Effectiveness, Implementation Capacity, and Policy Diffusion: Or, "Can We Make that Work for Us and Do We Care?"

Sean Nicholson-Crottya,*, Sanya Carleya,

a School of Public and Environmental Affairs, Indiana University* Corresponding author: 1315 E. 10th St., Bloomington, IN, 47408; seanicho@indiana.edu

AbstractPolicy learning has arguably been one the primary mechanism by which policy innovations are assumed to diffuse from one jurisdiction to another. Recent research suggests, however, that learning is more than simply observing policy adoption in other jurisdictions and must also include an assessment of the outcomes or effectiveness of those policies. This paper argues that implementation considerations can also help scholars distinguish learning from simple emulation. It argues that lawmakers using the experience of others as a decision criteria are likely to ask not only was the policy effective in other states that adopted it, but also can we make the policy work for us? We test hypotheses drawn from this general argument in analyses of renewable portfolio standards in the American states between 1990 and 2009. Results indicate that both shared implementation environments among jurisdictions and internal implementation capacity help determine the impact that policy effectiveness information has on adoption. This moderating impact is evident not only in the initial decision to adopt, but also in decisions regarding how stringent to make RPS policies. These findings confirm the occurrence of policy learning, and suggest the importance of implementation in distinguishing learning from other diffusion mechanisms.

Keywords: public policy, energy policy, policy adoption, renewable portfolio standard, diffusion, social learning, effectiveness, compliance

Introduction

Because states play a crucial role as incubators for policy innovation in the U.S. federal system, scholars have long been interested in the factors that affect policy adoption decisions within the American states. In a large and growing body of work, studies have demonstrated that both internal characteristics—such as ideology, wealth, and innovativeness—as well as external factors—including most notably the behavior of other states—influences those decisions.

The most persistent questions in the literature on policy adoption and diffusion focus on those external influences. When early studies recognized that public polices often spread among the states in patterns of regional contagion, they surmised that states must be learning about policies from their neighbors prior to adopting them themselves. In the ensuing decades, authors have spent a good deal of time and effort refining the concept of policy learning. They have investigated the mechanisms, such as interest groups and professional networks, by which information might travel from one state to another. They have studied which states are most likely to learn from one another, concluding that both ideological and geographic peers might serve as exemplars for potential adopters. And, most recently, they have sought to understand the ways in which interstate learning can be distinguished from intrastate decision processes that may lead to the same outcome.

It is to the general conversation about policy learning, and to this final line of research more specifically, that this paper aims to contribute. The work seeking to distinguish learning from internal decision processes has emphasized the importance of policy effectiveness, suggesting that successful policies are more likely to be emulated if learning is really driving the adoption process. We suggest herein that a consideration of implementation can also serve as an indicator of policy learning. In other words, we believe that lawmakers using the experience of others as a decision criteria are likely to ask not only was the policy effective in other states that adopted it, but also can we make the policy work for us? In the following pages we further develop the theoretical argument that lawmakers truly interested in learning about the effectiveness of a policy will consider their ability to implement it. Based on that general argument we offer the following expectations: 1) information about policy effectiveness will have a greater impact on adoption when a state shares implementation related characteristics with previous adopters; and 2) information about effectiveness should have less influence over the adoption decision when a state has high levels of internal implementation capacity.

We test these assertions in an analysis of the adoption of renewable portfolio standards (RPS) in the American states between 1990 and 2009. Specifically, we examine whether the impact of RPS policy effectiveness, measured as local utility compliance with policy targets, on policy adoption is moderated by similarity in the levels of electricity market deregulation between previous and potential adopters. We also test whether the importance of information about effectiveness diminishes as a state’s general environmental enforcement capacity increases. We look for these relationships across multiple operationalizations of the policy “adoption” variable, including a simple

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dichotomous indicator, a categorical variable that captures several dimensions of RPS policies at the time of adoption, and a continuous measure that captures both policy strength as well as adjustments to the policy after adoption.

The results consistently suggest that factors related to implementation help to determine the impact that effectiveness information has on the adoption decision. We conclude from these findings that states are indeed engaged in policy learning in the case of RPS policies and, more importantly, offer the observed consideration of implementation by lawmakers as a potential mechanism for verifying policy learning in other contexts. We also conclude that the results point to implementation capacity as an underexplored but potentially important influence on policy adoption decisions in the states.

Policy Learning in the Literature

The idea that state policymakers may learn from one another in the diffusion process grows from the earliest work on the diffusion of innovations among individuals, which suggested that the spread of something new is a social process dependent on communication among users and potential users (see Rogers 1995 for a review). Walker (1969) focused the discussion on governments and policy innovations, and suggested that jurisdictional decisions are driven by both internal state characteristics and information from other states, the latter providing a heuristic cognitive shortcut for policymakers considering an innovation. Later research suggests that this internal-external diffusion model has defined and continues to dominate the study of policy diffusion (Berry and Berry 1990).

Walker (1969) emphasized that state policymakers were most likely to imitate the policy choices of “similar” states, and as a proxy for this similarity he used geographic contiguity. The argument that neighboring states are likely to share relevant characteristics is an intuitive one, and the empirical research has consistently confirmed that a jurisdiction is more likely to adopt a policy innovation if a higher proportion of its neighbors has done so (see for example Berry & Berry, 1990, 1992; Gray, 1973; Mintrom, 1997; Mintrom & Vergari, 1996; Volden, 2002; Karch 2007a).

Despite the persistence of results confirming geographical diffusion, scholars have also begun to look for other “peers” that states may choose to emulate when considering policies. In this vein, scholars have demonstrated that policymakers are likely to learn from states that share their political ideology, particularly for policies that are ideologically charged (Grossback, Nicholson-Crotty, and Peterson 2004; Volden 2006). Research has also demonstrated that states learn from the policy example of the federal government (Gray 1973; Karch 2007a), which often serves to disseminate or amplify previous state-level innovations. Finally, they have shown that policies can diffuse up to state governments that can, under certain circumstances, learn from local governments within the state (Shipan and Volden 2006).

In addition to asking who states learn from, scholars have also investigated the

mechanisms and volume of information transfer among states. For example, one body of

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work has explored the degree to which unofficial political actors (i.e. interest groups, professional associations, and other policy entrepreneurs) help facilitate learning between states (see, e.g., Balla 2001; Haider-Markel 2001; Mintrom 2000). Recent scholarship has also demonstrated that characteristics of a policy itself, such as salience, complexity, and trialability affect the incentives that lawmakers have to gather information from peers (Nicholson-Crotty 2009; Boushey 2010; Makse and Volden 2011).

Interestingly, scholars have recently begun to question long held assumptions regarding the prevalence and importance of interjurisdictional learning in the diffusion process. As an example, Boehmke and Whitmer (2004) argue that social learning is often conflated with economic competition as a motivation for state behavior in the diffusion process and demonstrate that the former can explain initial adoption decisions, the latter is more likely responsible for subsequent changes to policy. Similarly, research has suggested that what it often termed “learning” is simply an emulation of behavior in other jurisdictions, rather than a conscious search for information about the effectiveness of a policy (see for example Weyland 2004).

In a related argument, Volden (2006) argues that a desire to avoid policy failure gives both lawmakers and administrators incentives to replicate only successful policies. Authors empirically confirm that effective State Children’s Health Insurance Program innovations were more likely to diffuse. The key implication of this finding is that conclusions about policy learning are more robust when scholars find evidence that policymakers rationally mimic only those policies that work. Work on policy diffusion in the developing world, though rarely cited in studies of U.S. diffusion, similarly argues that, in order to conclude that learning is occurring, studies should find evidence that potential adopters examine both policy actions and outcomes in previously adopting jurisdictions, rather than simply the first (See Weyland 2005; 2007; Meseguer 2005).

Volden, King, and Carpenter (2008) extend and refine this argument formally.

They demonstrate that a decision theoretic model that does not allow potential adopters to learn from one another can predict the same adoption outcome as a game theoretical approach that allows learning to be a central feature of the adoption decision. One of the key takeaways from this finding is that looking for emulation of effective policies is one of the primary methods by which scholars can distinguish adoption decisions driven by learning rather than by internal experimentation.1

Learning and Attention to Implementation in Diffusion Process

We propose in this paper that, in addition to assessments of effectiveness, true policy learning will likely involve a consideration by lawmakers of their ability to replicate that success. Our theoretical argument rests primarily on the widely accepted premises that policy effectiveness is inexorably linked to implementation choices and that lawmakers are aware of this connection and work hard to make sure that policies are 1 See Karch (2007) for the argument that the emulation of effectiveness is particularly likely when a policy is not highly controversial and potential adopters are more concerned with achieving substantive policy objectives than with simple political desirability.

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administered in a way that matches their preferences. From that premise, we make what we believe is a relatively straight forward argument that, for the same reasons they might prefer adoption information from states with which they share demographic and ideological information, lawmakers may place greater weight on effectiveness information from states that share similar implementation capacities. Additionally, we make the argument that information about effectiveness is likely to become less valuable to potential adopters with very high implementation capacity, who are likely to trust their ability to produce desired outcomes regardless of the experience of previous states. It is important to note that we are not suggesting that a focus on implementation will replace other criteria, such as an overweighting of neighbor’s policy decisions or attention to policy effectiveness, in the adoption decision, but rather that it will interact with these well-established influences.

Initially, we can note that an argument regarding the importance of implementation for lawmakers concerned with policy effectiveness should be uncontroversial for a couple of reasons. First, for at least 50 years scholars have demonstrated empirically that policy effectiveness and implementation choices are inexorably intertwined (see Pressman and Wildavsky 1963 for an early example). While the goals of some policies are intractable, thus attenuating the linkage between implementation and success (see Mazmanian and Sabatier 1983), in the vast majority of cases choices about the allocation of resources, stakeholder involvement, discretion afforded to street level personnel, collaborations, and various other implementation related factors have an enormous impact on the outcomes of public policy (see Hupe and Hill 2004 for a good modern review).

Even more germane to our argument is the very large literature suggesting that policy makers are well aware of the linkage between implementation and policy. Indeed, there exists a mountain of evidence that legislators go to great lengths to ensure that bureaucratic agents do not use their discretion to implement policies in a way that deviates from legislative preferences. Lawmakers overcome information asymmetries and other factors that facilitate policy drift through agency design (Moe 1989). They “stack the deck” in favor of bureaucratic decisions that match their preferences through ex ante controls such as advisory commissions and reporting requirements (McCubbins, Noll, and Weingast 1989; Balla 2001). They identify and seek to correct undesirable implementation decisions through ex post controls such as monitoring and auditing (McCubbins and Schwartz 1984). And, finally, lawmakers seek to control policy outcomes by writing detailed legislation that gives bureaucratic agents very little discretion in the implementation process (Huber, Shipan, and Pfaler 2001; Huber and Shipan 2002).

So, there is consistent evidence that 1) jurisdictions are more likely to learn from and emulate effective policies and that 2) lawmakers care about the implementation of laws they write. It is a relatively short leap, therefore, to assert that they will pay attention to implementation when assessing the effectiveness of policies previously adopted by other jurisdictions. Indeed, this fits quite well with Roger’s (1962: 173) assertion that knowledge regarding “how to use it correctly” is one of the three primary pieces of

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information that potential adopters are likely to gather about any innovation. We can also turn back to existing work on the use of information in the learning process in order to develop some specific expectations about the relationship between effectiveness, implementation, and adoption. Specifically, we can draw on the large literature suggesting that potential adopters prefer information from states with which they share relevant characteristics, and the findings that certain characteristics make information about policy less relevant to potential adopters.

One of the oldest and most consistent findings in work on diffusion is that those considering an innovation are more likely to trust information about its advantages and disadvantages from a previous adopter who shares their characteristics. Focusing primarily on individual adoption decisions, Rogers (1963) argued that “interpersonal diffusion networks are mostly homophilous,” or characterized by contacts among similar individuals. Walker (1969) extended the argument of diffusion networks to state-level actors and developed the expectation that regional peers would be considered as “legitimate guides to action” for potential adopters because of the relative homogeneity of states within a given region.

Interestingly, Walker did not find particularly strong empirical support for his expectations of regional clustering, but numerous other studies have hypothesized and demonstrated that adoptions in neighboring states have a large impact on the decisions of potential adopters (See for example Berry and Berry 1990; 1992; Volden 2002). The standard explanation for these results is that information from proximal states means more because shared demographic, economic, and political characteristics gives potential adopters a better idea of the capability of a policy solution with their needs and values. Grossback, Nicholson-Crotty, and Peterson (2004) extended this “sameness” argument by suggesting that ideological compatibility may not always be captured by geographic proximity and that potential adopters will be more likely, therefore, to trust policy information from ideological as well as regional peers (see also Volden 2006).

Thus, the literature is clear that shared characteristics increase the trust that potential adopters of an innovation have in information from previous adopters. In this analysis, we are simply offering the implementation environment as another element of sameness, which might increase the weight that potential adopters assign to information—particularly information about whether or not a policy worked in previously adopting jurisdictions.

By the implementation environment we mean those characteristics that lawmakers might logically think would affect the outcomes of a policy or, more importantly, the degree to which they could produce outcomes observed in other states. On the one hand, these could be structural or institutional characteristics that likely condition success, such as tax and expenditure limits, local government autonomy, the characteristics of the regulatory environment, or interest group strength, to name just a few. Alternatively, they could be things that bear on lawmakers’ ability to ensure that policies are implemented according to their preferences, such as monitoring and enforcement capacity. Whichever of these is the focus, we expect that evidence of policy success will mean more when it

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comes from states where the implementation environment is comparable to the one faced by a potential adopter.

We turn now to a second argument from the literature that bears on the potential relationship between previous effectiveness, implementation, and the decision to adopt. Research on diffusion has demonstrated fairly consistently that policy information is not always of the same value to potential adopters. Specifically, studies have shown that more complex innovations encourage the collection of more and better information about characteristics such as effectiveness, relative to those policies that are simpler and easier to understand (see Rogers 1963 for the original argument). Some of this work has focused on technical complexity (see for example Nicholson-Crotty 2009), but other studies have emphasized the importance of administrative complexity in search for information (see for example Gormley 1986; Boushey 2010; Makse and Volden 2011). These latter studies focus on Roger’s (1963) argument that complexity reflects “how difficult an innovation is to use” and argued that this characterization speaks to the challenges of implementing a policy (Makse and Volden 2011). The upshot of all of this research is that complex innovations diffuse more slowly because potential adopters take more time to gather desired information before making an adoption decision.

In our minds, administrative complexity is the natural inverse of administrative capacity. If complexity, and resultant uncertainty about the ability to “use” a policy, makes information more valuable to potential adopters, then a proven ability to implement laws should reduce that uncertainty and make information about things like effectiveness less important in the adoption decision. Indeed, a large literature demonstrates that capacity, measured as relevant technical skills, adequate resources and, most commonly, human capital or adequate personnel resources, correlates with the effectiveness of policy implementation at the federal, state, and local levels (see for example May 1993; Spillane and Thompson 1997; McDermott 2004; Howlett 2009). Because of this demonstrated linkage between capacity and policy effectiveness, we expect that the need for information about effectiveness among previous adopters will be lower among potential adopters with high administrative capacity.

U.S. State Energy Policy: The Renewable Portfolio Standard

We evaluate the relationship between implementation factors and policy learning through the lens of U.S. state energy policy. Over the past three decades, state governments have taken a prominent role in the energy policy arena. While the federal government over this time has adopted transportation policies, such as renewable fuel standards and increased the corporate average fuel economy standards, updated the production tax credit periodically after letting it lapse time and again, and, most recently, provided a number of incentives to alternative energy industries through the American Recovery and Reinvestment Act of 2009, their commitment to alternative energy in the electricity sector has been little more than rhetoric.2 State governments have responded to 2 The obvious exception to this statement is recent developments with the Environmental Protection Agency’s regulation of greenhouse gas emissions through the Clean Air Act. This type of activity, however, is arguably climate policy, and is not driven by the objective of increasing renewable energy, energy efficiency, or other energy alternatives.

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this absence of national leadership in energy policy by designing and implementing policies of their own. Collectively, state governments as well as American territories have adopted over 3,000 individual renewable energy or energy efficiency policies that are still active as of 2014 (NC Solar Center, 2014).3

One of the most popular state policies is the renewable portfolio standard, present in 45 states as of the beginning of 2014. While there is a wide degree of variation among states in their RPS design4, all states’ policies set a target for renewable energy by a specific year. The vast majority of policies set a percentage target for renewable energy out of total electricity generation or sales (e.g., 20% renewable energy by 2020); as the only two exceptions, Iowa and Texas set total capacity targets (e.g., Texas’ target is 5,880 MW by 2015). Once a state establishes a final target, it then sets annual benchmarks that must be achieved by all participating utilities, usually documented as annual Megawatt-hour (MWh) obligations. Utilities must then meet their annual obligations through developing and deploying their own renewable energy, paying an alternative compliance payment, or paying for renewable energy credits. A renewable energy credit (REC) is a certificate that represents one MWh of renewable electricity. RECs can be sold within states or across states, and are generally traded through specifically designed REC transaction and tracking systems.

RPS policies provide a good opportunity to test for the relationship between implementation concerns, policy information, and adoption for a number of reasons. First, as illustrated in Figure 1, the policy has diffused in a relatively traditional manner, suggesting that this is a case in which policy learning has played a role in diffusion decisions (see Nicholson-Crotty 2009; Boushey 2010).

[Insert Figure 1 about here]

Second, this policy is also a convenient platform in which to test a new theoretical argument about policy learning, because it has already been the focus of a number of empirical diffusion studies. The majority of these papers have evaluated which factors are associated with policy adoption and a consistent finding across them is that political ideology is one of the key factors in the adoption decision (Huang et al. 2007, Matisoff 2008, Chandler 2009, Lyon and Yin 2010, Carley and Miller 2012, Yi and Feiock 2012). Many also find that state affluence is important (Huang et al. 2007, Matisoff 2008, Chandler 2009, Wiener and Koontz 2010). Other factors are less consistent across studies. Of notable importance for this study, evidence on the effect of peer influence is

3 Of the total 3,123 policies active in 2014, 1,142 are classified as renewable energy incentives, 1,452 as energy efficiency incentives, 391 as renewable energy regulations, and 138 as energy efficiency regulations (NC Solar Center, 2014).4 Policies may differ in the following design features: which renewable and alternative energy resources are eligible; whether a portion of the energy target must come from a specific resource (i.e., “carve-outs” or “set-asides”); whether some resources count more than others (i.e., “multipliers”); whether a certain percentage of a target must come from in-state generation; whether all utilities in a state must comply to this regulation, or if it only pertains to investor-owned utilities; what level and how well enforced the penalty for non-compliance is; whether an alternative compliance payment option is offered; and whether the policy is voluntary or binding.

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mixed. All energy policy studies that include peer relations do so with a measure of geographic neighbors—usually operationalized as the percent of contiguous or regional neighbors that have a policy in the previous year. The majority of these studies do not find neighborly influence to be statistically significant (Matisoff 2008, Yi and Feiock 2012, Carley and Miller 2012; see also Stoutenborough and Beverlin 2008 for a similar finding regarding the adoption of net metering, a different energy policy), while one does (Chandler 2009). Of course, none of these studies have included information about policy effectiveness in previous adopters, similarities across the implementation environments of potential and previous adopters, or the administrative capacity of those considering a policy, which are the core of our theoretical contribution.

Before moving on to a description of our operationalization of those and other concepts, however, it is important to touch upon one additional advantage of RPS policies. Renewable portfolio standards offer an intriguing test of our theoretical argument because there is significant variation in the character and content of these policies as they have been adopted and amended across the nation. As one example, some states demanded that utilities produce 12.5% of electricity from renewable sources, while others set a much higher standard of 40%. Similarly, some states gave local utilities 4 years to meet targets, while others demanded compliance in 28 years. Finally, 44% of states that adopted RPS amended their standard at least once during the period under study. This variation allows us to test for the relationship between implementation factors and learning by examining not just policy adoption, but also the choice among different policy characteristics and the amendment of existing policies among adopters.

Data, Dependent Variables, and Estimation Strategy

We model RPS policies in the America states between 1990 and 2009. The RPS policy data, including the information used to devise the stringency score, are extracted from the Database for State Incentives for Renewables and Efficiency, DSIRE (NC Solar Center 2014). The RPS is assumed to be present in a state on the date by which it is registered as effective. Because we are interested in not only whether a state decides to adopt RPS, but also the choice among policy characteristics in the adoption process, and policy amendment post adoption, we use several different measures of the RPS adoption variable and thus estimate several different models.

In the first model, we operationalize policy adoption as a simple dichotomous measure, where a state either has a policy in a given year or does not. We employ a traditional event history analysis technique for this model, in which we drop a state from the sample in the year following first adoption. Specifically, we employ Cox proportional hazard model, where “failure” is coded as the adoption of the RPS policy, either a fully binding or voluntary policy. Because there are numerous “ties” or adoptions in the same year in our data, we use the Efron rather than the Breslow method for dealing with these.

In the second model, policy adoption is operationalized as a categorical variable that measures the characteristics of RPS policy at the time of adoption. Specifically, we model the stringency of a state’s policy using a score first proposed by Carley and Miller

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(2012). This score is a measure of the total percentage of renewable energy generation that must be added due to the RPS divided by the total number of years that a state has given to achieve this percentage, multiplied by the percent of a state’s electricity load that is regulated under this mandate. This score provides an estimate of how quickly a state must develop and deploy new renewable energy, weighted by how much of the state’s electricity market is actually regulated. The variable in our second model is equal to zero when a state has no RPS policy, one when a state has a voluntary policy, two when a state’s stringency score is under the median value of stringency scores in that year, and three when the stringency score is over the median (see Miller and Carley 2012).

We also employ event history approach in this model and, thus drop observations post-adoption. Because the dependent variable is categorical, we estimate a multinomial probit and deal with time dependence via the inclusion of three cubic splines, as suggested by Beck, Katz, and Tucker (1998). We also test whether dependent variable categories should be collapsed using a Cramer-Ridder test and the independence of irrelevant alternatives (IIA) assumption using the Hausman test. Neither category mix nor IIA is found to present problems.

In the final model, we operationalize the stringency of an RPS policy with a continuous measure. In this model, we are not only interested in how strong or weak the policy is at the time of adoption, but also over the entire study period. This model, therefore, does not drop observations following policy adoption; the dependent variable also varies over time for those states that revise their policy at some point over the study period. More specifically, we use a continuous measure of the calculated stringency score for each state in each year, again according to the approach devised by Carley and Miller (2012).5 If a state revises its policy in a given year, the stringency score will change accordingly. We model this variable via a standard panel data estimation using state and year fixed effects and robust standard errors.

Independent VariablesBecause we are interested in the degree to which the impact of information about

policy effectiveness is moderated by concerns about implementation, our first independent variable needs to capture both who a potential adopter is most likely to learn from and the information about policy effectiveness contained in the information they receive from those peers. In terms of effectiveness, we focus on compliance measured as the average percent of a state’s RPS annual mandate that is achieved by utilities in each year. So if, for example, a state must deploy 500,000 MWh to achieve their annual benchmark, but their utilities only deploy a total of 400,000, the compliance value for that state in that year would be 0.8. 6 In terms of those exemplars from which potential adopters are most likely to value information, we focus first on neighbors, which is the variable enjoys the greatest support in the literature.

5 All states with voluntary RPS policies have stringency scores equal to zero.6 These data are utility reported and tracked by DSIRE.

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As noted above, our key independent variable combines these separate components, effectiveness and geographic proximity, into a single measure. Specifically, for each state and year the variable measures the average compliance rate in the previous year among those contiguous neighbors that had already adopted an RPS standard.

Our next set of independent variables capture the relative and absolute implementation environment within a state. For the first, we use a dimension upon which we believe lawmakers might look for similarities when considering whether they could produce the RPS compliance rates observed in neighboring states. Specifically, we focus on the regulation of electricity markets within a state and, precisely, on the similarity in the regulatory environments of potential and previous adopters.

Though our primary interest is not in the direct impact of deregulation on RPS compliance, there are reasons to expect that this relationship would be positive. Intuitively, we might expect that states operating a regulated monopoly would see higher compliance rates because they avoid the multiple agent problem and can, therefore, more easily monitor a producer. However, there are several strains of literature that suggest that deregulation could increase compliance with RPS standards among local utilities. First, research suggests that deregulation incentivizes producers to take advantage of policies like RPS in order to “environmentally differentiate” themselves and court customers who value green power (Delmas et al. 2007). Additionally, research on vertical monopolies suggests that regulatory regimes in these arrangements are often weak and tend to suffer from very large information asymmetries, which makes the confirmation of compliance with regulations difficult (Fabrizio et al. 2007).7 Finally, empirical work suggests that deregulation correlates positively with the total renewable energy utilized within a state (Carley 2009).

However deregulation affects compliance, our argument is simply that states will value compliance information more when it comes from previous adopters with a similar regulatory environment. Thus, while we include the deregulated indicator in all models as a control, our independent variable is actually the similarity between the regulatory environment of a potential adopter and the environments in surrounding states. In order to create this measure, we first calculate the proportion of a state’s neighbors that are deregulated. The “same regulatory environment” is that proportion for potential adopters that are deregulated and the inverse of that proportion for those that are not.

Along with the expectation that the value of effectiveness information from peers will increase as similarities with the implementation environment in peer states increases, we also expect that such information will become less valuable as a state’s own implementation capacity grows. In order to begin testing that second hypothesis, we need an indicator of internal capacity. Ideally, we would use the size of public utilities commissions as our measure of capacity. These data, however, are only available for all 50 states in a very limited set of years. Instead we use the measure of “traditional”

7 Interestingly, analyses of our own data confirm that, in 2009, that public utilities commissions in deregulates stated employed significantly more staff than those in deregulated states even after controlling for a host of demographic, political, and economic factors.

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environmental enforcement created by Konisky (2007). The measure captures total enforcement actions initiated by state inspectors in each state and year. Unfortunately, these data are only available from 1985 to 2000, which does not overlap sufficiently with our period of study. It is a relatively long time frame, however, and enforcement at year t correlates at over .91 with enforcement at t-1 throughout the series and in the presence of numerous other controls. Because of these features of the data, we are able to create a stable “average” enforcement figure for each state based on the 1985 to 2000 values. We divide that figure by GSP in each year in order to normalize for state size and the need for environmental regulation. This procedure creates a time-variant variable that we use as our measure of capacity. This is not a perfect proxy of the regulation of public utilities, but we believe that it adequately captures the resources that a state is willing to devote to regulatory enforcement. We expect that states with a high enough commitment may have faith in their ability to produce high RPS compliance regardless of the expectation of their peers.

Finally, all models discussed below include multiplicative interaction terms, which are the variables that actually allow us to test our hypotheses. Specifically, we estimate one model for each of the dependent variables discussed above that contains an interaction between the measure of regulatory sameness and the average compliance rate in neighboring adopters. We also estimate three models where the interaction term contains the state’s own enforcement per GSP and the measure of compliance in neighboring states.

Control VariablesIn addition to the independent variables discussed above, we, of course, control

for other influences on adoption identified in previous diffusion and state energy policy adoption studies. The first of these is a more traditional measure of peer effects. While recent work suggests that information about policy effectiveness from peers is really what drives the learning process, there are also a number of studies that have shown a correlation between adoption decisions and adoptions in neighboring states, regardless of effectiveness. In order to capture this effect, we include a measure of the proportion of neighboring states that have adopted an RPS for each state and year.

Next, we include a second variable to capture the impact of policy effectiveness of

a state’s ideological peers. In this paper, we only test for the interaction between implementation factors and information about effectiveness coming from neighboring states both for the sake of parsimony and because we believe states will have better information about the implementation environment in neighboring versus geographically distant but ideologically similar states. We also do not include this variable directly in the tests of our theoretical expectations because we are not as confident in its validity as a measure of effectiveness information as we are the measure of contiguous state compliance discussed above. This is because the ideological peer variable’s construction is more complicated than the neighboring compliance measure. In a given year for a given state, we calculate the average difference in ideological values between the state of interest and all other states that had adopted the policy in the year prior, and then multiply this value by the average compliance value of all of those peers. The same calculation is performed for all policy adoptions in previous years, and the two values are then

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averaged together and lagged by one year.8 This series of calculations generates a measure that is weighted simultaneously by difference in ideology (i.e., peer status), policy compliance, and policy vintage.

The next set of controls includes economic and demographic measures, including the price of electricity, the status of a state’s electricity regulatory structure, the population growth rate, and gross state product per capita. The price of electricity, gathered from the Energy Information Administration (2010), measures the annual average real price of electricity in each state, in cents/kWh, averaged across all end users. The deregulation variable is dichotomous, equal to one if the state’s retail market is deregulated and equal to zero otherwise. A state’s annual population growth is extracted from annual Census Bureau data (1999, 2009) and GSP per capita are derived from Bureau of Economic Analysis data (2010).9

We control for three political variables. We capture state-level political ideology with the Berry et al.’s (2010) measures of citizen and government ideology. These variables range from 0 to 100, where 100 represents the highest level of liberalism and 0 represents the lowest. We also control for fossil fuel industry presence with a measure of carbon dioxide emissions per capita, drawn from the Environmental Protection Agency (2010).

We additionally include two other factors. First, we control for wind and solar energy potential as the total GWh possible per year. Wind potential is based on the available windy land area, after exclusions, with a wind turbine capacity factor of 30 percent at a height of 80 meters (DOE 2011). Solar potential represents average solar radiation measured between 1961-1990 for a south-facing flat-plat collector, with zero degree tilt, multiplied by the total area of land and the number of sunny days per year (NREL 1991). Second, we include a measure of whether the state has another energy policy instrument, a net metering policy, which is even more prevalent than the RPS. Net metering allows a customer that owns a small-scale renewable energy system of a certain capacity size or smaller to hook their system up to the electric grid and exchange electricity with the grid. We include this variable on the premise that previous adoption of energy policies encourages future adoption of other energy policies (Yi and Feiock, 2012). This variable is lagged one year and is composed using policy information from DSIRE (NC Solar Center, 2014).

Findings and Discussion

The results from our empirical tests are presented in Tables 1 and 2 and Figures 2-5. Table 1 contains the three models examining whether similarity in the regulatory environment moderates the impact of information regarding RPS compliance rates in neighboring states on adoption decisions. The first column presents the results from a

8 This technique helps to separate the impact of very recent adoptions among ideological peers, which the literature suggests should be more consequential (see Grossback et al. 2004) from older ones.9 This variable is based on Standard Industrial Classification (SIC) codes before 1997 and the North American Industry Classification System (NAICS) codes from 1997 onward.

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Cox model of the dichotomous adoption choice, while the second contains the multinomial probit regression using the categorical stringency at time of adoption, and the third contains the fixed effects panel regression with the continuous measure of stringency over the life of the policy. We will present the findings related to each hypothesis in order, dealing first with the expectation that shared implementation capacity should increase the value of information on policy effectiveness before turning to the assertion that high internal implementation capacity should decrease the impact that such information has on the adoption decision.

[Insert Tables 1 and 2 about here]

Before discussing the two primary hypotheses, we can note that, as the literature would suggest, political ideology is a consistent predictor of RPS policies, where more liberal states are more likely to adopt any RPS, to adopt more binding and stringent policies, and to adjust stringency upward through the policy amendment process. Additionally, the influence of the fossil fuel industry is evident in the CO2 per capita figure in the multinomial probit model, where higher rates of CO2 per capita decrease the likelihood that a state will adopt a binding policy. Finally, the models suggest that renewable energy potential and deregulation correlate positively with increases in the stringency level of RPS over the life of these policies.

Turning now to the independent variables of primary interest we see that, across all three models, the main effect of average compliance scores among neighboring adopters is not statistically significant. This is not at all surprising because, in the presence of the interaction between neighboring states’ compliance and similar regulatory environment, that coefficient represents the impact when there is no information on effectiveness coming from peers that share the same regulatory environment.

The real variable of interest for hypothesis testing is the interaction term and, in all three models, it is significant and in the expectedly positive direction. Turning first to the cox model, the impact of the interaction is easier to see graphically. Figure 2 graphs the survival function at the mean level of neighboring compliance rates and 1-standard deviation below (line 1) and 1-standard deviation above (line 2) the mean value of the proportion neighbors with the same regulatory environment. As the figure suggests the probability of a state “surviving” each year without adopting an RPS is lower for states where regulatory sameness with neighbors is high, even though both states are getting the same information about policy effectiveness from geographic peers.

[Insert Figure 2 here]

If we examine the model of the categorical RPS variable presented in Column 2, the interaction is again significant, but only in the final equation. Compliance rates in neighboring states had a positive, though only marginally significant, impact on the decision to adopt a voluntary RPS. That impact was not, however, moderated by similarity in the implementation environment. Neither neighboring compliance rates nor their interaction with regulatory sameness were significant predictors of the decision to adopt a mandatory but relatively low stringency RPS policy.

14

When we turn to those cases where states adopted a mandatory policy that was more stringent than the median, however, the interaction term is highly significant and in the expectedly positive direction. This suggests that compliance information has a larger impact on the decision to adopt this type of RPS, relative to no policy, when a greater proportion of neighboring states have the same regulatory environment. An assessment of predicted effects suggests that neighboring compliance rates do not begin to have a significant impact on the adoption decision until the proportion of neighbors with the same regulatory environment increases to 0.6.

The final column of Table 1 presents a model of the continuous measure of RPS stringency. Again, the interaction term is positive and significant, suggesting that neighboring compliance information has a larger impact on the adjustment of RPS stringency levels when a greater proportion of neighbors share the implementation environment. Figure 3 graphs the marginal effect of an increase in compliance rates across the range of the regulatory similarity measure for this dependent variable. Again, the graph suggests that the impact of neighboring compliance does not begin to have a significant impact on RPS stringency until the proportion of neighbors with the same regulatory environment reaches 0.7. From that point, an increase of 1-standard deviation in the sameness measure increases stringency by approximately 1/3-standard deviation.

[Insert Figure 3 about here]

Table 2 presents models testing the hypothesis that internal implementation capacity reduces the impact of policy effectiveness information on adoption decisions. As a reminder, our measure of capacity is state-level enforcement actions for clean air, clean water, and hazardous materials violations normalized by GSP. As in the first table, Column 1 contains results from a survival analysis of the dichotomous adoption choice. The interaction between enforcement actions and neighboring compliance is negative and significant, suggesting that the impact of compliance information from neighbors on the adoption decision decreases as internal enforcement capacity goes up.10 A graph of the survival function, as displayed in Figure 4, with enforcement actions per GSP set to 1-standard deviation below (line 1) and 1-standard deviation above (line 2) the mean shows that the probability of a state “surviving” each year without adopting an RPS is consistently higher at the greater level of enforcement in states whose neighbors report identical levels of compliance.

[Insert Table 2 and Figure 4 about here] Column 2 of the second table presents the model with the categorical measure of RPS stringency at initial adoption. In this case, an increase in neighboring compliance rates have a positive impact on both the likelihood of adopting both voluntary RPS and mandatory RPS that are less stringent than the average. In the latter case, however, that impact is moderated by enforcement capacity. The predicted effects suggests that, in the

10 The interaction term is individually significant at .1 on a 1-tailed test, but the interaction terms are jointly significant at .05 on a 2-tailed test.

15

case of the adoption of mandatory but low stringency policies, the impact of compliance information from neighboring states begins diminishing significantly once enforcement capacity rises above the mean level.

Results from the model of the continuous measure of RPS stringency are presented in the third column of Table 2. The interaction between enforcement actions and neighboring compliance information is negative and statistically significant, suggesting that the influence of effectiveness information from peers decreases as enforcement capacity increases. However, an examination of the marginal effects (Figure 5) suggests that enforcement capacity has a relatively limited impact on the importance of neighboring compliance information. The former only begins to significant moderate the latter once enforcement capacity reaches 2-sd above the mean level.

Nonetheless, when taken together, the findings presented above provide considerable evidence for our hypotheses. Whether we are modeling a simple adoption decision, or taking account of the different policy characteristics lawmakers might choose during and after adoption, implementation concerns consistently appear to be relevant. In all three models presented in Table 1, we find that information about compliance rates in neighboring states has a greater impact when a greater proportion of those states have the same regulatory environment as a potential adopter. In at least two of the three models in Table 2, depending on which statistical significance threshold one values, the results suggest that the influence of neighboring compliance rates decreases as the internal enforcement capacity of a potential adopter goes up. It appears, therefore, that states are concerned not only with how effective RPS has been for previous adopters, but also whether they will be able to duplicate that effectiveness within their borders. That concern leads them to prefer effectiveness information from states with similar implementation environments, but discount the importance of previous effectiveness when their own implementation capacity is sufficiently high.

Conclusion

In recent years, much of the debate in the literature on diffusion has focused on the challenges of distinguishing and understanding policy learning. Scholars have noted that it can be very difficult to tell the difference between the careful weighing of costs and benefits by potential adopters and other decision processes that may produce the same adoption outcome. As a result, they suggest that diffusion patterns ascribed to “learning” may, in some cases, be simple imitation or even the result of intrajurisdicitonal experimentation free from any external influence.

Fortunately, previous work has also suggested a method by which students of diffusion can distinguish policy learning from other decision processes. Specifically, they have argued that a focus on policy effectiveness is the best way to do so. The logic of this argument is that the simple spread of policies from one jurisdiction to another could be caused by any number of factors, but if we see that jurisdictions are copying successful policies and forgoing those that prove ineffective, then we can assert with greater confidence that policy learning is contributing to diffusion.

16

This paper offers another test that scholars can use to determine if observed diffusion patterns are a result, in part at least, of interjurisdicitonal learning. There is significant evidence that lawmakers are highly attuned to the relationship between implementation and policy success. We argue that if potential adopters are gathering information about the effectiveness of previously adopted policies, they are also likely to gather information about the ways in which those policies were implemented. If learning is driving the adoption decision, we should see evidence that lawmakers considering an innovation weight effectiveness information based on their ability to reproduce that success in the implementation process. Thus, we should see them give more weight to effectiveness information from states that share similar implementation conditions, but discount the importance of success among previous adopters when internal implementation capacity is high.

Based on these expectations, it appears that in the case of RPS policies learning did influence the diffusion of the innovation among the American states. The success of these policies really only had a meaningful effect on the likelihood of adoption in states that shared the regulatory environment of previous adopters and, thus, had the reasonable expectation that they would experience similar levels of success. Similarly, we find that states with high capacity to monitor policy outcomes post adoption were less likely to let low success rates among previous adopters deter them from embracing an RPS. It is very hard to imagine a process other than policy learning that could produce this pattern of results.

Before concluding, it is important to note that examination of the effectiveness-implementation interaction cannot offer a critical test of the learning hypothesis. In other words, if we fail to observe implementation factors moderating the impact of effectiveness information, it is not sufficient evidence to conclude that policy learning is not present. The observation of a moderating relationship can, however, provide an affirmative test, which gives researchers more confidence that learning is influencing diffusion patterns.

Of course, there is still a great deal of research that needs to be done to confirm that attention to implementation can help us better understand policy learning. First and foremost, the results from this study need to be replicated in a policy where “success” is not as easily observed. In the case of RPS, lawmakers face what is primarily an agency problem and by observing compliance with established targets they can easily conclude whether the policy has been successful or not. Such determinations are not so easily made in the case of numerous other policies where success is more difficult to measure. It is an open empirical question whether policy makers will give the same weight to implementation factors in these types of policies.

References

Balla, Steven J. 2001. Interstate Professional Associations and the Diffusion of Policy Innovations. American Politics Research 39:221-45.

17

Berry, William D., Richard C. Fording, Evan J. Ringquist, Russell L. Hanson, and Carl E. Klarner. 2010. “Measuring Citizen and Government Ideology in the U.S. States: A Re-appraisal.” State Politics & Policy Quarterly 10(2): 117 -135. Data through 2008 updated online at <http://rcfording.wordpress.com/state-ideology-data/>; retrieved January 19, 2011.

Boehmke, F. J., & Witmer, R. (2004). Disentangling diffusion: The effects of social learning and economic competition on state policy innovation and expansion. Political Research Quarterly, 57(1), 39-51.

Boushey, Graeme. 2010. Policy Diffusion Dynamics in America. New York, NY: Cambridge University Press.

Carley, Sanya, and Chris J. Miller. 2012. “Regulatory Stringency and Policy Drivers: A Reassessment of Renewable Portfolio Standards.” Policy Studies Journal.

Chandler, Jess. 2009. “Trendy Solutions: Why do States Adopt Sustainable Energy Portfolio Standards?” Energy Policy 37:3274-3281.

Delmas, M., Russo, M. V., & Montes‐Sancho, M. J. (2007). Deregulation and environmental

differentiation in the electric utility industry. Strategic Management Journal, 28(2), 189-209.

Gray, Virginia. 1973. Innovation in the States: A Diffusion Study. American Political Science Review 67:1174-85.

Grossback, Lawrence J., Sean Nicholson-Crotty, and David A. M. Peterson. 2004. “Ideology and Learning in Policy Diffusion.” American Politics Research 32(5):521-545.

Haider-Markel, Donald P. 2001. Policy Diffusion as a Geographical Expansion of the Scope of Political Conflict: Same-Sex Marriage Bans in the 1990s. State Politics and Policy Quarterly 1:5-26.

Howlett, M. 2009. Policy analytical capacity and evidence‐based policy‐making: Lessons from

Canada. Canadian public administration, 52(2): 153-175.

Huang, Ming-Yuan, Janaki R.R. Alavalapati, Douglas R. Carter, and Matthew H. Langholtz. 2007. “Is the Choice of Renewable Portfolio Standards Random?” Energy Policy 35(11): 5571-5575.

Huber, J. D., Shipan, C. R., & Pfahler, M. (2001). Legislatures and statutory control of bureaucracy. American Journal of Political Science, 330-345.

18

Huber, J. D., & Shipan, C. R. (2002). Deliberate discretion?: The institutional foundations of bureaucratic autonomy. Cambridge University Press.

Hill, M. J., & Hupe, P. L. (2002). Implementing public policy: governance in theory and practice. London: Sage.

Karch, Andrew. 2007a. Democratic Laboratories: Policy Diffusion among the American States. Ann Arbor: University of Michigan Press.

Lyon, Thomas P., and Haitao Yin. 2010. “Why Do States Adopt Renewable Portfolio Standards? An Empirical Investigation.” Energy Journal 31(3):133-157.

Makse, Todd, and Craig Volden. 2011. The Role of Policy Attributes in the Diffusion of Innovations. Journal of Politics 73:108-24.

Matisoff, Daniel C. 2008. “The Adoption of State Climate Change Policies and Renewable Portfolio Standards: Regional Diffusion or Internal Determinants?” Review of Policy Research 25(6):527-546.

May, P. J. (1993). Mandate design and implementation: Enhancing implementation efforts and

shaping regulatory styles. Journal of Policy Analysis and Management, 12(4), 634-663.

Mazmanian, D. A., & Sabatier, P. A. (1983). Implementation and public policy. Glenview, IL: Scott, Foresman.

McCubbins, M. D., Noll, R. G., & Weingast, B. R. (1989). Structure and process, politics and policy: Administrative arrangements and the political control of agencies. Virginia Law Review, 431-482.

McCubbins, M. D., & Schwartz, T. (1984). Congressional oversight overlooked: Police patrols versus fire alarms. American Journal of Political Science, 165-179.

McDermott, K. A. (2006). Incentives, capacity, and implementation: Evidence from Massachusetts education reform. Journal of Public Administration Research and Theory, 16(1), 45-65.

Meseguer, C. (2005). Policy learning, policy diffusion, and the making of a new order. The Annals of the American Academy of Political and Social Science, 598(1), 67-82.

Mintrom, Michael. 1997. Policy Entrepreneurs and the Diffusion of Innovation. American Journal of Political Science 42:738-770.

19

Mintrom, Michael. 2000. Policy Entrepreneurs and School Choice. Washington, DC: Georgetown University Press.

Mintrom, Michael, and Sandra Vergari. 1998. Policy Networks and Innovation Diffusion: The Case of Education Reform. Journal of Politics 60:126-148.

Moe, T. M. (1989). The politics of bureaucratic structure. Can the government govern, 267, 285-323.

Moe, T. M. (1991). Politics and the Theory of Organization. Journal of Law, Economics, and Organization, 7(special issue), 106-129.

National Renewable Energy Laboratory, 1991. Solar Radiation Data Manual for Flat-Plate and Concentrating Collectors. Available at < http://rredc.nrel.gov/solar/pubs/redbook/> Accessed January 3, 2008.

Nicholson-Crotty, Sean. 2009. “The Politics of Diffusion: Public Policy in the American States.” The Journal of Politics 71 (1): 192-205.

North Carolina Solar Center, 2014. Database of State Incentives for Renewable Energy (DSIRE). Available at <http://www.dsireusa.org>. Accessed Feb. 16, 2014.

Pressman, Jeffrey and Wildavvsky, Aaron (1973). Implementation. Berkeley: University of California Press.

Rogers, Everett M. 1995. Diffusion of Innovations. 4th ed. New York: The Free Press.

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

Spillane, J. P., & Thompson, C. L. (1997). Reconstructing conceptions of local capacity: The

local education agency’s capacity for ambitious instructional reform. Educational Evaluation and Policy Analysis, 19(2), 185-203.

U.S. Bureau of Economic Analysis. Nov. 24, 2010. Regional Economic Accounts: Gross Domestic Product by State. Online at <http://www.bea.gov/regional/gsp>. Retrieved Nov. 24, 2010.

U.S. Census Bureau. 1999 and 2009. State Population Estimates: Annual Time Series. Population Estimates Program, Population Division, Washington, DC. Retrieved online at <http://www.census.gov/popest/data/historical/index.html>, April 21, 2012.

U.S. Department of Energy. 2011. Energy Efficiency and Renewable Energy: Wind Powering America. Available online at <http://www.windpoweringamerica.gov/wind_maps.asp>. Retrieved September 21, 2011.

20

U.S. Energy Information Administration. 2010. Electricity Price by State and End-User, 1990–2008. Available at <http://www.eia.doe.gov/cneaf/electricity/page/>. Retrieved November 18, 2010.

U.S. Environmental Protection Agency. 2010. “State CO2 Emissions from Fossil Fuel Combustion.” Available at < http://www.epa.gov/statelocalclimate/resources/state_energyco2inv.html>. Retrieved Mar. 28, 2012.

Volden, Craig. 2002. The Politics of Competitive Federalism: A Race to the Bottom in Welfare Benefits? American Journal of Political Science 46: 352-363

Volden, Craig. 2006. “States as Policy Laboratories: Emulating Success in the Children's Health Insurance Program.” American Journal of Political Science 50(2): 294-312.

Volden, Craig, Michael Ting, Daniel Carpenter. 2008. “A Formal Model of Learning and Policy Diffusion.” American Political Science Review 102: 319-332.

Walker, Jack L. 1969. “The Diffusion of Innovations Among the American States.” American Political Science Review 63(3): 880-899.

Weyland, K. G. (2004). Learning from foreign models in Latin American policy reform. Johns Hopkins University Press.

Weyland, K. G. (2005). Theories of policy diffusion: lessons from Latin American pension reform. World politics, 57(2), 262-295.

Wiener, Joshua G., and Tomas M. Koontz. 2010. “Shifting Winds: Explaining Variation in State Policies to Promote Small-Scale Wind Energy.” Policy Studies Journal 38(4): 629-651.

Wiser, R., Barbose, G., Holt, E., 2011. Supporting solar power in renewables portfolio standards: Experience from the United States. Energy Policy 39(7), 3894-3905.

Yi, Hongtao, and Richard Feiock. 2012. “Policy Tool Interactions and the Adoption of State Renewable Portfolio Standards.” Review of Policy Research 29(2): 193-206.

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Table 1 Regulatory Similarity and the Impact of Compliance Information on RPS Adoption and Adjustment Cox Model Multinomial Probit Regression

Voluntary Mand. Low Mand. High

Neighbor Compliance 0.743 2.441 -0.458 -1.174 -8.524(0.805) (1.260) (0.917) (1.094) (3.829)

Regulatory Similarity

0.177 0.325 -0.285 -2.865 4.226

(0.188) (1.073) (0.904) (1.300) (3.243)Similar X Comp. 30.559 -1.088 0.922 4.578 17.849

(43.160) (1.537) (1.262) (1.577) (5.129)Ideo. Peer Comp. 1.430 0.067 -0.099 0.001 -2.083

(0.828) (0.152) (0.158) (0.138) (2.158)Electricity Price 1.058 -0.009 -0.205 0.091 4.077

(0.115) (0.167) (0.135) (0.119) (0.601)Deregulated 0.923 -1.612 -0.051 0.499 10.200

(0.497) (0.746) (0.552) (0.692) (2.058)Citizen Ideology 1.084 0.084 0.076 0.018 0.319

(0.025) (0.032) (0.027) (0.021) (0.102)Govt. Ideology 1.005 -0.014 -0.007 0.049 0.181

(0.010) (0.011) (0.013) (0.011) (0.034)Renew. Eng. Poten. 1.001 -0.002 0.002 0.001 41.201

(0.001) (0.001) (0.001) (0.001) (16.843)Co2 Emissions 0.975 -0.014 -0.036 -0.045 0.248

(0.016) (0.017) (0.013) (0.031) (0.316)Gross State Product 241.134 65.048 51.103 45.080 -86.776

(7657.109)

(44.791) (28.123) (41.121) (237.742)

Net Metering Policy 0.704 -0.554 -0.216 -0.984 7.558(0.267) (0.525) (0.490) (0.504) (1.798)

Adopting Neighbors 0.602 0.309 1.040 -0.903 3.686(0.474) (1.233) (0.855) (0.786) (3.550)

Pop. Growth Rte. 1.36E+31

33.811 4.532 13.419 -119.944

(4e+32) (22.731) (13.934) (33.598) (89.826)Intercept -89.850 -12.356 -10.402 -12116.720

(54.993) (2.893) (2.438) (4928.824)

Number of Observations

813 814 814 814 1000

R-squared 0.509

Numbers in parentheses are robust standard errors. Models 2,3, and 4 contain cubic splines of time. Model 5 includes year and state fixed effects.

22

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Table 2 Enforcement Capacity and the Impact of Compliance Information on RPS Adoption and Adjustment Cox Model Multinomial Probit Regression

Voluntary Mand. Low Mand. High

Neighbor Compliance 6.285 3.209 1.400 -0.255 4.696(5.683) (1.482) (0.885) (0.843) (5.263)

Enforcement Capacity

454.186 -198.449 93.978 -397.624 423.858

(403.190) (120.347) (66.999) (268.469) (265.654)Capacity X Comp. 7.22E-80 -313.840 -734.727 304.797 -1038.058

(8.1E-78) (270.548) (289.973) (280.240) (519.794)Ideo. Peer Comp. 1.485 0.125 0.105 0.037 -2.161

(0.839) (0.159) (0.129) (0.142) (4.494)Electricity Price 1.147 -0.387 -0.114 0.267 3.573

(0.131) (0.203) (0.162) (0.111) (1.104)Deregulated 0.852 -1.572 -0.407 0.410 8.426

(0.418) (0.639) (0.555) (0.623) (5.028)Citizen Ideology 1.108 0.097 0.099 0.040 0.154

(0.029) (0.029) (0.040) (0.020) (0.106)Govt. Ideology 1.001 -0.009 -0.015 0.040 0.152

(0.010) (0.012) (0.016) (0.010) (0.048)Renewable Eng. Potential

1.003 -0.003 0.004 0.001 0.029

(0.001) (0.002) (0.001) (0.001) (0.006)Co2 Emissions 0.991 -0.035 -0.028 -0.005 0.013

(0.010) (0.019) (0.011) (0.020) (0.051)Gross State Product 7.68E+26 -52.124 109.979 47.193 338.358

(2.33E+28

)

(47.082) (49.016) (41.145) (422.357)

Net Metering Policy 0.733 0.216 0.061 -0.818 6.811(0.321) (0.595) (0.496) (0.549) (3.359)

Prop. Adopting Neighbors

0.613 1.378 1.077 -0.953 3.465

(0.496) (0.923) (0.958) (0.857) (7.416)Pop. Growth Rte. 1.08E+23 46.145 1.183 11.658 -23.814

(3.38E+24

)

(26.217) (15.385) (26.230) (137.387)

Intercept -6.559 -13.001 -12.007 -42.998(2.432) (3.331) (2.310) (17.120)

Number of Observations

767 767 767 767 940

R-squared 0.509

Numbers in parentheses are robust standard errors. Models 2, 3, and 4 contain cubic splines of time. Model 5 includes year fixed effects.

Figure 1. Cumulative adoption of RPS policies over time

19901992

19941996

19982000

20022004

20062008

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Percentage of states with RPS

24

Figure 2. Impact of compliance information on RPS survival rate at different levels of regulatory similarity

.75

.8.8

5.9

.95

1S

urvi

val R

ate

1990 1995 2000 2005 2010Year

Low SimilarityHigh Similarity

Legend

25

Figure 3. Impact of compliance information on RPS stringency at different levels of regulatory similarity

-20

-10

010

20M

argi

nal E

ffect

s

0 .2 .4 .6 .8 1Regulatory Sameness

Dashed lines give 90% confidence interval.

26

Figure 4. Impact of compliance information on RPS survival rate at different levels of enforcement capacity

.5.6

.7.8

.91

Sur

viva

l Rat

e

1990 1995 2000 2005 2010_t

Low capacityHigh capacity

27

Figure 5. Impact of compliance information on RPS stringency at different levels of enforcement capacity

-30

-20

-10

010

Mar

gina

l Effe

cts

0 .005 .01 .015 .02 .025Enforcement Capacity

Dashed lines give 90% confidence interval.

28