本文採民國94、96、98、100、102年的台灣 「人力運用調查」 資料庫, 以關聯結構法找出組合誤差間的關聯結構密度函數與聯合機率密度函數, 建構隨機邊界關聯結構模型, 解決勞動市場上樣本選擇性問題。 之後分別針對男性及女性估計薪資方程式, 探討每位勞工的薪資效率程度。 本文男、 女性的勞工各按年齡、 工作經驗、 職業、 行業、 教育程度、 公司規模、 工作身分、 婚姻狀態、 工作地分為9大類, 在各類中分群比較薪資效率。 實證結果顯示, 公司規模、 工作身分、 工作地等 3 類, 不論有無考慮樣本選擇, 同一性別中的薪資效率變動趨勢大致一致, 但其餘 6 類, 有考慮樣本選擇的薪資效率變動明顯不同於未考慮樣本選擇。 在考慮了樣本選擇之後的實證結果大多打破以往文獻的預期, 可能是因為過往文獻探討薪資效率時, 大多未考慮樣本選擇, 即將無工作者樣本完全排除, 導致迴歸分析結果僅適用於有工作者。 除了探討薪資效率外, 本文也嘗試在隨機邊界法的架構下, 提出一個衡量性別歧視的新觀點, 將兩性薪資無效率的差異視為一種性別歧視。 若以新觀點衡量台灣的勞動市場, 會發現這5個年度中, 薪資差異幾乎可以完全歸諸於性別歧視。 這顯示即使兩性的薪資差異雖然逐年縮小, 但性別的刻板印象仍存於當今的台灣勞動市場, 造成明顯的性別歧視。 / This paper adopts the "Manpower Utilization Survey" data, a database conducted by Directorate General of Budget, Accounting & Statistics, Executive Yuan, ROC (DGBAS), to study the issues of gender wage differentials and underpayment. The econometric model considers sample selection under the framework of the stochastic frontier model with copula methods. It requires to correct for sample selection and derive the copula density function and joint probability density function by copula method. We separately estimate the male and female wage equations, respectively, to evaluate the wage efficiency and decompose the average wage differential between male and female into several components. The paper distinguishes workers into 9 categories, including age, experience, occupation, industry, education, firm size, working identity, marital status and working area, and compares the wage efficiency between those categories. The empirical results shows that, the trend of wage efficiency in the categories of firm size, working identity, and working area are almost the same in each gender whether correcting for the sample selection bias or not. However, in the remaining 6 categories, the wage efficiency changes substantially after correcting the sample selection bias. With the correction of the sample election bias, most of the findings differ from those from the past literatures. This may be attributed to the fact that the past works mainly focus on employed workers and lead to possible sample selection bias. The paper also tries to offer a new method to measure the gender discrimination, which considers the difference in wage inefficiency between the male-female inefficiency as an element of discrimination. The paper finds that the wage differential between male and female can explain almost the entire discrimination. This findings confirms that the gender discrimination still exists in the Taiwan’s labor market eventhough the wage differential between male and female decreases over time.

綜觀國內外探討金融產業經營效率之相關文獻，多數針對銀行、證券、保險中其一產業進行獨立實證研究，較無法將金融控股之綜效表現加以捕捉並體現在估計效率值中，也因此在本文中，將同時涵蓋銀行業務與證券業務，並以納入金控體制與否加以區分為不同子集合進行探討，透過數理方法聯結金控體制下銀行與證券業務部門兩者間因多角化經營所產生之綜效，以修正傳統方法上表現綜效法上之不足，使估計效率值具有更高的參考依據。 此外，研究產業效率所參考之研究方法，在相關文獻中無參數法乃以資料包絡分析法(Data envelopement analysis, DEA)為多數研究者所採用；參數法則以隨機邊界模型(stochastic frontier approach, SFA)為主要研究方法，進一步在不同群組技術效率之比較中，Battese et al. (2004) 與 O’Donnell et al. (2008)提出為多數研究者所接受之共同邊界模型(Metafrontier)，而Huang et al. (2013)所提出之修正模型使共同邊界模型在效率研究上更加完備，本文則希望以此修正模型作為主要研究模型，並同時引用Battese et al. (2004)傳統模型加以比較：(1)考慮綜效前後 (2)傳統模型與修正模型間效率值的差異，期待為後續相關研究者在方法論上提供參考依據。 關鍵字：經營效率、技術效率、共同邊界、計量方法、關聯結構法、金融控股銀行 / This study aims to discuss the evidence suggested that synergy improve the operating performance of Taiwan’s commerical banks and securites firms. Furthermore, efficiency comparisons amongst two basis metafrontier models, Haung et al.(2013) and Battese et al.(2004), and copula-adjusted metafrontier. The empirical results suggest that copula-adjusted metafrontier could significantly improve the efficiency of financial holding companies based on the data of sample period 2001-2012. On the framework of copula-adjusted metafrontier, the evidence also imply that financial holging companysubsidiary technical efficiency is better than non-financial holding company compared to the framework of Haung et al.(2013) and Battese et al.(2004), the conclusion emphasis that synergy is perhaps one crucial key factor influenced the efficiency estimation. Otherwise, this study appears to support the copula-adjusted metafrontier model is also well-adjusted compared to programming techniques because not only it enables the statistical inferences to be drawn, but also provides more efficiency estimations.