Challenges for
Monetary Policy in China:
I. Overheating and Financial Depth,
II. Adverse Selection and
Credit Rating Error,
III. Macro-economic Stability
and Loan-Loss-Reserve Regulation
Stodder: Monetary Pollicy, July
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I. Overheating
1.
2.
3.
4.
5.
Evidence of Overheating
Inefficient Capital Allocation
Informal Financial Intermediaries
Excessive Money Supply
Small Government Bond Market
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1. Evidence of Overheating:
Inflation Spike
Consumer Price Inflation in China
5
4.4
4.1
Annual % Change
4
3
CPI
2
1.8
1.5
1.2
1
0
-1
-0.8
2002
2003
Source: CIA Factbook,
and June 2007 Figures
2004
2005
Stodder: Monetary Pollicy, July
2006
2007
3
Evidence: Real Estate Prices
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Evidence: Shanghai Engineers’ Salaries Higher
than in Thailand, Indonesia, Philippines
Source: METI - China and ASEAN4
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2. Capital Allocation: State Banks
to State-Owned Enterprises
Source: McKinsey
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Capital Allocation: Too Scarce
in Inland Areas
Source: METI
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Capital Allocation: Can Heighten
Regional Inequalities
Source: METI
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3. Money Supply: China’s M2/GDP out of proportion
Figures for 1999/2000
Country
GDP per capita
M2/GDP(%)
CHINA
$2,001
150.0
Singapore
$12,963
90.0
Korea, Rep. of
$5,626
40.0
Malaysia
$2,368
70.3
Thailand
$1,466
69.5
Philippines
$706
31.8
Indonesia
$580
41.1
Sri Lanka
$466
30.5
India
$358
46.1
Pakistan
$336
39.7
Bangladesh
$200
31.3
http://www.allcountries.org/china_statistics/index.html
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4. Informal Finance:
“ All small business start with loans
from family and friends. I’m not
aware of any business that was
started with bank financing.”
- Manager of one of Shanghai’s 10 largest
Private Firms (McKinsey)
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Informal Financial Intermediaries
Distribution of Banking Deposits (2004),
in Billions of Dollars
100%
80%
$2,412
Corporate and
other Bank
Deposits
60%
40%
20%
28%
Lending from
Family and
Friends
$838
Underground
Bank Loans
$101
0%
Source: McKinsey
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5. Small Government Bond Market:
Makes Central Bank’s Job More Difficult
Source: McKinsey
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II. Adverse Selection by
Credit Rating Error



Think of two firms which must go
through a long difficult process – just
to achieve the same credit rating.
But say the 1st firm is a good risk,
while the 2nd firm is a poor risk.
(So there is Rating Error.)
Which firm will be more determined to
complete the rating process?
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Empirically Based Simulation of
Credit Rating Effects:

“Modeling the economic value of credit
rating systems”, by Jankowitscha, Pichlera,
and Schwaigerb
Journal of Banking & Finance
Volume 31, Issue 1, January 2007,
Pages 181-198
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Adverse Selection: History of
30,000 Austrian Corporate Loans
Source: Jankowitsch et. al., Journal of Banking & Finance (2007)
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Adverse Selection: Basis Point Improvement
in Change from Low Credit Rating Accuracy
Portfolio
Quality
Δ low to Δ low to
medium
high
Δ low to
perfect
Good
30.8
43.7
44.8
Average
32.6
45.9
46.8
Weak
39.0
56.4
58.7
Source: Jankowitsch et. al. (2007)
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Adverse Selection


The study finds this improvement in return
is mostly due to less adverse selection –
not better loan pricing.
As a very distinguished banker friend of
mine once said:
“If you lose the principle, it’s hard to make
it up on interest payments.”
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III. Macro-economic Stability
and Loan-Loss-Reserve Regulation
“A sound banker, alas, is not one
who foresees danger and avoids it,
but one who, when he is ruined, is
ruined in a conventional way, along
with his fellows, so that no one can
really blame him.”
– J.M. Keynes (1931)
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Macro Stability: Keynes noted
the “Paradox of Thrift”


Consumers cut back on their spending and
save more during a recession. This only
makes the recession worse.
Similarly, Loan Loss Reserves (LLR) are
often raised in a recession, just when banks
can least afford them – often ensuring their
collapse and worsening the recession.
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Macro Stability:
Perverse Loan-Loss Regulation


Laeven, Luc & Majnoni, Giovanni, 2003.
"Loan loss provisioning and economic
slowdowns: too much, too late?,"
Journal of Financial Intermediation, Vol.
12(2), April, pages 178-197.
Can be downloaded from World Bank:
http://ideas.repec.org/p/wbk/wbrwps/2749.html
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They argue that LLR should be
‘pro-cyclical’, built up in good times,
so that it is available for bad times.
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Instead, LLR growth ‘counter-cyclical’:
Weak LLR in booms spurs inflation,
Strong LLR in busts worsens recessions
Dependent Variable: LLR %
Latin
Japan America
ASIA
Europe
U.S.
Earnings (EBT)/Assets
***.209
***.207
***.545
***.280
*.053
Negative earnings (?)*
***-.373
***-.541
***-1.329
***-.444
***-.590
Loan growth
***-.008
***-.006
***-.016
***-.009
***-.010
GDP growth
***-.070
***-.160
***-.148
0
***-.091
R-squared
0.15
0.32
0.48
0.24
0.30
Bank-year observations
2,477
2,288
1,016
570
951
No of banks
438
388
145
126
166
(***: p-val < 1%, **: p-val < 5%, *: p-val < 10%)
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Tentative Conclusions:



China’s Monetary Policy challenge is very
difficult, with few policy instruments
A good Credit Rating System has great
potential for improved financial returns from
banking
However, poor regulation based on credit
rating has the potential to increase macroeconomic instability
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