International Max Planck Research School on the Life Course (LIFE).
Department of Educational Sciences, University of Potsdam.
Multivariate Behav Res. 2023 May-Jun;58(3):504-525. doi: 10.1080/00273171.2022.2029339. Epub 2022 Feb 7.
Wages and wage dynamics directly affect individuals' and families' daily lives. In this article, we show how major theoretical branches of research on wages and inequality-that is, cumulative advantage (CA), human capital theory, and the lifespan perspective-can be integrated into a coherent statistical framework and analyzed with multilevel dynamic structural equation modeling (DSEM). This opens up a new way to empirically investigate the mechanisms that drive growing inequality over time. We demonstrate the new approach by making use of longitudinal, representative U.S. data (NLSY-79). Analyses revealed fundamental between-person differences in both initial wages and autoregressive wage growth rates across the lifespan. Only 0.5% of the sample experienced a "strict" CA and unbounded wage growth, whereas most individuals revealed logarithmic wage growth over time. Adolescent intelligence and adult educational levels explained substantial heterogeneity in both parameters. We discuss how DSEM may help researchers study CA processes and related developmental dynamics, and we highlight the extensions and limitations of the DSEM framework.
工资和工资动态直接影响个人和家庭的日常生活。在本文中,我们展示了工资和不平等研究的主要理论分支——累积优势(CA)、人力资本理论和寿命视角——如何可以整合到一个连贯的统计框架中,并通过多层次动态结构方程建模(DSEM)进行分析。这为实证研究随时间推移导致不平等加剧的机制开辟了新的途径。我们利用具有代表性的美国纵向数据(NLSY-79)来演示新方法。分析结果表明,在整个生命周期中,初始工资和自回归工资增长率在个体之间存在根本差异。只有 0.5%的样本经历了“严格”的 CA 和无界工资增长,而大多数个体的工资随时间呈对数增长。青少年时期的智力和成年后的教育水平解释了这两个参数的大部分异质性。我们讨论了 DSEM 如何帮助研究人员研究 CA 过程和相关的发展动态,并强调了 DSEM 框架的扩展和局限性。