Title:
What Drives States to Restrict Stem Cell Research? An Event-History Analysis
What Drives States to Restrict Stem Cell Research? An Event-History Analysis
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Hearn, James C.
Lacy, T. Austin
Levine, Aaron D.
Lacy, T. Austin
Levine, Aaron D.
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Abstract
Since human embryonic stem cells were first isolated in 1998, stem cell science has rapidly emerged on the policy agenda both at the national and state levels. This policy activity reflects the contentious nature of this research. Supporters of human embryonic stem cell research point to the ability of these cells to give rise to any cell type in the human body and suggest that this science will usher in a new era of regenerative medicine. Detractors argue not about the potential of this research, but about its ethics. Specifically, they argue that the destruction of human embryos required by human embryonic stem cell research and the potential use of cloning technology to create patient-matched human embryonic stem cells make this technology immoral. Policymakers around the world have balanced these competing views in different manners. As a result, a heterogeneous patchwork of policies has emerged where some jurisdictions actively support research in this field while others deliberately restrict it (Knowles, 2004). This patchwork is particularly evident at the state level within the United States. Ten states have adopted policies that support human embryonic stem cell research, and eight states have acted to restrict research in this field. Supportive policies typically legalize human embryonic stem cell research and related technologies or provide state funding to support this research, while restrictive policies outlaw specific research practices or place limits on the use of state funding to support this field. Despite this atypically heterogeneous policy environment, the factors that influence adoption of state stem cell policies have not been systematically explored and remain poorly understood. This paper seeks to address this gap in the literature and offer insight into the factors that influence states to adopt stem cell policies. We focus on restrictive stem cell (RSC) policies and, building on insights from previous studies of state policy adoption, ask how state-level characteristics, such as the strength of its economy and the political make-up of its government, influence the adoption of these policies. In addition, we test a diffusion model of policy adoption, asking how the adoption of RSC policies is influenced by the adoption of stem cell policies in neighboring states. In this analysis, time is measured discretely as the calendar year in which a state first adopted such a policy. Our data set begins in 1998, when Michigan adopted an RSC policy, and continues until 2007, by which time a total of 8 states had adopted a policy of this kind. Our method utilizes a type of Event History Analysis (EHA) known as the Cox Proportional Hazards Model (CPH). The CPH focuses on the relationship in panel data between the event outcome and the covariates of theoretical interest, without the need for specifying a functional form of the duration dependence (Box-Steffensmeier and Jones, 2004). The dependent variable expresses the duration of time in years (t) until a state (i) adopts a policy. CPH calculates a survival function, representing the probability that a unit will "survive" (or fail to experience the event) longer than time t (Box-Steffensmeier and Jones, 2004; Box-Steffensmeier and Bradford, 2004; Singer and Willett, 2003). Next, the hazard function, our primary dependent variable of interest, is calculated. The hazard function represents the instantaneous rate of change in the probability of experiencing an event at time t, conditional upon "survival" up to the specified period of time. For our analysis, the hazard function indicates the probability that a state without a RSC policy will adopt one in a particular year, given its values of the independent variables that are hypothesized to influence change. Maximum partial likelihood estimation is used to calculate the parameter estimates. An advantage of CPH is its superior capability of dealing with the "right-censoring" problem, that is, the likelihood of event occurrence after the time period for which data are available. Our findings suggest that, in the context of a variety of control factors, states having a republican governor, a low unemployment rate, a conservative citizen ideology, and a higher number of contiguous states previously enacting any type of stem cell legislation were more likely to adopt these policies. An interaction effect between citizen ideology and the number of contiguous states previously adopting a policy reveals that conservative states and liberal states respond to the diffusion of stem cell policies differently. That is, partisan politics, a state's ideological characteristics, and interstate diffusion all contribute to the decision to adopt a prohibitive stem cell policy in both straightforward and dynamic manners. Future models on this project will update the analytic dataset to incorporate the adoption of a PSC by additional states in 2008 and will incorporate variables related to electoral timing and states' religious preferences. Prior studies have examined the adoption of morality policies, such as restrictions on access to abortion or capital punishment, as well as, to a lesser extent, economic development policies. The case of stem cell policy examined here extends the policy adoption literature by illuminating the factors that drive policy adoption when lawmakers are forced to balance ethical concerns with potential health benefits and economic development opportunities. References Box-Steffensmeier, J.M., & Jones, B.S. (2004). Event history modeling: A guide for social scientists. Cambridge: New York: Cambridge University Press. Box-Steffensmeier, J.M., & Bradford, S.J. (2004). Timing and political change: Event history modeling in political science. Ann Arbor: University of Michigan Press. Knowles, L. P. (2004). "A Regulatory Patchwork - Human ES Cell Research Oversight." Nature Biotechnology 22(2): 157-73) Singer, J. D., & Willett, J. B. (2003). Applied longitudinal data analysis: Modeling change and event occurrence. Oxford and New York: Oxford University Press.
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2009-10-03
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