薪資軌跡之年齡、時代與世代效果之實徵研究－以華人家庭動態資料庫為例
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2018
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本研究旨在透過縱貫資料與多層次模型的整合，檢驗年齡、時代及世代變數的效 果，以及性別、教育程度與工時等薪資溢酬變數對薪資軌跡的影響。過去這類型的研 究多半是由橫斷面資料及相對應的統計方法來完成，因此年齡、時代與世代變數兩兩 之間會有完全線性相依的問題；然而，透過兩階層的多層次模型，即可將這三個變數 分成兩個層次來處理，亦即時代效果在組內、世代效果在組間，而年齡效果則可以同 時作用在組內和組間層次，如此一來，三個變數便可以一起納入模型，解決年齡、時 代與世代變數所存在的共線性問題。本研究運用「華人家庭動態資料庫」自 1999 年至 2016 年長達 18 年共 16 波的追縱調查資料，以多層次模型分析 5,800 位受訪者的薪資 軌跡與其他變數的變動關係。 研究結果顯示，年齡效果的薪資軌跡不論是組內或組間，都是顯著的二次曲線模 型，且在 50 歲左右的時候會有最高點；其次，發生在組內的時代效果也同樣對薪資軌 跡有顯著影響，然而隨著其他共變數(控制變數)的加入，該二次曲線的最低點會從原本 的 2009 年遞延到 2014 年。再來，世代效果對薪資軌跡的二次曲線則說明了，1966 年 到 1970 年出生的人的薪資水準最高，可是一旦把年齡、時代和其他溢酬變數控制住之 後，世代效果就會變成不顯著。最後，性別、教育年數和工時的溢酬效果則再一次證 明，隨著人力資本的累積，薪資水準會跟著增加。本研究運用高階統計方法進行年齡、 時代與世代效果分析，除了具備學術意涵，也提供人力資源管理實務的決策性參考。
This study is aimed to combine longitudinal data analysis with multilevel models to examine effects of age, cohort and periods on wage trajectory. With an extension of those, premium effects of human capital factors on wage, such as gender, education and working hours, are also included. In the past, examination of such effects had relied on crosssectional data and methodology, thus confounding any two of the three variables—age, period and cohort. However, by adapting a two-leveled multilevel modeling, relationships among these three variables are able to be decomposed into within- and between-effects, where period is counted as within-variable in level one, cohort is a between-variable in level two, and age is viewed as both within- and between-variable in level one and level two, so that all three variables are simultaneously analyzed. In this study, a longitudinal data with 16 waves spanning 18 years of over 5,800 individuals in a Panel Study of Family Dynamics (PSFD) database was used to conduct wage trajectory research, and a series of multilevel models were proposed. It is found that age effect is a curvilinear trajectory across life span, with the highest level around 50s, and it is steadily significant both within an individual and among individuals. Period effects also bring about significant variations in one’s wage trajectory; moreover, the lowest points of this effect defer from the year 2009 to the year 2014, when other covariates are controlled. Lastly, cohort effect reveals that people born in 1966 to 1970 earn most in each month; however, this effect becomes insignificant as the other two temporal effects are simultaneously included. Three premium effects (i.e. gender, years of education and working hours) are also examined and thus verify the fact that the accumulation of human capital can result in an increase in wage. In all, this study not only successfully demonstrates effects of age, period and cohort with improved methodology, but also generate useful implications and empirical solutions to classical human resource practices.
This study is aimed to combine longitudinal data analysis with multilevel models to examine effects of age, cohort and periods on wage trajectory. With an extension of those, premium effects of human capital factors on wage, such as gender, education and working hours, are also included. In the past, examination of such effects had relied on crosssectional data and methodology, thus confounding any two of the three variables—age, period and cohort. However, by adapting a two-leveled multilevel modeling, relationships among these three variables are able to be decomposed into within- and between-effects, where period is counted as within-variable in level one, cohort is a between-variable in level two, and age is viewed as both within- and between-variable in level one and level two, so that all three variables are simultaneously analyzed. In this study, a longitudinal data with 16 waves spanning 18 years of over 5,800 individuals in a Panel Study of Family Dynamics (PSFD) database was used to conduct wage trajectory research, and a series of multilevel models were proposed. It is found that age effect is a curvilinear trajectory across life span, with the highest level around 50s, and it is steadily significant both within an individual and among individuals. Period effects also bring about significant variations in one’s wage trajectory; moreover, the lowest points of this effect defer from the year 2009 to the year 2014, when other covariates are controlled. Lastly, cohort effect reveals that people born in 1966 to 1970 earn most in each month; however, this effect becomes insignificant as the other two temporal effects are simultaneously included. Three premium effects (i.e. gender, years of education and working hours) are also examined and thus verify the fact that the accumulation of human capital can result in an increase in wage. In all, this study not only successfully demonstrates effects of age, period and cohort with improved methodology, but also generate useful implications and empirical solutions to classical human resource practices.
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薪資軌跡, 年齡-時代-世代分析, 人力資本理論, 縱貫資料, 多層次模型, wage trajectory, age-period-cohort analysis, human capital theory, panel data, multilevel modeling