Evaluating efficiencies of Chinese commercial banks in the context of stochastic multistage technologies

Tai Hsin Huang, Chung I. Lin, Kuan-Chen Chen

研究成果: 雜誌貢獻文章

19 引文 (Scopus)

摘要

This paper proposes a stochastic network model under the framework of the stochastic frontier approach, which allows firms to produce outputs through multistage processes so that we can characterize the underlying technologies and assess technical efficiency in each subsector of a firm. Our model explicitly considers the links among subsectors and overcomes the failure of network DEA that fails to estimate the fractions of shared inputs employed by subsectors, when only aggregate data are available. We compile data from the Chinese banking industry over the period 2002–2015 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 59% and 61% 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 68% and 84%. 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.
原文英語
頁(從 - 到)93-110
頁數18
期刊Pacific Basin Finance Journal
41
DOIs
出版狀態已發佈 - 二月 1 2017

指紋

Technical efficiency
Deposits
Commercial banks
Copula
Banking industry
Labor
Loans
State-owned banks
Aggregate data
Network model
Stochastic frontier approach

ASJC Scopus subject areas

  • Finance
  • Economics and Econometrics

引用此文

Evaluating efficiencies of Chinese commercial banks in the context of stochastic multistage technologies. / Huang, Tai Hsin; Lin, Chung I.; Chen, Kuan-Chen.

於: Pacific Basin Finance Journal, 卷 41, 01.02.2017, p. 93-110.

研究成果: 雜誌貢獻文章

@article{63dd213755bc4acb88b89ce0751dd33e,
title = "Evaluating efficiencies of Chinese commercial banks in the context of stochastic multistage technologies",
abstract = "This paper proposes a stochastic network model under the framework of the stochastic frontier approach, which allows firms to produce outputs through multistage processes so that we can characterize the underlying technologies and assess technical efficiency in each subsector of a firm. Our model explicitly considers the links among subsectors and overcomes the failure of network DEA that fails to estimate the fractions of shared inputs employed by subsectors, when only aggregate data are available. We compile data from the Chinese banking industry over the period 2002–2015 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 59{\%} and 61{\%} 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 68{\%} and 84{\%}. 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.",
keywords = "Chinese banks, Copula methods, Fraction of shared inputs, Multistage processes, Stochastic network model, Technical efficiency",
author = "Huang, {Tai Hsin} and Lin, {Chung I.} and Kuan-Chen Chen",
year = "2017",
month = "2",
day = "1",
doi = "10.1016/j.pacfin.2016.12.008",
language = "English",
volume = "41",
pages = "93--110",
journal = "Pacific Basin Finance Journal",
issn = "0927-538X",
publisher = "Elsevier",

}

TY - JOUR

T1 - Evaluating efficiencies of Chinese commercial banks in the context of stochastic multistage technologies

AU - Huang, Tai Hsin

AU - Lin, Chung I.

AU - Chen, Kuan-Chen

PY - 2017/2/1

Y1 - 2017/2/1

N2 - This paper proposes a stochastic network model under the framework of the stochastic frontier approach, which allows firms to produce outputs through multistage processes so that we can characterize the underlying technologies and assess technical efficiency in each subsector of a firm. Our model explicitly considers the links among subsectors and overcomes the failure of network DEA that fails to estimate the fractions of shared inputs employed by subsectors, when only aggregate data are available. We compile data from the Chinese banking industry over the period 2002–2015 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 59% and 61% 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 68% and 84%. 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.

AB - This paper proposes a stochastic network model under the framework of the stochastic frontier approach, which allows firms to produce outputs through multistage processes so that we can characterize the underlying technologies and assess technical efficiency in each subsector of a firm. Our model explicitly considers the links among subsectors and overcomes the failure of network DEA that fails to estimate the fractions of shared inputs employed by subsectors, when only aggregate data are available. We compile data from the Chinese banking industry over the period 2002–2015 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 59% and 61% 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 68% and 84%. 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.

KW - Chinese banks

KW - Copula methods

KW - Fraction of shared inputs

KW - Multistage processes

KW - Stochastic network model

KW - Technical efficiency

UR - http://www.scopus.com/inward/record.url?scp=85009387563&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85009387563&partnerID=8YFLogxK

U2 - 10.1016/j.pacfin.2016.12.008

DO - 10.1016/j.pacfin.2016.12.008

M3 - Article

AN - SCOPUS:85009387563

VL - 41

SP - 93

EP - 110

JO - Pacific Basin Finance Journal

JF - Pacific Basin Finance Journal

SN - 0927-538X

ER -