Research Paper Final Draft

Marijuana Policy: What Factors Affect Implementation? EBBERS 1

Marijuana Policy: What Factors Affect Implementation?

Ingrid Elizabeth Ebbers

Boise State University

12 December 2012


INTRODUCTION

On November 6, 2012 two states, Colorado and Washington, made history in the fight

against marijuana prohibition by passing voter-led initiatives legalizing marijuana for

recreational use for anyone 21 and older, essentially treating it like alcohol. Earlier this year

Connecticut’s legislature passed legislation legalizing medical marijuana. In addition,

Massachusetts’s voters passed Ballot Question 3, becoming the 18 th state to legalize it for

medicinal use. On the flipside, several states chose not to approve legislation for legalization

(Oregon) or medicinal use (Arkansas). Since the passage of the 1970 Controlled Substances Act

marijuana advocates have tried to persuade the federal government to reconsider their stance but

have failed. Realizing their efforts were futile they decided to pursue an avenue through the

states. The first successful medical marijuana law came in 1996 with the passage of California’s

Proposition 215 and ever since states continue to put forth medical marijuana ballot initiatives or

legislation. Some states have been more successful than others in their efforts to pass laws

leaving many to ask, “What affects marijuana policy in the United States?” For marijuana

advocates this is an important question to answer because it will help them determine how to

effectively pursue an avenue towards legislation. This is also an important question for

opponents of such measures because of their efforts to stop states from enacting marijuana laws.

The marijuana debate however is not as simple as medicinal use; the passage of such laws has

broader implications on the American-led “War on Drugs.” Before delving into what affects

marijuana policy a review of the current literature on US drug/marijuana policy is necessary.


1. LITERATURE REVIEW

Most of the literature on marijuana does not discuss what affects policy enactment but

instead examines the arguments for and against. Until more states enact marijuana policy I think

the research on this subject will focus more on these arguments, rather than what affects policy.

In the USA Today article “Slowly, Limits on Pot are Fading,” William M. Welchand and Donna

Leinwand write about the declining opposition to medical marijuana. It points out that medical

marijuana legislation is a rising trend and since 1996 141 states plus the District of Columbia

have passed legislation either through ballot initiatives or their state legislatures. This article is

also the basis for the dependent variable discussed later. One of the interviewees, James Gray,

“spent two decades as a superior court judge…and once ran for Congress as a Republican –

switched sides in the war on drugs, becoming an advocate for legalizing marijuana” (Welchand

and Leinwand 2010). He is not their only “onetime drug warrior” (Welchand and Leinwand

2010). Jeff Studdard, a former California police officer, changed his mind after an accident

resulted in a broken back and marijuana was the only thing that relieved his pain. The article also

presents polling data that shows 44%2 of Americans favor full legalization, where another poll

found 81%3 of Americans favored legalization for medical marijuana. They also show that the

Obama administration has a markedly different approach than previous administrations.

“Attorney General Eric Holder last fall announced that raiding medical marijuana facilities

would be the lowest priority for U.S. law enforcement agents…” (Welchand and Leinwand

2010). Finally, some of the interviewees, including Gray, have suggested that full legalization is

not far off.

1 This was the number of states with medical marijuana at the time this article was written but the number has risen to 18 plus the District of Columbia.2 Gallup Poll from October 2009 3 ABC News Poll from January 2010


In addition to the USA Today article, there have been countless journal articles and even

research conducted by the Congressional Research Service (CRS) in regards to marijuana policy.

The Congressional Research Service is a part of the Library of Congress that provides

nonpartisan research and analysis to members of Congress and their committees. In 2010, Mark

Eddy, a specialist in social policy, wrote the report “Medical Marijuana: A Review and Analysis

of Federal and State Polices” for the CRS. The purpose of his research paper was to give

Congress information to help them determine “whether to continue the federal prosecution of

medical marijuana patients…or whether to relax federal marijuana prohibition enough to permit

the medicinal use of botanical cannabis” (Eddy 2010, 1). His 47-page report breaks the topic

down into several sections: federal policy (congressional, executive, and judicial actions), state

policies, public opinion, and concludes with an analysis (not taking either side) of the arguments

for and against marijuana. Mr. Eddy gives a brief history of medical marijuana prior to the

passage of the Marijuana Stamp Act of 1937, and then discusses the federal marijuana policy for

the past 75 years. According to Mr. Eddy, medicinal marijuana can trace its origins to ancient

China, India, Greece, Rome, and even parts of the Middle East. He further states that prior to the

passage of the Stamp Act, “for most of American history, growing, and using marijuana was

legal under both federal and state laws of the individuals states” (Eddy 2010, 1). Then the federal

government enacted the first legislation in a long line of anti-marijuana legislation – the

Marijuana Stamp Act of 1937. The purpose of the Act was to “[impose] registration and

reporting requirements and a tax on the growers, sellers, and buyers of marijuana. Although the

act did not prohibit marijuana outright, its effect was the same” (Eddy 2010, 2). An interesting

point Mark Eddy makes, using testimony from a congressional hearing during that time, is that


the American Medical Association (even back then) did not agree with the federal government

and believed marijuana did possess medicinal value.

The next major legislation – the Controlled Substances Act (CSA) of 1970 – came during

the Nixon administration and was passed by a Congress controlled by Democrats. The CSA

provides the federal government a way, through the Food and Drug Administration (FDA), to

regulate narcotics based on: “(1) its actual or relative potential for abuse; (2) scientific evidence

of its pharmacological effect, if known; (3) the state of current scientific knowledge regarding

the drug or other substance; (4) its history and current pattern of abuse; (5) the scope, duration,

and significance of abuse; (6) what, if any, risk there is to the public health; (7) its psychic or

physiological dependence liability; and (8) whether the substance is an immediate precursor of a

substance already controlled under this subchapter” (Food and Drug Administration 2009). The

CSA classifies marijuana, along with opiates, opium derivatives, and hallucinogens (marijuana

falls under this subgroup) as a Schedule I. These drugs have “high potential for abuse…no

currently accepted medical use…and a lack of accepted safety for use…” (Food and Drug

Administration 2009). Mr. Eddy then discusses more current federal marijuana legislation,

including the 105th Congress’s anti-marijuana legislation (“Not Legalizing Marijuana for

Medicinal Use”) and the Hinchey-Rohrabacher Amendment (2003-2007). He concludes that any

attempt to relax federal marijuana law has failed. The next section in Mark Eddy’s report is

executive branch actions, which include the FDA’s Investigational New Drug (IND)

Compassionate Program (1978), the FDA’s approval of the synthetic form of marijuana Marinol

in 1985; several administrative judge rulings; and the DEA’s enforcement of federal law.

Under his judicial action section he presents three federal cases, two of which are

Supreme Court cases, involving marijuana policy: US v. Oakland Buyers’ Cooperative (2001),


Contant v. Walter (2002), and Gonzales v. Raich (2005). In US v. Oakland Buyers’ the “issue

was whether a medical marijuana distributor can use a medical necessity defense against federal

marijuana distribution charges” (Eddy 2010, 15). The Supreme Court in an 8-0 ruling, concluded

that a medical necessity defense “is at odds” with federal law and therefore cannot be used (Eddy

2010, 15). The next case Mark Eddy reviews, Contant v. Walter (2002), refers to a 1997 Clinton

administration threat against doctors prescribing marijuana. This case only reached a federal

appeals court, where a permanent injunction was issued “prohibit[ing] federal officials from

threating or punishing physicians for recommending marijuana to patients…” (Eddy 2010, 15).

Lastly, he presents the 2005 Supreme Court case Gonzales v. Raich, which argued the CSA

“exceeded Congress’s constitutional authority under the Commerce Clause” but the Court ruled

6-3, against this argument; thus upholding federal law (Eddy 2010, 16).

The CRS report then breaks down the 14 states plus the District of Columbia’s laws

regarding marijuana. Other more updated sources for federal and state marijuana laws include

ProCon.org (a website dedicated to the pros and cons arguments for marijuana) and the National

Organization for the Reform of Marijuana Laws, NORML.org (an organization that formed

during the 70s and is dedicated to reforming laws). State laws vary from very restrictive (e.g.

only specific conditions allowed, not accepting out-of-state medical cards, state mandated

registration) to less restrictive (e.g. allowing residents from other states to apply for a medical

card, open-ended lists of conditions permitted, and no state registration). In addition, the way in

which states enact legislation varies from ballot initiatives (most states with medical marijuana

laws used this method) to bills introduced and passed by state legislatures (Hawaii, Rhode Island,

New Mexico, New Jersey, Maryland, Vermont, and most recently Connecticut). The range of

approval in which these measures passed varies from as low as 54% in Colorado to as much as


69% in DC. The CRS report closes out with an analysis of the arguments for and against medical

marijuana. The nice thing about this report is it presents the arguments without taking a side.

However, the CRS report does not suggest anything that might affect the outcome of marijuana

policy. Despite the fact most literature is about the arguments for and against rather than what

affects marijuana policy, several opinion polls do exist that breakdown the population (e.g. age,

gender and ideology) and their views on marijuana policy. In the next section I will discuss these

polls and how they helped me generate my hypotheses.

2. HYPOTHESIS

In this section I present the evidence I used to develop my hypothesis, then I explain a

general hypothesis about the variables affecting marijuana policy, then I break down each

independent variable and how it affects marijuana policy, and I conclude with an estimated

equation and explain why I believe it to be correct.

Public Opinion Polls

Using NORML.org as a starting place in the search of public opinion polls on marijuana I

found three polls from CBS News, the Pew Research Center, and Gallup. In the October 19,

2009 article “U.S. Support for Legalizing Marijuana Reaches New High” Lydia Saad analyzes

Gallup poll data that focuses on opinions for full legalization.4 The Gallup survey was conducted

over the telephone (landline and cellular) October 1-4, 2009, it consisted of 1, 013 national

adults, aged 18 and older. “Gallup’s October Crime poll finds 44% of Americans in favor of

making marijuana legal and 54% opposed” (Saad 2009). The data also shows that since 1970,

when American approval of legalization was at its lowest (12%), ratings have increased 32-

points to reach its high of 44% of Americans believing marijuana should be made legal. The 4 Based off survey question #18 “Do you think the use of marijuana should be made legal, or not?”


Gallup poll breaks down areas, like region and partisanship (Democrat/Republican), which I do

not test but would be an interesting regression model to run if my data does not prove

statistically significant. When asked on their views on legalization and taxation respondents in

the west (53%) were more accepting of the idea than the Midwest (34%), South (38%), or East

(44%). When looking at politics and ideology, 78% of liberals approve of marijuana legalization

and only 27% of conservatives approve; and 54% of Democrats approve whereas only 28%

Republicans approve. The gap between gender has men (41%) favoring it more than women

(32%) by 9-points. Finally, age is categorized into three groups: 18-49, 50-64, and 65 and older.

Not surprising younger people supported legalization more than older people, 39% of people

aged 18-49 supported marijuana legalization; however, only 27% of seniors, aged 65 and older

support legalization.

The next poll was published the following April (2010) from the Pew Research Center

and an analysis can be found on the Pew Research Center’s website under the title “Broad Public

Support For Legalizing Medical Marijuana”. The poll was conducted over the telephone

(landline and cellular) March 10-14, 2009, and comprised 1,500 adults. Like the last poll it

breaks down the information based on age, gender, and ideology, but adds race, religion, and

education. In contrast to the last poll this one deals with full legalization and medical marijuana.

The poll shows an 11-point difference between support and opposition for full legalization, with

only 41% supporting it. This is a 3-point drop from the Gallup poll, but a 6-point increase from a

similar 2008 General Social Survey, which is presented for comparison in the poll. As for

medical marijuana 73% of respondents favor it and 23% oppose it. Even for medical marijuana,

younger people still favor it more than older people: 18-29 year olds – 80% favor and 17%

oppose; 65 and older – 63% approve and 30% oppose. This poll does discuss ideology, but in


terms of political affiliations – Conservative/Liberal/Moderate Democrat and

Conservative/Liberal/Moderate Republican. Democrats (80%) overall favor medical marijuana

almost 20-points more than Republicans (61%) and as one moves closer to the extreme ends of

the political spectrum approval/opposition becomes stronger (e.g. 54% Conservative

Republicans approve and 85% Liberal Democrats approve). Also similar to the last poll is the

approval rating data based on the respondent’s gender – men, 74% and women, 72%. I like this

poll because there is data based on education and religion. According to the poll, college

graduates (71%) support medical marijuana less than respondents with high school or less (72%),

and those with some college (77%) support it the most. As for the breakdown on religion –

Protestants favor medical marijuana 69%, whereas Catholics favor it 73%, and evangelicals

(64%) are less approving than other forms of Christianity (75%).

The third, final, and most recent of the three polls comes from CBS News and was

analyzed by Fred Backus in the article “Marijuana and Medical Marijuana (2011)”. A telephone

survey (landline and cellular) was conducted October 28-31, 2011, and consisted of 1,033 adults.

The two survey questions that regard marijuana policy are questions 15 and 16. Question 15

asks, “Do you think that the use of marijuana should be made legal, or not?” To which 40% of

the respondents believed it should be made legal, 51% did not, and 9% chose not to answer the

question. Question 16 referring to medical marijuana asks respondents, “Do you think doctors

should be allowed to prescribe small amounts of marijuana for patients suffering form serious

illnesses, or not?” This is far more popular, with 77% of respondents believing it should be

allowed, and only 17% believe it should not be allowed, with 6% not responding. This survey

does not break the data down as extensively as the Pew poll did but it is comparable to the

Gallup poll. After looking at the overall approval/opposition by respondents, it then separates


their responses based on age, gender, region, partisanship, and ideology. Similar to the other two

polls, the CBS News poll has the overall approval rating for full legalization at 40%. In fact most

of the results from this poll echo those of earlier ones. As with the other two polls, this one also

has younger people supporting it more than older people: 18-29 year olds give it an approval

rating of 52%, in contrast only 28% of those 65+ say it should be legal. Men support it (46%)

more than women (35%). Although the CBS poll also discusses region, the regions used are

different than the Gallup poll: Northeast (41%), Midwest (43%), South (33%), and West (48%).

Despite the regions being different, the west still wins out in overall support with 48% of

respondents believing marijuana should be legal. This poll also shows Democrats favor it more

than Republicans, as do liberals over conservatives. This poll, however, reveals only 45% of

Democrats support full legalization, a 9-point drop from the 2009 Gallup poll. On the other hand

Republicans also dropped in their approval ratings for legalization, from 28% in 2009 to 23% in

2011. Again, liberals have a higher approval rating for legalization than do conservatives (66%

vs. 25%). This also is a big drop from the 2009 Gallup poll; liberal approval fell from 78% to

66%. However, conservative views fell only 2-percentage points, from 27% to 25% approval of

legalizing marijuana.

Hypotheses

Based on my own knowledge of the subject and the three public opinion polls mentioned

above I believe the following five variables will have the largest affect on marijuana policy:

religiosity, ideology (liberal/conservative), education, age, and gender.

My first hypothesis is that states with higher levels of religiosity will not have any active

or pending legislation. The more religious a person is the more socially conservative they will

likely be and marijuana is a social issue; therefore, they will be more likely to oppose any


marijuana legislation. Next, states with higher levels of liberals will be more likely to have

enacted or pending legislation. I believe this because states like California, Oregon, and

Washington all have enacted medical marijuana laws and those states have large percentages of

liberals. Furthermore, I believe that the younger the population the more likely there will be

enacted or pending legislation. This is why I used the two variables that refer to age: pop_18_24

and over64. I believe age is a factor because older generations have a different set of values and

therefore look at social issues like marijuana differently. Next, I used gender, in particular

women in state legislatures. Despite the overall trend in the above polls I believe that women are

actually more likely to support marijuana policy than not. Women tend to be more socially

liberal and democratic; therefore, I believe that as the number of women in the state legislatures

goes up the more likely a state will have enacted or pending marijuana legislation. Finally, as a

person’s education level increases they tend to be more liberal; therefore, as a state’s education

level increases so will the likelihood that they will have enacted or pending legislation. The null

hypothesis states that there is no statistically significant relationship exists between the 6

independent variables and the dependent variable.

Estimated Equation

I believe the equation below will be correct based on the hypotheses presented above. I

suspect the sign on the regression coefficients for the variables, libpct_m, pop_18_24, college,

and womleg_2010, will all be positive and will increase marijuana policy. On the other hand the

variables, over64 and religiosity will have negative coefficients resulting in the decline of

marijuana policy. The following is the estimated equation with the correct signs in front of the

coefficients.

Pot Policy=a+b1 (libpct m )+b2 ( pop¿)+b3 (college )+b4 ( womleg2010)−b5 (¿64 )−b6 (religiosity )


3. DATA

The following section identifies the variables (the dependent and independent variables) and

how they were operationalized. All of the variables used in this paper come from the States

dataset in SPSS and is based on each of the 50 states’ responses.

Variables

For the dependent variable, I used the ordinal-level variable pot_policy. As stated before

this is based on information collected from the 2010 USA Today article “Slowly, Limits on Pot

are Fading.” The data collected refers to the legalization policies for all 50 states and SPSS codes

the data as follows: (1) none, (2) pending, and (3) enacted. However, there are a few problems

associated with using this data. For starters it is two years old and more states have been added to

the list of states with enacted policies, thus altering the values for the other categories. As for the

pending legislation, this changes with every new legislative term/election cycle because new

opportunities arise to propose legislation and/or ballot initiatives.

My first independent variable is libpct_m. This interval-level variable measures, for each

state, the percentage of people in the mass public who identify as a liberal. It is a collection of

more than two decades (1977-1999) worth of CBS News/New York Times polls, which looked

at party identification and ideology estimates. The next independent variable college, looks at the

percentage of population, 25 years and older, who have college degrees or higher. The

information collected was from the US census bureau chart titled, “Public High School

Graduates and Educational Attainment.” It breaks down each state’s educational data and the

interval-level variable college looks specifically at the educational attainments for 2003. The

information was collected from the Census Bureau’s American Community Survey. The problem


with using this data is that it is nearly 10 years old and may not accurately reflect the population

especially since education in the US is declining. Furthermore, the 2010 census data is now

available so using the updated data might change the outcome especially because the dependent

variable pot_policy is based off the 2010 list of states with medical marijuana laws. My third

independent variable, womleg_2010, comes from information compiled by Rutgers University

Center for American Women and Politics (CAWP). CAWP “is nationally recognized as the

leading source of scholarly research and current data about American women’s political

participation” (Rutgers Center for American Women and Politics 2012). This interval-level

variable measures the percentage of women in each state’s legislature for 2010. The next two

variables deal with the same subject – age. Data for both pop_18_24 and over64 was collected

from the US Census Bureau’s State and Metropolitan Area Data Book: 2006, Table A-4. The

interval-level variable pop_18_24 measures the percentage of each states population age 18 to 24

and the interval-level variable over64 measures the percentage of a state’s population over 64

years old for the year 2004. It would be interesting to see the change (if at all) from the new 2010

census data. A limitation to using this data is that is only covers 18-24 and over 64, which

completely ignores nearly four decades of people (25-64). My final independent variable is

religiosity5. This ordinal-level variable is the Pew index of religiosity. It is based on the Pew

Research Center’s Forum on Religion and Public Life poll “How Religious is Your State?” The

poll ranked each state on the basis of four factors “the importance of religion in people's lives,

frequency of attendance at worship services, frequency of prayer and absolute certainty of belief

in God” (Pew Research Center's Forum on Religion and Public Life 2009). The scale ranges

from -4 (highest religiosity) to -180 (lowest religiosity).

5 SPSS has also has the variable secularism, which is reverse of the religiosity scale


4. FINDINGS

In this section I will discuss the results from the different tests I ran to test my hypothesis; this

includes a correlation and a multivariate linear regression. I will close this section out with a

discussion of five regression assumptions, whether my model violated them, and how to fix them

if they were violated.

Correlations

Correlation (aka Pearson’s r) is a good place to start in research (that is after picking the

variables) because it allows the researcher to see the strength and direction of the relationship

between all the variables. All correlations range from -1 to +1, the closer to 1 the stronger the

relationship and the closer to 0 the weaker the relationship. A negative sign in front of Pearson’s

r denotes a negative or inverse relationship, meaning the variables move in opposite directions.

On the other hand a positive correlation means the variables move in the same direction. If the

variables do not correlate then different ones should be selected, but if they do correlate then the

researcher can move on to the fun stuff – regression.

The correlation for my dependent variable with my six independent variables revealed

some interesting and surprising information about the relationships (See Appendix, Table 1:

Correlation 1). The relationship between state legalization policies and the percent of the

population with college or higher is a moderate, positive relationship. This is evident by its

Pearson’s r, 0.439. The significance level is 0.001, meaning that this only occurred by chance

less than 1% of the time. In other words there is a 99.9% confidence level that the relationship

did not occur by random chance. This is well above the standard 95% confidence level used in

statistical analysis. Pearson’s r for the percent of mass public and legalization policies is 0.613,

thus a moderate to strong positive relationship exists. With a significance level of 0.000, this


means there is almost a 100% chance that this relationship did not occur randomly. The next

relationship I analyzed was between legalization policies and the 65 and older age group.

Pearson’s r, -0.114 reveals a negative, weak relationship. However the significance level proves

that this relationship is not statistically significant because its significance is .319. This means

that the results occurred randomly 32% of the time. Put another way, the confidence level that

the relationship between, pot policies and the percent of the population over 64, happened by

random chance is only 68% - well below the 95% standard. The fact that this relationship is not

statistically significant surprises me because of the literature and public opinion poll results. The

next variable also deals with age but this one shows statistical significance. The relationship

between the percent of the population 18-24 and legalization policies is a weak to moderate

negative relationship, Pearson’s r is -0.301. Like the last variable the results of this correlation

surprised me because it runs counter to everything in the literature. The significance level, 0.034,

suggests the relationship occurred by chance only 3.4% of the time. In other words there is a

96.6% confidence level that this relationship is not random. The next relationship I examined

was between legalization policies and religiosity. This was the strongest relationship out of all of

the correlations, with -0.672 for its Pearson’s r. The relationship is also negative meaning that as

religiosity goes up, legalization polices go down and vice-versa. The significance, 0.000, means

this relationship has a less than 1% chance of occurring by chance. Lastly, I examined the

relationship between legalization policies and the percent of state legislators who are women.

The Pearson’s r, 0.608, indicates a moderate to strong, positive relationship. This relationship

occurred by chance less than 1% of the time and there is 99.9% confidence level that this is true.

These results suggest there are several relationships with statistical significance and thus should


be used in a regression analysis. Those variables without statistical significance should be

dropped or replaced.

Regression

After using correlation to determine the relationship between variables a regression

analysis is used to explain the exact nature of the relationship. In other words, how much does

the independent variable(s) affect the dependent variable? For this particular model’s bivariate

regression this means how much will the legalization policy increase or decrease for every one-

unit increase in one of the independent variables. A multivariate regression is used when needing

to control for other variables. In SPSS the regression output also generates an R-square and

Adjusted R-Square value, which explains how much variation in the dependent variable is

accounted for by the independent variable(s). Adjusted R-square is preferred over the R-square

because “R-square’s errors can assume only positive values…inflating the estimated value of R-

square” (Pollock III n.d., 195-196).

Multivariate I

For my first multivariate regression I tested all the variables even the one that showed no

statistical significance in the correlation (See Appendix, Table 3: Regression 1). I did this

because I still needed to test my original hypothesis. The Adjusted R-squared for this model is

0.475, which translates into 47.5% of the variation in marijuana legalization policy is accounted

for by the six independent variables. To put it another way, by knowing libpct_m, pop_18_24,

college, womleg_2010, over64, and religiosity the prediction of a state’s legalization policy

improves by 47.5%. On the other hand more than half of the variation is still unexplained. If all

the significance values for this model turnout to be statistically significant then the equation will

look like this:


Pot Policy=1.221+.059 (libpct m )−.016 ( pop¿ )−.028 (college )+ .026 (womleg2010)−.071 (¿64 )−.007 (religiosity )

What do all these numbers mean? First, the constant, or “a”, signifies that if all the

independent variables equal zero then a state’s marijuana legalization policy will be 1.221, in

other words no enacted legislation. The coefficients for each independent variable represent its

partial effect on the dependent variable. When the other five independent variables are held

constant, for every one-point increase in the percentage of people who identify as liberals a states

legalization policy will increase by .059. While this is a low coefficient I did not expect any of

them to be very high because the dependent variable only goes up to a value of 3; therefore,

having a large coefficient would be suspect. Next, an examination of the significance level and

the t-ratio for this variable is needed to ensure the variable has a statistically significant

relationship with the dependent variable. The significance level is .151, meaning that the

confidence level that this relationship did not occur by random chance is only an 85%. In other

words this relationship happened by chance 15% of the time. Looking at the t-ratio, 1.464, also

implies there is no statistically significant relationship because it does not pass the “greater than

+/-2” requirement.

What impact does age have on legalization policies? For my model I tested two variables

for age – pop_18_24 and over64. The partial effect of the percent of the population aged 18-24

on legalization policy is -.016. In other words, when all other variables are held constant, every

1-point increase in the percent of the population aged 18-24 there is a .016 decrease in marijuana

legalization policy. This is counter to my hypothesis because I thought it would increase policy

when in fact it decreases it. The significance level, .907, and the t-ratio, -.118, suggest there is no

statistically significant relationship between the variables. The confidence level that this occurred


by random chance is 9.3%, or these observations occurred by chance 91% of the time. A quick

look at the t-ratio, -.118, confirms there is no statistically significant relationship. The fact that

this variable lacks statistical significance shocks me because polling data shows that younger

people are more accepting of marijuana legalization. The other age variable, over64, has similar

but less extreme results. The partial effect for the percent of the population over 64, when

controlling for all other variables is -.071. In other words, if all other variables are held constant,

every 1-point increase in the percent of the population over 64, there is a .071 decrease in

legalization policies. Like the other age variable, there is no statistically significant relationship

as evidenced by the significance level and t-ratio. The t-ratio equals -1.313, and thus is less than

the required +/-2. According to the significance level, .196, this relationship occurred by chance

about 20% of the time, or there is only an 80% that this did not occur by random chance.

Another coefficient that surprised me was the one for college, as stated before I suggested

that it would have a positive impact on pot policy when in fact it does not. The coefficient for

college is -.28, meaning that when all other variables are held constant, every 1-point increase in

the percent of the population, aged 25 or older, with a college degree results in a .28 decrease in

legalization policy. Thus the partial effect of college, when controlling for the other independent

variables, on legalization policies is -.28. This variable also does not pass the significance or t-

ratio test. The confidence level that this relationship did not occur by chance is only 69.5%. As

for the t-ratio -1.039, it is less than -2; therefore no statistical significant relationship exists.

Does gender really matter? According to the regression output it does not. If the

significance level were not .183, then the partial effect of the percent of women in state

legislatures, when controlling for the other independent variables, on pot policy would be .026.

To put it another way, if the other independent variables are held constant, for every 1-point


increase in the percent of women in state legislatures there is a .026 increase in pot policy. The

significance level however says that this relationship happened by chance 18.3% of the time;

therefore, no statistically significant relationship exists.

The sixth and final variable, religiosity, is the only one that shows any statistically

significant relationship in the regression model. If all other independent variables are held

constant, every 1-point increase in religiosity there is a .007 decrease in marijuana legalization

policy. Therefore, the partial effect of religiosity on pot policy, when the other independent

variables are held constant, is -.007. There is a 99.98% chance that this relationship is not

random and the t-ratio, -2.650 (slightly above the +/-2 threshold), confirms there is a statistically

significant relationship.

Multivariate II

As a result of my variables not confirming my hypothesis I decided to continue playing

around with my independent variables. After examining the regression results I was able to use

the correlation described above to determine which variables I should to keep and those I should

not. I first tried the combination of the three variables most strongly correlated with the

pot_policy, and came up with: religiosity, womleg_2010, and libpct_m (See Appendix, Table 4:

Regression 2). This regression also proved unsuccessful and once again only religiosity showed a

statistically significant relationship. I continued testing variables until I found one that had an

Adjusted R-square of .476 and was statistically significant (See Appendix, Table 5: Regression

3). Thus, the two variables religiosity and womleg_2010 explain 47.6% of the variation in

marijuana policy. Conversely, 52.4% of the variation in marijuana policy is not accounted for by

the variables in this model. The regression equation looks like this:

Pot Policy=.308+.035 ( womleg2010)−.007 (religiosity )


Before moving on a quick interpretation of this equation is needed. Again the constant

refers to what the legalization policy would be if both independent variables were zero. In this

case the constant is .308, meaning no enacted or pending legislation. In this model, the partial

effect of the percent of women in the state legislatures, controlling for religiosity is .035. In

other words, if religiosity is held constant, every 1-point increase in the percent of women in

state legislatures there is a .035 increase in legalization policy. There is a 95.7% confidence level

that this relationship did not occur randomly. The t-test helps confirm this because it is 2.077;

therefore it is statistically significant. The partial effect of religiosity on marijuana policy, when

controlling for the percent of women in state legislatures is -.007. Put another way, if the percent

of women in state legislatures is held constant, every 1-point increase in religiosity results in

a .007 decrease in legalization policy. One final observation about both these regressions, the

constant in neither of the models shows statistical significance but this could be related to a

possible violation of the regression assumptions discussed below.

Regression Assumptions

Ensuring that the regression assumptions are not violated is key for any regression to be

successful. If these assumptions are violated they can result in inaccurate coefficients, t-ratios,

and significance levels, just to name a few. However there are solutions if they are violated and

some are as easy as switching or throwing out a variable.


Autocorrelation

Autocorrelation is related to data that has been collected over time by the same person.

For example data from the National Election Survey can suffer from autocorrelation because a

lot of their data is considered time series sets. I used the States dataset from SPSS and my

variables were collected from different time periods and by different organizations, so it is

unlikely that my regression results suffer from autocorrelation.

Linearity

The regression assumption of linearity states that data will follow a straight line in a

linear fashion, e.g. when moving from 0 to 1 or 100 to 101 is the same value. Examining the

scatterplots can help diagnose this problem (See Appendix, Figures 1-6: Scatterplots 1-6). My

data likely violates this regression assumption because my dependent variable is an ordinal level

variable. The reason this might cause it to violate the assumption is because the variable the

numbers do not follow a straight line but a three-step scale. To fix this problem I could create

another legalization variable that deals with percentages of approval ratings or something

similar.

Constant Variance (Heteroscedasticity)

Constant variance is also known as the regression assumption of heteroscedasticity. This

assumption states that the data points will be equidistance from regression line. If the data does

suffer from heteroscedasticity it could throw the results off by suggesting there is a statistically

significant relationship when there is not, and vice-versa. To tell whether this affected my data I

used the standard measure of creating a scatterplot of my dependent variable with each of my

independent variables (See Appendix, Figures 1-6: Scatterplots 1-6). After visually inspecting

the scatterplots I believe most of my variables, except over64, show constant variance because


the data points are equal distance from the regression line as the data progresses along the line.

Looking at Figure 3: Scatterplot 3, located in the Appendix, a person can see that the data looks

lopsided; therefore, it violates the assumption.

Multicollinearity

The assumption of multicollinearity means that the independent variables are too highly

correlated and could therefore throw off the results. To test for multicollinearity a correlation of

only the independent variable is run in SPSS. If any of the variables have a correlation coefficient

greater than +/-0.75, then those variables are likely being affected by multicollinearity.

Furthermore this could influence the outcome of the significance level, suggesting there is no

statistical significance when in reality there is significance. To fix this problem, select one of the

variables that are highly correlated and throw the other(s) out. I do not believe my data suffers

from multicollinearity because I ran a correlation of the independent variables and none of my

variables reached the +/-0.75 threshold. (See Appendix, Figure 2: Correlation 2). The only one

that came close was religiosity and percent mass public liberal, but it only is only -0.705.

Although this might be why when I ran a regression of religiosity, percent mass public liberal,

and percent of women in state legislatures that the variables did not show statistical significance.

(See Appendix, Table 2: Regression 2)

Normality

The assumption of normality states that the data will follow the normal distribution curve.

If the data breaks the assumption of normality then it could also throw the statistical significance

level, implying there is no significance when there is. A histogram showing the normal curve is

produced of all of the variables (dependent and independent). (See Appendix Figures 7-12:

Histograms 1-7) My data, in particular my dependent variable, breaks this assumption because it


is positively skewed. The data used for pot_policy (Histogram 4) is two years old so it would be

interesting to see an updated version of the data and whether it is still skewed. Because my

dependent variable breaks the normality assumption it could be the reason my constant, as well

as my other independent variables, are not showing statistical significance.

5. CONCLUSION

Determining what affects US marijuana policy remains a difficult task because after

performing several regressions and numerous other statistical analyses, the null hypothesis was

confirmed because mine variables were not statistically significant. However, as discussed in the

regression assumption section, if any of the variables violate the assumptions (which mine

violate several) it could distort the results. By fixing the problems associated with violating them

it could alter my results and confirm my hypothesis instead of the null. Even though my original

hypothesis was wrong I was still determined to find something that affected policy that also had

a statistical significance, I was successful. I determined that religiosity and the percent of women

in state legislatures explained almost 48% of the variation in marijuana policy, but that still

leaves 52% unexplained. For anyone using my research as starting place in his or her own

research on what affects marijuana policy I would suggest they fix the violations of the

regressions assumptions and use the most recent data for the variables (e.g. update the dependent

variable to reflect the current legalization policies, use the 2010 census data, and update the

percent of women legislators). Furthermore, they should use my second regression because of

the variables pass the significance test.

The implications for my results could influence how marijuana activists or opponents

pursue their legislative goals. Based on the results of the second regression, activists could use


this to focus their efforts on getting women elected to state legislatures in the hopes of improving

their chances of getting marijuana laws enacted in their states. Conversely, it could trigger

religious lobbyists to advocate to their legislatures not to enact any legalization policies.

Additionally, they could promote more religiously minded candidates. Lastly, election analysts

could use this research as a predictor of whether a state will enact policy based on the make up of

the population. What happens next with marijuana legalization policy is still up in the air,

especially since Colorado and Washington legalized it for recreational use this year.


TABLE 1: Correlation w/ all variables

Correlations

Legalization policies

Percent of pop w/college or higher

Percent mass public Liberal

Percent age 65 and older

Percent age 18-24 (2004)

Relig observance-belief scale (Pew)

Percent of state legislators who are women (2010)

Legalization policies

Pearson Correlation

1 .439** .613** -.144 -.301* -.672** .608**

Sig. (2-tailed) .001 .000 .319 .034 .000 .000

N 50 50 50 50 50 50 50Percent of pop w/college or higher

Pearson Correlation

.439** 1 .623** -.187 -.258 -.611** .626**

Sig. (2-tailed) .001 .000 .194 .071 .000 .000

N 50 50 50 50 50 50 50Percent mass public Liberal

Pearson Correlation

.613** .623** 1 .055 -.546** -.705** .663**

Sig. (2-tailed) .000 .000 .706 .000 .000 .000

N 50 50 50 50 50 50 50Percent age 65 and older

Pearson Correlation

-.144 -.187 .055 1 -.272 .019 -.151

Sig. (2-tailed) .319 .194 .706 .056 .894 .294

N 50 50 50 50 50 50 50Percent age 18-24 (2004)

Pearson Correlation

-.301* -.258 -.546** -.272 1 .332* -.369**

Sig. (2-tailed) .034 .071 .000 .056 .019 .008

N 50 50 50 50 50 50 50Relig observance-belief scale (Pew)

Pearson Correlation

-.672** -.611** -.705** .019 .332* 1 -.667**

Sig. (2-tailed) .000 .000 .000 .894 .019 .000

N 50 50 50 50 50 50 50Percent of state legislators who are women (2010)

Pearson Correlation

.608** .626** .663** -.151 -.369** -.667** 1

Sig. (2-tailed) .000 .000 .000 .294 .008 .000

N 50 50 50 50 50 50 50**. Correlation is significant at the 0.01 level (2-tailed).*. Correlation is significant at the 0.05 level (2-tailed).


TABLE 2: Correlation w/ Independent Variables Only

Correlations


Percent mass public Liberal

Percent age 65 and older

Percent age 18-24 (2004)




Pearson Correlation

1 .623** -.187 -.258 -.611** .626**

Sig. (2-tailed) .000 .194 .071 .000 .000

N 50 50 50 50 50 50Percent mass public Liberal

Pearson Correlation

.623** 1 .055 -.546** -.705** .663**

Sig. (2-tailed) .000 .706 .000 .000 .000

N 50 50 50 50 50 50Percent age 65 and older

Pearson Correlation

-.187 .055 1 -.272 .019 -.151

Sig. (2-tailed) .194 .706 .056 .894 .294

N 50 50 50 50 50 50Percent age 18-24 (2004)

Pearson Correlation

-.258 -.546** -.272 1 .332* -.369**

Sig. (2-tailed) .071 .000 .056 .019 .008

N 50 50 50 50 50 50Relig observance-belief scale (Pew)

Pearson Correlation

-.611** -.705** .019 .332* 1 -.667**

Sig. (2-tailed) .000 .000 .894 .019 .000

N 50 50 50 50 50 50Percent of state legislators who are women (2010)

Pearson Correlation

.626** .663** -.151 -.369** -.667** 1

Sig. (2-tailed) .000 .000 .294 .008 .000

N 50 50 50 50 50 50**. Correlation is significant at the 0.01 level (2-tailed).*. Correlation is significant at the 0.05 level (2-tailed).


TABLE 3: Regression Output 1

Model SummaryModel R R Square Adjusted R Square Std. Error of the Estimate1 .734a .539 .475 .597a. Predictors: (Constant), Relig observance-belief scale (Pew), Percent age 65 and older, Percent age 18-24 (2004), Percent of pop w/college or higher, Percent of state legislators who are women (2010), Percent mass public Liberal

Coefficientsa

Model Unstandardized Coefficients Standardized Coefficients

t Sig.

B Std. Error Beta

1

(Constant) 1.221 2.069 .590 .558

Percent mass public Liberal .059 .040 .264 1.464 .151Percent age 18-24 (2004) -.016 .132 -.015 -.118 .907Percent of pop w/college or higher

-.028 .027 -.155 -1.039 .305


.026 .019 .215 1.353 .183

Percent age 65 and older -.071 .054 -.150 -1.313 .196Relig observance-belief scale (Pew)

-.007 .003 -.429 -2.650 .011

a. Dependent Variable: Legalization policies



Model SummaryModel R R Square Adjusted R Square Std. Error of the Estimate

1 .717a .514 .482 .593a. Predictors: (Constant), Relig observance-belief scale (Pew), Percent of state legislators who are women (2010), Percent mass public Liberal

Coefficientsa


t Sig.

B Std. Error Beta

1

(Constant) -.199 .519 -.383 .703

Percent mass public Liberal .043 .034 .192 1.235 .223


.027 .018 .221 1.494 .142


-.006 .002 -.389 -2.488 .017




Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .705a .498 .476 .596

a. Predictors: (Constant), Relig observance-belief scale (Pew), Percent of state legislators who are women (2010)

Coefficientsa


t Sig.

B Std. Error Beta

1

(Constant) .308 .319 .966 .339


.035 .017 .288 2.077 .043


-.007 .002 -.480 -3.460 .001



FIGURE 1: Scatterplot 1












FIGURE 7: Histogram 1












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