What factors drive state firearm law adoption? An application of exponential-family random graph models
What factors drive state firearm law adoption? An application of exponential-family random graph models
复制标题
哪些因素推动州枪支法的通过？
DOI：
10.1016/j.socscimed.2022.115103
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发表时间：
2022
影响因子：
5.4
通讯作者：
Porfiri, Maurizio
中科院分区：
文献类型：
--
作者：
Clark, Duncan A.;Macinko, James;Porfiri, Maurizio
枪支是当代美国文化中无处不在的一个特征，至少部分原因是宪法对枪支的崇拜。然而，大多数旨在限制或扩大枪支获取和使用的法律都是在各州制定和通过的，导致50个不同的与枪支有关的法律的环境。到目前为止，很少有人知道为什么一些州比其他州通过了更严格或更宽松的枪支法律。在这篇文章中，我们确定模式的枪支法律通过国家，通过框架的问题作为一个双边网络（国家连接到法律和法律连接到国家），这是一个复杂的，相互关联的系统不可观察的力量的结果。我们采用指数族随机图模型（ERGMs），一类统计网络模型，允许分配统计独立性的假设，以确定增加或减少国家在1979年至2020年期间采用许可或限制性枪支法律的可能性的因素。结果表明，更进步的州政府与更高的机会颁布限制性枪支法律，并制定一个较低的机会，宽容的。保守的州政府与类似的反向关联有关。如果邻国也通过了法律，那么国家更有可能通过法律。无论是限制性法律还是许可性法律，邻国法律的存在都增加了一个国家拥有该法律的条件可能性，也就是说，法律跨越国界扩散。高水平的凶杀案与一个州通过了更宽松，但不是更严格的枪支法律有关。总之，这些结果表明，国家内部和外部因素的复杂相互作用，似乎推动不同模式的枪支法律通过基于这些结果，未来的工作，使用相关类别的模型，考虑到网络结构的时间演变可能提供一种手段来预测未来的法律通过的可能性。
Guns are a ubiquitous feature of contemporary US culture, driven, at least partly, by firearms' constitutional enshrinement. However, the majority of laws intended to restrict or expand firearm access and use are formulated and passed in the states, leading to 50 different firearm-related legal environments. To date, little is known about why some states pass more restrictive or permissive firearm laws than others. In this article, we identify patterns of firearm law adoption across states, by framing the problem as a bipartite network (states connected to laws and laws connected to states) that is the result of a complex, and interconnected system of unobserved forces. We employ Exponential-family Random Graph Models (ERGMs), a class of statistical network models that allow for the dispensing of the assumptions of statistical independence, to identify factors that increase or decrease the likelihood of states adopting permissive or restrictive firearms laws over the period 1979 to 2020. Results show that more progressive state governments are associated with a higher chance of enacting restrictive firearm laws, and a lower chance of enacting permissive ones. Conservative state governments are associated with the analogous reversed association. States are more likely to adopt laws if bordering states have also adopted that law. For both restrictive and permissive laws the presence of a law in a neighboring state increased the conditional likelihood of a state having that law, that is laws diffuse across state borders. High levels of homicides are associated with a state having adopted more permissive, but not more restrictive, firearm laws. In summary, these results point to a complex interplay of state internal and external factors that seem to drive different patterns of firearm law adoption Based on these results, future work using related classes of models that take into account the time evolution of the network structure may provide a means to predict the likelihood of future law adoption.