跨生命週期的薪資軌跡與人力資本溢酬異質性：潛在成長模式與成長混合模式的實徵分析

Wage is the major earning compensation of the labor forces. In the progress across the life span, the earned wage of the individual employee may differ among people, but also revealing a growth trajectory changing over time. Based on the concept of wage dynamics, the trajectory of earning is a life-cycle wage growth in terms of aging or potential experiences. The differential premium effects due to human capital variables reflect the observed heterogeneity, as the sample consists of explicitly identifiable or defined groups; in contract, composition of individuals is not known ahead of time and must in some way be inferred from the wage data itself, which is entitled as latent heterogeneity in this study. In the present study, the multilevel modeling was adapted for analyzing the wage trajectory on both within-person level and between-person level. Furthermore, both types of heterogeneity of wage trajectory were examined by latent growth modeling (LGM) and growth mixture modeling (GMM). The empirical data selected from “Panel Study of Family Dynamics” (2000-2018), in which a total of 16 waves penal data of family activities along with salary and human capitals contained in the dataset is used for the study of wage trajectory. A total of 93936 observations collected from 5871 adults, with average formal education year of 12.73, average age 37.34 years old, and average work hours of 47.29 were used. MLM results indicated a quadratic trajectory of wage change, with the highest point at 45.18 years old in the within-person level, at 41.37 years old in the between-person level. Gender as well as educational year can significantly predict wage trajectory, as the same findings for work structure variables, such as tenure and work hour per week. Results of LGM revealed a better fit quadratic trajectory model as well, and could be separated to two latent class, a Major group (97.7%) and a Special minor group (2.3%). The effects of explanatory human capital variables in Major group is similar with the total sample, however, the Special group have in-stable relationship among variables, reflecting a specifically reaction response in dataset. Beside the academic concern, the major purpose of this present study is to utilize the existing secondary dataset and to provide constructive findings for applications of human resource management in practice.