Copyright
by
Xiaojun Yang
2003
The Dissertation Committee for Xiaojun Yang Certifies that this is the
approved version of the following dissertation:
Essays on Income Inequality, Exchange Rate,
and Policy Coordination
Committee:
David A. Kendrick, Supervisor
Li Gan
Vince Geraci
William Glade
Hong Yan
Essays on Income Inequality, Exchange Rate,
and Policy Coordination
by
Xiaojun Yang, B.A., M.I.A., M.S.
Dissertation
Presented to the Faculty of the Graduate S
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chool of
The University of Texas at Austin
in Partial Fulfillment
of the Requirements
for the Degree of
Doctor of Philosophy
The University of Texas at Austin
May 2003
UMI Number: 3116243
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iv
Acknowledgements
I am deeply indebted to my supervisor Professor David A. Kendrick for
his kind guidance, advice and encouragement. Under his consistent,
conscientious and patient supervision, I have gained insight about
macroeconomics and computational economics, importantly how to approach
academic research. I will continue to value his friendship.
I also owe thanks to the other member of the dissertation committee: Dr.
Li Gan, Dr. Vince Geraci, Dr. William Glade, Dr. Hong Yan, for their valuable
discussions and suggestions, and for their generosity and kindness. Also, I would
like to express my appreciation to Seung-Rae Kim and Marco Tucci for their
intellectual and technical advice.
Finally, I express my special thanks to my parents for their lasting support
and love.
v
Essays on Income inequality, Exchange Rate,
and Policy Coordination
Publication No._____________
Xiaojun Yang, Ph.D.
The University of Texas at Austin, 2003
Supervisor: David A. Kendrick
The goal of this dissertation is to develop and to use computational
methods to study issues in policy coordination, exchange rates and income
inequality.
The topic of policy formulation among interdependent economies has
received much attention in the literature. In the first essay a two-country model is
used to illustrate the interdependence of China’s and Hong Kong’s economies.
Not surprising, we find that the policy effect is asymmetric, due to difference in
size. Shocks to the Chinese economy will affect Hong Kong’s stable economic
growth. In order for Hong Kong to keep a stable growth, both governments must
act in certain ways. Particularly, by importing more China can help Hong Kong’s
economy, especially during the financial crisis years. We find that fiscal policy is
vi
more effective than monetary policy in affecting economic activities in this
model.
In the second essay, we develop a model to study the behavior of the
Yuan/Dollar exchange rate. The parameter values estimated for the model are
such that when China increases its relative money supply, the exchange rate
appreciates, which is different than the conventional result. Also the parameter
values indicate that in order for China to have a higher level of GDP, China has to
increase its money supply; and for a stable exchange rate, China either decreases
its money supply then increases it or increases its money supply during the entire
period. The right policy hinges on the desired path for the exchange rate. Since
the model is simple in essence, the results should be interpreted with caution.
The third essay analyses the contribution of different factors to the
determination of income inequality. The questions regarding whether greater
income inequality is conducive to China's growth and the role of degree of reform
for growth and inequality have been studied in the paper. We find a positive
correlation between income inequality and growth, that reform plays a dominant
role in determining growth and income inequality, and that steady growth can not
be emphasized too much, otherwise the reform process will be reversed, which is
not practical. Finally, the tradeoff between income inequality and growth is
analyzed.
vii
Table of Contents
Chapter One: Introduction...................................................................................... 1
Chapter Two: Economic Interaction and Policy Coordination Between China
and Hong Kong .............................................................................................. 3
2.1 Introduction .............................................................................................. 3
2.2 The Model ................................................................................................ 4
2.3 The Optimal Control Theory.................................................................. 10
2.4 The Model with Price Variables............................................................. 32
2.4.1 The Model Equations… ............................................................. 32
2.4.2 Simulations................................................................................. 39
2.4.2.1 Historical Simulation....................................................... 39
2.4.2.2 Policy Simulation ............................................................ 43
2.4.2.2.1 Fiscal Expansion ................................................. 43
2.4.2.2.2 Monetary Expansion ........................................... 49
2.5 The Model with Price Variables in the Control Theory Framework ..... 50
Chapter Three: Yuan - Dollar Exchange Rate Model ......................................... 62
3.1 Introduction ............................................................................................ 62
3.2 Developments of China's Foreign Exchange System............................ 63
3.3 The Model .............................................................................................. 71
3.3.1 The Model Equations… ............................................................. 72
3.3.2 Data ............................................................................................ 74
3.3.3 Estimation................................................................................... 75
3.3.4 Policy Simulation ....................................................................... 77
3.4 The Control .......................................................................................... 82
3.4.1 The Control Framework ............................................................. 82
3.4.2 Results and Experiments ............................................................ 86
viii
3.5 Conclusion ........................................................................................... 95
Chapter Four: Reform, Inequality, and Growth .................................................. 96
4.1 Introduction ............................................................................................ 96
4.2 Factor Consideration .............................................................................. 97
4.3 The Model ............................................................................................ 102
4.4 Control and Sensitivity Analyses ......................................................... 112
4.4.1 The Control Framework ........................................................... 112
4.4.2 Sensitivity Analysis.................................................................. 117
4.4.2.1 Sensitivity Analysis from 1991 to 1998........................ 119
4.4.2.2 Sensitivity Analysis from 1998 to 2010........................ 132
4.4.2.2.1 Caring More about Income Inequality .............. 135
4.4.2.2.2 Caring More about Growth Rate ....................... 140
4.4.2.2.3 Greater Difficulty to Further Reform ................ 144
4.4.2.2.4 Inequality verse Growth ................................... 149
4.5 Conclusion ......................................................................................... 152
Appendix A-1 Historical Simulation................................................................... 154
Appendix A-2 Simulation Results for China's Fiscal Expansion........................ 158
Appendix A-3 Duali Input File 1 ........................................................................ 162
Appendix A-4 Duali Input File 2 ........................................................................ 179
Appendix B-1 Comparison of Different Money Supply ..................................... 198
Appendix B-2 Duali Input File............................................................................ 199
Appendix C-1 Constant Inequality, Growth and Inflation.................................. 204
Appendix C-2 Duali Input File............................................................................ 206
Appendix D-1 Data-China .................................................................................. 212
Appendix D-2 Data-Hong Kong ......................................................................... 220
ix
Appendix D-3 Data-US....................................................................................... 226
Bibliography ....................................................................................................... 228
Vita ................................................................................................................... 231
1
Chapter 1: Introduction
The topic of policy formulation among interdependent economies has
received much attention in the literature. China and Hong Kong are economically
closely linked. Policy initiatives in one economy may influence the evolution of
economics variables in the other. In the first essay a two-country model is used to
illustrate the interdependence of these two economies. Not surprisingly, we found
that the policy effects are asymmetric, due to differences in size. China’s
economic policies have a big effect on Hong Kong, but the reverse is not true.
However, China and Hong Kong’s economies are intertwined. A shock to the
Chinese economy will affect Hong Kong’s stable economic growth. In order for
Hong Kong to keep a stable growth, both governments must act in certain ways.
Particularly, by importing more China can help Hong Kong’s economy, especially
during financial crisis years. In doing so, China has to have higher government
expenditure and Hong Kong has to have higher money growth. At the same time,
Hong Kong should steadily increase its government expenditure and China should
keep a stable money growth. Fiscal policy is more effective than monetary policy
in affecting economic activities in this model.
In the second essay, we develop a model to study the behavior of the
Yuan/Dollar exchange rate. We connect the exchange rate with China and
America’s income, money supply, interest rate, and current account. The
parameter values estimated for the model are such that when China increases its
2
relative money supply, the exchange rate appreciates. Also the parameter values
indicate that in order for China to have a higher level of GDP, China has to
increase its money supply; and for a stable exchange rate, China can either
decrease its money supply then increases it or increase its money supply during
the entire period. The correct policy depends on the desired path of the exchange
rate. Since the model is simple in essence, the results should be interpreted with
caution.
The reform and open-door policies in China have liberated people’s work
incentive and enthusiasm. Important aspects of this change are that people have
more job choices and more opportunities, and that income inequality has
increased. The final chapter analyses the contribution of different factors in the
determination of income inequality. The questions regarding whether income
inequality is conducive to China's growth and the role of degree of reform for
growth and inequality have been studied in the paper. A two-period model with
two-group households—rural and urban—is introduced to illustrate factors that
should be considered in income distribution and growth. Based on this
framework, equations are developed for urban income inequality, rural income
inequality, growth and inflation. Contradicting a popular view regarding East
Asian countries, a positive correlation between income inequality and growth was
found. The other findings are that reform plays a dominant role in determining
growth and income inequality, and that steady growth can not be emphasized too
much, otherwise the reform process will be reversed, which is not practical.
Finally, the tradeoff between income inequality and growth is analyzed.
3
Chapter 2: Economic Interaction and Policy Coordination
Between China and Hong Kong
2.1 INTRODUCTION
The economic reforms that took place in mainland China in the late 1970s
began a new process that fundamentally changed the economic relationship
between mainland China and Hong Kong. In the 1960s and 1970s, Hong Kong’s
economic growth rate reached, on average, almost 10 percent per year. However
by the early 1980s high land rents and wages began to erode Hong Kong’s
international competitiveness that had been the basis of its success.
Coincidentally, the emergence of such pressures coincided with China’s open-
door policies. Thus a mutual benefit situation arose between the two and the
forging of much closer economic relations began. We want to know how the two
economies interact, how policy interaction can increase their welfare, and what
policy instrument is more effective in affecting the economies. In this paper a
two-country model is used to illustrate the interdependence of these two
economies and also answer those questions. We found the policy effect is
asymmetric, due to different size. China’s economic policies have a big effect on
Hong Kong, but the reverse is not true. However, China and Hong Kong’s
economies are intertwined. The shock of the Chinese economy will affect Hong
Kong’s stable economic growth. In order for Hong Kong to keep a stable growth,
both governments must act in certain ways. China can help Hong Kong
government reduce expenditure without hurting Hong Kong’s economic growth.
4
Fiscal policy is more effective than monetary policy in affecting economic
activities.
The chapter is organized as follows. Section 2 describes the model. The
optimal control theory is presented in section 3, where we describe the quadratic
linear problem, give the solution process for the system, and associate the
dynamic optimization method with our problem. In section 4, the price variables
are added to the model to see the role of monetary policy, where we also present
policy simulations. Section 5 puts the expanded model in the control theory
framework and gives a sensitivity analyse.
2.2 THE MODEL
2.2.1 The Model Setup
The model consists of the GDP identity and functions for each of its
components. Specifically, for the Chinese economy we have:
CYt+1 = CCt+1 + CIt+1 + CGt+1 + CXt+1 – CMt+1
CCt+1 = a0 + a1CCt + a2CYt+1 + a3CYt
CIt+1 = b0 + b1CIt + b2CYt+1 + b3CYt
CMt+1 = c0 + c1CMt + c2CYt+1
CXt+1 = d0 + d1CXt + d2HYt+1
where CY, CC, CI, CG, CX, and CM stand for China’s GDP, consumption,
investment, government expenditure, exports, and imports respectively;
5
For the Hong Kong economy we have:
HYt+1 = HCt+1 + HIt+1 + HGt+1 + HXt+1 – HMt+1
HCt+1 = e0 + e1HCt + e2HYt+1
HIt+1 = f0 + f1HIt + f2HYt+1 + f3HYt
HMt+1 = g0 + g1HYt + g2HYt+1
HXt+1 = h0 + h1CYt + h2CYt+1
where HY, HC, HI, HG, HX, and HM stand for Hong Kong’s GDP,
consumption, investment, government expenditure, exports, and imports
respectively. Economic theory has little to say about the lag structure, and the lag
variables are included in the stochastic equations, since they result in a better fit
of the model to the data.
Since all the endogenous variables are interrelated between China and
Hong Kong as well as within each economy, we use two-stage least squares to
estimate the model. We use all the relevant variables as the instrumental
variables. When the Durbin-Watson statistic indicates a first-order serial
correlation, we use the Cochrane-Orcutt technique to correct it.
The following is the estimation result1:
For the Chinese economy:
CYt+1 = CCt+1 + CIt+1 + CGt+1 + CXt+1 – CMt+1
1 The data we used is from “Statistical Yearbook of China” from 1987 to 2001 by SSB.
6
CCt+1 = -6402 + 0.85 CCt + 0.48 CYt+1 - 0.4 CYt
(11300) (0.26) (0.044) (0.14) R2 = 0.99
CIt+1 = 10660 + 0.65 CIt + 0.46CYt+1 – 0.35 CYt
(9863) (0.22) (0.045) (0.1) R2 = 0.98
CMt+1 = -8486 + 0.75 CMt + 0.07 CYt+1
(13960) (0.39) (0.068) R2 = 0.8
CXt+1 = -10677 + 0.9 CXt + 0.3 HYt+1
(19072) (0.22) (0.3) R2 = 0.93
For the Hong Kong economy:
HYt+1 = HCt+1 + HIt+1 + HGt+1 + HXt+1 – HMt+1
HCt+1 = -2799 + 0.5 HCt + 0.41 HYt+1
(15177) (0.23) (0.15) R2 = 0.99
HIt+1 = -9118 + 0.42 HIt + 0.74HYt+1 – 0.5HYt
(3901) (0.23) (0.13) (0.14) R2 = 0.95
HMt+1 = -28400 + 2.27HYt+1 – 0.680 HYt
(12419) (0.46) (0.41) R2 = 0.95
HXt+1 = 35263 + 0.4CYt+1 - 0.216 CYt
(24486) (0.19) (0.19) R2 = 0.55
7
The standard errors are in parentheses. All the signs meet our expectation.
The interaction of the two economies is represented by the export functions:
China’s export is a function of Hong Kong’s GDP, and Hong Kong’s export is a
function of China’s GDP.
2.2.2 The Estimation Technique2
This section describes a consistent estimator of a simultaneous-equations
model in which there is a lagged dependent variable and serial correlation.
Consider the following equations (in deviation form):
t1t3t2t qapaq ε++= − (1)
t1tt v+ρε=ε − (2)
tt2t uybp += (3)
where ut and vt are independent over time and are uncorrelated with each other.
The first equation is identified and contains an autoregressive error term.
Plugging (2) into (1), we get
t2t1t31tt21tt v)qq(a)pp(aqq +ρ−+ρ−=ρ− −−−− (4)
Since ρ is not known, the estimate of the serial correlation coefficient, r, may not
equal ρ . Then (4) becomes
2 This section is heavily drawn from Pindyck and Rubinfeld (1998).
8
])r(v[)rqq(a)rpp(arqq 1tt2t1t31tt21tt −−−−− ε−ρ++−+−=− (5)
According to Fair, a consistent estimator can be obtained by the following
procedure.
Stage one: estimate the equation
t2t51t41t3t2t wqpqyp +γ+γ+γ+γ= −−− (6)
and get the predicted values pˆ t.
Stage two: estimate the equation
]wˆa)r(v[
)rqq(a)rppˆ(arqq
t21tt
2t1t31tt21tt
+ε−ρ+
+−+−=−
−
−−−−
(7)
where ttt ppw ˆˆ −= , the residual from the first stage. The estimate of ρ can be
obtained via the Cochrane-Orcutt procedure.
The following figure is out of sample prediction based on above
estimation.
9
Figure 1.1 Actual verse Prediction
310
320
330
340
350
360
370
380
390
400
1997 1998 1999 2000
Panel a: China's Consumption
actual predicted
160
210
260
310
360
410
1997 1998 1999 2000
Panel b: China's Investment
actual predicted
0
20
40
60
80
100
120
140
160
180
1997 1998 1999 2000
Panel c: China's Import
actual predicted
0
10
20
30
40
50
60
1997 1998 1999 2000
Panel e: Hong Kong's Investment
actual predicted
0
50
100
150
200
250
1997 1998 1999 2000
Panel e: Hong Kong's Import
actual predicted
64
66
68
70
72
74
76
78
80
82
84
86
1997 1998 1999 2000
Panel d: Hong Kong's Consumption
actual predicted
10
2.3 OPTIMAL CONTROL THEORY3
Many problems in economics are formulated as dynamic models. Control
theory is a dynamic optimization method, in which controls are used to move an
economic system over time from a less desirable to a more desirable state. The
basic idea of control theory is that an objective function is optimized subject to a
set of state or system equations. The objective function of a model depends on the
decision maker’s objectives. Variables in the system are separated into two
groups: state and control variables. The state of the economic system at any point
in time is represented by the state variables. Controls represent policy variables,
that can be altered by decision makers. The application of optimal control in
economics normally centers on a class of control problems called quadratic linear
tracking problems. The goal in the quadratic linear tracking problem is to cause
the state variables and control variables to follow their desired paths as closely as
possible. That is also the model we use in this study.
The objective function in the quadratic linear tracking problem is
)~()~(
2
1 '
NNNNN xxWxxJ −−=
[ ]∑−
=
−Λ−+−−+
1
0
'' )~()~()~()~(
2
1 N
t
tttttttttt uuuuxxWxx
and the system equations are in the structural form used by Pindyck (1973), i.e.
3 See Kendrick (1981) (2002) for discussion of control theory methods.
11
ttttt zCuBxAxAx 111101 +++= ++
where
xt = state vector of period t
tx~ = desired path for the state vector
tu = control vector of period t
tu~ = desired path for the control vector
zt = purely exogenous variable vector of period t
tW = penalty weight matrix for the state vector which is a diagonal matrix
tΛ = penalty weight matrix for the control vector which is a diagonal
matrix
A0, A1, B1, and C1 are the coefficient matrices and vectors
To specify, in our model we have:
=
t
t
t
t
t
t
t
t
t
t
t
HM
HX
HI
HC
HY
CM
CX
CI
CC
CY
x ,
=
t
t
t HGLead
CGLead
u , [ ]Constzt = .
12
Since we are going to represent this model in Duali software written by Amman
and Kendrick (1999), and Duali software does not allow variables in concurrent
terms, we introduce Lead CGt and Lead HGt, where
Lead CGt = CGt+1
Lead HGt = HGt+1
−
−
=
000027.200000
0000000004.0
000074.000000
000041.000000
1111000000
00000000007.0
00003.000000
00000000046.0
00000000048.0
0000011110
0A
13
−
−
−
−
−
=
00006.000000
000000000216.0
0042.005.000000
0005.0000000
0000000000
0000075.00000
0000009.0000
000000065.0035.0
0000000085.04.0
0000000000
1A
=
00
00
00
00
10
00
00
00
00
01
1B
−
−
−
−
−
=
28400
35263
9118
2799
0
8486
10677
10660
6402
0
1C
The desired paths of all the state variables are computed using their average
growth value -- the same growth rate -- from 1987 to 2000 except for the control
variables which are from 1988 to 2000, for example, China’ GDP in 1987 and
2000 are 359 billion and 815 billion US dollars (both in 1990 price) respectively.
The growth rate of GDP during this period is 6.5 percent per year. The desired
14
path of China’s GDP then is calculated according the growth rate of 6.5% each
year. The desired paths are shown in Table 2.1.
Table 2.1: Desired Paths for the State and Control Variables
Year CY CC CI CX CM HY HC HI HX HM CG HG
1987 359 184 133 45 50 54 32 15 66 63
1988 383 195 142 51 55 58 34 16 72 68 51 4
1989 407 207 151 56 60 62 36 17 78 74 55 5
1990 434 219 160 63 66 66 39 18 84 80 58 5
1991 462 233 171 70 73 70 41 19 91 87 62 5
1992 492 247 181 79 80 74 44 21 98 94 66 6
1993 524 261 193 88 88 79 47 22 106 102 70 7
1994 558 277 205 98 97 84 49 23 115 111 74 7
1995 595 294 218 109 106 90 53 25 125 120 79 8
1996 633 312 232 122 117 96 56 26 135 130 84 8
1997 675 330 246 136 129 102 60 28 146 141 89 9
1998 719 350 262 152 141 109 63 30 158 153 95 10
1999 765 371 278 170 155 116 67 32 171 165 101 11
2000 815 394 296 189 171 123 72 34 185 179 107 12
Following Shih (1997), The penalty weights for the base case are chosen
according to:
)/(559 iofmeanWi =
Where 559 is the average for China’s GDP.
i: state variables or control variables.
For example, China’s consumption average from 1987 to 2000 is 277
billion dollars. The penalty weight for China’s consumption is 559 divided by
277, and which is about two. 559 is China’s GDP average, which is the largest
15
average value among all variables. We chose 559, since we want the penalty
weights to be greater than one.
Since variables in our model have different units, this normalization will
give the same importance on each variable. Sometimes the weights are
normalized with different methods, for example, normalized with squares.
Fonseca (1999) gave a detailed description about different normalization
approaches. Here we follow Shih (1997)’s method. This normalization is simple
to calculate and also it achieves normalization goal. The following table lists the
penalty weights.
Table 2.2: Penalty Weights on State and Control Variable
CY CC CI CX CM HY HC HI HX HM CG HG
1 2 2.7 5.5 5.6 6.6 11.3 24 4.8 5 7.9 80.5
In fact the quadratic linear tracking problem can be transformed to the
quadratic linear problem (QLP), as described in Kendrick (1981, page 6-8). The
quadratic linear problem (QLP) is to obtain the solution paths for all the relevant
variables by optimizing a quadratic objective function subject to system equations
and a given initial condition. The variables in the model are separated into two
groups: state and control variables. Kendrick (1981) states the QLP as to find
1
0)(
−
=
N
kku to minimize the criterion (2.1) next page.
16
∑−
=
+Λ++++
+=
1
0
'''''
''
2
1
2
1
2
1
N
k
kkkkkkkkkkkkk
NNNNN
uuuuFxxwxWx
xwxWxJ
λ
(2.1)
subject to the system equations
kkkkkk cuBxAx ++=+1 for k = 0,1,2,…,N-1 (2.2)
and the given initial condition 0x ,
where
kx = state vector of period k with n elements
ku = control vector of period k with n elements
kW = n by n weight matrix of period k
kw = n element weight vector of period k
kF = n by m weight matrix of period k
kΛ = m by m weight matrix of period k
kλ = m element weight vector of period k
kA , kB and kc = coefficient matrices and vectors
Thus the problem is to find the time paths for the m control variables in
each period for the time periods from 0 to N-1 to minimize the quadratic form
(2.1) given 0x and following (2.2).
17
Solution Process
The problem (2.1) to (2.2) can solved by the method of dynamic
programming to obtain the feedback-control solution. The derivation of the
solution for this model is described in detail in Chapter 2 of Kendrick (1981). The
cost-to-go at time k is defined as
∑−
=
+Λ++++
+=
1
'''''
''
2
1
2
1
2
1)(
N
kt
ttttttttttttt
NNNNNk
uuuuFxxwxWx
xwxWxxf
λ
(2.3)
Which is the summation of the objective function from period k to the terminal
period.
Suppose the economic system is in state kx at time k and the optimal cost-
to-go at time k+1 is )( 1
*
1 ++ kk xf . Then the problem at k is to choose ku to
minimize:
)(
2
1
2
1)( 1
*
1
'''''
++++Λ+++= kkkkkkkkkkkkkkkkk xfuuuuFxxwxwxxf λ (2.4)
The optimal cost-to-go at k+1 will be:
1
'
111
'
11
*
1 2
1)( +++++++ += kkkkkkk xpxPxxf (2.5)
We ignore the constant term since the optimal cost-to-go is quadratic. 1+kP and
1+kp are coefficient matrix and vector determined backward from the terminal
period, and we will explain it later.
Substituting the system equation (2.2) for 1+kx in equation (2.5) to express
the optimal value in terms of kx , we get:
++++= ++++++ kkkkkkkkkkkkkkkkkk uBPAxxpAcPAxAPAxxf 1'''1'1'1''1* 1 )(2
1)(
18
kkkkkkkkkkk upBcPBuBPBu
'
1
''
1
'
1
'' )(
2
1
+++ ++ (2.6)
Plugging (2.6) into (2.4) and taking the first order condition with respect
to ku , we get the optimal solution:
kkkk gxGu +=* (2.7)
where
][][ '1
'1
1
'
kkkkkkkkk FAPBBPBG +Λ+−= +−+ (2.8)
][][ 1
''
1
'1
1
'
++
−
+ ++Λ+−= kkkkkkkkkkk pBcPBBPBg λ (2.9)
(2.7) is called a feedback rule, which says that if the economy is in state kx at k,
the best policy is *ku .
Now substituting the feedback rule in equation (2.6) and further
substituting the feedback rule and the resulting equation (2.6) in equation (2.4),
we get:
kkkkkkk xpxPxxf
''*
2
1)( += (2.10)
where
kkkkkkkkkkkkkkk GGGFWAPAGBPBGP Λ++++= ++ '1'1'' 22 (2.11)
++++= +++ kkkkkkkkkkkkk gBPBGcPApGBAp ' 1''1'1')(
kkkkkkkkkkkk GgGgFwcPBG λ'''' 1'' +Λ++++ (2.12)
Equations (2.11) and (2.12) are the Riccati equations for the problem. The Riccati
equations dictate the backward relationships in the time dimension and kP and
kp are functions of 1+kP and 1+kp . That means if we have the terminal values for
NP and Np , then we can solve kP and kp by integrating the Riccati equations
19
backward in time. NP and Np can be obtained from the minimization of the
terminal period cost-to-go
NnNNNNN xwxWxxf
''
2
1)( +=
we get
NN WP = (2.13)
NN wp = (2.14)
Because the objective function at N is constant in terms of the control vector Nu
and thus is the same as its optimal value.
Results and Experiment
If we apply the desired paths and penalty weights above and use the Duali
software written by Amman and Kendrick (1999), we get the optimal values for
each variable, and also this is our base case value for each variable in the
following experiment. The experiments here are a warm up, they set the stage
for the second model with prices.
Experiment one: lower government expenditure.
Due to the relative size of the two economies, China’s policy change will
have a substantial effect on the Hong Kong economy, but not vice versa. For
example, if China should decide to lower government expenditure to slow
inflation, the effect on Hong Kong would be substantial. However if Hong Kong
should cut gove._.rnment expenditure, the effect on China’s GDP would be
negligible. To do this experiment, we first let Chinese government’s desired
20
expenditure be 80% of its previous level each year, which is reflected in “low1”
case in Figures 2.1 – 2.4. Then we restore Chinese government expenditure to its
initial level and let Hong Kong government expenditure be 80% of its previous
level, which is reflected in “low2” case in those Figures 2.1 – 2.4. “low1” and
“low2” stand for optimal solutions for all variables under the reduced China and
Hong Kong’s government expenditure respectively. Figures 2.1 and 2.2 reflect
what happens to China and Hong Kong’s government expenditure after the
change respectively.
Figure 2.1: China’s Government Expenditure
40
60
80
100
120
140
160
88 89 90 91 92 93 94 95 96 97 98 99 2000
base low1 low2
21
Figure 2.2: Hong Kong’s Government Expenditure
It is obvious, from Figure 2.1, that China’s government expenditure is lower
under “low1” than in the base case which reflects optimal solutions for all
variables before making any change, and from Figure 2.2, Hong Kong’s
Government expenditure is lower under “low2” than the base case. At the same
time, the reduction of China’s government expenditure has a big effect on Hong
Kong’s government expenditure, see Figure 2.2 “low1” case. Hong Kong’s
government expenditure is increased substantially over the base path in order to
offset the loss of income which comes from the decrease in exports to China.
However the reverse in not true – China’s government expenditure under “low2”
is almost the same as the base case, see Figure 2.1. The reduction of China’s
government expenditure also has a big effect on Hong Kong’s export. Figures 3
below reflect the optimal paths for Hong Kong’s export. From Figure 2.3, Hong
1
6
11
16
21
26
88 89 90 91 92 93 94 95 96 97 98 99 2000
base low1 low2
22
Kong’s export path is apparently lower than the base level. So the Hong Kong
government must greatly increase expenditure to offset the loss in exports caused
by a decrease in government expenditure in China but the reverse is not true.
Not surprisingly, Hong Kong’s government expenditure change has a negligible
effect on China’s export, see Figure 2.4 under “low2” case.
Figure 2.3: Hong Kong's Export
50
70
90
110
130
150
170
190
87 89 91 93 95 97 99
base low1 low2
23
Figure 2.4: China’s Export
From this experiment, we have seen that the size of an economy matters. Since
China’s economy size is bigger than Hong Kong’s, China’s economic policy has
a large effect on Hong Kong, but the reverse is not true.
Experiment two: China has lower GDP growth
In this experiment, we want to see what happens to Hong Kong’s
economy if China’s GDP growth is slower. In order to mitigate this adverse
effect on Hong Kong’s economy, what should both governments do? First we let
the growth rate of China’s GDP be 4% each year, which is lower than the base
case which was 6.5%. “lowy1” in Figures 2.5 – 2.8 reflects the optimal paths for
30
50
70
90
110
130
150
170
190
87 88 89 90 91 92 93 94 95 96 97 98 99 2000
base low1 low2
24
all variables. From Figure 2.5 panel a and Figure 2.6 panel a, we can see both
China and Hong Kong have a lower optimal GDP path (lower than the base case)
after the change. Now we want China’s GDP growth rate still to be the lower
level – 4% – each year, but at the same time, we want Hong Kong’s optimal GDP
stays as almost the same level as the base case, the case where China’s GDP
growth rate is 6.5%. In order to achieve this, we increase Hong Kong GDP’s
desired level – higher than the base case and “lowy1” case (Hong Kong’s GDP
has same desired level under base and “lowy1” case). This is reflected in
“lowy2” in panel b of Figures 2.5 – 2.8. Panel a of figures 5 – 8 show base and
“lowy1” and panel b of Figures 2.5 – 2.8 then add “lowy2”. Now we describe
this scenario. As mentioned above, the optimal path for China’s GDP stays at
lower level under “lowy1”, as can be seen from Figure 2.5 panel a.
Figure 2.5: China’s GDP
250
350
450
550
650
750
850
87 89 91 93 95 97 99
b
base low y1 low y2
250
350
450
550
650
750
850
87 89 91 93 95 97 99
a
base low y1
25
The shock of the Chinese economy also makes Hong Kong’s GDP stay at lower
level, as can be seen in Figure 6 panel a under “lowy1”.
Figure 2.6: Hong Kong’s GDP
In order to reduce this adverse effect on Hong Kong’s economy, we increase
Hong Kong GDP’s desired path, that is the “lowy2” case. Under “lowy2”, the
optimal path of Hong Kong’s GDP is almost the same as that in the base case, as
can be seen in Figure 2.6 panel b. The higher growth rate of Hong Kong’s GDP
helps China’s growth in the presence of the adverse shock. We can see the
optimal path of China’s GDP is higher under “lowy2” than under “lowy1”, as can
be seen in Figure 2.5 panel b. That means under “lowy2” both Hong Kong and
China gain. In order to achieve this, what should the governments do? The
answer lies in Figures 2.7 and 2.8 panel b, which represents the optimal paths of
40
60
80
100
120
140
160
87 89 91 93 95 97 99
a
base low y1
40
60
80
100
120
140
160
87 89 91 93 95 97 99
b
base low y1 low y2
26
China and Hong Kong’s government expenditure respectively. Comparing the
optimal paths under “lowy1” with that under “lowy2”, we can see after the shock
of the Chinese economy, in order for Hong Kong to avoid the adverse effect,
China should reduce its government expenditure initially, then increase their
expenditure thereafter, and Hong Kong should increase its government
expenditure significantly, as can be seen from Figures 2.7 and 2.8 panel b.
Figure 2.7: China’s Government Expenditure
50
60
70
80
90
100
110
120
130
140
150
88 90 92 94 96 98 2000
a
base low y1
50
60
70
80
90
100
110
120
130
140
150
88 90 92 94 96 98 2000
b
base low y1 low y2
27
Figure 2.8 Hong Kong’ Government Expenditure
3
4
5
6
7
8
9
10
11
88 89 90 91 92 93 94 95 96 97 98 99 2000
a
base lowy1
3
5
7
9
11
13
15
88 89 90 91 92 93 94 95 96 97 98 99 2000
b
base lowy1 lowy2
28
Experiment three: Hong Kong has a lower government expenditure.
In this experiment, we want to see how much China can help Hong Kong
with its economic growth, when Hong Kong has to cut its government
expenditure. We let Hong Kong’s government desired expenditure be 80% of its
previous level each year. Figure 2.9 panel a reflects what happens to Hong Kong
government’s expenditure after the change. It is obvious that Hong Kong
government’s expenditure is lower than the base case. “lowg1” in the figure
reflects this change. “lowg1” stands for optimal solutions for all variables under
the reduced government expenditure. Panel a in Figures 2.9 – 2.11 show the
optimal path under the base case and the “lowg1” case.
Figure 2.9: Hong Kong’s Government Expenditure
1
3
5
7
9
11
13
15
17
88 90 92 94 96 98
a
base low g1
1
3
5
7
9
11
13
15
17
88 90 92 94 96 98
b
base low g1 low g2
29
“lowg2” is the case that we let Hong Kong ‘s GDP track its desired path as
closely as possible after Hong Kong government expenditure reduction. Hong
Kong’s GDP weight under “lowg2” case is as large as 10 times the previous
weights. Panel b of Figure 2.9 – 2.11 add the “lowg2” case. From Figure 2.10
panel b, we can see Hong Kong’s GDP is closer to its desired path under
“lowg2”. At the same time, Hong Kong’s government expenditure also stays at
lower level, as can be seen in Figure 2.9 panel b.
Figure 2.10: Hong Kong’s GDP
Under “lowg2”, Hong Kong has both stable economic growth and lower
government expenditure. But what happens to the Chinese economy? From
30
50
70
90
110
130
150
170
87 89 91 93 95 97 99
b
desired low g1 low g2
30
50
70
90
110
130
150
170
87 89 91 93 95 97 99
a
desired low g1
30
Figures 2.11 and 2.12, we can see the reduction of government expenditure in
Hong Kong and stable economic growth have little effect on China.
Figure 2.11: China’s GDP
200
300
400
500
600
700
800
900
1000
1100
87 88 89 90 91 92 93 94 95 96 97 98 99
a
desired base low g1
200
300
400
500
600
700
800
900
1000
1100
87 88 89 90 91 92 93 94 95 96 97 98 99
b
desired base low g1 low g2
31
Figure 2.12 shows when Hong Kong has to reduce its government expenditure,
China should reduce its government expenditure in both the initial and last
periods, in order to help Hong Kong has a more stable economic growth.
It is obvious that because of difference in size, the Chinese and Hong
Kong economies will have different effects on one another. Since China’s
economy size is bigger than Hong Kong’s, China’s economic policy has big effect
on Hong Kong, but the reverse is not true. However the question is open as to the
size of these effects over time. From those experiments, we can see China and
Hong Kong’s economies are intertwined. The shock of the Chinese economy will
affect Hong Kong’s stable economic growth. In order for Hong Kong to keep a
stable growth, both governments should act accordingly to make that happen.
30
50
70
90
110
130
150
170
190
88 89 90 91 92 93 94 95 96 97 98 99
Figure 2.12: China's Governmen Expenditure
lowg1 lowg2
32
China can help Hong Kong government reduce expenditure without hurting Hong
Kong’s economic growth.
2.4 THE MODEL WITH PRICE VARIABLES
In this section, price variables are added to the previous model. The
reason for adding price variables is that we want to see the role of monetary
policy in the interaction of the two economies, the effect of monetary policy on
the domestic economy and the difference between fiscal and monetary policy.
The price variables are the price level, wages, the interest rate and the
exchange rate. The unemployment rate also is added. The basic relationship
among GDP, consumption, investment, exports and imports are the same as
before.
2.4.1 The Model Equations.
Consumptions
China’s consumption (cc):
cc = -6518 + 0.856cc(-1) + 0.48cy – 0.40cy(-1)
(11253) (0.2563) (0.04) (0.13) Adjusted R-squared = .98
Hong Kong’s consumption (hc):
hc = -5577 + 0.036779hc(-1) + 0.62hy;
(12177) (0.07) (0.05) Adjusted R-squared = .99
33
There is a positive relationship between national income and consumption. The
explanatory variables also include lagged consumption. Wages were not one of
the explanatory variables due to multicollinearity with national income. The
equations explain the consumption in China and Hong Kong respectively. In
theory, consumption is also a function of the interest rate and the price level: the
higher the interest rate or the price level, the lower consumption. However, the
interest rate and the price level are not included in either of the consumption
equations, since the data doesn’t support these two variables.
Investment
China’s investment (ci):
ci = 10611 + 0.65ci(-1) + 0.46cy – 0.35cy(-1)
(9858) (0.22) (0.05) (0.1) Adjusted R-squared = .977751
Hong Kong’s investment (hi):
hi = -8653 + 0.4237hi(-1) + 0.718hy – 0.4839hy(-1)
(3884) (0.23) (0.13) (0.14) Adjusted R-squared = .93
In theory, investment is a positive function of national income, a negative
function of the interest rate, and also a function of lagged investment and national
income. We don’t have the interest rate in either China’s or Hong Kong’s
investment functions, since interest rates are not significant in the investment
34
functions. This is important, because it means monetary policy has a very limited
effect on investment.
Export
China’s exports (cx):
cx = -12910 + 0.86cx(-1) + 0.355hy
(19165) (0.22) (0.34) Adjusted R-squared = .927
Hong Kong’s exports (hx):
hx =213572 + 0.22cy - 0.18cy(-1) – 78882er
(26593) (0.07) (0.065) (11031) Adjusted R-squared = .94
Exports are a function of foreign income. Both China and Hong Kong’s economic
growth hinge on the growth of the demand for each other’s export. If foreign
income increases, the demand for domestic goods and services also increase. In
theory, exports are also a function of the exchange rate. If a country depreciate its
currency, the foreign demand for the country’s good will increase, since exporting
goods and services become cheaper for foreigners. However, the data does not
support the use of the exchange rate in China’s export function and thus is
omitted.
Import
China’s import (cm):
cm = 60485 + 0.137cy - 34364er
(41560) (0.04) (17581) Adjusted R-squared = .83
35
Hong Kong’s import (hm):
hm = -33299 + 2.54hy – 0.915hy(-1)
(130270) (0.49) (0.44) Adjusted R-squared = .949060
If a country has higher income, the country will have higher demand for imports.
If a country depreciates its currency, imports will become more expensive, and
the demand for imports will decrease. Thus, import is a function of national
income and exchange rate. The exchange rate is not significant in Hong Kong
import function, and thus it is excluded. That means currency depreciation played
a very limited role for export expansion in China.
Price level
China’s price level (cp):
cp = 0.12 + 0.68387cp(-1) + 0.1347cmg(-1);
(3.9) (0.26) (0.16) Adjusted R-squared = .297384
Hong Kong’s price level (hp):
hp = 5.1533 + 0.882358hp(-1) -0.0000545hy + 0.0836hmg(-1)
(3.4) (0.16) (0.000025) (0.067) Adjusted R-squared = .82
The inflation rate is a function of unemployment rate, national income, and
money supply. We want to find whether there is a tradeoff between inflation and
unemployment, and the data tells there is no such relationship. Also for
36
institutional reasons, we don’t have data on China’s overall unemployment rate. If
an economy prints too much money, the overall price level will increase. So the
money supply growth (mg) has a positive relationship with inflation. By the same
logic, if an economy’s money supply is fixed, but goods supply increases, and
then the price level will decrease. Here we look at national income from supply
perspective, not a source of demand pull. We observe this in Hong Kong’s
economy. The data doesn’t support national income as an explanatory variable in
China’s price equation.
Wage
China’s rural income (crw):
crw = -4.66 + 0.27crw(-1) + 0.000256cy
(9.7) (0.11) (0.000037) Adjusted R-squared = .973402
China’s urban wage (cuw):
cuw = 2193 + 0.150806cy + 95.537cp(-1)
(5159) (0.0077) (144) Adjusted R-squared = .97
Hong Kong’s wage (hw):
hw = 236 + 0.95hw(-1) + 0.003hy(-1)
(159) (0.09) (0.004) Adjusted R-squared = .99
37
The explanatory variables for wage equations are the unemployment rate, prices
and national income. The wage rate is positively related to prices and national
income while negatively related to the unemployment rate. A rise in prices leads
to an increase in wages when workers demand more pay to offset losses incurred
under higher price level. A reduction in unemployment and an increase in national
income mean a higher demand for labor, and higher demand for labor leads to
higher wages. Since unemployment is not significant in both China and Hong
Kong’s wage equations, the variable is excluded. It is also the same reason that
price variables are not included in Hong Kong’s wage equation. For China, we
have two wage equations: one for the urban area, and one for the rural area. The
urban area’s wage we use the wages of staff and workers, and the rural area’s
wage we use annual individual net income instead.
Unemployment rate
China’s urban unemployment rate (cun):
cun = 1.535 + 0.000013355cuw – 0.00184cp
(0.32) (0.000003) (0.010) Adjusted R-squared = .77
Hong Kong’s unemployment rate (hun):
hun = 0.26 -0.22hun(-1) + 0.0021hw -0.000057hy
(0.99) (0.31) (0.00064) (0.000025) Adjusted R-squared = .79
Unemployment rate is negatively related to national income and positively
related to wage. Wage increases reduce employment by raising input costs. This
38
relationship is important in the simulations. Due to lack of data, we don’t have
unemployment equation for China’s rural area. Since GDP is not significant in
China’s urban unemployment equation and thus it is omitted.
Interest rate
China’s interest rate (cr):
cr = -12.7 + 0.53cr(-1) + 0.000094ci(-1) -0.7cmg(-1)
(9.6) (0.4) (0.000058) (0.4) Adjusted R-squared = .13
Hong Kong’s interest rate (hr):
hr = 4.223 + 0.237hr(-1) -0.127hmg
(1.4) (0.28) (0.06) Adjusted R-squared = .25
The interest rate is negatively related to money growth, and also the interest rate
is influenced by investment demand. Higher investment spending increases the
demand for credit and therefore increases the interest rate. The data doesn’t
support investment as an explanatory variable in Hong Kong’s interest rate
equation and thus is omitted.
Exchange rate
er = 0.044 + 0.86er(-1) +0.0000036(cx(-1)-cm(-1));
(0.1) (0.07) (0.00000186) Adjusted R-squared = .87
39
In theory, the exchange rate is a function of the differential between China’s
interest rate and Hong Kong’s interest rate, and it is also a function of trade
balance –either Hong Kong’s trade balance or China’s trade balance. Here we use
China’s trade balance. An increase in the differential between China’s interest rate
and Hong Kong’s interest cause China’s RMB Yuan appreciate. An increase in
the China’s trade surplus leads China has more US dollar, and thus China’s RMB
Yuan appreciates relatively to Hong Kong dollar.
2.4.2 Simulations
In this section we report on simulations, including historical simulations
and policy simulations. In the historical simulation, we will see the fitness of our
estimated model. In the policy simulations, we will see how state variables in
our model respond to the changes in policy variables. Both temporary and
permanent changes are considered. We will do four different policy simulations:
both fiscal and monetary policy for each economy.
2.4.2.1 Historical Simulation
Figures 2.13 and 2.14 present the results of historical simulation from
1988 to 2000 for the Chinese and Hong Kong’s economies, respectively. These
results also will be used as a base case to which the policy simulation results are
compared. Each figure consists of four panels: GDP, price level, unemployment
and wage. A complete presentation of historical simulation is in Appendix A-1.
40
Figure 2.13: Simulation Result for the Chinese Economy
From Figures 2.13 and 2.14 we can see all the trends for each variable are well
captured, especially the two wage variables and the two GDP variables are quite
300
400
500
600
700
800
900
88 90 92 94 96 98
20
00
Panel a: China's GDP
Historical Simulated
-5
0
5
10
15
20
25
88 90 92 94 96 98
20
00
Panel b: China's Inflation
Historical Simulated
1
1.5
2
2.5
3
3.5
88 90 92 94 96 98
20
00
Panel c: China's Urban Unemployment Rate
Historical Simulated
50
60
70
80
90
100
110
120
130
140
88 90 92 94 96 98
20
00
Panel d: China's Urban Wage Income
Historical Simulated
41
well simulated. The predicted unemployment rates deviate a little from the
historical values in both economies.
Figure 2.14: Simulation Result for Hong Kong’s Economy
50
60
70
80
90
100
110
120
130
140
150
88 90 92 94 96 98
20
00
Panel a: Hong kong's GDP
Historical Simulated
-6
-4
-2
0
2
4
6
8
10
12
14
88 90 92 94 96 98
20
00
Panel b: Hong Kong's Inflation
Historical Simulated
0
1
2
3
4
5
6
7
88 90 92 94 96 98
20
00
Panel c: Hong Kong's Unemployment Rate
Historical Simulated
1
2
3
4
5
6
7
88 90 92 94 96 98
20
00
Panel d: Hong Kong's Wage
Historical Simulated
42
Figure 2.15 presents the simulation result for exchange rate and current
account for each economy. The exchange rate is well predicted and the
simulated current accounts capture well the trends of the variable.
Figure 2.15: Simulation Result for Current Accounts and the Exchange Rate
0.5
0.7
0.9
1.1
1.3
1.5
1.7
1.9
2.1
2.3
88 90 92 94 96 98
20
00
Exchange Rate
Historical Simulated
-12
-7
-2
3
8
13
18
23
28
33
38
88 90 92 94 96 98
20
00
China's Current Account
Historical Simulated
-5.5
-3.5
-1.5
0.5
2.5
4.5
6.5
8.5
10.5
88 90 92 94 96 98
20
00
Hong Kong's Current Account
Historical Simulated
43
2.4.2.2 Policy Simulations
We will see what happens if each economy changes its government
expenditure and money growth rate. Only policy expansion cases ― increases of
government expenditure and money growth rate ― are considered. The increases
of each policy instrument include both temporary and permanent increases. The
temporary increase refers to the increase in the initial year, and the permanent
increase refers to the increase during the entire time period.
2.4.2.2.1 Fiscal expansion
First we let China’s government expenditure increase by10% in 1988, that
is an increase of $5,127 million. This leads to China’s GDP increase of $26,025
million ―about 5 times the increase in the government expenditure. All other
variables are affected to different extents by this change in 1988. Figures 2.16
and 2.17 show the results of China’s fiscal expansion for the Chinese and Hong
Kong’s economies respectively, where “tempg” stands for the case: China’s
temporary fiscal expansion. Each figure consists of two variables: GDP and
unemployment rate, and each variable has two panels – one shows the base case
and temporary fiscal expansion, and the other shows the base case and permanent
fiscal expansion. A complete presentation of the simulation is attached in
Appendix B. We can see the temporary fiscal expansion has negligible effect on
the variables in the subsequent years in the model. One reason for this is that the
fiscal expansion has little effect on the price variables in the model for both
economies, even for the first couple of periods ( the price variables are shown in
Appendix A-2 ), so the price variables are almost intact in the subsequent years.
44
The other reason is that the effects due to the expansion are canceled out. For
example, in China’s investment function:
CI = 10611 + 0.65CI(-1) + 0.46CY – 0.35CY(-1)
We can see, initially when China’s government expenditure increases, both CI
and CY increase in the first year, and the increase in CY is bigger than the
Figure 2.16: China’s Fiscal Expansion Results for the Chinese Economy
Temporary Changes
1
1.5
2
2.5
3
3.5
88 90 92 94 96 98
20
00
Panel c: China's Urban Unemployment Rate
base tempg
Temporary Changes
300
400
500
600
700
800
900
88 90 92 94 96 98
20
00
Panel a: China's GDP
base tempg
Permanent Change
300
400
500
600
700
800
900
1000
88 90 92 94 96 98
20
00
Panel b: China's GDP
base permg
Permanent Changes
1
1.5
2
2.5
3
3.5
88 90 92 94 96 98
20
00
Panel d: China's Urban Unemployment Rate
base permg
45
increase in CI. So, when we calculate CI in the second year according to the
equation, the increase in CY and the rise in CI are cancelled out, because CY’s
coefficient is negative, and further the absolute value of CY’s coefficient is
Figure 17: China’s Fiscal Expansion Results for the Hong Kong’s Economy
Temporary Changes
0
1
2
3
4
5
6
88 90 92 94 96 98
20
00
Panel c: Hong Kong's Unemployment Rate
base tempg
Temporary Changes
50
60
70
80
90
100
110
120
130
140
88 90 92 94 96 98
20
00
Panel a: Hong Kong's GDP
base tempg
Permanent Changes
50
60
70
80
90
100
110
120
130
140
150
88 90 92 94 96 98
20
00
Panel b: Hong Kong's GDP
base permg
Permanent Changes
0
1
2
3
4
5
6
88 90 92 94 96 98
20
00
Panel d: Hong Kong's Unemployment Rate
base permg
46
smaller than CI’s. Even though CY has a bigger increase from the beginning, it
played a smaller role in the investment equation due to the smaller coefficient.
So, the effects are cancelled out when we calculate subsequent year’s investment.
In the permanent China’s fiscal expansion case, we let China’s
government expenditure increase 10% each year from 1988 to 2000. Figures 2.16
and 2.17 also present this change, which is represented by “permg” in each
graph. As in the temporary case, China’s permanent fiscal expansion not only has
a big effect on China’s GDP and unemployment, but also has important effects
on Hong Kong’s GDP and unemployment. Higher government expenditure leads
to higher GDP, higher GDP gives rise to higher consumption, higher investment
and higher wage rate, and higher wage rate results in higher unemployment rate.
At the same time, China’s higher GDP lets China import more, and Hong Kong
export more. With higher export, Hong Kong has a higher GDP, which lets Hong
Kong have a higher consumption, higher investment and higher wage rate, and
higher wage rate gives rise to higher unemployment. China’s fiscal expansion has
small effect on the price variables in the model, which is not shown here and can
be seen in Appendix B. China’s interest rate has a positive relationship with
fiscal expansion, since higher investment leads to higher interest rate, but the
effect is small. China’s price level is a function of its own lag and China’s money
supply, and government expenditure is negligible in the effect on the price level.
Hong Kong’s interest rate is a function of its own lag and Hong Kong’s money
supply, so China’s fiscal expansion also has a negligible effect on Hong Kong’s
interest. Hong Kong’s price level is a function of its own lag, Hong Kong’s
47
money supply and Hong Kong’s GDP. Higher GDP means higher supply and
higher supply leads to lower price. Since China’s fiscal expansion affect Hong
Kong’s GDP, it also affects Hong Kong’s price, but the effect is small. Table 2.3
shows this effect.
Table 2.3: The Effect of China’s Fiscal Expansion on Hong Kong’s Price
Above we have seen what happens in the two economies if China increases its
government expenditure. Figure 2.18 presents what happens to the Hong Kong
Year Price Change(%)
1988 0.13005
1989 0.14429
1990 0.1194
1991 0.12942
1992 0.14956
1993 0.1791
1994 0.15536
1995 0.17429
1996 0.19962
1997 0.21722
1998 0.23285
1999 0.24985
2000 0.27233
48
economy if Hong Kong has a fiscal expansion. The analysis of Hong Kong’s
fiscal expansion is the same as China’s fiscal expansion. The only difference is
that Hong Kong’s fiscal expansion has very small effect on China’s economy due
to its small size of economy.
Figure 2.18: Hong Kong’s Fiscal Expansion Results for the Hong Kong’s
Economy
80
100
120
140
160
180
200
88 90 92 94 96 98
20
00
Panel d: Hong Kong's Import
base permhg
-4
-2
0
2
4
6
8
10
12
14
88 90 92 94 96 98
20
00
Panel b: Hong Kong's Price Level
base permhg
0
1
2
3
4
5
6
88 90 92 94 96 98
20
00
Panel c: Hong Kong's Unemployment Rate
base permhg
50
60
70
80
90
100
110
120
130
140
88 90 92 94 96 98
20
00
Panel a: Hong Kong's GDP
base permhg
49
2.4.2.2.2 Monetary Expansion
Both a monetary expansion in China and a monetary expansion in Hong
Kong have a negligible effect on the other economy. Even in the same economy,
the effect of monetary expansion is limited: Only the price variables are
significantly affected, since interest rates do not affect investment in the model.
Figures 2.19 and 2.20 show the results of China’s monetary expansion on the
Chinese price variables and Hong Kong’s monetary expansion on the Hong
Kong’s price variables respectively, where “permm” stands for China’s
permanent monetary expansion, and “permhm” represents Hong Kong’s
permanent monetary expansion. In both economies, higher money growth lead to
higher price level and lower interest rate.
Figure 2.19: China’s Monetary Expansion Results on China’s Price Variables
-10
-5
0
5
10
15
20
25
30
88 90 92 94 96 98
20
00
Panel b: China's Interest Rate
base permm
-2
0
2
4
6
8
10
12
14
16
18
88 90 92 94 96 98
20
00
Panel a: China's Inflation
base permm
50
Figure 2.20: Hong Kong’s Monetary Expansion Results for Hong Kong’s Price
Variables
2.5 THE MODEL WITH PRICE VARIABLES IN THE CONTROL THEORY
FRAMEWORK
Now we put the model into the control theory framework. Table 4 presents
the desired paths for the new state and control variables: the added price variables.
With Table 2.1, Table 2.4 completes the desired paths for all variables. The
desired paths for the new variables are the means of their historical values. Table
2.5 shows the penalty weights for the new state and control variables. To get the
penalty weights we used the same procedure as we did for the old variables in
Table 2.2.
-5
-3
-1
1
3
5
7
9
11
13
15
88 90 92 94 96 98
20
00
Panel a: Hong Kong's Inflation
base permhm
0
1
2
3
4
5
6
7
88 90 92 94 96 98
20
00
Panel b: Hong Kong's Interest Rate
base permhm
51
Table 4.4: Desired Paths for the new State and Control Variables
Year CP CRW CUW CUN CR HP HW HUN HR ER CMG HMG
1987 7.4 124 51 2.67 0.17 6.2 1.73 2.66 3.8 1.26 12 8.5
1988 7.4 132 54 2.67 0.17 6.2 1.92 2.66 3.8 1.26 12 8.5
1989 7.4 140 58 2.67 0.17 6.2 2.12 2.66 3.8 1.26 12 8.5
1990 7.4 149 63 2.67 0.17 6.2 2.35 2.66 3.8 1.26 12 8.5
1991 7.4 158 67 2.67 0.17 6.2 2.6 2.66 3.8 1.26 12 8.5
1992 7.4 168 72 2.67 0.17 6.2 2.88 2.66 3.8 1.26 12 8.5
1993 7.4 178 78 2.67 0.17 6.2 3.18 2.66 3.8 1.26 12 8.5
1994 7.4 190 84 2.67 0.17 6._. 3288.00 3288.00 3288.00
6.60 6.60 6.60 6.60
11.30 11.30 11.30 11.30
24.00 24.00 24.00 24.00
4.80 4.80 4.80 4.80
193
5.00 5.00 5.00 5.00
90.00 90.00 90.00 90.00
154.00 154.00 154.00 154.00
210.00 210.00 210.00 210.00
147.00 147.00 147.00 147.00
444.00 444.00 444.00 444.00
lams
7.90 7.90 7.90 7.90 7.90
47.00 47.00 47.00 47.00 47.00
80.50 80.50 80.50 80.50 80.50
66.00 66.00 66.00 66.00 66.00
lams
7.90 7.90 7.90 7.90 7.90
47.00 47.00 47.00 47.00 47.00
80.50 80.50 80.50 80.50 80.50
66.00 66.00 66.00 66.00 66.00
lams
7.90 7.90 7.90
47.00 47.00 47.00
80.50 80.50 80.50
66.00 66.00 66.00
x0
359236.00
184088.00
194
133468.00
45333.00
49667.00
7.30
124.00
50539.00
2.00
-0.10
54185.00
32090.00
14945.00
66163.00
62814.00
5.50
1731.00
1.70
2.13
2.09
xtws
359236.00 382606.00 407496.00 434006.00 462240.00
184088.00 195170.00 206918.00 219374.00 232579.00
133468.00 141896.00 150855.00 160380.00 170507.00
45333.00 50600.00 56479.00 63041.00 70365.00
49667.00 54620.00 60067.00 66057.00 72645.00
195
7.40 7.40 7.40 7.40 7.40
124.00 132.00 140.00 149.00 158.00
50539.00 54307.00 58357.00 62708.00 67384.00
2.67 2.67 2.67 2.67 2.67
0.17 0.17 0.17 0.17 0.17
54185.00 57730.00 61506.00 65530.00 69817.00
32091.00 34140.00 36319.00 38638.00 41105.00
14945.00 15922.00 16962.00 18070.00 19251.00
66163.00 71616.00 77518.00 83906.00 90821.00
62814.00 68094.00 73819.00 80025.00 86753.00
6.20 6.20 6.20 6.20 6.20
1731.00 1916.00 2121.00 2348.00 2599.00
2.66 2.66 2.66 2.66 2.66
3.80 3.80 3.80 3.80 3.80
1.26 1.26 1.26 1.26 1.26
xtws
492310.00 524337.00 558448.00 594778.00 633471.00
246579.00 261422.00 277159.00 293843.00 311531.00
181273.00 192719.00 204888.00 217825.00 231579.00
78540.00 87664.00 97849.00 109217.00 121906.00
79890.00 87857.00 96618.00 106254.00 116850.00
7.40 7.40 7.40 7.40 7.40
168.00 178.00 190.00 201.00 214.00
72409.00 77808.00 83610.00 89844.00 96544.00
196
2.67 2.67 2.67 2.67 2.67
0.17 0.17 0.17 0.17 0.17
74385.00 79251.00 84436.00 89960.00 95845.00
43730.00 46522.00 49492.00 52652.00 56014.00
20509.00 21849.00 23276.00 24797.00 26417.00
98306.00 106408.00 115177.00 124669.00 134944.00
94046.00 101953.00 110524.00 119815.00 129888.00
6.20 6.20 6.20 6.20 6.20
2877.00 3184.00 3524.00 3901.00 4318.00
2.66 2.66 2.66 2.66 2.66
3.80 3.80 3.80 3.80 3.80
1.26 1.26 1.26 1.26 1.26
xtws
674681.00 718572.00 765318.00 815105.00
330284.00 350166.00 371244.00 393591.00
246202.00 261747.00 278275.00 295845.00
136069.00 151878.00 169523.00 189218.00
128504.00 141319.00 155412.00 170911.00
7.40 7.40 7.40 7.40
227.00 241.00 256.00 272.00
103743.00 111479.00 119791.00 128724.00
2.67 2.67 2.67 2.67
0.17 0.17 0.17 0.17
102116.00 108796.00 115914.00 123497.00
197
59591.00 63396.00 67443.00 71750.00
28144.00 29982.00 31942.00 34029.00
146065.00 158102.00 171132.00 185236.00
140808.00 152546.00 165479.00 179390.00
6.20 6.20 6.20 6.20
4779.00 5290.00 5856.00 6481.00
2.66 2.66 2.66 2.66
3.80 3.80 3.80 3.80
1.26 1.26 1.26 1.26
utws
51269.00 54526.00 57990.00 61674.00 65592.00
12.00 12.00 12.00 12.00 12.00
4248.00 4628.00 5042.00 5492.00 5984.00
8.50 8.50 8.50 8.50 8.50
utws
69760.00 74191.00 78905.00 83917.00 89249.00
12.00 12.00 12.00 12.00 12.00
6519.00 7102.00 7737.00 8429.00 9183.00
8.50 8.50 8.50 8.50 8.50
utws
94919.00 100949.00 107362.00
12.00 12.00 12.00
10004.00 10899.00 11874.00
8.50 8.50 8.50
198
Appendix B-1: Comparison of Different Money Supply
ADebug File
-1
-0.8
-0.6
-0.4
-0.2
0
0.2
0.4
0.6
0.8
1
1981 1984 1987 1990 1993 1996 1999
Panel a: Exchange Rate Difference
higher-cm highest-cm
-7
-6
-5
-4
-3
-2
-1
1981 1984 1987 1990 1993 1996 1999
Panel b: Interest Rate Difference
higher-cm highest-cm
-7.5
-7
-6.5
-6
-5.5
-5
-4.5
-4
-3.5
1981 1984 1987 1990 1993 1996 1999
Panel c: Income Difference
higher-cm highest-cm
-5
-4
-3
-2
1981 1984 1987 1990 1993 1996 1999
Panel d: Money Supply Difference
higher-cm highest-cm
0
1
2
3
4
5
6
1981 1984 1987 1990 1993 1996 1999
Panel e: China's Current Account
higher-cm highest-cm
199
Appendix B-2: Duali Input File
a
0.88 0.00 2.15
0.00 0.00 27.22
0.00 0.00 0.81
b
-5.46 -0.04
-60.58 0.00
0.34 0.00
capc
-5.71
-49.82
-0.14
zs
1.00 1.00 1.00 1.00 1.00
zs
1.00 1.00 1.00 1.00 1.00
zs
1.00 1.00 1.00 1.00 1.00
zs
1.00 1.00 1.00 1.00
cs
200
-5.71 -5.71 -5.71 -5.71 -5.71
-49.82 -49.82 -49.82 -49.82 -49.82
-0.14 -0.14 -0.14 -0.14 -0.14
cs
-5.71 -5.71 -5.71 -5.71 -5.71
-49.82 -49.82 -49.82 -49.82 -49.82
-0.14 -0.14 -0.14 -0.14 -0.14
cs
-5.71 -5.71 -5.71 -5.71 -5.71
-49.82 -49.82 -49.82 -49.82 -49.82
-0.14 -0.14 -0.14 -0.14 -0.14
cs
-5.71 -5.71 -5.71 -5.71
-49.82 -49.82 -49.82 -49.82
-0.14 -0.14 -0.14 -0.14 Debug File for
QLP
ws
16.70 16.70 16.70 16.70 16.70
1.80 1.80 1.80 1.80 1.80
1.00 1.00 1.00 1.00 1.00
ws
16.70 16.70 16.70 16.70 16.70
1.80 1.80 1.80 1.80 1.80
201
1.00 1.00 1.00 1.00 1.00
ws
16.70 16.70 16.70 16.70 16.70
1.80 1.80 1.80 1.80 1.80
1.00 1.00 1.00 1.00 1.00
ws
16.70 16.70 16.70 16.70 16.70
1.80 1.80 1.80 1.80 1.80
1.00 1.00 1.00 1.00 1.00
lams
2.30 2.30 2.30 2.30 2.30
1.00 1.00 1.00 1.00 1.00
lams
2.30 2.30 2.30 2.30 2.30
1.00 1.00 1.00 1.00 1.00
lams
2.30 2.30 2.30 2.30 2.30
1.00 1.00 1.00 1.00 1.00
lams
2.30 2.30 2.30 2.30
1.00 1.00 1.00 1.00
x0
0.75
-2.51
202
-3.90
xtws
0.75 0.70 0.65 0.60 0.55
-2.70 -2.70 -2.70 -2.70 -2.70
-3.96 -4.06 -4.16 -4.26 -4.37
xtws
0.50 0.45 0.40 0.35 0.30
-2.70 -2.70 -2.70 -2.70 -2.70
-4.47 -4.58 -4.69 -4.81 -4.93
xtws
0.25 0.20 0.15 0.10 0.05
-2.70 -2.70 -2.70 -2.70 -2.70
-5.05 -5.17 -5.30 -5.43 -5.56
xtws
0.00 0.00 0.00 0.00 0.00
-2.70 -2.70 -2.70 -2.70 -2.70
-5.70 -5.83 -5.98 -6.12 -6.27
utws
-1.33 -1.40 -1.47 -1.55 -1.62
5.00 5.00 5.00 5.00 5.00
utws
-1.71 -1.79 -1.88 -1.98 -2.08
5.00 5.00 5.00 5.00 5.00
utws
203
-2.18 -2.29 -2.41 -2.53 -2.66
5.00 5.00 5.00 5.00 5.00
utws
-2.79 -2.93 -3.08 -3.23
5.00 5.00 5.00 5.00
204
Appendix C-1: Constant Inequality, Growth, and Inflation
1
2
3
4
5
1998 2001 2004 2007 2010
Panel a: Urban Income Inequality
desired solution new
10
13
16
19
1998 2001 2004 2007 2010
Panel b: Rural Income Inequality
desired solution new
3
7
11
1998 2001 2004 2007 2010
Panel c: Per Capita GDP Growth
desired solution new
6
8
10
12
1998 2001 2004 2007 2010
Panel d: Inflation
desired solution new
0
2
4
6
1998 2000 2002 2004 2006 2008
Panel e: Urban Degree of Reform
desired solution new
49.45
49.48
49.51
49.54
1998 2001 2004 2007
Panel f: Foreign Direct Investment
desired solution new
205
70
73
76
79
1998 2000 2002 2004 2006 2008
Panel g: Rural Degree of Reform
desired solution new
0.2
0.4
0.6
0.8
1998 2000 2002 2004 2006 2008
Panel h: Agriculture Tax
desired solution new
8
12
16
20
1998 2000 2002 2004 2006 2008
Panel i: International Trade
desired solution new
8
12
16
20
1998 2000 2002 2004 2006 2008
Panel j: Fixed Assets Investment
desired solution new
4.2
4.3
4.4
4.5
4.6
1998 2000 2002 2004 2006 2008
Panel k: Human Capital
desired solution new
23
24
25
26
27
1998 2000 2002 2004 2006 2008
Panel l: Money Growth
desired solution new
206
Appendix C-2 Duali Input File
a
0.14 0.00 0.00 0.00
0.00 0.67 0.00 0.01
0.08 0.12 0.31 0.00
0.00 0.01 0.02 0.53
b
0.92 0.00 0.00 -0.00 0.00
0.00 0.00 0.52 -2.29 0.00
0.55 0.00 0.09 -0.41 0.09
0.03 0.00 0.00 -0.02 0.00
b
0.00 0.00 0.00
0.00 0.00 0.01
0.11 0.05 0.00
0.01 0.00 0.53
capc
0.72
-33.43
-6.96
-9.04
z
207
1.00
cs
0.72 0.72 0.72 0.72 0.72
-33.43 -33.43 -33.43 -33.43 -33.43
-6.96 -6.96 -6.96 -6.96 -6.96
-9.04 -9.04 -9.04 -9.04 -9.04
cs
0.72 0.72 0.72 0.72 0.72
-33.43 -33.43 -33.43 -33.43 -33.43
-6.96 -6.96 -6.96 -6.96 -6.96
-9.04 -9.04 -9.04 -9.04 -9.04
cs
0.72 0.72
-33.43 -33.43
-6.96 -6.96
-9.04 -9.04 Debug File for QLP
ws
18.00 18.00 18.00 18.00 18.00
4.00 4.00 4.00 4.00 4.00
8.00 8.00 8.00 8.00 8.00
8.00 8.00 8.00 8.00 8.00
ws
18.00 18.00 18.00 18.00 18.00
208
4.00 4.00 4.00 4.00 4.00
8.00 8.00 8.00 8.00 8.00
8.00 8.00 8.00 8.00 8.00
ws
18.00 18.00 18.00
4.00 4.00 4.00
8.00 8.00 8.00
8.00 8.00 8.00
lams
22.00 22.00 22.00 22.00 22.00
1.50 1.50 1.50 1.50 1.50
1.00 1.00 1.00 1.00 1.00
165.00 165.00 165.00 165.00 165.00
5.00 5.00 5.00 5.00 5.00
5.00 5.00 5.00 5.00 5.00
17.00 17.00 17.00 17.00 17.00
3.00 3.00 3.00 3.00 3.00
lams
22.00 22.00 22.00 22.00 22.00
1.50 1.50 1.50 1.50 1.50
1.00 1.00 1.00 1.00 1.00
165.00 165.00 165.00 165.00 165.00
5.00 5.00 5.00 5.00 5.00
5.00 5.00 5.00 5.00 5.00
209
17.00 17.00 17.00 17.00 17.00
3.00 3.00 3.00 3.00 3.00
lams
22.00 22.00
1.50 1.50
1.00 1.00
165.00 165.00
5.00 5.00
5.00 5.00
17.00 17.00
3.00 3.00
x0
4.40
18.00
9.53
9.50
xtws
4.40 4.40 4.40 4.40 4.40
18.00 18.00 18.00 18.00 18.00
9.53 9.53 9.53 9.53 9.53
9.50 9.50 9.50 9.50 9.50
xtws
4.40 4.40 4.40 4.40 4.40
18.00 18.00 18.00 18.00 18.00
210
9.53 9.53 9.53 9.53 9.53
9.50 9.50 9.50 9.50 9.50
xtws
4.40 4.40 4.40
18.00 18.00 18.00
9.53 9.53 9.53
9.50 9.50 9.50
utws
3.54 3.54 3.54 3.54 3.54
49.50 49.50 49.50 49.50 49.50
77.39 77.39 77.39 77.39 77.39
0.47 0.47 0.47 0.47 0.47
14.40 14.40 14.40 14.40 14.40
17.00 17.00 17.00 17.00 17.00
4.50 4.50 4.50 4.50 4.50
26.00 26.00 26.00 26.00 26.00
utws
3.54 3.54 3.54 3.54 3.54
49.50 49.50 49.50 49.50 49.50
77.39 77.39 77.39 77.39 77.39
0.47 0.47 0.47 0.47 0.47
14.40 14.40 14.40 14.40 14.40
17.00 17.00 17.00 17.00 17.00
4.50 4.50 4.50 4.50 4.50
211
26.00 26.00 26.00 26.00 26.00
utws
3.54 3.54
49.50 49.50
77.39 77.39
0.47 0.47
14.40 14.40
17.00 17.00
4.50 4.50
26.00 26.00
212
Appendix D-1: Data-China
Year GDP (1 million us$ 1990 price) C I G
1978 427864.0415 209198.4489 163864.7847 57083.31275
1979 469941.5831 231989.3969 170538.9294 71028.96665
1980 480025.5147 245120.6956 168202.454 69714.09884
1981 411762.3527 219281.3506 133129.9932 59365.36697
1982 390191.2091 205121.5269 125895.2236 55072.90203
1983 400962.0591 211195.2174 133054.646 55610.8695
1984 385351.4796 198495.0813 133352.8256 55100.00896
1985 360728.7738 189806.4905 140048.9816 48971.64627
1986 344017.7298 178480.7343 132644.8124 47146.50508
1987 359235.9903 184088.3916 133468.0984 46012.83357
1988 432436.8439 226602.3116 163128.965 51269.10327
1989 458972.834 238497.0269 170544.8911 56885.60517
1990 381021.4499 190501.2751 134704.6281 47075.54664
1991 380281.5311 185997.6322 135532.9347 51025.43638
1992 436062.5834 210362.8994 162687.7557 58961.64894
1993 540998.3939 246088.7064 235349.0804 70609.43172
1994 475996.38 212918.3479 197067.4938 61248.63354
1995 604981.759 276725.1915 245221.3772 68712.72036
1996 680433.2271 322262.9157 269290.2905 78696.68759
1997 739954.0848 342108.7383 279320.1939 85636.62529
1998 770001.5922 357336.1204 285956.1952 91797.41759
1999 785508.1908 372959.7293 291105.5063 98499.47009
2000 815105.4965 393591.3652 295845.4295 107361.9441
213
Year X M
Exchange
rate7 US Cpi (ifs) US Cpi percent (ifs)
1978 19521.42265 21803.92745 1.6836 49.94513041 7.54
1979 24572.43631 28188.14619 1.555 55.59074334 11.30363036
1980 28722.42876 31734.16246 1.4984 63.08658697 13.48397806
1981 31601.95401 31616.31201 2.2524 69.64759202 10.4
1982 30213.44608 26111.88956 2.38 73.87439334 6.06884058
1983 29145.81 28044.48385 2.396 76.27168365 3.245089667
1984 32858.93356 34455.36989 2.558 79.55218617 4.301075269
1985 33220.7869 51319.1315 2.9367 82.32797159 3.489263526
1986 36844.50467 51098.82661 3.4528 83.97453102 2
1987 45333.33333 49666.66667 4.55 87 3.602841176
1988 52508.28729 61071.8232 5.01 90.5 4.022988506
1989 55363.54057 62318.22972 4.77 94.9 4.861878453
1990 62090 53350 5.29 100 5.374077977
1991 68944.33781 61218.80998 5.77 104.2 4.2
1992 79087.52328 75037.24395 6.82 107.4 3.071017274
1993 82947.55877 93996.38336 8.1 110.6 2.979515829
1994 106710.7584 101948.8536 8.6187 113.4 2.53164557
1995 127598.6278 113276.1578 8.3507 116.6 2.821869489
1996 125875 115691.6667 8.3142 120 2.915951973
1997 148730.6753 115842.1481 8.2898 122.9 2.416666667
1998 147283.6538 112371.7949 8.2791 124.8 1.545972335
1999 153006.2794 130062.7943 8.2783 127.4 2.083333333
2000 189217.9195 170911.1617 8.2784 131.7 3.4
7 The data from 1987-1992 is from the world bank (1994), 93’s data is estimated; 94-2000’s data
is from China Statistical Yearbook; 78-79 and 85-86 year data are official rate according to
Zhongxia Jin (1995); 1981-1984’s data is estimated (per settlement rate 2.8 and official rate
weighted by export divided by total trade.
214
Year China cpi (General Retail price index)
interest
rate
real interest
rate
m1 (1 million
us$)
1978 100.7 4 3.3 112799.0045
1979 102 4 2 136169.7014
1980 106 5.4 -0.6 152693.9761
1981 102.4 5.4 3 144059.9572
1982 101.9 5.8 3.9 136924.1086
1983 101.5 5.8 4.3 144833.798
1984 102.8 5.8 3 158363.9081
1985 108.8 7.2 -1.6 138183.5921
1986 106 7.2 1.2 145964.4761
1987 107.3 7.2 -0.1 152818.1934
1988 118.5 8.6 -9.9 177702.215
1989 117.8 11.3 -6.5 178581.0671
1990 102.1 8.6 6.5 159055.5625
1991 102.9 7.6 4.7 168731.9227
1992 105.4 7.6 2.2 198066.7711
1993 113.2 11 -2.2 255472.5409
1994 121.7 11 -10.7 210165.014
1995 114.8 11 -3.8 246352.1253
1996 106.1 7.7 1.6 285804.2065
1997 100.8 5.7 4.9 341830.965
1998 97.4 3.8 6.4 377008.3782
1999 97 2.3 5.3 434617.7433
2000 98.5 2.42 3.92 487470.3522
215
Year
m2(1 million
us$)
total industry gross value
(million us$)
state-owned industry
(million us$)
1978 137844.3079 503879.1586 391161.0222
1979 168676.4434 541543.8137 424970.7034
1980 194956.165 545257.3461 414222.3451
1981 188158.7411 454694.9238 339948.827
1982 185230.9113 415637.3373 309409.5769
1983 204061.3648 428723.9687 314513.3113
1984 223981.5364 411483.6258 284289.0364
1985 215032.6789 401884.7397 260663.1684
1986 231797.327 386079.1777 240427.1723
1987 257267.3256 426560.2751 254771.8242
1988 299830.7407 541029.4581 307296.3771
1989 334363.1223 616061.8703 345368.6541
1990 319691.4587 500104.5194 273088.3398
1991 348882.3644 480053.7963 269641.4845
1992 428873.6932 584146.2909 300928.4514
1993 547334.9016 759525.6826 356601.4036
1994 480104.2825 718015.4534 268079.1566
1995 623919.3062 943769.0344 320635.3979
1996 762700.1596 998241.9635 362562.4434
1997 893147.1678 1116324.764 353037.1055
1998 1011380.139 1152190.765 325396.5267
1999 1136845.246 1195755.482 337276.0366
2000 1234656.395 1362989.382 296010.5269
216
Year
annual average wages of
staff and workers(us$)
total wages ( 1 million usd) of
staff and workers
1978 731.3799446 67655.61797
1979 772.7581388 74800.21146
1980 806.1023265 81710.4248
1981 650.0718199 69049.07932
1982 570.7555301 63090.65829
1983 548.1453247 62021.38262
1984 526.151066 61225.83349
1985 474.8264351 57202.52263
1986 458.3592191 57241.44439
1987 450.5551958 58090.43035
1988 518.6283927 68760.56572
1989 541.4345598 73268.54754
1990 447.3431164 61689.4519
1991 421.9064351 59930.54699
1992 457.707042 66506.80856
1993 528.9783637 77145.16262
1994 464.3117487 68105.87757
1995 564.8605664 83188.55615
1996 622.4290972 91008.95656
1997 635.0506208 92315.94442
1998 723.8454013 89974.98025
1999 791.3485146 93637.2
2000 859.5155851 97740
217
Year Per Capital Annual net income of Rural(us$) # of staff and workers (10000persons)
1978 158.8818872 9499
1979 185.2884298 9967
1980 202.3718833 10444
1981 188.1503205 10940
1982 193.1914489 11281
1983 205.567769 11515
1984 191.9479038 11890
1985 164.4520824 12358
1986 146.1645125 12809
1987 142.8559517 13214
1988 161.7633722 13608
1989 168.3064019 13742
1990 143.4633555 14059
1991 137.9143264 14508
1992 132.3652973 14792
1993 144.6177573 14849
1994 124.9283044 14849
1995 162.032821 14908
1996 193.0532503 14845
1997 205.1498149 14668
1998 209.2463909 12337
1999 209.5755598 11773
2000 206.6854775 11259
218
Year employed person total (10000persons) employed person rural (10000persons)
1978 40152 30638
1979 41024 31025
1980 42361 31836
1981 43725 32672
1982 45295 33867
1983 46436 34690
1984 48197 35968
1985 49873 37065
1986 51282 37990
1987 52783 39000
1988 54334 40067
1989 55329 40939
1990 63909 47293
1991 64799 47822
1992 65554 48313
1993 66373 48784
1994 67199 48786
1995 67947 48854
1996 68850 49035
1997 69600 49393
1998 69957 49279
1999 70586 49572
2000 71150 49876
219
Year employed person urban (10000persons) urban unployment rate(%)
1978 9514 5.3
1979 9999 5.1
1980 10525 4.9
1981 11053 3.8
1982 11428 3.2
1983 11746 2.3
1984 12229 1.9
1985 12808 1.8
1986 13292 2
1987 13783 2
1988 14267 2
1989 14390 2.6
1990 16616 2.5
1991 16977 2.3
1992 17241 2.3
1993 17589 2.6
1994 18413 2.8
1995 19093 2.9
1996 19815 3
1997 20207 3.1
1998 20678 3.1
1999 21014 3.1
2000 21274 3.1
220
Appendix D-2: Data-Hong Kong
Year
GDP(us$ million 1990
price) C I G
1987 54184.68632 32090.60384 14945.03454 3799.824889
1988 64410.29213 36051.31624 18438.99649 4247.75955
1989 70770.987 38863.71619 18868.30942 4897.598011
1990 74781.64313 42420.92426 20475.48139 5556.225931
1991 82559.02957 48299.31452 22455.03547 6356.375431
1992 93739.54303 54327.52206 26701.88026 7706.432435
1993 104908.0553 60228.03136 29045.29398 8454.285265
1994 115351.0586 67628.39999 36785.88317 9546.129241
1995 119415.0757 72559.11636 41599.41003 10447.24626
1996 128425.3513 77805.57711 41169.8345 11247.41402
1997 139135.4082 83915.59444 48058.84822 11954.80613
1998 130285.2543 78859.18955 37806.44254 12183.21167
1999 124210.4671 74220.74666 30992.77072 12289.43597
2000 123497.3438 71749.55196 34028.6141 11873.79437
221
Year Export Import
Per Capita
GDP(us$)
GDP
Deflator CPI
1987 66162.85343 62813.63037 8.9 5.5
1988 85505.97854 79833.7587 11445.36785 9.5 7.5
1989 94249.81762 86108.45424 12446.02956 12.3 10.1
1990 100410.1412 94081.12965 13109.24262 7.5 9.7
1991 114478.1116 109029.8074 14353.1576 9.2 11.6
1992 134030.0997 129026.3914 16160.65464 9.7 9.3
1993 147696.8954 140516.4506 17775.44596 8.5 8.5
1994 160971.3733 159580.7271 19144.5204 6.9 8.1
1995 178462.3715 183653.0684 19907.81555 2.5 8.7
1996 182538.682 184336.1564 19955.82277 5.9 6
1997 184355.2409 189149.0815 21440.7523 5.8 5.7
1998 168471.4911 167035.0805 19910.07432 0.4 2.6
1999 165684.4426 158976.9289 18801.27386 -5.4 -3.3
2000 185235.8313 179390.4479 18529.25523 -6.6 -2.9
222
Year
Real
GDP
Ex rate(year
average)
unemployment
rate
M1(HK current $ million,
end year)
1987 13 7.798 1.7 81900
1988 8 7.806 1.4 88800
1989 2.6 7.8 1.1 94900
1990 3.4 7.79 1.3 107509
1991 5.1 7.771 1.8 128500
1992 6.3 7.741 2 155600
1993 6.1 7.736 2 187600
1994 5.4 7.728 1.9 185334
1995 3.9 7.736 3.2 190471
1996 4.5 7.734 2.8 217460
1997 5 7.742 2.2 208093
1998 -5.3 7.745 4.7 197666
1999 3 7.758 6.2 225156
2000 10.5 7.791 4.9 243847
223
Year
M1(us $ million,
end year)
M2(HK current $ million,
end year)
M2(us $ million,
end year)
1987 12072.06092 677000 99789.80758
1988 12570.01626 824600 116725.624
1989 12820.51282 988800 133581.9081
1990 13800.89859 1210050 155333.7612
1991 15869.32665 1371000 169313.9832
1992 18715.79346 1518800 182683.4647
1993 21926.0927 1761000 205820.092
1994 21148.27412 1992351 227345.145
1995 21116.10683 2282849 253082.5341
1996 23431.16973 2532236 272846.7374
1997 21870.18322 2742993 288283.4093
1998 20450.12498 3066089 317211.3729
1999 22780.55609 3313534 335252.6566
2000 23765.03387 3605213 351359.7012
224
Year
savings deposites
rate
12-month time deposite
rate
labor
force
1987 2.127691667 4.234066667 272.8
1988 3.293525 5.386308333 276.3
1989 5.788108333 8.038108333 275.3
1990 5.913883333 8.163883333 274.8
1991 4.714516667 6.964516667 280.4
1992 2.325266667 4.575266667 279.2
1993 1.5 3.75 285.6
1994 2.447583333 5.174408333 292.9
1995 4.1975 6.268675 300.1
1996 3.773333333 5.187583333 316.1
1997 4.075833333 6.38365 323.5
1998 5.1875 8.30855 327.6
1999 3.746666667 5.762216667 332
2000 4.465833333 5.401833333 337.4
225
Year
wage (hk current$
million)
wage (us$
million)
1987 13500 1989.900151
1988 15828 2240.520467
1989 18855 2547.215693
1990 23443 3009.370988
1991 25286.5 3122.799443
1992 25852 3109.516019
1993 28702.4 3354.645433
1994 32077 3660.273825
1995 34832 3861.565451
1996 37404 4030.256012
1997 40114 4215.906012
1998 44092 4561.669232
1999 46488 4703.505532
2000 50497 4921.376582
226
Appendix D-3: Data-US
Year
(1990
price)
GDP(billion
of US$) Exports G I C
1979 4433.112155 401.3258082 841.5070062 818.301704 2818.274961
1980 4255.104181 432.1045298 840.5907269 692.698751 2746.38411
1981 4308.117356 419.684287 844.3938735 740.1548066 2749.700233
1982 4216.481326 365.8913295 868.6365748 605.4872057 2775.928041
1983 4393.373582 346.6555179 885.9120026 658.0423769 2922.840946
1984 4682.209477 355.238509 925.0531449 841.7116263 3055.352866
1985 4827.277927 341.6821702 996.9880032 781.1439874 3193.325366
1986 5007.946992 367.9687118 1038.648254 783.45183 3331.367221
1987 5169.195402 406.3218391 1059.08046 804.0229885 3459.08046
1988 5348.287293 478.6740331 1063.535912 824.0883978 3578.121547
1989 5532.982086 535.3003161 1027.608008 876.9230769 3712.434141
1990 5522.2 557 1043.2 799.5 3748.4
1991 5492.226488 577.2552783 1054.990403 707.1017274 3748.944338
1992 5605.400372 594.1340782 1047.765363 736.7783985 3851.862197
1993 5929.566004 594.755877 1166.726944 787.6130199 4027.21519
1994 6220.723104 639.4179894 1170.987654 967.5485009 4159.082892
1995 6346.912521 702.058319 1176.672384 980.8747856 4261.578045
1996 6511 728.5 1184.916667 1035.083333 4364.583333
1997 6754.109032 787.6322213 1205.044752 1125.874695 4495.036615
1998 7043.429487 774.0384615 1234.695513 1241.907051 4688.221154
1999 7275.196232 776.9230769 1281.397174 1284.77237 4905.965463
2000 7496.507213 837.4335611 1321.943812 1342.0653 5108.883827
227
Year
(1990
price) Imports
Interest
Rate
m2
(b.od)
US Cpi Acc
(ifs)
US Cpi percent
(ifs)
1979 446.4772102 11.22 1501.8 55.59074334 11.30363036
1980 456.6739363 13.07 1635.5 63.08658697 13.48397806
1981 445.8158438 15.91 1798.7 69.64759202 10.4
1982 399.5971901 12.35 1959.6 73.87439334 6.06884058
1983 419.9461513 9.09 2194 76.27168365 3.245089667
1984 489.3643013 10.37 2378.3 79.55218617 4.301075269
1985 485.7401346 8.05 2580.5 82.32797159 3.489263526
1986 525.3973969 6.52 2814.7 83.97453102 2
1987 572.0689655 6.86 2933.9 87 3.602841176
1988 597.5690608 7.73 3089.8 90.5 4.022988506
1989 619.2834563 9.09 3245.1 94.9 4.861878453
1990 625.9 8.16 3357 100 5.374077977
1991 596.0652591 5.84 3472.7 104.2 4.2
1992 622.3463687 3.68 3533.6 107.4 3.071017274
1993 651.4466546 3.17 3606.1 110.6 2.979515829
1994 716.1375661 4.63 3520.6 113.4 2.53164557
1995 774.271012 5.92 3665.8 116.6 2.821869489
1996 802.5833333 5.39 3836.5 120 2.915951973
1997 859.4792514 5.62 4053.2 122.9 2.416666667
1998 895.4326923 5.47 4408.2 124.8 1.545972335
1999 973.7833595 5.33 4677.3 127.4 2.083333333
2000 1113.819286 6.46 4973.7 131.7 3.4
228
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Vita
Xiaojun Yang was born in Shaanxi, China, on April 15, 1966, the son of
Zhuxiang Li and Yaodong Yang. He received his Bachelor of Arts from Shaanxi
Finance and Economics Institute in 1987. Following graduation from Renmin
University where he received a Master of Arts in Economics in 1992, he joined
the State Planning Commission of P. R. China where he was employed as a policy
maker and researcher. In 1995 he entered Columbia University, and received a
Master of International Affairs in 1997. During his study at Columbia, he spent
half year at World Bank where he conducted quantitative analysis regarding
countries’ effective rates of protection and tariff equivalents of non-tariff barriers.
In August, 1997 he entered the Graduate School of the University of Texas at
Austin.
Permanent address: Anjiu Xiang #10
Heyang, Shaanxi
715300 P. R. China
This dissertation was typed by the author.
._.
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