Measuring Neighbourhood Effects Non-experimentally: How Much Do Alternative Methods Matter?
Measuring Neighbourhood Effects Non-experimentally: How Much Do Alternative Methods Matter?
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DOI：
10.1080/02673037.2013.759544
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发表时间：
2013-04-01
期刊：
影响因子：
3.2
通讯作者：
Hedman, Lina
中科院分区：
文献类型：
--
作者：
Galster, George;Hedman, Lina
欧洲试图量化邻里效应的研究几乎完全依赖于对观测数据的分析。没有达成共识，也许是因为采用了各种统计程序。我们通过探索替代的非实验性统计方法在多大程度上产生不同的估计邻里收入组合和个人工作收入之间的关系时，适用于同一纵向数据库。我们发现，结果是高度敏感的统计方法。控制地理选择偏差的方法通常会减少低收入邻居和个人收入之间的负相关，但模型之间仍然存在很大差异。同时控制选择和内隐会产生更大的关联和非线性的证据，这在只控制选择的模型中是隐藏的。所有的方法都有缺点，所以我们主张多方法的调查，以确定强大的调查结果，与工具变量和固定的影响，非移动样本被首选。在我们的例子中，我们发现一个实质性的邻里效应，无论采用的方法。
European research attempting to quantify neighbourhood effects has relied almost exclusively on analyses of observational data. No consensus has emerged, perhaps because a variety of statistical procedures have been employed. We investigate this by exploring the degree to which alternative, non-experimental statistical methods yield different estimates of the relationship between neighbourhood income mix and individual work income when applied to the same longitudinal database. We find that results are highly sensitive to the statistical approach employed. Methods controlling for geographic selection bias generally reduce the negative association between low-income neighbours and individual earnings, but substantial differences across models remain. Controlling for both selection and endogeneity produces larger associations and evidence of non-linearity, something that is hidden in models only controlling for selection. All methods suffer shortcomings, so we argue for multi-method investigations to identify robust findings, with instrumental variables and fixed effects on non-mover samples being preferred. In our case, we find a substantial neighbourhood effect, regardless of the method employed.