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A Longitudinal Mixed Logit Model for Estimation of Push and Pull Effects in Residential Location Choice

机译:估计居住区位选择中的推拉效应的纵向混合Logit模型

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摘要

We develop a random effects discrete choice model for the analysis of households' choice of neighbourhood over time. The model is parameterised in a way that exploits longitudinal data to separate the influence of neighbourhood characteristics on the decision to move out of the current area (push" effects) and on the choice of one destination over another (pull" efdfects). Random effects are included to allow for unobserved heterogeneity between households in their propensity to move, and in the importance placed on area characteristics. The model also includes area-level random effects. The combination of a large choice set, large sample size and repeated observations mean that existing estimation approaches are often infeasible. We therefore propose an effcient MCMC algorithm for the analysis of large-scale datasets. The model is applied in an analysis of residential choice in England using data from the British Household Panel Survey linked to neighbourhood-level census data. We consider how effects of area deprivation and distance from the current area depend on household characteristics and life course transitions in the previous year. We find substantial differences between households in the effects of deprivation on out-mobility and selection of destination, with evidence of severely constrained choices among less-advantaged households.
机译:我们建立了一个随机效应离散选择模型,用于分析家庭随着时间推移对邻里的选择。对模型进行参数化,该方法利用纵向数据来分离邻域特征对移出当前区域的决策的影响(“推”效果)和对选择另一个目的地的目标的影响(“拉”效果)。包括随机效应,以允许家庭之间在迁徙倾向以及对地区特征的重视中观察到异质性。该模型还包括区域级随机效应。大选择集,大样本量和重复观察的结合意味着现有的估算方法通常不可行。因此,我们提出了一种用于分析大型数据集的有效MCMC算法。该模型被用于英国的住户选择分析中,该分析使用的是来自英国家庭面板调查的数据,该数据与社区一级的人口普查数据相关联。我们考虑区域剥夺和与当前区域之间距离的影响如何取决于上一年的家庭特征和生活历程的转变。我们发现,在贫困对外出和目的地选择的影响之间,家庭之间存在巨大差异,证据表明,弱势家庭的选择受到严重限制。

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