Sociological research is increasingly using survey panel data to examine changes in diverse outcomes over life course events. Most of these studies have one striking similarity: they analyze changes between yearly time intervals. In this article, we present a simple but effective method to model such trajectories more precisely using available data. The approach exploits month-specific information regarding interview and life event dates. Using fixed effects regression models, we calculate monthly dummy estimates around life events and then run nonparametric smoothing to create smoothed monthly estimates. We test the approach using Monte Carlo simulations and Socio-economic Panel (SOEP) data. Monte Carlo simulations show that the newly proposed smoothed monthly estimates outperform yearly dummy estimates, especially when there is rapid change or discontinuities in trends at the event. In the real data analyses, the novel approach reports an amplitude of change that is roughly twice as large as the yearly estimates showed. It also reveals a discontinuity in trajectories at bereavement, but not at childbirth; and remarkable gender differences. Our proposed method can be applied to several available data sets and a variety of outcomes and life events. Thus, for research on changes around life events, it serves as a powerful new tool in the researcher’s toolbox.

Close

社会学研究越来越多地使用调查小组数据来检查生命历程事件中不同结果的变化。大多数这些研究都有一个惊人的相似之处：它们分析每年时间间隔之间的变化。在本文中，我们提出了一种简单但有效的方法，可以使用可用数据更精确地对此类轨迹进行建模。该方法利用有关访谈和生活事件日期的特定月份信息。使用固定效应回归模型，我们计算围绕生活事件的每月虚拟估计，然后运行非参数平滑以创建平滑的每月估计。我们使用蒙特卡罗模拟和社会经济小组 (SOEP) 数据测试该方法。蒙特卡洛模拟表明，新提出的平滑月度估计优于年度虚拟估计，特别是当事件趋势发生快速变化或不连续时。在实际数据分析中，这种新颖的方法报告的变化幅度大约是每年估计值的两倍。它还揭示了丧亲时轨迹的不连续性，但在分娩时则不然。以及显着的性别差异。我们提出的方法可以应用于多个可用的数据集以及各种结果和生活事件。因此，对于围绕生活事件的变化的研究，它可以作为研究人员工具箱中强大的新工具。

关闭