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Does Print Media Inf luence Publ ic Opinion? An Analysis of Marijuana Legalization Opinion Coverage in Newspapers

   

Julia Verbrugge December 2014

Abstract

This paper studies the influence of mass media on public opinion of marijuana legalization. Using a measure of media framing in the most widely circulated newspapers in 11 states over the period from 2010 to 2014, I seek to explain the changes in public opinion on the issue of marijuana legalization. To identify the position of the print media, I rely on editorials and op-ed columns, which I then use to evaluate the changes in newspaper support over time. Controlling for social demographic factors of each state, I find that there is an 8.24 percent—statistically significant and positive—relationship between media framing and public opinion over time. I also find insignificant relationships between changes in media support and changes in public support over the period of interest. Analysis of public opinion trends on framing shifts over time also suggests that direction of causality is such that the media influences public opinion. In sum, the paper uncovers preliminary relationships between the media’s coverage of the topic and the public’s support of marijuana legalization. I. Introduction  

Multiple studies have evaluated the role that mass media plays in shaping public opinion

and driving public policy innovation, but none have done so with the highly topical subject of

recreational marijuana legalization. This study seeks to answer the following question: does the

framing of marijuana legalization in newspaper opinion content influence public opinion on a

state-by-state basis? To be clear from the outset, I define framing as the way in which

newspapers define and package the issue (Colvin, 2006) in order to position the issue so that it

conveys certain meaning (Menashe and Siegel, 1998). Additionally, I define newspaper opinion

content as inclusive of both editorial articles as well as op-ed columns.

Before diving into analysis, it is necessary to set up a basic context for the study. The

path towards decriminalization, medical marijuana legalization, and now recreational marijuana

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legalization, has been a winding one, as reflected on the graph of support over time shown in

Figure 1, which provides an overview of support for marijuana legalization from 1969 through

2014. As apparent in the graph, support has been steadily increasing since the 1990s. Before that,

however, there exist a few interesting trends worth pointing out. For instance, in June 1971,

President Nixon declared the national “War on Drugs.” However, between 1973 and 1977,

eleven states decriminalized marijuana possession, during which time support levels spiked

(“History of Marijuana”). Within just a few years, though, the tide shifted, leading up to the drug

war hysteria of the late 1980s, directly following Nancy Reagan’s “Just Say No” campaign.

Disapproval became so extreme that in 1989, 64 percent of Americans polled saw drug abuse as

the nation's "number one problem,” up from 5 percent in 1985 (“History of Marijuana”). This is

reflected in the lower support levels in the late 1980s. Since then, support for legalization has

steadily increased, and my interest is in the last 4 years in particular, during which it has became

a hotly debated topic in the media and politics. The growth in support is also apparent in state-

level public policy innovation, the details of which are shown in Figure 2. According to the

Marijuana Policy Project (2014), as of December 2014, recreational marijuana is legal in 4

states: Alaska, Colorado, Washington, and Oregon (Washington D.C. voted to legalize but this

decision needs to undergo Congressional review).

Given its relevance in the news and public policy, understanding the way in which public

opinion on marijuana legalization develops is important. Beyond its topical nature, evaluating the

relationship between media framing and public opinion also proves important for theoretical and

public policy reasons. Within economic theory, the topic is relevant because it explores the idea

that demographic factors are not adequate for a full explanation of public opinion. Numerous

studies (Wald et al. 1996, Haider-Markel and Meier 1996) note that demographics are important

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explanations for public opinion. One study by Erikson, Wright and McIver (1993) argues that

public opinion in states reflects demographic characteristics of residents, but also acknowledges

that a mysterious factor labeled ‘state culture’ substantially influences public opinion. Thus, this

study provides relevant theoretical information in that it evaluates the role the media plays in

influencing public opinion while also taking demographics into account. In doing so, it also

expands upon recent Economics literature that addresses how media bias can affect attitude and

voting behaviors (Chiang and Knight 2011, DellaVigna and Kaplan 2007). The topic is also

relevant in the realm of public policy, because if mass media shapes public policy preferences, it

could influence public policy innovation regarding the legalization of marijuana. Numerous

researchers, like Erikson, Wright and McIver (1993), have argued that public opinion in states

affects state policies. This finding has implications for the political process, because it means that

those who shape media messages could have influence over public opinion and even political

outcomes.

This study also advances over relevant existing literature. Though a large body of

literature exists on mass media’s impact on public opinion, there is not yet literature specifically

on the media’s coverage of marijuana legalization. That being said, there are 3 areas of literature

that I looked into: literature that connects media to marijuana legalization, literature that

influences my methodology, and literature on the topic in a broader sense. Within the context of

marijuana, Golan (2010) looked into differences in framing strategies between newspapers and

between op-eds versus editorials. For the development of my methodology, literature on gay

marriage and framing proved most useful. For example, Chomsky and Barclay (2006) looked

into how public opinion on gay marriage shifts due to media. They find that the media’s position,

as indicated by editorials and op-ed columns, “has a significant effect on the level of public

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support for gay rights, even after taking into account social demographic factors.” Colvin (2006),

on the other hand, looks into how framing of gay rights issues in the media influences public

policy innovation, concluding that media coverage does not have a significant impact. More

generally, much literature exists on the broader topic of how media influences public opinion.

Page, Shapiro, and Dempsey (1987) did a comprehensive analysis entitled “What Moves Public

Opinion,” which looks into how news content moves with the public’s policy preferences. I also

looked to McCombs and Shaw (1972), who evaluated the power of mass media in setting

political agendas given that media chooses what to publish and how to frame it.

With all of the aforementioned literature and context in mind, I set out to evaluate the

relationship between newspaper’s opinion coverage on marijuana legalization and public opinion

on the topic on a state-by-state basis over time. To do so, I collect data on demographics and

public opinion for multiple years and construct a media framing measure using a manually

created dataset. To measure media’s framing of the topic over time, I identify the largest

newspapers in the states in my sample and find all relevant opinion content from 2010 to 2014,

which I then manually code based on positive/negative framing. In doing this, I assume that the

polling datasets provide representative samples and that the opinions content as a whole

accurately represents the policy stance of newspapers. I then calculate changes in framing over

time for each state. With all of my data, I regress public opinion on media framing, taking into

account all relevant social demographic factors. This leads to a positive and statistically

significant result. I also compare changes in support levels in states where newspapers become

more supportive over time versus less supportive over time, finding insignificant differences.

Lastly, I complete a trends analysis on public opinion, finding that the trends begin after framing

shifts take place in newspapers, suggesting that the media is influencing public opinion, and not

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the other way around. All in all, relationships exist between the newspaper’s coverage and public

opinion, and my analysis suggests that the relationship is causal such that the newspaper’s

opinion content moves public opinion on the topic of marijuana legalization.

 The paper is organized as follows. Section II explains my data, section III discusses my

methodology, and section IV presents my results. Section V provides a discussion of these

results, all of which are summarized in the conclusion, section VI.

II. Data

This section presents the data on polling information and newspaper coverage. The tables

included provide summary statistics from the data.

II.A. Polling Information

To create a measure of public opinion, polling information on a state-by-state basis was

needed. To collect this data, I utilized datasets made publicly available by the Pew Center for the

People and the Press. Each year since 2010, Pew has conducted a Political Survey using a

random sample of people contacted via landline and cell phone. The question of interest for this

paper reads as follows: “Do you think the use of marijuana should be made legal, or not?” The

wording of this question has not varied over time, making it a strong indicator for opinion on

marijuana legalization. Unfortunately, Pew did not include this question in publicly available

datasets for 2012. Furthermore, the full dataset has not yet been made available for the February

2014 Political Survey, which did include questions regarding marijuana legalization, since it is

the largest survey Pew has conducted to date. Despite these data gaps, full datasets were

available for 2010, 2011, and 2013, so this study focuses on those three years.

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After downloading the datasets, I removed all unnecessary information and created a new

spreadsheet of all the relevant information: state name, numerical state ID, race, income, age,

education, urban density, sex, political party, and ideology. These demographic factors in

particular are used because these same indicators have been used successfully in similar studies

to account for state differences and political cultures (Erikson, Wright, and McIver 1993,

Chomsky and Barclay 2006, Barclay and Fisher 2003). An advantage of the edited dataset is that

all needed demographic information is available alongside the public opinion information,

allowing for group-level analysis. In Tables 1 and 2, for instance, I provide summary statistics on

support for marijuana legalization over time by state and by demographic group.

When compiling data, I removed data for states that had less than 15 respondents for any

given year because these sample sizes were too small for use in the analysis. The relatively small

sample sizes for each state constitute one weakness of the polling data.

II.B. News Coverage

The second phase of data collection required manual data creation, given that I compiled

the dataset myself rather than using existing work. Inspired by the methodologies of previous

studies (Chomsky and Barclay 2006, Colvin 2006), I used the most widely circulated newspaper

as the primary news source for each state analyzed in this study. To find the needed circulation

information, I used the Editor and Publisher’s International Yearbook, which provided state-level

daily circulation numbers. Unfortunately, the Yearbook does not provide data on state-level

distribution of national newspapers, like the New York Times and the Washington Post. For

these, I made the assumption that these were the most widely circulated newspaper in their states

of publication.

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After identifying the newspapers of interest, I moved into data creation for the media

framing measure. To construct this measure, I chose to analyze opinion content in newspapers in

which enough relevant opinion content on marijuana legalization was published between 2010

and 2014. Opinion content includes both op-ed articles and editorials, in this case. I focused only

on opinion content rather than all related newspaper content because it creates a more

manageable data set, provides content that is easier to evaluate for framing, and because, as

Golan (2010) notes, “highly subjective editorial and opinion pages provide readers with

important benchmarks regarding salient issues against which readers can evaluate their own

opinion.” Opinion content, as a whole, should provide relatively straightforward expressions of

the newspapers’ policy preferences.

To find all relevant articles, I used two electronic databases: LexisNexis and NewsBank’s

Access World News. LexisNexis provides a comprehensive list of content from leading national

newspapers included in this study, but covered few of the state-level newspapers. For the state-

level content, Access World News proved more useful. However, both had gaps in opinion

content coverage of marijuana legalization, so I also used newspaper website archives to ensure

my dataset was comprehensive. Because less content on the subject existed than originally

anticipated, I focused on the top newspapers in 11 states: New York, New Jersey, Colorado,

Oregon, Washington, Virginia, Maryland, Nevada, Texas, California, and Illinois. For states with

two widely circulated newspapers, like Texas (Houston Chronicle and Dallas Morning News)

and California (San Francisco Chronicle and the Los Angeles Times), both were used for the

state’s media framing measure.

To compute the media framing measure for each state for 2010, 2011, and 2013, I read

each of the 160 opinion articles and manually coded them. If the article emphasized negative

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ramifications of full legalization, highlighted the negative health consequences, or expressed

concern about abuse by minors, I coded the article as a “0”. If the article pushed for full

legalization and elucidated the positive effects of doing so, I coded the article as a “1”. Table A

in the Appendix provides a snapshot of the dataset compiled, and Table 3 provides clippings of

articles that I coded to illustrate my coding methodology. Also, it is important to note that

articles that did not take any identifiable position were excluded from the sample. After doing

this for every article in the selected newspapers, I calculated the average annual frame for each

of the 11 states for 2010, 2011, 2012, 2013, and 2014. Figure 3 provides a visual for this

information, and Table B in the Appendix shows the summary statistics of this data. Because

public opinion information is not available for 2012 or 2014, I focus on the media framing

measure for 2010, 2011, and 2013. As aforementioned, one weakness is that there are not a

substantial number of opinion pieces on marijuana legalization in the chosen time frame. Gaps

exist for the media framing measure in certain years as a result. Beyond that, very few

newspapers have taken official editorial stances on the topic other than the New York Times, the

New Jersey Star Ledger, and the Las Vegas Review Journal.

 III. Methodology  

My methodological approach to the question is twofold. First, I look to quantify the

relationship between public opinion and frame using a regression while taking social

demographic factors into account. Second, I compare the changes in public support over time

between states with newspapers that become more supportive and states with newspapers that

become less supportive over time. To do this, I rely on a t-test, Mann-Whitney test, and a follow-

up regression.

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III.A. Regression Analysis

To model the source for shifts in public opinion, I use methods similar to those employed

in relevant recent studies (Chomsky and Barclay 2006, Colvin 2006). I use a time-series

regression model, shown below, and introduce a number of measures that shape public opinion

in the 11 states in the sample. The unit of analysis is the individual in the regression given that

the Pew datasets present public opinion on marijuana on the individual level. Therefore, I

analyze how media framing impacts the way in which individuals in the 11 states think about

marijuana legalization.

Public Opinion = β0 + β1(Media Framing) + αX + ε

All in all, I seek to regress public opinion on media framing while controlling for a

variety of demographic factors in order to evaluate if and how the position of the print media has

an effect on public support levels regarding recreational marijuana legalization. To identify the

demographics necessary to include, I look to previous papers, and choose to focus on the

following: race, population density of area, income level, education level, age, political party,

and ideology (Wald et al., 1996). To include them in the regression, I create dummy variable

indicators for all demographic factors of interest, allowing me to evaluate how differences in

demographics influence the relationship between public opinion and media framing.

Furthermore, as stated earlier, demographics prove highly important in determining policy

preferences, so I control for them with the objective of isolating the influence of mass media on

public opinion. After running this regression, I do it again and include state fixed effects to

control for time-invariant differences between the states that the primary regression did not

capture.

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As is clear from the summary statistics provided, most states display increases in public

support levels no matter what the demographic characteristics of the state are. Even more

conservative states like Alabama and Kansas display upward trends in support, indicating that

there exist trends towards increased support outside of politics and demographics. Because of

this, I expect that even when a newspaper is unsupportive of marijuana legalization, the state’s

population will still become increasingly supportive of legalization, but less so than in states

where newspapers advocate strongly for legalization, like in Nevada. To control for these time

trends, I include dummy variables for each year in the analysis.

To further strengthen the regression, I separate the sample into groups that are more or

less likely to read the newspaper, according to the Pew Research Center’s State of the Media

(2012). The report concludes that people who are above 45 and have high levels of education and

high income are most likely to read the newspaper. That said, the relationship between public

opinion and framing should be most pronounced within these demographic groups, so I run

regressions on these groups individually. Because there is no question on the Pew poll that I used

in my dataset about readership of newspapers, I use these demographic factors as proxies.

III.B. Framing and Support Shifts Analysis

The second piece of analysis splits newspapers into 3 categories: newspapers that become

more supportive of marijuana legalization over time, newspapers that become less supportive,

and newspapers that display no change in position. The average media framing measure for each

newspaper for each year indicates these changes in support over time. For clarification, I define

an “up frame” as the instance when a newspaper becomes more supportive of full marijuana

legalization between 2010 and 2013. I define a “down frame” as the instance when a newspaper

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becomes more opposed to legalization policies between 2010 and 2013. From my sample size of

11 newspapers, 2 exhibit down frames and 3 exhibit up frames. The remaining 6 newspapers

have framing approaches that do not change significantly.

With this division in place, I evaluate how changes in framing in newspaper opinion

content impacts changes in public opinion. To do so, I run two separate t-tests. The first

compares the change in public support levels from 2010 to 2013 between states where there are

up frames and states where there are not up frames. The second compares the change in public

support levels from 2010 to 2013 between states where there are down frames and states where

there are not down frames. I then supplement the t-test analysis with a Mann-Whitney test

analysis to measure sensitivity. The Mann-Whitney test, or Wilcoxon rank sum test, is a

nonparametric test that compares two unpaired groups. To complete it, I rank all values in

change in public opinion for the 11 states without paying attention to whether the value is for an

up frame, down frame, or other frame state. I assign the smallest value for change in public

opinion a rank of 1, and the largest a rank of 11. I then average the ranks by group in the same

way that I separate the frame groups for the t-tests. In other words, I compare the average rank

between up frames and other frames, and also compare the average rank between down frames

and other frames. I add this additional test as a supplement to the t-tests because the Mann-

Whitney test doesn’t require the underlying assumption of normality when comparing samples,

and is thus a generalization of the t-test.

Lastly, I aggregate the data to the state-year level and regress the change in opinion from

2010 to 2013 on indicators for up frame and down frame, as shown in the regression below.

Change in Public Opinion = β0 + β1(Indicator for Up Frame) + β2(Indicator for Down Frame) + ε

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To address direction of causality, I complete my analysis work by evaluating public

opinion trends before the changes in framing and comparing them to public opinion trends

during the framing shifts.

In completing these regressions and t-tests I make numerous necessary assumptions. The

first few relate directly to my datasets in that I assume that both the polling datasets and the

newspaper framing datasets are representative. In the Pew datasets, samples are relatively small,

vary in size over the years included, and also change in terms of people chosen as respondents.

Despite these potential challenges, it proves necessary to assume that the people surveyed in the

11 states analyzed provide an accurate representation of the populations in those states. A similar

assumption is necessary for the collected opinion content, which I assume, on an aggregate level,

is representative of the newspapers’ policy preferences. Op-ed content can diverge significantly

from a newspaper’s official editorial stance, but for the purposes of this study, I assume that the

opinion content as a whole represents the newspapers’ views. Because of how few newspapers

have released official editorial stances on the topic of recreational marijuana legalization,

including op-ed content is a necessity. Beyond these assumptions of representativeness, I also

assume that people read the most widely circulated newspaper in their state of residency, and

more specifically, that they read the opinion content. To strengthen this assumption, as

aforementioned, I identify the demographic groups that are most likely to read the newspaper

and separate them in part of the analysis.

Finally, I assume that nothing, other than the variables I control for, is correlated with

both newspaper framing and public attitudes towards marijuana. I assume that I have not omitted

any variables, and that the following controls prove sufficient: time trends, political party,

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ideology, income, education, race, and urban density. I make this assumption given my controls

because these control variables have been successfully employed in similar studies and because

it is necessary to assume the absence of confounding variables left unaccounted for in order to

successfully estimate the relationship between media framing and public opinion. Because I

recognize that there might be differences across states that I am not yet controlling for, I use state

fixed effects as a part of my regression analysis. In doing so, I find that the regression loses its

statistically significant result.

 IV. Results

This section contains my empirical results. I first discuss the results of the regression

quantifying the relationship between public opinion and framing. Following that, I present the t-

test and Mann-Whitney results that address how changes in framing impact public opinion over

time. Finally, I address the challenge of causality.

The first regression I run is of public opinion on opinion content frames over the time

period. My hypothesis is that framing of opinion content in newspapers influences public

opinion, as evident in β1, at a statistically significant level.

Public Opinion = β0 + β1(Media Framing) + αX + ε

I include race, sex, education, income, political party, ideology, and urban density as

controls (αX) by creating dummy variables for all possible values of each of the factors. I also

include the years as dummy controls in order to control for time trends. Results of this regression

can be seen in Table 4. As is evident, a positive framing of marijuana legalization in newspaper

opinion content is associated with an 8.24 percent increase in public support of legalization. The

result is statistically significant at the 95 percent level with a p-value of .047 and a confidence

interval ranging from .001 to .1636. The time trends and social demographic factors can also be

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seen in Table 4. Some of the demographics are of more importance than others, such as sex,

political party, race, and ideology.

To briefly evaluate the relationship between frame and readers’ predispositions, I drop all

demographic controls in the regression but leave in the year dummies. When I do so, the frame

effect jumps from 8.24 percent to 12 percent and is statistically significant with a p-value of

.001, as shown in Table 5. Given that this coefficient is higher when I drop the demographic

controls and that I think the relationship between readers’ predispositions and the dependent

variable is positive, the relationship between frame and readers’ predispositions is positive as

well. Essentially, dropping the demographic controls allows me capture this positive relationship

between frame and predispositions.

Going back to the first regression with demographic controls, I expect the results to be

stronger in demographic groups that are more likely to read the paper, including older people,

more educated people, and wealthier people. These factors serve as proxies for whether the

individuals polled read the paper. To test this hypothesis, I run the same regression on these

specific demographic groups. All results are shown in Table 6. First, I run the regression on

people with different income levels, once using a sample of people whose annual incomes are

above $75,000 and again with people whose incomes fall below that threshold. The coefficient is

4 percent higher for people whose annual income is above $75,000; more specifically, a change

to a positive frame is associated with a 10 percent increase in support for legalization among

richer people. However, the results are not statistically significant. Then, I run the same

regression for people who have at least an associate’s degree and compare the results to people

who have lower levels of educational attainment. These two groups have almost identical

coefficients (.083 and .085), and neither result is statistically significant. Lastly, I run the

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regression for people who are fifty years old or above and compare the results to people younger

than that. The coefficient for people aged 50 or above is only .6 percent higher, and again, the

results are insignificant. In all of these cases, the 95 percent confidence intervals include zero.

The statistical insignificance of these results could also be attributed to the lower sample sizes

given that they are cut in half. Regardless, the fact that none of these lead to significant

differences is not ideal, because I would expect that people who are more likely to read the

newspaper are also more likely to be influenced by the framing of the opinion content.

To complete the regression analysis, I return to the initial full regression and include state

fixed effects to address the possibility of omitted variable bias. I recognize that there could be

some time-invariant differences that have not already been controlled for between the 11 states

that might be correlated with the independent and dependent variables. Adding state fixed effects

allows me to control for these unobserved differences, and doing so leads to a negative 10

percent insignificant correlation between framing and public opinion. The full regression output

including state fixed effects is shown in Table 7. When completing the regression, I omit the

dummy indicator for California and find that only two states have statistically significant

coefficients. Texas has a coefficient of -0.248 and Virginia has a coefficient of -0.1647, which

makes sense given the relative conservativeness of these states. The 95 percent confidence

interval of the regression of public opinion on frame goes from -.342 to .139, so I cannot reject

the hypothesis that there is a positive effect of framing on public opinion. This range also

includes the coefficient of 8.24 percent, as found in the first regression. However, the negative

coefficient remains surprising, and suggests that if there is an effect, it is likely very small. The

time-invariant differences between the states seem to account for more of the relationship. It is

also important to note that adding more variables while keeping the same number of observations

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lowers the statistical strength of the regression. With more observations, I would likely see more

of a significant relationship between framing and public opinion.

All in all, it appears that there is a correlation between framing and public opinion, even

after taking social demographic factors into account. This supports my hypothesis that print

media framing influences public opinion, and the results are high in magnitude, because an 8

percent increase in public support for legalization is significant. However, it remains

disconcerting that these effects are not higher for demographic groups more likely to read the

opinion content. It is also predicted based on the results of adding state fixed effects that the

relationship between framing and public opinion is very small.

The second analysis I conduct looks into another hypothesis: that shifts in opinion

content framing should influence shifts in public opinion. To analyze this hypothesis, I now

complete a full analysis using a t-test, Mann-Whitney test, and regression. Using the designated

groups of “up frame”, “down frame”, and “other frame”, I compare movement in public opinion

between 2010 and 2013. To do so, I run two separate t-tests. The first compares the change in

public opinion between states with up frames and states with other frames. The second compares

the change in public opinion between states with down frames and states with other frames. The

results of these t-tests are displayed in Figure 4. Neither t-test provides statistically significant

results, which can again partially be attributed to the sample size. However, the relationships do

match my hypothesis. States with up frames experience an average 14 percent increase in public

support, while states without up frames experience lower increases of 10 percent. States with

down frames experience increases in support of 3 percent, while other states experience average

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increases of 13 percent. As aforementioned, it is not surprising that down frame states also

experience increases in support levels, because time trends suggest that support is increasing in

all states regardless. Though insignificant at the 95 percent level, the magnitudes of difference

are large, because a 14 percent increase in public support could influence passage of legislation

on a state level.

To supplement the t-test analysis, I also run a Mann-Whitney two-sample test. This tests

the hypothesis that two independent samples are from populations with the same distribution. To

put it another way, I answer the following question: if the groups are sampled from populations

that have identical distributions, what is the chance that random sampling would lead to the mean

ranks being as far apart as I observe in this study? As in the t-tests explained previously, I

compare up frame states to other frame states, and also run a test comparing down frame states to

other frame states. Thus, my null hypothesis is that the samples were drawn from populations

with the same distribution. Results of my Mann-Whitney tests are shown in Table 8. In my

comparison between up frames and other frames, I find insufficient evidence to reject the null

hypothesis, given a p-value of 0.8383. In my comparison between down frames and other

frames, I again find insufficient evidence to reject the null hypothesis due to a p-value of 0.1573.

These results do not necessarily means that the two populations are the same in both tests;

however, as with the t-tests, I simply lack compelling evidence to prove that they differ.

Lastly, I incorporate these findings into a regression, in which I aggregate the data to the

state-year level and then regress the change in opinion from 2010 to 2013 on indicators for up

frame and down frame. I show the regression equation below, the results of which are evident in

Table 9.

Change in Public Opinion = β0 + β1(Indicator for Up Frame) + β2(Indicator for Down Frame) + ε

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The regression outputs a β0 of .1247, β1 of .02, and β2 of -.0923, and although neither of

the coefficients for the frame indicators prove statistically insignificant, the relationships match

expectations. Relative to people in states with newspapers that don’t exhibit a shift in framing,

residents of states with up frames, on average, are 2 percent more likely to support marijuana

legalization. Furthermore, residents of states with down frames, on average, are 9 percent less

likely to support legalization, relative to residents in states with other frames.

None of the aforementioned analyses provide convincing evidence for direction of

causality to suggest that newspaper content influences public opinion. To address this, I did an

analysis on public opinion trends before the changes in framing and compared them to public

opinion trends during the framing shifts as a preliminary defense of causation. Results of this

analysis are shown in Figure 6. The figure shows that the significant trends begin after

newspapers begin to write increasingly framed opinion content, suggesting that the media drives

public opinion. For example, in states where the newspapers become increasingly supportive of

marijuana legalization between 2011 and 2013 (up frames), public opinion, on average, actually

fell 3 percent from 2010 to 2011 before jumping an average 17% between 2011 and 2013. All

states show upward trends from 2011 to 2013, including states where newspapers exhibit down

frames, so it remains difficult to separate the time trends from the impact of framing. On the

whole, however, the distinguishable trends from 2011 to 2013, when the newspapers began to

shift their frames, provides introductory evidence to support the hypothesis that newspapers

influence public policy preferences, and not the other way around.

I return to the initial question: does the framing of marijuana legalization in newspaper

opinion content influence public opinion on a state-by-state basis? Unfortunately, the results of

all of the aforementioned analyses provide no conclusive answer to this question. The

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correlations in the regression suggest that a relationship exists, and the final trends analysis

suggests that direction of causality is such that newspapers influence public opinion. The t-tests

and Mann-Whitney tests provide support as well in that they illustrate that support moves with

shifts in framing, but they lack statistical strength. All in all, I can conclude that framing of

marijuana legalization in print media opinion content relates to public opinion on the topic, but

with this dataset, I cannot decipher the specifics of that relationship.

 V. Discussion  

Numerous limitations exist in this study. First and foremost, the sample size of 11

newspapers is too small to make decisive conclusions with. Although I analyze 160 articles, the

average framing measure method I use shrinks this to 1 frame number per year per newspaper

per state. These small sample sizes could not be resolved due to the general lack of opinion

content on the topic of marijuana legalization, however. Figure 5 illustrates the issue of limited

print media coverage on the topic, displaying the fact that terms like “recreational marijuana”

and “marijuana legalization” only entered the newspaper lexicon in the previous 3 years. Related

studies rely on at least 14 years of data (Chomsky and Barclay, 2006), and this study draws from

only 3 years. Adding more years of polling data and framing information would significantly

strengthen the analysis, but this is not yet possible.

Furthermore, very few newspapers have published official editorials asserting their

position on recreational marijuana policies, leaving me to rely more heavily on op-ed content,

which has more variation in bias. The missing years in polling data and gaps in media framing

data also become a primary limitation, as does the reality of the difficulties in ensuring a

comprehensive opinion content dataset. It proved difficult to find all relevant articles in the

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databases and newspaper archives, but I remain confident that for the newspapers in my sample

set, the articles coded make up a sufficiently inclusive sample.

In addition to the limitations related to sample size shortfalls, there exist a few issues in

approach to the analysis. For instance, a bias could exist in the way in which I coded the opinion

content as positive or negative. To check for this bias, I asked another student to read 10 random

pieces of opinion content from the study and code them as positive or negatively framed. The

student’s framing results matched mine for all of the articles, which validates my framing

analysis and supports the robustness of the measure. A final limitation is the difficulty in fully

controlling for social demographic factors in the regression. For example, I did not include

religion in my analysis because of gaps in the data, but this could be an important driver of

public opinion. To put it another way, it is difficult to isolate the influence of media framing on

public opinion due to the plethora of other factors at play, as shown with the introduction of state

fixed effects.

The discussion of this difficulty leads right into the main challenge someone could raise

to my conclusions: how can I assert direction of causality? The regression suggesting a positive

and statistically significant relationship between framing and public opinion shows correlation

rather than causation. A challenge is deciphering whether this relationship exists because

newspapers influence public opinion, or because public opinion influences newspaper content.

As Chomsky and Barclay (2006) posit, the media may alter their messages in response to

existing public policy preferences on the local level. Newspapers are profit-driven commercial

entities, which provides an incentive to please readers by aligning their content with existing

opinions. This reflects the point of Hamilton (2004), who argues that the media takes stances in

reflection of their audience’s viewpoints, as evident in the demographic characteristics of

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audience members. In response to this challenge of causality, I admit it is difficult, using my

data, to provide proof of causality. However, the analysis previously outlined on public opinion

trends before and during newspaper framing shifts serves as a preliminary defense. Another,

more minor, challenge that could be brought up is the presence of strong time trends in support

levels. Regardless of demographic composition, all states appear to display higher support levels

for legalization over time. To address this challenge, I controlled for time trends in the

regression.

With more time and resources, I would attempt to get the full datasets for Pew’s 2014

Political Survey. This would allow me to increase my sample size in public opinion as well as

include more available opinion content. For statistical strength, the analysis requires more years

of information, so I would also try to get more detailed 2009 data. This would also allow me to

strengthen my argument of direction of causality. Additionally, I would increase my state sample

size above 11 by searching for more news coverage on LexisNexis, Newsbank’s Access World

News, and newspaper archives. Essentially, additional time and resources would allow for

increased sample sizes, though doing so could require waiting for continued increases in relevant

articles on marijuana legalization.

 VI. Conclusion  

This study provides numerous findings regarding the relationship between print media

and public opinion. First, in the specific case of marijuana legalization, it presents the finding

that a relationship exists between the way in which newspapers frame the topical issue and the

public’s support for recreational marijuana becoming fully legalized. It also addresses the

commonly addressed question of whether the media drives public opinion, or whether the media

adapts to the preferences of their audiences for profit-based reasons. Preliminary evidence in this

  22  

paper suggests the media is leading public opinion. Lastly, the findings suggest that frame shifts

over time move with public opinion on a state-by-state basis.

It is also interesting to note the variety of perspectives that newspapers take regarding

marijuana legalization. In similar studies, like Chomsky and Barclay (2006), the newspaper

coverage was relatively homogeneous on the topic of gay marriage, a detail that the authors note

as a possible explanation for why the newspaper had such a significant influence. Within

marijuana legalization, far less content has been published in state-level newspapers, and the

opinion coverage to date varies quite a bit within each newspaper. As the topic continues to

come to the forefront of social and political discussions, I posit that articles will converge

towards more positive framing.

Proponents of legalization have achieved significant victories in legislation to date, and

with growing support by the public, I anticipate more support for these proponents moving into

the 2016 election year and beyond. Results of this paper provide evidence for a relationship

between print media and public support for legalization, and also suggest that the direction of

influence is such that the public alters its opinion based on the media. Thus, this remains an

important topic to study moving forward as more content and relevant polls emerge, because if

public opinion shifts as a result of the position of print media sources, then the media messages

may hold political power.

     

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References Barclay, Scott and Shauna Fisher. 2003. The states and the differing impetuses for divergent

paths on same-sex marriage, 1990—2001. Policy Studies Journal 31(3): 331-352. Chomsky, Daniel and Scott Barclay. 2006. Shaping the same sex marriage discussion: Mass

media, government action, social demographic factors, and public opinion in the 50 States. Paper presented at the annual meeting of the American Political Science Association, Marriott, Loews Philadelphia, and the Pennsylvania Convention Center, Philadelphia, PA.

Chiang, Chun-Fang and Brian Knight. 2011. Media bias and influence: Evidence from

newspaper endorsements. Review of Economic Studies 78(3):795-820. Colvin, Roddrick. 2006. Understanding policy adoption and gay rights: The role of the media

and other factors. The Innovation Journal 11(2): 1-16. DellaVigna, Stefano and Ethan Kaplan. 2007. The Fox News effect: Media bias and voting. The

Quarterly Journal of Economics 122(3): 1187-1234. Erikson, Robert S., Gerald C. Wright, and John P. McIver. 1993, Statehouse democracy: Public

opinion and policy in the American states. New York: Cambridge University Press. Golan, Guy J. 2010. Editorials, op-ed columns frame medical marijuana debate. Newspaper

Research Journal 31(3): 50-60. Haider-Markel, Donald P. and Kenneth J. Meier. 1996. The politics of gay and lesbian

rights: Expanding the scope of the conflict. Journal of Politics 58: 332-349. Hamilton, James T. 2004. All the news that’s fit to sell: How the market transforms information

into news. Princeton: Princeton University Press. History of marijuana in the United States. (2014, June 1). Retrieved December 10, 2014, from

http://www.pbs.org/wgbh/pages/frontline/shows/dope/etc/cron.html Marijuana state policies. (2014, November 10). Retrieved December 10, 2014, from

http://www.mpp.org/ McCombs, Maxwell E., and Donald L. Shaw. 1972. The agenda-setting function of the press.

Public Opinion Quarterly, 36(2): 176-187. Menashe, Claudia L. and Michael Siegel. 1998. The power of a frame: An analysis of newspaper

coverage of tobacco issues--United States, 1985-1996. Journal of Health Communication 3: 307 ­ 325.

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Page, Benjamin I., Robert Y. Shapiro, and Glenn R. Dempsey. 1987. What moves public opinion?. American Political Science Review 83(1): 23-43.

Pew state of the news media 2012. (2012, February 11). Retrieved December 10th, 2014, from

http://www.stateofthemedia.org/2012/newspapers-building-digital-revenues-proves- painfully-slow/

Wald, Kenneth, James W. Button and Barbara A. Rienzo. 1996. The politics of gay rights in

American communities: Explaining antidiscrimination ordinances and policies," American Journal of Political Science 40(1): 1152-1178.

  25  

Table 1: Support for Marijuana Legalization by Demographic Groups

Year

2010 2011 2013 2014 By age: 18-29 57.3% 54.0% 66.2% 70.0% 30-49 43.7% 48.0% 56.4% 56.0% 50-64 44.4% 47.1% 53.6% 55.0% 65+ 24.8% 33.9% 37.0% 32.0%

Sex Male 47.0% 48.3% 46.8% . Female 36.5% 42.4% 57.6% .

Race White 41.4% 44.7% 51.0% 55.0% Black 38.7% 49.0% 55.0% 60.0% Hispanic 37.0% 41.6% 56.5% 43.0%

Urban Density Rural 36.9% 38.9% 48.1% . Suburban 37.2% 37.9% 45.8% . Urban 44.4% 51.1% 53.3% .

Political Party: Democrat 48.9% 55.0% 59.8% 63.0% Republican 22.3% 28.3% 37.1% 39.0% Independent 49.1% 50.0% 57.8% 58.0%

Income <30,000 44.7% 43.8% 54.4% . 30-50,000 42.3% 50.9% 49.5% . 50-75,000 39.6% 46.1% 54.3% . 75-100,000 50.0% 45.8% 47.4% . 100,000+ 40.4% 52.0% 57.7% .

Notes: This table illustrates support over time based on demographic groups. Full datasets for 2014 from the Pew Research Center for the People and the Press have not yet been released.

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Table 2: Support for Marijuana Legalization by State

State 2010 2011 2013 Alabama 31.8% 42.9% 42.9% Arizona 61.3% 71.4% 53.3% California 50.7% 57.9% 57.0% Colorado 56.5% 43.3% 61.8% Florida 41.8% 54.7% 52.1% Georgia 22.7% 46.5% 35.1% Idaho 40.0% 42.9% 58.3% Illinois 45.5% 43.9% 68.6% Indiana 28.1% 50.0% 67.4% Kansas 27.3% 26.7% 42.9% Kentucky 30.0% 29.2% 57.1% Louisiana 36.8% 29.2% 40.9% Maryland 45.0% 46.2% 50.0% Massachusetts 32.1% 74.1% 78.9% Michigan 51.2% 42.5% 65.1% Minnesota 45.0% 31.3% 58.6% Mississippi 29.4% 35.7% 60.0% Missouri 31.0% 44.7% 52.2% Nevada 37.5% 36.4% 81.8% New Hampshire 75.0% 85.7% 80.0% New Jersey 56.1% 53.5% 61.0% New York 47.9% 43.4% 54.0% North Carolina 44.4% 47.7% 48.9% Ohio 43.9% 35.1% 44.6% Oklahoma 18.8% 62.5% 40.9% Oregon 59.1% 65.2% 52.9% Texas 32.9% 32.4% 50.0% Utah 15.8% 50.0% 60.0% Virginia 46.8% 42.9% 48.3% Washington 37.5% 61.9% 54.8% West Virginia 12.5% 38.9% 58.3% Total 41.6% 45.2% 52.0%

Note: This table summarizes average support levels for marijuana legalization for each year analyzed on a state level. It shows state-year averages for the answer to the following question: “Do you think the use of marijuana should be made legal, or not?” Information is from the Pew polls in my dataset.

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Table 3: Coding Methods Examples

Positive Framing (Code as 1) Newspaper Year Text Clipping

Denver Post 2013

The Denver Post opposed Amendment 64 mainly because of the conflict with federal law. But we have long supported the concept of legalizing marijuana nationwide and putting an end to the massive squandering of resources on prosecuting and punishing people for possessing and using marijuana. It seems that many others now agree.

Las Vegas Review Journal 2014

The banned substance remains everywhere — at schools and streetcorners, in public housing and affluent suburbs. Bringing all this commerce into the sunshine, and turning all the people who grow, process and sell marijuana into taxpayers, is a far more practical course. Colorado and Washington state voters were the first to legalize the sale and use of marijuana for recreational purposes, and Colorado governments already are collecting more than $1 million per week in tax revenues from all marijuana sales.

Negative Framing (Code as 0) Newspaper Year Text Clipping

The Seattle Times 2012

Marijuana addiction is common. About 9 percent of people who smoke marijuana even once become addicted to it, and that figure approximately doubles when people begin using the drug as adolescents. To prevent addiction from spreading, Washington voters should not legalize marijuana and reject Initiative 502 on the Nov. 6 ballot.

Dallas Morning News 2013

By legalizing marijuana, we are gambling with our children’s futures. Why add to the already serious alcohol and prescription drug abuse problem by legalizing yet another powerful narcotic drug that is so addictive? Why risk sacrificing the health and well-being of our children at the altar of purported personal liberty and self-pleasure? They deserve better.

Notes: This table provides samples of opinion content that I read in the study, illustrating how I coded the articles. I coded the first two as “1”, or positively framed, because they both propose legalizing recreational marijuana. I coded the second two as “0”, or negatively framed, because they both speak to the potential dangers of marijuana and thus oppose legalization.

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Table 4: Full Regression Results

Public opinion coefficient Std. Error t P>|t| [95% Conf. Interval] frame 0.082 0.041 1.99 0.047 0.001 0.164

2011 0.016 0.041 0.39 0.7 -0.065 0.097 year 2013 0.078 0.038 2.07 0.039 0.004 0.153

2 -0.025 0.043 -0.58 0.564 -0.110 0.060 3 -0.056 0.047 -1.2 0.232 -0.147 0.036 4 -0.018 0.052 -0.34 0.735 -0.120 0.085

income 5 -0.035 0.045 -0.78 0.435 -0.124 0.053 (2)Democrat 0.132 0.043 3.08 0.002 0.048 0.216

party (3) Independent 0.123 0.039 3.12 0.002 0.046 0.200 (2) Black -0.096 0.051 -1.9 0.058 -0.196 0.003 (3) Asian -0.171 0.071 -2.4 0.017 -0.311 -0.031 4 0.010 0.065 0.15 0.878 -0.117 0.137 5 -0.062 0.092 -0.67 0.501 -0.241 0.118 6 -0.574 0.331 -1.74 0.083 -1.223 0.075

race (7) Hispanic 0.004 0.083 0.05 0.96 -0.159 0.168 sex (1) Male 0.110 0.029 3.76 0 0.052 0.167

2 -0.014 0.132 -0.11 0.915 -0.273 0.245 3 0.183 0.121 1.52 0.129 -0.053 0.420 4 0.237 0.126 1.88 0.06 -0.010 0.485 5 0.206 0.121 1.7 0.09 -0.032 0.444 6 0.166 0.122 1.36 0.174 -0.073 0.406 7 0.170 0.127 1.34 0.179 -0.078 0.419

education 8 (most education) 0.089 0.136 0.65 0.515 -0.178 0.356 2 (sparse) -0.018 0.048 -0.37 0.708 -0.112 0.076 3 -0.019 0.049 -0.39 0.694 -0.115 0.076 4 0.018 0.048 0.38 0.704 -0.075 0.111

density 5 (densest) 0.057 0.047 1.22 0.224 -0.035 0.148

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(2) conservative -0.014 0.057 -0.25 0.804 -0.125 0.097 3 0.226 0.058 3.9 0 0.112 0.339 4 0.291 0.065 4.49 0 0.164 0.418

ideology (5) liberal 0.456 0.078 5.85 0 0.303 0.609 _cons -0.005 0.134 -0.04 0.969 -0.267 0.257

Notes: This table provides the detailed results of the regression of public opinion on media framing. It includes dummy variable indicators for 7 demographic factors (income, party, race, sex, education, density, and ideology) as well as for time trends. The coefficient between public opinion and framing comes out to 8.24 percent, and is statistically significant.

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Table 5: Regression without Demographic Controls

Notes: I run the same regression as shown in Table 4 but remove all demographic controls. As is evident, the coefficient jumps to 12.06 percent from 8.24 percent. This analysis allows me to pinpoint the correlation between frame and readers’ predispositions. Given that this coefficient is higher without the demographic controls and that I think the relationship between readers’ predispositions and the dependent variable is positive, the relationship between frame and readers’ predispositions is positive as well.

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Table 6: Regression Results for more Likely Newspaper Readers

Coefficient P-value 95% Confidence Interval Education High (at least Associates) 0.0832452 0.115 -0.02042 0.18691 Low 0.085513 0.204 -0.046553 0.2175811

Income High (75,000+) 0.105789 0.145 -0.03678 0.2483601 Low 0.0648352 0.212 -0.03697 0.16664

Age 50 or above 0.09156 0.121 -0.02425 0.20738 Below 50 0.084824 0.149 -0.030445 0.2000947

Notes: This table repeats the regression from Table 4, but separates the sample into different demographic groups based on education, income, and age. These factors serve as proxies for reading the newspaper. I expect that the coefficients would be higher for groups that are more likely to read the newspapers. However, the results do not show significant differences.

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Table 7: Regression with State Fixed Effects

Public Opinion Coefficient Std Error t P>|t| [95% Confidence Interval] Frame -0.1007645 0.1226957 -0.82 0.412 -0.3415277 0.1399987 Density 2 (sparsest) -0.0153675 0.0482359 -0.32 0.75 -0.1100197 0.0792848 3 -0.0158228 0.0489777 -0.32 0.747 -0.1119306 0.0802851 4 0.0229966 0.0493515 0.47 0.641 -0.0738448 0.119838 5 (densest) 0.0601378 0.0482842 1.25 0.213 -0.0346093 0.1548849 Education 2 0.0013948 0.1326167 0.01 0.992 -0.2588361 0.2616257 3 0.1906241 0.1208788 1.58 0.115 -0.0465738 0.4278221 4 0.2843841 0.1251385 2.27 0.023 0.0388275 0.5299407 5 0.1982282 0.1216458 1.63 0.104 -0.0404747 0.4369311 6 0.1714614 0.1224056 1.4 0.162 -0.0687326 0.4116554 7 0.1415526 0.1268823 1.12 0.265 -0.1074258 0.390531 8 (most education) 0.1424413 0.1347122 1.06 0.291 -0.1219015 0.4067841 Income 2 -0.0166416 0.0437528 -0.38 0.704 -0.1024967 0.0692135 3 -0.0453843 0.0468112 -0.97 0.333 -0.137241 0.0464724 4 -0.0014751 0.0524752 -0.03 0.978 -0.1044461 0.1014958 5 (richest) -0.0270958 0.0455932 -0.59 0.552 -0.1165625 0.0623709 Party (2) Dem 0.1247417 0.0429599 2.9 0.004 0.0404424 0.2090409 (3) Ind 0.1186708 0.0395167 3 0.003 0.041128 0.1962136 Race 2 (black) -0.0655839 0.0513466 -1.28 0.202 -0.1663402 0.0351723 3 (asian) -0.170823 0.0716978 -2.38 0.017 -0.311514 -0.030132 4 -0.0262438 0.0647347 -0.41 0.685 -0.1532714 0.1007838 5 -0.0726592 0.0897484 -0.81 0.418 -0.2487707 0.1034523 6 -0.5777333 0.3309759 -1.75 0.081 -1.2272 0.0717337 7 (hispanic) 0.0423235 0.0827481 0.51 0.609 -0.1200513 0.2046982

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Ideology 2 (conservative) -0.0097939 0.0570368 -0.17 0.864 -0.121716 0.1021282 3 0.2266995 0.0583729 3.88 0 0.1121555 0.3412435 4 0.2891781 0.0649914 4.45 0 0.1616469 0.4167094 5 (liberal) 0.4669464 0.0785504 5.94 0 0.3128087 0.6210842 Sex (1) Male 0.1089243 0.0293508 3.71 0 0.0513298 0.1665188 State ID 8 - CO 0.0511468 0.0897186 0.57 0.569 -0.124906 0.2271996 17 - IL 0.0086456 0.0644903 0.13 0.893 -0.1179024 0.1351935 24 - MD -0.0487377 0.0811003 -0.6 0.548 -0.2078792 0.1104037 32 - NV -0.2220461 0.1731962 -1.28 0.2 -0.5619053 0.1178131 34 - NJ 0.0162138 0.0935968 0.17 0.863 -0.1674493 0.1998769 36 - NY -0.0069191 0.063897 -0.11 0.914 -0.1323028 0.1184645 41 OR -0.0387779 0.0964513 -0.4 0.688 -0.2280423 0.1504865 48 - TX -0.1957071 0.0940124 -2.08 0.038 -0.3801856 -0.0112286 51 - VA -0.1353096 0.0638964 -2.12 0.034 -0.2606922 -0.009927 53 - WA 0.0189383 0.0600709 0.32 0.753 -0.0989376 0.1368143 _cons 0.1726547 0.1552699 1.11 0.266 -0.1320282 0.4773376

Notes: To check for omitted variables, I run the same regression as shown in Table 4, but also include dummy indicators for State ID, thus adding state fixed effects. With state fixed effects added in, the coefficient between public opinion and framing becomes negative (-10 percent) and insignificant. Two states added have statistically significant coefficients: Texas and Virginia. Overall, the negative relationship is surprising, but the 95 percent confidence interval is very large and crosses zero. Therefore, I cannot reject my hypothesis that there is a positive effect, but think that if there is an effect, it is very small.

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Table 8: Mann-Whitney Two-Sample Test Results

Notes: This table provides the results to the Mann-Whitney two-sample test conducted as the non-parametric supplement the t-test previously done. It is used to answer the following question: if the groups are sampled from populations that have identical distributions, what is the chance that random sampling would lead to the mean ranks being as far apart as I observe in this study? As in the t-tests, I compare up frame states to other frame states, and also run a test comparing down frame states to other frame states. The results above provide insufficient evidence to reject the null hypothesis that the distributions are the same because both p-values are statistically insignificant.

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Table 9: Regression for Change in Opinion on Framing Shifts Notes: This table builds on the t-test comparing change in public opinion from 2010 to 2013 with the type of newspaper frame shift. I aggregate the data to the state-year level and regress the change in opinion from 2010 to 2013 on indicators for up frame and down frame. Results are not statistically significant but match expectations, as an “up frame” is associated with a jump in public support while a “down frame” is associated with a drop in support.

  36  

Figure 1: Support for Marijuana Legalization over time

Notes: Figure shows support levels for marijuana legalization from 1969 to 2014 in the United States. The data points are from the Pew Research Center for the People and the Press.

0  

10  

20  

30  

40  

50  

60  

Percent  of  those  Surveyed  

Year  

Should Marijuana be Legal or Illegal in the United States?

  37  

Figure 2: Current State Policies on Marijuana

Note: The Marijuana Policy Project created this map of current state policies. It illustrates the variety of policies currently in place across the country and the progress that has been made in drug policy recently (given the full legalization measures in Alaska, Oregon, Washington, Colorado, and Washington D.C.).

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Figure 3: Average Frames Over Time in States

Notes: The visual shows the average newspaper frame for each of the 11 states over the 3 years analyzed in order to express the shifts in frames over the years. Average newspaper frame per year was calculated by averaging the frames of all articles published by the paper in the specific year. Average frames closer to 1 (highlighted in dark blue) means the largest newspaper in the state expressed strong favorability towards marijuana legalization, and average frames closer to 0 (highlighted in light blue) means the largest newspaper in the state expresses strong opposition. Full summary of average frames available in Appendix Table B.

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Figure 4: Changes in Public Support by Framing Shifts

Notes: T-test results comparing the average change in support for marijuana legalization in states with up frames (newspapers that become more supportive over time) against states with other frames. Figure on right compares the average change in support in states with down frames (newspapers that become less supportive over time) against states with other frames. Results match expectations, but neither is statistically significant (as evident in the high p-values).

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Figure 5: Lack of Newspaper Content on Marijuana Legalization

Notes: Figure created used the Chronicle Tool from the New York Times. Figure shows the percent of total articles that the terms “recreational marijuana”, “marijuana legalization”, “medical marijuana”, and “marijuana” are included in over time. It illustrates the limitation that marijuana legalization has only become a more spoken about topic in newspapers in recent years.

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Figure 6: Trends in Public Opinion Before and After Framing Shifts Notes: This figure provides preliminary evidence suggesting direction of causality. It is similar to Figure 4, and shows average public opinion changes over two time periods, 2010-2011, and 2011-2013, when framing shifts took place. The figure groups states by the type of framing shift that the newspaper in the state exhibits.

-0.05

0

0.05

0.1

0.15

0.2

Up Frame Other Frame Down Frame

Change in Support by Framing Change

2010-20112011-2013

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Appendix

Table A: Framing Analysis Results

Year Paper Frame 2011 Las Vegas Review Journal 0 2014 Las Vegas Review Journal 1 2014 Las Vegas Review Journal 1 2014 Las Vegas Review Journal 1 2009 Washington Post 1 2010 Washington Post 1 2010 Washington Post 0 2013 Washington Post 1 2013 Washington Post 0 2014 Washington Post 1 2014 Washington Post 0 2014 Washington Post 0 2014 Washington Post 0 2009 Chicago Sun Times 1 2010 Chicago Sun Times 1 2010 Chicago Sun Times 1 2012 Chicago Sun Times 1 2012 Chicago Sun Times 1 2012 Chicago Sun Times 1 2012 Chicago Sun Times 1 2013 Chicago Tribune 1 2013 Chicago Tribune 1 2013 Chicago Tribune 0 2013 Chicago Sun Times 1 2013 Chicago Sun Times 0 2014 Chicago Tribune 0 2010 Denver Post 0 2012 Denver Post 1 2012 Denver Post 0 2013 Denver Post 1 2013 Denver Post 0 2014 Denver Post 1 2014 Denver Post 1 2010 Los Angeles Times 1 2010 San Francisco Chronicle 0 2010 San Francisco Chronicle 0 2010 San Francisco Chronicle 0 2010 San Francisco Chronicle 1

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2010 San Francisco Chronicle 1 2011 Los Angeles Times 0 2011 Los Angeles Times 1 2011 Los Angeles Times 1 2011 San Francisco Chronicle 0 2011 San Francisco Chronicle 1 2011 San Francisco Chronicle 1 2011 San Francisco Chronicle 1 2012 San Francisco Chronicle 1 2012 San Francisco Chronicle 1 2012 San Francisco Chronicle 1 2012 San Francisco Chronicle 1 2012 San Francisco Chronicle 1 2013 Los Angeles Times 0 2013 Los Angeles Times 1 2013 Los Angeles Times 1 2013 San Francisco Chronicle 1 2013 San Francisco Chronicle 1 2013 San Francisco Chronicle 1 2014 Los Angeles Times 0 2014 Los Angeles Times 0 2014 Los Angeles Times 1 2014 Los Angeles Times 1 2014 Los Angeles Times 0 2014 Los Angeles Times 1 2014 Los Angeles Times 1 2014 Los Angeles Times 0 2014 San Francisco Chronicle 1 2014 San Francisco Chronicle 1 2014 San Francisco Chronicle 1 2014 San Francisco Chronicle 1 2014 San Francisco Chronicle 1 2014 San Francisco Chronicle 0 2014 San Francisco Chronicle 0 2014 San Francisco Chronicle 1 2014 San Francisco Chronicle 1 2014 San Francisco Chronicle 1 2014 San Francisco Chronicle 1

Notes: This table provides a snapshot into the framing analysis I conducted. After reading each article I coded it as positive “1”, or negative “0”. I then noted the article title.

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Table B: Average Frames in States Over Time

Year State Average Frame 2010 California 0.7 2011 California 0.70833333 2012 California 1 2013 California 0.83333333 2010 Colorado 0 2012 Colorado 0.5 2013 Colorado 0.5 2014 Colorado 1 2009 Illinois 1 2010 Illinois 1 2012 Illinois 1 2013 Illinois 0.70833333 2014 Illinois 0 2009 Maryland 1 2010 Maryland 0.5 2013 Maryland 0.5 2014 Maryland 0.25 2011 Nevada 0 2013 Nevada 1 2014 Nevada 1 2011 New Jersey 1 2013 New Jersey 1 2014 New Jersey 1 2010 New York 1 2012 New York 0.8 2013 New York 1 2014 New York 1 2010 Oregon 0.33333333 2012 Oregon 0.6 2013 Oregon 1 2014 Oregon 1 2010 Texas 0 2011 Texas 0 2012 Texas 0.5 2013 Texas 0 2014 Texas 0.44444444 2009 Virginia 1 2010 Virginia 0.5 2013 Virginia 0.5 2014 Virginia 0.25 2011 Washington 0.92857143 2012 Washington 0.75

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2013 Washington 0.66666667 2014 Washington 1

Notes: This table shows the average media frame measure for each of the 11 states in my sample. To compile this, I coded 160 opinion pieces as positive (1) or negative (0) and calculated the average for each year for each newspaper.