由人力資本投資理論探討個人薪資之影響要素：以商管學院畢業生為核心

Abstract

個人薪資與人力資本投資息息相關，根據Mincer（1968）的薪資方程式，教育與工作經驗是影響薪資的主要因素，雖然其模型之後被廣泛地使用，但卻較少應用在某個特定群體的研究。 本研究首先針對商管、法律、文學與教育等學院的畢業生，建立薪資決定模式，其迴歸變數包括個人的教育、工作經驗、性別、公司所在地、公司規模以及畢業學院等。其次，由於在上述四個學院的資料中，行業別與職業別的分類太過繁雜，因此，本研究以在特定行業與職業工作的商管學院畢業生為樣本，做進一步的分析，包含薪資上的性別歧視。 在第一組資料的分析中，除教育與工作經驗外，本研究亦發現婚姻、公司規模、公司所在地、是否為戶長、性別與畢業學院等因素會影響個人薪資，而已婚的男性相較於已婚的女性則可賺取薪資上的溢酬。然在畢業學院對於薪資的影響上，雖然商管學院畢業生的薪資是顯著低於法學院畢業生，不過其並未顯著高於文學院與教育學院的畢業生。 在第二組資料的分析中，由於加入其他的解釋變數，迴歸式的解釋能力明顯提高。除了上述的影響變數之外，新加入的行業別與職業別變數亦顯著地影響薪資。本研究亦顯示，個人如能經由取得專業認證，主動發展專業職能，將有助於個人人力資本的提升，而在其專業領域上賺取更多的薪資。 在薪資的性別歧視分析中，並無證據顯示在「兩性工作平等法」施行之後，薪資性別歧視的情況較為減緩。當使用「分解法」（Decomposing method）進行薪資差異分析時，本研究發現在兩性的薪資差異中，有73.89%是由於稟賦上的差異，26.11%則可歸因於性別歧視。另由上述結果可以發現，教育對性別薪資歧視具有平減的效果，也就是說，若女性可以得到更多的教育，似乎可以有效地改變性別薪資歧視的情況。

Human capital investment theory plays a momentous role in personal wage determination. According to Mincer’s eminent model (1968), schooling and work experience are two main essentials of wage determination. Although Mincer’s model is generally applied, it is rare to be used in a specific group. In this study, we first focus on people graduated from four particular colleges, i.e., management/commerce, literature, law, and education colleges. We establish an extended model to test the relationship between wage and a worker’s schooling, work experience, gender, marriage, work place, firm-size and college. Second, because industrial and occupational categorizations are too broad within the four colleges, we use the sample of management/commerce college graduates employed in certain industries and occupations as the second data subset to do further analyses, including the analysis of gender wage discrimination. In the first data subset, in addition to schooling and work experience, we also find other wage determinants such as marriage, firm-size, work place, householder, gender, and college. We demonstrate that married males earn wage premiums, and people graduated from management/commerce college significantly earn less than those from law college, but it is not evident that whether they earn more than literature and education college graduates. In the second data subset, the adjusted R2 of the regression is larger than that in the first data subset due to the addition of other explanatory variables. Besides the variables in the first data subset, industrial and occupational variables also significantly affect wage determination. The results also show that a person can develop her/his professional skills by obtaining some certifications; then s/he can increase her/his human capital and thus earn higher wages in a professional domain. As for gender wage discrimination, there is no evidence that it has lessened after the enactment of Gender Equality in Employment Act. By using the decomposing method, 73.89% of gross gender wage differentials come from the endowment differences, and 26.11% are attributed to gender discrimination. We find that education has a counteracted effect on gender wage discrimination. It seems that obtaining more education for females is an effective way to avoid gender wage discrimination.