了解两轮选举中的选票转移,而无需诉诸申报数据。生态推论的贡献,结合 2014 年蒙彼利埃市政选举案例研究的事实信息

Julien Audemard
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引用次数: 0

摘要

在选举社会学中,对选票转移的分析历来依赖于从调查中获得的个人数据。由于此类数据存在大量的申报和记忆偏差,因此用可获得的投票站级别的选举统计数据取而代之可能更有优势。生态推断模型的最新发展使我们能够利用这些综合数据来确定选票转移的估计值,同时将生态误差的风险降至最低。然而,迄今为止,用于估算选票转移的生态推断模型的可靠性很少以经验评估的形式受到关注。本文旨在通过分析一个预测两轮选举(即 2014 年在蒙彼利埃举行的市政选举)中选举波动性的模型来揭示这一盲点。这一方法的独创之处在于它使用了观察到的信息--两轮选举中的非投票者比例--首先将这一数据与模型得出的估计值进行比较,然后将其整合为一个建模参数,以衡量其对估计选票分配的影响。分析表明,初始模型的结果相对可靠,尽管略微高估了已知参数的振幅并低估了其变化。整合了一致非投票者比例信息的模型得出的估计结果与使用 "原始 "模型得出的结果接近。因此,就解释能力而言,整合这些额外信息的附加值很小。然而,整合这些信息确实可以建立更窄的密度区间,减少与其他参数解释相关的不确定性,特别是与第一轮得票少的候选人相关的比例。
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Understanding vote transfers in two-round elections without resorting to declared data. The contribution of ecological inference, consolidated with factual information from a case study of the 2014 municipal elections in Montpellier
In electoral sociology, the analysis of vote transfers has traditionally depended on individual data obtained from surveys. Because such data suffer from a significant amount of declaration and memory bias, replacing them with the electoral statistics available down to polling station level may be advantageous. Recent developments in models of ecological inference allow us to use these aggregated data to establish estimates of vote transfers while minimising the risk of ecological error. Nonetheless, the reliability of ecological inference models for estimating vote transfers has thus far received little attention in the form of empirical evaluations. The purpose of the present article is to cast light on this blind spot by analysing a model for predicting electoral volatility in a two-round election, namely the municipal election held in Montpellier in 2014. What makes this approach original is its use of observed information – the proportion of non-voters in both rounds – first to compare this data with the estimates produced by the model and then to integrate it as a modelling parameter to measure its impact on estimated vote allocation. This analysis reveals that the initial model's results are relatively reliable regarding the known parameter, although they slightly overestimate its amplitude and underestimate its variability. The model that integrates information regarding the proportion of consistent non-voters yields estimates close to those obtained using the “raw” model. In terms of interpretative capacity, the value added by integrating this additional information is, therefore, slight. However, integrating the information does make it possible to establish narrower density intervals, reducing the uncertainty associated with the interpretation of the other parameters, particularly proportions associated with candidates who received few votes in the first round.
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