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<title level="a" type="main">Agenda Setting and State Policy Diffusion: The Effects of Media Attention, State Court Decisions, and Policy Learning on Fetal Killing Policy*</title>
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<title level="a" type="main">Agenda Setting and State Policy Diffusion: The Effects of Media Attention, State Court Decisions, and Policy Learning on Fetal Killing Policy*</title>
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<forename type="first">M. R.</forename>
<surname>Oakley</surname>
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<affiliation> <orgName type="institution">Mount. St. Mary's University</orgName> </affiliation>
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<abstract xml:lang="en" style="main"> <p> <hi rend="bold">Objectives. </hi> This study combines theories on agenda setting, policy innovation, and policy learning to develop an improved model of state policy change. The case of fetal killing policy change in the states is used to develop a model that incorporates national media attention and the decisions of state courts, in addition to policy learning variables that account for the policy changes of neighboring states and the passage of time.</p> <p> <hi rend="bold">Methods. </hi> I test the effect of national media attention, decisions by the courts, and the actions of neighboring states on the likelihood that states will change their fetal homicide policies. Using time-series cross-sectional data from 1970 to 2002, the model is tested using logistic regression analysis. In addition to testing the theories mentioned above, control variables in the model include citizen and government ideology and the percentage of state residents who are fundamentalist Protestants.</p> <p> <hi rend="bold">Results. </hi> Three of the four research hypotheses are supported by the statistical analysis. The results demonstrate that increased media attention to fetal homicide in a given year increases the likelihood that a state will change its policy the next year. Support is also found for the hypothesis that state court decisions will affect policy change. One of the control variables, government liberalism, is also found to decrease the likelihood that states will change their fetal homicide policies.</p> <p> <hi rend="bold">Conclusions. </hi> This study lends insight into why states change their policies by including agenda-setting variables such as media attention and decisions made by the courts. States do react to the actions of the courts by making changes to policies affected by the decisions.</p> </abstract>
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<p>Much media attention is given to national politics and policy; however, many of the most high-profile and controversial policies are made at the state level, such as gay-marriage bans, abortion regulations, and capital punishment policy. Also, policy change can sweep across the states in a relatively short amount of time. For example, prior to <_date>1970</_date>, few states had no-fault divorce, but by 1974 all but five states had the policy, and by 1985 all 50 states had adopted some form of no-fault divorce (<ref type="bibr" target="#b15">Jacob, 1988</ref>:80). In the case of end-of-life policy, in the late <_date>1980s</_date> state supreme courts clarified the right to refuse life-saving treatment and by 1992 all 50 states had either a living-will law, or authorization for appointment of a health-care agent, or some combination of these policies (<ref type="bibr" target="#b8">Blank, 1995</ref>:171). Another example is "Megan's Law," which swept across the country in a few short years. <_ref type="bibr">In 1994</_ref>, when <_placeName>New Jersey</_placeName> passed the highly publicized law, only a few states had registration requirements for sex offenders, yet by 1999, all 50 states and the national government had enacted some form of the policy. In the area of fetal rights, 11 states passed fetal homicide policies in the <_date>1990s</_date> that made the killing of a fetus, at any stage of development, essentially equal to the murder of a born person, and this trend has continued into the present decade (<ref type="bibr" target="#b1">AUL, 2003a</ref>).</p>
<p>The phenomenon of the diffusion of policy innovation is nothing new to policy scholars, but the process continues to require further explanation. What sets this process in motion? Why are some policies adopted so rapidly? This study contributes to the understanding of state policy making by combining theories of agenda setting and policy learning to build on existing models of policy innovation to provide further insight into the innovation process than previous studies. This model of policy innovation incorporates the role of media attention to high-profile fetal killing cases in prompting innovation by state legislatures. The model also incorporates the importance of decisions of other branches of government-namely, the judicial branch in helping to set the policy agenda in the legislative branch. Additionally, the concept of policy learning is further developed by including both time and neighboring state behavior in the model.</p>
</div>
<div xml:lang="en" xml:id="ss0">
<head>The Test Case</head>
<p>In a case garnering a tremendous amount of national attention, Scott Peterson was charged and later convicted of two counts of homicide for the murder of his wife, Laci Peterson, and his unborn son Conner. Immediately, the charge of homicide for the fetus, under California's homicide law, was the subject of much discussion in the media. How can a fetus be a victim of homicide from eight weeks' gestation under the law, when a woman could abort the fetus legally? Laci Peterson's family has been active in supporting federal legislation, which passed both chambers of <_orgName>Congress</_orgName> and was signed into law by <_persName>President Bush</_persName> on April 1, 2004. The Act was originally named the Unborn Victim's of Violence Act, but was renamed Laci and Conner's Law at the request of Peterson's parents, according to the May 27, 2003, <hi rend="italic"><_placeName>Pittsburgh</_placeName> Post-Gazette</hi> article by Cindy Lash. Additionally, according to a June 1, 2003, article posted on the <_orgName>National Right</_orgName> to Life Committee's website, the Texas Legislature and several other states passed fetal homicide legislation after receiving a letter in support of the legislation from Laci's mother, Sharon Rocha.</p>
<p>The fetal homicide issue is a timely one to explore. The Peterson case has brought national attention to the issue of fetal homicide legislation, but the policy has been diffusing through the state legislatures since <_date>1970</_date>. The federal government has now adopted legislation and states are continuing to consider and adopt these policies. The national attention recently given to the issue is not likely to abate any time soon given the double murder trial, conviction, and death penalty sentence in the Peterson case. This makes understanding the forces driving fetal homicide policy innovation and reinvention over time particularly important for policy scholars. Most importantly, the model developed here combines the strengths of agenda-setting theory and policy-learning theory to create a model that can be applied to a wide range of policy areas.</p>
<p>This study differs from Schroedel's excellent work on fetal killing policies because it focuses on what prompts states to change their policies in a given year, while Schroedel's study is a broader one aimed at determining whether states with the most fetal protection and abortion restrictions also protect children after birth with more comprehensive health, welfare, and adoption policies. Schroedel looks at the comprehensiveness of fetal killing policy as an independent variable explaining policies aimed at the welfare of children, whereas this study focuses on explaining the change in fetal killing policy itself as a case in testing agenda-setting and policy-learning theories of policy change.</p>
</div>
<div xml:lang="en" xml:id="ss1">
<head>Understanding Policy Innovation</head>
<p>Institutions often resist change. Something must occur that motivates state legislatures to take a new path, rather than following the stable, predictable course followed in the past. As <ref type="bibr" target="#b20">David Nice</ref> puts it: "In view of the obstacles to the adoption of policy innovations, we might expect state governments to continue doing the same things decade after decade" (1994:20). Yet, in the case of the comprehensive fetal homicide policies adopted in the <_date>1980s</_date>, <_date>1990s</_date>, and after, state after state has made the nonincremental change of recognizing the fetus as a homicide victim after decades of following the "born-alive" rule. What prompts such innovation?</p>
<p>This article develops a model of the diffusion and reinvention of state fetal killing policies that builds on previous research on policy innovation and agenda setting. Nice has argued in the innovation literature that the factors that influence state policy innovation are the problem environment, available resources, and the state orientation toward government activity (1994:20). In terms of the problem environment, a crisis may prompt media attention to an area where a serious problem was not previously perceived. A well-publicized problem or failure may also prompt the public and interest groups to pressure officials to respond to a particular problem. Agenda-setting scholars have long incorporated the concept of focusing events and media attention to an issue into their models of policy change (<ref type="bibr" target="#b18 #b3">Kingdon, 1984; Baumgartner and Jones, 1993</ref>). This study applies agenda-setting theories to a quantitative model of state policy diffusion.</p>
<p>The model developed here presents four research hypotheses: two agenda-setting hypotheses and two policy-learning hypotheses. The model asserts that increased media attention, state court decisions out of sync with public opinion, the simple passage of time, and policy adoptions by neighboring states will all prompt state policy innovation in the case of fetal killing policy, while controlling for other factors that affect state policy innovation.</p>
<p>The agenda-setting hypotheses asserted here focus on the specifics of the problem environment, which should be especially important in the case of fetal killing policy because of the brutal nature of the crimes and media coverage of them. An event that could create a sense of urgency or need to act is the focusing event discussed in the agenda-setting literature. Kingdon defines a focusing event as "a crisis or disaster that comes along to call attention to the problem, a powerful symbol that catches on, or the personal experience of the policy maker" (1995:95). Events like the Laci Peterson murder have brought a great deal of attention to the issue of fetal homicide and other fetal killings. High-profile fetal killing incidents also occurred in Ohio, Delaware, and other states just prior to the adoption of new laws in those states (<ref type="bibr" target="#b12 #b24">CR, 1999; SW, 1999</ref>). Of course, events such as these come to public and government attention primarily through the media. Thus, media attention to an issue can prompt legislative action. <ref type="bibr" target="#b3">Baumgartner and Jones (1993)</ref> attribute the instability in U.S. politics to the shifting attention of the media.</p>
<p>In the case of intense media attention to a major incident of fetal murder or abuse by a third party, a need for innovation is more likely to be perceived. Also, if there is conflict over how to define an issue (in this case "tough on crime" or "pro-fetal rights/anti-abortion"), the event may serve to focus attention on one interpretation and reduce controversy. The incidence of a fetal killing, which is a fairly rare event, is less abstract when an actual event is covered in the media. For example, after a domestic violence case in Delaware, a prosecutor in the state worked with the attorney general to pass a fetal killing law, characterizing it as "closing a loophole in criminal law" and "catching up with other states" (<ref type="bibr" target="#b24">SW, 1999</ref>). <ref type="bibr" target="#b3">Baumgartner and Jones (1993)</ref> explain the importance of media attention not only in bringing an issue to the agenda but in helping to define it by focusing on one aspect of the issue or another. To determine whether media attention to fetal killing policy and high-profile fetal killing cases like those in California and Delaware serve to prompt policy innovation, the following hypothesis is developed.</p>
<p>
<list xml:id="l1" style="custom">
<item n=" ">
<p>Media Attention Hypothesis: <hi rend="italic">States will be more likely to change fetal killing policies in years when the overall level of media attention is higher than in years when media attention is lower</hi>.</p>
</item>
</list>
</p>
<p>Media attention to horrific fetal killing cases like the Laci Peterson case may prompt legislative action, but an additional agenda-setting factor is the state courts. Although more attention has focused on the legislative branch for explaining policy change, the judicial branch can have a significant effect as well (<ref type="bibr" target="#b26">Langer and Brace, 2005</ref>). If a state has a standing court decision upholding the born-alive rule, the legislature may feel pressure to pass fetal homicide legislation. This rule is a common-law principle dating back to the <_date>1600s</_date> that states that the fetus must be born alive and draw breath before dying of its injuries for the death to be considered a homicide. Since the born-alive rule is followed in the absence of a fetal homicide law, the application of that rule by the courts in a prominent case is likely to create a demand for legislative action. Opinion polls show that 80 percent of Americans support fetal killing policies (<ref type="bibr" target="#b22">Rosenberg, 2003</ref>), so decisions upholding the "born-alive" rule may be especially likely to prompt legislative action. This occurred in California, the first state to adopt a fetal homicide law, in <_date>1970</_date>, when the day after the California State Supreme Court applied the born-alive rule in the <hi rend="italic">Keeler</hi> case the legislature acted to include fetuses in the homicide statute. Although this seems to explain policy innovation in California, whether court decisions serve as an impetus to innovation for other states is an empirical question. The model developed here will test the following hypothesis regarding state court decisions.</p>
<p>
<list xml:id="l2" style="custom">
<item n=" ">
<p>State Court Decision Hypothesis: <hi rend="italic">States with a court decision upholding the born-alive rule will be more likely to change fetal killing policies in the year following the decision than states without such a decision</hi>.</p>
</item>
</list>
</p>
<p>The agenda-setting factors of media attention and court decisions may prompt state policy innovation, but policy learning is another important factor to consider. As more states adopt a fetal killing law, policy learning may take place between state officials from across different states, leading to more comprehensive policies. <ref type="bibr" target="#b14">Glick and Hays (1991)</ref> found this to be the case for living-will policies in the <_date>1970s</_date> and <_date>1980s</_date>. Incremental learning took place over time, leading to more expansive policies for late adopters. As policies diffuse through the states, they may become more comprehensive as states become bolder due to experience with the policy and its effects. <ref type="bibr" target="#b15">Jacob (1988)</ref> points out that the spread of innovations can often be attributed to diffusion networks. Sponsoring organizations may endorse an innovation and seek to disseminate knowledge of the innovation to policymakers across states. In the case of no-fault divorce, endorsement by the American Bar Association helped increase awareness of the policy. National pro-life organizations and their state affiliates have worked to promote comprehensive state fetal killing policies called Unborn Victims of Violence Acts or fetal homicide laws, similar to the policy adopted by the federal government in <_date>2004</_date> (<ref type="bibr" target="#b19 #b2">NRLC, 2003; AUL, 2003b</ref>). Under these policies, intentional fetal killing is treated in much the same way as any other homicide. A key feature of the policies endorsed by pro-life groups is that they apply at any stage of gestation-from conception to birth.</p>
<p>Also, in <_date>1987</_date>, Americans United for Life, based in Chicago, began to circulate model fetal homicide legislation to all 50 state legislatures. Although some states had had the less comprehensive fetal manslaughter laws on the books since the 19th century, and California adopted a law making the killing of a fetus homicide in <_date>1970</_date>, in the <_date>1980s</_date> and <_date>1990s</_date> most states adopted more comprehensive fetal homicide policies.</p>
<p>To illustrate the diffusion of fetal killing policy, <ref type="table" target="#t1">Table 1</ref> shows the comprehensiveness of policies and new adoptions taking place between 1970 and 2002. <ref type="figure" target="#f1">Figure 1</ref> further shows the cumulative adoptions over time. The pattern of adoptions follows that of the "s curve" expected when policy learning takes place (<ref type="bibr" target="#b25">Gray, 1973</ref>). Further, <ref type="table" target="#t2">Table 2</ref> shows the increasing comprehensiveness of fetal killing policy from 1970 to 2002. Inclusion of a time counter independent variable in the analysis will test the effect of time that <ref type="table" target="#t2">Table 2</ref> seems to show, while controlling for other variables. Although the statistical analysis will focus on whether a state made a change to a policy in a given year, rather than the overall level of comprehensiveness, it is important to remember that each change a state makes in the case of fetal killing policy has been to a more comprehensive policy. No state has changed its policy to be less comprehensive.</p>
<figure type="table" xml:id="t1">
<head>&#x2028;Comprehensiveness of Third-Party Fetal Killing Laws as of December 2002</head>
<table cols="3"> <row role="label"> <cell>State</cell>
<cell style="align(center)">Score</cell>
<cell style="align(center)">New Adoptions 1970-2002</cell>
</row>
<row>
<cell>Iowa</cell>
<cell>3</cell>
<cell>1983</cell>
</row>
<row>
<cell>Delaware</cell>
<cell>4</cell>
<cell>1999</cell>
</row>
<row>
<cell>New Hampshire</cell>
<cell>4</cell>
<cell/>
</row>
<row>
<cell>Louisiana</cell>
<cell>4</cell>
<cell>1989</cell>
</row>
<row>
<cell>Kansas</cell>
<cell>4</cell>
<cell>1995</cell>
</row>
<row>
<cell>Nevada</cell>
<cell>5</cell>
<cell/>
</row>
<row>
<cell>Rhode Island</cell>
<cell>6</cell>
<cell/>
</row>
<row>
<cell>Arizona</cell>
<cell>6</cell>
<cell>1992</cell>
</row>
<row>
<cell>South Dakota</cell>
<cell>6</cell>
<cell>1995</cell>
</row>
<row>
<cell>Arkansas</cell>
<cell>6</cell>
<cell>1999</cell>
</row>
<row>
<cell>Florida</cell>
<cell>6</cell>
<cell/>
</row>
<row>
<cell>Michigan</cell>
<cell>6</cell>
<cell>1999</cell>
</row>
<row>
<cell>Mississippi</cell>
<cell>6</cell>
<cell/>
</row>
<row>
<cell>Georgia</cell>
<cell>6</cell>
<cell>1982</cell>
</row>
<row>
<cell>Illinois</cell>
<cell>6</cell>
<cell>1987</cell>
</row>
<row>
<cell>California</cell>
<cell>7</cell>
<cell>1970</cell>
</row>
<row>
<cell>Washington</cell>
<cell>7</cell>
<cell/>
</row>
<row>
<cell>North Dakota</cell>
<cell>7</cell>
<cell>1987</cell>
</row>
<row>
<cell>Tennessee</cell>
<cell>7</cell>
<cell>1989</cell>
</row>
<row>
<cell>Oklahoma</cell>
<cell>7</cell>
<cell/>
</row>
<row>
<cell>Pennsylvania</cell>
<cell>7</cell>
<cell>1997</cell>
</row>
<row>
<cell>Indiana</cell>
<cell>7</cell>
<cell>1997</cell>
</row>
<row>
<cell>Wisconsin</cell>
<cell>7</cell>
<cell>1998</cell>
</row>
<row>
<cell>Utah</cell>
<cell>7</cell>
<cell>1994</cell>
</row>
<row>
<cell>Idaho</cell>
<cell>9</cell>
<cell>2002</cell>
</row>
<row>
<cell>Nebraska</cell>
<cell>9</cell>
<cell>2002</cell>
</row>
<row>
<cell>Ohio</cell>
<cell>9</cell>
<cell>1996</cell>
</row>
<row>
<cell>Minnesota</cell>
<cell>9</cell>
<cell>1986</cell>
</row>
</table>
<note xml:id="t1n1"> <p>N<hi rend="smallCaps">ote</hi>: Schroedel's (2000) point system is used to rate state fetal killing policies (in effect as of December 2002) as to their comprehensiveness. The policies are rated based on: (1) the severity of the class of the crime, (2) the stringency of the maximum allowed sentence, and (3) the gestational period at which the law applies. The points are assigned based on the three dimensions as follows: (1) 3 points are assigned if the murder statute is applied to fetuses, 2 points if only the manslaughter law can be applied to fetuses, (2) 4 points for a capital crime, 3 for life in prison, 2 for 15-30 years, and 1 for less than 15 years, (3) 3 points if the law applies at any state of gestation, 2 points if the law applies at quickening (16-20 weeks), and 1 point if the law applies only in the third trimester or is not specified. States with no legislative statutes regarding fetal killing (score of 0 are not listed). States not adopting laws from 1970 to 2002 adopted fetal manslaughter laws in the 19th or early 20th century.</p> </note>
<note xml:id="t1n2"> <p>S<hi rend="smallCaps">ource</hi>: Updated from Schroedel (2000) based on a search of individual state statutes.</p> </note>
</figure>
<figure xml:id="f1">
<label>1</label>
<media mimeType="image" url="urn:x-wiley:00384941:media:SSQU609:SSQU_609_f1"/>
<media mimeType="image/gif" url="" rendition="webOriginal"/>
<media mimeType="image/gif" url="" rendition="webLoRes"/>
<figDesc> &#x2028;Cumulative Frequency Distribution of States Adopting New Fetal Killing Laws from 1970 to 2002 </figDesc>
</figure>
<figure type="table" xml:id="t2">
<head>&#x2028;Average State Comprehensiveness Scores from 1970 to 2002 for All States and for States with a Legislative Fetal Killing Policy</head>
<table cols="2"> <row role="label"> <cell>Year</cell>
<cell style="align(center)">Ave. Score (All States)</cell>
</row>
<row>
<cell>1970</cell>
<cell>0.98</cell>
</row>
<row>
<cell>1972</cell>
<cell>1.06</cell>
</row>
<row>
<cell>1974</cell>
<cell>1.06</cell>
</row>
<row>
<cell>1976</cell>
<cell>1.19</cell>
</row>
<row>
<cell>1978</cell>
<cell>1.19</cell>
</row>
<row>
<cell>1980</cell>
<cell>1.31</cell>
</row>
<row>
<cell>1982</cell>
<cell>1.65</cell>
</row>
<row>
<cell>1984</cell>
<cell>1.92</cell>
</row>
<row>
<cell>1988</cell>
<cell>2.06</cell>
</row>
<row>
<cell>1990</cell>
<cell>2.15</cell>
</row>
<row>
<cell>1992</cell>
<cell>2.27</cell>
</row>
<row>
<cell>1994</cell>
<cell>2.67</cell>
</row>
<row>
<cell>1998</cell>
<cell>3.25</cell>
</row>
<row>
<cell>2000</cell>
<cell>3.25</cell>
</row>
<row>
<cell>2002</cell>
<cell>3.75</cell>
</row>
</table>
<note xml:id="t2n1"> <p>S<hi rend="smallCaps">ource</hi>: Updated from Schroedel (2000) based on a search of individual state statutes.</p> </note>
</figure>
<p>
<list xml:id="l3" style="custom">
<item n=" ">
<p>Policy-Learning Hypothesis 1: <hi rend="italic">States will be more likely to change fetal killing policies over time</hi>.</p>
</item>
</list>
</p>
<p>In addition to the learning that should take place between earlier and later adopters generally, states should learn most from the experiences of other states. Innovation in one state should prompt innovation in nearby states, as they naturally try to keep up (<ref type="bibr" target="#b5">Berry and Berry, 1990</ref>).</p>
<p>
<list xml:id="l4" style="custom">
<item n=" ">
<p>Policy Learning Hypothesis 2: <hi rend="italic">States with more neighbors changing fetal killing policies in previous years will be more likely to change fetal killing policies than states with fewer neighbors changing fetal killing policies</hi>.</p>
</item>
</list>
</p>
<p>In addition to the four research hypotheses above, two other factors are expected to explain fetal killing policy innovation, and they are also included in the model as control variables. These factors are pro-life group strength and citizen and government liberal ideology (<ref type="bibr" target="#b6">Berry et al., 1998</ref>). Although pro-choice groups have opposed fetal homicide policies in any form that identifies the fetus rather than the pregnant woman as the victim of a crime, pro-life groups have been supportive of the most comprehensive policies (<ref type="bibr" target="#b11">CRLP, 1996</ref>). In states where the mobilization of pro-life groups is stronger, such as states with larger numbers of residents affiliating with fundamentalist Protestant churches, we would expect policies to be more comprehensive.</p>
<p>Government and citizen liberalism is also included in the model to control for the ideological tendencies of the legislature and state residents. States with liberal government officials and citizens are expected to more strongly oppose changing to a more comprehensive fetal homicide policy because these are the fetal killing policies most opposed by pro-choice groups, while states with more conservative government officials and citizens should change policies to be more comprehensive.</p>
</div>
<div xml:lang="en" xml:id="ss2">
<head>The Model</head>
<p>The model tested here uses time-series cross-sectional data from <_date>1970</_date>-2002. The data set begins with the year the first fetal killing policy was adopted in the "modern" era (five states had fetal manslaughter policies going back to the 19th and early 20th centuries). The data are modeled using logistic regression with robust standard errors. Robust standard errors are appropriate for time-series cross-sectional data because they are adjusted to account for the effects that this type of data (primarily autocorrelation and heteroskedasticity) have on the standard errors. When working with time-series cross-sectional data with a binary dependent variable, as we have in this case, <ref type="bibr" target="#b4">Beck, Katz, and Tucker (1998)</ref> urge the direct modeling of time using a grouped duration model with temporal dummies or the natural spline to account of the effects of time. Unfortunately, one of the problems with the model that they point out is that it cannot accommodate independent variables that vary over time, but not states. This is because the variable would be highly correlated with the temporal dummies. For this reason, the robust standard errors are used, but not the grouped duration modeling, as one of the key hypotheses (overall national media attention) varies only with time, not across states. The model does incorporate time in the sense that Policy Learning Hypothesis 1 incorporates the year of a policy change into the model. This will be explained further below.</p>
<div xml:lang="en" xml:id="ss3">
<head>Dependent Variable</head>
<p>The dependent variable is change in fetal killing policy. If a state made a change to its policy in a given year, the variable is coded 1 for that year; if it did not change its policy, the variable is coded as 0 for that year. Change in policy is the key concept to measure here because this study aims to explain what prompts change in policy, not just the initial adoption or the overall comprehensiveness of the policy. Tables showing comprehensiveness of policies are included to demonstrate the range in policies and the types of changes being made, but the key is that the state made a change. In the case of fetal killing policy, all changes were toward a more comprehensive fetal killing policy as measured by Schroedel's index (see <ref type="table" target="#t1 #t2">Tables 1 and 2</ref>). As <ref type="table" target="#t2">Table 2</ref> demonstrates, several states made changes to more comprehensive policies, so the variable is substantively different from a variable that just captures first adoption of the policy. This will allow a test of whether the independent variables influence subsequent changes in state policy, in addition to initial adoptions of the policy. Although each change that was made increased the comprehensiveness of the policy, using the full 10-point scale of Shroedel as the dependent variable would be problematic. Schroedel uses her index as an independent variable rather than as a dependent variable. As a dependent variable, the index would not be normally distributed. Some scores are more likely than others. Further, the focus of policy-learning and agenda-setting theories tested here are on what prompts change in a policy, rather than what prompts states to have different levels of comprehensiveness in their policies. The descriptions and rationales for the measures of the independent variables are explained below.</p>
</div>
<div xml:lang="en" xml:id="ss4">
<head>Independent Variables</head>
<p>Media attention is measured through a content analysis of the Lexis/Nexis U.S. news and wire articles for each year, searching for the terms, "feticide,""fetal homicide," fetal manslaughter,""unborn victims," and "fetal killing." For the state court decision variable, if a state court decision applying the "born-alive" rule in a fetal killing case occurred in a state in a given year, it is coded 1, as is each following year in which the decision stands for that state. For state years where no court decision on the "born-alive" rule has been handed down, the variable is coded 0 (see <ref type="table" target="#t3">Table 3</ref>). The passage of time for Policy Learning Hypothesis 1 is captured simply by the year. As year increases, policy changes should become more likely, due to the interaction taking place in the policy-learning process. For Policy Learning Hypothesis 2, the number of neighboring states that have changed fetal killing policies in a previous year is included in the model. Since changes do not take place very often, neighbors are counted if they have made a change, not just in the previous year, but in any year up to that point since the start of the data set (1970).</p>
<figure type="table" xml:id="t3">
<head>&#x2028;State Murder and Negligent/Vehicular Homicide Cases Applying the Born-Alive Rule Between 1970 and 2002</head>
<table cols="4"> <row role="label"> <cell>State</cell>
<cell style="align(center)">Event</cell>
<cell style="align(center)">Adoption of&#x2028;Legislative&#x2028;Policy</cell>
<cell style="align(center)">Case Citation</cell>
</row>
<row>
<cell>AZ</cell>
<cell>1991</cell>
<cell>1992</cell>
<cell> <hi rend="italic">Vo v. Superior Court</hi>, 836 P.2d 408 (Ariz. Ct. App. 1992)</cell>
</row>
<row>
<cell>CA</cell>
<cell>1969</cell>
<cell>1970</cell>
<cell> <hi rend="italic">Keeler v. Superior Court</hi>, 2 Cal.3d 619, 87 Cal. Rptr. 481, 470 P.2d 617 (Cal. 1970) (viable fetus)</cell>
</row>
<row>
<cell>CA</cell>
<cell>1975</cell>
<cell/>
<cell> <hi rend="italic">People v. Smith</hi>, 129 Cal. Rptr. 498 (Ct. App. 1976) (nonviable fetus)</cell>
</row>
<row>
<cell>IL</cell>
<cell>1978</cell>
<cell>1987</cell>
<cell> <hi rend="italic">People v. Greer</hi>, 79 Ill.2d 103, 402 N.E.2d 203 (1980)</cell>
</row>
<row>
<cell>KS</cell>
<cell>1987</cell>
<cell>1995</cell>
<cell> <hi rend="italic">State. v. Green</hi>, 781 P.2d 678 (Kan. 1989)</cell>
</row>
<row>
<cell>KS</cell>
<cell>1988</cell>
<cell>1995</cell>
<cell> <hi rend="italic">State v. Trudell</hi>, 243 Kan. 29, 755 P.2d 511 (1988)</cell>
</row>
<row>
<cell>KY</cell>
<cell>1983</cell>
<cell/>
<cell> <hi rend="italic">Hollis v. Commonwealth</hi>, 652 S.W.2d 61 (Ky. 1983)</cell>
</row>
<row>
<cell>LA</cell>
<cell>1974</cell>
<cell>1989</cell>
<cell> <hi rend="italic">State v. Gyles</hi>, 313 So. 2d 799 (La. 1975)</cell>
</row>
<row>
<cell>NC</cell>
<cell>1986</cell>
<cell/>
<cell> <hi rend="italic">State v. Beale</hi>, 324 N.C. 87, 376 S.E.2d. 1 (1989)</cell>
</row>
<row>
<cell>MI</cell>
<cell>1980</cell>
<cell>1999</cell>
<cell> <hi rend="italic">People v. Guthrie</hi>, 97 Mich.App. 226, 293 N.W.2d 775 (1980), <hi rend="italic">leave to appeal denied</hi>, 417 Mich. 1007, 334 N.W.2d 616 (1983)</cell>
</row>
<row>
<cell>UT</cell>
<cell>1978</cell>
<cell>1994</cell>
<cell> <hi rend="italic">State v. Larsen</hi>, 578 P.2d. 1280 (Utah 1978)</cell>
</row>
</table>
<note xml:id="t3n1"> <p>N<hi rend="smallCaps">ote</hi>: "Event" is the year that the crime or accident took place. "Adoption" is the year the state adopted a legislative fetal killing policy. If a year is not listed, the state did not adopt a policy after the ruling and prior to 2002.</p> </note>
<note xml:id="t3n2"> <p>S<hi rend="smallCaps">ource</hi>: Americans United for Life (2003).</p> </note>
</figure>
<p>The measure of state ideology used here is the measure of government and citizen liberalism developed by <ref type="bibr" target="#b6">Berry et al. (1998)</ref>. This measure is well suited for the analysis conducted here because it is calculated for each year <_date>from 1960 to 1999</_date>. <_ref type="bibr">Berry et al.</_ref> argue that an annual measure is needed because both citizen and government ideology are less stable over time than previously thought. Although others argue that ideology is mostly stable for all but a few states (<ref type="bibr" target="#b9 #b7">Brace et al., 2007), Berry et al. (2007</ref>) make a strong case that their measure captures policy mood, which does show changes over time and should be a predictor of the types of policies a state will adopt. States with more liberal citizens and government officials are expected not to change fetal killing policies. This is because pro-choice forces oppose changing fetal killing policies to be stricter, as they fear they will conflict with abortion rights by establishing personhood for the fetus. These forces tend to be allied with more liberal legislators and have more liberal citizen members in general.</p>
<p>The measure of pro-life mobilization in support of fetal homicide policies is the percentage of the state population that is fundamentalist Protestant. In the absence of direct measures of pro-life support, this measure has been used in previous research and was collected by the <_orgName>Glenmary Research Center</_orgName> for the years 1980, 1990, and 2000 (<ref type="bibr" target="#b21 #b10 #b16">Quinn et al., 1982; Bradley et al., 1992; Jones et al., 2002</ref>).</p>
<p>As all scholars of political institutions know well, it may take time for states to react to forces prompting change. For this reason, the independent variables are all lagged, except for the percentage of fundamentalist Protestants (this variable is only measured by decade). This means that the variables are measured in the year previous to the dependent variable of policy change. This allows time for legislatures to react. Although California passed its fetal homicide law one day after the <ref type="bibr" target="#b17"> <hi rend="italic">Keeler</hi> </ref> decision in <_date>1970</_date>, this is not expected to be the norm.</p>
<p>Obviously, some of the independent variables may be correlated with one another, so multicollinearity diagnostics were run to see if the variables could safely be included in one model. Although the percentage of residents that are fundamentalist Protestants has a regional pattern (highest in the South), there were no problems with this variable and the neighboring states variable, which also has a regional aspect. Citizen and government ideology, while correlated (0.60), did not show any problems in the diagnostics. Indeed, the variance inflation factors (VIF) were close to 1, with only one reaching 2.5 (10 is considered a problem). There was one case where a problem arose. In the case of national media attention, the variables do not vary within states, only from year to year. Including it with the variable for year could be problematic, so these two variables are run in separate models.</p>
</div>
</div>
<div xml:lang="en" xml:id="ss5">
<head>Results</head>
<p>The results of the analysis of the effects of the independent variables on fetal killing policy change are shown in <ref type="table" target="#t4">Table 4</ref>. Model 1 includes the agenda-setting variable, national media attention, while Model 2 includes the policy-learning variable, year. The other results for the other variables in the two models are very similar.</p>
<figure type="table" xml:id="t4">
<head>&#x2028;Change in State Third-Party Fetal Killing Laws from 1970-2002</head>
<table cols="3"> <row role="label"> <cell>Determinants</cell>
<cell style="align(center)">Model 1</cell>
<cell style="align(center)">Model 2</cell>
</row>
<row>
<cell> <hi rend="italic">Model Variables</hi> </cell>
</row>
<row>
<cell role="label">Overall issue attention (Lexis/Nexis news articles)</cell>
<cell>0.08<hi> <ref type="note" target="#t4n1"> <hi rend="superscript">*</hi> </ref> </hi> </cell>
<cell/>
</row>
<row>
<cell>(0.04)</cell>
<cell/>
</row>
<row>
<cell role="label">State court decision</cell>
<cell>2.36<hi> <ref type="note" target="#t4n1"> <hi rend="superscript">*</hi> </ref> </hi> </cell>
<cell>2.4<hi> <ref type="note" target="#t4n1"> <hi rend="superscript">*</hi> </ref> </hi> </cell>
</row>
<row>
<cell>(1.08)</cell>
<cell>(1.10)</cell>
</row>
<row>
<cell role="label">Neighbors</cell>
<cell>−0.04</cell>
<cell>−0.15</cell>
</row>
<row>
<cell>(0.21)</cell>
<cell>(0.23)</cell>
</row>
<row>
<cell role="label">Year</cell>
<cell/>
<cell>0.06<hi> <ref type="note" target="#t4n1"> <hi rend="superscript">*</hi> </ref> </hi> </cell>
</row>
<row>
<cell/>
<cell>(0.03)</cell>
</row>
<row>
<cell> <hi rend="italic">Control Variables</hi> </cell>
</row>
<row>
<cell role="label">Government ideology</cell>
<cell>−0.02<hi> <ref type="note" target="#t4n1"> <hi rend="superscript">*</hi> </ref> </hi> </cell>
<cell>−0.02<hi> <ref type="note" target="#t4n1"> <hi rend="superscript">*</hi> </ref> </hi> </cell>
</row>
<row>
<cell>(0.04)</cell>
<cell>(0.01)</cell>
</row>
<row>
<cell role="label">Citizen ideology</cell>
<cell>0.03</cell>
<cell>0.03</cell>
</row>
<row>
<cell>(0.02)</cell>
<cell>(13)</cell>
</row>
<row>
<cell role="label">Percent fundamentalist Protestant</cell>
<cell>0.03</cell>
<cell>0.01</cell>
</row>
<row>
<cell>(0.02)</cell>
<cell>(0.01)</cell>
</row>
<row>
<cell> <hi rend="italic">N</hi> </cell>
<cell>1,535</cell>
<cell>1,535</cell>
</row>
<row>
<cell>Wald chi sq.(6)</cell>
<cell>20.14<hi> <ref type="note" target="#t4n2"> <hi rend="superscript">**</hi> </ref> </hi> </cell>
<cell>18.09<hi> <ref type="note" target="#t4n2"> <hi rend="superscript">**</hi> </ref> </hi> </cell>
</row>
</table>
<note xml:id="t4_note46"> <p> <emph> <span> <hi rend="superscript">*</hi> <hi rend="italic">p</hi>&lt;0.05</span> </emph> ; <emph> <span> <hi rend="superscript">**</hi> <hi rend="italic">p</hi>&lt;0.01; <hi rend="superscript">***</hi> <hi rend="italic">p</hi>&lt;0.001.</span> </emph> &#x2028; N<hi rend="smallCaps">ote</hi>: The analysis is on 48 states. Hawaii and Alaska are not included due to missing data. All independent variables are lagged except for Percent fundamentalist Protestant, which only varies by decade. All other independent variables are coded annually. </p> </note>
</figure>
<p>The findings provide support for three of the four research hypotheses put forth in this study. Both agenda-setting hypotheses are supported and one of the policy-learning hypotheses is also supported. As shown in <ref type="table" target="#t4">Table 4</ref>, all statistically significant variables are significant at the 0.05 level. The overall model is significant at the 0.01 level.</p>
<p>The media attention variable (Model 1 in <ref type="table" target="#t4">Table 4</ref>) is statistically significant, and in the expected direction. As media coverage in a given year increases, the likelihood that a state will change its fetal killing policy also increases in the next year. Additionally, the state court decision hypothesis is supported by the analysis.</p>
<p>Of the two policy-learning variables, year is statistically significant (Model 2 of <ref type="table" target="#t4">Table 4</ref>), but neighboring state policy change is not. As the policy-learning theory predicts, states are more likely to make changes to their fetal killing policies (thus making them more comprehensive in this case) in later years, rather than earlier years in the time period observed (1970-2002). This finding is not surprising based on the results demonstrated in <ref type="table" target="#t2">Table 2</ref> showing an increasing number of fetal killing laws over time.</p>
<p>In terms of the control variables, the state percentage of fundamentalist Protestants was not in the expected direction and did not reach statistical significance. This may be due to the fact that the variable is a proxy for pro-life support, but it may be showing a regional effect since the highest percentages of fundamentalist Protestants are in the South, which has fewer fetal killing policies than the Midwest and Great Plains states. A more direct measure (if one can be obtained) may be helpful in the future. Citizen and government elite ideology were also included to account for the fact that the policy may be more appealing to conservative states due to its pro-life support and the opposition of many pro-choice groups to these laws (<ref type="bibr" target="#b23">Schroedel, 2000</ref>). Interestingly, government ideology is statistically significant and in the expected direction, while citizen ideology is not significant or in the expected direction. States with a higher governmental liberalism score (as measured by <_ref type="bibr">Berry et al.</_ref>) are less likely to change fetal killing policies. This was expected, but it is unclear why citizen ideology is not significant. The two variables are correlated (0.60), but running them in separate models did not change the results and the VIF factors for all variables, including these two, were very low.</p>
</div>
<div xml:lang="en" xml:id="ss6">
<head>Conclusion</head>
<p>The model and results presented in this study contribute to scholarly knowledge of the state policy process by improving our understanding of why states innovate and how policies change as they diffuse through the states. The most significant finding is that legislatures do respond to increased media attention in the case of fetal killing policies by changing to a stricter fetal killing policy. Additionally, state legislators respond to unpopular state court decisions by changing their policies. This study provides evidence that legislators do not innovate in a vacuum, but react to actions of other branches of government. Previous studies of state policy innovation and diffusion have focused on a state's tendency to innovate, but have not typically incorporated the concepts developed in the agenda-setting literature.</p>
<p>Further evidence for policy learning is also provided. States are more likely to change policies as time goes on and more states have experience with the fetal killing policies. To truly understand the process by which state officials learn about fetal killing policy from one another, further research including case studies would be required. The quantitative variable in this study provides us with the overall pattern, which can be further investigated.</p>
<p>Another interesting finding is that the more liberal a state's government officials are, the less likely they are to change their fetal killing policy. This makes sense in the context of state adoptions of fetal homicide and other fetal killing policies because no state has ever changed its policy to make it less comprehensive. All changes have been toward policies that mete out a harsher penalty to those who commit this offense, or to decrease the gestational age of the fetus at which the law applies, as pro-life groups have advocated. The fact that citizen ideology is not significant may seem troubling in terms of democratic theory. One would hope policies would reflect citizen preferences. However, an issue like fetal killing policy, while important to elites and pro-life and pro-choice activists, may not be as salient to the public as the abortion issue itself and other high-profile morality policies. Instead, since the issue may be less known to the public, the media and state courts have a major role to play in bringing attention to fetal killing policy and getting the issue onto the agenda.</p>
<p>This study looks only at fetal killing policy, but the objective of this project was to demonstrate the value of adding agenda-setting variables to models of state policy change in addition to traditional diffusion and internal state variables. With both agenda-setting variables showing significant effects on policy change, the model is ready to be tested in other policy settings by scholars seeking to model similar change in other contexts.</p>
</div>
</body>
<back>
<div type="fn-group">
<note place="inline" xml:id="fn1"> <p> <hi rend="superscript">*</hi>Direct correspondence to M. R. Oakley, Mount St. Mary's University, Department of Political Science, 16300 Old Emmitsburg Rd., Emmitsburg, MD 21117 〈<email>oakley@msmary.edu</email>〉 All data and coding information will be shared with anyone interested in obtaining it for replication of the analysis. I thank the members of the Mount <_placeName>St. Mary</_placeName>'s University Faculty Writing Group, Trudy Steuernagel, Karen Mossberger, Caroline Tolbert, Christopher Mooney, Francis Stokes Berry, and anonymous reviewers for helpful comments on earlier versions of this article.</p>
</note>
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