States as Policy Laboratories: Experimenting with the Children s Health Insurance Program

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1 States as Policy Laboratories: Experimenting with the Children s Health Insurance Program Craig Volden * University of Michigan and Ohio State University Abstract For more than a decade, scholars of policy diffusion across the states have relied on state-year event history analyses. Such work has been limited by: (1) focusing mainly on neighbor-to-neighbor diffusion paths, rather than other similarities across states; (2) neglecting the role of the success or failures of policies in their diffusion; (3) studying singular specific policy adoptions rather than the choice among policy variants; and (4) setting aside questions about how diffusion mechanisms vary across different policies and different political processes. This paper proposes the alternative approach of dyad-year event history analysis, commonly used in international relations, and applies it to the study of policy diffusion in Children s Health Insurance Program from This approach uncovers strong evidence of the emulation of states with similar political, demographic, and budgetary characteristics, and those with successful policies. Moreover, the diffusion mechanisms differ substantially across different policy types and political processes. * Prepared for the 2003 Summer Political Methodology Meetings, Minneapolis, MN. Thanks to Chuck Shipan, Julie Cullen, Jake Bowers, and seminar participants at Harvard University, the University of Michigan, the Midwest Political Science Association Meetings, and the Robert Wood Johnson Aspen Meetings for helpful comments, to Tracy Finlayson for research assistance, and to the Robert Wood Johnson Foundation for their generous support. Please send questions and comments to: Craig Volden, Robert Wood Johnson Scholar, School of Public Health II, University of Michigan, 109 Observatory, Ann Arbor, MI , or

2 States as Policy Laboratories: Experimenting with the Children s Health Insurance Program Scholars of federalism frequently discuss the costs and benefits of policy formation at the state and local level, as opposed to the national level (Elazar 1987, Peterson 1995, Kincaid 2001). Among the potential benefits of setting policy at the state or local level are the ability to address heterogeneous needs, greater responsiveness to citizens, efficiency gains from competition among governments (Tiebout 1956), and the possibility of learning from the policy experiments taking place in other jurisdictions. As Louis Brandeis wrote in his oft-cited opinion: It is one of the happy accidents of the federal system that a single courageous state may, if its citizens choose, serve as a laboratory, and try novel social and economic experiments without risk to the rest of the country. 1 Over the past three decades, political scientists have examined countless state programs in attempts to determine whether states are actually serving as policy la boratories, abandoning failures and adopting successful policies from similar states. Recently, such work has focused on whether policies diffuse across states, and those diffusion studies almost universally base their conclusions on whether states adopt policies that are present in geographically neighboring states. This is problematic. Policymakers today are not confined to communications within their geographical regions, and might prefer to compare policies with states outside their region that share other similar features, be they political, economic, or demographic. Beyond this consideration, little attention has been paid to whether the policies diffusing across states are actually successful at addressing the policy problems states face. States can only be thought of as effective laboratories of democracy if beneficial policies are emulated and failures are abandoned. Moreover, the effectiveness of learning across states may vary by policy area and by different political processes within states. To explore these considerations, this paper proposes an alternative to the standard state-year event history analysis used widely in diffusion studies. I argue that the dyad-year event history analysis, 1

3 common in studies of wars and trade disputes in international relations, can be adapted to examine the flow of public policies from one state to another. When a state adopts a policy present in another state, the dependent variable under this approach captures this as a potential emulation for this pair (or dyad) of states. Independent variables capture the relationship between these pairs of states, not only whether or not they are geographic neighbors, but also whether they have similar political, demographic, budgetary, and other characteristics. The success of the policies in the states can also be examined in this approach to determine whether states indeed act as policy laboratories, abandoning failing policies and adopting successes. The use of this approach is demonstrated here with an examination of the Children s Health Insurance Program (CHIP) from 1998 through When Congress established the CHIP program in 1997, it gave the states an unprecedented degree of flexibility in how to use the federal matching grants. States experimented extensively with variations in how to insure poor children, from using a Medicaid expansion model to setting up a separate state program, from actively targeting a small number of the neediest children to extending broad coverage, from instituting cost sharing mechanisms and waiting periods to smoothing the transition from Medicaid to CHIP. Some of these policies worked better than others. And, over the five years since their initial adoption, the states have amended their programs more than 100 times. In the following section, I discuss the limitations of the current approach to diffusion analysis and how the dyad-year approach can help researchers overcome these obstacles. I then turn to an empirical examination of numerous amendments that states have made to their CHIP programs, in order to demonstrate the usefulness of this alternative approach and to evaluate the effectiveness of states as policy laboratories. 1 New State Ice Co. v. Liebmann, 285 U.S. 262, 311 (1932). Tarr (2001) explains how Brandeis s opinion in this case was more greatly influenced by his support of the Scientific Management movement than by his views on federalism. 2

4 Approaches to Studying Policy Diffusion across the States The number of studies of the diffusion of innovations has soared in recent decades, to more than 4000 books and articles in fields ranging from anthropology and agricultural sociology, to medicine, marketing, and management (Rogers 1995). In political science, the study of policy innovations across the American states was given a spark by the work of Walker (1969). His study examined 88 policies adopted across the states to create an index of innovativeness and to uncover regional diffusion patterns. Gray (1973) followed this work with an examination of national diffusion patterns across three main policy areas, noting that the speed and nature of diffusion varied by policy area. These diffusion studies were set in contrast to previous work, such as that of Dye (1966), which tried to explain state policy adoptions based on factors internal to each state. Numerous studies followed each of these two approaches, enhancing scholars understandings of state politics and policy choice. Berry and Berry s (1990) examination of state lottery adoptions was a major breakthrough in this line of research. Their innovation came in fusing together the internal determinants tradition with the diffusion approach through the use of the state-year event history analysis (EHA). This approach, which has now become standard in the study of policy diffusion across states, has a variety of permutations. Its most common form is that of a limited dependent variable model, often studied with the use of a logit analysis. The unit of observation is the state-year. Each state that does not have the policy under examination in a given year is entered in the dataset. The dependent variable for this state-year takes a value of zero if the state does not adopt the innovation, and one if it does. At the point when the state adopts the innovation, it is removed from the dataset for future years. The model is thus examining the hazard rate, or likelihood that the state will adopt the innovation. The independent variables include all sorts of internal determinants of whether a state has the conditions that would lead it to adopt the change. Models also include features of diffusion research, typically through a variable capturing the number or proportion of the state s neighbors that have adopted the innovation in the past. A positive coefficient on this Neighbors variable is commonly considered to show the diffusion of the innovation from one neighbor to the next. The absence of such an effect is purported to show that internal factors, rather than 3

5 diffusion, are most important in determining policy choices. This state-year event history analysis approach became widely adopted, as more and more scholars took an interest in policy diffusion across states, localities, and countries. 2 Despite its broad adoption, the standard use of this sort of event history analysis to study diffusions across states has faced a number of limitations. First, scholars using this approach have tended to focus on a quite narrow mode of diffusion the imitation of geographically contiguous neighbors often with mixed evidence of neighborhood effects (see Berry and Berry 1992, Hays and Glick 1997, Mintrom 1997, Stream 1999, Mooney 2001, among many others). Yet, with extensive communication across policy networks, there may be a variety of diffusion mechanisms. States may define their neighbors more broadly as any states that share similar political, economic, budgetary, or demographic characteristics (Case, Hines, and Rosen 1993). As Lutz (1986) and Gray (1994) point out, with the greater flow of information available more recently, states may be beginning to learn from others beyond their immediate neighbors. Even Walker (1969, p. 894), who was focused on regional diffusion patterns, recognized: As the American political system has developed, an increasing number of specialized communication systems have been created which cut across traditional regional lines and bring officials from many different regions into contact with each other and with federal and local officials, journalists, academic experts, and administrative consultants. Second, the standard state -year EHA approach has not led scholars to focus on the role of policy success in the diffusion process. Theoretically, states would emulate policies found to be successful at addressing comparable problems across states. A state would not copy its neighbor if the policy were found to lead to severe adverse consequences. If states are to serve as effective policy laboratories, it is more important that they adopt successful policies wherever such policies may be found, than that states simply adopt whatever their neighbors are doing. Although there has been much written about the role of 2 Although there remain other approaches to studying policy diffusion, the state-year event history analysis method has become the most widely accepted. See Berry (1994b) and Berry and Berry (1999) for a summary and assessment of alternative methods. 4

6 success in policy diffusion, measures of success have not been incorporated into diffusion analyses across states (see Berry and Berry 1999 for a summary). 3 Third, diffusion studies using the state-year EHA approach have been limited to work focusing on whether single innovations are adopted or not. When a researcher s goal is to study the adoption of lotteries, school choice, HMO reform, and the like, each in isolation, this approach is entirely appropriate. Often, however, complex policy puzzles can be addressed in many different ways. For example, are education dollars best spent on decreasing class size, updating buildings and equipment, or training teachers with new techniques? Which model is most effective in the transition from welfare to work? Or, if states need to contain costs in the CHIP programs, should they reduce the size of their eligible population, eliminate particular services, add premiums and copayments, or impose waiting periods? For scholars interested in how these competing models are assessed and politically diffused across states, a focus solely on the overall adoption of welfare-to-work or CHIP is insufficient. Finally, since the work of Gray (1973), scholars have believed that diffusion patterns may differ substantially across policy areas, but no strong typology has been developed because it is problematic to compare econometric results across separate models. For example, Mooney and Lee (1995, 1999) have raised the possibility that adoption of morality policies may follow different patterns than the adoption of other policy types. However, outside of a method that allows the inclusion of multiple policy changes within a single model, it is difficult to determine whether the results may differ systematically or for atheoretical reasons perhaps simply because the variables capturing the states internal determinants are better specified in one policy area than in another. Similarly, diffusion mechanisms may vary with different political processes and conditions underlying policy changes in the states (Berry 1994a). Some of these limitations can be overcome within the standard state-year EHA approach. For example, beyond simple neighbor effects, scholars have incorporated policy entrepreneurs (Mintrom 1997), federations of voluntary groups (Skocpol et al 1993), and professional associations (Balla 2001) 3 Soule (1999) raises an example in which an unsuccessful innovation was broadly imitated, but does not analyze its diffusion using the EHA approach. 5

7 into the EHA framework. Overcoming other limitations may be impossible or may require substantial alterations of the standard approach. As an alternative, I argue that these four limitations can be overcome by adjusting our frame of reference from the state-year unit of analysis to the dyad-year. The dyad-year approach has been used extensively in international relations (see, for example, Bueno de Mesquita and Lalman 1992; Beck, Katz, and Tucker 1998; and Reiter and Stam 2003). Each dyad-year observation consists of a pair of countries in a given year. The dependent variable captures whether or not the pair were at war in that year. Explanatory variables include such features as whether the countries were both democracies, whether they were geographic neighbors, and the degree of trade between the two. Thankfully, the U.S. states have not been at war with one another in over a century. But they do emulate one another s policies. And the dyad-year event history analysis approach can be used to uncover conditions under which such emulation takes place. For example, when Arizona adopts a policy change, such as adding a fifteen-dollar CHIP monthly premium, Florida (and other states) may already have this policy component. If so, the dependent variable for the Arizona-Florida dyad for this year takes a value of one as a potential emulation. Independent variables capture whether the pair of states are geographic neighbors, whether both have unified Republican governments, whether they share similar demographics, and so on, as well as whether each state s policy has been successful at addressing the health insurance needs of poor children in the state. In addition to using this approach to determine whether similar and successful states are more likely to be emulated, researchers can uncover whether diffusion mechanisms vary for different types of policy changes and for different political processes leading to policy adoptions. Before turning to the specifics of how such an analysis is carried out, it is useful to describe the types of policy changes that are found within the CHIP program. 6

8 Dimensions of the CHIP Program As part of the 1997 Balanced Budget Act, Congress developed the Children s Health Insurance Program as a series of grants to the states to provide health insurance coverage to children from families with incomes above the Medicaid thresholds but below about 200% of the federal poverty level (FPL). 4 The program was set up through a series of capped matching grants, with the federal government paying about 65-85% of the program s cost, with a higher matching rate for states with lower per capita incomes. All states found the matching grants offered by the federal government through the CHIP program too attractive to pass up. By the end of 1999, each state had sent a proposal for federal funding to the Health Care Financing Administration, now renamed the Centers for Medicare and Medicaid Services (CMS), which oversees the CHIP program. Initial state plans for providing health insurance coverage to poor children differed on a number of dimensions, as highlighted in Table 1. One major decision for the states was whether to adopt the CHIP program as an expansion to their existing Medicaid coverage, as a separate state program, or as some combination of the two approaches. 5 Half of the states initially adopted CHIP as a Medicaid expansion; sixteen states established a separate program; nine used a combination of Medicaid expansion and separate elements. [Insert Table 1 about here] States set eligibility standards based on the family s income relative to the federal poverty level. Table 1 shows the maximum eligibility levels in initial state plans, covering children up to 133% FPL in some states, ranging up to 300% FPL in others. 6 Programs also differed in the benefits offered to enrollees. Over half of the states offered the same benefits as those available through the Medicaid program, while the rest relied on benefits offered to state employees, offered in the state s largest health maintenance organization (HMO), or meeting other statewide benchmarks. In hopes of dissuading people 4 The actual coverage of the grants depends on each state s previous Medicaid policy, which varied across the s tates in 1997 based on states Medicaid waivers. 5 Under the combined approach, children meeting particular criteria (such as being below a certain percent of FPL) would enroll in the Medicaid part of CHIP, with those above the threshold enrolling in the separate program. 7

9 from dropping their private (often employer-sponsored) insurance and immediately enrolling their children in the CHIP program, many states imposed a waiting period from the time a family drops its private insurance to the time that the children can be enrolled in CHIP. The waiting period extended up to a year in Alaska, New Jersey, New Mexico, and Virginia. Also varying substantially across states were the cost-sharing measures utilized. Nearly half of the states adopted some form of cost sharing, typically requiring both a monthly premium and copayments for doctor visits and prescription medications. Premium levels often varied within each state based on family size and income levels. The highest level was in Missouri, where families above 225% FPL were offered health insurance with a $65 per month premium. Far more typical were premiums in the range of ten to twenty dollars per child per month. Copayments, often in the range of five to ten dollars per visit or per prescription, are common across cost-sharing states. If states are to function as policy laboratories, a number of alternatives should be initially attempted across the states, followed by the diffusion of successful models. As should be clear from Table 1, the states appeared to be experimenting with many different policies to insure the target population. A variety of political and economic arguments help explain these initial state programs, often with a focus internal to each state s own circumstances (Beamer 1999; Goggin 1999; and Ullman and Hill 2001). As one state CHIP official put it: Initially, at the time of implementation, all the states were in the same boat. We were scrambling to formulate policies. Over time, we started looking to other states, what they were doing with waiting periods, with outreach. 7 Indeed, over the first five years of the program, the states amended their programs many times. As a federal grant program, CHIP required states to submit their proposals for major changes to CMS, where the proposals were evaluated, accepted or rejected, or sent back for revisions needed to meet specific federal criteria. By the end of 2001, more than one hundred state amendments had been 6 The TennCare program in Tennessee, adopted through a Medicaid waiver, allows all children to take part in the state s public health insurance program. Tennessee s cost sharing measures provide the main limitation on program size. 7 Interview with author. 8

10 approved by CMS. Every state except South Carolina had sent an amendment proposal to the federal government. Most adopted two or three amendments; Florida adopted seven. Forty-nine of these amendments substantively changed the state policies listed in Table 1 and are examined here for evidence of diffusion and policy learning. 8 Often these changes altered many program characteristics simultaneously, such as when Mississippi expanded its program in 1999 to 200% FPL while adding copayments. Expe rimentation and Policy Diffusion within CHIP I think you might be able to term it a bit as an experiment it was kind of an experiment to take a look at some different areas. I think [legislators] wanted, at least for the first two years, to see how the program would operate, to see what problems there were or how well it worked before they moved on to other things, like expanding the program or changing the asset level in the Medicaid program. 9 Based on Table 1 and the scores of subsequent policy changes, there should be little doubt that the states are experimenting with their Children s Health Insurance Programs. Questions remain about whether the states are modifying their programs based on their own experiences, internal to each state, or whether they are learning from one another. Moreover, if we are observing policy diffusion, how is it occurring? Which states are being emulated and why? To answer these questions, I here examine policy changes across the states from The time period is limited by lack of data availability for many independent variables in The types of innovations examined are those in Table 1 changes in the policy type, maximum eligibility, benefits, waiting period, premiums, and copayments. For each of these, data were available regarding whether the adopted innovation was present in each of the forty-nine other states at the time of its adoption. 8 Other changes were idiosyncratic to particular states (such as the provision of car safety seats in Texas), were required by the federal government (such as indication of compliance with final CHIP regulations or the exemption of Native Americans from cost sharing), or were beyond the scope of the present analysis (such as the use of premium assistance programs). They are not included in the analysis below. 9 Author interview with a state CHIP official. 9

11 The CHIP program is ideal for this study for a variety of reasons. As an intergovernmental grant program, CHIP requires states to request approval of all major program amendments. Therefore, unlike in most state policy areas where it is difficult to determine what is being attempted in each state over time, here a centralized record is available to researchers. Also, as a recent and politically salient program, CHIP provides a study of diffusion in an information-rich environment. State officials are advised about activities in other states by centralized and regional CMS offices. CHIP administrators communicate with CHIP directors in other states at national meetings and through CHIP-chat, an service provided by the National Academy for State Health Policy. They receive extensive studies from the federal government and from research organizations such as the Urban Institute. And organizations such as the National Governors Association highlight many states best practices under the CHIP program. Diffusion may indeed be taking place locally, but possibilities of other, broader diffusion patterns are available. To examine such possibilities, I collected data across the states on state similarities and differences, and conducted a series of semi-structured qualitative interviews with CHIP officials in fifteen states. 10 The bulk of the analysis presented here is based on the quantitative data work, with the interviews serving to guide the research direction. The dyad-year event history analysis examines every pair of states in each of the four years of this study, for a total of 9800 observations (50 states x 49 states x 4 years). Each of the fifty states may adopt policies in each year that imitate the policies in place in any of the other forty-nine states. Each pair of states is represented twice in each year under this approach, as it is reasonable to assume that, for example, policymakers in Iowa have a different view of New York than those in New York have of Iowa. The analysis therefore follows the directed dyad year approach of Reiter and Stam (2003), here examining the direction of the polic y flow. The dependent variable used throughout the analysis is dichotomous, taking a value of one for a dyad-year in which State A adopts a 10 States were selected in order to focus on policy amendments across states with varying degrees of legislative professionalism and population sizes. States included in the interviews are: Alabama, Arizona, Florida, Georgia, Idaho, Illinois, Iowa, Kentucky, Mississippi, North Carolina, North Dakota, Ohio, Pennsylvania, Vermont, and Virginia. 10

12 policy change that State B already has, and zero otherwise. 11 As described below, a logit analysis is thus carried out to determine the characteristics of the dyads that are more likely to serve as pathways for policy diffusions from one state to another. Unlike the analysis of war, in which we know with certainty whether the two countries are at war with one another, here we do not know with certainty whether there was any actual contact and learning between the state that adopts a policy change and the state (or states) that already have that policy. Rather, as with previous diffusion analyses, we are interested in discerning whether there are patterns, such as the adoption of policies that are present in neighboring states rather than those found randomly across the country. In this analysis, several such paths of diffusion are assessed through independent variables operationalized to test the following hypotheses. All variable sources and descriptive statistics are included in the appendix. Geographic Neighbors Hypothesis: A state is more likely to emulate its geographic neighbors than to emulate other states. Common throughout the literature on policy diffusion across states is the idea that states adopt policies that have been advanced in their neighboring states. Interviews with CHIP officials provide some evidence that neighbors policies may be important, as well as a conception of why they may matter more than others. A CHIP administrator in Mississippi spoke of that state s experience: 12 If I go to the legislature and I say, This is kind of what we re doing, and it s also being done this way, if I compare ourselves to Alabama and North Carolina I m going to be listened to a lot more than if I say, We re modeling California. 11 If a state s amendment contains many of the components in Table 1, the dependent variable is given a value of one if the majority of the components adopted by State A move in the direction of those present in State B. Changes in policy components that are continuous (such as the eligibility level, waiting period, or premium amount), rather than categorical (such as the program or benefit type), are coded as moving in the direction of another state only if those changes do not move beyond the policy in that state. For example, a state that extends its waiting period from three months to nine months is seen as moving in the direction of those states with a similar nine-month (or longer) waiting period, but not toward those with a six-month waiting period (because the policy change, while initially in the direction of a longer waiting period, exceeded the policy in such a state, and was thus unlikely to be based on learning from that state). 12 Interview with author. 11

13 Other states, however, downplayed the importance of geographic proximity. Another state CHIP official commented: 13 We ve been looking to other states, regardless of their region, just to find out what their experiences have been. To examine the possibility of diffusion from one neighboring state to the next, I create a Geographic Neighbor dummy variable, taking a value of one for dyads comprised of contiguous neighbors, and zero otherwise. 14 If states are emulating policies that are more prevalent in their neighboring states than in all states nationwide, this variable will have a positive coefficient, supporting the Geographic Neighbors Hypothesis. Political Similarity Hypothesis: A state is more likely to emulate those states controlled by the same political party or with the same ideological leanings. Beyond neighborhood effects, diffusion may take place across states based on strict partisan grounds. Republican governors may be more likely to take policy ideas from their fellow Republican governors, rather than from Democrats. State legislators may do the same. To capture this possibility, I include three independent variables in the analysis conducted here. The first, Same Unified Republican Government, is a dummy variable taking a value of one if the governor, house, and senate are all controlled by Republicans in both states in the dyad. Same Unified Democratic Government is similarly constructed based on Democratic control. 15 A positive coefficient is expected on each of these variables if the Political Similarity Hypothesis is to be supported. A third independent variable, Difference in Government Ideology, is constructed following the work of Berry et al (1998). They develop a measure of the government s ideology in each state based on a variety of factors, including partisan control in the state legislature, voting patterns of congressional representatives, and other ideological measures. Difference in Government Ideology is the absolute 13 Interview with author. 14 An alternative specification, coding each dyad for whether the two states were in the same broad region, was also examined, with effects that did not differ substantially from those for the Geographic Neighbor variable. 15 Alternative specifications, comparing the partisan control of the governor, house, and senate separately within the dyad, showed no significant diffusion patterns. 12

14 difference between the government s ideology score in the two states in the dyad. If both states are quite moderate, both are conservative, or both are liberal, this variable will take a low value. If the coefficient on this variable is negative, it would lend support to the Political Similarity Hypothesis. Demographic Similarity Hypothesis: A state is more likely to emulate states with similar demographic characteristics. States may adopt the innovations that are found in demographically similar states. Although neighbors, the populations of Oregon and California look quite different from one another. Such differences may lead these two states to not emulate one another s CHIP policies. As one CHIP official described: 16 [In the states we look to] I think there are probably similarities in our programs because of similarities in our populations and similarities in just cultural types of issues. To capture these concepts, I include four independent variables. The first, Population Ratio, simply focuses on the size of the population of the two states in the dyad. This variable takes the ratio of the larger of these two states to the smaller. A comparison of Texas to New York, or of North Dakota to Vermont, will produce a value close to one for this independent variable, while Oregon compared to California will produce a value of about ten. If states tend to emulate those of approximately the same size, the coefficient on this variable will be negative. The second independent variable generated to examine demographics is based on race. Absolute Difference in Minority Population captures the absolute difference between the two states in the dyad in the percent of their populations comprised of minorities. 17 If state governments are shaping their CHIP policies around those in states with similar racial population bases, this variable should have a negative coefficient. The third variable related to demographics included here is Absolute Difference in Per Capita Income. If states with similar poverty or wealth are more likely to emulate one another, this variable s coefficient should be negative. 16 Interview with author. 17 Alternative specifications broke this category down further into African-American and Hispanic groups. The results were similar to those reported here. 13

15 The final variable used to examine the Demographic Similarity Hypothesis is specific to the policy area of health care. Absolute Difference in HMO Penetration captures the absolute difference between the two states in the dyad in the percent of their populations enrolled in health management organizations. If states are more likely to imitate those with similar health care structures serving their populations, this variable should have a negative coefficient. Budgetary Similarity Hypothesis: A state is more likely to emulate states facing the same budgetary constraints or slack. Scholars of diffusion have long speculated that budgetary resources are crucial to the innovativeness of a government or organization (Mohr 1969). Some debate has been generated, however, about just how resources matter. On the one hand, slack resources allow states to experiment; on the other, fiscal crisis may drive states to desperation and policy change. The Budgetary Similarity Hypothesis suggests that states in either position, in good times or bad, will imitate those facing similar budgetary conditions. And, in interviews, CHIP officials expressed keen awareness of how declining state revenues may affect their programs: 18 If anybody comes up with some wonderful ideas to save money, they d probably be initiated around the country. Two independent variables are included here to operationalize this hypothesis. The first, Absolute Difference in Percent Change in Revenue, captures the relative direction of states fiscal health. This variable is the absolute difference between the two states in the dyad in the percent change in state government revenues between the current year and the previous year. States with growing revenues will look similar to others with growing revenues, thus taking a low value for this variable. Likewise, two states facing dropping revenues will look quite similar on this front. A negative coefficient on this variable will indicate support for the Budgetary Similarity Hypothesis. The second independent variable in this area is Absolute Difference in Percent Spent on Health and Hospitals. This budgetary consideration is specific to the policy area being examined, and is functionally the absolute difference 18 Interview with author. 14

16 between the two states in the dyad in the percent of the budget dedicated to health and hospitals. States with comparable fiscal commitments in this area will have low values for this variable. A negative coefficient will show support for the hypothesis. Follow the Leader Hypothesis: Smaller and poorer states are more likely to emulate larger, wealthier, and more advanced states. Some states are more likely to take the lead in developing policy ideas and are subsequently more likely to be emulated. Early work on diffusion processes focused on these lead states. Walker (1969), for example, examined leader states through factor analysis. Prior to that work, Crain (1966) found that leader cities in the use of fluoridation tended to be larger and richer than others. Grupp and Richards (1975) pointed to the emulation of esteemed states. And Berry (1994a) found greater diffusion when states were fiscally strong. These works, in combination with those across many fields of study (Rogers 1995) point to early innovator states and organizations being those that are larger and wealthier with others emulating them later on. The CHIP program may be no exception. As a Kentucky CHIP official described: 19 But we also talk to people in California. I mean you couldn t get much more different from California than we are. To explore this possibility, I incorporate three independent variables. First, Difference in Population simply captures the difference of State A (the emulating state) subtracted from State B (the potentially emulated state in the dyad) in state population in millions. 20 If smaller states look to larger states as policy leaders, they are more likely to be emulated. We would see a positive coefficient on this variable. Second, Difference in Per Capita Income, expressed in thousands of dollars, captures the difference in wealth of State A subtracted from State B. 21 If wealthier states are policy leaders, we would expect a positive coefficient on this variable. Finally, Difference in HMO Penetration is the difference of State A subtracted from State B in the percent of their population enrolled in HMOs. A positive 19 Interview with author. 20 As an alternative specification, the logarithm of population was incorporated, with effects similar to those presented here. 15

17 coefficient is expected. These last two variables are somewhat similar to those included in testing the Demographic Similarity Hypothesis. They are incorporated in this manner to explore nonlinearities in these relations, with State A emulating states that are similar and/or leaders on these dimensions. 22 Best Practices Hypothesis: A state is more likely to emulate states with policies that are judged to be successful. The idea that states function as effective policy laboratories would be best supported by states emulating the practices that are the most successful elsewhere, regardless of factors like their geographic location. Yet, surprisingly, very few studies of policy diffusion include any measures of policy success. In the area of CHIP policy, there are numerous criteria upon which success could be determined. For example, Shi, Oliver, and Huang (2000) delineate eighteen objectives that states had as they were formulating their CHIP plans. However, the gold standard amongst these, espoused by 46 states in their initial plans and mentioned broadly in interviews, is the reduction in the uninsured rate among the target population of poor children. I therefore create an independent variable Lowering Uninsured Rate Among Poor Children. Construction of this variable involved several stages. First, I generated an intermediate variable capturing the percent decrease in the uninsured rate among children from families below 200% FPL between 1997 and the current year within each state and year. Second, to control for the effect of different eligibility standards on this percent decrease and its variance over time, I conducted linear regressions for each year, with this percent decrease measure as the dependent variable and the state s eligibility standards (truncated at 200% FPL for states with higher eligibility) as the explanatory variable. Third, states that exceeded their predicted value were labeled successes, with those not exceeding their predicted value labeled failures. Finally, the Lowering Uninsured Rate Among Poor Children variable was created for each dyad in each year, taking a value of one if State A is below its predicted value in uninsurance decline (a failure) and State B is successful. Otherwise, the variable takes a value of zero. If 21 The alternative of per capita state government revenue was explored with similar results. 22 There appear to be no multicollinearity problems in the analysis below, with low levels of correlation among these variables and no substantial changes in the results upon the inclusion or exclusion of these variables of interest. 16

18 states with poorly performing policies are emulating successful states, the coefficient on this variable should be positive. 23 Political Process Differences Hypothesis: The patterns of diffusion will vary systematically based on whether the policy change in State A was brought about through new legislation or through administrative rulemaking. Specifically, political similarities will be more important for emulations made through legislative action. Scholars of policy diffusion seem to accept the idea that diffusion takes different paths for different types of policy changes made through different political processes (Gray 1973; Canon and Baum 1981; Berry 1994a; Mooney and Lee 1995, 1999; Lutz 1997; Berry and Berry 1999). Yet there is little systematic evidence of what features of a policy change affect the diffusion process, partly because of the reliance on the study of singular specific adoptions with the common state-year EHA approach. The dyad-year EHA approach espoused here allows scholars to overcome such limitations. With regard to the CHIP program, just under three-fourths of the forty-nine policy changes examined here were made through new legislation in the states, with the rest brought about through state-level administrative rulemaking. It is entirely possible that the diffusion pathways will differ based on the political process behind these policy adoptions. Due to the limited ability of scholars to systematically characterize diffusion patterns across political processes, there is not much theoretical guidance about what form such differences might take. The current examination is thus quite exploratory. At a minimum, however, we might expect that policy adoptions through the legislative process would be more political, and would thus show a greater reliance on states with similar partisan leanings. Policy Area Differences Hypothesis: The patterns of diffusion will vary systematically across different policy areas. Specifically, policymakers will rely to a greater extent on evidence of policy success when adopting policies with elements increasing the program costs than when adopting cost-saving changes. As with the Political Process Differences Hypothesis, there may be good reason to believe that diffusion pathways differ based on the nature of the policy choices being examined. One of the more 23 Alternative specifications, characterizing only the success or failure of State B or characterizing the relative success of State A to State B regardless of whether each exceeds its predicted value threshold, showed similar 17

19 well-articulated conjectures in this area is advanced by Downs and Mohr (1976, p. 702). They suggest that, the determinants of high-cost innovations would seem to be markedly different from those of lowcost innovations. With a focus on CHIP policy changes, we can isolate financially costly innovations (e.g., increasing eligibility levels, reducing cost sharing, and easing the waiting period) from those that are financially beneficial to state budgets. Of the forty-nine policy adoptions examined here, a handful were solely comprised of cost-saving measures, with just over half of the changes clearly increasing costs, and just over a third containing a mix of components (such as an increase in eligibility coupled with cost sharing). We would expect the diffusion patterns to differ across these types of innovations, perhaps with a greater reliance on evidence of policy success when program changes are fiscally costly. Data Results To test the above hypotheses, a number of econometric models were examined. Using the dyadyear event history analysis approach, 2450 dyads are observed in each of four years. As described above, the independent variables capture various relationships between the two states in each dyad. The dependent variable is a dummy for whether State A adopts a policy change moving it on average toward State B s CHIP policy. Given the dichotomous nature of this variable, the analysis is conducted using logit models. 24 Scholars have recently taken an interest in the potential dependence across observations in cross-section-time-series models such as those presented here. For instance, Beck, Katz, and Tucker (1998) introduce a method to control for temporal dependence in models with a dichotomous dependent variable. Unfortunately, their method is inappropriate for models with a small number of time periods. Instead, I make three adjustments to the standard logit model to address these concerns. First, I incorporate year dummies into the model to account for different hazard rates over time. Second, I include a Number of Prior Emulations variable that captures the number of previous times State A adopted a policy that State B had. Third, I cluster the data by dyad and rely on Huber/White robust results to those reported here. 24 Results reported here are robust to the use of the alternatives of probit and cloglog models. 18

20 standard errors. Because each pair of states is included twice in each year (New York may emulate California or vice versa), there may be concern about whether these observations are truly independent. By clustering both such pairs together, this potential dependence is addressed, following the directed dyad approach of Reiter and Stam (2003). 25 The robust standard errors also are used to address the possibility of heteroskedasticity. Multicollinearity does not appear to be a problem, given the low level of correlation across the independent variables. Outliers appear to have little effect on the outcomes reported here. Further robustness checks are noted below. All analyses were carried out using Stata 7. [Insert Table 2 about here] Model 1 in Table 2 shows the results of the analysis with variables capturing the first six hypotheses raised above. Coefficients and their standard errors are reported along with the point estimate of the percent change in the odds ratio that accompanies a one-unit change in each independent variable. As expected under the Geographic Neighbors Hypothesis, the coefficient on Geographic Neighbor takes a positive value. However, it is not statistically distinct from zero at conventional levels. The Political Similarity Hypothesis receives substantial support through the Same Unified Republican Government variable and the Difference in Government Ideology variable. Both have statistically significant coefficients with the anticipated signs. The odds of the adoption of a policy moving State A toward State B are thirty-three percent greater when both states are under unified Republican control than under other political configurations. A one-point increase in the absolute ideological distance between dyad states is associated with a 0.7-percent decline in the odds of a policy emulation. Given that this independent variable is a construct, it is difficult to interpret this result. Perhaps more intuitively, a one-standarddeviation increase in the absolute difference in ideology is associated with a 13.7-percent decrease in the odds of State A imitating State B. Contrary to expectations, the coefficient on Same Unified Democratic Government is negative, although it does not attain statistical significance. For cases like this where multiple independent variables are used to test a hypothesis, the best standard for comparison is a joint 25 Each cluster therefore includes eight observations both pairs of states in each of the four years. Independence is assumed across, but not within, clusters. Clustering each directed dyad separately does not substantially change the 19

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