Public health and agenda setting: determinants of state attention to tobacco and vaccines.

Journal of Health Politics, Policy and Law

Public Health and Agenda Setting: Determinants of State Attention to Tobacco and Vaccines Julianna Pacheco University of Iowa Graeme Boushey University of California, Irvine

Abstract What determines government attention to emerging health issues? We draw

on research in agenda setting and policy diffusion to explore the determinants of public health attention in the fifty American states. We find that intergovernmental influence has a strong and consistent influence over state attention to tobacco and vaccines from 1990 to 2010. While national attention to tobacco or vaccines also sparks attention in the states, this effect is smaller than the internal impact of gubernatorial attention and the horizontal influence of neighboring state attention. We find some support that problem severity matters; however, these results are highly dependent on the measures used. Finally, we find no evidence that interest groups influence the attention that states pay to tobacco or vaccines. Our results suggest that institutions play a critical role in explaining government attention to health policy.

In April 2009 public health officials in the United States detected the first case of the H1N1 influenza virus, a novel strain of the ‘‘swine flu’’ that scientists feared could trigger a severe global influenza pandemic. In response to growing alarm over the emergence of the H1N1 virus, federal public health officials called on state and local governments to enact new emergency preparedness measures to mediate the potential spread of the influenza. While the federal government worked to coordinate national A version of this paper was presented at the State Politics and Policy Conference, University of Houston and Rice University, February 16–18, 2012. This research was generously supported by the Robert Wood Johnson Foundation. We would like to thank the following individuals for their hard work in data collection: Thomas Michael Klein, Adam Wilson, Rose Afriyie, Jeannie Gong, Megan Grant, Maria Trikolas, and Sang-Jung Hun. Tim Jurka and Loren Collingwood were instrumental in writing computer programs to parse and code the bills. Finally, we thank the anonymous reviewers for their helpful comments and suggestions. Journal of Health Politics, Policy and Law, Vol. 39, No. 3, June 2014 DOI 10.1215/03616878-2682612 2014 by Duke University Press

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emergency preparedness by disseminating information and grant funding for new programs through the Centers for Disease Control and Prevention (CDC), state governments assumed a prominent role in developing and implementing local responses to mediate the threat of the H1N1 virus. Despite the widespread publicity and urgency, however, state legislative responses to H1N1 flu varied dramatically. In many state governments, policy makers introduced a slate of new proposals to update their general emergency preparedness plans—expanding vaccine delivery, implementing new sanitation programs, making budget appropriations for infectious disease control, providing for mandatory sick leave for workers, and regulating the content of H1N1 vaccine itself (NCSL 2010). In other states, the legislatures proposed few—if any—new policies in response to the threat of the flu pandemic (NCSL 2010). The different responses of state governments to the threat of the H1N1 virus raise an important question for students of public health policy making. What determines governmental attention to emerging health issues? Explaining governmental attention allocation has interested scholars since at least the 1960s when Schattschneider (1960: 66) observed, ‘‘He who determines what politics is about runs the country.’’ After decades of research, we know that government attention to public problems is influenced by myriad factors including issue characteristics and problem definition (Rochefort and Cobb 1994; Donovan 2001), interest group activity (Baumgartner and Jones 1993), intergovernmental influence (Karch 2010; McCann, Volden, and Shipan 2012), focusing events (Birkland 1998), the media (Colby and Cook 1991), and the public ( Jacobs 1993). The majority of this research, however, has been conducted on the federal government, and often on issues not related to health. Focusing on the federal government and its subsidiaries has limited our ability to evaluate how variations in the political system influence variations in attention allocation. In short, we know little about why governments allocate vastly different agenda space to common policy pressures and even less about the determinants of attention to health. A comparative model of state agenda setting has implications for our understanding of how governments identify health policy problems and develop new policy solutions in the United States. Theories of federalism contend that decentralized policy making encourages a ‘‘marketplace of ideas,’’ as autonomous state governments develop new policies in response to locally severe problems (Oates 1999). Although scholars in the fields of political science and public health have developed a robust literature for modeling the spread of health policy innovations in the United States



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(Carter and LaPlant 1997; Shipan and Volden 2006; Pacheco 2012), we have only a limited understanding of how actively state governments engage in public health agenda setting prior to policy adoption. For instance, states may place health policy reforms on the agenda through committee hearings, floor resolutions, and bill introductions for years before finally passing a formal law addressing an emerging health policy concern. The legislative proposals from various states may reflect competing ways to define and design policy innovations well before governments converge on a preferred policy solution. This article draws on the fifty policy arenas of the American states to test how institutions, problem characteristics, and policy participants simultaneously influence government attention to public health problems. Drawing on recent research on the horizontal and vertical diffusion of policies (Shipan and Volden 2006; Karch 2010; McCann, Volden, and Shipan 2012), we evaluate three distinct paths of intergovernmental influence. First, we explore paths of internal influence, extending research on presidential agenda setting to evaluate whether gubernatorial pressure leads to changes in legislative attention. Second, we look at vertical intergovernmental influence (also called ‘‘top-down’’ influence), exploring whether agenda setting in Congress triggers similar agenda-setting responses in the states. Third, we extend a classic expectation of state politics research, exploring whether the allocation of legislative attention in one state leads to a change in the agenda-setting behavior of neighboring governments (also called horizontal influence). In addition to mapping the paths of intergovernmental influence, we also extend research on national agenda setting to understand how local variation in problem severity and interest group participation shapes the allocation of state attention. We adopt a new empirical strategy to explore state-level agenda-setting dynamics. While other scholars have typically looked at state agendas at a few points in time (see, e.g., Lowery, Gray, and Baumgartner 2011) or for a subset of states (see, e.g., Karch 2007), we draw on a more extensive measure of state legislative attention. We collected comprehensive data on state legislative agenda setting for all the states over twenty years, focusing on bill introductions related to tobacco control and vaccine regulation from 1990 through 2010. These descriptive data are interesting in their own right, as they are the first to document the considerable differences in state agenda setting on these two critical public health problems over time. We find that internal and horizontal intergovernmental influence has the strongest and most consistent influence over state attention to tobacco and vaccines. While national attention to tobacco and vaccines also sparks attention in the states, the vertical intergovernmental effect is smaller than



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the internal impact of gubernatorial attention and the horizontal influence of neighboring state attention. We find some support that problem severity matters; however, these results are highly dependent on the measures. Finally, we find no evidence that interest groups influence the attention that states pay to tobacco or vaccines. Our results point to the importance of institutions, as well as disease burden, in explaining governmental attention to health policy. Determinants of Attention Allocation

There are countless issues on which policy makers can take action, from budgetary decisions to same-sex marriage to foreign policy, yet the agenda space, or the amount of time available for a particular issue, is incredibly limited. In the face of considerable constraints in time and resources, officials often engage in disproportionate information processing, selectively updating a majority of issues through incremental adjustment while dedicating considerable resources to policy development on issues demanding urgent intervention ( Jones and Baumgartner 2005). Given these dynamics, many policy solutions receive limited if any attention from policy makers. The implications of nonattention are obvious; particular interests from the public, elites, or other important groups will be excluded from the political system if they do not receive government attention (Schattschneider 1960; Bachrach and Baratz 1962). As importantly, the scarcity of agenda space means that governments may dedicate only limited attention to emerging problems. Why, then, do some issues receive more government attention compared with others? Research at the national level suggests several distinct mechanisms that drive governmental attention, including issue characteristics (Schneider and Ingram 1993), problem definition (Rochefort and Cobb 1994), and exogenous shocks (Kingdon 1995; Birkland 1998), as well as pressure from organized interests (Baumgartner and Jones 1993), political leaders (Cox and McCubbins 2005; Beckmann 2010), and the public ( Jacobs 1993). We draw on these theories to identify discrete hypotheses for how variation in the structure of government, problem characteristics, and policy participants may trigger different state-level agenda-setting responses to common public health problems. Intergovernmental Influence

In a system of separated powers, perhaps one of the most important influences over attention is intergovernmental influence across branches



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or levels of government. We consider three types of intergovernmental influence including internal influence across branches of government, vertical influence from the federal government to the states, and the horizontal diffusion of attention across neighboring states. There is evidence at the national level that institutional agendas are linked. For instance, Congress and the president are more likely to pay attention to civil rights issues after the Supreme Court has ruled on such cases (Fleming, Wood, and Bohte 1999). On the other hand, Congress tends to lead the way on environmental issues (Fleming, Wood, and Bohte 1999), while the president influences congressional attention to domestic issues, such as health care and education (Edwards and Wood 1999). The president is especially important in setting the policy agenda because of his ability to focus attention on one particular topic and pressure other elected officials to make policy changes (Baumgartner and Jones 1993; Kingdon 1995). We consider the link between gubernatorial and legislative agenda setting. In many states, the governor is the most powerful person in state government (Rosenthal 1990; Beyle 2004). One of the major ways that governors influence state policy is by setting the agenda (Dometrius 1979). Formally, governors set the legislative agenda through the State of the State address to the legislature (Ferguson 2006) as well as calling special legislative sessions to address a particular topic. Informally, the governor’s power to persuade is much like the president; governors are the symbolic leader of the state and can skillfully use the mass media with the help of an enormous staff (Morehouse and Jewell 2004). Consequently, we expect state legislatures will dedicate more agenda space to health issues when the governor deems health important (H1).1 Federalism itself also shapes the agenda-setting process in two distinct ways. First, there may be vertical diffusion of attention, as national consideration of issues influences state-level attention (‘‘top-down’’ influence) or vice versa (‘‘bottom-up’’ influence). Evidence suggests that while there are various forms of attention linkage between the federal government and the states (for an overview, see Lowery, Gray, and Baumgartner 2011), national attention tends to foster widespread consideration across the states (Karch 2007; Baumgartner, Gray, and Lowery 2009; McCann, Volden, and Shipan 2012). Karch (2010), for example, finds that national attention to stem cell research from 1999 to 2008 measured via President George W. Bush’s nationally televised addresses increased the probability that state 1. It could be that governors follow the agenda setting of state legislatures or that under some conditions the governor leads, while in others the legislature leads in attention allocation. Analyzing the determinants of gubernatorial attention and the interplay between the governor and the state legislature is beyond the scope of this article.



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officials introduced similar bills. Consequently, we expect for states to pay more attention to health issues when the federal government pays attention to health (H2). Second, federalism creates a system of policy learning, as publics and elites are directly influenced by the actions of their geographic neighbors (Walker 1969). A consistent finding in the policy diffusion literature is that as the proportion of neighboring states adopts a policy, the probability of adoption in the home state also increases (see, e.g., Berry and Berry 1990). There is also evidence that state legislative attention is influenced by the legislative attention of neighboring states (Mintrom 1997). Indeed, officials often look to neighboring or nearby states to learn about policies for reasons of political and demographic similarity (Walker 1969; Berry and Berry 1990) and political networking (Mintrom and Vergari 1998). States are also most likely to compete economically with nearby states because of the mobility constraints of residents (Boehmke and Witmer 2004). Recent research suggests that residents in the home state learn about policies in neighboring states and then pressure their own officials to adopt similar policies (Pacheco 2012). All of this suggests that states will pay more attention to health when neighboring states pay attention to health (H3). Problem Severity

In addition to intergovernmental influence, problem characteristics also matter for attention allocation. Public perceptions of political problems evolve, and the changing nature of political problems influences why some specific policies are politically controversial and garner government’s attention, while others are consensual and virtually nonexistent in the political realm. Researchers in public policy have noted how facets of the target population (Schneider and Ingram 1993), the content of the issue, the public or elite understanding of the issue (McCombs and Shaw 1972), and objective conditions, such as problem severity (Best 1990), influence attention allocation. We focus on one issue characteristic: problem severity. At its most basic level, the severity of a problem (e.g., disease incidence or the cost of a problem) should be positively related to governmental attention. We would expect, for example, that an increase in traffic-related deaths will motivate policy makers to shift their attention to finding solutions to make it safer to drive. This clear expectation, however, has received mixed empirical support. Several researchers claim that empirical indicators of severity are unrelated to issue attention (see, e.g., Blumer 1971). For instance, Baumgartner and Jones (1993) find that problem severity is a poor predictor of governmental attention to drug and alcohol abuse. Many of the



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null effects, however, come from studies conducted at the national level using single case studies. Research using a comparative approach across time and issues suggests that empirical measures of severity influences attention. Armstrong, Carpenter, and Hojnacki (2006) find, for example, that newspaper attention to specific diseases increased as the number of deaths from that disease increased. Other researchers have demonstrated that problem severity is an important stimulus for state governments to adopt new policies (Nice 1994; Sapat 2004). Consequently, we expect that states will allocate more attention to health when problems are locally severe (H4). By locally severe, we are referring to problem severity in a particular state, absent from national conditions. States vary tremendously on the severity of the problems that they face because of differences in geography, culture, demographics, ideology, or other factors, which differentiate them from each other and the federal government. Immigration policy is one example. Southern states like Alabama, Georgia, and South Carolina have become attentive to immigration policy because of a sudden and sharp growth in their immigrant populations over the last two decades (Boushey and Luedtke 2011). We use the variation in problem severity in tobacco and vaccines across the states to explore the role that it has on governmental attention over time. Interest Groups

Finally, policy participants influence governmental attention allocation. The importance of interest groups in drawing government attention to problems is well established at the national level. Indeed, the explicit aim of interest groups is to focus government and public attention to their specific problem (Best 1990). Interest groups can increase governmental attention by communicating to policy makers or the public through general advocacy activity (Caldeira and Wright 1988), mobilizing voters in response to a particular issue (Donovan 2001), and providing information to the media (Colby and Cook 1991; Armstrong, Carpenter, and Hojnacki 2006), which influences how the public thinks about policies. There is evidence that as the mobilization of interests changes over time, so too does the likelihood that certain issues receive government attention. For instance, in times when the policy community is rife with political conflict and controversy, such as in the 1960s on nuclear power, governmental attention allocation is likely to increase (Baumgartner and Jones 1993). Therefore, we expect that states with a large and more powerful interest group population operating within a given health area will allocate more attention to health (H5).



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Again, there is a vast amount of variation in interest group activity across the states. States vary in both the number and type of organized interests that are active (see, e.g., Lowery and Gray 1993; Lowery and Gray 1995; Goldstein and Bearman 1996). For instance, in 1994, 28 percent of the lobbyists in Pennsylvania worked on health-related issues compared with 7 percent in Illinois (Goldstein and Bearman 1996); in 1980 Pennsylvania had sixty-nine registered banking-interest groups, while Arkansas’s legislature registered only eleven (Lowery and Gray 1995). We expect for these state variations to translate into different patterns of governmental attention to health.2 To summarize, we have five hypotheses about the determinants of state attention to health: H1 State legislatures will dedicate more agenda space to health issues when the governor deems health important. H2 States will pay more attention to health issues when the federal government pays attention to health. H3 States will pay more attention to health when neighboring states pay attention to health. H4: States will allocate more attention to health when problems are locally severe. H5: States with a large and more powerful interest group population operating within a given health area will allocate more attention to health. We test our hypotheses using a unique data set on state agenda setting from 1990 to 2010 on two important public health issues, tobacco legislation and vaccine regulation, as we describe below. Data on State Attention Allocation to Tobacco and Vaccines

To measure state attention, we collected data on the number of bills introduced in American state legislatures related to tobacco and vaccines from 1990 to 2010 using the State Bill Tracking database on LexisNexis State Capital.3 Employing bill introductions to assess legislative attention 2. Prior research on the role of interest group populations on state-level policy making has produced mixed results. For example, Gray and Lowery (1993) find that as interest group density increases, the number of bills introduced and enacted decreases. They find no relationship between interest group diversity and lawmaking, although their research suggests that the volume of proposed and enacted legislation increases as the proportion of not-for-profit interest increases. These studies have focused on the effect of the general interest group population, rather than the participation of specific industries or sectors of the economy. 3. The database is maintained by LexisNexis, a division of Reed Elsevier Inc., and is available at http://web.lexis-nexis.com. The database contains bill synopses for each bill introduced by each statehouse in a calendar year. More details about our data collection methods, including keywords used and intercoder reliability, can be found in the supplemental text.



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is common at the national level (see, e.g., Adler and Wilkerson 2010) as well as in the states (see, e.g., Baumgartner, Gray, and Lowery 2009). We collected data on tobacco legislation and vaccine regulation for several reasons. First, states have primary jurisdiction across the entire time frame on both of these issues, ensuring ample variation across states and time on attention allocation. For instance, while the FDA regulates and licenses all vaccines to ensure safety and effectiveness and the National Vaccine Advisory Committee makes recommendations for vaccine coverage, the fifty states have considerable autonomy over who is vaccinated and for which diseases. Second, for both issues we can measure problem severity using widely available public health data, allowing us to test H4. Third, interest group activity at the state level has been particularly high for both domains, allowing us to test H5. Tobacco remains a divisive issue in state politics, as tobacco manufacturers and distributors have a vested interest in preventing strong tobacco regulation, while major public health advocates have organized to enact ever more restrictive tobacco legislation. Similarly, major pharmaceutical manufacturers and public health officials are active in immunization policy. Finally, from a public health perspective it is important to understand what influences state attention allocation to these two significant health issues. Cigarette smoking remains the single most preventable cause of death in the United States, responsible for more than 430,000 premature deaths each year, including an estimated 53,800 people who die from exposure to secondhand smoke (CDC 2005). Cigarettes take a toll economically as well; smoking causes more avoidable illness and work absenteeism than any other behavior and accounts for 6–10 percent of all health care costs (Warner 2006). Immunizations, heralded as one of the twentieth century’s most successful public-health achievements, protect both individuals and the larger population from serious illness and death (National Conference of State Legislatures 2011). Vaccines are responsible for the control of many infectious diseases that were once common, including polio, measles, diphtheria, pertussis, rubella, mumps, and tetanus (CDC 2009). Yet each year as many as forty-two thousand adults die and thousands more are hospitalized for diseases that are preventable, with the costs of treatment exceeding $10 billion each year (Council of State Governments 2007). From 1990 to 2010, 20,634 bills related to tobacco were introduced across the fifty states, and 3,257 vaccine-related bills were introduced



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Figure 1 Number of Tobacco-Related Bills Introduced by State Legislative Session

during the same time frame. To be consistent across states with different legislative schedules, we divide our times-series data into eleven legislative sessions: 1990, 1991–92, 1993–94, 1995–96, 1997–98, 1999–2000, 2001–2, 2003–4, 2005–6, 2007–8, and 2009–10.4 Figure 1 shows the number of tobacco-related bills introduced in four select states in each region for each legislative session.5

4. Five states (Kentucky, Louisiana, Mississippi, New Jersey, and Virginia) have legislative sessions on even years. The eleven sessions are 1990–91, 1992–93, 1994–95, 1996–97, 1998–99, 2000–2001, 2002–3, 2004–5, 2006–7, 2008–9, and 2010. 5. These states were selected for descriptive purposes only and are not exhaustive. See the supplemental text for time trends of each state.



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From figure 1, there are clear regional differences in attention to tobacco, with the Northeast introducing more tobacco-related bills compared with other regions, particularly the South. A closer look at figure 1 reveals large variation in tobacco attention across states and legislative sessions. Some states, like Massachusetts and Illinois, allocate much more attention to tobacco compared with other states, like Colorado and Ohio. Similarly, some states (e.g., New York and Illinois) have increased their attention to tobacco, others (e.g., Pennsylvania and Connecticut) have decreased their attention, particularly since the early 2000s, and still others (e.g., California and Texas) have generally been stable in their attention to tobacco. The majority of the variance is between states (63 percent); however, 38 percent of the variance is within states, suggesting that there are temporal differences across the states. Detailed descriptive information is available in table S1 in the supplemental text file found online. To access this file, please click the article’s ‘‘Supplemental Material’’ link on the journal’s website (http://jhppl.dukejournals.org/). We see a similar degree of heterogeneity with state attention to vaccines. As shown in figure 2, the West generally introduces fewer vaccine-related bills compared with other regions. Some states, such as New York and Texas, spend much more attention on vaccine regulation compared with other states, such as California and Ohio. Similarly, it appears that many states (e.g., Massachusetts and Washington) increased their attention to vaccines since the 2000s, while other states (e.g., Alabama and Connecticut) show spikes in attention at certain times. The majority of variance in state attention to immunizations is between states (54 percent); however, there is an ample amount of temporal variance (47 percent). Analysis: Determinants of State Attention to Tobacco and Vaccines

To test our three hypotheses about intergovernmental influence, we include measures of gubernatorial, national, and neighboring state attention. Recall that H1 suggests that states will pay more attention to health when governors see health as important. We measure gubernatorial attention to tobacco or vaccines using State of the State (SOS) addresses.6 SOS 6. In cases when SOS addresses were not given, we used the governor’s budget address. If the SOS and budget address were not given, we used the inaugural speech. Some speeches were given, but we could not find a record of such a speech (text or video) to code. In other instances, it was confirmed that no such SOS speech was given in a particular year. In both of these instances, the governor’s agenda variable is coded as missing.



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Figure 2 Number of Vaccine-Related Bills Introduced by State Legislative Session

addresses are similar to the president’s State of the Union address and are indicative of the governor’s top legislative priorities (Ferguson 2006). Given their relevance, SOS addresses have been used by a number of scholars as a valid way to measure a governor’s agenda across time and states (see, e.g., DiLeo 1997; Kousser and Phillips 2012). For each speech, we coded whether tobacco or vaccines were mentioned in each year. We then averaged the number of mentions per legislative session so that our variable ranges from 0 to 1. For example, a value of .5 on this measure indicates that the governor mentioned a particular topic in one of the two years during a legislative session. Thirty-four percent of the governors mentioned tobacco, and 21 percent mentioned vaccine regulation in their SOS addresses across the states over the eleven legislative sessions.



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To test H2, which suggests that states pay more attention to health when the federal government does, we measure congressional attention to tobacco and vaccines over time using the Policy Agendas Project data.7 Specifically, we include measures of the number of congressional hearings focused on tobacco or vaccines across the eleven legislative sessions.8 We include the average number of tobacco- or vaccine-related bills introduced in neighboring states for each legislative session to test H3. Specifically, we add up all the tobacco- or vaccine-related bills introduced in neighboring states and divide this value by the number of neighboring states. We expect the neighboring attention variable to be positively related to attention allocation to tobacco control and immunization. To test whether states allocate more attention to health when problems are locally severe (H4), we measure problem severity of tobacco and vaccine using various variables. For tobacco, we use two variables: the percentage of smokers in a state and the smoking attributable mortality rate (SAMR). We expect for states with a large number of smokers to pay more attention to tobacco simply because of the burden that smokers place on the state’s health and economy. This variable is time varying and taken from the CDC’s Behavioral Risk Factor Surveillance System (BRFSS). For SAMR, we measure the average annual number of deaths caused by cigarette smoking for each state from 1997 to 2001. We expect for severity of tobacco to increase as the number of deaths by cigarette smoking increases. For immunization, we measure three variables that we believe capture severity. First, we pool the rate of deaths per 100,000 caused by vaccine preventable diseases from 1999 to 2008 for each state, with the expectation that states with a high death rate will pay more attention to vaccines.9 Second, we measure the rate of immunization from two important age groups. For children, we measure the average percentage of children from nineteen to thirty-five months of age vaccinated across a variety of diseases including diphtheria, tetanus, pertussis (DTaP), poliovirus, measles, mumps, and rubella (MMR), Haemophilus influenza type b (Hib), 7. The data used here were originally collected by Frank R. Baumgartner and Bryan D. Jones, with the support of National Science Foundation grant numbers SBR 9320922 and 0111611, and were distributed through the Department of Government at the University of Texas at Austin. Neither NSF nor the original collectors of the data bear any responsibility for the analysis reported here. 8. For tobacco-related hearings, we use the major topic code 341: Tobacco Abuse, Treatment and Education. For vaccine-related hearings, we use major topic codes 331: Prevention, Communicable Diseases and Health Promotion, and 332: Infants and Children. 9. These data come from the CDC’s Wonder searchable database. Specifically, these diseases include viral hepatitis (B15–B19) and influenza ( J11, J11.1, and J09).



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hepatitis B, and varicella (chickenpox) pooled from 1995 to 2009.10 For seniors, we measure the percentage of residents aged sixty-five and older who report receiving a flu vaccine in the past year pooled from 1995 to 2010 using the CDC’s BRFSS. Here, we expect that states with high levels of immunization rates will pay less attention to vaccines. Finally, as stated by H5, we expect for states with large and more powerful interest group populations operating within a given health area to allocate more attention to health. The battle over tobacco control is primarily between the tobacco industry and health advocates, particularly since the 1990s (Givel and Glantz 2001). Consequently, we include four measures of state organized interests for the tobacco analyses: the ratio of the number of health (or tobacco) lobbyists in the state to the total number of registered lobbyists and whether health (or tobacco) interests were listed as one of the ten most effective lobbies within a state (coded as 2), one of the top twenty groups (coded as 1), or not mentioned. Together these variables capture the presence and perceived power of health and tobacco lobbyists compared with other organized interests in each state. These variables come directly from Shipan and Volden’s (2006) analysis of antismoking legislation and are non–time varying. For vaccines, we include the two variables on health lobbyists only.11 We expect more attention to tobacco legislation or vaccine regulation in states where health organizations are present and powerful; similarly, we expect less attention to tobacco control in states where tobacco organizations are present and powerful.12 We also include various control variables that may influence state agenda setting separately from our hypotheses. These include state ideology and state partisanship, as measured by Pacheco (2011). In general, states with large numbers of liberals or Democrats are more likely to adopt healthrelated bills (e.g., Paul-Shaheen 1998; Kousser 2002). We include a measure of democratic strength with the typical expectation that states under Democratic control will be more likely to pay attention to health-related 10. These data come from the National Immunization Survey. Specifically, we took the average percentage of children aged nineteen to thirty-five months who were administered four or more doses of DTAP, three or more doses of poliovirus vaccine, one or more doses of any MMR vaccine, three or more doses of Hib vaccine, three or more doses of HepB vaccine, and one or more doses of varicella vaccine. 11. Ideally, we would have a comparable measure to capture the presence and power of the pharmaceutical interest groups; however, these variables are not, to our knowledge, available. 12. We recognize that our interest group variables are less than ideal and potentially outdated. At the same time, Shipan and Volden’s (2006) data are state of the art in terms of measuring interest group activity across the states on health-related issues. Creating new measures of interest group activity across the states and over time is an important endeavor, but beyond the scope of this article.



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bills. This variable is the sum of percentages of state houses and senates that are Democrats plus one hundred if the governor is a Democrat (Bailey and Rom 2004).13 For the tobacco analyses, we include a dummy variable to indicate whether a state is a tobacco producer; tobacco-producing states are less likely to pass smoking legislation. We also include Squire’s (2007) measure of legislative professionalism with the expectation that highly professionalized legislatures will introduce more bills. Finally, we account for differences in the capacity of state public health agencies by including a measure of the per capita health expenditures by state, taken from the US Census survey of state governments. Our expectation is that states that spend more on public health agencies will, by nature, be more likely to pay attention to emerging health issues. Methodological Strategy

The dependent variables are count variables, and analyses suggest the presence of overdispersion.14 Using Gaussian techniques for nonnormally distributed data can lead to biased estimates of the regression parameters (King 1988). Additionally, autocorrelation function (ACF) plots suggest the presence of autocorrelation in both the tobacco and the vaccine counts; models that ignore time dependence lead to incorrect inferences and inefficient estimates, particularly in event count data (Brandt and Williams 2001). To correct for autocorrelation, scholars typically include a lagged dependent variable (LDV). As Brandt and Williams (2001) point out, the coefficient of the LDV in a negative binomial regression is no longer an autocorrelation coefficient; instead, it estimates a linear exponential growth rate. They suggest other time-series event count models, such as a linear poisson autoregressive model (PAR[p]). Unfortunately, the PAR(p) model is not suited for time-series cross-sectional data with non-time-varying variables. To control for overdispersion and autocorrelation, we transform our dependent variables to approximate a normal distribution (e.g., by taking 13. An annual estimate was not available for the 2009–2010 session, so 2007–2008 estimates were included. We also used Klarner’s measure of state legislative partisan control, which is an additive scale of Democratic power in the legislature. A value of 1 = Democratic control of chambers, 0 = Republican control of both chambers, .5 = Democrats control one chamber, Republicans the other, .25 = Republican control of one chamber, split control of the other, and .75 = Democratic control of one chamber, split control of the other. Specifically, we measured the average score on this scale for each legislative session across states. These measures of Democratic power failed to reach statistical significance for both tobacco and vaccines. 14. The mean number of counts for tobacco is 28 with a variance of 557. The mean number of counts for vaccine regulation is 5.92 with a variance of 45.9.



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the square root) and estimate the model using ordinary least squares regression with an LDV. This specification, while statistically parsimonious, is difficult to interpret in terms of substantive effects. In addition, with only eleven time points, we lose 10 percent of the data when employing an LDV. There is also a concern that time-series models are not appropriate, since T is relatively small (see, e.g., Nickell 1981). Correspondingly, we report results from a negative binomial regression15 in the main text and provide alternative model specifications in the supplemental text (see tables S2–S3).16 Finally, we include a countervariable for session to account for systemic influences on state attention that are not captured by the model.17 The session counter also accounts for all election effects as well as any other annual-level influences on state attention allocation to tobacco and vaccine (Lowery, Gray, and Baumgartner 2011). Results are shown in table 1. As shown in table 1, there is moderate support for H1, which suggests that states will pay more attention to health when the governor deems health important. Specifically, our analyses suggest that states are more likely to pay attention to tobacco when governors mention tobacco regulation in their SOS addresses. We see a similar result for vaccines; states are more likely to pay attention to vaccine regulation when governors explicitly mention immunizations in their SOS addresses.18 There is

15. The negative binomial regression is preferable over the poisson regression model because it does not assume that events are independent and allows for the variance to be greater than the mean (see, e.g., Long 1997). We employ a random effects estimator, which is the default and allows for the dispersion parameter to vary randomly from state to state. Estimates with a fixedeffects overdispersion model (e.g., the dispersion parameter in a state can take on any value) are identical. 16. We were unable to estimate the negative binomial regression using a state fixed-effects approach to account for any unit-specific effects; the model did not converge. Moreover, even if the model did converge, our results would not be as informative, since many of our non-timevarying independent variables are unable to be estimated with a fixed-effects approach. We do, however, report fixed-effects models controlling for autocorrelation with an LDV on the transformed dependent variables in table S3 in the supplemental text. 17. We also included a squared version of the session variable. It was negative and statistically significant for both issues. The addition of the squared session variable causes the percentage of smokers and SAMR to fail to reach statistical significance for tobacco (B = - .0006, p-value = .969 for the smokers variable; B = - .002, p-value = .298 for SAMR). The addition of the squared session variable causes the gubernatorial attention variable to fail to reach statistical significance for vaccines (B = .094, p-value = .15). All other conclusions are nearly identical. 18. The gubernatorial attention variable is not significant in alternative model specifications that include an LDV (see tables S2 and S3). As suggested by a reviewer, this may indicate that governors and legislators are responding to similar state-level factors that are not included in the model or that governors may be responding to legislative behavior in the previous session. Exploring the causal direction of gubernatorial and legislative attention to issues is an interesting avenue for future research.



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Table 1 Negative Binomial Regression Models Predicting State Attention Allocation to Tobacco and Vaccines, 1990–2010 Tobacco Legislation (N = 529) Intergovernmental Influence (H1, H2, and H3) Gubernatorial attention to tobacco (t)

Vaccine Regulation (N = 532)

Intergovernmental Influence (H1, H2, and H3) .20*** Gubernatorial attention (.04) to vaccines (t)

.11* (.07)

National attention to tobacco (t) .00003 National attention to vaccines (t) (.004)

.002* (.002)

Neighboring state attention to tobacco (t)

.02*** Neighboring state attention (.004) to vaccines (t)

.04** (.02)

Problem Severity (H4) Percentage smokers (t)

Problem Severity (H4) .06*** Percentage aged 19–35 months (.01) immunized for various childhood diseases

.04* (.02)

Smoking attributable mortality rate

- .01** Percentage aged 65+ immunized - .02 (.002) for the flu (.02) Death rate of vaccine-preventable - .01 diseases (.018)

Interest Groups (H5) Power tobacco lobbyists

Interest Groups (H5) - .12 (.15)

Power health lobbyists

.07 (.08)

Power health lobbyists

.06 (.09)

Ratio tobacco lobbyists

- 5.33 (7.37)

Ratio health lobbyists

- .45 (1.22)

Ratio health lobbyists

- 1.14 (1.40)

Control Variables Percentage Democrat (t)

Control Variables .03*** Percentage Democrat (t) (.01)

.02* (.01)

Percentage liberal (t)

- .01 (.01)

- .01 (.02)

Tobacco state

- .12 (.14)

Percentage liberal (t)

(continued )



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Table 1 Negative Binomial Regression Models Predicting State Attention Allocation to Tobacco and Vaccines, 1990–2010 (continued ) Tobacco Legislation (N = 529)

Vaccine Regulation (N = 532)

Democratic strength (t)

- .0003 (.0004)

Democratic strength

.0002 (.001)

Legislative professionalism

.99* (.52)

Legislative professionalism

2.10*** (.56)

Per capita health expenditures .23 (.42)

Per capita health expenditures - .03 (.51)

Session counter

.07*** (.01)

Session counter

.12*** (.01)

Constant

.80 (.59)

Constant

- 1.33 (2.07)

Log likelihood

- 2013.36 Log likelihood

- 1300.38

Standard errors are shown in parentheses. *p < .10, **p < .05, ***p < .001 with a two-tailed test

minimal support for H2, which suggests that states pay more attention to health when the federal government does. As shown in table 1, states are no more likely to pay attention to tobacco when the federal government does. However, for vaccines, the national attention variable is significant, suggesting that states are more likely to pay attention to vaccines when the federal government does. Even still, the federal government’s influence over state attention to vaccines is comparatively similar to that of the governor.19 Finally, there is strong empirical evidence that states pay more attention to health when neighboring states pay attention to health (H3). In table 1 for tobacco, states are more likely to pay attention to tobacco when neighboring states also pay attention to tobacco. Results are similar with vaccine regulation. There is moderate support that problem severity increases attention (H4) to health across both issues. States are more likely to pay attention to tobacco as the percentage of smokers increases, but less likely as the SAMR increases. For vaccines, the percentage of children immunized is 19. Using the negative binomial regression, the coefficient of gubernatorial attention to vaccines indicates that for a one-unit increase in gubernatorial attention, there is a .11 increase in the difference in the logs of the expected counts, keeping all other variables constant. The comparable effect for national attention is .002. However, the range for gubernatorial attention is 0 to 1, so the coefficient indicates the maximum effect. The maximum effect for national attention to vaccines is .108 (the maximum, 54*.002).



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positively related to state attention. Across both issues, there is no empirical support for H5, which suggests that states will pay more attention to health when interest groups advocate strongly for health issues within the state. As shown in table 1, the interest group variables are consistently not significant for both tobacco and vaccine regulation.20 Other control variables conform to expectations. States with a high percentage of Democrats or liberals are more likely to introduce healthrelated legislation. Finally, highly professional legislatures are more likely to introduce bills related to tobacco or vaccine compared with those that are less professional. Democratic strength of the legislature does not matter for an explanation of state attention to emerging health issues. Conclusion

Using an original data set on bill introductions on tobacco and vaccines from 1990 to 2010, we identify factors influencing the agenda-setting process across the states on health. Our results suggest several novel things about agenda setting and public health policy making. First, intergovernmental influence, particularly internal and horizontal influence, is a driving force in determining attention allocation to health. Our analyses suggest that when governors highlight public health problems in SOS speeches, state legislatures respond with policy-making activity. Like the president, governors play a strong role in shaping and influencing state agenda setting. Given that gubernatorial power has increased over time (Beyle 2004), governors now play a prominent role in determining attention allocation compared with forty years ago. Governors who are highly popular or skilled at influencing legislative behavior may be particularly successful at pushing their agenda. This suggests that public health officials concerned with slow legislative responses to issues such as infectious disease control, obesity, and distracted driving should emphasize the importance of executive leadership in elevating the salience of public health problems. We also find that attention allocation is shaped by the agenda-setting activity of neighboring states. It is therefore not simply the act of policy 20. This null finding may be the result of the imperfect measures of interest group participation available in this analysis. We would expect that state-level attention to public health issues would increase as the health and pharmaceutical lobbies play a more prominent role in state policy making and that state attention to health would decrease as the tobacco lobby plays a more prominent role in health policy making. We note that this null result is in keeping with prior research on interest group participation in state policy making, which has not demonstrated a clear link between the density and diversity of state interest group populations on lawmaking (Gray and Lowery 1996).



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adoption that triggers policy emulation and imitation but an earlier process of legislative agenda setting. This suggests an interesting line of inquiry for future research, as scholars may wish to explore whether adopting and nonadopting states differ both in the decision to enact legislation and in their agenda-setting and problem-definition activities. For policy makers, public health officials may wish to pressure locally influential state governments to attend to emerging health problems. Our results suggest that such a concentrated effort could lead to a spillover of legislative attention in subsequent years. The national agenda is also influential for state attention; however, we find that internal and horizontal intergovernmental influences are stronger in magnitude compared with vertical intergovernmental influence. Identifying instances where the federal government is particularly influential, for instance, on issues that are primarily the jurisdiction of the national government and when partisanship is shared between Congress and state legislatures are interesting avenues for future research. Future research may also look at how federal signals, such as grant programs or policy recommendations, shape state attention allocation. We find mixed empirical support that internal dynamics such as local problem severity drives attention to emerging health issues, echoing previous research at the national level. The inconsistency in our results may reflect the difficulty that policy makers have in identifying and deciphering appropriate indicators of population health. Future research may benefit from developing comparable measures of bureaucratic influence, since public health departments are a critical information source for policy makers. Some states, with highly professionalized public health departments, may be particularly successful at informing policy makers about emerging health problems and focusing the agenda on these problems. We find no evidence that interest groups influence attention to tobacco or vaccines. These results may be a function of the limited measures available to capture interest group activity in the American states. While we are able to capture the presence and power of the tobacco and health industries within states, our measures are static. Therefore, we are unable to pick up meaningful changes in interest group activity, which may have an impact over time on state attention. We also are unable to locate comparable measures of pharmaceutical groups, which may be particularly important for attention to vaccines. Future research would benefit from more detailed measures of interest group activity across states and time. In addition, our control variables point to other factors that drive statelevel attention to emerging public health problems. For example, state



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legislative professionalism is strongly associated with state-level attention to immunization policy, suggesting that legislative expertise may play an underappreciated role in problem recognition. We also find some evidence that state partisanship may shape preferences for public health agenda setting. States with a higher percentage of residents identified with the Democratic Party are likely to dedicate more attention to public health policy, although this finding is especially strong for tobacco regulation. Empirical analyses of other policies would help further determine what influences attention allocation, not just policy adoption, in the states. To this end, scholars can benefit from taking advantage of the longitudinal variation in attention allocation across the states, as we have done in this article. We urge scholars to look at broad issues rather than a single policy so that conclusions are more generalizable. In this regard, our data collection effort is a template for scholars studying the agenda-setting process in the states on other policy areas. Finally, while the current study explores the determinants of agenda intensity over time, it does not capture changes in the underlying policy alternatives considered by state governments from one year to the next. Future research could add to our understanding of state-level agenda-setting dynamics by exploring variation, not just in the intensity of attention as we have done here, but also in the range and content of public health policy alternatives over time.

n

n

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Julianna Pacheco is assistant professor of political science at the University of Iowa. Her research explores political behavior, public opinion, state politics, and health policy, widely defined. Her subfields of interest include youth voter turnout, dynamic state public opinion, policy diffusion, state agenda setting, dynamic policy responsiveness, and antismoking legislation. Her most recent publications have appeared in the Journal of Politics, State Politics and Policy Quarterly, and Public Opinion Quarterly. Graeme Boushey is assistant professor of political science at the University of California, Irvine. His research explores public policy innovation and political decision making in the United States, with a focus on state-level public health, immigration, and criminal justice policy making. His book, Policy Diffusion Dynamics in America (2010), explores the triggers of policy outbreaks—the rapid diffusion of policy innovation across states. His work has appeared in the Policy Studies Journal, State Politics and Policy Quarterly, and the Journal of Comparative Policy Analysis.



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References Adler, E. Scott, and John D. Wilkerson. 2010. ‘‘The Evolution of Policy: Congress and the Timing and Degree of Policy Change.’’ Paper presented at the annual meeting of the Western Political Science Association, San Francisco, April 1. Armstrong, Elizabeth M., Daniel P. Carpenter, and Marie Hojnacki. 2006. ‘‘Whose Deaths Matter? Mortality, Advocacy, and Attention to Disease in the Mass Media.’’ Journal of Health Politics, Policy and Law 31, no. 4: 729–72. Bachrach, Peter, and Morton S. Baratz. 1962. ‘‘Two Faces of Power.’’ American Political Science Review 56, no. 4: 947–52. Bailey, Michael A., and Mark Carl Rom. 2004. ‘‘A Wider Race? Interstate Competition across Health and Welfare Programs.’’ Journal of Politics 66, no. 2: 326–47. Baumgartner, Frank R., Virginia Gray, and David Lowery. 2009. ‘‘Federal Policy Activity and the Mobilization of State Lobbying Organizations.’’ Political Research Quarterly 62, no. 3: 552–67. Baumgartner, Frank R., and Bryan D. Jones. 1993. Agendas and Instability in American Politics. Chicago: University of Chicago Press. Beckmann, Mathew. 2010. Pushing the Agenda. Cambridge: Cambridge University Press. Berry, Frances Stokes, and William Berry. 1990. ‘‘State Lottery Adoptions as Policy Innovations: An Event History Analysis.’’ American Political Science Review 84: 395–415. Best, Joel. 1990. Threatened Children: Rhetoric and Concern about Child-Victims. Chicago: University of Chicago Press. Beyle, T. L. 2004. ‘‘The Governors.’’ In Politics in the American States, edited by V. Gray, R. L. Hanson, and H. Jacob, 194–231. 8th ed. Washington, DC: CQ Press. Birkland, Thomas A. 1998. ‘‘Focusing Events, Mobilization, and Agenda Setting,’’ Journal of Public Policy 18, no. 1: 53–74. Blumer, Herbert. 1971. ‘‘Social Problems as Collective Behavior.’’ Social Problems 18: 296–306. Boehmke, Frederick J., and Richard Witmer. 2004. ‘‘Disentangling Diffusion: The Effects of Social Learning and Economic Competition on State Policy Innovation and Expansion.’’ Political Research Quarterly 57, no. 1: 39–51. Boushey, Graeme, and Adam Luedtke. 2011. ‘‘Immigrants across the US Federal Laboratory: Explaining State-Level Innovation in Immigration Policy.’’ State Politics and Policy Quarterly 11, no. 4: 390–414. Brandt, Patrick T., and John T. Williams. 2001. ‘‘A Linear Poisson Autoregressive Model: The Poisson AR( p) Model.’’ Political Analysis 9, no. 2: 1–21. Caldeira, Gregory A., and John R. Wright. 1988. ‘‘Organized Interests and Agenda Setting in the US Supreme Court.’’ American Political Science Review 82: 1109–27. Carter, Larry E., and James T. LaPlant. 1997. ‘‘Diffusion of Health Care Policy Innovation in the United States.’’ State and Local Government Review 29, no. 1: 17–26.



Pacheco and Boushey

-


587

CDC (Centers for Disease Control and Prevention). 2005. ‘‘Annual Smoking-Attributable Mortality, Years of Potential Life Lost, and Productivity Losses—United States 1997–2001.’’ MMWR 54: 265–68. CDC (Centers for Disease Control and Prevention). 2009. ‘‘How Vaccines Prevent Disease.’’ www.cdc.gov/vaccines/vac-gen/howvpd.htm (accessed February 2, 2012). Colby, David C., and Timothy E. Cook. 1991. ‘‘Epidemics and Agendas: The Politics of Nightly News Coverage of AIDS.’’ Journal of Health Politics, Policy and Law 16, no. 2: 215–49. Council of State Governments. 2007. ‘‘Increasing Vaccination Rates.’’ www.healthy states.csg.org/NR/rdonlyres/E921A0B1–CBD5–4A29–8C93–8612CA6E2E77/0 /IncreasingVacLPB.pdf (accessed February 2, 2012). Cox, Gary, and Mathew McCubbins. 2005. Setting the Agenda. Cambridge: Cambridge University Press. Dometrius, Nelson C. 1979. ‘‘Measuring Gubernatorial Power.’’ Journal of Politics 41, no. 2: 589–610. Donovan, Mark C. 2001. Taking Aim: Target Populations and the Wars on AIDS and Drugs. Washington, DC: Georgetown University Press. DiLeo, Daniel. 1997. ‘‘Dynamic Representation in the United States: Effects of the Public Mood on Governors’ Agendas.’’ State and Local Government Review 29, no. 2: 98–109. Edwards III, George C., and B. Dan Wood. 1999. ‘‘Who Influences Whom? The President, Congress, and the Media.’’ American Political Science Review 93, no. 2: 327–44. Ferguson, Margaret Robertson. 2006. The Executive Branch of State Government: People, Process, and Politics. Santa Barbara, CA: ABC-CLIO. Fleming, Roy B., B. Dan Wood, and John Bohte. 1999. ‘‘Attention to Issues in a System of Separated Powers: The Macrodynamics of American Policy Agendas.’’ Journal of Politics 61, no. 1: 76–108. Givel, Michael, and Stanton Glantz. 2001. ‘‘Tobacco Lobby Political Influence on US State Legislatures in the 1990s.’’ Tobacco Control 10: 124–34. Goldstein, Adam O., and Nathan S. Bearman. 1996. ‘‘State Tobacco Lobbyists and Organizations in the United States: Crossed Lines.’’ American Journal of Public Health 86: 1137–42. Gray, Virginia, and David Lowery. 1993. ‘‘Stability and Change in State Interest Group Systems, 1975–1990.’’ State and Local Government Review 25, no. 2: 87–96. Gray, Virginia, and David Lowery. 1996. ‘‘Environmental Limits on the Diversity of State Interest Systems: A Population Ecology Simulation.’’ Political Research Quarterly 49, no. 1: 103–18. Jacobs, Lawrence. 1993. The Health of Nations: Public Opinion and the Making of American and British Health Policy. Ithaca, NY: Cornell University Press. Jones, Bryan, and Frank Baumgartner. 2005. The Politics of Attention: How Government Prioritizes Problems. Chicago: University of Chicago Press. Karch, Andrew. 2007. Democratic Laboratories: Policy Diffusion among the American States. Ann Arbor: University of Michigan Press.



588


Karch, Andrew. 2010. ‘‘Vertical Diffusion and the Policy-Making Process: The Politics of Embryonic Stem Cell Research.’’ Political Research Quarterly 65, no. 1: 48–61. King, Gary. 1988. ‘‘Statistical Models for Political Science Event Counts: Bias in Conventional Procedures and Evidence for the Exponential Poisson Regression Model.’’ American Journal of Political Science 32, no. 3: 838–63. Kingdon, John W. 1995. Agendas, Alternatives, and Public Policies. New York: HarperCollins. Kousser, Thad. 2002. ‘‘The Politics of Discretionary Medicaid Spending, 1980– 1993.’’ Journal of Health Politics, Policy and Law 27, no. 4: 639–71. Kousser, Thad, and Justin Phillips. 2012. The Power of American Governors. Cambridge: Cambridge University Press. Long, J. Scott. 1997. Regression Models for Categorical and Limited Dependent Variables. Vol. 7. Thousand Oaks, CA: Sage. Lowery, David, and Virginia Gray. 1993. ‘‘The Density of State Interest Group Systems.’’ Journal of Politics 55: 191–206. Lowery, David, and Virginia Gray. 1995. ‘‘The Population Ecology of Gucci Gulch, or the Natural Regulation of Interest Group Numbers in the American States.’’ American Journal of Political Science 39, no. 1: 1–29. Lowery, David, Virginia Gray, and Frank R. Baumgartner. 2011. ‘‘Policy Attention in State and Nation: Is Anyone Listening to the Laboratories of Democracy?’’ Publius: The Journal of Federalism 41, no. 2: 286–310. McCann, Pamela, Craig Volden, and Charles Shipan. 2012. ‘‘Intergovernmental Policy Diffusion: National Influence on State Policy Adoption.’’ Paper presented at the annual meeting of the Association for Public Policy Analysis and Management, Baltimore, MD, November 9. McCombs, Maxwell, and Donald L. Shaw. 1972. ‘‘The Agenda-Setting Function of Mass Media.’’ Public Opinion Quarterly 36, no. 2: 176–87. Mintrom, Michael. 1997. ‘‘Policy Entrepreneurs and the Diffusion of Innovation.’’ American Journal of Political Science 41, no. 3: 738–70. Mintrom, Michael, and Sandra Vergari. 1998. ‘‘Policy Networks and Innovation Diffusion: The Case of State Education Reforms.’’ Journal of Politics 60, no. 1: 126–48. Morehouse, Sarah M., and Malcolm E. Jewell. 2004. ‘‘States as Laboratories: A Reprise.’’ Annual Review of Political Science 7: 177–203. NCSL (National Conference of State Legislatures). 2010. ‘‘2009 State Pandemic and H1N1 (Swine) Flu Legislation.’’ www.ncsl.org/issues-research/health/2009–state -pandemic-and-h1n1–swine-flu-legislati.aspx (accessed January 15, 2013). NCSL (National Conference of State Legislatures). 2011. ‘‘Immunizations.’’ www .ncsl.org/issues-research/health/immunizations-policy-issues-overview.aspx (accessed February 2, 2012). Nice, David C. 1994. ‘‘The States and the Death Penalty.’’ Western Political Quarterly 45, no. 4: 1037–48. Nickell, Stephen. 1981. ‘‘Biases in Dynamic Models with Fixed Effects.’’ Econometrica: Journal of the Econometric Society 49, no. 6: 1417–26.



Pacheco and Boushey

-


589

Oates, Wallace E. 1999. ‘‘An Essay on Fiscal Federalism.’’ Journal of Economic Literature 37, no. 3: 1120–49. Pacheco, Julianna. 2011. ‘‘Using National Surveys to Measure State Public Opinion over Time: A Guideline for Scholars and an Application.’’ State Politics and Policy Quarterly 11, no. 4: 415–39. 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, no. 1: 187–202. Paul-Shaheen, Pamela A. 1998. ‘‘The States and Health Care Reform: The Road Traveled and Lessons Learned from Seven That Took the Lead.’’ Journal of Health Politics, Policy and Law 23, no. 2: 319–61. Rochefort, David A., and Roger W. Cobb. 1994. The Politics of Problem Definition: Shaping the Policy Agenda. Lawrence: University Press of Kansas. Rosenthal, Alan. 1990. Governors and Legislatures: Contending Powers. Washington, DC: CQ Press. Sapat, Alka. 2004. ‘‘Devolution and Innovation: The Adoption of State Environmental Policy Innovations by Administrative Agencies.’’ Public Administration Review 64, no. 2: 141–51. Schattschneider, E. E. 1960. The Semisovereign People. New York: Holt, Rinehart, and Winston. Schneider, Anne, and Helen Ingram. 1993. ‘‘Social Construction of Target Populations: Implications for Politics and Policy.’’ American Political Science Review 87: 334–47. Shipan, Charles R., and Craig Volden. 2006. ‘‘Bottom-up Federalism: The Diffusion of Antismoking Policies from US Cities to States.’’ American Journal of Political Science 50: 825–43. Squire, Peverill. 2007. ‘‘Measuring State Legislative Professionalism: The Squire Index Revisited.’’ State Politics and Policy Quarterly 7, no. 2: 211–27. Walker, Jack L. 1969. ‘‘The Diffusion of Innovations among the American States.’’ APSR 63, no. 3: 880–89. Warner, Kenneth. 2006. ‘‘Tobacco Policy Research: Insights and Contributions to Public Health Policy.’’ In Tobacco Control Policy, ed. Kenneth Warner, 3–86. San Francisco: Jossey-Bass.


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