JOURNAL OF COMPARATIVE ECONOMICS
ARTICLE NO.
25, 180–195 (1997)
JE971461
Economic Fluctuation, Macro Control, and Monetary Policy
in the Transitional Chinese Economy1
Qiao Yu
National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260
Received October 27, 1995; revised March 12, 1997
Yu, Qiao—Economic Fluctuation, Macro Control, and Monetary Policy in the Transitional Chinese Economy
Since 1978, China has experienced four episodes of economic fluctuations, during
which the government used macro controls to restore stability. This paper finds that
(i) tight monetary policy reinforced by administrative actions checks the economic
overheating, (ii) monetary policy affects significantly industrial output, retail sales,
and prices, but it has no effect on fixed-asset investment and merchandise imports,
(iii) a long-run stable relationship exists between financial variables and economic
activity, and (vi) money aggregates outperform bank credits in predicting future
changes in activities. The evidence provides ways for the People’s Bank of China
to improve the effectiveness of monetary policy. J. Comp. Econom., October 1997,
25(2), pp. 180 – 195. National University of Singapore, 10 Kent Ridge Crescent,
Singapore 119260. q 1997 Academic Press
Journal of Economic Literature Classification Numbers: E32, E52, P21.
1. INTRODUCTION
In the past two decades, China achieved both rapid economic growth and
basic macro stability during its transition toward a socialist market economy.
The core of China’s reforms is a dual economic framework under which the
market mechanism is developed progressively while the traditional plan-based
system continues to function. As a result of market-oriented reforms, China’s
price volatility has increased. For example, average annual inflation measured
by the retail price index was 2.5% in 1978–1984, 8.6% in 1985–1992, and
1
This research is co-sponsored by the Research Institute of Finance and Banking in the
People’s Bank of China and the National University of Singapore (research grant RP930003).
The views expressed here are mine, and no responsibility for them should be attributed to any
institutions. I wish to thank two anonymous referees and Josef Brada for helpful comments and
suggestions on the revision of the paper.
0147-5967/97 $25.00
Copyright q 1997 by Academic Press
All rights of reproduction in any form reserved.
AID
JCE 1461
/ 6w10$$$101
180
09-23-97 11:28:22
ceal
ECONOMIC FLUCTUATION
181
19.4% in 1993–1994. In order to preserve basic economic stability during
the transition, the Chinese government imposed strong macroeconomic controls four times since 1978.
The conduct of China’s macroeconomic policy has been discussed by many
authors, including Chow (1987), Naughton (1987), Portes and Santorum
(1987), Chen (1989), Feltenstein and Ha (1991), Khor (1992), Hafer and
Kutan (1993), Mackinnon (1994), and Yusuf (1994). Using monthly macroeconomic data, this paper assesses the effectiveness of China’s macroeconomic management in general, and monetary policy, in particular. Section 2
outlines the background of China’s macroeconomic controls since 1978. Section 3 discusses the long-term relationships between financial variables and
economic activity. Section 4 assesses the usefulness of monetary policy.
Section 5 studies the predictive power of financial variables for economic
activities, and Section 6 contains our conclusions.
2. BACKGROUND OF CHINA’S MACROECONOMIC CONTROLS
Since the late 1970’s, China’s economy has experienced four episodes of
Party-Congress-led fluctuations in aggregate output. These four CCP’s Party
Congresses were held in 1977, 1982, 1987, and 1992. In general, the economy
gained momentum after a new Party Congress proposed development targets
and relaxed political controls.2 Economic overheating would occur when inflation accelerated and bottlenecks in energy, electricity, transportation, and
communication sectors emerged. The economic slow-down, however, would
not set in until the elite policymakers decided to launch an austerity campaign.
Up to now, China’s macroeconomic management has been based mainly
on tight monetary policy, enforced through direct controls over bank credit.
When the People’s Bank of China (PBC) was transformed into a central bank
and a banking system with four state specialized banks was constructed to
take over regular bank business in the early 1980’s, a framework for conducting monetary policy was established formally.3 The central bank controls
bank credits in both quantitative and qualitative ways. The PBC allocates
2
As a rule, the newly-selected party chief presided over the following Party Congress and raised
new economic development goals. In the past two decades, Hua Guofeng, Hu Yaobang, Zhao
Ziyang, and Jiang Zemin, respectively, chaired the 11th, 12th, 13th, and 14th Party Congress.
3
In the pre-reform era, the PBC was a de facto accounting subsidiary of the Finance Ministry
expected to assist in the fulfilment of state physical production plan. As a mono-bank combining
the roles of both the central bank and commercial banks, the PBC controlled all deposits and
loans directly, while issuing currency to fill deposit–loan mismatch. In 1979–1984, the ‘‘Big
Four’’ state specialized banks were established, the Bank of China (1979), the Agricultural Bank
of China (1979), the People’s Construction Bank of China (1981) (renamed the Construction
Bank of China in 1996), and the Industrial & Commercial Bank of China (1984). They were
ranked among the top 100 largest global banks in the 1990’s (see The Banker, July 1996). In
addition, a few smaller-sized commercial banks were organized in the 1980’s and 1990’s.
AID
JCE 1461
/ 6w10$$$101
09-23-97 11:28:22
ceal
182
QIAO YU
FIG. 1. Growth rate of money aggregates.
credit quotas to banks, while designating bank credit usage. In practice, the
People’s Bank uses the overall credit ceiling as an intermediate objective to
enforce direct monetary policy. The main policy tools include the overall
credit plan, PBC loans to banks, and interest rate controls.4 Occasionally, the
government has taken administrative actions to reinforce monetary policy by
issuing government decrees and dispatching task teams to inspect policy
implementation by lower authorities.
Figures 1–4 present diagrams that construct the time series of growth rates
over 12 months for major financial and macroeconomic variables including
money aggregates, bank credit, industrial output, and prices during 1979–
1994. To all appearances, China’s first economic cycle occurred in 1978–
1983, the second in 1984–1986, the third in 1987–1990, and the fourth from
4
The PBC’s overall credit plan aggregates all banks’ credit plans and matches national fund
sources and usages. Recently, PBC loans account for 25–33% of the funds of state banks. There
are four types of PBC loans: (1) pre-planned annual loans (1–2 years), which are used mainly
for policy lending, such as poverty alleviation loans, land development loans, and key state
project loans; (2) quarterly loans (within 4 months), which are used to relieve the banks’ seasonal
fund shortage; (3) overnight loans (within 10 days), which meet banks’ temporary liquidity
needs; and (4) discount loans (within 3 months), which meet banks’ discount needs, although
their amount is trivial.
AID
JCE 1461
/ 6w10$$$102
09-23-97 11:28:22
ceal
ECONOMIC FLUCTUATION
183
FIG. 2. Growth rate of bank credit.
1991 to present. Correspondingly, the People’s Bank has used tight monetary
policy four times to combat the economic overheating. The first tight money
episode was enforced by a government decree, ‘‘the Decision to Strengthen
Credits Management and Tighten Money Supply,’’ in January 1981. The
second was undertaken through the National Banks’ Working Conference in
April 1985. The third was conducted by the CCP’s Working Conference in
September 1988, and the most recent monetary tightening was initiated by
the sixteen-point austerity campaign in July 1993.
Vertical lines in the diagrams indicate the dates of shifts to tight monetary
management. These dates usually occur at the peak level of growth of the
financial and real variables. Moreover, the growth of money aggregates, bank
credit, output, and prices declined immediately after the tight monetary policies. Even though these diagrams are illustrative, they are not decisive confirmation of the usefulness of monetary policy. Thus, we employ a battery
of comprehensive statistical tests in the following sections to provide formal
evidence of the effectiveness of monetary policy in China’s transition.
3. LONG-RUN RELATIONSHIPS BETWEEN FINANCIAL
VARIABLES AND ECONOMIC ACTIVITIES
This section conducts unit root tests and cointegration tests to identify
appropriate autoregressive models. The first step is to test for the univariate
AID
JCE 1461
/ 6w10$$$102
09-23-97 11:28:22
ceal
184
QIAO YU
FIG. 3. Growth rate of industrial output.
properties for all time series variables. The variables include currency in
circulation (M0), currency plus checkable deposits (M1), currency plus overall
deposits (M2), state bank credit (L1), overall credit (L2), gross industrial
output (Y1), output in the heavy-industry sector (Y2), output in the lightindustry sector (Y3), industrial output by state-owned enterprises (Y4), industrial output by non-state-owned enterprises (Y5), overall retail sales (S1),
retail sales to households (S2), retail sales to institutions (S3), fixed-asset
investment (FI), merchandise exports (EX), merchandise imports (IM), and
the retail price index (P1).5
Following the Dickey–Fuller method as revised by Perron (1989), the unitroot tests performed here include a non-zero mean and a deterministic linear
time trend and also account for possible correlation in the first differences of
the time series. The Augmented Dickey–Fuller test results are presented in
Table 1. According to the critical values reported by Mackinnon (1990), the
estimated statistics show that we cannot reject the null hypothesis of a unit root
for all series in logarithmic level forms, but we can reject the null hypothesis
of a unit root for the series in first-difference forms at the 0.05 level or lower.
Although individual series are non-stationary in levels, there may exist a
5
The Appendix provides detailed explanations of the data.
AID
JCE 1461
/ 6w10$$$102
09-23-97 11:28:22
ceal
ECONOMIC FLUCTUATION
185
FIG. 4. Growth rate of retail price index.
stationary linear combination of these series or a so-called cointegration relationship between them. A statistical model that omits this cointegrating relationship would be misspecified. When series are characterized as non-stationary in
levels and stationary in first differences, Johansen (1991) expressed the pdimensional vector autoregressive model with Gaussian errors as
k01
DX t Å
∑ GiDXt0i / PXt0k / m / FDt / et
t Å 1, . . . , T,
(1)
iÅ1
where D is the first difference operator, Xt and et are of dimension (p 1 1),
X10k , . . . , X0 are fixed variables, et Ç N(0, V), and Dt is a vector of deterministic variables. The parameters G1 , rrr Gk01 , m, F, and V are assumed to vary
without restrictions. The hypothesis of at most r cointegration vectors is the
matrix P Å ab*, where b are the cointegrating vectors and a are the adjustment
coefficients; both a and b are p 1 r matrices. If the series are cointegrated,
then P has rank 0 õ r õ p and Eq. (1) is a vector error-correction model
(VECM). Johansen and Juselius used two likelihood testing methods to determine the number of cointegrating vectors. The first one is based on the maximal
eigenvalues, testing for the null hypothesis that at most r cointegrating vectors
exist against the alternative of r / 1 vectors. Another method is based on the
trace of the stochastic matrix, testing for the null hypothesis that at most r
vectors exist against the alternative of r or more vectors.
AID
JCE 1461
/ 6w10$$$102
09-23-97 11:28:22
ceal
186
QIAO YU
TABLE 1
INTEGRATION TEST
FOR
MACROECONOMIC VARIABLES
Level form
lnM0
lnM1
lnM2
lnL1
lnL2
lnY1
lnY2
lnY3
lnY4
lnY5
lnS1
lnS2
lnS3
lnFI
lnEX
lnIM
lnP1
First difference form
ADF
t-statistic
for C
t-statistic
for T
01.70
01.65
02.74
02.95
02.38
00.37
00.82
00.44
01.86
00.32
02.28
02.79
01.70
00.36
01.96
02.26
01.82
1.84*
1.70*
2.80**
3.03***
2.44**
0.41
0.86
0.48
1.91*
0.38
2.58**
3.18***
01.59
00.66
01.87*
02.21**
1.86*
1.63
1.75*
2.73**
2.79**
2.34**
0.54
0.91
0.65
1.74*
0.49
1.77*
2.14**
1.60
1.38
2.00*
1.99**
1.89*
ADF
DlnM0
DlnM1
DlnM2
DlnL1
DlnL2
DlnY1
DlnY2
DlnY3
DlnY4
DlnY5
DlnS1
DlnS2
DlnS3
DlnFI
DlnEX
DlnIM
DlnP1
07.23***
05.00***
05.36***
07.27***
06.38***
04.15***
04.52***
03.81***
04.96***
03.74***
07.36***
07.35***
03.71***
03.66***
06.56***
04.11***
03.73***
t-statistic
for C
t-statistic
for T
4.33***
2.75**
4.28***
6.55***
5.77***
2.02**
2.21**
1.66*
3.18***
1.58*
2.07**
1.90*
0.55
00.56
2.30**
0.24
1.38
00.51
0.63
0.36
03.86***
03.22***
1.26
0.87
1.33
00.87
1.37
00.61
00.72
0.32
1.46
0.39
0.31
0.54
Notes. ADF, augmented Dickey–Fuller statistic; C, constant; T, trend. Also, see the Appendix
for explanations of the variables.
* Statistically significant at the 10% level.
** Statistically significant at the 5% level.
*** Statistically significant at the 1% level.
Table 2 reports maximal eigenvalue testing results of the VECM systems
with different financial variables. The vector error-correction autoregressive
systems contain a non-price macro variable, a price variable, and a financial
variable to test the null hypothesis of no more than r cointegrating vectors of
the underlying series versus the alternative of at most r / 1 cointegrating
vectors. The systems also include exogenous tight-money controls, which are
proxied by a dummy equal to one on the current and twenty-four lags of each
tight-money date and zero otherwise. The estimated statistics reject significantly
the null hypothesis, indicating that there exist at least two cointegrating vectors
between the macroeconomic variables, a price variable, and financial variables.6
Since these test results confirm the existence of long-term relationships
between economic activities and the financial variables, the vector error6
For brevity, the trace-test results are not reported here, but they agree with maximal-test
outcomes.
AID
JCE 1461
/ 6w10$$$102
09-23-97 11:28:22
ceal
ECONOMIC FLUCTUATION
187
TABLE 2
MAXIMAL-EIGENVALUE TEST FOR THE LONG-RUN RELATIONSHIP
ACTIVITIES AND FINANCIAL VARIABLES
BETWEEN
ECONOMIC
(non-price macro variable, price, financial variable)
Three-variable
system
lnY1, lnP1
rÅ0
r£1
r£2
lnY2, lnP1
rÅ0
r£1
r£2
lnY3, lnP1
rÅ0
r£1
r£2
lnY4, lnP1
rÅ0
r£1
r£2
lnY5, lnP1
rÅ0
r£1
r£2
lnS1, lnP1
rÅ0
r£1
r£2
lnS2, lnP1
rÅ0
r£1
r£2
lnS3, lnP1
rÅ0
r£1
r£2
lnFI, lnP1
rÅ0
r£1
r£2
lnEX, lnP1
rÅ0
r£1
r£2
lnIM, lnP1
rÅ0
r£1
r£2
lnM0
lnM1
lnM2
lnL1
lnL2
58.35***
25.76***
3.57*
50.64***
31.37***
1.71
53.77***
37.69***
4.82**
45.96***
17.97***
3.75*
48.30***
25.40***
4.36**
61.42***
24.30***
4.49**
54.70***
26.79***
1.91
60.95***
39.34***
5.41**
55.71***
21.33***
4.15**
55.62***
26.22***
5.01**
56.04***
24.85***
4.89**
49.41***
28.37***
1.86
51.77***
34.53***
5.59**
48.65***
18.49***
4.47**
47.01***
25.29***
5.47**
55.68***
24.45***
4.72**
46.40***
26.98***
1.72
52.39***
40.62***
5.55**
55.98***
20.56***
4.36**
46.64***
26.60***
5.38**
35.48***
27.59***
3.83*
33.16***
24.64***
1.81
30.03***
27.55***
3.79*
32.68***
15.79**
3.44*
32.95***
18.83***
3.83*
23.24***
20.15***
5.97**
30.48***
18.09***
1.67
33.51***
23.09***
4.96*
31.12***
20.03***
5.37**
37.23***
19.71***
5.22**
52.07***
20.81***
6.18**
38.09***
25.34***
2.68
45.84***
30.68***
5.04**
56.82***
22.68***
5.67**
62.95***
24.77***
5.53**
38.95***
21.27***
4.76**
33.04***
24.80***
2.43
32.63***
24.66***
4.23**
28.32***
21.37***
4.89**
27.76***
24.02***
4.78**
37.63***
23.73***
8.30***
37.48***
28.82***
3.69*
41.16***
31.37***
8.01***
31.57***
26.73***
6.10**
34.25***
30.29***
7.36**
44.34***
23.98***
6.70**
39.08***
27.64***
1.94
40.92***
28.03***
5.56**
40.67***
21.15***
4.26**
39.72***
27.19***
5.29**
42.72***
25.35***
6.49**
45.60***
26.61***
2.20
42.84***
28.33***
6.23**
41.54***
21.29***
4.50**
42.99***
26.13***
5.71**
Note. All tests are computed with six lagged differences, a ‘‘constant term’’. and exogenous
tight-money dates. The critical values are reported by Johansen and Juselius (1990) in Table A.
See the Appendix for explanations of the variables.
* Statistically significant at the 10% level.
** Statistically significant at the 5% level.
*** Statistically significant at the 1% level.
09-23-97 11:28:22
ceal
188
QIAO YU
correction model expressed by Eq. (1) will be used to assess the usefulness
of tight monetary management and to investigate the information content of
financial variables for macroeconomic activities.
4. TIGHT MONETARY POLICY AND ITS USEFULNESS
During the reforms, progressive economic marketization created a real
need, and a favorable environment, for the People’s Bank to conduct meaningful monetary policy.7 Indeed, the PBC has integrated direct monetary policy
to the macro management objective of stabilizing economic volatility in the
early 1980’s. In many cases, especially in boom periods, the PBC has found
it difficult to preserve the overall credit ceiling under political pressure from
both central and local governments. When the overall credit ceiling was
exceeded, the PBC had no alternative but to provide additional liquidity for
banks to fill their funding gap. Such an accommodative money supply has
strong pro-cycle tendencies. Nevertheless, bank credits and money aggregates
are by no means endogenous during the entire business cycle. On the tightmoney dates, when the PBC was authorized to cut the growth of bank credit
and money aggregates, financial variables were exogenously determined or
at least pre-determined. As discussed in Section 2, there have been four times
since 1978 when the People’s Bank has tightened monetary management to
cool down the economy. Because these episodes were independent of economic activities and were immediately followed by economic slow-downs,
tight monetary policy seems to work.
A set of Granger-causality tests based on a variety of vector error-correction
models (VECMs) is conducted to provide a statistical assessment of the
usefulness of tight monetary policy. Table 3 reports the marginal significance
levels for the null hypothesis that tight monetary management can be excluded
from the VECM systems that predict economic activities. Tight monetary
policy is proxied by a dummy variable that is equal to one for the current
and twenty-four lags on each tight-money episode, since tight monetary management began on the observed dates and lasted over time.
In order to verify the robustness of the tests, estimated results from the
various VECM systems are reported in equations 1–4 of Table 3. In each
VECM system, twelve lags of the growth rates for the relevant series and
the proxy for tight monetary policy are included. Not surprisingly, the outcomes suggest consistently that tight monetary management has a strong
effect on economic variables such as industrial output, overall retail sales,
retail sales to households, merchandise exports, and prices, regardless of the
different autoregressive specifications. However, tight monetary policy has
7
Two additional developments also emphasize the importance of monetary policy. First, bank
credit replaces budgetary grants as the major source of capital. Second, a long-lasting budget
deficit hinders flexible fiscal policy.
AID
JCE 1461
/ 6w10$$$102
09-23-97 11:28:22
ceal
ECONOMIC FLUCTUATION
189
TABLE 3
SIGNIFICANCE TEST
FOR
MONETARY CONTROL
IN
VECM SYSTEMS
Equation
System 1
System 2
System 3
System 4
DlnY1
DlnY2
DlnY3
DlnY4
DlnY5
DlnS1
DlnS2
DlnS3
DlnFI
DlnEX
DlnIM
DlnP1
0.0165
0.0741
0.0132
0.0195
0.1099
0.2213
0.3076
0.8983
0.9236
0.1871
0.7313
0.0894
0.0213
0.1093
0.0996
0.0099
0.0803
0.0091
0.0286
0.6733
0.6691
0.0081
0.2611
0.1586
0.0104
0.0442
0.0096
0.0433
0.0256
0.7007
0.7215
0.9743
0.2066
0.0302
0.8282
0.0602
0.0186
0.1116
0.0345
0.0723
0.0483
0.0070
0.0018
0.5927
0.1150
0.0194
0.7143
0.2380
Note. D is the first difference operator. Dummies for tight-money management consist of 24 lags
of tight-money dates. System 1 includes a 12 lagged macro variable, 12 lagged price, and tightmoney dummies. System 2 includes a 12 lagged macro variable, 12 lagged price, 12 lagged M0,
and tight-money dummies. System 3 includes a 12 lagged macro variable, 12 lagged P, 12 lagged
L1 and tight-money dummies. System 4 includes a 12 lagged macro variable, 12 lagged L1, 12
lagged M2, and tight-money dummies. Also see the Appendix for the explanations of the variables.
no statistically significant impact on retail sales for institutions, fixed-asset
investment, and merchandise imports.8
Table 4 presents estimated coefficients and t-statistics for the tight-money
proxy in different VECMs. As would be expected, the results illustrate negative values and robustness for the systems including different variables. Again,
tight monetary management has a significantly negative impact on industrial
output, overall retail sales, retail sales to households, and prices, but its
influence on fixed-asset investment and merchandise imports is not statistically significant.9
Thus, China’s tight monetary management is an efficient and useful policy
in checking economic overheating and it plays a prominent role in the slow
down of growth in industrial output, retail sales, and prices, even though it
shows no significant impact on fixed-asset investment, retail sales to institutions, and merchandise imports.
8
Since official interest rates change rarely and market-clearing rates are not available, they
are not included in VECMs. Our previous study shows that the interest rate effect on activities
is insignificant (see Yu et al., 1994). When tight-money policy is proxied by a dummy with
current and twelve lags of focal episodes or nominal macro variables are used, the significance
levels are very similar.
9
I also examined dynamic responses of activities to the tight-money dates, and the results are
quite similar. For brevity, they are not reported here.
AID
JCE 1461
/ 6w10$$$103
09-23-97 11:28:22
ceal
190
QIAO YU
TABLE 4
ESTIMATES
Equation
DlnY1
DlnY2
DlnY3
DlnY4
DlnY5
DlnS1
DlnS2
DlnS3
DlnFI
DlnEX
DlnIM
DlnP1
System 1
00.0271
00.0245
00.0272
00.0262
00.0220
00.0099
00.0133
00.0013
0.0047
00.0514
00.0188
00.0042
(02.4467)
(01.8083)
(02.5309)
(02.3815)
(01.4178)
(01.2327)
(01.0267)
(00.1282)
(0.0961)
(01.3301)
(00.3445)
(01.7180)
OF
MONETARY CONTROL
System 2
00.0258
00.0207
00.0191
00.0386
00.0281
00.0220
00.0243
00.0046
0.0310
00.1085
00.0755
00.0040
(02.3531)
(01.6215)
(01.6674)
(02.6489)
(01.7741)
(02.6831)
(02.2368)
(00.4235)
(0.4292)
(02.7101)
(01.1329)
(01.4246)
IN
VECM SYSTEMS
System 3
00.0412
00.0400
00.0415
00.0290
00.0475
00.0050
00.0072
00.0004
00.0969
00.0944
00.0166
00.0069
System 4
(02.6333)
(02.0485)
(02.6607)
(02.0580)
(02.2799)
(00.3860)
(00.3580)
(00.0324)
(01.2746)
(02.2113)
(00.2177)
(01.9094)
00.0381
00.0325
00.0352
00.0274
00.0403
00.0256
00.0487
00.0066
00.0147
00.0678
00.0266
00.0045
(02.4091)
(01.6112)
(02.1560)
(01.8241)
(02.0090)
(02.7818)
(03.2397)
(00.5375)
(00.4935)
(02.3919)
(00.3669)
(01.1900)
Note. The t-statistics are in parentheses. Also see the notes to Table 3 and the Appendix for
explanations of the variables.
5. PREDICTIVE POWER OF FINANCIAL VARIABLES
FOR ECONOMIC ACTIVITIES
Starting in the 1990’s, the effectiveness of direct monetary policy has
deteriorated.10 This is due to problems in the bank-credit control system. First
of all, because the overall credit ceiling is determined jointly by the state
growth target and central–local bargaining, the People’s Bank accommodates
bank asset growth without constraining its own liabilities in periods of expansion, while bearing the cost of bank deposits built up in times of austerity.11
Second, the bank-credit allotment system tends to produce allocative inefficiency and to foster rent-seeking among government officials and bank staff.
Third, asymmetric information between the PBC and the banks prevents the
central bank from enforcing efficient credit discipline in the short term and
enables banks to circumvent credit quotas in the longer term. Fourth, state
10
Compared to previous macro controls, it takes a longer time (about 20 months) for the
current tight monetary management initiated in July 1993 to slow down inflation.
11
When the central bank tightened credit quotas to reduce bank loans and raised deposit rates
to attract deposits in austere periods, massive unusable bank deposits were accumulated and the
difference between lending and deposit rates was narrowed or phased out in banks. The large
cost of this policy was shifted eventually to the PBC. This problem is even more serious in the
recent period of tight monetary management. For example, the net profit of the big-four state
banks dropped from 34.3 billion yuan in 1992 to 12 billion yuan in 1994 and became negative
in 1995. The sharp decline of bank profits is caused by two main factors, high deposit costs and
non-performing loans (see Xinhua News Agency, Liahe Zaobao (United Morning Post, Singapore), August 30, 1996).
AID
JCE 1461
/ 6w10$$$103
09-23-97 11:28:22
ceal
ECONOMIC FLUCTUATION
191
TABLE 5
SIGNIFICANCE TEST
FOR
FINANCIAL VARIABLES
IN
VECM SYSTEMS
Equation
DlnM0
DlnM1
DlnM2
DlnL1
DlnL2
DlnY1
DlnY2
DlnY3
DlnY4
DlnY5
DlnS1
DlnS2
DlnS3
DlnFI
DlnEX
DlnIM
DlnP1
0.0005
0.0361
0.0086
0.2601
0.0019
0.0044
0.0013
0.4606
0.0271
0.0003
0.8748
0.1659
0.0002
0.0087
0.0337
0.0897
0.0029
0.4826
0.1753
0.6767
0.0364
0.0273
0.2741
0.0017
0.0826
0.1992
0.2764
0.4276
0.0086
0.5074
0.7310
0.4771
0.0460
0.6287
0.9935
0.0290
0.0605
0.6044
0.5315
0.5381
0.0002
0.4860
0.2097
0.0947
0.3766
0.4342
0.4598
0.1210
0.0554
0.0831
0.1445
0.1881
0.0001
0.5770
0.2783
0.1609
0.4389
0.3511
0.1318
0.1952
Note. The systems include a 12 lagged macro variable, 12 lagged price, and 12 lagged financial
variable. Also see the Appendix for explanations of the variables.
banks are no longer the sole suppliers of capital. Last, market-clearing interest
rates in segmented loanable funds markets are generally higher than official
rates, so that the PBC’s interest rate control is largely nullified.
In order to improve the effectiveness of monetary policy in a changing
economic environment, the government has decided recently to transform
direct control of bank credit into indirect management of money aggregates.
This policy change, however, relies immediately on the information content
of money aggregates predicting future movements of economic activities.
Based on growth rates of the underlying variables, I regressed a set of VECMs
to obtain a battery of F-test statistics to diagnose the predictive ability of
money aggregates and bank credits for activities.
Table 5 reports significance levels of financial variables in different VECMs
for the purpose of robustness. Each VECM system contains a macro variable,
a price variable, and a financial variable. The null hypothesis assumes that the
growth of each financial variable has no predictive information about the growth
of economic activities. In short, the F-test results enable us reach the following
conclusions. First, money aggregates (M0, M1 M2) and overall credit (L2) have
significant predictive power for industrial output. Second, money aggregates,
especially M0 and M1, contain statistically significant information about
changes in industrial output produced by different sectors and owners, whereas
credits provide useful information for changes in output produced by non-stateowned enterprises. Moreover, both money aggregates and credits may have
predictive power for changes in overall retail sales and household spending,
but they do not contain information about changes in institutional consumable
AID
JCE 1461
/ 6w10$$$103
09-23-97 11:28:22
ceal
AID
JCE 1461
/ 6w10$$1461
09-23-97 11:28:22
ceal
0.12
(0.37)
00.86
(1.55)
00.68
(0.26)
00.90
(1.26)
0.59
(1.76)
00.78
(4.42)
01.34
(5.36)
00.41
(1.33)
00.17
(0.51)
00.62
(1.56)
00.94
(2.78)
0.05
(0.62)
lnY01
00.23
(1.30)
00.39
(1.84)
00.39
(2.79)
00.27
(1.90)
0.30
(0.94)
01.01
(4.55)
01.81
(5.19)
00.91
(1.56)
01.09
(0.64)
00.53
(1.32)
02.04
(1.92)
00.04
(0.85)
lnP01
0.05
(0.24)
0.63
(1.75)
0.63
(1.37)
0.41
(1.48)
00.59
(1.49)
0.58
(4.60)
1.03
(5.23)
0.48
(1.55)
0.59
(0.74)
0.51
(2.92)
1.12
(2.21)
00.01
(0.17)
lnM001
00.82
(0.37)
01.46
(3.01)
00.47
(1.74)
00.55
(1.61)
00.51
(1.34)
00.34
(3.33)
00.71
(5.30)
00.30
(1.30)
00.95
(2.63)
00.31
(0.62)
00.97
(2.85)
0.15
(1.52)
lnY01
00.29
(1.30)
0.37
(0.29)
00.42
(4.21)
00.06
(0.50)
00.26
(2.11)
00.34
(3.54)
00.64
(4.83)
00.45
(1.56)
04.51
(3.20)
00.04
(0.78)
01.95
(2.73)
00.08
(1.60)
lnP01
0.67
(2.56)
0.92
(3.26)
0.53
(2.79)
0.23
(2.04)
0.67
(1.71)
0.25
(3.78)
0.48
(5.22)
0.31
(1.55)
2.70
(3.25)
0.20
(1.33)
1.32
(2.97)
00.83
(1.23)
lnM101
0.33
(0.17)
00.16
(0.53)
0.06
(0.39)
00.54
(1.45)
0.06
(0.33)
00.40
(3.02)
00.76
(4.61)
00.11
(0.66)
00.06
(0.26)
01.65
(3.04)
00.95
(2.53)
0.05
(1.10)
lnY01
00.35
(2.97)
00.33
(2.42)
00.40
(3.11)
00.23
(1.92)
00.38
(2.48)
00.44
(3.00)
00.86
(4.28)
00.33
(1.28)
01.55
(1.51)
00.72
(1.83)
01.90
(1.94)
00.05
(2.04)
lnP01
IN
0.14
(1.16)
0.23
(1.40)
0.11
(1.47)
0.27
(2.29)
0.13
(0.76)
0.26
(3.05)
0.51
(4.50)
0.18
(1.24)
0.76
(1.58)
0.55
(3.02)
1.07
(2.16)
00.002
(0.08)
lnM201
System (Y, P, M2)
FIRST ERROR-CORRECTION TERMS
System (Y, P, M1)
OF THE
0.20
(1.42)
0.16
(0.74)
0.26
(1.82)
00.11
(0.32)
0.09
(0.81)
00.28
(0.93)
00.67
(2.19)
00.08
(0.75)
0.34
(1.79)
01.53
(3.29)
00.81
(2.05)
0.03
(0.85)
lnY01
00.12
(1.03)
00.10
(0.60)
00.19
(1.49)
00.11
(0.85)
0.03
(0.17)
00.22
(0.85)
00.59
(1.65)
00.13
(0.85)
1.28
(1.69)
0.55
(1.34)
00.002
(0.01)
00.02
(0.76)
lnP01
00.04
(0.50)
00.03
(0.25)
0.03
(0.41)
0.09
(0.84)
00.05
(0.44)
0.17
(0.96)
0.41
(1.95)
0.12
(1.26)
00.56
(1.40)
0.61
(3.45)
0.46
(1.10)
00.003
(0.14)
lnL101
System (Y, P, L1)
VECM SYSTEMS
0.14
(0.92)
0.02
(0.08)
0.15
(1.00)
00.21
(0.58)
0.13
(0.10)
00.37
(1.17)
00.75
(2.38)
00.07
(0.61)
0.29
(1.22)
01.68
(3.71)
01.26
(3.00)
0.13
(0.34)
lnY01
00.09
(0.83)
00.001
(0.01)
00.16
(1.28)
00.08
(0.65)
0.08
(0.56)
00.27
(1.00)
00.54
(1.69)
00.09
(0.58)
1.00
(1.21)
0.60
(1.56)
00.47
(0.69)
00.02
(0.76)
lnP01
00.01
(0.15)
0.03
(0.21)
0.01
(0.14)
0.11
(0.99)
00.02
(0.15)
0.22
(1.17)
0.45
(2.09)
0.10
(1.05)
00.40
(0.86)
0.65
(3.84)
0.88
(2.04)
00.007
(0.30)
lnL201
System (Y, P, L2)
Note. The systems include a 12 lagged macro variable (represented by Y), 12 lagged price variable (represented by P), and 12 lagged financial
variable (M or L). The figures in parentheses are the absolute value of the t-statistics. Also see the Appendix for explanations of the variables.
DlnP1
DlnIM
DlnEX
DlnFI
DlnS3
DlnS2
DlnS1
DlnY5
DlnY4
DlnY3
DlnY2
DlnY1
Equation
System (Y, P, M0)
ESTIMATES
TABLE 6
192
QIAO YU
ECONOMIC FLUCTUATION
193
spending. Third, money aggregates are able to forecast changes in fixed-assets
investment, while credits have no significant predictive power. Money aggregates have useful information about fluctuations in merchandise exports, while
financial variables, either money or credits, have no predictive power for
changes in merchandise imports. Thus, in general, money aggregates are superior to credits as predictive variables for inflation.12
Finally, Table 6 displays the estimated coefficients of the first error correction terms in the VECM systems. In the systems with money aggregates, at
least one of the error correction terms is statistically significant, although
their performance is less impressive in the systems with bank credits. In
addition, all the first-error correction terms are significant in at least one
equation. Hence, these results suggest that the lagged variables on levels
contain useful information in predicting future economic movement.
6. CONCLUSIONS
This paper documents the central role that monetary policy has played in
China’s macroeconomic control of economic overheating in the past two decades. Hence, China’s macroeconomic stability during the transition will depend basically on successful monetary management over the foreseeable future.
In recent years, institutional problems have weakened direct monetary enforcement. As a result, it is essential for the People’s Bank to improve the efficiency
of monetary policy. The empirical findings of this paper indicate ways for the
People’s Bank to fulfill this goal. Because money aggregates outperform bank
credit in predicting future economic fluctuations, the PBC is correct to replace
bank credit with money aggregates as a major policy target and to switch from
direct control of bank credit to indirect management of money aggregates. In
addition, the existence of long-run stable relationships between financial variables and economic activities enables the central bank to conduct monetary
policy by changing not only the growth rates of policy variables but also their
levels. This would be an attractive option for the PBC in its policy practice.
APPENDIX
1. The Definitions of the Variables
M0 currency in circulation
M1 currency plus institutional checkable deposits
M2 currency plus overall deposits
12
I also used methods of Sims’ variance decomposition for vector autoregressions and dynamic
responses of activities to financial variables as alternatives to assess information contained in
money aggregates and bank credits. These methods, again, provide similar empirical results. For
brevity, they are not reported here.
AID
JCE 1461
/ 6w10$$$103
09-23-97 11:28:22
ceal
194
L1
L2
Y1
Y2
Y3
Y4
Y5
S1
S2
S3
FI
EX
IM
P1
QIAO YU
outstanding indebtedness of domestic non-financial borrowers with state
banks
outstanding indebtedness of domestic non-financial borrowers with state
banks and credit cooperatives
real gross industrial output
real industrial output in the heavy-industry sector (capital-intensive
sector)
real industrial output in the light-industry sector (labor-intensive sector)
real industrial output in the state-owned enterprises
real industrial output in the non-state-owned enterprises
overall retail sales, which includes retail sales of agricultural producer
goods and consumer goods
retail sales of consumer goods to households
retail sales of consumer goods to institutions
fixed-asset investment
merchandise exports
merchandise imports
the overall retail price index (RPI); the basket contains food, clothes,
miscellaneous, entertainment, medicine and medical treatment, and
agricultural input
2. The Data Source and the Sample Period
The data are from the Chinese Statistics Monthly published by the State
Commission of Statistics and the Monthly Assets and Liabilities published by
the People’s Bank of China. Industrial output variables are in real values measured
by 1980 constant prices. Retail sales and fixed-asset investment are nominal
variables measured in current prices, but their real values are obtained as the
nominal terms are deflated by the overall retail price index. Merchandise exports
and imports are in nominal terms measured by current U.S. dollars. Similarly,
the real values for exports and imports are obtained as they are deflated by the
U.S. consumer price index reported in International Financial Statistics by the
IMF. Series are seasonally adjusted and transformed into logarithms. The sample
period used for estimations is from December 1983 to May 1994.
REFERENCES
Ambler, Steve, ‘‘Does Money Matter in Canada? Evidence from a Vector Error Correction
Model.’’ Rev. Econom. Statist. 71, 4:651–658, Nov. 1989.
Almanac of China’s Finance and Banking. Beijing: Xinhua Press, 1986–1995. [annual; in Chinese, Zhongguo Jinrong Nianjian].
Bernanke, Ben S., and Blinder, Alan S., ‘‘The Federal Funds Rate and the Channels of Monetary
Transmission.’’ Amer. Econom. Rev. 82, 4:901–921, Sept. 1992.
Chen, Chien-Hsun, ‘‘Monetary Aggregates and Macroeconomic Performance in Mainland
China.’’ J. Comp. Econom. 13, 2:314–324, June 1989.
AID
JCE 1461
/ 6w10$$$104
09-23-97 11:28:22
ceal
ECONOMIC FLUCTUATION
195
Chen, Nai-Ruenn, and Hou, Chi-Ming, ‘‘China’s Inflation, 1979–1983: Measurement and Analysis.’’ Econom. Develop. Cultural Change 34, 4:811–835, July 1986.
Chow, Gregory, ‘‘Money and Price Level Determination in China.’’ J. Comp. Econom. 11,
3:319–333, Sept. 1987.
Dickey, David A., and Fuller, Wayne A., ‘‘Distribution of the Estimators for Autoregressive
Time Series with a Unit Root.’’ J. Amer. Statist. Assoc. 74, (366), Part 1, 427–431, June
1979.
Feltenstein, Andrew, and Ha, Jiming, ‘‘Measurement of Repressed Inflation in China.’’ J. Develop. Econom. 36, 2:279–294, Oct. 1991.
Friedman, Benjamin M., and Kuttner, Kenneth N., ‘‘Money, Income, Prices, and Interest Rates,’’
Amer. Econom. Rev. 82, 3:472–492, June 1992.
Gertler, Mark, and Gilchrist, Simon, ‘‘Monetary Policy, Business Cycles, and the Behavior of
Small Manufacturing Firms,’’ Quart. J. Econom. 109, 2:309–340, May 1994.
Hafer, R. W., and Kutan, A. M., ‘‘Further Evidence on Money, Output and Prices in China:
Note.’’ J. Comp. Econom. 17, 3:701–709, Sept. 1993.
International Monetary Fund, World Economic Outlook. Washington, DC, 1993.
Johansen, Soren, and Juselius, Katarina, ‘‘Maximum Likelihood Estimation and Inference on
Cointegration—With Applications to the Demand for Money.’’ Oxford Bulletin Econom.
Statist. 52, 2:169–210, May 1990.
Johansen, Soren, ‘‘Estimation and Hypothesis Testing of Cointegration Vectors in Gaussian
Vector Autoregressive Models.’’ Econometrica 59, 6:1551–1580, Nov. 1991.
Khor, Hoe Ee, ‘‘China—Macroeconomic Cycles in the 1980s.’’ China Econom. Rev. 3, 2:173–
194, Fall 1992.
King, Robert G., Plosser, Charles I., and Rebelo, Sergio T., ‘‘Production, Growth and Business
Cycles.’’ J. Monetary Econom. 21, 213:195–232, 309–341, March/May 1988.
McKinnon, Ronald I., ‘‘Financial Growth and Macroeconomic Stability in China, 1978–1992:
Implications for Russia and Other Transitional Economies,’’ J. Comp. Econom. 18, 3:438–
469, June 1994.
Naughton, Barry, ‘‘Macroeconomic Policy and Response in the Chinese Economy: The Impact
of the Reform Process.’’ J. Comp. Econom. 11, 3:334–353, Sept. 1987.
Perron, Pierre, ‘‘The Great Crash, the Oil Price Shock, and the Unit Roots Hypothesis.’’ Econometrica 57, 6:1361–1404, Nov. 1989.
Perkins, Dwight, ‘‘Completing China’s Move to the Market.’’ J. Econom. Persp. 8, 2:23–46,
Spring 1994.
Portes, Richard, and Santorum, Anita, ‘‘Money and the Consumption Goods Market in China.’’
J. Comp. Econom. 11, 3:354–371, Sept. 1987.
Romer, Christina D., and Romer, David H., ‘‘New Evidence on the Monetary Transmission
Mechanism.’’ Brookings Papers Econom. Activity 0, 1:149–198, 1990.
Sims, Christopher A., ‘‘Macroeconomics and Reality.’’ Econometrica 48, 1:1–48, Jan. 1980.
Statistical Yearbook of China. Beijing: China Statistical Press, 1981–1995. [annual; in Chinese,
Zhongguo Tongji Nianjian].
United Nations, World Investment Report 1994. New York, 1994.
Watson, Andrew, ‘‘China’s Economic Reforms 1987–1993: Growth and Cycles.’’ Asian Pacific
Econom. Lit. 8, 1:48–65, May 1994.
Watson, Mark W., ‘‘Business-Cycle Durations and Postwar Stabilization of the U.S. Economy.’’
Amer. Econom. Rev. 84, 1:24–46, March 1994.
Yu, Qiao, Qin, Chijiang, and Lin, Tao, ‘‘Money, Output, Prices and Interest Rates in China’s
Heterogenous Economy during Reforms.’’ Working paper, the Institute of Banking and
Finance of the PBC, 1994.
Yusuf, Shahid, ‘‘China’s Macroeconomic Performance and Management during Transition.’’ J.
Econom. Persp. 8, 2:71–92, Spring 1994.
AID
JCE 1461
/ 6w10$$$104
09-23-97 11:28:22
ceal