coding order altered)
Independent Variables
The Bible is the literal word of God
(BibleLiteral)
The Bible is not the literal word of God
(BibleNot)
Church attendanceper month (Church)
People view the Bible in different ways.
Which of these comes closest to your
view? The Bible is the word of God but
should not be taken literally word for
word. The Bible is the actual word of God
and should be taken word for word. The
Bible is a book written by men but is not
necessarily the word of God.
On average, how many times a month do
you attend church or religious services?
1= literal, 0=else
(coding order altered)
1=not literal, 0=else
(coding order altered)
(Range 0-20)

23. Snider 23
Table 3: Variable Names, Questions, and Coding for 2010 Data
Names Questions Coding
Dependent Variables
Alabama Lottery (lottery1) If a state-run lottery were createdin
Alabama, it would increase tax revenue
and stategovernment services. How
strongly would you support or oppose a
lottery?
1=SD; 2=D; 4=A; 5=SA
(coding order altered)
Independent Variables
The Bible is the literal word of God
(BibleLiteral)
The Bible is not the literal word of God
(BibleNot)
Church attendanceper month (Church)
People view the Bible in different ways.
Which of these comes closest to your
view? The Bible is the word of God but
should not be taken literally word for
word. The Bible is the actual word of God
and should be taken word for word. The
Bible is a book written by men but is not
necessarily the word of God.
On average, how many times a month do
you attend church or religious services?
1= literal, 0=else
(coding order altered)
1=not literal, 0=else
(coding order altered)
(Range 0-20)

24. Snider 24
Table 4: Lottery*Survey Year Crosstabulation
Survey Year Difference from
2003 to 20102003 2010
Alabama needs a
lottery
strongly disagree 21.2% 18.7% -2.5%
disagree 18.9% 11.3% -7.6%
agree 31.6% 29.5% -2.1%
strongly agree 28.4% 40.5% +12.1%
Total 100.0% 100.0%

25. *I would like to acknowledge Professor Bob Slagter of Birmingham-Southern College for his help throughout all stages of this paper.
Table 5: Pearson’s Correlation Matrix
Lottery BibleLit BibleNot Church Age Ethnicity College Income Genderr Ideology PartyID Year
Lottery 1
BibleLit -.222 1
BibleNot .088 -.670 1
Church -.406 .365 -.129 1
Age -.170 .095 -.004 .162 1
Ethnicity -.112 -.138 .115 .017 .138 1
College -.055 -.158 .146 -.011 -.063 .041 1
Income -.047 -.093 .120 .027 -.012 .162 .303 1
Gender .037 -.113 .021 -.109 -.018 .074 .089 .164 1
Ideology .321 -.240 .046 -.241 -.196 -.119 .005 -.082 -.045 1
PartyID -.216 .037 .095 -.107 .035 .417 .085 .260 .062 -.413 1
Year .111 .054 -.017 -.055 .169 -.040 -.041 -.012 .036 -.049 .015 1

26. Snider 26
Table 6: Multiple Regression- Dependent Variable is “Support for a Lottery”
Independent Variables Model 1 Model 2
Demographic and Political
Variables b SE Beta b SE Beta
Age -.012 *** .003 -.133 -.008 *** .003 -.094
Ethnicity -.063 .135 -.015 -.161 .133 -.039
CollegeGrad -.186 * .097 -.060 -.240 * .094 -.077
Income .055 .101 .018 .031 .097 .010
Gender .198 ** .096 .062 .072 .093 .023
Ideology .374 *** .048 .259 .263 *** .048 .181
PartyID
Year
-.099
.480
***
***
.035
.093
-.103
.155
-.070
.337
**
***
.034
.090
-.072
.109
Religious Variables
The Bible is not the literal word .051 .130 .015
The Bible is the literal word of God -.226 * .133 -.073
Church Attendance -.114 *** .012 -.304
Constant 3.272 0.257 4.096 .274
R2 (adjusted) 0.142 0.241
N 982 948
***<.01, **<.05, *<.10 (two-tailed)