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    题名: 銀行產業效率分析: 網絡隨機邊界模型與其應用
    Studies on Bank Efficiency: The Network SFA Model and Its Applications
    作者: 林忠億
    Lin, Chung I
    贡献者: 黃台心
    Huang, Tai Hsin
    林忠億
    Lin,Chung I
    关键词: 網絡資料包絡分析法
    技術效率
    比例參數
    關聯結構法
    隨機生產和成本邊界
    多階段生產技術
    存款創造
    放款擴張
    中國銀行
    合資銀行
    國有銀行
    Network DEA
    Technical efficiency
    Fractional parameters
    Copula method
    Stochastic production and cost frontiers
    Multi-stage technologies
    Deposit-gathering
    Loan-expansion
    Chinese banks
    Joint-stock banks
    State-owned banks
    日期: 2016
    上传时间: 2016-05-02 13:50:31 (UTC+8)
    摘要: 銀行產業的效率研究中，網絡資料包絡分析法的顯著貢獻，在於可處理存款在銀行生產過程所扮演的雙重角色。透過一定比例的勞動與實質資本投入創造出存款，因此，存款先被視為中間產出；在接續的生產過程中，存款這項中間產出又將被視為要素投入，結合其餘的勞動與實質資本，共同再創造出最終銀行諸多的產出組合。然而，網絡資料包絡分析法無法處理在銀行生產過程中關鍵的勞動與實質資本的比例參數。有鑒於此，第一篇網絡隨機邊界分析法的理論研究中，我們建構生產函數、成本函數與成本份額方程式以刻劃銀行多階段生產技術的經濟模型，運用最大概似法計量結合關聯結構，有效地估計聯立方程式中的各項參數，尤其是關鍵的勞動與實質資本比例參數。同時，模型亦將以2009年美國銀行產業為例，估計隨機生產與成本邊界的技術效率值，進而闡釋所創計量模型的實用與可行性。在第二篇實證論文研究中，即假設銀行的生產過程包括兩個階段:吸納存款的階段與創造放款擴張的階段，我們將探討2006年至2013年中國商業銀行的效率;結果發現中國大陸的商業銀行於存款創造階段，勞動與實質資本的配置約在35%及50%；且平均在存款創造階段與放款擴增階段的技術效率值約64%與69%。此外，實證顯示兩階段皆具規模經濟，但第二階段並未顯現範疇經濟。我們的實證結果也支持相關文獻的發現：中型合資商業銀行最具生產技術效率，然而，主要大型商業銀行，包括傳統的四家大型國有銀行，則在技術效率上表現最差。
    The main contribution of network DEA deals with the dual role of deposits in the bank production process. Deposits are first viewed as an intermediate output, produced by, e.g., fractions of labor and capital. This intermediate output is next used as an input in the second process, together with the remaining labor and capital, to produce output combinations. A problem occurs in that network DEA suffers from the difficulty of determining the fractions of labor and capital used in the first process. This first research thus develops an economic model to characterize the underlying multi-stage technologies and proposes a copula-based econometric model to identify parameters of the structural equations, including the fractional parameters, by the maximum likelihood. Our model also estimates technical efficiencies of the stochastic production and cost frontiers. We collect data from U.S. banks in 2009 to illustrate the feasibility and usefulness of our modeling, and the results are promising. In the second empirical application, we compile data from the Chinese banking industry over the period 2006-2013 to exemplify our approach with the help of copula methods. Under the assumption of two production stages - i.e., deposit-gathering and loan-expansion stages - we find that banks allocate roughly 35% and 50% of labor and capital, respectively, to collect deposits in the first stage and that the average technical efficiency scores in both production stages are respectively 64% and 69%. Additionally, both production stages enjoy economies of scale, however, we do not verify the presence of scope economies Our study supports the previous findings that joint-stock banks are the most technically efficient, while larger commercial banks, including the big four state-owned banks, are the least technically efficient.
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