The Politics of Expertise in Policymaking: The Case of Erin’s Law Adoption and Diffusion Across the U.S. States

Abstract

This study examines whether and how policy entrepreneurs and their interactions with state legislatures influence the adoption and diffusion of a child abuse prevention policy, that is, Erin’s Law, across U.S. state legislatures. Employing 8 years of state-level data (2011–2018), we claim that a policy entrepreneur’s impact on policy adoption is conditional on the degree of legislative professionalism and the state’s political ideology. The event history analysis (EHA) and logistic regression (Logit) analyses reveal that policy entrepreneurs’ speaking engagements decrease the time to adoption and increase the likelihood of adoption, and the effect becomes stronger when states’ political ideology aligns with the political landscape surrounding the issue. However, our findings did not support the countervailing role of a policy entrepreneur in leveling gaps in the degree of legislative professionalism and ideological preferences across state legislatures.

Keywords

policy adoption and diffusion legislative professionalism political ideology Erin’s Law

Introduction

The literature on policy adoption and diffusion has advanced to demonstrate diverse factors that shape policy landscapes within various settings. The factors include state politics (Berry 1994; Berry and Berry 1990, 2018), regions (Berry 1994; Mooney 2001), legislative professionalism (Anderson, DeLeo, and Taylor 2019; Jansa, Hansen, and Gray 2019), policy entrepreneurs (e.g., Callaghan and Sylvester 2019; Kingdon 1995[1984]; Mintrom and Luetjens 2017; Vallett 2020), and the individual interactions with legislators (Jones et al. 2016). In his seminal work, Kingdon (1995[1984]) highlighted the pivotal role of policy entrepreneurs in agenda-setting and seeking windows of opportunity within the Multiple Streams Framework. This framework provides a foundation by examining key components or streams (i.e., problems that capture legislative attention, available policies that solve perceived problems, and politics surrounding the legislative process), which are combined through a policy window as entrepreneurs “push for [a] solution or to focus attention on a certain problem” (Kingdon 1995[1984], 179–84).

However, the evidence of policy entrepreneurs’ impact on policy choices is somewhat mixed, thus requiring empirical investigation to determine the conditions that affect policy adoption and diffusion. This study examines whether and how a policy entrepreneur and their interactions with state legislatures influence policy adoption and diffusion and whether the influence is conditional on the varying degree of legislative professionalism and states’ political ideology. As the primary lawmaking entity in a state, state legislatures are one of the essential strongholds for policy entrepreneurs advocating for specific legislation and changing the political environment. Since policy entrepreneurs’ ideas and beliefs are shaped and reinforced by policy problems under institutional constraints and political environments (Bakir and Javis 2017; Berry 1994), “[i]t is an open question of whether PE [policy entrepreneur] advocacy systematically promotes policy change” (Arnold 2021b, 972).

Leveraging a unique dataset on the visits made by a policy entrepreneur to state legislatures in pursuit of child abuse reform, we examine the role of Erin Merryn as a policy entrepreneur and expert in the adoption and diffusion of the child abuse prevention policy known as Erin’s Law from 2011 to 2018, during which 34 states gradually adopted the law. Child abuse within the United States became a major public concern after the publication of The Battered-Child Syndrome (Kempe et al. 1962). However, until Erin’s Law was enacted, state legislatures had entirely relied on mandatory reporting laws, which was laid upon street-level bureaucrats and healthcare professionals.

Erin’s Law is a compelling case study for two reasons. First, Erin’s Law was lobbied by a policy entrepreneur, Erin Merryn, who publicly represented the policy to state legislatures “from outside the formal governmental system” (Roberts and King 1991, 152) and was “willing to invest their resources in return for future policies they favor” (Kingdon 1995[1984], 214). Merryn was engaged in entrepreneurial activities, such as idea generation, problem framing, disseminating, lobbying, collaborating, enlisting support from elected officials, and attracting media attention (Roberts and King 1991). Second, Merryn’s visits solidify her role as an entrepreneur and policy expert, which pairs with the definition of street-level policy entrepreneur as a participant “who invest more time, energy and resources” (Arnold 2021a, 440) in establishing political networks and expertise toward achieving a policy goal. Erin Merryn has established these networks through formal encounters and connections with legislators, which is one of the key components of policy entrepreneurism (Stone 2019). She was already a policy expert in child abuse prevention and related reforms, as demonstrated by her background as a licensed social worker with a master’s degree (MSW) and the author of multiple books on child sex abuse prevention and experiences.

The next section reviews the roles, incentives, and impacts of policy entrepreneurs in the policy process and introduces the study context of child abuse, focusing on the role of Erin Merryn in adopting Erin’s Law across multiple states within the United States. We then present hypotheses connecting the policy entrepreneur to the child abuse prevention policy as a part of the policy process, professional legislatures in policymaking, and a case for interactive effects between legislative professionalism and political ideology. The data and measures are then described before we present the results of our statistical models in the following section. Finally, we discuss the implications of our findings and several caveats for future research.

Literature and Hypotheses

Roles of Policy Entrepreneurs

Policy entrepreneurs facilitate policy change by providing new information and pushing their policy solutions, even when not initially accepted by policymakers, through the window into which a policy can be adopted or altered (Kingdon 1995[1984]; Mintrom 2019; Shipan and Volden 2006; Roberts and King 1991). Policy entrepreneurs are alert to the political climate in order to utilize resources, particularly political capital, to drive political action (Kirzner 1973, 1985), and disturb “economic and social equilibriums” (Petridou and Mintrom 2021, 944).¹

From an economic approach to entrepreneurship, policy entrepreneurs are regarded as individual change agents, opportunists, and rational actors confined by institutional constraints, attempting to alter the status quo of policy arrangements (Callaghan and Sylvester 2019; Mintrom 2000; Petridou et al. 2015; Schneider and Teske 1992). Policy entrepreneurs use their information strategically to access lawmakers, obtain trust, and influence the agenda-setting process during opportune moments (Anderson et al. 2019; Mintrom 2019; Ruvalcaba-Gomez et al. 2023). Policy entrepreneurs seek to visit places and strategically create events that best utilizes their political capital in those states that are more likely to adopt (Christopoulos 2006). Policy entrepreneurs’ advocacy could mitigate collective action problems by bringing lawmakers and community members together through their political and social capital (Schneider and Teske 1992; Teske and Schneider 1994). Teske and Schneider (1994) showcase how policy entrepreneurs are motivated to foster greater efficiency by facilitating collaboration among public employees and other relevant political stakeholders.

Erin Merryn’s entrepreneurship and her formal encounters with legislators may help them to increase their awareness of the problem and process relevant information, potentially leading to the adoption and diffusion of Erin’s Law as a policy solution. First, Merryn took an active role in sharing her experience and leveraging her expertise. This effort altered the policy narrative by framing the issue as one of educating and safeguarding children, rather than solely focusing on the reporting of abuse. The adapted narrative also softened the discussion away from the sensitive topic of a sex education curriculum and focused on the more politically beneficial topic of child abuse prevention. Previous literature demonstrates that policy entrepreneurs are also motivated by normative and cognitive ideas and beliefs based on the logic of appropriateness (Bakir and Jarvis 2017; March and Olsen 2008). They advocate policies focused on morality politics (Mintrom 2013), such as child abuse, by shaping the conversation to be more politically palatable.

Second, state legislators, particularly those with limited resources, use the information obtained from policy entrepreneurs as a heuristic for decision-making (Anderson et al. 2019). While they would ultimately decide to adopt Erin’s Law on moral grounds (Mooney and Lee 1999), legislators may hold off on policy adoption until the policy turns out to be effective elsewhere (Gilardi, Füglister, and Luyet 2009; Shipan and Volden 2012). We expect that the policy process is significantly affected by the intensity of the entrepreneur’s efforts (Arnold 2021b; Mintrom 2013) and the frequency of formal interactions with legislatures, such as visits and testimonies (Vallett 2020).

Hypothesis 1: State legislatures that received more encounters with a policy entrepreneur are more likely to adopt Erin’s Law and to do so more quickly.

Legislative Professionalism and State Politics Shaping the Entrepreneurial Roles

The literature suggests professional legislatures are more effective in adopting legislation (Hoffman and Lyons 2016) and more responsive to their constituents’ needs (Harden 2013; Miller 2013). Professional legislatures have several advantages in that they have more time and resources (Squire and Moncrief 2015, 64–65) and experience less turnover, which allows for more expertise in playing their role over time (Moncrief, Niemi, and Powell 2004), such as managing challenging policy environments (Gamm and Kousser 2010), devising innovative policies (McCann, Shipan, and Volden 2015), and emulating other states’ policies (Shipan and Volden 2014). With greater expertise, time, and access, we expect that professional legislatures will more likely adopt a policy and do it more rapidly.

However, we lack a clear picture of how legislatures with different degrees of professionalism respond to interactions with policy en-trepreneurs and how these interactions impact policy adoption. Moreover, our understanding of the contextual factors of a policy in relation to legislative professionalism is limited. Bakir and Jarvis (2017, 471) noted the lack of understanding of “how interactions within and across structural, institutional, organizational and individual levels create complementarities that enable or constrain institutional entrepreneurship.” As reinforced by Zahariadis and Exadaktylos (2016) the work of the policy entrepreneur is strategic including the context in which they choose to operate. Being aware of the resources available to a particular group of legislatures may influence entrepreneurs’ actions when choosing to influence the policy process. We expect the legislature-entrepreneur relationships will differ by the degree of legislative professionalism and hypothesize the role of policy entrepreneurs in countervailing the gaps.

On the one hand, professional legislatures may or may not seek outside expertise because of the wealth of internal resources already available to them. More professionalized legislatures may have their own expertise and are less inclined to rely on information provided by a policy entrepreneur (Berkman 2001), especially when the entrepreneur does not provide new or reliable information (Anderson et al. 2019). But when interacting with a capable and reliable policy entrepreneur, professional legislatures tend to process the information more quickly and efficiently (Anderson et al., 2019) while collaborating with outside groups more frequently (Maestas 2003).

On the other hand, legislators may be more receptive to a policy proposed by a policy entrepreneur when their legislatures are short of time, expertise, and resources. Legislatures lacking the capacity to collect the information independently could exhibit a higher propensity to reach out to professionals or entrepreneurs to help secure a particular policy, in which case, entrepreneurs may serve as an alternative source of information and make up for the deficiency of expertise in legislatures. If we observe the countervailing effect, any difference in the time spent on adoption or the likelihood of adoption between more and less professional legislatures will dissipate as the presence of a policy entrepreneur increases.

Hypothesis 2A: State legislatures with a higher level of professionalism are more likely to adopt Erin’s Law and to do so more quickly.

Hypothesis 2B: The effect of legislative professionalism on policy adoption and diffusion will decrease when state legislatures receive more encounters with policy entrepreneurs.

Policy adoption and diffusion are highly political decisions reflecting politics surrounding the legislative process. Among other factors, states’ political/ideological preferences likely shape the politics of expertise engaged in policy entrepreneurs’ strategic activities to shift the political context concerning the policy issue. Policy entrepreneurs must navigate the politics stream within the legislatures to push all three streams together through the policy window (Kingdon 1995[1984]). Thus, policy entrepreneurs form a “symbiotic relationship” with legislators because they enact policies that policy entrepreneurs approve, and policy entrepreneurs help legislators to “survive in their election-dependent world” (Polsby 1984, 171–2). State legislatures are more likely to act when constituents are involved through requests, testimonies, lobbies, and statements in the committee process brought together through the efforts of a policy entrepreneur (Cluverius 2017; Grasse and Heidbreder 2011).

Considering the role of policy entrepreneurs participating in policy debates within various ideological circumstances, we present two hypotheses. First, in our policy context, we expect states that align with a liberal ideology will be more amenable to policy entrepreneurs’ efforts to enact Erin’s Law. States governed by a more liberal ideology are likely to adopt Erin’s Law and more promptly do so, as they tend to dedicate more funds toward education and social justice causes (Beland and Oloomi 2017; Hill and Jones 2017). Erin’s Law requires additional education spending as it alters the policy approach from mandatory reporting to an education policy (Fowler and Vallett 2021). Politically liberal states often prioritize the well-being of vulnerable populations such as children and are likely to make policy choices investing substantial resources into education, and social welfare programs, such as initiatives addressing child abuse prevention. Studies demonstrated that legislatures controlled by Democrats significantly increase spending toward family assistance programs (Besley and Case 2003) and prioritize violence and adverse childhood experiences (child abuse) as major risk factors, compared to their Republican and conservative counterparts (Purtle et al. 2019). Crowley et al. (2022) also illustrate that Democrats are more likely to sponsor or co-sponsor legislation to minimize adverse childhood experiences. Therefore, we expect policy entrepreneurs’ legislative efforts will be more effective depending on states’ political ideology.

Further, we hypothesize that the effect of policy entrepreneurs’ legislative efforts will depend on states’ political ideology. On the one hand, policy entrepreneurs could mitigate the gaps in policy adoption and diffusion among states with different ideological preferences. The ideological knowledge gaps in framing problems, identifying solutions, and adopting policies may diminish when policy entrepreneurs actively spread innovative ideas and change political realities (Polsby 1984). As policy entrepreneurs aim to influence the policy process through various mechanisms such as defining the issue, manipulating the policy narrative, funding policy adoption and diffusion, and providing expertise and information (Anderson et al. 2019; Arnold 2021a, 2021b; Callaghan and Sylvester 2019; Petridou and Mintrom 2021), the effect of state ideology may decrease when the state receives more encounters with policy entrepreneurs.

On the other hand, instead of seeking to countervail the ideological gaps, policy entrepreneurs may rather wait for the “background conditions” (political context) to become more favorable for the changes as part of strategic deliberation (Roberts and King 1991, 172). Policy entrepreneurs endeavor “to gain an advantage in the pursuit of their policy goals,” and reading and using the politics stream is one of their key strategies (Jones et al. 2016, 14). They also seek an advantage by framing the policy problem (Mintrom 2019) and a preferred solution in a way that aligns with policymakers’ interests and preferences (e.g., framing Erin’s Law as a social problem with an educational solution). Thus, entrepreneurial efforts to persuade legislators to become allies will move the policy agenda and preferred solutions forward (Roberts & King 1991), but the changes may occur only in states where the policy issues and solutions align with the political ideology of the government. In this case, we may observe policy adoption and diffusion through Erin’s Law legislation facilitated by Merryn’s visits and testimonies in liberal states but not conservative states.

Hypothesis 3A: States with a more liberal ideology are more likely to adopt Erin’s Law and to do so more quickly.

Hypothesis 3B: The effect of state government ideology on policy adoption and diffusion will increase when state legislatures receive more encounters with policy entrepreneurs.

Data and Measures

Dependent Variable

The dependent variable of this study is the adoption of Erin’s Law, recorded as the year each state adopted the law: 1 for the year the law was enacted and 0 for other years. The use of this variable differs depending on the model being analyzed, as described in the sections below.

Independent Variables

Policy Entrepreneur’s Influence

Policy entrepreneurs’ influence is measured by counting how often they participated in formal legislative speaking engagements for each state each year (0 for non-participating states). Merryn (2013), a social worker from the State of Illinois and a child sex abuse victim, recognized the lack of action and lobbied the Illinois State Legislature in 2010 with Senator Tim Bivins. Merryn’s speaking engagement began in 2011 and continued until 2018, through 111 visits to 36 states, and 34 states adopted the law (Figures 1 and 2). We gathered this data from Merryn’s website, which lists her engagements from 2010 through 2018. The invitation for these engagements was confirmed through personal communication. Examining the number of visits is important, as it aligns with the frequency of interactions a legislature has with a policy entrepreneur and demonstrates her efforts and capacity to network.

Figure 1.

Number of states adopting Erin’s Law each year.

Figure 2.

U.S. map of Erin’s Law adoption.

Merryn’s visits were typically spurred through her mass emails to legislators explaining the purpose of Erin’s Law and resulting in invitations to testify in legislative committees. However, this was not always the case. As detailed in Merryn’s (2013) book, An Unimaginable Act, the invitation structure and triggering events varied across states. For example, the request to testify in Michigan followed a chance meeting at a family wedding, whereas she testified to a legislature reeling from the Sandusky scandal in Penn-sylvania. In some cases, such as with Arkansas and New York, Merryn visited multiple times in a year or over several years (Figure 3).

Figure 3.

Number of visits for each state per year through 2018.

Legislative Professionalism

Legislative professionalism has been conceptualized and measured in two distinct ways (Bowen and Green 2014). One is to use an index of professionalism focusing on the legislative capacity “of the legislature to perform its role in the policymaking process” (Mooney 1994, 70–71) or “the capacity of both legislators and legislatures to generate and digest information in the policymaking process” (Squire 2017, 362). Ranging from 0 to 100, a revised Squire Index provides a comprehensive understanding of the capabilities available across state legislatures (Squire 2017). Another way is to use the components of “professional” legislatures, such as longer sessions, more staff and expenditures, and higher salaries (Squire 2017; Squire and Moncrief 2015). Scholars argue that using each component better accounts for the divergent effect of legislative professionalism on both policy adoption and diffusion (Bowen and Greene 2014; Jansa et al. 2019).

We use three conceptual components of the Squire Index: time in session, legislative expenditure, and salary. First, the longer duration of sessions increases the opportunity for legislators to interact with a policy entrepreneur and deliberate the policy, increasing the likelihood of legislative actions. We posit that a professional legislature will allow more time for Merryn to testify on child abuse and consider the policy problem. Using the Book of the States (BOS), we place all dates into legislative days by deflating those numbers reported in calendar days by five-sevenths. This process turns a seven-calendar day schedule into a five working day comparable schedule. While this method is imperfect,² it has been universally used and serves as a reasonable proxy for legislative professionalism, more nuanced than an index score (Bowen and Greene 2014; Jansa et al. 2019; Squire 2017).

Second, we anticipate that states with a higher per-legislator expenditure have greater access to staffing, research, and expertise, facilitating legislative efforts to address policy problems. Moncrief and Squire (2017) argue that as staff numbers increase, the capacity for policy making also increases. The literature suggests that professional legislatures rely on internally generated information from professional staff and resources (Berkman 2001). Following Bowen and Greene (2014), we calculate expenditures per legislator in 2018 constant dollars based on the U.S. Census Bureau’s Annual Survey of State Government Finances expenditure data.

The last component of legislative professionalism is legislative salary, which provides insight into legislators’ professional experience. Higher salary encourages talented employees to run for office (Carnes and Hansen 2016) and allow them to “find the new, professional institution an attractive place in which to build a career” (Moncrief et al. 1996, 57). Conversely, legislators in low-salary states are hampered by an additional workload as they “often juggle outside careers in addition to their legislative workload” (Maestas 2003, 444). We pull the base salary data from the BOS to sufficiently approximate total compensation for a state com-parison.³

Political Ideology of State Government

We use Berry et al. (1998)’s revised government ideology series to measure the political ideology of the state government, with zero representing the most conservative and 100 the most liberal position. The data provides a more nuanced examination of state government ideology as it is not limited to presidential years (e.g., Warshaw andTausanovitch 2022). It has continued to be validated as an effective measurement of government ideology and is recommended for state-year panel data analysis (Berry et al. 2013).

Control Variables

A list of control variables is included to account for their influence on policy adoption and diffusion. First, the literature on policy coalition, diffusion, and institutional isomorphism explains how innovations are communicated through social systems (Rogers 1995) and political structures (Fay and Wenger 2016). We measure the regional effect of geographic proximity by counting the proportion of bordering states that have already adopted Erin’s Law for a given year compared to the total number of bordering states. However, evidence demonstrates a mixed effect of geographical distance, suggesting that neighboring states may choose not to adopt when the policy is not in the state’s best economic interest or may suggest a political connotation the state is unwilling to assume (Flink et al. 2021; Mooney 2001). Therefore, we consider ideological distance to account for the effect of ideological distance between states and previous adopters on policy adoption (Boehmke and Skinner 2012; Grossback et al. 2004; Mallinson 2021), expecting that states are less inclined to adopt a policy when previous adopters are more ideologically distant at the time of policy adoption. We employ the updated 2017 government ideology series by Berry et al. (1998).

Second, we include two other variables to account for the legislative makeups: first, we consider the political polarization of members in the house of a state legislature by employing Shor’s (2018) aggregated state legislator data measured at the chamber level for each state (for details, Shor and McCarty 2011). The ideological distances within the legislative body may slow the legislative process (for detailed review, Coleman 1999; Hughes and Carlson 2015), while it may depend on the policy issue. We expect more polarized states to experience more conflicts when negotiating policy differences, which confounds or delays policy adoption. Closely linked to political polarization, a divided government may also influence the length of policymaking processes. The legislative impact of divided or unified government has been extensively studied, while the empirical findings are mixed (Coleman 1999; Edwards, Barrett, and Peake 1997). Some scholars did not find evidence of legislative gridlock and more conflicts under a divided government (Mayhew 1991), while others argue that important legislation is being delayed due to the conflicts between the legislative and executive branches (Hughes and Carlson 2015; Sundquist 1992). On the other hand, a unified government where the same party controls the governor and both legislative houses may easily find consensus among the government on legislation (Berry and Berry 1990; Coleman 1999).

Third, we recognize that advocacy for child abuse prevention policy is occurring beyond the work of Erin Merryn. We consider this effect by including three variables that measure different forms of advocacy focused on child abuse prevention. First, using records from the U.S. Department of Health and Human Services and the Child Welfare Information Gateway, we count the number of National Child Abuse Prevention Partner Organizations in each state.⁴ Each of these national organizations may perform different levels of advocacy for child abuse prevention at a national level, but their presence in a particular state demonstrates the specific advocacy efforts. Second, we consider the presence of the Prevent Child Abuse America chapter within a state. As one of the most prominent child abuse prevention in the United States, Prevent Child Abuse America supports state chapters tasked with greater child abuse advocacy efforts at the state level. Currently, 40 states are hosting a Prevent Child Abuse America chapter that is actively engaging in state advocacy. Third, we account for the number of parent advocacy groups in a state recorded by the U.S. Department of Health and Human Services to monitor non-Merryn advocacy efforts within a state. These groups are important to study as recent literature suggests that policy entrepreneurship is a group affair (Capano and Galanti 2021; Mintrom and Norman 2009), and the efficacy of these different advocacy groups may differ.

Fourth, we account for the perceived severity of the policy problem by including the number of child abuse reports by educators per 10,000 population and the number of child abuse reports that received an investigation per 10,000 population, given that lawmakers are more likely to attend to perceived problems and proceed with potential policy solutions. We also include racial discrepancies that may exist between the number of white victims and non-white victims by including the percentage of white victims to non-white victims per 10,000 population. This is particularly important because minorities disproportionately experience child abuse reporting (Diyaolu et al. 2023; Luken, Nair, and Fix 2021). In addition, scholars have observed that children born to teen mothers or mothers with limited education are more vulnerable to abuse (Brown et al. 1998). This does not mean that a child born in these situations will be abused, but the environment surrounding these factors may result in additional pressures, which lead to a greater possibility of abuse (Gelles and Cavanaugh 2009). While these variables are measured at intervals, they are reported annually, producing yearly values for each attribute.

Fifth, the model includes a set of variables for legislative characteristics: first, according to gender politics scholarship, we account for the potential gender difference in agenda-setting by including the gender of state governors and the percentage of women legislators in a state legislature (Bratton and Haynie, 1999; Childs and Krook 2009; Flink et al. 2021). Women officeholders may increase legislative attention and favorable action concerning issues traditionally associated with women, such as a child support policy (Carroll 2001; Wilkins 2006). Both Purtle et al. (2019) and Crowley et al. (2022) found that women legislators were more supportive in their sponsoring and voting for adverse childhood experience legislation that addressed the prevention of child abuse. However, the findings surrounding gender differences among state legislators remain inconsistent (Poggione 2004). In particular, Rosenthal (1998) found that male and female legislators have similar leadership styles within professional legislatures. Second, we include the number of legislators in each state to control the size of the legislature. Size is important for two reasons; previous literature demonstrates that large organizations are typically more innovative (Berry 1994), as they have more resources or slack resources available (Cyert and March 1963). Third, we also examined the existence of term limits, which may shift legislators’ attention and increase responsiveness to new policies (Lax and Philips, 2012). Term limits force legislatures to work within a limited timeframe to accomplish desired policies, encourage rapid adoption, and focus more on specific policy goals. Carey, Niemi, and Powell (1998) demonstrated that legislatures with term limits spend significantly more time developing legislation and pushing personal legislative priorities while passing laws more quickly.

Finally, the model includes the state GDP per capita and the share of social spending measured by the percentage of public welfare expenditures serving as a predictor of state legislatures’ commitment to public welfare (Barrilleaux, Holbrook, and Langer 2002). States with higher GDP tend to have more resources available at their disposal, which can support legislative research or agencies to review and vet a policy, leading to a policy being adopted more quickly.

Descriptive statistics and correlations among variables are presented in Tables 1 and 2.

Table 1.

Descriptive Statistics of Key Variables.

Variable	Observations	Mean	Standard deviation	Min	Max
Number of visits by Erin Merryn	400	0.278	0.642	0.000	4.000
Legislative professional score	400	22.546	10.414	4.800	62.900
Number of legislative days in session	400	80.367	58.985	0.000	260.714
Expenditures per legislator	400	515.032	944.119	35.571	12,290.24
Legislative salary	400	43,908.9	33,529.46	0.000	173,510
State government ideology	400	41.144	17.583	17.512	73.495
Percent of neighboring states	400	0.334	0.306	0.000	1.000
Ideology distance	400	16.422	9.442	0.047	49.236
Polarization of members in the house	381	1.646	0.493	0.487	3.041
Unified government	400	0.673	0.470	0.000	1.000
National Prevention Partners	400	0.840	1.272	0.000	5.000
Prevent Child Abuse America	400	0.82	0.385	0.000	1.000
Parent advocacy groups	400	1.140	1.300	0.000	6.000
Number of reports of suspected abuse¹	400	12.524	5.724	0.000	45.605
Number of total cases investigated¹	400	103.122	44.128	0.000	306.125
Percent of white victims to non-white victims¹	399	1,156.793	667.514	133.542	3,589.081
Gender of a state governor	400	0.133	0.339	0.000	1.000
Number of women in a state legislature	400	0.244	0.070	0.094	0.420
Number of teen births per 10,000	400	7.774	3.014	2.380	18.004
Number of teen dropouts per 10,000	400	23.087	7.417	7.483	51.228
Total number of legislators in each state	400	147.66	59.67	49	424
Presence of term limits	400	0.298	0.458	0.000	1.000
Gross Domestic Product (GDP)	400	0.053	0.011	0.324	0.087
Percentage of public welfare expenditures	400	0.255	0.051	0.130	0.380

¹Data made available by the National Data Archive on Child Abuse and Neglect, Cornell University, Ithaca, NY, are used with permission. These data were originally supplied by state Child Protective Service agencies and the Children’s Bureau of the Administration on Children, Youth and Families, U.S. Department of Health and Human Services. Neither the collectors of the original data, the funder, the archive, Cornell University, nor its agents or employees bear any responsibility for the analyses or interpretations presented here.

Table 2.

Correlations Among Variables.

	Number of visits by Erin Merryn	Legislative professional score	Number of legislative days in session	Expenditures per legislator	Legislative salary	Percent of neighboring states	Number of reports of suspected abuse	Number of total cases investigated	Percent of white victims to non-white victims	Gender of state governor	Number of women in state legislator	Polarization of members in the house	Unified government	Legislative control	Presence of term limits	Party of president that carried the state	Total number of legislators in each state	Number of teen births per 10,000	Number of teen dropouts per 10,000	Gross Domestic Product (GDP)	Percentage of public welfare expenditures
Number of visits by Erin Merryn	1.000
Legislative professional score	0.107	1.000
Number of legislative days in session	0.089	0.601	1.000
Expenditures per legislator	0.045	0.631	0.266	1.000
Legislative salary	0.071	0.826	0.680	0.517	1.000
Percent of neighboring states	0.022	0.072	0.047	0.070	0.019	1.000
Number of reports of suspected abuse	−0.052	−0.101	0.013	−0.046	−0.024	0.139	1.000
Number of total cases investigated	−0.023	−0.085	−0.057	−0.031	−0.051	0.106	0.688	1.000
Percent of white victims to non-white victims	−0.033	0.306	0.159	0.160	0.237	0.056	0.167	0.289	1.000
Gender of state governor	−0.078	−0.121	−0.076	−0.094	−0.163	−0.039	−0.022	0.046	0.212	1.000
Number of women in state legislator	−0.012	0.175	0.114	0.037	0.047	0.010	−0.027	−0.265	−0.047	−0.015	1.000
Polarization of members in the house	0.056	0.225	−0.011	0.344	0.083	0.183	−0.091	−0.095	−0.068	0.070	0.405	1.000
Unified government	0.028	0.013	−0.062	0.065	0.003	0.015	−0.011	0.051	−0.052	0.051	−0.270	−0.090	1.000
Legislative control	−0.018	0.325	0.218	0.185	0.236	−0.089	−0.046	−0.197	0.148	0.016	0.591	0.022	−0.213	1.000
Presence of term limits	−0.038	0.199	0.044	0.206	0.046	0.160	−0.029	0.126	0.061	−0.020	0.067	0.268	−0.006	−0.151	1.000
Party of president that carried the state	0.019	0.317	0.250	0.168	0.263	−0.140	−0.158	−0.342	0.062	0.007	0.509	0.049	−0.271	0.678	−0.124	1.000
Total number of legislators in each state	0.036	−0.020	0.127	−0.078	0.062	0.019	0.047	−0.110	−0.216	0.070	−0.013	0.030	−0.134	−0.071	−0.187	0.115	1.000
Number of teen births per 10,000	0.112	−0.201	−0.262	−0.083	−0.211	−0.250	−0.031	0.268	0.133	0.140	−0.464	−0.101	0.216	−0.366	0.157	−0.501	−0.231	1.000
Number of teen dropouts per 10,000	0.007	−0.182	−0.189	−0.134	−0.193	−0.083	−0.054	0.156	0.209	0.199	−0.250	0.044	0.120	−0.297	0.205	−0.394	−0.214	0.669	1.000
Gross Domestic Product (GDP)	−0.024	0.341	0.245	0.196	0.362	0.066	0.043	−0.223	0.227	−0.199	0.210	−0.046	−0.117	0.396	−0.272	0.401	0.011	−0.543	−0.425	1.000
Percentage of public welfare expenditures	−0.003	0.263	0.239	0.163	0.244	0.439	0.239	0.281	0.189	0.045	0.063	0.145	−0.001	0.038	0.155	−0.019	0.230	−0.140	−0.121	−0.017	1.000

Findings

This section presents our estimation results for the timing and the likelihood of policy adoption using event history analysis (EHA) and logistic regression (Logit), respectively. Previous studies validate applying both EHA and Logit models with a random effects estimator for analyzing the adoption and diffusion process (Flink et al. 2021; Shipan and Volden 2008; Vallett 2020; Zhang and Zhu 2019). Examining multiple model types, Berry and Berry (2018) determined EHA models as an ideal methodology for diffusion studies while not perfect. Using both an EHA and Logit model provides two advantages in testing our hypotheses. First, it allows us to examine the timing (EHA) and the likelihood (Logit) of adopting the policy of interest by accounting for both internal and external characteristics in a single model (Berry and Berry 2018). Second, as explained by Berry and Berry (1990), these models “can assess the effects on the probability of adoption of characteristics of states that vary substantially from year to year” (p. 399), which is not the case for traditional cross-sectional methods. Our analysis estimates the EHA model using a Weibull distribution that provides the lowest Bayesian information criterion (BIC) and Akaike information criterion (AIC) compared to alternative distributions.

We present the EHA and Logit results in Tables 3 and 4, including the linear and interaction models. In the linear EHA and Logit models, we find support for Hypothesis 1, suggesting the number of visits made by the policy entrepreneur remains statistically significant (p < .10). In the EHA model, the degree of legislative professionalism measured by the Squire index does not significantly influence the length of time before adoption (HR = 1.019, p > .10), contrary to Hypothesis 2A (Model 3A). We found similar results in Model 3C where the number of entrepreneur visits interacted with the professional index score (HR = 1.021, p > .10). The logistic regression (Logit) demonstrates comparable findings (Models 4A and 4C). Thus, Hypothesis 2B, expecting the moderating role of policy entrepreneurism in more or less professionalized legislatures, was not supported. However, we find that entrepreneurism measured by the number of visits does influence the likelihood of adoption regardless of the professional status of the state legislature (LR = 1.622, p < .05, Model 4C).

Table 3.

EHA Models for Time to Adoption of Erin’s Law.

Model	(3A)	(3B)	(3C)	(3D)	(3E)	(3F)	(3G)
Model	Squire index	Professional components	Squire index interacted w/visits	Session interacted w/visits	Expenditure interacted w/visits	Salary interacted w/visits	Ideology interacted w/visits
Number of visits	2.061** (0.556)	2.184** (0.621)	1.305 (1.083)	2.873^† (1.423)	2.043^† (0.781)	1.566 (0.851)	0.469 (0.031)
Squire index	1.019 (0.030)	—	1.008 (0.036)	—	—	—	1.018 (0.030)
Number of days in session	—	0.997 (0.005)	—	1.000 (0.008)	0.997 (0.005)	0.998 (0.005)	—
Expenditures per legislator	—	1.000 (0.000)	—	1.000 (0.000)	1.000 (0.000)	1.000 (0.000)	—
Legislative salary	—	1.000 (0.000)	—	1.000 (0.000)	1.000 (0.000)	1.000 (0.000)	—
State government ideology	1.157** (0.033)	1.149** (0.034)	1.156** (0.032)	1.151** (0.035)	1.149** (0.034)	1.152** (0.035)	1.135** (0.031)
Squire × No. of visits	—	—	1.021 (0.037)	—	—	—	—
No. of visits × Legislative days	—	—	—	0.996 (0.007)	—	—	—
No. of visits × Expenditures	—	—	—	—	1.000 (0.000)	—	—
No. of visits × Salary	—	—	—	—	—	1.000 (0.000)	—
No. of visits × State government ideology	—	—	—	—	—	—	1.043** (0.015)
% Neighboring states	0.031** (0.033)	0.030** (0.033)	0.028** (0.030)	0.034** (0.038)	0.029** (0.032)	0.036** (0.039)	0.052** (0.055)
Ideology distance	0.895** (0.048)	0.906^† (0.050)	0.895* (0.048)	0.901^† (0.052)	0.906^† (0.051)	0.907^† (0.051)	0.900^† (0.050)
Polarization of the house	2.040 (1.170)	1.894 (1.253)	1.895 (1.104)	2.095 (1.433)	1.908 (1.285)	2.138 (1.489)	3.869* (2.365)
Unified government	3.974^† (3.249)	3.215 (2.610)	4.009^† (3.318)	3.517 (2.934)	3.245 (2.642)	3.328 (2.763)	3.258 (2.769)
National Prevention Partners	1.027 (0.244)	1.037 (0.251)	1.041 (0.246)	1.027 (0.249)	1.044 (0.254)	1.015 (0.245)	0.807 (0.197)
Prevent Child Abuse America	9.219** (7.516)	8.001* (6.673)	9.039** (7.450)	8.086* (6.823)	8.107* (6.755)	7.043* (5.941)	9.946** (8.320)
Parent advocacy groups	0.808 (0.196)	0.813 (0.210)	0.819 (0.199)	0.786 (0.212)	0.814 (0.211)	0.779 (0.211)	0.826 (0.214)
No. of reports of suspected abuse	1.263* (0.114)	1.258* (0.121)	1.262* (0.115)	1.262* (0.122)	1.261* (0.123)	1.268* (0.128)	1.255** (0.106)
No. of the total cases investigated	0.970** (0.009)	0.971** (0.009)	0.969** (0.009)	0.971** (0.009)	0.970** (0.010)	0.970** (0.010)	0.969** (0.009)
% White to non-white victims	1.001^† (0.000)	1.001 (0.000)	1.001^† (0.000)	1.001 (0.001)	1.001 (0.000)	1.001 (0.000)	1.001* (0.000)
No. of teen births per 10,000	1.299 (0.233)	1.311 (0.237)	1.318 (0.239)	1.306 (0.236)	1.322 (0.242)	1.318 (0.240)	1.291 (0.229)
No. of teen dropouts per 10,000	1.056 (0.056)	1.059 (0.057)	1.054 (0.056)	1.059 (0.058)	1.057 (0.058)	1.057 (0.057)	1.054 (0.055)
Gender of a state governor	1.758 (1.230)	1.936 (1.398)	1.743 (1.240)	1.950 (1.404)	1.910 (1.385)	1.823 (1.336)	1.333 (0.934)
No. of women in a state legislature	0.000* (0.000)	0.000** (0.000)	0.000* (0.000)	0.000* (0.001)	0.000* (0.000)	0.000** (0.000)	0.000** (0.000)
No. of legislators in each state	1.015** (0.005)	1.013* (0.005)	1.014** (0.005)	1.014* (0.005)	1.013^* (0.006)	1.014^* (0.005)	1.019** (0.005)
Presence of term limits	3.811* (2.303)	3.814* (2.323)	3.581* (2.212)	3.871* (2.379)	3.919* (2.419)	3.575* (2.225)	2.506 (1.496)
Gross Domestic Product (GDP)	0.000** (0.000)	0.000** (0.000)	0.000** (0.000)	0.000** (0.000)	0.000** (0.000)	0.000** (0.000)	0.000** (0.000)
% Welfare expenditures	0.000 (0.000)	0.000 (0.000)	0.000 (0.000)	0.000 (0.000)	0.000 (0.001)	0.000 (0.000)	0.000* (0.000)
Constant	0.000** (0.000)	0.000** (0.000)	0.000** (0.000)	0.000** (0.000)	0.000** (0.000)	0.000** (0.000)	0.000** (0.000)
Summary statistics
N	270	270	270	270	270	270	270
BIC	186.633	191.894	195.742	201.075	201.269	200.840	183.432
χ²	93.92 (23 df)	96.01 (24 df)	94.26 (25 df)	96.27 (26 df)	96.08 (26 df)	96.51 (26 df)	102.72 (24 df)

Note: Standard errors in parentheses.

p < .05. **p < .01. ^†p < .10.

Table 4.

Logit Models for the Likelihood of Adoption of Erin’s Law.

Model	(4A)	(4B)	(4C)	(4D)	(4E)	(4F)	(4G)
Model	Professional index	Professional components	Index interacted w/visits	Session interacted w/visits	Expenditure interacted w/visits	Salary interacted w/visits	Ideology interacted w/visits
Number of visits	1.256** (0.282)	1.236** (0.273)	1.622* (0.779)	1.320** (0.423)	1.131** (0.362)	1.216** (0.436)	0.340 (0.548)
Squire index	−0.028 (0.029)	—	−0.021 (0.032)	—	—	—	−0.026 (0.028)
No. of days in session	—	−0.005 (0.005)	—	−0.004 (0.006)	−0.005 (0.005)	−0.005 (0.005)	—
Expenditures per legislator	—	0.000 (0.000)	—	0.000 (0.000)	0.000 (0.000)	0.000 (0.000)	—
Legislative salary	—	0.000 (0.000)	—	0.000 (0.000)	0.000 (0.000)	0.000 (0.000)	—
State government ideology	0.060** (0.021)	0.047* (0.022)	0.060** (0.021)	0.047* (0.022)	0.047* (0.022)	0.047* (0.022)	0.050* (0.022)
Squire × No. of visits	—	—	−0.015 (0.030)	—	—	—	—
No. of visits × Legislative days	—	—	—	−0.001 (0.004)	—	—	—
No. of visits × Expenditures	—	—	—	—	0.000 (0.000)	—	—
No. of visits × Salary	—	—	—	—	—	0.000 (0.000)	—
No. of visits × State government ideology	—	—	—	—	—	—	0.026^† (0.014)
% Neighboring states	0.536 (0.840)	0.750 (0.858)	0.593 (0.852)	0.749 (0.860)	0.686 (0.867)	0.750 (0.858)	0.371 (0.843)
Ideology distance	−0.050 (0.032)	−0.050 (0.032)	−0.051 (0.032)	−0.051 (0.032)	−0.050 (0.032)	−0.050 (0.032)	−0.054^† (0.033)
Polarization of the house	0.565 (0.593)	−0.008 (0.639)	0.530 (0.597)	−0.008 (0.640)	0.039 (0.651)	0.033 (0.666)	1.014 (0.657)
Unified government	0.548 (0.590)	0.272 (0.587)	0.578 (0.593)	0.282 (0.588)	0.271 (0.587)	0.271 (0.588)	0.515 (.605)
National Prevention Partners	0.011 (0.223)	−0.049 (0.294)	0.016 (0.222)	−0.053 (0.230)	−0.049 (0.229)	−0.052 (0.234)	−0.177 (0.259)
Prevent Child Abuse America	0.539 (0.652)	0.657 (0.662)	0.585 (0.659)	0.662 (0.663)	0.632 (0.660)	0.655 (0.664)	0.505 (0.671)
Parent advocacy groups	−0.514^† (0.285)	−0.494^† (0.294)	−0.509^† (0.283)	−0.487^† (0.294)	−0.480^† (0.293)	−0.496^† (0.296)	−0.545^† (0.294)
No. of reports of suspected abuse	0.035 (0.055)	0.047 (0.055)	0.033 (0.055)	0.046 (0.055)	0.046 (0.055)	0.047 (0.055)	0.030 (0.057)
No. of the total cases investigated	−0.001 (0.007)	−0.001 (0.007)	0.000 (0.007)	−0.001 (0.007)	−0.001 (0.007)	−0.001 (0.007)	−0.001 (0.007)
% White to non-white victims	0.000 (0.000)	0.000 (0.000)	0.000 (0.000)	0.000 (0.000)	0.000 (0.000)	0.000 (0.000)	0.000 (0.000)
No. of teen births per 10,000	−0.197 (0.128)	−0.218^† (0.132)	−0.205 (0.130)	−0.220^† (0.133)	−0.214^† (0.132)	−0.217 (0.133)	−0.208 (0.130)
No. of teen dropouts per 10,000	0.028 (0.040)	0.043 (0.041)	0.031 (0.041)	0.044 (0.041)	0.041 (0.042)	0.043 (0.042)	0.018 (0.041)
Gender of a state governor	0.275 (0.639)	0.501 (0.647)	0.295 (0.639)	0.502 (0.647)	0.487 (0.647)	0.499 (0.648)	0.375 (0.660)
No. of women in a state legislature	−7.261 (5.098)	−4.343 (5.077)	−6.911 (5.143)	−4.322 (5.087)	−4.656 (5.149)	−4.394 (5.155)	−9.554^† (5.448)
No. of legislators in each state	0.001 (0.004)	0.002 (0.004)	0.002 (0.004)	0.002 (0.004)	0.002 (0.004)	0.002 (0.004)	0.001 (0.004)
Presence of term limits	0.245 (0.516)	−0.203 (0.515)	0.111 (0.532)	−0.188 (0.516)	−0.183 (0.514)	−0.206 (0.517)	−0.041 (0.523)
Gross Domestic Product (GDP)	−21.475 (31.531)	−36.902 (32.928)	−22.382 (31.679)	−37.556 (33.099)	−36.110 (33.004)	−36,541 (33.491)	−15.223 (31.765)
% Welfare expenditures	−6.254 (5.732)	−8.362 (5.896)	−6.837 (5.857)	−8.654 (6.002)	−7.867 (6.002)	−8.311 (5.962)	−4.729 (5.935)
Constant	−1.068 (3.086)	0.213 (3.163)	−1.210 (3.102)	0.222 (3.167)	0.196 (3.178)	0.203 (3.169)	−0.883 (3.134)
Summary statistics
N	380	380	380	380	380	380	380
Wald χ²	26.98 (21 df)	29.16 (23 df)	27.19 (22 df)	29.07 (24 df)	29.22 (24 df)	29.20 (24 df)	30.12 (22 df)

Note: Standard errors in parentheses.

p < .05. **p < 0.01. ^†p < .10.

For each professional component, Models 3B and 3D illustrate that the length of a state’s legislative session does not appear to influence adoption timing, notwithstanding whether the interaction effect was considered (HR = 0.997, p > .10) or not (HR = 0.996, p > .10). The Logit models demonstrated similar findings that longer legislative sessions did not influence the likelihood of adoption (LR = −0.005, p < .10, Model 4B). As the interaction variable was not significant (LR = −0.001, p > .10, Model 4D), states with longer time in session have a similar likelihood of adoption as those states with shorter sessions regardless of the number of visits made by the policy entrepreneur (Figure 4).

Figure 4.

Marginal effects (legislative session).

In addition, we find that legislatures with greater expenditures do not adopt Erin’s Law any sooner than those with fewer expenditures (HR = 1.00, p > .10, Model 3B). The Logit models also do not support Hypothesis 2A (LR = 0.000, p > .10, Model 4B). This same trend continues even when accounting for the potential interaction in the EHA (HR = 1.00, p > .10, Model 3E) and Logit models (LR = .000, p > .10, Model 4E). These findings run contrary to Hypotheses 2A and 2B. Again, we do not find the differential effect of legislative salary as a professional incentive to legislators on adopting Erin’s Law. Both the EHA and Logit models did not demonstrate a statistical difference between legislatures with higher-paying salaries and those with low-paying salaries, rejecting Hypothesis 2A. However, the policy entrepreneur’s visits continued to shorten the time spent until the adoption (HR = 2.184, p < .01, Model 3B) (Figure 5) and increase the likelihood of adoption (LR = 1.216, p > .01, Model 4F). Overall, these findings suggest little evidence for the interaction effects of entrepreneurism and legislative professionalism expected in Hypotheses 2B.

Figure 5.

Marginal effects (Legislative salary).

However, we found that state government ideology affects the adoption of Erin’s Law and also moderates the effect of policy entrepreneur visits on the likelihood of adoption and the time to adoption. States that are ideologically more liberal are more likely to adopt Erin’s Law (LR = 0.060, p > .05, Model 4A) and do it faster than a state more conservative on the ideological spectrum (HR = 1.157, p > .01, Model 3A), supporting Hypothesis 3A.

Further, the coefficient of the interaction variable is positive and significant (HR = 1.043, p > .01, Model 3G), suggesting that the number of entrepreneur visits has a positive influence on the legislation of Erin’s Law in liberal states but not in conservative states. The result supports Hypothesis 3B, which anticipates the increased effect of policy entrepreneurs’ advocacy aligned with states’ ideological preferences but rejects the idea of policy entre-preneurism mitigating the ideological gaps across states. Figure 6 shows the marginal effect of the policy entrepreneur visits changing with the government ideology. Similar results were found in the Logit (Model 4G) that the frequency of the entrepreneur visits increases the likelihood of adoption (LR = 0.026, p > .10). The marginal effect of visits increases as the government ideology becomes more liberal, but the entrepreneur visits are most effective when it occurs up to two times in states more liberal than the median (Figure 7).

Figure 6.

Marginal effects (government ideology—EHA).

Figure 7.

Marginal effects (government ideology—Logit).

Several control variables are significant in determining either or both the likelihood and the length of time until adoption. First, Erin’s Law tends to be adopted early in states where more women legislators are present and the state GDP is large, although the coefficients were minor for both variables. Second, we found somewhat mixed impacts of non-Merryn advocacy groups. The presence of parent advocacy groups within a state had a negative impact on the likelihood of adoption. It may be because their presence may divert state legislatures’ attention from Erin’s Law to alternative policy solutions for child safety. In this case, Erin’s Law served as a competing policy to the policies advocated for by parental groups. Holyoke (2003) highlights a similar example, where the involvement of additional interest groups, regardless of their supportive or oppositional stance, provide competing policies affecting interest groups’ lobbying decisions and legislators’ policy actions. The findings also show that the presence of a Prevent Child Abuse America chapter within a state decreased the time to adoption, suggesting the significant role of the well-organized national organization in providing information and directly supporting Erin’s Law within their state chapters.⁵

Third, we align with Kingdon’s (1995[1984]) Multiple Streams Framework by revealing that the severity of the problem, quantified by the number of reports from educators, accelerates the time to adoption as the reports of suspected abuse increase. However, we found the opposite effect when the number of investigated reports increased. The mixed findings suggest the nuance associated with defining the policy problem. Legislatures respond to a child abuse education policy more favorably when they see the problem as an educational issue, particularly the reporting from education professionals. However, when the reports are viewed as an investigation, the problem is no longer solvable through a child abuse education policy, and the adoption of Erin’s Law is ignored or not adopted as quickly. This finding lends itself to future research on better understanding how legislatures view the policy solution within the policy problem.

Lastly, our study presents evidence that states are more likely to follow their ideological neighbors rather than geographic neighbors. States took more time to adopt the policy when neighboring states had enacted the law, counter to previous scholarship (Berry and Berry 1990; Shipan and Volden 2006). Yet it is not entirely unanticipated within the adoption and diffusion literature (Hays and Glick 1997; Mooney 2001). As technology and information-sharing become more readily available, policy learning and imitation are not limited to bordering states (Mallinson 2021; Mitchell and Petray 2016), but states are likely to move quickly following the states that are ideologically more similar. The time to adoption increases as the ideological distance grows between states.

Discussion and Conclusion

This study advances our understanding of state-level policy adoption by presenting several findings drawn from the child abuse case and related legislation. First, with a focus on the policy entrepreneur and the adoption of Erin’s Law, this study finds that policy entrepreneurs play a significant role in facilitating legislative action while accounting for various forms of policy advocacy, such as local or national advocacy pressures. The time spent for adoption decreases and the likelihood of adoption increases as policy entrepreneurs make continual visits to the legislature. It demonstrates that policy entrepreneurs’ advocacy plays a significant role in opening the policy window and altering long-established policies, such as those in place with child abuse protection in the form of mandatory reporting laws, by providing legislatures with additional information and perspectives essential for tackling a wicked problem with limited policy solutions. State legislatures, particularly more liberal states, sought Merryn’s expertise to reinvent child abuse protection policies after mandatory reporting failures (Hays 1996). These findings also support the notion that policy entrepreneurs work together in a “symbiotic relationship” with legislators and serve each other when the political environment aligns and the political stream comes together.

Second, this study illuminates that policy entrepreneurs’ influence on policy adoption within state legislatures depends on legislative politics rather than legislative professionalism, while the extent and effectiveness of policy entrepreneurs’ roles remain open. Our findings did not support the argument for a countervailing role of a policy entrepreneur in leveling gaps in the degree of legislative professionalism across state legislatures. However, this finding does not demonstrate that professionalism is unimportant in policy adoption but, instead, may be tied to other factors with greater influence, such as legislative politics, the size of legislatures, and other institutional arrangements, such as term limits. It is these factors that may also influence the actions the policy entrepreneur takes within their role in the Multiple Streams Framework and aligns with the work of Bakir and Jarvis (2017). Specifically, the policy entrepreneurs’ efforts are complemented by context-dependent and dynamic interactions or relationships with legislatures. Our findings confirm the expectation and previous studies that legislatures placing term limits pass laws more quickly (Carey et al. 1998). It also highlights the heterogeneity in state legislatures’ lawmaking capacity and the concept of legislative professionalism. These discoveries are important and provide valuable paths of future research in understanding both legislative professionalism and policy entrepreneurs. We cannot rule out operationalization and measurement errors. More attention should be given to additional conceptual characteristics (Bowen and Green 2014; Jansa et al. 2019) beyond what is examined here and whether or which single indicator would better explain the politics of expertise associated with policy entrepreneurs in a specific policy context.

Second, our findings provide additional support for the partisan nature of policy processes in that legislatures adopt a policy responding to the policy advocacy that occurs and aligns with the political landscape surrounding the issue. The political ideology of the state shapes the patterns of legislation advocated by a policy entrepreneur in the case of child abuse reform. These findings align with the recent work demonstrating that states with a more liberal ideology are more accepting of legislation preventing adverse childhood experiences (Crowley et al. 2022; Purtle et al. 2019). We show that a policy entrepreneur plays an important role in toward encouraging policy adoption among these states, but expertise may not be a determinant factor in policy processes when considering political ideology, although it may depend on a policy issue. We find little evidence for the countervailing role of policy entrepreneurs in narrowing the ideological gaps in policy adoption and diffusion.

This study has several limitations that illuminate avenues for further examination. First, of particular concern is using witness appearance as a proxy for entrepreneurship on policy. The measure reflects the frequency of the policy entrepreneur’s visit but not necessarily the intensity and duration of the advocacy. We consider non-Merryn advocacy that may occur as formal advocacy through national organizations, state chapters, and parent groups. However, some advocacy activities involved in “problem framing, team building, networking, leading by example, and exploring ways to scale up change processes” may occur using informal mechanisms but may also influence legislative action (Arnold 2021b; Mintrom and Luetjens 2017; Petridou and Mintrom 2021, 945), which should be explored with more depth in the future.

Second, we also acknowledge the potential endogeneity of the policy entrepreneur and where they choose to testify depending on legislatures’ sympathy to the cause or amenable to policy changes. Although it is hard to disentangle the effect of entrepreneurs’ visits from the effect of legislators’ decision to invite Merryn, we argue that it is the actual visit that led to policy adoption. This demonstrates that even when legislatures have an interest in adopting the law and request the visit, the final adoption decision is not made until after the policy entrepreneurs’ visit and their engagement with legislators. While policy entrepreneurs originate from within the policy process (Bakir and Jarvis 2017), their work with legislatures, particularly their legislative visits, advances policy adoption.

Relatedly, more attention needs to return to policy entrepreneurs themselves and their resources, skills, and attributes beyond the policy and legislatures (Arnold 2021b). While previous literature has explored the role of policy entrepreneurs as policy experts (Callaghan and Sylvester 2019; Mintrom 2019; Schneider & Teske 1992), some other factors or attributes may determine policy entrepreneurs’ influence on legislatures, such as demographic characteristics and strong narratives regarding the specific policy issue. The potential of public entrepreneurs’ role as policy marketers, framers, and interpreters of policy problems could be better understood using different methodological approaches.

Lastly, this study did not explicitly account for the increasing role of social media and mass media (Kuhlmann et al. 2020; Soroka and Wlezien 2019) and the degree to which a policy entrepreneur draws media attention. We also acknowledge that it lacks legislators’ perspectives on policy entrepreneurs’ presence and their efforts. Future research could address how legislatures compare expertise and whether legislatures prefer information generated internally or externally from various sources. Moving forward, we suggest that the relationship between policy entrepreneurs and lawmakers varied by policy contexts, such as education, morality, and economic policies, be investigated more thoroughly.

Footnotes

Data Availability

Data is available upon reasonable request from the corresponding author. Data Citation: Vallett, Joel, 2022, “Replication Data for: The Politics of Expertise in Policy Adoption: Policy entrepreneurs and legislative professionalism in combating child abuse in the U.S. States.” . Harvard Dataverse.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Joel D. Vallett

Notes

Joel D. Vallett is an Assistant Professor at Southern Utah University, Department of Political Science and Criminal Justice. Joel teaches courses in Ethics, Research Methods, and Comparative Policy. His research interests center around policy incentives as a driving force for policy adoption, implementation, and ethical decision-making.

Sanghee Park is an Associate Professor and PhD Program Director of Public Policy & Administration in the School of Public Service at Boise State University. Her research focuses on diversity and representation, public management and governance, and bureaucratic politics.

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