The Diffusion of Policy Perceptions

The Diffusion of Policy Perceptions:

Evidence from a Structural Topic Model∗

Fabrizio Gilardi† Charles R. Shipan‡ Bruno Wueest§

June 22, 2015

Abstract

Policy diffusion means that policies in one unit (such as states, cantons, and cities) are inﬂuenced by policies in other units. The literature has established convincingly that diffusion is an important determinant of policy choices, but the same process can also shape how policies are perceived, both before and after they are adopted, if they are adopted at all. This paper studies this process in the case of smoking bans in U.S. states. It relies on an original dataset of over 370,000 articles published in sixteen American newspapers between 1996 and 2014 and uses structural topic models to estimate how smoking bans have been perceived and how perceptions changes as a function of policy adoption in nearby states. The preliminary results show how the structural topic model can be used to study the diffusion of policy perceptions.

∗We thank Katrin Affolter, Nina Buddeke, Sarah Däscher, Jeremy Gelman, Andrea Häuptli, Geoff Lorenz, Adriano Meyer, Christian Müller, and Thomas Willi for excellent research assistance, Klaus Rotherhäusler for his help with the implementation of the computational linguistic methods, and Jude Hays for helpful comments. The ﬁnancial support of the Swiss National Science Foundation (grant nr. 100017_150071/1) is gratefully acknowledged. †Department of Political Science, University of Zurich (http://www.fabriziogilardi.org/). ‡Department of Political Science, University of Michigan ([email protected]). §Department of Political Science, University of Zurich ([email protected]).

1 1 Introduction

A large number of studies have examined how policies in one country, state, or city are shaped by policies in other countries, states, or cities—that is, how policies diffuse, cross-nationally or sub-nationally

(Dobbin, Simmons and Garrett, 2007; Gilardi, 2012; Graham, Shipan and Volden, 2013; Maggetti and

Gilardi, 2015). The literature has established convincingly that diffusion occurs in a wide range of policy areas. There is strong evidence that policies in one unit are inﬂuenced by policies in other units.

However, the interdependencies driving policy diffusion matter not only for policy adoption, but also for shaping the way in which policy problems and solutions are deﬁned and understood.

In this paper, we study how the perception of policy problems and solutions changes as a function of the adoption of policies elsewhere. We thereby rely on the concept of frames, namely, patterns of justiﬁcations linking speciﬁc policies with selective interpretations of their causes and consequences

(Entman, 1993; Helbling, Hoeglinger and Wueest, 2010). Frames can be identiﬁed by looking at the words, images, and phrases that are used when conveying information about a policy (Gamson and

Modigliani, 1989). In the context of policy diffusion, frames allow us to identify whether policy makers connect a policy with different types of outcomes in relation to the experience of other units.

We focus on smoking bans—policies restricting smoking in public places—in the United States.

This choice is motivated by several considerations. First, several American studies (e.g., Shipan and

Volden, 2006b, 2008; Pacheco, 2012), as well as abundant anecdotal evidence indicate that smoking bans have indeed been subject to a diffusion process. This allows us to concentrate on the nature of the process instead of its mere existence. Second, smoking bans have been adopted in a convenient time frame (about ten years), which is long enough to detect variations and to supply sufficient information but short enough to be practically manageable. Third, the policy has well-defined characteristics and is comparable across units. Fourth, there was significant uncertainty on the consequences of the policies with respect to their economic consequences, popular support, interest group support, and ease of implementation.

Our empirical analysis relies on an original dataset of over 370,000 articles published in sixteen

American newspapers between 1996 and 2014 and uses structural topic models (Roberts et al., 2014;

Roberts, Stewart and Airoldi, 2015) to estimate how smoking bans have been perceived in the states and how perceptions changes as a function of policy adoption in nearby states. Preliminary results from an analysis of sixteen American newspapers shows how the structural topic model can be used to

2 study the diffusion of policy perceptions.

2 Frames as indicators of policy diffusion

We deﬁne policy diffusion in terms of interdependence. That is, we consider that policy diffusion occurs if the policy choices of one unit (such as countries, states, cities, etc.) are inﬂuenced by the policy choices of other units (Dobbin, Simmons and Garrett, 2007; Gilardi, 2012). Diffusion processes are driven by three broad classes of mechanisms: competition, learning, and emulation (Simmons,

Dobbin and Garrett, 2006; Braun and Gilardi, 2006; Dobbin, Simmons and Garrett, 2007; Gilardi,

2012). First, competition means that a unit reacts to the policies of other units in an attempt to attract resources. Second, learning means that the experience of other units is used to estimate the consequences of policy choices. Third, emulation is the idea that the socially constructed properties of policies and their relationship with dominant norms, instead of their objective characteristics, drive policy diffusion. Thus, some policies may become accepted as appropriate regardless of their actual consequences. Emulation can also be seen as a process in which the “burden of proof” shifts from the proponents to the opponents of the policy. When a policy is new or heterodox, its proponents must

ﬁght against prevailing norms. However, as the policy becomes more accepted, opponents of the policy

(that is, defenders of the status quo) are under greater pressure to make their case.

We argue that the frames used in public discourses can be used as indicators of diffusion. Frames can be defined as patterns of justifications linking specific policies with certain beliefs (Entman, 1993;

Helbling, Hoeglinger and Wueest, 2010). Empirically, they can be measured through the words, images, and phrases used when conveying information about a policy (Gamson and Modigliani, 1989). Frames inform us on how a given policy is perceived in a given unit. This perception changes as a function of the policy being adopted elsewhere, thereby informing us on the nature of the diffusion process.

Regardless of whether the media reﬂect or inﬂuence how policies are framed in public debates, they can be used as an accurate source for ways in which smoking bans are perceived in a given unit—and, crucially, how this perception changes as a function of the adoption of smoking bans in other units.

In our context, frames allow us to identify patterns of justiﬁcations that link smoking bans with certain expectations about their effectiveness or appropriateness. As with any other issue, smoking bans allow for a variety of competing frames (Chong and Druckman, 2007, 112–115). Although most of the existing literature on the media coverage of smoking bans tends to focus on tobacco-related

3 news in general or on speciﬁc medical issues such as the health consequences of passive smoking, it provides some useful guidelines for the development of our classiﬁcation (Champion and Chapman,

2005; Nelson et al., 2007; Smith et al., 2008; Foster et al., 2012; Champion and Chapman, 2005). In particular, Champion and Chapman (2005, 680) identify frames related to health, economics, public opinion, or practical arguments and distinguish positive, neutral, and negative tones. Building on these insights, we expect to observe seven frames. each connects smoking bans with a different topic, namely:

1. The health of employees in businesses subject to smoking bans, or of the general population;

2. The economic implications of smoking bans for businesses subject to the ban;

3. Public support for smoking bans;

4. The implications of smoking bans for politicians;

5. Interest group support for smoking bans;

6. The implementation and enforcement of smoking bans;

7. The implications of smoking bans for individual freedom.

Table 1 shows four excerpts from newspaper articles that can be linked to the frames deﬁned above.

Excerpt A contains the “health” frame, while excerpt B exempliﬁes the “economic implications” frame.

We use these frames in two steps. First, we identify the frames empirically and look descriptively at their distribution, both cross-sectionally and over time. This allows us to establish, for instance, which frames are most used, whether their “mix” (for instance, the ratio of different frames or another composite measure) varies over time, whether the frames used focus less on economic consequences over time, and whether states used the same frames found in similar states.

Second, we analyze frames as a function of policy adoption in other units. There are two ways in which the frames can be connected with the process by which policies diffuse. If the frame “implementation,” for instance, becomes less prevalent or assumes a more positive tone after adoption by other units, then we would consider this to be evidence of diffusion because the perception of a given consequence of the policy changes as a reaction to the experience of others. Moreover, we could examine whether arguments in favor of smoking bans are more frequent than those against them in earlier periods, but the frequency reverses over time. If that is the case, it would suggest that the “burden of proof” shifts

4 A North Carolina, the nation’s largest grower of tobacco, will soon prohibit smoking in restaurants and bars. The ban, signed into law on Tuesday by Gov. Bev Perdue, is another defeat for the ailing tobacco industry on home turf in the South. [...] “This is a historic day for North Carolina,” said Governor Perdue, a Democrat. “By banning smoking in our restaurants and bars, we will greatly reduce the dangers of secondhand smoke and lower health care costs for families.” [...] Since March, smoking has been banned in most bars and restaurants in Virginia, where tobacco has been grown for 400 years. (New York Times, 19.5.2009)

B Several recent studies support the casino owners’ fears that they will lose business. An analysis of the Delaware smoking ban by Richard Thalheimer, an authority on the horse racing industry and casino gambling who teaches at the University of Louisville, found that the ban was followed by reductions of 11 to 19 percent in slot machine wagering at racetracks. [...] Another study, commissioned last year by the Casino Association of New Jersey, a trade group, and using Delaware as a baseline, estimated that a ban in Atlantic City would depress annual gambling revenue, now about $5 billion, by about $1 billion after two years. (New York Times, 29.11.2006)

Table 1: Examples of frames found in newspapers (A: health; B: economic implication). from proponents to opponents, which is in line with the idea that some policies may gain widespread acceptance and be internalized as appropriate.

3 Methodology

The units of analysis in this study are U.S. states. The United States features a strong federal structure in which subnational units retain considerable autonomy in most policy areas, including public health and, specifically, smoking restrictions. Furthermore, many antismoking policies were first debated and adopted at the local and then at the subnational level (Shipan and Volden, 2006a). Concerning the time frame, we set the beginning of the observation period two years before the first full state-wide adoption, namely, 1996 (the first statewide smoking ban was adopted in California in 1998)1. To analyze public debates, we rely on articles published in the newspapers listed in Table 2. The construction of the newspaper corpus is still under way. The final corpus eventually will include the largest newspaper in terms of circulation for every state. At the moment, 16 newspapers are available. We use the print media rather than television or radio programs for technical reasons but especially because they generally report more extensively on political matters than do on-air media (Druckman, 2005, 469).

1Debates on smoking bans go back at least to the introduction of the ﬁrst smoke-free spaces in the 1980s. The Minnesota Clean Indoor Air Act, for example, called for a partial smoking ban in bars and restaurants as early as 1975. However, the analysis requires signiﬁcant public debates associated with highly visible events.

5 State Newspaper N articles N paragraphs N ﬁltered Georgia Atlanta Journal-Constitution 23,281 114,843 8,195 West Virginia Charleston Gazette 18,228 116,099 8,354 New York Daily News 14,202 60,828 2,536 Colorado Denver Post 13,088 79,843 5,693 Utah Deseret News 15,884 58,817 5,882 Michigan Detroit Free Press 6,021 54,014 1,795 Connecticut Hartford Courant 14,821 83,980 3,698 California Los Angeles Times 29,597 196,061 12,213 Pennsylvania Philadelphia Inquirer 18,975 105,861 6,652 Rhode Island Providence Journal 15,264 89,549 5,951 New Jersey Record 19,453 95,395 7,685 Missouri St. Louis Post-Dispatch 27,516 137,830 6,380 Minnesota Star Tribune Minneapolis 13,693 120,220 11,977 New York USA Today 11,246 59,637 3,444 New York Wall Street Journal 22,971 139,448 8,962 New York New York Times 53,411 344,898 11,675 Total 376,146 2,358,875 108,311

Table 2: Selected sources for the content analysis.

We retrieved newspaper texts using a simple keyword search2 from different databases such as

LexisNexis. Then we split the texts into paragraphs of a similar length3, which produced a corpus containing 2,358,875 paragraphs. A manual evaluation of a random sample of 14,519 paragraphs revealed a very low share of paragraphs actually covering smoking bans (about 1.5% on average). Usually, a much smaller sample of hand-coded documents is necessary (Grimmer and Stewart, 2013). However, in our case, the search string matched a lot of documents which covered smoking in other contexts

(e.g. smoking in movies, health problems unrelated to regulation, restaurant reviews mentioning that a restaurant is non-smoking). Consequently, we had to increase the sample for the manual annotation in order to produce enough relevant paragraphs for the supervised classification. e annotated whether a paragraph is actually about smoking bans, that is, state or federal regulation of smoking in public places or specific workplaces. This definition includes statements about any kind of restriction of smoking

(smoking bans) in public places and/or businesses introduced through legislative action, executive action or other democratic actions (e.g. direct democratic processes). By contrast, paragraphs discussing, for

2The keyword string for the different newspaper database was an adaptation of “tobacco OR non-smoking OR antismoking OR smoking OR cigar! OR (lung AND cancer) OR smoker”, depending on the options available for Boolean operators and truncation wildcards. 3The original paragraph structure of the documents was kept, however, paragraphs with less than 150 tokens were collapsed until the collapsed paragraph exceeded 150 tokens. This ensures the basic comparability of the texts from different newspapers.

6 example, smoking bans introduced by private actors (e.g. companies, businesses), or bans of specific tobacco products (e.g. mentholated cigarettes) are irrelevant for our purposes and are excluded from the sample. Thus, in a last preparatory step, the paragraphs were filtered using a naive bayes classifier which was first optimized for the recall of positives.4 Evaluated on four randomly sampled test tests, the filter is able to remove 97-98% of the irrelevant paragraphs, while keeping 57-76% of the relevant ones.5 This

filter results in a corpus of 108,311 paragraphs containing 54,573 unique terms and 7,804,736 instances of these terms (see Table 2 for an overview of the corpus). Although there still is noise in our data, we are confident that our estimations reveal the general trend in the newspapers’ coverage of smoking bans, especially since most classification runs we tested agreed with an overall F-Score of 0.8 or higher—a further sign for the consistency and thus reliability of the classification (Collingwood and Wilkerson,

2012).

We identify the frames described in Section 2 inductively with a structural topic model (STM)

(Roberts et al., 2014; Roberts, Stewart and Airoldi, 2015). The STM builds on well-established generative topic models, such as the Latent Dirichlet Allocation (LDA) (Blei, Ng and Jordan, 2003). The LDA is a mixed-membership model, meaning that it assumes that each document consists of a mixture of topics

(Grimmer and Stewart, 2013, 283–285). The LDA estimates the proportion of each document allocated to a given topic. Concretely, the LDA is a hierarchical model in which a document’s i proportion of topics has a common prior drawn from a Dirichlet distribution:

πi Dirichlet(α). ∼

Then, the topic of the j-th word in the i-th document is drawn from a multinomial distribution:

τi j Multinomial(πi ). ∼

Finally, the j -th word in the i-th document is drawn from a multinomial distribution, conditional on its probability of being drawn from topic k, θk :

wi j Multinomial(θk ). ∼ 4Parameter-tuning in scikit-learn yielded the following setting: No normalization of the document-term matrix, no TFIDF calculation, 50% maximum frequency of terms in documents, removal of stopwords, lowercase transformation, unigrams, and a maximum of 5000 considered features. 5For future analyses we will use other classiﬁers, such as support vector machines and ensemble classiﬁers, that usually better than the naive bayes.

7 The STM’s major innovation is that the prior distribution of topics can be inﬂuenced by covariates

(Roberts et al., 2014; Roberts, Stewart and Airoldi, 2015):

πi LogisticNormal(X β,Σ). ∼

Furthermore, covariates can also be speciﬁed for the word distribution over topics.

The STM model is estimated with a semi-collapsed variational Expectation-Maximization algorithm, which yields estimates of the quantities of interests, in our case the document-topic and word-topic probabilities (Roberts, Stewart and Airoldi, 2015; Roberts, Stewart and Tingley, 2014). Prior to the estimation, we pre-processed all documents with standard procedures such as text segmentation into paragraphs and sentences, tokenizing, removal of punctuation, as well as stemming and converting all words to lowercase (Hopkins and King, 2010).

We assume ten topics and include three covariates in the estimation of topic prevalence: a monthly trend, newspaper names, and the proportion of neighboring states with a smoking ban. The last covariate is the most interesting substantively and theoretically and we use it to estimate diffusion effects. It corresponds roughly to the spatial lags used in many diffusion analyses. We constructed it by updating the dataset on state-wide government buildings and restaurant restrictions by Shipan and

Volden (2006a). To code prior adoptions in neighboring states, we relied on newspapers’ locations and considered policies adopted in the previous year or earlier. In future analyses we will apply the

OpenNLP Named Entity Recognizer, an effective tool to detect locations in texts, to identify units mentioned in the New York Times.

4 Results

4.1 Media attention to smoking bans

Figure 1 shows how the newspapers reported on smoking bans over time. The points indicate the number of monthly published paragraphs since 1996; the line and the grey area indicate the loess smoothed trend with its 95% conﬁdence interval, respectively.6 We see a clear trend. The coverage of smoking bans soared to around 1,600 paragraphs in the late 1990s, reached another peak in 2003 and then gradually decreased to around 300 paragraphs per month towards the end of the period. Moreover,

6The development of the number of relevant articles over time (not shown) is very similar to the trends in Figure 1.

8 ● 1600 California New York smoking ban (98) Public Health Law (03)

1200 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 800 ● ● ● ● ● ● ●● ●

● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 400 ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●●● ● ● Paragraphs on smoking bans per month Paragraphs ●●● ● ● ● ● ● ● ● ● ●● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ●●●●● ● ● ● ● ● ● ● ●● ● ●

1996−01 1998−01 2000−01 2002−01 2004−01 2006−01 2008−01 2010−01 2012−01 months

Figure 1: Coverage of smoking bans in US newspapers in all newspapers except the Minnesota Star Tribune,7 coverage was intense in the period prior to the introduction of federal or statewide smoking bans, it peaked when legislation was passed and then decreased (figures for each newspaper are presented in the appendix). Further, the peak in the late 1990s seems related to California’s extension of the smoking bans to bars, making it the first US state to enact a complete ban in all enclosed workplaces. Overall, reports on smoking bans in the US spiked again at the end of 2003, which is likely related to the introduction of a statewide smoking ban for all enclosed workplaces in New York, closely followed the very similar Smoke-Free Air Act in New York City. It seems plausible that US newspapers paid particular attention to the two highest-profile anti-smoking policies of the last two decades.

It is encouraging for the external validation of our supervised classiﬁcation that the development of

US newspapers’ coverage on smoking bans seems to mirror the proposition, debate, and introduction of major legislative acts. Our newspaper sample will eventually cover all US regions and, therefore, we are conﬁdent that all major legislative activity related to smoking bans will be included.

4.2 The perception of smoking bans: topic models

The topics estimated by our STM model are shown in Table 3. It lists the 25 words (stems) most closely associated with each topic and includes labels reﬂecting our interpretation of the topics. The ﬁrst topic

7The Minnesota Star Tribune’s coverage correlates with the non-smoking ordinance in Minneapolis, adopted and implemented in 2005.

9 State policy Local policy Residual (restau- Lifestyle (“Popu- Legislative pro- (“Implementa- (“Implementa- rant reviews) lar support”) cess (“Political tion I”) tion II”) consequences”) law, state, requir, citi, counti, said, restaur, smoke, peopl, thi, can, bill, legisl, senat, public, govern, council, school, food, reserv, park, one, smoke, say, hous, said, vote, rule, allow, use, board, will, ordin, card, bar, dine, like, get, just, committe, pass, feder, right, pro- public, mayor, area, dinner, ser- think, even, will, republican, demo- tect, legal, issu, member, offici, vic, lunch, casual, want, new, make, crat, support, can, regul, limit, park, depart, area, rate, price, credit, smoker, time, measur, thi, pro- amend, polici, town, propos, menu, wine, mani, cigarett, pos, lawmak, employe, thi, meet, build, stu- access, atmospher, now, becaus, take, leader, approv, enforc, act, make, dent, resid, health, list, entre, open, thing, cigar, know sen, legislatur, issu, provid, worker district, plan, fire onli, dish governor, session, year, major, rep Interest groups Lawsuits (“Inter- Public finances Businesses Health conse- (“Interest group est group sup- (“Economic con- (“Economic quences support I”) port II”) sequences I”) consequences II”) tobacco, cigarett, court, compani, tax, state, million, smoke, ban, smoke, health, industri, product, tobacco, morri, year, money, said, bar, restaur, smoker, said, compani, said, ad- philip, said, increas, program, casino, busi, new, cigarett, studi, vertis, settlement, case, lawsuit, fund, budget, owner, law, citi, cancer, quit, per- market, regul, state, industri, health, cost, per- smokefre, place, cent, diseas, year, will, campaign, cigarett, rule, judg, cent, new, will, will, state, allow, peopl, report, risk, health, antismok, attorney, suit, rais, pay, spend, public, area, effect, use, american, public, year, appeal, file, lawyer, cut, revenu, plan, room, nonsmok, research, nicotin, fda, gener, drug, smoker, claim, said, care, sale, year, gambl, lung, caus, second- congress, govern, damag, maker, propos, billion exempt, club hand, say, adult, nation, administr, reynold, billion, prevent, death say, sale decis

Table 3: 25 most closely associated words in terms of their probability ranking for a ten topic model. seems to reflect policies at the state level; the second topic seems to identify policies at the local level; the third topic picks up the few irrelevant paragraphs (likely restaurant reviews) not weeded out by our classifier; the fourth topic seems to relate to smoking seen from a lifestyle perspective; the fifth topic seems to cluster texts discussing the legislative process; the sixth topic seems to be about tobacco and cigarette companies; the seventh topic seems to pick up discussions of lawsuits related to smoking; the eighth topic seems to group texts on the implications of smoking or smoking bans for public finances; the ninth topic seems to be about businesses affected by smoking bans; and the tenth topic quite clearly reflects the consequences of smoking on health.

These topics do not overlap perfectly with our expectations (Section 2) but can be usefully interpreted

10 in their light. Smoking bans are discussed not only with respect to their content, but also to the political processes associated with them and to several actors affected by the policies, such as cigarette companies and businesses subject to smoking bans. The economic dimension is present in connection with public

ﬁnances. Public opinion is not clearly represented, or at most from a lifestyle rather than political angle.

Finally, the health dimension is clearly apparent in one topic.

Figure 2 shows the evolution of the ten topics from 1996 until 2013. “State policy (implementation

I)” steadily declined after the introduction of smoking bans in California, while “Legislative process

(political consequences)” became more relevant. “Health consequences” peaked after California’s decision, then declined, then regained relevance. The “lifestyle (popular support)” topic sinked at the same time, then caught up again.

The analysis is preliminary and we refrain from interpreting the trends more in detail. We emphasize that the current STM implementation only allows to model time quite crudely. However, In many text corpora similar to the one presented here time dependence follows more complicated dynamics (Blei and Lafferty, 2006), which makes the further development of the STM in this regard a priority (Wueest,

2015).

4.3 The diffusion of policy perceptions

Figures 3 and 4 present first evidence on how topics in one state (newspaper) are influenced by the presence of smoking bans in neighboring states. Both figures show the expected prevalence of topics conditional on the percentage of neighboring states that has already adopted a smoking ban. Figure 4 considers smoking bans in restaurants and bars while Figure 3 considers smoking bans in government buildings.

The following effects are consistent across both types of smoking bans. The “local policy (implementation II)” and “legislative process (political consequences)” topics becomes less prevalent as more neighboring states adopt smoking bans, while “lawsuits (interest group support II)” and “businesses

(economic consequences II)” follow the opposite pattern. Again, the analyses are preliminary and because the speciﬁc results will likely change as we add newspapers and reﬁne the analysis, we do not wish to push the interpretation. The important point is that the STM allows to model topic prevalence as a function of policies in other states, which establishes a direct connection with the diffusion framework.

Figures 3 and 4 illustrate the kind of empirical ﬁndings that can be produced with this approach.

11 Implementation I Implementation II

● 0.20 ● 0.25 ● ● ● ● ● ● ● ● ●

● ●● 0.20 ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.15 ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● 0.15 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● 0.10 ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ●● ●● 0.10 ● ● ● ● ● ● ● ● ● ● ● ● ● Topic proportion Topic proportion Topic ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.05 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.05 ● ● ● ● ● ● ● ● ● ● ● ● ●

0.00 1996−01 1998−01 2000−01 2002−01 2004−01 2006−01 2008−01 2010−01 2012−01 1996−01 1998−01 2000−01 2002−01 2004−01 2006−01 2008−01 2010−01 2012−01 months months

Popular support Political consequences

● ● ● ● ● ●

● ● 0.12 ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● 0.10 ● ● ● ● ● ● ● ● ● ● ● ● 0.2 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.08 ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

Topic proportion Topic ● ● proportion Topic ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● 0.06 ● ● ● ● ● 0.1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.04 ● ●

1996−01 1998−01 2000−01 2002−01 2004−01 2006−01 2008−01 2010−01 2012−01 1996−01 1998−01 2000−01 2002−01 2004−01 2006−01 2008−01 2010−01 2012−01 months months

Interest group support I Interest group support II 0.25 ● ●

●

● ● 0.20 ● 0.20 ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● 0.15 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● 0.15 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Topic proportion Topic ● ● proportion Topic ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● 0.10 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● 0.05 ● ● ●● ● ● ● ● 0.10 ● ● ●

1996−01 1998−01 2000−01 2002−01 2004−01 2006−01 2008−01 2010−01 2012−01 1996−01 1998−01 2000−01 2002−01 2004−01 2006−01 2008−01 2010−01 2012−01 months months

Economic consequences I Economic consequences II

● ●

● ● ● ● ● 0.25 0.100 ● ●● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.20 ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.075 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.15 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Topic proportion Topic ● proportion Topic ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● 0.050 ● ● ● ● ● ● 0.10 ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.05 1996−01 1998−01 2000−01 2002−01 2004−01 2006−01 2008−01 2010−01 2012−01 1996−01 1998−01 2000−01 2002−01 2004−01 2006−01 2008−01 2010−01 2012−01 months months

Health consequences 0.16 ●

●

● ● ● 0.14 ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.12 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.10 ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● Topic proportion Topic ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●●● ● ● ● ● 0.08 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● 0.06 ●

1996−01 1998−01 2000−01 2002−01 2004−01 2006−01 2008−01 2010−01 2012−01 months

Figure 2: Topic proportions indicating how smoking bans are framed over time

12 0.60 0.60 0.60

0.67 0.67 0.67

0.80 0.80 0.80

0.83 0.83 0.83

0.86 0.86 0.86

1.00 1.00 1.00

0.60 0.60 0.60

0.67 0.67 0.67

0.80 0.80 0.80

0.83 0.83 0.83

0.86 0.86 0.86

1.00 1.00 1.00

0.60 0.60 0.60

0.67 0.67 0.67

0.80 0.80 0.80

0.83 0.83 0.83

0.86 0.86 0.86

1.00 1.00 1.00

Figure 3: Dependence of smoking ban frames on government building restrictions in neighboring states

13 0.16 0.16 0.16

0.20 0.20 0.20

0.33 0.33 0.33

0.40 0.40 0.40

0.57 0.57 0.57

0.60 0.60 0.60

0.63 0.63 0.63

0.67 0.67 0.67

0.75 0.75 0.75

0.80 0.80 0.80

0.83 0.83 0.83

0.86 0.86 0.86

0.88 0.88 0.88

1.00 1.00 1.00

0.16 0.16 0.16

0.20 0.20 0.20

0.33 0.33 0.33

0.40 0.40 0.40

0.57 0.57 0.57

0.60 0.60 0.60

0.63 0.63 0.63

0.67 0.67 0.67

0.75 0.75 0.75

0.80 0.80 0.80

0.83 0.83 0.83

0.86 0.86 0.86

0.88 0.88 0.88

1.00 1.00 1.00

0.16 0.16 0.16

0.20 0.20 0.20

0.33 0.33 0.33

0.40 0.40 0.40

0.57 0.57 0.57

0.60 0.60 0.60

0.63 0.63 0.63

0.67 0.67 0.67

0.75 0.75 0.75

0.80 0.80 0.80

0.83 0.83 0.83

0.86 0.86 0.86

0.88 0.88 0.88

1.00 1.00 1.00

Figure 4: Dependence of smoking ban frames on restaurant and bar restrictions in neighboring states

14 5 Conclusion

This paper has argued that the diffusion literature should focus not only on how policies themselves diffuse, but also on how their perception is subject to a similar process. We have put forward a preliminary analysis of the diffusion of the perception of smoking bans in US states based on a structural topic model (STM) of over 100,000 paragraphs in sixteen newspapers. We could show that the evolution of attention to smoking bans in newspapers follows important policy initiatives. The topics estimated by the STM appear relevant based on our theoretical expectations and manual annotations and vary as a function of the adoption of smoking bans in nearby states. Thus, we could establish a connection between topic models and the diffusion of policy perceptions.

Obviously, this is very much work in progress. Future steps include the completion of our newspaper sample, the improvement of the classiﬁer used to weed out irrelevant texts, the use of Named Entity

Recognition tools to identify states and cities in the texts, and more generally the overall improvement of the topic models.

15 References

Blei, D. M., A. Y. Ng and M. I. Jordan. 2003. “Latent Dirichlet Allocation.” Journal of Machine Learning 3:993–1022.

Blei, D. M. and J. D. Lafferty. 2006. Dynamic Topic Models. In Proceedings of the 23rd International Conference on Machine Learning. Pittsburgh, PA: .

Braun, Dietmar and Fabrizio Gilardi. 2006. “Taking ‘Galton’s Problem’ Seriously: Towards a Theory of Policy Diffusion.” Journal of Theoretical Politics 18(3):298–322.

Champion, David and Simon Chapman. 2005. “Framing pub smoking bans: an analysis of Australian print news media coverage, March 1996–March 2003.” Journal of Epidemiology and Community Health 59(8):679–684.

Chong, Dennis and James N. Druckman. 2007. “Framing Theory.” Annual Review of Political Science 10(1):103– 126.

Collingwood, Loren and John Wilkerson. 2012. “Tradeoffs in Accuracy and Efﬁciency in Supervised Learning Methods.” Journal of Information Technology & Politics 9(3):298–318.

Dobbin, Frank, Beth Simmons and Geoffrey Garrett. 2007. “The Global Diffusion of Public Policies: Social Construction, Coercion, Competition, or Learning?” Annual Review of Sociology 33:449–472.

Druckman, J. N. 2005. “Media Matter: How Newspapers and Television News Cover Campaigns and Inﬂuence Voters.” Political Communication 22(4):463–481.

Entman, R. M. 1993. “Framing: Toward Clariﬁcation of a Fractured Paradigm.” Journal of Communication 43(4):51–58.

Foster, Caroline D, James F Thrasher, Sei-Hill Kim, John C Besley and Ashley Navarro. 2012. “Agenda-Building Inﬂuences on the News Media’s Coverage of the US Food and Drug Administration’s Push to Regulate Tobacco, 1993-2009.” Journal of Health and Human Services Administration 35(3):303.

Gamson, William A and Andre Modigliani. 1989. “Media discourse and public opinion on nuclear power: A constructionist approach.” American Journal of Sociology pp. 1–37.

Gilardi, Fabrizio. 2012. Transnational Diffusion: Norms, Ideas, and Policies. In Handbook of International Relations, ed. Walter Carlsnaes, Thomas Risse and Beth Simmons. Second ed. SAGE Publications pp. 453–477.

Graham, Erin R., Charles R. Shipan and Craig Volden. 2013. “The Diffusion of Policy Diffusion Research in Political Science.” British Journal of Political Science 43(3):673–701.

16 Grimmer, Justin and Brandon M Stewart. 2013. “Text as data: The promise and pitfalls of automatic content analysis methods for political texts.” Political Analysis .

Helbling, M., D. Hoeglinger and B. Wueest. 2010. “How Political Parties Frame European Integration.” European Journal of Political Research 49(4):495–521.

Hopkins, Daniel J. and Gary King. 2010. “A Method of Automated Nonparametric Content Analysis for Social Science.” American Journal of Political Science 54(1):229–247.

Maggetti, Martino and Fabrizio Gilardi. 2015. “Problems (and Solutions) in the Measurement of Policy Diffusion Mechanisms.” Journal of Public Policy .

Nelson, David E, W Douglas Evans, Linda L Pederson, Stephen Babb, Joel London, Jeffrey McKenna et al. 2007. “A national surveillance system for tracking tobacco news stories.” American Journal of Preventive Medicine 32(1):79.

Pacheco, Julianna. 2012. “The Social Contagion Model: Exploring the Role of Public Opinion on the Diffusion of Antismoking Legislation across the American States.” Journal of Politics 74(1):187–202.

Roberts, M. E., B. M. Stewart and D. Tingley. 2014. stm: R Package for Structural Topic Models.

Roberts, M. E., B. M. Stewart, D. Tingley, C. Lucas, J. Leder-Luis, S. Gadarian, B. Albertson and D. Rand. 2014. “Structural topic models for open-ended survey responses.” American Journal of Political Science 58:1064–1082.

Roberts, Margaret E., Brandon M. Stewart and Edoardo M. Airoldi. 2015. “A model of text for experimentation in the social sciences.”.

Shipan, C. R. and C. Volden. 2006a. “Bottom-Up Federalism: The Diffusion of Antismoking Policies from U.S. Cities to States.” American Journal of Political Science 50(4):825–843.

Shipan, Charles R. and Craig Volden. 2006b. “Bottom-Up Federalism: The Diffusion of Antismoking Policies from U.S. Cities to States.” American Journal of Political Science 50(4):825–843.

Shipan, Charles R. and Craig Volden. 2008. “The Mechanisms of Policy Diffusion.” American Journal of Political Science 52(4):840–857.

Simmons, Beth, Frank Dobbin and Geoffrey Garrett. 2006. “Introduction: The International Diffusion of Liberalism.” International Organization 60(4):781–810.

Smith, Katherine Clegg, Catherine Siebel, Luu Pham, Juhee Cho, Rachel Friedman Singer, Frank Joseph Chaloupka, Michael Griswold, Melanie Wakeﬁeld et al. 2008. “News on tobacco and public attitudes toward smokefree air policies in the United States.” Health Policy 86(1):42.

17 Wueest, B. 2015. Time dependency in topic models of political attention. University of Zurich.

18 Appendix

19 AtlantaJournal CharlestonGazette

● ●

150 ● ● ● 100 ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 100 ● 75 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● 50 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 50 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● 25 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●

Paragraphs on smoking bans per month Paragraphs ● on smoking bans per month Paragraphs ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 ● ●

● 0 1996−01 1998−01 2000−01 2002−01 2004−01 2006−01 2008−01 2010−01 2012−01 1996−01 1998−01 2000−01 2002−01 2004−01 2006−01 2008−01 2010−01 2012−01 months months

DailyNewsNewYork DenverPost

● ● 100

100 ● ●

● 75 ●

● ●● ● 75 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 50 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 50 ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 25 ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 25 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●

Paragraphs on smoking bans per month Paragraphs ● ● ● ● ● ● on smoking bans per month Paragraphs ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● 0 0

1996−01 1998−01 2000−01 2002−01 2004−01 2006−01 2008−01 2010−01 2012−01 1996−01 1998−01 2000−01 2002−01 2004−01 2006−01 2008−01 2010−01 2012−01 months months

DeseretNewsSaltLakeCity DetroitFreePress 75 120 ● ●

●

50 ● ●

80 ● ● ● ● ● ● ● ● ●

● ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 25 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 40 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● Paragraphs on smoking bans per month Paragraphs ● ● ● ● ● ● ● ●● on smoking bans per month Paragraphs ●● ● ● ● ●● ● ● ● ● 0 ● ●

1996−01 1998−01 2000−01 2002−01 2004−01 2006−01 2008−01 2010−01 2012−01 1996−01 1998−01 2000−01 2002−01 2004−01 2006−01 2008−01 2010−01 2012−01 months months

HartfordCourant LosAngelesTimes

● ●

●

60 ● ● ●

●

● 200

● ● ● ● ● ● ● ● ● ● ● 40 ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 100 ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 20 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● Paragraphs on smoking bans per month Paragraphs on smoking bans per month Paragraphs ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● 0 ● ●● ● ● ●● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● 0 ● ●

1996−01 1998−01 2000−01 2002−01 2004−01 2006−01 2008−01 2010−01 2012−01 1996−01 1998−01 2000−01 2002−01 2004−01 2006−01 2008−01 2010−01 2012−01 months months

Figure A1: Coverage of smoking bans in US newspapers I

20 NewYorkTimes PhiladelphiaInquirer 250 ● ●

200

100

● ● ● 150 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 100 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 50 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 50 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● Paragraphs on smoking bans per month Paragraphs ● ● ● ● ● ● on smoking bans per month Paragraphs ● ● ● ● ● ● ● ● ● ● ●●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 ● 0 1996−01 1998−01 2000−01 2002−01 2004−01 2006−01 2008−01 2010−01 2012−01 1996−01 1998−01 2000−01 2002−01 2004−01 2006−01 2008−01 2010−01 2012−01 months months

ProvidenceJournal Record

● ●

●

● ●● 100 ● ● 100 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 50 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● 50 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● Paragraphs on smoking bans per month Paragraphs 0 on smoking bans per month Paragraphs ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● 0 ● ● ●

1996−01 1998−01 2000−01 2002−01 2004−01 2006−01 2008−01 2010−01 2012−01 1996−01 1998−01 2000−01 2002−01 2004−01 2006−01 2008−01 2010−01 2012−01 months months

StarTribuneMinneapolis StLouisPostDispatchMissouri 150 ● ●

● ● ● ● 150 ● ● ● ● ●

● ● ● ● ● ● ● ● ● ● ● 100 ● ● ● ● ● ●● ● ● ● ● ● ● ● 100 ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 50 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 50 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ●● ● ●● ● ● ●● ● ●●● ● ● ● ● ●● ● Paragraphs on smoking bans per month Paragraphs ● ● ●● ● ● ● on smoking bans per month Paragraphs ● ● ● ● ● ●● ● ● 0 ● ● ● 0

1996−01 1998−01 2000−01 2002−01 2004−01 2006−01 2008−01 2010−01 2012−01 1996−01 1998−01 2000−01 2002−01 2004−01 2006−01 2008−01 2010−01 2012−01 months months

USATODAY WallStreetJournal

● ●

200

75 ● ●

●

● ● ● 150 ● ● ● 50 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 100 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 25 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● 50 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Paragraphs on smoking bans per month Paragraphs on smoking bans per month Paragraphs ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● 0 0 1996−01 1998−01 2000−01 2002−01 2004−01 2006−01 2008−01 2010−01 2012−01 1996−01 1998−01 2000−01 2002−01 2004−01 2006−01 2008−01 2010−01 2012−01 months months

Figure A2: Coverage of smoking bans in US newspapers II