Essays on Income Inequality, Exchange Rate, and Policy Coordination

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 ________________________________________________________ UMI Microform 3116243 Copyright 2004 by ProQuest Information and Learning Company. All rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code. ____________________________________________________________ ProQuest Information and Learning Company 300 North Zeeb Road PO Box 1346 Ann Arbor, MI 48106-1346 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 Bibliography Alesina, A., & Rodrik, D. 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(1993). The East Asian Miracle. New York: Oxford University Press. World Bank. (1994). China: Foreign Trade Reform. A World Bank Country Study. Xu, L. , & Zou, H. (2000). Explaining the changes of income distribution in China. China Economic Review 11 (2000), 149-170. Xu, Yingfeng (2000). China’s Exchange Rate Policy. China Economic Review 11 Zhang, Zhichao (2001). Choosing an Exchange Rate Regime During Economic Transition: the Case of China. China Economic Review 12 Zhou, Zhengqing (1992). Studies on China Monetary Policy. China Financial Press. 231 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. ._.