Forthcoming in Journal of Banking & Finance
Sources of Bank
Productivity Growth in
China:
A Disaggregation View
Tzu-Pu Chang
Jin-Li Hu (http://jinlihu.tripod.com)
Ray Yutien Chou
Lei Sun
July 15, 2011
Outlines
Introduction
 Methodology
 Empirical results
 Conclusions

2
Introduction (1/4)

In the past three decades, the Chinese banking
system has reformed gradually and gained
remarkable successes in many respects.
 The
total assets of Chinese banking industry have
been more than 60 trillion RMB and increased about
300 times than that in 1978.
 On November, 2009, the capital adequacy ratio and
the provision coverage of Chinese banking industry
has been larger than 10% and 150%, respectively.
 Industrial and Commercial Bank of China (ICBC),
China Construction Bank (CCB), and Bank of China
(BOC) are the largest three listed banks in the world.
3
Introduction (2/4)



The financial reforms also make efficiency and
productivity improvements in the banking sector (Chen et
al., 2005; Matthews et al., 2009).
This application aims to investigate how the total-factor
productivity (TFP) changes and to disaggregate the
sources of productivity change in Chinese banking
industry from 2005 to 2009.
It is worthy that the ‘Big Four’ state-owned banks (SOBs)
have partially privatized to take on minority foreign
ownership since 2005.
4
Introduction (3/4)
Previous studies focused on productivity
change in banking typically adopt the
Malmquist TFP index or Luenberger TFP
index approach.
 However, these indices are aggregative
indices, meaning that it might lack some
insights if we want to see the change of
each factor.

5
Introduction (4/4)
Therefore, this paper tries to overcome the
disadvantage of total factor productivity
index and traditional partial productivity
measures .
 This paper proposes a total-factor input
productivity index (TIPI) to deal with the
above concerned. The productivity growth
of each factor under total factor concerns
can be calculated.

6
Methodology (1/4)


We first assumed that the production technology
Ft models the transformation of multiple
t
M
inputs, x  R , into multiple outputs, yt  RS , for
each time period t
The Luenberger productivity index relies on
directional distance functions. Following
Chambers et al. (1998), the directional distance
functions could be defined at t as:
D(t ) (xt , yt ; g x , g y )  max{  : (xt   g x , yt   g y )  Ft }.
S
M
R
R
where (gx, gy) is a nonzero vector in  ×  .
7
Methodology (2/4)

The Luenberger productivity index is measured
as follows:
L(xt 1 , y t 1 , xt , y t ) 

1
D (t ) (xt , y t )  D (t ) (xt 1 , y t 1 )
2



 D (t 1) (xt , y t )  D (t 1) (xt 1 , y t 1 )  ,

if the Luenberger productivity index is less than,
equal to, or greater than zero, then it
respectively stand for productivity regress, no
change, or progress between period t and t+1.
8
Methodology (3/4)

Briec (2000) introduces a Färe-Lovell efficiency measure
that has the advantage to select a strong efficient vector
onto the frontier.
1
D (t ) (x , y )  max
( 1 
M
t
t
 M )
N
s.t.
t
t

x

x
 j ij io (1  i ), i  1,..., M ,
j 1
N
t
t

y

y
 j rj ro , r  1,..., S ,
j 1
 j  0, i  0,
j  1,..., N ; i  1,..., M ; r  1,..., S .
9
Methodology (4/4)

The TFP change can be decomposed into the
productivity change of the M individual inputs as
follows:
1
TFPCH  TIPI1  TIPI 2   TIPI M 
M
1
1
  EFFCH1   EFFCH M   TECHCH1   TECHCH M 
M
M
 EFFCH  TECHCH
10
Data and variables’ descriptions

Based on intermediation approach, this article
specifies two outputs and three inputs to
investigate the total-factor input productivity
change of banks in China.
 The
output variables include total loans, and other
earning assets.
 In input variables, labors, capitals, and funds are the
conventional inputs in previous researches. Funds
defined as the total deposits; capital is measured by
the total fixed assets; labor is the total number of
employees of a bank.
11
Data and variables’ descriptions



This application collects a balanced panel data covering
2005-2009 from 21 Chinese commercial banks, including
Big Four state-owned banks, national shareholding
commercial banks, and major city commercial banks in
China.
The financial data, including the items of the balance
sheets and income statements, are taken from
Bankscope. The information on the numbers of
employees is quite incomplete in Bankscope. Therefore,
this variable is complemented through each bank’s
annual report.
All nominal prices are transferred using GDP deflator
with 2009 as the base year.
12
13
Productivity analysis at the
industry level
14
Figure 1 Cumulative changes of TFP and its components
15
Figure 2 Annually change of TFP and total-factor input productivity
16
17
Productivity analysis at the
group level
18
Note: SOB is state-owned banks; JSB is joint-stock banks; CCB is city commercial banks.
19
Change of total-factor input productivity
20
Empirical results - firm level





Innovators: 5 banks; only Bank of Beijing (#2) is the
innovator which shifts the frontiers of all inputs.
Negative growth of TFP: Bank of China
TFP growth is driven by efficiency improvement: 2
banks
TFP growth is driven by technical progress: 4 banks
Technological gains transcend the efficiency
regressions and results in TFP growth: 9 banks
21
Conclusion

There’re two advantages of TIPI
 TIPI
can calculate productivity change of particular
input factor under total-factor framework
 TIPI measures TFP change as the arithmetic mean of
the productivity change of each input
 Thus, we can find out what is the main driver for TFP
growth

In China’s bank industry, the TFP gains are
principally driven by technical progress,
especially for capital usage.
22
The End
23