Theoretical System of China's Macroeconomic Analysis 9789814402347, 9789814402330

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Theoretical System of China’s Macroeconomic Analysis

Theoretical System of

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Contents PART I

The AD–AS Model

Chapter 1

China‘s Aggregate Supply and Demand Functions ....................................................1

Chapter 2

The Statics and Dynamics of the AD–AS Model of China.........................................17

PART II

Growth and Inflation

Chapter 3

The Mode, Structure, and Efficiency of China’s Economic Growth.........................27

Chapter 4

Structural Inflation and Cost-Push Inflation in China...............................................45

PART III

The Open Economy

Chapter 5

China’s Economic Fluctuations: Demand Driver and International Coupling ..........................................................................................63

Chapter 6

The Dynamic Purchasing Power Parity Theory: Concept, Evidence, and Prediction.................................................................................................................79

PART IV

Demand Management

Chapter 7

The Macroeconomic Analysis and Forecasting Model of China (CMAFM)..........101

Chapter 8

Hysteresis, Multiple Equilibriums, and Reverse Soft Landing: China’s Experience in Demand Management........................................................... 119

Chapter 9

China’s Monetary Policy Rules and the Effects of Monetary Policy......................141

Appendix: The Empirical and Theoretical Foundation of China’s Macroeconomic Policy Orientation..............................................................154 Notes..............................................................................................................................173 References......................................................................................................................193 Index...............................................................................................................................213

Part I

The AD–AS Model

1

Chapter

China’s Aggregate Supply and Demand Functions

Theoretical System of China's Macroeconomic Analysis

An Analysis and Synthesis of Traditional Aggregate Supply Functions An expectations-augmented aggregate supply function Labor demand behavior, labor supply behavior, and the way they are combined underlie the specific structure of the labor market. They determine the actual labor supply, which can be formulated as the map

O : (P , P E, W ) → N (1–1) Then, the labor supply function O : (P , P E , W ) → N and the aggregate production function Y = F (K , N ) can be condensed into the aggregate supply function

Y = S (P , P E) (1–2) (Romer 2006; Snowdon and Vane 2005). Various traditional aggregate supply (AS) functions, from classical to new classical and new Keynesian variants, adopt differing configurations of labor supply and demand, which incarnate their specific assumptions for expectation formation mechanisms: In the classical aggregate supply function, the labor demand function is d N = N d(W /P ), and the labor supply function N s = N s(W /P ). With labor market clearing, N = N d = N s, determining W /P . Therefore, dY /dP | AS = 0. In the Keynesian aggregate supply function, the labor demand function is d N = N d(W /P ), and labor supply exhibits wage rigidity and money illusion. If N < N f, W = W c. If N = N f, W = N d–(N f) • P , in which N f is the full employment level and W c represents an exogenous nominal wage. The map O takes the shape of N = min{N d(W c/P ), N f}. Therefore, dY /dP | AS > 0 when Y < F (K , N f); dY /dP | AS = 0 when Y = F (K , N f). New Keynesianism tends to interpret wage stickiness from the perspective of staggered contracts, and explains price stickiness from the perspective of menu costs. N d = N d(W c/P ), and N s = N s(W c/P E), in which W c is the contracted nominal wage. Here, the map O takes the shape of N = N d(W c/P ), while ((N d(W c/P E) – N ) indicates involuntary unemployment. In the long run, P E = P , determining W c such that N f = N d(W c/P ) = N d(W c/P ), and dY /dP | AS = 0. In the short run, however, P E is fixed, determining W c such that N f = N d(W c/P E) = N d(W c/P E), and therefore, dY /dP | AS > 0.1 In the new classical aggregate supply function, N d = N d(W /P ), N s = N s(W / P E), and N = N d = N s in a cleared labor market. If P = P E, dY /dP | AS = 0, and N

4

China’s Aggregate Supply and Demand Functions

= N f, as in the paradigm of the classical aggregate supply function. If P E is fixed, dY /dP | AS > 0.2 The Lucas aggregate supply function directly aggregates firm supply functions in decentralized markets (with incomplete information). For market i , Y it = b • (P it – E (P t | I it), and b > 0, indicating that the firm only reacts to perceived changes in relative prices. P it = P t + Z it, and E (P t | I it) = (1 – α) • E (P t | I t) + α • P it, in which Z it is an idiosyncratic shock to market i , ∑Z it = 0, and α = δP2/(δP2 + δ2Z). Consequently, Y it = b • (1 – α) • (P it – E (P t | I t)), which can be aggregated to derive the aggregate supply function Y t = β • (P t – E (P t | I t), where β = b • (1 – α) (Hoover 1988; Blanchard and Fisher 1989). The Lucas aggregate supply function, which falls within the new classical paradigm, is constructed under the assumption of rational expectations. However, its conceptualization of aggregate supply reacting to unanticipated inflation, through discretionary presumption regarding the formation of inflation expectations, serves to reflect the nature of the aggregate supply curves in other paradigms, i.e., dY t/d(P t – E (P t)) > 0 being in line with the qualitative calculus of dY /dP | AS under a special E (P t). For the classical aggregate supply function, the long-run new Keynesian aggregate supply function, and the new classical aggregate supply function conditioned by P = P E, dY /dP | AS = 0, and when E (P t) = P t, dY t/d (P t – E (P t)) > 0, implying that dY t/dP t = 03. For the Keynesian aggregate supply function, the short-run new Keynesian aggregate supply function, and the new classical aggregate supply function with a fixed P E, dY /dP | AS = 0. Given that E (P t) is fixed, the fact that dY t/d (P t – E (P t)) > 0 implies that dY t/dP t = 0. Hence, traditional aggregate supply functions can be synthesized in an expectations-augmented Lucas aggregate supply function: Y = S (P /P E ), assuming that dY /d(P /P E ) | AS > 0, in which P /P E is the equivalence ratio of the natural log(P t – E (P t)).

General forms of traditional aggregate supply functions With its diagrammatic nature, the Keynesian AS curve manifests the second order condition for the function S (P /P E) with an inverted L-shape, as illustrated in Fig. 1.1(a). In Fig. 1.1(b), the LRAS and SRAS curves, respectively, represent the long-run and short-run Keynesian aggregate supply functions, as well as the corresponding new classical aggregate supply functions with P = P E or a fixed P E. For the same economy, the new Keynesians predict a steeper AS curve than new classical economics. Although the potential supply models of both the Keynesian model, YK* in Fig.1.1(a), and the new classical or new Keynesian model, YC* in Fig. 1.1(b), seem to be similarly determined by Y = F (K , N f) pro forma, they bear

5

Theoretical System of China's Macroeconomic Analysis

distinctive economic significance: whereas YK* is the upper bound of Y , YC* can be transcended by Y . Approximately,

F –(YK*) • (1 – u *) = F –(YC*), (1–3) in which u * is the NAIRU (non-accelerating inflation rate of unemployment). Fig. 1.1 (a)  Keynesian AS curve P

AS

YK*

0

Y

Fig. 1.1 (b)  New Keynesian and new classical AS curves P

LRAS E

0

SRAS (P = P )

0

P

0

Y*C

Y

Meanwhile, the aggregate supply function Y = S (P /P E ) is defined with the structural characteristics illustrated in Fig. 1.2 in order to generalize the traditional aggregate supply functions in Fig. 1.1 (a) and (b). Thus, on the plane P –Y , the function is presented as an inverted expectations-augmented L-shaped

6

China’s Aggregate Supply and Demand Functions

Keynesian AS curve. The curve is asymptotic to the line Y = YK*; the critical point J where it transitions from relative mildness to relative steepness (which should be distinguished from its inflection point) takes place near Y = Y C*, while its vertical position is dependent on P E. Fig. 1.2

General curve for traditional AS functions P

AS

0

P

E

0

(P = P ) J

0

YC*

YK*

Y

An Aggregate-Demand﹣Augmented General Aggregate Supply Function The effects of aggregate demand on effective potential supply

Traditional aggregate supply functions adopt the representative firm hypothesis and assume that firms supply standard products from the aggregation of firm supply to aggregate supply so as to abstract away possible asymmetry between the supply and demand structures.4 Under certain economies, if the mechanism for resource allocation fails to adapt to the supply and demand structures ex ante , resulting in the presence of ineffective constituents in potential supply, and if the demand structure changes in the (Marshallian) short run as a result of aggregate demand (AD) fluctuations, affecting its compatibility with the supply structure, any aggregate demand fluctuations will lead to (non-negligible) variations in the ineffective constituents of potential aggregate supply and hence effective potential aggregate supply. The symmetry between the supply and demand structures and their response to aggregate demand fluctuations give birth to unconventional channels other than inflation expectations, through which aggregate demand fluctuations can shift the aggregate supply curve. While approving such firm supply behavior claimed by traditional aggregate supply functions, the general aggregate supply function abandons the

7

Theoretical System of China's Macroeconomic Analysis

representative firm hypothesis, aggregating firm supply to constitute effective aggregate supply according to the short-side rule. It defines effective potential aggregate supply as the maximum production fit for the demand structure. The gap between effective potential aggregate supply and potential aggregate supply constitutes the structural slackness of potential aggregate supply, i.e., the total surplus capacity of all non-bottleneck sectors when the capacity of bottleneck (or short-side) sectors is fully utilized. Only in the traditional context where potential aggregate supply and demand structures completely match can effective potential aggregate supply be equivalent to potential aggregate supply. In a pure exchange economy with n products, Y M and [s i] are, respectively, potential aggregate supply and its structural coefficient matrix, which is assumed to be fixed in the short run, whereas [d i] is the aggregate demand structural coefficient matrix. The selection mechanism min{s i/d i} = s b/d b identifies the potential bottleneck product b , and s b/d b ≤ 1. For effective potential aggregate supply under the constraint of the short-side Y E, Y E = (s b/d b) • Y M, and Y E ≤ Y M.5 Suppose Y d has moved from Y 1 to Y 2 at speed g and [d i] has moved from [d 1i] to [d 2i] correspondingly. Furthermore, e i denotes the aggregate demand elasticity coefficient for product i , and E is an n × n diagonal matrix with the main diagonal element e i, i.e., E = diag(e i). Then, [d 2i] • Y 2 = (I + (Y 2/Y 1 – 1) • E ) • ([d 1i] • Y 1), (1–4) in which I is an n × n unit matrix. Because Y 2 = (1 + g ) • Y 1, [d 2i] = ((I + g • E ) • [d 1i])/(1 + g ), and d 2i = d 1i • ((1 + g • e i) /(1 + g )), min{s i/d 1i} = s u/d 1u identifies the potential bottleneck product u when Y d = Y 1 and Y E = (s u/d 1u) • Y M, while min{s i/d 2i} = s v/d 2v identifies the potential bottleneck product v when Y d = Y 2 and Y E = (s v/d 2v) • Y M. Only in the small probability event where s u/d 1u = s v/d 2v will the movement of Y d movement Y 1 to Y 2 not affect Y E; otherwise, Y E will react to the movement of Y d. It is permissible that s u/d 1u = s v/d 2v even if u ≠ v , when the extreme event of bottleneck switch happens. With g indicating the dynamics of Y d, Y E /Y M = s b/d b = (s u/d 1u) • ((1 + g • e u)/(1 + g )), so aggregate demand fluctuations possess unconventional supply effects through its impact on effective potential aggregate supply.

An aggregate supply function augmented by both expectations and aggregate demand Based on the traditional form S (P /P E) augmented only by expectations, this aggregate supply function embeds aggregate demand into the general form of Y s = S (P /P E), Y d), in order to embody the effects of aggregate demand on

8

China’s Aggregate Supply and Demand Functions

effective potential aggregate supply. In Fig. 1.3, this general aggregate supply function is depicted as an inverted L-shaped Keynesian AS curve asymptotic to the line Y = YK* (instead of Y = Y M), whose vertical and horizontal positions are sequentially dependent on P E and AD . Fig. 1.3

General AS curve P

E

0

AS

d

0

(P − P ) , (Y − Y )

0

E

Y 0 (Y = Y ) d

M

Y

Y

Ultimately, the movement of Y d may change Y E so as to shift the AS curve in the

horizontal direction, diagrammatically defining the effects of aggregate demand

on effective potential aggregate supply. As the movement of Y d changes the structural slackness of potential aggregate supply, i.e., (Y M – Y E), the response of the equilibrium output to the movement of Y d can be decomposed into the AD curve

moving along the AS curve and the AS curve shifting due to the AS curve.6

In the traditional aggregate supply function Y = S (P /P E), its upper bound YK*,

i.e., the Keynesian potential aggregate supply, should be effective in meeting the aggregate demand, and hence YK* = Y M = Y E. It is technically feasible to express Y

= S (P /P E) as Y s = f (P /P E) • Y E, in which f (P /P E) is implicitly defined by f (P /P E) •

YK* = S (P /P E). To formulate the effects of aggregate demand on effective potential aggregate supply, Y E = φ(Y d) • Y M, (1–6) where φ(Y d) = min{s i/d i} = s b/d b and d (Y M – Y E)/dY d = –φ' • Y M. In consequence,

the general aggregate supply function Y s = S (P /P E, Y d) is structured into Y s = f (P /P E) • Y E and Y E = φ(Y d) • Y M, and then expressed as

Y s = f (P /P E) • φ(Y d) • Y Mφ,

(1–5)

which degenerates into the traditional aggregate supply function Y s = f (P /P E) • Y E only in the special case of φ(Y d) = 1.

9

Theoretical System of China's Macroeconomic Analysis

The numerical demonstration of effective potential aggregate supply Table 1.1 investigates a (constructed) two-sector economy in order to calculate

the effective and structurally slack continents of potential aggregate supply during the growth of actual aggregate demand, demonstrating the shortside rule Y E = min{s i/d i} • Y M as determined by effective potential aggregate supply and the corresponding effects of aggregate demand on it. Suppose

Y M 200, and s 1 = s 2 = 0.5, the potential supply of both products 1 and 2 will be 100, while their marginal propensity to demand 0.8 and 0.2, respectively. With Y d rising from 100 to 200, the real demand for products 1 and 2 will be 40 and 60, respectively, when Y d = 100, whereas the values become 120 and 80 when Y d = 200. Table 1.1 AD

Yd

Short-side rule and effective potential aggregate supply

Demand structural coefficient Product 1

Product 2

d1

d2

Short-side rule min{s i/d i}

Potential AS

Demand elasticity

Effective

Structural

Product 1

Product 2

supply

slackness

e1

e2

Y

E

M

Y —Y

E

100

0.400 0

0.600 0

0.833 3

166.67

33.33

—

—

110

0.436 4

0.563 6

0.887 1

177.42

22.58

2.000 0

0.333 3

120

0.466 7

0.533 3

0.937 5

187.50

12.50

1.833 3

0.354 8

130

0.492 3

0.507 7

0.984 8

196.97

3.03

1.714 3

0.375 0

140

0.514 3

0.485 7

0.972 2

194.44

5.56

1.625 0

0.393 9

150

0.533 3

0.466 7

0.937 5

187.50

12.50

1.555 6

0.411 8

160

0.550 0

0.450 0

0.909 1

181.82

18.18

1.500 0

0.428 6

170

0.564 7

0.435 3

0.885 4

177.08

22.92

1.454 5

0.444 4

180

0.577 8

0.422 2

0.865 4

173.08

26.92

1.416 7

0.459 5

190

0.589 5

0.410 5

0.848 2

169.64

30.36

1.384 6

0.473 7

200

0.60 0

0.400 0

0.833 3

166.67

33.33

1.357 1

0.487 2

AS

M

Y = 200, s 1 = s 2 = 0.5

As illustrated in Table 1.1, although product 1, which has a high marginal

propensity to demand, is a luxury good with elastic demand (e 1 > 1), its e 1

decreases with Y d. On the contrary, product 2, a necessity good with inelastic

demand (e 2 < 1) and a low marginal propensity to demand, has an e 2 which

10

China’s Aggregate Supply and Demand Functions

increases with Y d. Decomposing potential aggregate supply on the basis of Table 1.1, Fig. 1.4 portrays an inverted-U path of effective potential aggregate supply, recording the bottleneck switch phenomenon where min{s i/d i} = s 2/d 2 when Y d ≤ 130, but s 1/d 1 when Y d ≥ 140.7 Fig. 1.4

Effective supply and structural slackness of potential aggregate supply

Potential AS

200 Structural slackness

190 180

Effective supply

170 160

100

120

140

160

Real AD

180

200

China’s Monetarist Aggregate Demand Function The rationing equilibrium in the IS–LM model In the basic IS–LM model of a closed economy, the equation system of IS : Y =

C (Y ) + I (R ) + G and LM : M /P = K (Y ) + L (R ) is combined to produce Y = C (Y )

+ I (L –(M /P – K (Y ))) + G , which implicitly defines the traditional aggregate

demand function Y d = D (G , M /P ). The intersection of the IS and LM curves determines the equilibrium (E *) of the model, as illustrated in Fig. 1.5. Thus, dY /dP < 0 on the AD curve, while at equilibrium E *, dY /dG > 0, as is dY /dM .

Aggregate demand in China, a regime of regulated interest rates, follows

the rationing equilibrium model of IS–LM. 8 Fig. 1.5 shows that under a regulated interest rate (R r), the interest rate policy line R = R r intersects with

the IS curve on the right of the LM curve, and the rationing equilibrium (E r)

determines the actual aggregate demand according to the short-side rule, i.e.,

Y = min{Y | IS (R = R r), Y | LM (R = R r)} = Y | LM (R = R r). (1–7)

11

Theoretical System of China's Macroeconomic Analysis

Because Y | LM (R = R r) = K –(M /P – L (R r)), China’s aggregate demand function should take the monetarist form of Y = D (M /P , R r) (Zheng 1998). With the continuous clearing of the money market under credit rationing, any excess investment demand over the saving supply in the goods market is offset by excess supply in the credit market. As such, the IS–LM model exhibits a general equilibrium, which satisfies the Walras’ law.9 Fig. 1.5

Rationing equilibrium in the IS–LM model IS

R

IS’

LM

LM’

E* E*’ r

E

E’ r

0

r

R=R

Y

Comparative policy efficiency in demand management China’s aggregate demand function Y d = D (M /P , R r) demonstrates the normal

efficiency of the monetary policy instrument (M ) with a negative slope, but

excludes the fiscal policy instrument (G ), implying an effective monetary

policy and yet an ineffective fiscal policy (without the accommodation of the monetary policy) in demand management. Thus, dY /dP < 0, dY /dM > 0, and dY /dG = 0.

Regarding the monetary policy, as Fig. 1.5 has illustrated, the decline of

prices (P ) or the increase of money supply (M ) shifts the LM curve rightward

to LM’ through increasing the real money balance (M /P ). While the speculative

demand for money (L ) is invariable on account of R R r, and accordingly disrupts the conventional transmission mechanism P (M ) (M /P ) R

I

Y , the increase of the real money balance (M /P ) is fully distributed

to meet the transactions demand for money (K ), easing credit rationing.

Furthermore, the movement of the rationing equilibrium from E r to E r’ necessarily surpasses the horizontal distance from E * to E *’ , which represents

the conventional expansionary effect of aggregate demand.

As for the fiscal policy, which is illustrated by Fig. 1.6, the increase of G

12

China’s Aggregate Supply and Demand Functions

shifts the IS curve to the right at IS’ , but the actual rationing equilibrium (E r) does not move. The incompetence of government expenditure is caused by the disruption of the conventional transmission mechanism G K R L (M /P – L ) K Y by R R r. If M is not increased, the increase of

G will have the effect of pushing up the government’s transactions demand (L ) and hence reducing the credit available to the non-government sector, ultimately crowding out the consumers (C ) and/or investors (I ). Fig. 1.6

Policy effects of government expenditure and interest rates IS

R

E’ r

r

E

IS’

LM

R=R’ r

r

R=R

0

Y

The regulated interest rate (R r) constitutes the parameter of the (possible)

policy instrument in China’s aggregate demand function Y = D (M /P , R ), whose policy effects depend on the interest elasticity of the demand for money. If

the money demand is interest-elastic with L’ < 0, the rationing equilibrium moves positively along the titling LM curve from E r’ to E r when the interest

rate policy line shifts up from R = R r to R = R r’ , as illustrated in Fig. 1.6, so dY /dR > 0. Through the unconventional transmission mechanism R L (M /P – L )

K

Y , the increase of R r reduces the speculative demand

for money (L ) and contributes to a relative rise in the money supply, thereby

meeting the transactions demand (K ) and easing credit rationing in general,

realizing the unconventional expansionary (as opposed to contractionary) effect

of aggregate demand. By contrast, if the money demand is interest-inelastic with L’ = 0, dY /dR = 0, and the aggregate demand function for China Y = D (M /

P , R ) degenerates into Y = D (M /P ). Moreover, if the money demand displays abnormal interest elasticity with L’ > 0, dY /dR < 0, and the rise of R r will restore the conventional contractionary effect of aggregate demand.10

13

Theoretical System of China's Macroeconomic Analysis

The Research Outline of the AD–AS Model of China Concerning the general dynamic system of dP /dt = α • (Y d – Y s) and dP E/

dt = β • (P – P E), which assumes the Walrasian adjustment of excess demand

and the formation of adaptive inflation expectations, the precondition for its stability is that the elasticity of φ(Y d) is smaller than 1 near Y d = Y * (Zheng 1998), but (φ’ • Y *)/φ < 1 is not sufficient for φ’ or φ’ < 0. The qualitative

effects of aggregate demand on effective potential aggregate supply is

indeterminate on the basis of the dynamic stability conditions suggested by Samuelson’s Correspondence Principle (1983).

Suppose a two-sector exchange economy possesses capital good 1 and

consumption good 2. In respect of China’s growing economy, the potential aggregate supply structure matches with the actual aggregate demand

structure, which is inclined toward elastic investment demand, (only) during high growth. 11 If e 1 > 1 and e 2 < 1, min{s i/d i} = s 2/d 2, and b = 2, the

consumption goods sector faces bottlenecks in potential aggregate supply.

When Y d grows from Y 1 to Y 2 at the speed g , d 22 = d 12 • ((1 + g • e 2)/(1 + g )),

and d 22 = d 12 = 1 + (e 2 – 1)/(1 + 1/g ), so d (Y E/Y M)/dg > 0. On this occasion,

the aggregate supply function Y s = f (P /P E ) • φ(Y d ) • Y M is provided with

the qualitative calculus φ’ t > 0, and consequently, aggregate demand has a positive effect on effective potential aggregate supply.

As such, an AD–AS model of China has been generated through the

specific-to-general construction of an aggregate supply function and the general-to-specific construction of an aggregate demand function, i.e.,

Y s = f (P /P E) • φ(Y d) • Y M;

(1–8)

Y d = D (M /P , R ). (1–9) In the absence of R , M is the only policy instrument for demand

management, which has an effect on effective potential aggregate supply in the short run. Based on the research outline for the AD–AS model of China set forth in Fig. 1.7, Chapter 2 will continue to investigate the operational

equation of China’s aggregate supply function and the statics and dynamics of this particular AD–AS model, and then discover the equilibrium business cycle generated by the optimal control of M .

14

China’s Aggregate Supply and Demand Functions

Fig. 1.7

Research outline of the AD–AS model AD function: Rationing equilibrium of the IS-LM model Operational equation of the AD function

AS function: Effects of aggregate demand on potential effective aggregate supply General form of AS function

Comparative statics: Demand shocks

Demand management: M instrument

Dynamic adjustment: M shock

Effects of aggregate demand: φ’ sign

Optimal control of M

Monetary business cycle

15

2

Chapter

The Statics and Dynamics of the AD–AS Model of China

Theoretical System of China's Macroeconomic Analysis

A Comparative Static Analysis of the AD–AS Model The AD–AS model of Y s = f (P /P E) • φ(Y d) • Y M and Y d = D (M /P ) is driven

only by money supply (M ) other than potential aggregate supply (Y M). To give

a diagrammatic analysis of the AD–AS model, the AS curve shifts vertically

in the short run on account of the variations of P E, and horizontally in the long run on account of the growth of Y M . The latter is mainly determined

by technological and population factors, while the short-run effect can be

decomposed into the movement of the AD curve along the original AS curve

and the horizontal shift of the AS curve as a result of the variations of Y E.1

Fig. 2.1 presents a comparative static analysis of the AD–AS model in

response to the increase of M . While the AD curve moves to AD’ on the right,

the AS curve and the general equilibrium (E ) at (P *,Y *) shift, respectively, to AS’ and E’ toward different directions according to specific conditions. If d(Y M – Y E)/dY d > 0, the AS curve shifts to the left in a direction opposite to that of the movement of AD , causing P * to rise. The direction for Y * is indeterminate, so both its rise and decline are laid out in Fig. 2.1(a) and Fig. 2.1(b), respectively. In cases where d(Y M – Y E)/dY d < 0, the AS curve shifts in the same direction as the movement of AD , and Y * rises with an indeterminate direction for P *, whose rise and fall are explicated in Fig. 2.1(c) and Fig. 2.1(d). This comparative static analysis of the AD–AS model demonstrates the complex conditions for attaining an equilibrium, covering all possible combinations of the qualitative calculus dY */dY d and dP */dY d in contrast to the conventional determinateness of dY */dY d > 0 and dY */dY d > 0. In particular, when aggregate demand expands and the AD curve moves to the right, not only will it lead to deflation as suggested by the traditional AD– AS model; the AS curve may also shift to the left due to the simultaneous contraction of aggregate supply, resulting in stagflation. Table 2.1 demonstrates a qualitative calculus for the comparative static analysis of the AD–AS model, which corresponds to the diagrams of Fig. 2.1. According to the calculus, an aggregate demand shock only has an inflationary effect in the critical state of dY */dY d = 0 and dP */dY d > 0, and only an income effect in the critical state of dY */dY d > 0 and dP */dY d = 0. In the monetary business cycle of the AD–AS model, the observable time path EE’ for the intersection of the AD and AS curves is set apart from the AS curve, sometimes so far as to evolve in its opposite direction.2 Even if the structure of f (P /P E) and φ(Y d) is stable so the aggregate supply function Y s =

18

The Statics and Dynamics of the AD–AS Model of China

f (P /P E) • φ(Y d) • Y M is immune to the Lucas critique, conventional estimation of the AS curve on the basis of the time path EE’ is yet invalid in methodology. Fig. 2.1

Comparative statics in diagrams

(a)

(b)

P

P

AD

AS’

AD’

AS

AD

AS’

AD’

AS

E’ E

0

E

E’

Y

M

Y

Y

Y

0

(c)

E

E’

M

Y

Y

Y

Y

(d) P

P AD

AD’

AS’

AD

AS

AS

AD’

AS’

E

E 0

E’

E’

E

Y

Table 2.1

E’

Y

M

Y

Y

0

E

E’

Y

Y

M

Y

Y

Comparative statics using qualitative calculus

Effective potential aggregate supply dY */dY d

Equilibrium national income and price (dY */dY d, dP */dY d)

—

(0, +)

—

(–, +)

Fig. 2.1 (b)

+

(+, +)

Fig. 2.1 (c)

+

(+, 0)

+

(+, –)

—

(+, +)

AD–AS diagram Fig. 2.1 (a)

Fig. 2.1 (d)

Dynamic Adjustments of the AD–AS Model In the AD–AS model, static real money supply (m N) is determined by D (m N) = f (1) • φ(D (m N)) • Y M. Therefore, the static national income is Y N = D (m N),

19

Theoretical System of China's Macroeconomic Analysis

static effective potential aggregate supply is Y EN = φ(D (m N)) • Y M, and the equilibrium price is P * = M /m N, which is proportional to nominal money supply (M ). Meanwhile, suppose price adjustments follow a Walrasian mechanism and price expectations are adaptive in a dynamic AD–AS model: dP/dt = λ • (Yd – Y s)

dP E/dt = θ • (P – P E) (2–1)

The equilibrium solution (P *, P E*) to the dynamic adjustment of the AD–

AS model is defined by dP /dt = dP E/dt = 0. Then, an approximate equation

system is established through first order linearization at the equilibrium (P *, P E*), where e D, e f and e φ are the elasticity coefficients of the functions D , f , and φ, respectively; e D > 0 and e f > 0:

< dP/dt < = A • < P – P * < E E E dP /dt

A=

P – P * (2–2) ;

φ N f D N f < – λ • (Y /P *) • (e + (1 – e ) • e ), λ • (Y /P *) • e
1 and thr2 > thr3. In Fig. 2.2, OB = thr2, OC = (λ • (Y N/P *) • (e f + e D) + θ)2, OL = (λ • θ) • (Y N/P *) • e D, and OC > OL . At T with e φ = thr3, the tangent line of the parabola tr(A )2 is parallel to the line det(A ) at the shortest vertical distance from the line 4det(A ).3 Since tr(A )2

20

The Statics and Dynamics of the AD–AS Model of China

lies above 4det(A ) within the value range of e φ, ΔA > 0. Table 2.2 provides a phase analysis of the dynamic adjustment process of the AD–AS model with respect to e φ, and consequently highlights the role that the sensitivity of potential effective aggregate supply (Y E) to actual aggregate demand (Y d) plays in designating the dynamics of the AD–AS model. Fig. 2.2

Diagrammatic solution to ΔA

4det(A) C

tr(A)

L

T

0

Table 2.2

2

B

1

e

φ

Phase analysis of dynamic adjustment Algebraic sign

Value range

Asymptotic property

Phase pattern

+

Stable and monotonic

Node

0

+

Stable and monotonic

Parallel

—

—

+

Unstable and monotonic

Saddle

thr2

0

—

+

Unstable and monotonic

Saddle

(thr2, ∞)

+

—

+

Unstable and monotonic

Saddle

e

φ

tr(A )

det(A )

ΔA

(–∞, 1)

—

+

1

—

(1, thr2)

The Monetary Business Cycle Under the realistic circumstance where 0 < e φ < 1, the dynamic adjustment system of the AD–AS model is close to global asymptotical stability at the equilibrium (P *, P E*), where the equation dP E/dt = 0 is reduced to P = P E and its slope dP E/dP = 1, while the equation dP /dt = 0 is reduced to D (M /P ) = f(P /

21

Theoretical System of China's Macroeconomic Analysis

P E) • φ(D (M /P )) • Y M and its slope is dP E/dP = 1 + (1 – e φ) • (e D/e f) > 1.4 Fig. 2.3 presents a phase analysis of the global asymptotical stability of the dynamic AD–AS model on the plane P –P E, based on which Fig. 2.4 portrays the monetary business cycle of the dynamic AD–AS model in response to and propagating money supply shocks, or M shocks. Fig. 2.3

The global asymptotic stability of the dynamic AD–AS model E

P

dP/dt = 0

E P*

0

E

dP /dt = 0

E

P*

P

In Fig. 2.4 (a), when M increases from M 1 to M 2 and creates a positive

demand shock, the curve dP /dt = 0 jumps to the right and upward, whereas the

equilibrium (P *, P E*) moves continuously from E 1 to E 2 to trace the locus E 1aE 2. It

is mainly due to the adaptability of the formation of inflation expectations that P

> P E and Y > Y N along the path E 1aE 2, similar to the intertemporal consequences of

the traditional AD–AS model.

However, as real money supply (M /P ) exceeds its static value (m *) in

transition, and correspondingly, effective potential aggregate supply (Y E) stands

above its static value (Y EN), the AS curve shifts rightward, making the M increase more expansionary than in the traditional AD–AS model. On the plane P –Y , the

equilibrium (P *, Y *) first jumps from E 1 to J 1, and then moves gradually from J 1 to

E 2 along the path J 1aE 2, which differs from the smooth adjustment process along E 2aE 2 in the traditional AD–AS model, as illustrated in Fig. 2.4 (b). The dynamic adjustment process of the AD–AS model under the symmetrical condition where M decreases from M 2 to M 1 is plotted on the planes P –P E and P –Y as the loci E 2bE 1 and E 2J 1bE 1 in Fig. 2.4 (a) and Fig. 2.4 (b), respectively. The dynamic adjustment of the traditional AD–AS model, in comparison, follows the same locus on P – P E in Fig. 2.4 (a) but a slightly different one (E 2b’E 1) on P – Y in Fig. 2.4 (b).

22

The Statics and Dynamics of the AD–AS Model of China

Fig. 2.4

Monetary business cycle of the dynamic AD–AS model E

P

dP/dt = 0 (M = M1)

E

dP /dt = 0

P2

E2(J2) b a

E1(J1)

P1

(M = M2)

dP/dt = 0 0

P

The Cyclicity of Structural Imbalance between Aggregate Supply and Demand In China’s economy, static and dynamic efficiency are internally consistent in the sense that, on the one hand, the high capacity utilization of capital goods promotes the growth of potential aggregate supply, and on the other, strong investment demand that stems from high growth reduces the structural slackness of potential aggregate supply. The comparative static analysis of the AD–AS model in Fig. 2.5 shows that although the cyclical behavior of the unconventional output gap (Y E – Y *) is indeterminate in a monetary cycle, structural slackness (Y M – Y E) and the conventional output gap (Y M – Y*) are countercyclical.5 Fig. 2.5

Comparative statics on the AD–AS model with positive aggregate demand shocks P

AD AD’

E

0

AS

AS’

E’

E

Y

E’

Y

M

Y

Y

23

Theoretical System of China's Macroeconomic Analysis

The imbalance of China’s economic structure is a combination of longrun structural imbalance, which is associated with the economic system and economic development, and short-run structural imbalance, which is caused by economic fluctuations. The long-run structural imbalance is essentially acyclical, whereas the short-run structural imbalance is a result of the structural slackness of potential aggregate supply, which is sensitive to the fluctuations of aggregate demand. Taking into account both forms of imbalance, the total structural imbalance of China’s economy is inclined toward countercyclicality.6 Table 2.3 is a simple vector autoregressive (VAR) model of the dynamic relationship between China’s real GDP indexes and GDP deflators between 1981 and 2009, where second-order estimation captures the statistically significant coefficients for the inflation rate on the lagged GDP growth rate, with C 1 > 0 and C 2 < 0. The inertia of inflation in response to growth or expected inflation in adaptation to actual inflation accounts for the fact that d(ΔlogP t)/d(ΔlogY t – 1) > 0. d(ΔlogP t)/d(ΔlogY t – 2) < 0 will require that d(ΔlogY Et – 1)/d(ΔlogY t – 2) > 0 so that P t (deflation pressure) of Y Et – 1 – Y t – 1 (output gap) increases with Y t – 2, proving the positive effect of aggregate demand on effective potential aggregate supply from an intertemporal perspective. Table 2.3

VAR model of China’s national income and inflation

z t = C 1 • ΔlogY t – 1 + C 2 • ΔlogY t – 2 + C 3 • ΔlogP t – 1 + C 4 • ΔlogP t – 2 + C 0 C1 C2 C3 C4 C0 R

2

Adj. R

SE

24

2

z t = ΔlogY t

z t = ΔlogP t

0.928 024

0.837 459

(4.680 89)

(2.828 39)

–0.485 584

–0.725 081

(–2.431 16)

(–2.430 76)

–0.193 495

0.831 711

(–1.344 79)

(3.870 46)

0.202 239

–0.145 418

(1.534 05)

(–0.738 58)

0.053 042

0.004 255

(2.924 71)

(0.157 09)

0.485 794

0.660 282

0.400 093

0.603 662

0.020 271

0.030 274

Part II

Growth and Inflation

3

Chapter

The Mode, Structure, and Efficiency of China’s Economic Growth

Theoretical System of China's Macroeconomic Analysis

An Overview of Issues in China’s Economic Growth Since the 1980s, China’s economy has been experiencing rapid growth. Today, the nation has accomplished the transformation from a closed, planned economy into an open, market economy, synchronizing the triple transitions of economic development, reform, and opening up to the outside word. Placed in a longer historical context, the Chinese economy had completed the Rostovian take-off by the early 1950s, entering an epoch of Kuznets’ modern, sustainable economic growth, and in the early 1980s, became strong enough to join the convergence clubs as it began to catch up with the national income per capita of developed countries. With its own adaptation of international experiences, the Chinese “economic growth miracle” has to be interpreted post ante using traditional economic growth theories, which will necessarily lead to the modification and refinement of traditional frameworks of economic growth analysis in order to accommodate the universal significance of its experience. Theoretical and empirical research on China’s economic growth has sought to prove its extensiveness and unsustainability on account of its large fixed investment when compared against international and historical levels. However, this chapter embraces the alternative method of growth accounting. By dissecting economic growth in light of the contribution of the input factors (capital, labor, and technology), the factor intensity of China’s economic growth will be examined to provide a basis for identifying the type of growth. As shall be seen, this method will render China’s economic growth capital-intensive and investment-driven. Theoretical and empirical research on China’s policy for economic growth along the line of the Phillips curve has assumed the trade-off between economic growth and structural equilibration (or upgrade). This approach balances the real cost of economic slowdown with the potential benefits of structural improvement, especially when conceptually verifying contractionary demand management. An alternative analysis, as shall be performed in this chapter, is to examine the symbiosis between growth and structural changes based on the conceptual dichotomy between economic growth and development. Adhering to Chenery’s development patterns, Chinese industrialization has been sustained by the mutual constraints as well as facilitation between rapid economic growth and structural upgrading. China’s industrial system was established through the preferential development of heavy industry under a planned economy. Although marketization led to reduced policy and institutional discrimination against

28

The Mode, Structure, and Efficiency of China’s Economic Growth

agriculture and the light industry, and hence their “compensatory growth” in the 1980s, the 1990s saw the return of heavy and chemical industrialization. Since then, the growth of heavy industries has once again outpaced light industries, and secondary industries have overtaken the primary and tertiary sectors, exerting an increasing pressure on natural resources, energy, and the environment. That said, static comparisons of the energy efficiency between the economies of China and developed economies are inclined to underrate the dynamic improvement in China’s energy efficiency, thereby negating the policy of heavy and chemical industrialization and advanced processing as well as neglecting the role of the tertiary industry in moderating resources, energy, and environmental constraints. In view of the issues facing China’s economic growth that have important theoretical and practical significance, this chapter will investigate the endogenous growth model, cyclical structural changes, and energy efficiency of China’s economy so as to revise traditional theoretical hypotheses on China’s economic growth and prepare the theoretical ground for China’s economic growth policy.

Investment-Driven Endogenous Economic Growth A quasi-AK growth model With dual sectors, developing economies retain nearly unlimited labor supply. As the classical Lewis model in Fig. 3.1 demonstrates, the labor supply curve L S (of the modern sector) is perfectly wage-elastic below the Lewis turning point (L LTP) and asymptotic to the labor force (L max). During the industrialization era, actual employment (L *) lies below L LTP and the actual wage rate lies at the subsistence wage level ( ). Fig. 3.1

Classical Lewis dual-sector model w L

0

D

L

D L ’

L*

L*’

L

LTP

S

L

max

L

29

Theoretical System of China's Macroeconomic Analysis

The remarkable rise of the nominal and real wages of simple labor since the late 2000s has given birth to the theoretical conjecture that China’s economy is approaching the Lewis turning point and the hypothesis of such structural characteristics of development as illustrated in Fig. 3.2 (a). However, since the subsistence wage necessarily increases with economic development and social progress in virtue of both natural and social needs, it is unreasonable to position the Lewis turning point just on the basis of wage increase or the acceleration of wage increase. Comprehensive considerations of the national income, industrial structure, and agricultural development of China will show that its economy is still in the stage of dual sectors and has yet to surpass the Lewis turning point. The rise of the actual wage level should chiefly be attributed to the upward movement of the L S curve as a result of the increase of , as illustrated in Fig. 3.2 (b).1 Fig. 3.2 (a) Classical Lewis model: beyond the Lewis turning point w

L

L

D

D’

L

S

w*’

0

L*

L*’

L

LTP

L

max

L

Fig. 3.2 (b) Revised Lewis model: the rising subsistence wage w L

0

L

D L ’

D

L*

L*’

L

LTP

S

L

max

L

According to the Cobb–Douglas production function, Y = F (K , L ) = A • K α •

L 1 – α, and the marginal product of labor is given by MPL = dY /dL = (1 – α) • A •

(K /L )α. After determining the actual employment level (L *) by the labor demand

30

The Mode, Structure, and Efficiency of China’s Economic Growth

equilibrium condition MPL = , the absolute surplus labor can be obtained by (L’ – L *), and the relative surplus labor (L max – L’ ). The equilibrium capital–labor ratio (K /L )* is a function of the subsistence wage ( ), and the technological level can be expressed as (A ) : (K /L )* = ( /(A • (1 – α)))1/α ω( , A ). Therefore, China’s aggregate production function should take the form of

Y = A • K • (K /L )α – 1 = A • K • (ω( , A ))α – 1. (3–1) To embody the intertemporal evolutions of the parameters function has to be formulated: φ(t )

and A , another

A (t ) • (ω( (t ), A (t )))α – 1. (3–2)

As such, the aggregate production function becomes

Y = φ(t ) • K , (3–3) and the corresponding marginal product of capital is defined as

MPK = φ(t ), (3–4) meaning that MPK is constant in the short run but variable in the long run. Owing to the non-diminishing returns to capital accumulation in the short run, China’s economic growth is functionally analogous to the AK model. Capital accumulation is sufficient to sustain China’s economic growth, and the nondiminishing returns to capital accumulation promise its endogeneity.

Capital deepening in a quasi-AK production function The temporal differential equation of China’s aggregate production function (Y = φ• K ) is: dY /dt = φ • (dK /dt ) + (dφ/dt ) • K , (3–5) and thus, dY /dt = φ • (dK /dt ) + (dφ/dt ) • (Y /φ), (3–6) i.e.,

Y’ = φ • K’ + (φ’ /φ) • Y . (3–7) Under the coefficient constraint α = β’ /β, it is feasible to estimate the implicit difference formula of the aggregate production function as

31

Theoretical System of China's Macroeconomic Analysis

ΔY t = α • Y t – 1 + β • ΔK t – 1

(3–8)

without data on the initial capital stock.2 Table 3.1 shows an ordinary least squares (OLS) estimation of the production function ΔY t = α • Y t – 1 + β • ΔK t – 1 for the period of 1980–2009, which generates the implicit difference equation

Y = φ • K. (3–9) Both the real GDP and capital formation variables are calculated in RMB in the constant prices of 1981. Their cyclical components have not been smoothed out, but are summed up in an appended autoregressive process. The discrete time variable T is defined as 1 in 1981 and 29 in 2009. The estimating production function ΔY t = C 1 • Y t–1 + exp(C 1 • T ) • ΔK t–1 + C 2 implies the following time function: φ(t ) = exp(C 1 • T ) = e

–0.035 414 • T

, (3–10)

and the resulting capital-output ratio:

K /Y = 1/φ(t ) = e

–0.035 414 • T

. (3–11)

The capital–output ratio grew exponentially at the average annual rate of 3.541 4% from 1981 to 2009, proving that China’s economic growth was driven by capital accumulation and hence characterized by capital deepening. Table 3.1

Estimation of China’s aggregate production function ΔYt = C 1 • Y t – 1 + exp (C 1 • T ) • ΔK t – 1 + C 2 + [AR (1) = C 3]

Coefficient

C1

–0.035 414

C2 C3

SE

Estimate

0.001 837

–869.758 1

291.296 3

0.519 796 2

0.175 586

t -stat

P > |t |

–19.279 73

0.000 0

–2.985 820

0.006 1

2.960 353

0.006 5

2

R = 0.940 985, adj. R = 0.936 445, SE = 511.485 7, DW = 1.543 096.

Economic Growth Rates and Structural Changes Economic growth rates in terms of sectoral distribution and dispersion Suppose the total national income (Y ) moves from Y 0 to Y 1 at the growth rate of g during the period ΔT , and the income from sector i (Y i) moves from Y 0i to Y 1i at

32

The Mode, Structure, and Efficiency of China’s Economic Growth

the growth rate of g i simultaneously. The growth rate (g ) and growth period (ΔT ) from the initial (Y 0) to the target (Y 1) national income level then constitute the hyperbolic function

g • ΔT = log(Y 1/Y 0). (3–12) If the structure of national income {s i} is dependent on the national income level (Y ), it will be S 0 = {s 0i } at the initial level (Y 0), with the share of sector i being Y 0i = s 0i • Y 0; and S 1 = {s 1i } at the target level (Y 1), with the share of sector i being Y 1i = s 1i • Y 1. Thus,

s 1i = Y 1i /Y 1 = exp((g i – g ) • ΔT )) • (Y 0i /Y 1) = exp((g i – g ) • ΔT )) • s 0 i ; (3–13) g i – g = log(s 1i /s 0i )/ΔT . (3–14) The national income elasticity of sector i is e i = g i/g . e i = 1 + log(s i1/s i0)/ (g • ΔT ), so

e i = 1 + log(s 1i /s 0i )/log(Y 1/Y 0), (3–15) in which e i is determined by the initial and target national income levels instead of following the exact growth path conditioned by g and ΔT . Meanwhile, we can establish a coefficient for sectoral dispersion (Χ) to measure the amount of variation in the income growth of various sectors (g i) against the total national income growth (g ): Χ = (∑ i{s 0i • (g i – g )2})1/2. (3–16) Here, as g i – g = log(s 1i /s 0i )/ΔT , Χ = (∑ i {s 0i • log 2 (s 1i /s 0i )}) 1/2 /ΔT , and its numerator (∑ i {s 0i • log 2 (s 1i /s 0i )}) 1/2 is only dependent on Y 0 and Y 1 , but independent of ΔT . In consequence, dΧ/d(ΔT ) < 0 and dΧ/dg > 0, predicting that the growth rate and sectoral dispersion of the total national income are positively correlated.

Cyclical correlations between GDP growth rates and their sectoral dispersion coefficients To calculate the coefficient for the sectoral dispersion of the annual GDP growth rate, we can break down China’s economy into nine sectors: (1) agriculture; (2) industry; (3) construction; (4) transportation, storage, and postage; (5) wholesale and retail trade; (6) hotel and catering; (7) financial intermediation; (8) real estate; and (9) other services. From 1981 to 2009, China’s GDP growth rates developed with close affinity to their sectoral dispersion coefficients, as

33

Theoretical System of China's Macroeconomic Analysis

illustrated in Fig. 3.3. Sectoral dispersion tended to decline over time, in line with the moderating trend of economic fluctuations. Fig. 3.3

Sectoral dispersion coefficients of China’s GDP growth rates 16 12 % 8 4 0

1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

GDP growth rate

Sectorial deispersion

Table 3.2 presents a cointegration test that indicates the positive cointegrating relationship between China’s GDP growth rates and their sectoral dispersion from 1981 to 2009, and establishes a normalized first-order cointegrating equation with a deterministic linear trend: Χ = 4.888 827 • g – 0.031 707 • (T + 1) – 45.654 28,

(3–17)

in which T = 1 in 1981 and T = 29 in 2009. Table 3.2

Cointegration analysis on sectoral dispersion coefficients Eigenvalue

Likelihood ratio

Critical value: 5%

None

0.432 705

28.581 23

25.32

Maximum 1

0.342 088

12.141 84

12.25

H0: Number of cointegrating equations

Table 3.3 shows a cross-correlation analysis on the cyclical GDP growth rates (g – g _HP ) and their sectoral dispersion coefficients (Χ – Χ_HP ) after extracting the trend components using the Hodrick–Prescott (H–P) filter (which yields g _HP for the GDP growth rates and Χ_HP for the sectoral dispersion coefficients). The analysis finds that during 1981 to 2009, China’s GDP growth rates were positively correlated with their sectoral dispersion coefficients, with the correlations of the cyclical components being most statistically significant. If we divide China’s economy into two phases using the reform of 1992 as the watershed and analyze the planned economy of 1981 to 1991 and market economy of 1992 to 2009 separately, we will find the positive correlations increasing along with the implementation of marketization.

34

The Mode, Structure, and Efficiency of China’s Economic Growth

Table 3.3

Cyclical correlations between China GDP growth rates and sectoral dispersion coefficients: ρ(x , y) y Period

x Χ

1981–2009

0.284 7

0.557 1

1992–2009

0.279 7

0.687 9

1978–1991

y

x

Period

Χ – Χ_HP

i = –1

g (i ) i =0

0.343 0

0.501 3

–0.149 7

i = –1

g (i ) – g _HP (i ) i =0

i =1

0.364 9

0.537 8

1981–2009

0.321 4

1992–2009

0.250 4

1981–1991

i =1

0.075 2

0.6017

0.731 0

0.331 7

0.094 8

–0.103 1 0.385 1

Structural changes during economic development were preconditioned by

unequal growth rates across sectors. Sectoral differences tended to widen in

phases of economic expansion or rapid development but narrow in times of economic contraction or slow development. China’s experience in economic development since the 1980s has nullified the trade-off relationship between

economic growth and economic structural adjustment, at least rejecting the validity of the so-called China’s Phillips curve in this matter.

Consumption Demand for Energy and Energy Efficiency China’s energy demand function To formulate an energy demand function for China, we can extend the Cobb– Douglas aggregate production function into

Y = K α • L β • (A • E )1 – α – β,

(3–18)

incorporating the input factors of capital, labor, and energy while adopting the

parameter A as an indicator of energy efficiency. Hence, the marginal product of capital is given by

MPK = dY /dK = α • (Y /K ), (3–19) the marginal product of labor is

MPL = dY /dL = β • (Y /L ), (3–20)

35

Theoretical System of China's Macroeconomic Analysis

and the marginal product of energy is

MPE = dY /dE = (1 – α – β) • (Y /E ). (3–21) Suppose China’s economy has the following characteristics: (1) in terms of the stage of economic development, it is still a dual economy with the wage rate exogenously fixed at the subsistence level ( ) by natural and social needs; and (2) in terms of economic influence, its interest rate is still largely constrained by the international capital market, maintaining at around the international level ( ).3 Based on the capital demand equilibrium condition where MPK = α • (Y /K ) = , K = (αY)/ ; based on the labor demand equilibrium condition where MPL = , L = β • (Y /L ) = (βY )/ . Substituting (αY )/ for K and (βY )/ for L , the α

β

aggregate production function Y = K •L • (A • E )1 – form of α

β

α–β

β

α–β

Y = (αY )/ ) • ((βY )/ ) • (A • E )1 –

α–β

takes the substantial

, (3–22)

so

Y1 –

α–β

α

= (α/ ) • (β/ ) • (A • E )1 –

, (3–23)

from which a structural equation for China’s energy demand function can be generated: α

β

E = (Y /A ) • (( /α) • ( /β) )1/(1 –

α – β)

. (3–24)

Suppose China’s dual-sector economy consists of a service sector and a non-service sector; the density of capital relative to labor (K /L ) and energy efficiency (A ) of the two sectors during production do not differ, but differences do exist in their energy intensity, which is expressed by the parameter (α + β). Meanwhile, introduce the control parameter κ to delineate sectoral differences in energy intensity and assume that 0 < κ < 1/(α + β). Denoting the non-service and service sectors by the subscripts I and S , respectively, α

β

Y = Y I + Y S, E = E I + E S, Y I = K I • L I • (A • E I)1 – κα

κβ

Y S = K S • L S • (A • E S)1 –

κα – κβ

α–β

; (3–25)

.

In the same manner as the national economy, α

β

E I = (Y I/A ) • (( /α) • ( /β) )1/(1 –

α – β)

. (3–26)

The energy demand equilibrium condition for the non-service sector at the energy price ep can be formulated as

MPE I = (1 – α – β) • (Y I/E I) = ep , (3–27)

36

The Mode, Structure, and Efficiency of China’s Economic Growth

whereas that for the service sector is

MPE S = (1 – κα– κβ) • (Y S/E S) = ep .

(3–28)

Hence, (1 – α – β) • (Y I/E I) = (1 – κα – κβ) • (Y S/E S) ;

(3–29)

E S = E I • (Y S/Y I) • (1 – κα – κβ)/(1 – α – β).

(3–30)

Acommodating the proportion of the output of the service sector in the

national income, s = Y S/Y , and E S = (s /(1 – s )) • (1 – κα – κβ)/(1 – α – β) • E I.

Finally, the equation for China’s (total) energy demand has come into shape:

E = E I + E S = (1 + (s /(1 – s )) • (1 – κα – κβ)/(1 – α – β)) • (( /α)α • ( /β)β)1/(1 – α – β) • (1 – s ) • (Y /A ). (3–31) In a simplified form,

E = (Y /A ) • (1 – s • (κ – 1) • (α + β)/(1 – α– β)) •(( /α)α • ( /β)β)1/(1 – α – β). (3–32)

Energy efficiency, national income, and production Table 3.4 estimates China’s energy demand function log(E t/Y t) = C 0 + C 1 • T

+ Δlog(1 – C 2 • s t–1) + C 3 • log(1 + R t) + C 4 • log(W t/P t), for the period of 1984

to 2009. The coefficient s takes the form of a temporal finite difference, which

makes manifest the annual structural variations during the expansion of the service sector. The value of T is 1 in 1981 and 29 in 2009. Table 3.4

Estimation of China’s energy demand function

log(E t/Y t) = C 0 + C 1 • T + Δlog(1 – C 2 • st – 1) + C 3 • log(1 + R t) + C 4 • log(W t/P t) Coefficient

Estimate

SE

t -stat

P > |t |

C0

4.865 803

0.312 182

15.586 44

0.000 0

C1

–0.088 409

0.011 652

–7.587 523

0.000 0

C2

1.177 612

0.438 665

2.684 538

0.013 9

C3

1.916 710

0.695 634

2.755 343

0.011 9

0.150 549

4.297 900

0.000 3

C4

0.647 043 2

2

R = 0.971 324, adj. R = 0.966 861, SE = 0.057 739, DW = 0.665 923.

37

Theoretical System of China's Macroeconomic Analysis

Apart from the negative substitution effect of a normal qualitative calculus

(with dE /d > 0 and dE /d

> 0, China’s energy demand function has a

positive scale effect (dE /dY > 0), a negative efficiency effect (dE /dA < 0),

and a negative structural effect (dE /ds < 0). With reference to the theoretical

α prototype of E = (Y /A ) • (1 – s • (κ – 1) • (α + β)/(1 – α– β)) •(( /α) • β

α

β

( /β) ) 1/(1 – – ), the estimating log-linear equation in Table 3.4 reveals the numerical regularity of China’s energy demand:

(1) ΔA /A = –C 1 = 0.088 409. China’s energy efficiency measured with the

parameter A grew exponentially at the average annual rate of 8.840 9% during

1984 to 2009.

(2) α/(1 – α – β) = C 3 = 1.916 710, and β/(1 – α – β) = C 4 = 0.647 043, so

α : β = 2.962 261 : 1, fitting the factor endowment of a developing economy, where capital is scarce and labor is redundant, but capital contributes more to economic growth than labor.

(3) (κ – 1) • (α + β)/(1 – α – β) = C 2 = 1.77 612. On account of (α + β)/(1 – α– β)

= C 3 + C 4 = 2.563 753, k – 1 = 0.459 331. Thus, China’s non-service sector is more

energy-intensive than its service sector during production.

High Savings, High Investments, and High Growth Assume that the life cycle of humankind includes a productive young age and a purely consumptive old age. Suppose the young population (N Yt) grows at

the natural rate of η, and N Yt = N Yt – 1 • (1 + η); the old population is given by N Ot = N Yt – 1, and the total population N t = N Yt + N Ot. Furthermore, let the national

income contributed by the young population be y t, which grows at the natural

rate of δ, making y t = y t – 1 • (1 + δ), and the total national income can be written

as Y t = N Yt • y t. Under the time preference rate θ and interest rate r , the young

population makes intertemporal decisions with the constant relative risk γ

aversion (CRRA) utility function U (c ) = c 1 – /(1 – γ), and abides by the Keynes– γ

Ramsey rule of optimal consumption (c t/ ct + 1) = (1 + θ)/(1 + r ) . Hence: γ

γ

max{c t1 – /(1– γ) + c t1 – /(((1– γ) • (1 + θ))} s.t.

y t = c t + c t + 1/(1 + r ) (3–33)

In the simple case of γ = 1, c t = ((1 + θ)/(2 + θ)) • y t = (1 – 1/(2 + θ)) • y t, and the young population’s propensity to save out of national income is expressed as s = 1/(2 + θ). Thus, the total national savings are given by

38

The Mode, Structure, and Efficiency of China’s Economic Growth

S t = N Yt • (s • y t) – N Ot – 1 • (s • y t – 1) • (1 + r ) = N Yt • (s • y t) • (1 – (1 + r )/((1 + η) • (1 + δ))), (3–34) and the aggregate propensity to save out of national income is

S /Y = s • (1 – (1 + r )/((1 + η) • (1 + δ))), (3–35) or approximately,

S /Y = s • (η + δ – r ). (3–36) The high propensity to save can be interpreted fully using the life cycle hypothesis which correlates young age and high rapid economic growth. Both the precautionary saving motive, which stems from the uncertainty of future income, and the traditional virtue of thrift increase the personal propensity to save (s ) through reducing the time preference rate (θ). Moreover, the combination of the factors of the young population, high economic growth, and a low (real) interest rate also pushes up the aggregate propensity to save by increasing the population growth rate (η) and enlarging the gap between the economic growth rate and the interest rate (δ – r ). Mathematically, this is expressed as s • (η + δ – r ). The proportion of the old population in the total population is 1/(2 + η), with η declining with population aging. Although population aging, the weakening of traditional values, and the improvement of social security may to some extent lower the propensity to save out of national income, substantial factors argue that such a decline will only take place slowly and limitedly in the foreseeable future: First, as China’s population remains young in general, population aging can hardly elevate the dependency ratio to above the critical value of 1. Second, the massive migration of rural labor, the large-scale accumulation of human and physical capital, and rapid technological progress by imitation and innovation embedment will continue to sustain rapid economic growth, maintaining a wide gap between the economic growth rate and real interest rate (δ – r ).4 As a result, the propensity to save in China will remain at a relatively high level. Whether according to Rostow’s stages of economic growth or Kuznets’ theory of modern economic growth, the prominent rise of investments in proportion to national income is the precondition for the transformation of a traditional economy into a modern economy. High national investment rates distinguish a modern economy from a traditional one; large-scale capital accumulation and rapid embedded technological progress with the support

39

Theoretical System of China's Macroeconomic Analysis

of a high investment rate have been the gist of the catch-up strategies of late-starting countries. The traditional-to-modern economic transition is also accompanied by the transformation from extensive to intensive economic growth. The evolution of China’s economy, however, is gradual, mainly depending on fast and lasting national income growth and yet prone to stagnation or reversal in such extreme economic conditions as overheating and depression. According to the golden rule of capital accumulation by Phelps (1961), dynamic inefficiency can be caused by both overinvestment in a centralized planned economy and underinvestment in a decentralized market economy.5 However, applying it to China’s economy, the rule fails to observe the universal phenomenon that the (net) investment returns are below the sum of the population growth rate and the rate of technological progress. In addition, the steady state modeling adopted by the golden rule theory in comparing and selecting the national savings rate is inconsistent with the tendency that the capital per capita of developing economies grows faster, leading to the eventual convergence of all economies. Confronted with a high savings rate in the distribution of national income, China’s macroeconomic policies should be designed and implemented in accordance with the Keynesian approach. The nation should establish a demand management system centering on domestic investment demand in order to facilitate the effective conversion of savings into investments, supporting the aged population with an amount of capital that is sufficient to substitute for labor in the long term. At present, unemployment in China is characterized by the coexistence of the classical and Keynesian categories. The two types of unemployment have to be redressed by capital accumulation and demand management, respectively. A pro-investment inclination in the expenditure structure of national income will increase effective demand and then promote capital accumulation, concurrently meeting the short-run and long-run objectives regarding employment during economic depression.

Data Appendix Based on data from China Statistical Yearbook 2010 , Table 3.5 calculates the GDP deflators, real GDP, and real capital formation of China in RMB based on the prices of 1981 using the following formulas:

40

P t = (GDPt/Y’ t)/(GDP1981/Y’ 1981) • 100;

(3–37)

Y t = (GDPt/Pt) • 100;

(3–38)

ΔK t = (CF t/Pt) • 100.

(3–39)

The Mode, Structure, and Efficiency of China’s Economic Growth

Table 3.5

China national income, inflation, and capital formation

Year

GDP Current price GDP t (RMB billion)

1978=100 Yt’

1981 price Yt (RMB billion)

GDP deflator 1981=100 Pt

1978

364.52

100.0

400.62

90.99

1979

406.26

107.6

431.07

1980

454.56

116.0

464.72

1981

489.16

122.1

489.16

1982

532.34

133.1

1983

596.27

147.6

1984

720.81

1985

Capital formation Current price CF t (RMB billion)

1981 price ΔK t (RMB billion)

107.39

118.03

94.24

115.31

122.35

97.81

132.24

135.20

100.00

133.93

133.93

533.22

99.83

150.32

150.57

591.31

100.84

172.33

170.90

170.0

681.05

105.84

214.70

202.86

901.60

192.9

772.80

116.67

267.20

229.03

1986

1,027.52

210.0

841.31

122.13

313.97

257.07

1987

1,205.86

234.3

938.66

128.47

379.87

295.70

1988

1,504.28

260.7

1,044.42

144.03

470.19

326.45

1989

1,699.23

271.3

1,086.89

156.34

441.94

282.68

1990

1,866.78

281.7

1,128.56

165.41

482.78

291.86

1991

2,178.15

307.6

1,232.32

176.75

607.03

343.44

1992

2,692.35

351.4

1,407.79

191.25

851.37

445.17

1993

3,533.39

400.4

1,604.09

220.27

1,330.92

604.21

1994

4,819.79

452.8

1,814.02

265.70

1,731.27

651.60

1995

6,079.37

502.3

2,012.33

302.11

2,088.50

691.31

1996

7,117.66

552.6

2,213.84

321.51

2,404.81

747.98

1997

7,897.30

603.9

2,419.36

326.42

2,596.50

795.45

1998

8,440.23

651.2

2,608.85

323.52

2,856.90

883.06

1999

8,967.71

700.9

2,807.96

319.37

3,052.73

955.87

2000

9,921.46

759.9

3,044.33

325.90

3,384.44

1,038.49 1,135.21

2001

10,965.52

823.0

3,297.12

332.58

3,775.45

2002

12,033.27

897.8

3,596.79

334.56

4,363.21

1,304.18

2003

13,582.28

987.8

3,957.35

343.22

5,349.07

1,558.51

2004

15,987.83

1087.4

4,356.37

367.00

6,511.77

1,774.33

2005

18,493.74

1210.4

4,849.13

381.38

7,423.29

1,946.42

2006

21,631.44

1363.8

5,463.69

395.91

8,795.41

2,221.55

2007

26,581.03

1557.0

6,237.69

426.14

10,394.86

2,439.33

2008

31,404.54

1707.0

6,838.63

459.22

12,808.44

2,789.16

2009

34,050.69

1862.5

7,461.59

456.35

15,667.98

3,433.35

Table 3.6 decomposes China’s economy into the nine sectors of (1)

agriculture; (2) industry; (3) construction; (4) transportation, storage, and

postage; (5) wholesale and retail trade; (6) hotel and catering; (7) financial intermediation; (8) real estate; and (9) other services. The superscripts of g

41

Theoretical System of China's Macroeconomic Analysis

and s are numbered according to the parenthesized numbers preceding the

sectors above. Based on data from China Statistical Yearbook 2010 , the sectoral dispersion coefficients of China’s annual GDP growth rates are calculated by the following formula:

X t = (∑1 → 9 (s it (g it – g)2))1/2 (3–40) Table 3.6

Sectoral distribution of China GDP growth Real GDP growth rate (%)

Total

Year

g1

g2

g3

g4

g5

g6

g7

g8

g9

g

1980

–1.5

13.6

12.7

4.3

–1.9

3.9

6.6

7.9

15.1

7.8

1981

7.0

1.9

1.7

1.9

29.5

17.5

4.3

–3.5

7.6

5.2

1982

11.5

5.6

5.8

11.4

–0.7

31.6

44.6

9.1

13.6

9.1

1983

8.3

10.4

9.7

9.5

21.2

19.4

27.0

5.2

12.0

10.9

1984

12.9

14.5

14.9

14.9

24.7

8.1

31.1

27.7

15.5

15.2

1985

1.8

18.6

18.2

13.8

33.5

6.3

16.9

25.0

11.7

13.5

1986

3.3

10.2

9.6

13.9

9.4

15.6

31.6

25.9

3.0

8.8

1987

4.7

13.7

13.2

9.6

14.7

9.7

23.3

29.3

10.4

11.6

1988

2.5

14.5

15.3

12.5

11.8

25.1

19.5

12.7

9.4

11.3

1989

3.1

3.8

5.1

4.2

–10.7

9.9

25.9

15.9

4.9

4.1 3.8

1990

7.3

3.2

3.4

8.3

–5.3

3.5

1.9

6.2

3.7

1991

2.4

13.9

14.4

10.6

5.2

8.2

2.3

12.0

15.7

9.2

1992

4.7

21.2

21.2

10.1

10.5

27.0

8.0

34.7

11.5

14.2

1993

4.7

19.9

20.1

12.5

8.6

8.2

10.9

10.8

16.9

14.0

1994

4.0

18.4

18.9

8.5

8.2

27.1

9.4

12.0

12.7

13.1

1995

5.0

13.9

14.0

11.0

8.2

10.2

8.5

12.4

10.3

10.9

1996

5.1

12.1

12.5

11.0

7.6

6.8

7.5

4.0

12.7

10.0

1997

3.5

10.5

11.3

9.2

8.8

10.9

8.5

4.1

15.9

9.3 7.8

1998

3.5

8.9

8.9

10.6

6.5

11.1

4.9

7.7

9.7

1999

2.8

8.1

8.5

12.2

8.7

7.7

4.8

5.9

11.4

7.6

2000

2.4

9.4

9.8

8.6

9.4

9.3

6.5

7.1

13.0

8.4

2001

2.8

8.4

8.7

8.8

9.1

7.6

6.4

11.0

12.9

8.3

2002

2.9

9.8

10.0

7.1

8.8

12.1

6.9

9.9

13.6

9.1

2003

2.5

12.7

12.8

6.1

9.9

12.4

7.0

9.8

10.8

10.0

2004

6.3

11.1

11.5

14.5

6.6

12.3

3.7

5.9

12.6

10.1

2005

5.2

12.1

11.6

11.2

13.0

12.3

13.8

12.2

11.9

11.3

2006

5.0

13.4

12.9

10.0

19.5

12.6

25.9

15.5

10.8

12.7

2007

3.7

15.1

14.9

11.8

20.2

9.6

27.6

24.4

11.3

14.2

2008

5.4

9.9

9.9

7.3

15.9

9.6

13.3

1.0

11.0

9.6

2009

4.2

9.9

8.7

3.7

12.1

5.5

17.9

11.3

7.4

9.1

42

The Mode, Structure, and Efficiency of China’s Economic Growth

(Cont'd) Sectoral share in real GDP (%)

Dispersion

Χ

Year

s1

s2

s3

s4

s5

s6

s7

s8

s9

1980

30.2

43.9

4.3

4.69

4.26

1.04

1.65

2.12

7.83

5.513 578

1981

31.9

41.9

4.2

4.51

4.72

1.11

1.63

2.04

7.99

1.194 658

1982

33.4

40.6

4.1

4.64

3.22

1.17

2.16

2.08

8.58

1.946 566

1983

33.2

39.9

4.5

4.61

3.33

1.22

2.50

2.04

8.74

1.495 862

1984

32.1

38.7

4.4

4.70

5.04

1.34

2.83

2.25

8.62

1.304 814

1985

28.4

38.3

4.6

4.68

8.90

1.53

2.88

2.39

8.29

6.219 913

1986

27.2

38.6

5.1

4.85

8.30

1.59

3.47

2.90

8.03

3.324 681

1987

26.8

38.0

5.5

4.71

8.79

1.55

3.73

3.17

7.68

3.575 700

1988

25.7

38.4

5.4

4.56

9.86

1.61

3.89

3.15

7.45

4.459 743

1989

25.1

38.2

4.7

4.78

9.04

1.63

5.67

3.33

7.60

0.540 891

1990

27.1

36.7

4.6

6.25

6.80

1.62

5.45

3.55

7.88

1.817 112

1991

24.5

37.1

4.7

6.52

8.42

2.03

4.85

3.51

8.36

3.849 769

1992

21.8

38.2

5.3

6.27

8.93

2.17

4.85

4.09

8.44

4.523 481

1993

19.7

40.2

6.4

6.15

7.97

2.02

4.73

3.90

8.95

4.205 920

1994

19.8

40.4

6.2

5.78

7.83

2.09

4.64

3.96

9.27

4.037 761

1995

19.9

41.0

6.1

5.34

7.86

1.97

4.60

3.87

9.22

2.646 498

1996

19.7

41.4

6.2

5.31

7.87

1.88

4.51

3.68

9.52

2.330 704

1997

18.3

41.7

5.9

5.25

8.01

1.98

4.57

3.70

10.67

3.282 610

1998

17.6

40.3

5.9

5.52

8.19

2.12

4.38

4.07

11.95

1.923 076

1999

16.5

40.0

5.8

5.76

8.33

2.16

4.24

4.09

13.09

2.382 977

2000

15.1

40.4

5.6

6.21

8.22

2.16

4.12

4.18

14.12

2.903 423

2001

14.4

39.7

5.4

6.27

8.32

2.19

3.97

4.30

15.41

2.750 825

2002

13.7

39.4

5.4

6.23

8.31

2.26

3.83

4.44

16.39

2.942 842

2003

12.8

40.5

5.5

5.83

8.22

2.30

3.67

4.54

16.66

2.709 627

2004

13.4

40.8

5.4

5.82

7.79

2.29

3.37

4.49

16.62

1.734 385

2005

12.1

41.8

5.6

5.77

7.55

2.27

3.29

4.61

17.03

2.129 034

2006

11.1

42.2

5.7

5.63

7.64

2.22

3.74

4.79

16.91

2.677 433

2007

10.8

41.6

5.8

5.49

7.88

2.09

4.64

5.20

16.60

3.610 042

2008

10.7

41.5

6.0

5.21

8.34

2.11

4.73

4.69

16.74

1.497 915

2009

10.3

39.7

6.6

5.01

8.49

2.09

5.21

5.48

17.06

1.738 407

Table 3.7 quotes China’s total energy consumption and the average wage of

an urban employed person from China Statistical Yearbook 2010 . It also shows

the time-weighted average interest rates of saving deposits calculated based on the official interest rates for one-year deposits published in Almanac of China’s

Finance and Banking 2010 :

43

Theoretical System of China's Macroeconomic Analysis

R t = Σi {R ti • Δt i}/Σi {Δt i}, (3–41) where the interest rate R ti is in effect during the time Δt i in the year t . Table 3.7 Year

1983

44

China Energy consumption, wages, and interest rates Energy consumption

Average wage of an urban

Average interest rate

E

(Current RMB)

R

(10,000 TCE)

66,048.6

employed person

W

(1-year deposit, %)

—

5.760

973.70

5.760

1984

70,936.2

1985

76,682.0

1,148.0

6.720

1986

80,850.0

1,329.0

7.200

1987

86,632.0

1,459.0

7.200

1988

92,997.0

1,747.0

7.680

1989

96,934.0

1,935.0

11.115

1990

98,703.0

2,140.0

9.928

1991

103,783.0

2,340.0

7.890

1992

109,170.0

2,711.0

7.560

1993

115,993.0

3,371.0

9.423

1994

122,737.0

4,538.0

10.980

1995

131,176.0

5,500.0

10.980

1996

135,192.0

6,210.0

9.172

1997

135,909.0

6,470.0

7.130

1998

136,184.0

7,479.0

5.035

1999

140,569.0

8,346.0

2.930

2000

145,531.0

9,371.0

2.250

2001

150,406.0

10,870.0

2.250

2002

159,431.0

12,422.0

2.018

2003

183,792.0

14,040.0

1.980

2004

213,456.0

16,024.0

2.027

2005

235,997.0

18,364.0

2.250

2006

258,676.0

21,001.0

2.349

2007

280,508.0

24,932.0

3.203

2008

291,448.0

29,229.0

3.925

2009

306,647.0

32,736.0

2.250

4

Chapter

Structural and CostPush Inflation in China

Theoretical System of China's Macroeconomic Analysis

Historical Trends of Inflation in China Stages and mechanisms of the evolution of inflation trends Following the course of marketization, China’s economy underwent a period of high inflation during the mid-to-late-1980s, followed by another high inflation phase during the early-to-mid-1990s, and then a low inflation era after the late 1990s, completing a transformation from steady high inflation to steady low inflation. Table 4.1 describes the characteristics of China’s steady inflation rates during the three periods, and attempts to make a conceptual hypothesis about their evolution. Table 4.1

Evolution of China’s inflation trends Phase I: mid-to-late1980s

Phase II: early-to-mid 1990s

Phase III: post-late 1990s

High inflation

High inflation

Low inflation

Driver

Repressed inflation

Labor compensation

Competitive market

Expression

Product price liberalization

Increased nominal wage Increased productivity

Pattern Mechanisms

Background

Goods market reform

Labor market reform

Market Integration

Under a planned economy, price controls led to repressed and hidden inflation in China, and incomplete wage compensation greatly limited the household’s function of capital accumulation. The mid-1980s began the reform of the goods market, which kicked off a complete transformation of the economic system. As a result, price liberalization and adjustments released previously repressed pressure on inflation overnight, giving rise to open inflation and widespread inflation expectations throughout the mid-to-late 1980s. In the early 1990s, the establishment of the socialist market economy was clearly designated as the official orientation of the economic reform. Thereafter, factor market reforms, especially the reform of the labor market, became a reform focus. During the redistribution of national income for the purpose of rebuilding the household’s function of capital accumulation, wage growth outpaced the improvement of productivity, and household incomes increased at a faster rate than household consumptions. Without a scale-down of the share of the non-household sector in the national income, the result was wagepush inflation in the early-to-mid 1990s. By and large, the radical liberalization

46

Structural and Cost-Push Inflation in China

of long-repressed inflation and the completion of labor compensation policies constituted the major structural causes of high inflation from the mid-1980s to the mid-1990s. The constitutional framework for a market economy fell in place in the late 1990s, laying the foundation for the acceleration of technological progress. With the dissipation of repressed inflation, the competitive goods market and labor market came to eliminate the wage factor in cost-push inflation and the related inflation inertia, ushering in a trend of low and steady inflation. Headline inflation, too, was moderated. 1 While the domination of heavy industries, strict regulations on environmental protection, and improved public ownership policies have made the inflation of commodity prices, especially the prices of natural resources, unavoidable in the long run, this cost is gradually absorbed by the increase of productivity along the production chain as a result of technological progress backed up by a competitive market, which eliminates the factor of commodity prices in cost-push inflation. In short, it is the stepwise absorption of the resource commodity costs by technological progress rather than the possible lag in price transmission that has caused the divergence between the consumer price index (CPI) and the producer price index (PPI) in China.

Steady-state inflation rates through autoregression The autoregressive process for inflation π t = C (0) + ∑ i {C (i ) • π t – i } is intertemporally stable under the coefficient constraint ∑i{C (i ) < 1. It provides the equilibrium solution π = C (0)/(1 – ∑i{C (i )}) as a formula to measure the steadystate inflation rate. The GDP deflators and retail price indexes (RPI) from 1983 to 2009 can be simulated through a second-order autoregressive process with a dummy variable: πt = C (0) + ∑i{C (i ) • πt – i} + DUM ,

(4–1)

in which DUM equals 0 from 1984 to 1996, and then 1 from 1997 to 2009. Thus, the equation for a stepwise calculation of their steady-state inflation rates is given by π = (C (0) + DUM t)/(1 – C (1) – C (2)). (4–2) As illustrated in Fig. 4.1, China’s steady-state GDP deflator inflation rates were 8.967 705% during 1984 to 1996 and 2.826 585% from 1997 to 2009, and its steady-state RPI inflation rates were 9.290 835% for 1984 to 1996 and 0.641 812% for 1997 to 2009.

47

Theoretical System of China's Macroeconomic Analysis

Fig. 4.1 (a) China steady-state GDP deflator inflation rates 25 20 15 % 10 5 0 –5

1984

1986

1988

1990

1992

Actual

1994

1996

1998

2000

2002

Regressed

2004

2006

2008

Steady

Fig. 4.1(b) China steady-state RPI inflation rates 25 20 15 10 %

5 0 –5

1984

1986

1988

Actual

1990

1992

1994

1996

1998

2000

2002

Regressed

2004

2006

2008

Steady

The cointegration test on China’s CPI, PPI, and purchasing price index for

raw materials, fuel, and power (RMFPPI) inflation rates from 1990 to 2009 reveals the following long-run equilibrium relationships:

πCPI = 0.801 095 1 • πPPI – 0.219 014 • (T + 2) + 6.221 351;

(4–3)

πCPI = 0.658 271 • πRMFPPI – 0.317 252 • (T + 2) + 7.774 349,

(4–4)

in which the time variable T is 1 for 1981 and 29 for 2009. Since the 1990s,

the steady-state CPI inflation rates have been moderating and expressing

an incomplete response to the corresponding rates of the PPI and RMFPPI. Comparing its response toward the two, the CPI appears less cost sensitive to the RMFPPI than to the PPI. China’s economy has demonstrated and will continue to exhibit a dynamic structure characterized by the gradual descending of the mentioned indexes in the order of the RMFPPI, the PPI, the GDP deflator, and the CPI, the last of which maintains a moderate growth rate.

48

Structural and Cost-Push Inflation in China

Nominal Price Stickiness and Structural Inflation Structural inflation during price equilibration In classical theories on structural inflation, the national economy is assumed to be inhomogeneous and divided into economic sectors with different technological progress. Uniform wage settlements across sectors tend to make wages grow faster than productivity in a stagnant sector, leading to overall inflation.2 On the basis of the cost-push inflation model, the structural inflation model contributes to the identification of differences in sectoral productivity as the technological cause of wage-push inflation, and specifies the sclerotic labor market as its transmission channel. As a natural result of the adoption of a market economy, China’s target of achieving rational prices and the corresponding price rationalization process conform to the Walrasian price equilibrium model. Nominal price stickiness is inclined to create structural inflation during price equilibration: It is inevitable as long as nominal prices are not perfectly flexible and thus do not to decline to a sufficient extent. Likewise, as long as relative prices are not perfectly flexible to allow for instantaneous adjustment, price equilibration will necessarily perpetuate structural inflation.3 Consider a pure exchange economy with n products. Suppose: (1) S i, D i, and P i denote the supply, demand, and price level of product i , respectively; (2) {S i}, as Marshallian supply, is determined by the initial price {P 0i } and fixed in the market period during price equilibration; (3) the circulation of money from the national income is closed, with Y = ∑i{P i • S i} = ∑i{P i • D i}; and (4) D i = D j(Y ; {P i}), and, as a homogeneous demand function of the zero order, D j = D j(Y /k ; {P i/ k }),where k is a positive constant. Thence, the equilibrium price {P *} i is a fixed point of the equation system S j = D j(∑ i{P i • S i}; {P i}), which is indeterminate according to the Walrasian equation. Only the relative equilibrium price {p i*} can be determined: {P *} i = Y • {p *}, (4–5) i given the monetary national income Y . Because {P i0} ≠ (∑i{P i0 •S i}) • {p i*}, S j is generally not equivalent to D j(∑ i{P 0i • Si}; {P 0i }). Suppose {P 0i } is adjusted to {P i’ }, and Y grows from Y 0 to Y’ . Then, Y’ = ∑i{P i’ • S i}, and {P i’ } = Y’ • {p }. Considering the inflation rate of product i (πi), 1 + πi = P i’ /P i0, and because P i’ = Y’ • p , 1 + πi = Y’ • (p /P i0). Meanwhile, identify the benchmark product u with the selection mechanism p u*/P u0 = min{p i*/P 0i } for the

49

Theoretical System of China's Macroeconomic Analysis

calculation of downward stickiness. Under the aggregate inflation rate π, 1 + π = Y’ /Y 0, and as 1 + πu = Y’ • (P u0/p u*), 1 + π = (1 + πu) • (P u0/(p u* • Y 0)), in which (P u0/(p u* • Y 0)) represents the degree of disequilibrium of the initial price of the benchmark product (P u0). If π u = 0, 1 + π = P u0/(p u* • Y 0). Then, the structural inflation rate is given by πs = P u0/(p u* • Y 0)) – 1,

(4–6)

which is equivalent to the lowest inflation rate under nominal rigidity.

Simple arithmetic of structural inflation rates Since 1 + π = (1 + πu) • (1 + πs), (1 + πu) • (1 + πs) = 0 when π = 0, and the critical inflation rate for the benchmark product is defined as πus = 1/(1 +us) – 1.

(4–7)

Therefore, approximately, π = π u + π s;

(4–8)

πus = –πs. (4–9) With perfect flexibility, a price can equilibrate under the precondition that π = 0, which implies that πu = –πs = πus. When πu > πus, π > 0, indicating a sticky nominal price. Inflation is mandatory in the case of downward rigidity where πi ≥ 0 and πu = 0, and thus π = πus. When πu > 0 and π > πs, the excess portion of π over πs belongs to non-structural inflation. Table 4.2 calculates China’s PPI structural inflation rates during the period of 1986 to 1989, when price liberalization and rationalization caused severe inflation, according to an algorithm for computing structural inflation rates under nominal price stickiness. Among the goods of all industrial sectors where the PPI applies, products of the electric power industry are identified as the benchmark product. In the latter half of the 1980s, the PPI of China’s industrial goods followed an almost monotonically accelerating inflation path during price equilibration. However, despite the dominance of structural inflation as conditioned by sticky nominal prices, this structural factor began to diminish toward the end of the 1980s, as the structural inflation rates and their contribution to total inflation proceeded along a U-shaped time path.4

50

Structural and Cost-Push Inflation in China

Table 4.2 Year

China PPI structural inflation rates (%) PPI Inflation rate

π

1986

6.800 0

1987

8.900 0

1988

13.833 3

1989

12.566 7

Electric power products πu

Structural inflation rate πs

2.966 7

2.400 0

Contribution of structural inflation in PPI πs/π

3.722 9

54.748 3

6.347 7

3.566 7

71.322 0

9.913 1

5.000 0

71.661 0

7.206 3

57.345 0

Inflation through the Wage-Push Mechanism The theoretical model of price formation The equation system of the price formation model comprises a monetary wage adjustment equation, a connecting equation between the cost of living and the price index, and a defining equation of the national income distribution coefficient, which are laid out in order as follows: W = W (C, ρ, y) C = C (P, t) ρ = W / (P ˙ y) , (4–10) where W , C , and P denote the monetary wage, cost of living, and price index, respectively, while y refers to labor productivity, and ρ indicates the wage share of national income. Fig. 4.2 presents a diagrammatic model of price formation based on the control theory. Fig. 4.2

Block diagram of price formation

P

C = C (P, t)

ρ = W /(P • y)

C t

y = y(t)

ρ = ρ(t)

y

W = W (C, ρ, y)

W

ρ

51

Theoretical System of China's Macroeconomic Analysis

In W = W (C , ρ, y ), although W is adjusted according to compensation based on the cost of living, the national income redistribution mechanism, and labor productivity, the incomplete response of W to C , ρ, and y is permitted as a growth constraint (gW = gC + g ρ + gy ) has not been imposed.5 As illustrated in Fig. 4.2, the wage-push mechanism leading to cost-push inflation includes a feed-forward channel going from W to P and a feedback channel going from C to W , forming a closed loop among W , P , and C . Driven by the time function y = y (t ) and ρ = ρ(t ), it may result in self-sustaining wage-push inflation. The static equilibrium solutions for the log-linear equation system of the price formation model are: π = ((k 2 – 1) • g ρ + (k 3 – 1) • gy + k 1l 2)/(1 – k 1l 1); (4–11)

gW * = ((k 2 – k 1l 1) • g ρ + (k 3 – k 1l 1) • gy + k 1l 2)/(1 – k 1l 1). (4–12) Therefore, the necessary and sufficient condition for price stability is: (k 2 – 1) • g ρ + (k 3 – 1) • gy + k 1l 2 = 0.

(4–13)

As the price index and cost of living demonstrate different time trends, even if the monetary wage undergoes complete adjustment in the respects of compensation, redistribution, and productivity using the conventional formula of gW = gC + g ρ + gy so that k 1 = k 2 = k 3 = 1 and π = l 2/(1 – l 1), unless in the small probability event where l 2 = 0, it will continue to be inflationary:6 lnW = k1 ˙ lnC + k2 ˙ lnρ + k3 • lny lnC = l1 ˙ lnP + l2 ˙ t

lnρ = lnW – lnP ˙ lny (4–14)

The wage-push inflation of China’s industrial goods The price index function of China’s industrial goods are determined by the monetary wage adjustment equation W = W (C , ρ, y ) and the national income distribution coefficient ρ = W /(P • y ):

P = (ρ • y ) • W (C , ρ, y ). (4–15) We can fit this function and the CPI connecting equation between 1985 and 2009 in a recursive system for the price formation model by OLS estimation, where in 1981, T = 1, and in 2009, T = 29: logPt = 0.819 033 + 0.735 441 • logCt + 0.208 257 • logρt – 1

52

Structural and Cost-Push Inflation in China

(2.000 117) (9.133 919)

(2.120 418)

– 0.085 524 • logyt – 1 + [AR (1) = 0.774 401] (3.621 146) (7.678 494)

(4–16)

R = 0.995 855, adj. R = 0.995 026, SE = 0.022193, DW = 1.647712; 2

2

logC t = 0.596 473 + 1.312887 • logP t – 1 – 0.476 119 • logP t – 2 – 0.094 285 (1.704 459) (9.003 352) (–321 239 7) (10.421 28) • T – 0.002 147 • T 2 (–11.825 57)



(4–17)

R = 0.995 860, adj. R = 0.995 032, SE = 0.031 676, DW = 1.728 005. 2

2

Then, extrapolate the time trends of labor productivity and the national income distribution coefficient during the period of 1985 to 2009, respectively, using the following equations:

logy t = 2.392 939 + 1.383 771 • logy t – 1 – 0.963 012 • logy t – 2 + 0.039852 • T + (4–18) 4.69E – 05 • T 3 + [MA (1) = –0.989 869]; log ρt = 0.907 716 • log ρt – 1 – 0.026 937 • T + 0.000 707 • T 2 + [MA (1) = 0.454 525].

(4–19)

Finally, a dynamic forecast of the GDP deflator inflation rates from 2010 to 2015 can be carried out using the price formation model, as illustrated in Table 4.3. Since the equation C = C (P , t ) uses a time variable that reflects the costpush effect of goods from sectors other than industy and excludes the factor of future demand shocks, the forecast primarily provides approximate wage-push inflation rates of China’s industrial goods in a steady state. Table 4.3

Wage-push GDP deflator inflation of China’s industry

Year of extrapolation Inflation rate (%)

2010

1.960

2011

1.917

2012

1.885

2013

1.966

2014

2.208

2015

2.580

Food Prices and Core Inflation Rates The food price hurdle to economic development The original Lewis dual-sector model predicts that the continuous migration of surplus labor will not reduce food supply or elevate food prices until the completion of industrialization, and that the subsistence wage will remain stable. Here is a revision of the model in the structuralist approach based on

53

Theoretical System of China's Macroeconomic Analysis

the experiences of South American countries: First, regarding food supply, its contraction is necessitated by labor migration as agricultural surplus labor is not absolute. Second, regarding food prices, they will eventually rise in response to the gap between food supply and demand, as the income inelasticity of food demand — which in fact increases during industrialization and urbanization — is often not as low as the prediction of Engel’s law. The increase in household food expenditure will in turn push up wages, resulting in wage-push structural inflation. Apart from South America’s negative experience of structural inflation, the U.K., the Soviet Union, and the U.S. have set successful examples of overcoming the food price hurdle to industrialization and urbanization. Table 4.4 provides a summary of their experiences: the U.K, as an early-starting country, repealed the Corns Law in favor of free trade in food, and encouraged importing cheap food from peripheral agrarian economies; the Soviet Union established a collective farming system to administer agricultural labor and food circulation, providing the urban population and industrial labor with cheap but limited food supply through central planning; and the U.S. took advantage of its rich natural resource endowment and promoted large-scale cultivation on fertile land in order to increase food supply and labor productivity, thereby stabilizing food prices while enhancing the comparative income of agricultural production. Table 4.4

Historical models of food price stabilization during industrialization

Trend of food prices

Mechanism

  Advantage

  Performance

Economic system

U.K.

Low inflation Early-starting

Food importation

Market economy

Soviet Union

Repressed inflation Central command

Collective farming

Planned economy

U.S.

Low inflation Natural resources

Extensive farming

Market economy

Unfortunately, none of these models are applicable for today’s China. The American model is not feasible given China’s inferior natural resources for food production. The British model also lacks validity to this late-coming and populous country. Furthermore, the practice of food importation itself will in the end escalate food prices worldwide. The Soviet model of collective farming, which China did model on in the form of people’s communes during the initial stage of industrialization, once served its cause in controlling food prices and labor migration. However, with its gradual transition from a planned economy to a market economy, China has long abandoned forced agricultural production. Under the constraints of the international economic order, the domestic economic system, and the availability of natural resources, it will continue to

54

Structural and Cost-Push Inflation in China

confront food price inflation as industrialization proceeds. Nonetheless, the near depletion of potential for the growth of land productivity makes the migration of surplus labor and the increase of food prices both unavoidable and favorable for China’s economy. The nation can draw lessons from Japan, which boosts labor productivity through increasing wages as a form of human investment and enjoys low unit labor costs thanks to this improvement in labor productivity. For China, the sustainability of industrialization and the acceleration of economic development will depend on high labor productivity supported by a large human capital. The “longrange match” against high wages induced by increasing food prices will be won through maintaining low unit labor costs, the comparative cost advantage, and a low core inflation rate.7

The propagation and impact of food inflation The accounting identity for the headline CPI is logP = α • log P Food + (1 – α) • log P Core, (4–20) where P Food denotes the food price, and P Core refers to the core inflation rate. The adjustment equation to obtain the core CPI is Δ log P Core = β • (log P – log P Core). (4–21) Thus, the intertemporal equation of motion of the headline CPI is given by log P t = α • log P Foodt – α • (1 – β) • log P Foodt – 1 + (1 – αβ) • log P t – 1. (4–22) In Table 4.5, this equation is estimated by OLS with a constraint on the coefficients α and β for the period of 1995 to 2009, where P = P Food = 100 in 1994, and the time variable T is 1 in 1995 and 15 in 2009. Table 4.5

Estimation of China’s CPI equation of motion

log Pt = C 0 + (C 1 + C 2 • T) • log P – (C 1 + C 2 • T ) • (1 – (C 3 + C 4 • T )) • log P + (1 – (C 1 + C 2 • T ) • (C 3 + C 4 • T )) • log P t – 1

Coefficient

SE

Estimate

0.010 862

C0

0.721 158

C1 C2

–0.029 732

C4

–0.064 443 2

3.653 454

P > |t |

0.005 825

14.020 02 –5.104 333

0.000 0

0.025 414

–2.535 701

0.029 6

0.051 438

0.733 457

C3

0.002 973

t -stat

0.249 866

2.935 407

0.004 4 0.000 5 0.014 9

2

R = 0.987 508, adj. R = 0.982 511, SE = 0.009 913, DW = 1.402 778.

55

Theoretical System of China's Macroeconomic Analysis

According to Table 4.5, the weight of food in the headline CPI and its adjustment speed to the core CPI are identified as α = C 1 + C 2 • T = 0.483 301 and β = C 3 + C 4 • T = 0.217 916, respectively. Fig. 4.3 illustrates the recursive system formed from the adjustment equation Δ logP Core = β • (logP – logP Core) and the intertemporal equation of motion log P t = α • logP – α • (1 – β) • P αβ) log + (1 – • logP t – 1 for simulating the headline CPI’s dynamic response to food price shocks at an inflation rate of 1%. Within the 15-year range of dynamic forecast, the dynamic impact of temporary food inflation shocks gradually wanes, nearly disappearing at the end of the period, whereas that of permanent food inflation shocks increasingly accumulates, approaching complete pass-through toward the end.8 Fig. 4.3

Dynamic effects of food inflation shocks on China’s CPI

1.00 0.75 % 0.50 0.25 0.00

1

3

5

7

9

11

13

15

Headline/Temporary Core/Temporary Headline/Permanent Core/Permanent

Econometrics Appendix The inflation of both the GDP deflator, which represents the prices of all products, and RPI, which represents the prices of final goods, during 1984 to 2009 was an intertemporally stable second-order autoregressive process. The

dummy variable is defined as DUM = 0 in from 1984 to 1996 and DUM =1 from 1997 to 2009, to distinguish between Phases II and III:

56

Structural and Cost-Push Inflation in China

πtGDPPI = 6.440 037 + 0.698 780 •

– 0.416 917 •

(3.668 706) (3.407 155)

(–2.312 877)

• DUM t (–2.716 667)

(4–23)

R 2 = 0.622 625, adj. R 2 = 0.571 164, SE = 3.416 415, DW = 1.740 414 ; = 7.745 686 + 0.653 390 • (4.031 785) (3.539 787)



– 0.487 081 • (–2.959 332)

– 7.210 614 • DUM t (–3.417 649)

R 2 = 0.688 522, adj. R 2 = 0.646 047, SE = 4.101 243, DW = 1.977635.

(4–24)

Table 4.5 presents a short-run error correction model associated with the cointegration test to obtain the long-run equilibrium relationships between the CPI, PPI, and RMFPPI inflation rates from 1990 to 2009 in the presence of a time trend, where T equals 1 in 1981 and 29 in 2009. Although the CPI inflation rates demonstrate a time lag in the first period, the PPI and RMFPPI inflation rates serially induce reverse adjustments to them in two consecutive periods, implanting a mean reversion mechanism into the error correction model. Table 4.6

Short-run error correction model of China’s inflation Δ

C0

= C 0 • ec t + C 2 • Δ

C2

C4

2

CPI

+ C 3 • Δx t – 1 + C 4 • Δx t – 2 + C 5

–2.589 269 (–7.186 28)

–1.796 300 (–6.202 81)

0.133 858 (0.902 25)

–0.114 664 (–0.909 45)

–0.890 486 (–4.467 86)

–0.519708 (–4.164 09)

–0.695 702 (–1.659 11)

C5

+ C2 • Δ

ec = EC RMFPPI/x = π RMFPPI

–1.420 470 (–5.054 44)

C3

CPI

ec = EC PPI / x = π PPI

1.841 290 (6.027 47)

C1

R

CPI

1.296 001 (5.488 11)

–0.796 745 (–4.408 78)

–0.567 840 (–1.232 08)

0.919 507

0.901 572

Adj. R

0.882 920

0.856 832

SE

1.713 681

1.895 008

2

57

Theoretical System of China's Macroeconomic Analysis

EC

=π

EC

– 0.810 951 • π (– 25.752 3)

=π

+ 0.219 014 • (T + 2) – 6.221 351 (6.664 17)

– 0.658 271 • π

(4–25)

+ 0.317 252 • (T + 2) – 7.774 349

(– 17.808 1)

(6.874 95)

(4–26)

Data Appendix The PPI inflation rates of China’s industrial goods in Table 4.7 are accessible

in China Statistical Yearbook 2010 , where they are presented in 14 sub-sectors

of industry. They are then smoothed through a three-year moving average

in an attempt to eliminate the influence of economic fluctuations. Because of industrial structural changes, the conventional combination-and-decompositionbased index algorithm is inapplicable to the averaged PPI inflation rates from 1986 to 1989. Table 4.7

PPI inflation rates of China’ s industrial goods (%)

Industry

Annual inflation rate (3-year moving average) 1986

1987

1988

1989

Overall

6.800 0

8.900 0

13.833 3

12.566 7

Metallurgical

9.566 7

9.933 3

14.466 7

15.566 7

Electric power

2.966 7

2.400 0

3.566 7

5.000 0

Coal

5.733 3

3.400 0

8.533 3

9.666 7

Petroleum

5.266 7

5.133 3

6.400 0

7.433 3

Chemical

6.000 0

11.833 3

17.333 3

13.800 0

Machine manufacturing

6.500 0

6.500 0

12.633 3

11.933 3

Building material

11.566 7

10.900 0

14.200 0

12.200 0

Timber

22.300 0

23.866 7

26.733 3

9.966 7

Food

5.800 0

9.400 0

13.333 3

10.533 3

Textile

5.066 7

11.066 7

17.666 7

17.300 0

Tailoring

4.900 0

8.600 0

14.900 0

14.733 3

Leather

5.566 7

6.333 3

11.866 7

13.000 0

10.500 0

12.833 3

18.600 0

15.333 3

7.600 0

10.566 7

14.366 7

10.133 3

Paper Cultural, educational & handicraft

58

Structural and Cost-Push Inflation in China

Based on data from China Statistical Yearbook 2010 , Table 4.8 calculates the

GDP deflators, labor productivity rates, and wage shares of the industrial sector from 1981 to 2009 using the following formulas:

P t = (GDPt/Y t)/(GDP1985/Y 1985) • 100;

(4–27)

N t = (L t + L t – 1)/2, y t = (Y t/N t)/(Y 1985/N 1985) • 100;

(4–28)

ρt = (W t/GDPt)/(W 1985/GDP1985) • 100.

(4–29)

Table 4.8

GDP deflators, labor productivity rates, and wage shares of China’s industrial sector

GDP Labor force Wage Current Constant Deflator Size at year Average Productivity Current Share Year (RMB (1978=100) (1985=100) end size (1985=100) (RMB billion) (million) (million) billion) GDP

Y

P

L

N

y

W

ρ

1981

204.84

124.5

93.587 5

6,975.0

6,844.50

75.474 0

55.50

0.185 462

1983

237.56

144.5

93.522 1

7,397.0

7,300.50

82.120 0

57.28

0.176 040

1982 1984 1985 1986 1987

1988

216.23 278.90 344.87 396.70 458.58

577.72

131.7 166.0

93.400 3 95.597 2

196.2

100.000 0

243.6

107.095 2

215.2 280.8

104.913 1 117.063 7

7,204.0 7,930.0 8,349.0 8,980.0 9,342.0

9,661.0

7,089.50 7,663.50

77.071 6 89.850 1

8,139.50

100.000 0

9,161.00

110.317 5

8,664.50 9,501.50

102.998 6 122.587 2

56.61 67.44 76.73 86.53

93.58

0.185 589 0.185 299 0.181 083 0.188 984

0.186 947

111.82 0.183 913

1989

648.40

295.0

125.063 6

9,569.0

9,615.00

127.263 9

127.18 0.188 587

1991

808.71

348.8

131.937 1

9,947.0

9,822.50

147.280 8

157.88 0.191 762

1990

685.80

1992 1,028.45 1993 1,418.80

1994 1,948.07 1995 2,495.06 1996 2,944.76 1997 3,292.14

1998 3,401.84

304.9 422.6 507.5

603.5 688.2 774.3 861.9

938.6

127.987 3

9,698.0

138.473 3

10,219.0

10,083.00

183.678 2

10,774.0

10,620.50

159.072 9

206.285 6 216.403 2 217.328 5

206.217 3

10,467.0

10,993.0 10,938.0 10,763.0 9,323.0

1999 3,586.15

1,018.6

200.328 9

9,061.0

2001 4,358.06

1,215.2

204.056 9

8,932.0

2000 4,003.36

1,118.3

9,633.50

203.701 0

8,923.7

10,343.00

10,883.50 10,965.50 10,850.50

10,043.00 9,192.00 8,992.37 8,927.89

131.277 0 173.848 0 203.526 0

235.691 9 262.292 6 292.887 1 329.499 0

387.675 1 459.639 0 515.821 5 564.592 8

142.52 0.200 204 178.84 0.175 332 215.65 0.157 211

268.90 0.146 596 319.16 0.139 219 341.02 0.126 988 353.58 0.116 535

351.65 0.103 814 389.24 0.099 770 415.89 0.093 417 440.53 0.090 247

59

(Cont'd) GDP Labor force Wage Year Current Constant Deflator Size at year Average Productivity Current Share (RMB (1978=100) (1985=100) end size (1985=100) (RMB billion) (million) (million) billion) GDP Y P L N y W ρ 2002 4,743.13

1,336.4

201.951 4

9,155.4

9,043.74

2004 6,521.00

1,680.2

220.832 4

3,852.1

3,809.21

1,829.610 2 1,521.33 0.088 868

4,101.94

2,139.833 1 1,987.68 0.089 292

2003 5,494.55 2005 7,723.08 2006 9,131.09

1,506.8 1,874.7 2,116.1

2007 11,053.49

2,431.5

2009 13,523.99

2,906.4

2008 13,026.02

2,673.0

207.488 7 234.404 4 245.525 2 258.658 5 277.281 9

264.766 2

3,766.3 4,020.0 4,183.8 4,303.8 4,281.2

4,353.3

6,460.88 3,936.08 4,243.82 4292.49

4317.21

612.932 0 967.360 9

473.21 0.090 227 771.96 0.090 772

1,975.619 3 1,735.15 0.088 432 2,376.609 8 2,298.49 0.088 247

2,582.995 3 2,685.29 0.088 489

2,792.426 8 2,935.87 0.093 721

Part III

The Open Economy

5

Chapter

China’s Economic Fluctuations: Demand Driver and International Coupling

Theoretical System of China's Macroeconomic Analysis

Coupling and Decoupling For a long time before the outbreak of the American subprime crisis, the synchronization of business cycles between developed and developing economies was fading out, although the center-periphery pattern of international economic growth had continued. Paradoxically, a “decoupling” phenomenon rose against the backdrop of economic globalization. The typical study by Kose, Otrok, and Prasa (2008) demonstrated that between 1985 and 2005, the business cycles of newly emerging and developed economies went on increasingly separate courses while the two types of markets each converged among themselves. After 2007, when the American economy was hard hit by recession from the burst of the real estate bubble, the question of whether the emerging economies could continue to decouple from the American business cycle and lead an independent path of expansion became an urgent and practical issue, rekindling enthusiasm in the coupling and decoupling theme in the field of macroeconomics. Unfortunately, perhaps due to the methodological defects of the simple extrapolation of historical tendencies, mainstream theoretical and policy research often underestimated the profound impact of the crisis and mistakenly affirmed the possibility of further decoupling. Even at the trough of the subprime crisis in mid-2008, Federal Reserve Board Vice Chairman Donald Kohn still expressed cautious optimism about the sustainability of decoupling, citing evidence from the international specialization of production, the limited spillover effects of the housing implosion, and the soundness of economic policies and financial institutions in emerging economies (Kohn 2008). A partaker in the economic globalization process, China adopted an exportoriented development model and trade policy while implementing marketoriented institutional transformation. Its accession to the WTO made it an important growth pole and engine of the world economy alongside the U.S. in the early 2000s. Contrary to the decoupling hypothesis, the business cycles of China and the U.S., as representatives of the emerging and developed markets, respectively, were basically synchronized before 2005. While temporarily set apart between 2006 and 2007, when China experienced vibrant growth and contraction went on in America, they went back to synchronization from 2008 onward, as illustrated in Fig. 5.1. The temporary, pre-crisis optimism from decoupling misled macroeconomic analysis in China to neglect the serious impact of the American subprime crisis on the world economy as well as China’s economy, and as a result, delayed the implementation of easing policies in demand management.

64

China’s Economic F luctuations: Demand Driver and International Coupling

Fig. 5.1

China GDP growth and U.S. GDP gaps 14

8

12

4

% 10

0 –4

8 6

%

1Q00 1Q01 1Q02 1Q03 1Q04 1Q05 1Q06 1Q07 1Q08 1Q09 China GDP (5QMA)

–8

U.S. GDP gap

It was only after the American subprime crisis had developed into a global financial crisis that more substantial theoretical insights were given into the role of international finance as the crisis’s transmission mechanism. Nonlinear transmission models of international financial shocks were built to interpret emerging economies’ transition from decoupling to recoupling, such as the hypotheses of decoupling under small shocks by Dooley and Hutchison (2009) and coupling under large shocks by Korinek, Roitman, and Végh (2010). Despite this, most analyses on the transmission of the crisis through international trade were carried out from an empirical perspective. For instance, Grossman and Meissner (2010) compared the 21st-century collapse of international trade with the similar breakdown during the Great Depression, and Haddad, Harrison, and Hausman (2010) gave a summary of the stylized facts of the collapse. By dividing national income into investment, consumption, and net exports, SNA statistics allows for the classification of economic growth according to the type of demand by which it is driven. This, however, fails to distinguish between internal and external demand-driven economic fluctuations. 2 Fig. 5.2 illustrates the conventional approach to empirical proof of the reaction of China’s economy to America’s economic conditions, which tests three hypotheses in sequence: (1) U.S. economic expansion promoting demand for imports in the U.S.; (2) U.S. import demand increasing demand for exports in China; and (3) China’s export demand boosting the expansion of China’s economy. This approach is problematic as its hypothesis on international trade and the coupling of international business cycles is not backed up by theoretical foundations of economic fluctuations. To fill in the gap, this chapter begins by

65

Theoretical System of China's Macroeconomic Analysis

formalizing the co-movement between national income and international trade when reacting to demand shocks and presenting an econometric approach to identifying the demand drivers of economic fluctuations based on international trade cycles. It then identifies the nature of China’s and America’s economic fluctuations by extracting the cyclical components of national income and international trade via the residual method, before analyzing the transmission of business cycles from the U.S. to China through international trade. Fig. 5.2

Empirical hypothesis on the reaction of China’s economy to the U.S. economy H1

U.S. national income

H2 U.S. import demand

H3 China’s export demand

China’s national income

The Cyclical Behavior of National Income and International Trade Effects of demand shocks on national income and international trade For an open economy composed of the three sectors of households, firms, and the government, we can define national income as

Y = C + I + (X – M ), (5–1) where the consumption function (C ), investment function (I ), export function (X ), and import function (M ) are, respectively, given by

C = C (Y ); (5–2) I=

+ u ; (5–3)

X=

+ v ; (5–4)

M = M (Y ). (5–5) Moreover, let the marginal propensity to consume be c , and 0 < c < 1; the marginal propensity to import be m , and 0 < m < 1. In the equations above, the independent and identically distributed (i.i.d.) random variables u and v represent internal and external demand shocks, respectively, each carrying a variance: σu2 and σv2. The equilibrium national income under effective demand constraints can

66

China’s Economic F luctuations: Demand Driver and International Coupling

thus be expressed as

Y = C (Y ) + ( + u ) + ( + v ) – M (Y ). (5–6) Therefore, dY = (c – m ) • dY + du + dv ;

(5–7)

dY = (du + dv )/(1 – c + m ).

(5–8)

In the condition for dynamic stability where 1 – c + m > 0, dY /du > 0 and dY / dv > 0, i.e. both internal and external demand shocks impact positively on national income. The net export the equilibrium can be expressed as

X –M =

+ v – M (Y ). (5–9)

Hence, d(X – M ) = dv – m • dY , (5–10) and in view of dY = (du + dv )/(1 – c + m ), d(X – M ) = ((1 – c ) • dv – m • du )/(1 – c + m ). (5–11) Thus, d(X – M )/du < 0 and d(X – M )/dv > 0, i.e., internal demand shocks impact negatively on net exports while external demand shocks impact positively on net exports.

Types of demand shocks and modes of economic fluctuations Based on the above formulas, the steady-state national income and steady-state net exports can be expressed as

Y * = C (Y *) + (X – M )* =

+

– M (Y *); (5–12)

– M (Y *). (5–13)

Hence, the equation for the deviation cycles of national income is given by

Y – Y * = (C (Y ) – C (Y *)) + u + v (M (Y ) – M (Y *)), (5–14) and its first-order Taylor approximation formula is

Y – Y * = c • (Y – Y *) + u + v – m • (Y – Y *),

(5–15)

67

Theoretical System of China's Macroeconomic Analysis

so

Y – Y * ≈ (u + v )/(1 – c + m ). (5–16) As for the deviation cycles of net exports, the equation can be written as (X – M ) – (X – M )* = v – (M (Y ) – M (Y *)), (5–17) and its first-order Taylor approximation formula (X – M ) – (X – M )* = v – m • (Y – Y *),

(5–18)

(X – M ) – (X – M )* ≈ ((1 – c ) • v – m • u )/(1 – c + m ).

(5–19)

so

Therefore, the covariance between net exports and national income is given by cov(X – M , Y ) = ((1 – c ) • σv2 – m • σu2)/(1 – c + m )2. (5–20) If only internal demand shocks exist, i.e., when σu2 > 0 and σv2 = 0, cov(X – M , Y ) < 0, implying countercyclical net exports. If only external demand shocks exist, i.e., when σu2 = 0 and σv2 > 0, cov(X – M , Y ) > 0, implying procyclical net exports. In the presence of both internal and external demand shocks, where σ u2 > 0 and σ v2 > 0, cov(X – M , Y) and the nature of cyclical net exports are indeterminate. They are dependent on the comparative strength of internal to external demand shocks (σ u2/σ v2), and the comparative extent of the marginal propensity to save to the marginal propensity to import ((1 – c )/m ). The demand driver of China’s economic fluctuations is identifiable based on the correlation coefficient between the cyclical components of net exports (nx ) and national income (y ), ρ(nx , y ), in which nx = (X – M ) – (X – M )*) and y = Y – Y *. The fluctuations are driven by internal demand if ρ(nx , y ) < 0, but by external demand if ρ(nx , y ) > 0.3

China’s External-Demand-Driven Economic Fluctuations For a static forecast of China’s real GDP time trend, estimate the log-linear equation logY CN,t = α + β • T for 1978 to 2009, setting T as 1 in 1981 and 29 in 2009, based on the time series Y CN. This generates a potential GDP time series (Y *CN) under the assumption of natural growth, where the natural growth rate (β) is 9.486%. Then, generate another Y *CN by applying the H–P filter to Y CN.

68

China’s Economic F luctuations: Demand Driver and International Coupling

Thus, the time series of China's (relative) GDP gap under the natural growth assumption and through the H–P filter can be derived from the relative income gaps, which serve to measure China’s business cycle:4

y CN = (Y CN – Y *CN)/Y *. (5–21) To obtain the time series of relative trade surplus nx CN, which will serve as the net exports to GDP ratio, estimate the log-linear equation nx CN,t = C 0 + C 1 • logY *CN, t + C 2 • loge CN,t + C 3 • Δloge CN,t + 1 by OLS for 1981 to 2009 based on Y *CN both under the natural growth assumption and the H–P filter, as illustrated in Table 5.1. The variables for the RMB real effective exchange rate (REER, e CN) and potential GDP (Y *CN), respectively, reflect the substitution effect of the real exchange rate and the income effect of potential national income.5 Based on the regression equation in Table 5.1, generate the time trend of nx CN (nx *CN) across the Y *CN scenario by static forecast, and establish the time series of the net export gap making use of its residual, which reflects China’s trade cycle:

nx cCN = nx CN – nx *CN. (5–22) Table 5.1

Estimation of China’s net export time trend nx CN,t = C 0 + C 1 • logY *CN, t + C 2 • loge CN,t + C 3 • Δloge CN,t + 1 Natural growth

H–P filter

C0

–32.498 39 (–2.9443 67)

–32.087 72 (–2.898 906)

C1

3.017 030 (4.6024 11)

2.973 684 (4.549 947)

C2

3.231 808 (1.925 644)

3.320 062 (1.897 055)

C3

8.644 338 (2.573 256)

8.666 921 (2.566 138)

R2

0.636 457

0.632 462

Adj. R 2

0.589 038

0.584 522

SE

1.798 659

1.808 513

DW

0.922 988

0.914 843

The time paths of China’s GDP gaps (y CN) and net export gaps (nx cCN) during 1981 to 2009 are plotted in Fig. 5.3, which exhibits that the trends of fluctuations

69

Theoretical System of China's Macroeconomic Analysis

between trade surplus and national income were contrary before the mid1990s, but similar since the late-1990s. To further illustrate the relationship between national income and trade cycles, Table 5.2 presents a cross-correlation analysis on this subject. As a whole, their cross-correlation was weakly negative throughout the period. Dividing the period into two halves before and after 1997 will consolidate the observation from Fig. 5.3: a strongly negative crosscorrelation from 1981 to 1996, but a strongly positive one between 1997 and 2009. To make it plain, economic fluctuations in China were mainly driven by internal demand shocks in the former period but external demand shocks in the latter period. In other words, China’s economic fluctuations in the present days are primarily external-demand-driven. Table 5.2

Cross-correlation analysis of China GDP gaps and net export gaps ρ(y CN,t , nx cCN,t + i)

i = –1

i=0

i=1

Natural growth 1981–2009

–0.094 1

–0.222 1

–0.048 9

1981–1996

–0.563 6

–0.837 5

–0.451 0

1997–2009

0.768 3

0.867 2

0.525 6

1981–2009

–0.008 8

–0.136 5

–0.001 6

1981–1996

–0.592 3

–0.850 5

–0.429 7

1997–2009

0.796 6

0.832 7

0.458 7

H–P filter

Fig. 5.3 (a) China GDP gaps and net export gaps: natural growth 8 4 % 0

yCN

70

nxcCN

2009

2007

2005

2003

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

–8

1981

–4

China’s Economic F luctuations: Demand Driver and International Coupling

Fig. 5.3 (b) China GDP gaps and net export gaps: H–P filter 8 4 % 0

yCN

2009

2007

2005

2003

2001

1999

1997

1995

1993

1991

1989

1987

1985

1983

–8

1981

–4

nxcCN

U.S. Economic Fluctuations and International Propagation Internal-demand driven economic fluctuations Make a static forecast of the U.S. GDP gaps (y US) through OLS by fitting the third-order polynomial equation y US,t = α + β • T 2 + γ • T 3 for 1981 to 2009, with T being 1 in 1981 and 29 in 2009, which generates the time trend y *US, and build its second-order time series from the residuals of the original time series:

y cUS = y US – y *US. (5–23) Regarding the relative trade surplus, fit the log-linear model nx US,t = C 0 + C 1 • logY *US, t + C 2 • loge US,t + C 3 • Δloge US,t + 1 for 1981 to 2009 by OLS, with the variables for the USD REER (e US) and potential GDP (Y *US) reflecting the substitution effect of the real exchange rate and the income effect of potential national income, respectively, in order to obtain the time trend nx *US. 6 Then, establish its time series from the residuals of the forecast equation for measuring the U.S. trade cycle:

nx cUS = nx US – nx *US. (5–24) Hence,

nx US,t = 95.498 60 – 4.802 129 2 • logY (6.977 973) (– 6.630 073) • Δloge US,t + 1

– 11.745 87 • loge US,t + 13.677 71 (– 4.821 017) (3.752 536)

R 2 = 0.738 249, adj. R 2 = 0.705 530, SE = 0.898 754, DW = 1.145 303.

(5–25)

71

Theoretical System of China's Macroeconomic Analysis

The time paths of the U.S. GDP gaps (y cUS) and net export gaps (nx cUS) from 1981 to 2009 are plotted in Fig. 5.4, and their cross-correlations are presented in Table 5.3. The fluctuations of net export and national income cycles were in reverse throughout the period; likewise, the GDP gaps and export gaps were negatively correlated. Therefore, U.S. economic fluctuations were mainly driven by internal demand shocks. Furthermore, the cyclical coincidence between trade and national income fluctuations, as was the negative correlation between the GDP and net export gaps, increased in stages, from the early phase of 1981 to 1996 to the more recent phase of 1997 to 2009. Fig. 5.4

U.S. GDP gaps and net export gaps 4 2

% 0

yCN

Table 5.3

2009

2007

2005

2003

2001

1999

1995

1997

nxcCN

Cross-correlations of U.S. GDP gaps and net export gaps

i = –1 1981–2009

1993

1991

1989

1987

1985

1983

–4

1981

–2

–0.184 0

ρ(y cUS,t , nx cUS,t + i)

i=0

i=1

–0.459 6

–0.240 9

1981–1995

–0.298 7

–0.392 1

–0.323 2

1996–2009

–0.100 9

–0.595 0

–0.134 2

International trade: the international propagation mechanism of U.S. economic fluctuations Under the assumption that U.S. exports to China (NX US–CN) are determined by

U.S. net exports (NX US) and the real exchange rate between the USD and RMB

(e US–CN), estimate the test equation NX US–CN,t = C 1 • NX US–CN,t + C 2 • NX US,t + C 3

• log(e US–CN,t/e US,t) by OLS for 1985 to 2009. Taking the trade terms between

72

China’s Economic F luctuations: Demand Driver and International Coupling

the two economies into consideration, it is found that the U.S. trade surplus positively determines the U.S. trade surplus with China:

NX US–CN,t = 0.819 840 • NX US–CN,t + 0.082 463 •NX US,t – 56.709 70 • (25.572 55) (9.008 805) (– 4.354 919) log(e US–CN,t/e US,t) R 2 = 0.993 908, adj. R 2 = 0.993 328, SE = 6.765 138, DW = 1.575 419.

(5–26)

Furthermore, under the assumption that the size of world trade (EX W ) is determined by the size of U.S. trade (X US + M US) and the USD REER (e US), estimate the test equation logEX W,t = C 0 + C 1 • log((X US,t + M US,t)/P US,t) + C 2 • nx US,t + C 3 • loge US,t by OLS for 1981 to 2009. Taking into consideration the size of U.S. trade and terms of trade, it is found that the U.S. trade surplus negatively determines the size of world trade: logEX W,t = 8.392 527 + 0.756 428 • log((X US,t + M US,t)/P US,t) – 0.091 521 • (7.536 265) (10.438 13) (– 5.839 758) nx US,t – 1.185 289 • loge US,t + [AR (1) = 1.200 729, AR (2) = –0.535 564] (– 5.183 260) (6.142 664) (–2.744 607)

R 2 = 0.990 827, adj. R 2 = 0.988 742, SE = 0.048 851, DW = 2.035 394.

(5–27)

Now, assume that China’s trade surplus (NX CN) is determined by the U.S. trade surplus with China (NX US–CN), the size of world trade (EX W), and the RMB REER (e CN), and estimate the test equation NX CN,t = C 0 + C 1 • NX CN,t – 1 + C 2 • NX US–CN,t + C 3 • ΔEX W,t + C 4 • Δe CN,t + C 5 • Δe CN,t – 1 using data from 1997 to 2009. Considering China’s terms of trade, it can be concluded that the U.S. trade surplus negatively determines China’s trade surplus, while the size of world trade positively determines it:

NX CN,t = –55.536 82 + 0.412 596 • NX CN,t – 1 –0.612 773 • NX US–CN,t + 0.017532 (–3.413461) (3.024010) (–3.918125) (3.873618) • ΔEX W,t + 3.408 075 • Δe CN,t + 3.721 439 • Δe CN,t – 1 (2.825836) (2.604171) R 2 = 0.977 618, adj. R 2 = 0.961 631, SE = 17.308 93, DW = 1.821 157.

(5–28)

In an economic setting where fluctuations of China’s economy are primarily driven by external demand, and U.S. economic fluctuations are largely internaldemand-driven, the conditions of the U.S. economy are propagated through

73

Theoretical System of China's Macroeconomic Analysis

international trade, leading to the coupling between China’s and the U.S.’s economic cycles, as illustrated by Fig. 5.5.7 Fig. 5.5

International propagation of U.S. economic fluctuations onto China

eUS XUS + MUS

f (XUS + MUS , NXUS , eUS)

eCN

YUS NXUS

f (NXUS–CN , EXW , eCN)

EXW f (NXUS , eUS–CN)

NXCN

NXUS–CN

YCN

eUS–CN

Non-Walrasian Equilibriums Since the American subprime crisis, mainstream macroeconomics has

come to resurrect Keynesianism, from which practical, problems-oriented research programs are developed, as policy lessons are drawn from the Great

Depression. The typology of non-Walrasian equilibriums by the school of disequilibrium economics, a fundamentalist form of Keynesianism, proves

conducive for the interpretation of the structural and cyclical nature of China’s

economy. Table 5.4 outlines the appearance of non-Walrasian equilibrium patterns in different phases of China’s economy from the perspective of the

Barro–Grossman–Malinvaud (BGM) model (Benassy 1986; Yang 1994). On the

basis of the classical unemployment equilibrium, China’s business cycle has graduated from the repressed inflation equilibrium of the planned economy

era and is alternating between the Keynesian unemployment equilibrium in

times of depression and the under-consumption equilibrium during prosperity in today’s market economy, in sync with international business cycles.

Placing such economic equilibriums in a structural spectrum, the

continuation of a dual-sector economy implies the excess of labor supply as

well as capital demand in the long run. That is to say, classical unemployment

74

China’s Economic F luctuations: Demand Driver and International Coupling

in the agricultural and non-agricultural sectors is neither related to the economic situation nor to the economic system.

The cyclical spectrum reveals a different perspective. Regarding the non-

agricultural sector in a market economy, under-consumption during prosperity denotes excess labor demand, which is manifested in the rapid migration of surplus rural labor, as opposed to Keynesian unemployment during depression,

which is expressed in the slow, or even reverse migration of surplus rural labor. Under-consumption only suggests excess domestic production in relation to domestic demand; when measured against international demand, goods supply

is deficient. This accounts for the phenomenon of excess labor demand in spite of surplus goods in the domestic market.

Table 5.4  Non-Walrasian equilibriums in China’s economy Spectrum Cyclical

Market economy Depression

Prosperity

Planned economy

Keynesian unemployment

Under-consumption

Repressed inflation

Structural

Classical unemployment Goods market D

Labor market

Keynesian unemployment

Y LS

Classical unemployment

YD > YS

LD < LS

Repressed inflation

YD > YS

LD > LS

Data Appendix Table 5.5 presents the definitions, original sources, and formulas of the basic data for econometric analysis in Chapter 5. Table 5.5 (a) displays the primitive variables, based on which the model variables are defined in Table 5.5 (b),

while being normalized or deflated. Original sources of the primitive variables

include China Statistical Yearbook 2010 by the National Bureau of Statistics

of China (NBS); “Federal Reserve Economic Data” (FRED), database of the

Federal Reserve Bank of St. Louis; “International Financial Statistics” (IFS) by the International Monetary Fund (IMF); and the online statistical database of

the World Trade Organization (WTO). In the table, the sources are identified with the parenthesized abbreviations as noted.

75

Theoretical System of China's Macroeconomic Analysis

Table 5.5 (a)  Primitive variables and data sources Definition

Unit

Source

GDPCN

China’s nominal GDP

100 million, current RMB

NBS

Y CN

China’s real GDP

1978 = 100

NBS

GDPUS

U.S. nominal GDP

Billon, current USD

FRED

Y US

U.S. real GDP

Billion, chained 2005 USD

FRED

y US

U.S. GDP gap

%

FRED

X CN

China’s nominal exports

Billion, current USD

WTO

M CN

China’s nominal imports

Billion, current USD

WTO

X US

U.S. nominal exports

Billion, current USD

FRED

M US

U.S. nominal imports

Billion, current USD

FRED

X US–CN

U.S. nominal exports to China

Billion, current USD

FRED

M US–CN

U.S. nominal imports from China

Billion, current USD

FRED

X MW

World nominal merchandise exports

Billion, current USD

WTO

XS W

World nominal commercial service exports

Billion, current USD

WTO

E US–CN

Nominal exchange rate between USD and RMB

RMB per USD

FRED

e CN

RMB REER

2005 = 100

IFS

e US

USD REER

2005 = 100

IFS

Table 5.5 (b)  Model variables and calculation formulas Definition

Formula

Unit

P CN

China’s GDP deflator

P CN,t = (GDPCN,t/Y CN,t)/(GDPCN,2005/ YCN,2005)

2005 = 100

P US

U.S. GDP deflator

P US,t = (GDPUS,t/Y US,t)/(GDPUS,2005/ Y US,2005)

2005 = 100

NX CN

China’s real net exports

NX CN = (NX CN – M CN)/P US

Billion, chained 2005 USD

nx CN

China’s relative trade surplus

nx CN = (NX CN – M CN)•E US–CN/GDPCN

%

NX US

China’s relative trade surplus

NX US = (NX CN – M CN)/P US

Billion, chained 2005 USD

76

China’s Economic F luctuations: Demand Driver and International Coupling

(Cont'd)

nx US

Definition

Formula

Unit

U.S. relative trade surplus

nx US = (X US – M US)/GDPUS

%

NX US–CN

U.S. real net exports to China

EX W

World real exports EX W = (XM W + XS W)/P US

e US–CN

Real exchange rate between USD and e US–CN = E US–CN • P US/P CN RMB

NX US–CN = (X US–CN – M US–CN)/P US

Billion, chained 2005 USD Billion, chained 2005 USD Billion, chained 2005 USD

Table 5.6 illustrates the time series of China and U.S. GDP gaps and net

export gaps to facilitate the measurement of the national income and trade cycles. The U.S. figures are second-order detrended. Table 5.6

China and U.S. GDP gaps and net export gaps (%) China Natural growth

U.S. H–P filter

GDP gap y CN

Net export gap nx cCN

GDP gap y CN

Net export gap nx cCN

GDP gap y cUS

Net export gap nx cCN

1981

–4.212 828

––

–2.161 738

––

1.215 880

––

1982

–5.033 063

1.005 074

–4.016 399

1.005 074

–3.496 761

–0.259 383

1983

–4.218 106

0.806 698

–3.799 838

0.806 698

–2.291 411

–0.636 024

1984

0.334 126

0.470 419

0.431 147

0.470 419

1.318 971

–0.039 371

1985

3.546 319

–2.247 165

3.530 340

–2.247 165

1.804 559

0.223 296

1986

2.523 735

–1.857 333

2.576 249

–1.857 333

1.654 752

–1.565 855

1987

4.035 150

–0.755 340

4.278 709

–0.755 340

1.387 793

0.115 455

1988

5.281 370

–2.828 130

5.776 914

–2.828 130

2.055 883

–0.480 091

Year

1989

–0.353 287

0.622 710

0.330 097

0.622 710

2.220 916

–0.026 558

1990

–5.897 162

3.621 244

–5.157 557

3.621 244

0.791 945

–0.622 619

1991

–6.544 494

3.117 277

–5.875 816

3.117 277

–2.454 545

0.575 041

1992

–2.899 156

0.220 368

–2.418 702

0.220 368

–2.003 299

0.602 302

1993

0.627 806

–0.583 020

0.830 417

–0.583 020

–2.096 035

0.991 244

1994

3.498 258

0.038 553

3.413 742

0.038 553

–1.147 602

–0.019 763

1995

4.422 115

–0.113 882

4.110 616

–0.113 882

–1.766 147

0.287 135

77

Theoretical System of China's Macroeconomic Analysis

(Cont'd) China Year

2000

Natural growth

U.S. H–P filter

GDP gap y CN

Net export gap nx cCN

GDP gap y CN

Net export gap nx cCN

GDP gap y cUS

Net export gap nx cCN

–1.689 662

–1.529 374

–2.504 602

–1.529 374

2.574 000

0.391 075

2001

–3.162 121

–1.680 015

–4.196 174

–1.680 015

0.082 277

0.873 521

2002

–3.921 167

–1.114 165

–5.222 915

–1.114 165

–1.303 129

0.288 652

2003

–3.856 522

–2.128 662

–5.421 708

–2.128 662

–1.545 635

–0.063 621

2004

–3.740 673

–2.418 290

–5.470 010

–2.418 290

–0.221 067

–0.322 508

2005

–2.549 270

0.050 314

–4.257 550

0.050 314

0.921 752

–1.117 239

2006

–0.135 863

1.564 295

–1.513 736

1.564 295

1.781 306

–1.533 264

2007

3.693 174

1.955 363

3.110 970

1.955 363

1.964 185

–1.354 819

2008

3.394 578

0.881 936

4.219 307

0.881 936

0.451 951

–1.011 496

2009

2.603 745

––

5.421 418

––

–3.091 248

1.786 806

78

6

Chapter

The Dynamic Purchasing Power Parity Theory: Conception, Evidence, and Prediction

Theoretical System of China's Macroeconomic Analysis

Static Purchasing Power Parity Purchasing power parity (PPP) is an equilibrium condition that holds when the law of one price (LOP) is enforced by international arbitrage in tradable goods. According to Dornbusch (1987)’s survey, the PPP theory first originated in the Spanish School of Salamanca in the 16th century, and then underwent further development in English bullionism, mercantilism, and classical economics during the 17th , 18th, and 19th centuries before its preliminary completion by Swedish economist Gustav Cassel (1918, 1922) in the 20th century. While it is related to the quantity theory of money and essentially falls within the spectrum of monetarism, the classical PPP theory has become the benchmark hypothesis of modern exchange rate theories.1 Open to theoretical and empirical falsification, the PPP theory continued expanding and deepening through the interaction between theoretical hypotheses and evidence. Soon after Cassel’s completion of the theory, Keynes (1923) judiciously perceived a major defect in the model: the overlooking of the nonprice transaction costs of tradable goods and the purchasing power of nontradable goods. Thereafter, major progress was found in the accommodation and conceptualization of these omitted factors, which led to the refinement of the theoretical framework and applicability of PPP as a long-run equilibrium exchange rate condition. The transportation costs, tariff and non-tariff barriers, pricing power of monopolistic firms, and other transaction costs in international trade were investigated to reveal the imperfect integration of international markets and the border effect resulting from departures from the LOP (Engel and Rogers 1996). At the same time, the Harrod–Balassa–Samuelson (HBS) effect, which considers the prices of non-tradable goods and productivity bias between the tradable and non-tradable good sectors, was conceived to provide partial explanation for the deviation of the actual exchange rates from the PPP level on the ground of international productivity differences.2 Over the years, the PPP puzzle of the enormous short-run volatility of real exchange rates versus the slow damping out of PPP deviations in the medium term seems to have dominated PPP research in the form of negative proof (Rogoff 1996). Nevertheless, under the assumption of imperfect markets and rational expectations, intertemporal exchange rate arbitrage tends to result in over-shooting adjustments, while nominal demand shocks bear real supply-side effects due to price stickiness. Moreover, movements in the exchange rate equilibrium have made the dynamic adjustment of exchange rates nonlinear mean-reverting at an evidently accelerated speed.3 Hence, not only can the temporary violation of the

80

The Dynamic Purchasing Power Parity Theory: Conception, Evidence, and Prediction

stable correlation between exchange rates and money supply as well as deviations of exchange rates from the PPP level reconcile with the PPP theory in the short run; PPP reversion is rather smooth and swift in the intermediate run. After repeated modifications of its long-run equilibrium condition and medium-run equilibration mechanism, exchange rate theories and historical evidence finally arrived at a positive consensus regarding PPP in the late 1990s. From the long-run perspective of the modern PPP theory, this chapter attempts to construct a dynamic PPP theory for developing economies, in order to depict the systemic PPP deviations of exchange rates, as well as their evolutionary trends, during economic development. Such a theory will be structuralist per se, as it will not only expand the modern PPP theory to model such structural factors as marketization, economic opening, and international economic integration, but also specify long-run structural relationships on the basis of historical experience, without the necessity of microeconomic and general equilibrium analyses.4 Articulated along the sequence of theoretical modeling, econometric analysis, and forecasting, the chapter first sketches an international economic system consisting of two dual-sector economies for capturing the structural properties of national and international economies, based on which an analytical framework of the dynamic PPP theory is founded to explore the evolutionary nature of exchange rates in developing economies. Subsequently, an econometric analysis of panel data on national income is conducted to establish a dynamic PPP econometric model while testing its theoretical structure. Finally, the model is refined into a computable dynamic PPP theory for a scenario-based forecast of the revaluation and/or appreciation trends of RMB exchange rates.

The Structural and Dynamic Framework The international economic system and structure of national economies The constructed international economic system consists of a developed country (W ) and a developing country (D ), each of which comprises a tradable sector (T ) and a non-tradable sector (N ). For country W , national production is given by N Q W = Q WT + Q W , (6–1)

whereas for country D ,

Q D = Q DT + Q ND. (6–2)

81

Theoretical System of China's Macroeconomic Analysis

Set the parameter λ to indicate the degree of marketization of the national economies, so that only λ portion of the production of the non-tradable sector (Q N) enters into the exchange market. For country W , λ 1; for country D , λ < 1. In SNA statistics, country W ’s real national income is expressed as

Y W = Q TW + Q NW, (6–3) and nominal national income

P W • Y W = P TW • Q TW + P NW • Q NW, (6–4) while country D ’s real and nominal national incomes are:

Y D = Q DT + λ • Q ND; (6–5) P D • Y D = P DT • QDT + λ • P ND • Q ND. (6–6) Then, set the parameter θ to indicate the openness of the national economies, so that only θ portion of national production (Q N) is tradable in the international market, i.e., θ = Q T/Q . Accordingly, the equations for countries W and D are: T θW = Q W /Q W; (6–7)

θD = Q DT /Q D.

(6–8)

Next is the parameter ρ that represents the relative price of tradable to nontradable goods in the national economies; ρ = P N/P T. For countries W and D , T ρW = P NW/P W ; (6–9)

ρD = P ND/P DT . (6–10) Lastly, create the parameter σ for international trade friction, so that at the nominal exchange rate (E ), the LOP only holds in the tradable sector when modified as follows:

E • P WT = (1 + σ) • P DT .5 (6–11)

The parametric expression of the real exchange rate For country W , given the accounting identity of nominal national income P W • Y W = P W • Q W = P WT • Q WT + P NW • Q NW, its price level is defined in parametric form as

P W = (1 – θW) • P NW + θW • P WT , (6–12)

82

The Dynamic Purchasing Power Parity Theory: Conception, Evidence, and Prediction

which also takes the expression of P W = (ρW • (1 – θW) + θW) • P WT . (6–13) For country D , with the accounting identity of nominal national income P D • Y D = P D • (Q DT + λ • Q ND) = P TD • Q DT + λ • P ND • Q ND, the price level is given by

PD =

λ • (1 – θD) • P ND + θD • P DT λ • (1 – θD) + θD

, (6–14)

and alternatively,

e =E •

P WT P DT

ρW • (1 – θW) + θW

•

λ • ρD (1 – θD) + θD λ • (1 – θD) + θD

.

(6–15)

To express the real exchange rate between countries D and W (e = E • (P W/ P D)) in a parametric equation,

e =E •

P WT P

T D

•

ρW • (1 – θW) + θW

λ • ρD (1 – θD) + θD λ • (1 – θD) + θD

(6–16)

T = (1 + σ) • P DT for Finally, substitute the equation of the modified LOP E • P W

P WT /P DT , and get the parametric equation of the real exchange rate: e = (1 + σ) •

ρW • (1 – θW) + θW

λ • ρD (1 – θD) + θD λ • (1 – θD) + θD

, (6–17)

which can be reduced using the surrogate parameter ηD:

e = (1 + σ) •

ρW • (1 – θW) + θW ρD • (1 – ηD) + ηD ,

(6–18)

where ηD = θD/(λ • (1 – θD) + θD).6

According to the quantity theory of money, the price level of an economy is

determined by money supply (M ) and real national income (Y ). For countries W and D , respectively, this gives the equations M W • V W = P W • Y W and M D • V D = P D • Y D. At a determined real exchange rate (e ), the nominal exchange rate between the two countries is determined by the domestic price levels P W and P D; therefore, E = e • (P D / PW).

83

Theoretical System of China's Macroeconomic Analysis

Comparative statics and dynamics on economic development Make the following suppositions about the evolution of country D ’s structural parameters λ, θ, ρ, and σ based on historical trends of economic development, economic opening, and economic globalization: (1) Although λ < 1, λ increases during economic development, so dλ/dt > 0. (2) Although θD < θW, θD increases during economic opening, so dθD /dt > 0. (3) Although ρD < ρW, ρD increases due to advantages of late development, so dρD/dt > 0.7 (4) Although σ > 0, σ decreases during economic integration into the global economy, so dσ/dt < 0. Table 6.1 shows a comparative static and dynamic analysis of country D ’s real exchange rate (e ) with country W with respect to the structural parameters λ, θ, ρ, and σ in order to demonstrate the operational significance of the dynamic PPP theory, which predicts that de /dθ D < 0, de /dρ D < 0, de /dσ > 0 and de / dλ > 0. Since a developing country is inferior to a developed country in terms of marketization, openness, globalization, and the efficiency of the tradable sector, country D ’s real exchange rate is perpetually undervalued relative to the conventional PPP criterion 1, i.e., e > 1, on account of λ, θ, ρ, and σ. Yet, with the country’s advancement in the respects of openness, globalization, and the efficiency of the tradable sector, its real exchange rate appreciates toward 1 over time; therefore de /dt < 0 on account of θ, ρ, and σ. At the same time, however, marketization has the effect of depreciating real exchange rates; therefore, de /dt > 0 on account of λ. Table 6.1

Comparative statics and dynamics on real exchange rates ρW • (1 – θW) + θW

Model

e = (1 + σ) •

Parameter

ηD

λ

θD

ρD

σ

ηD < θW

λ0

Statics

Dynamics

Property

ρD • (1 – ηD) + ηD

Prediction

e > 1*

uc **

λ↑

θD ↑

ρD ↑

σ↓

Prediction uc **

e↑

e↓

e↓

e↓

Property

* For the purpose of normalization only, assume ρW = 1. ** Uncertain.

84

The Dynamic Purchasing Power Parity Theory: Conception, Evidence, and Prediction

In the era of economic globalization, economic development and opening take place concurrently in developing countries. 8 Thus, it is reasonable to assume the rise of λ, θ, and ρ to coincide with the decline of σ during economic development, which can be epitomized by the growth of national income (per capita). In other words, in a developing country, the dynamics of its exchange rates are manifested in national-income-growth-driven appreciation toward 1. Fig. 6.1 depicts the synchronous evolution of the exchange rate and national income of country D during its convergence to country W . 9 Fig. 6.1

National incomes and exchange rates during economic catch-up Y

Y = YW

b

b

a

a Y = YD

45 ̊ e

1

0

Y/e

When country D succeeds in economic catch-up, the exchange rate (e ) arrives at 1 along the time path a in the quadrant e –Y , and the national income Y /e , measured in terms of the real exchange rate, reaches Y W along the time path a in the quadrant Y –Y /e . Because of the appreciation of the real exchange rate, Y /e asymptotically approaches the 45° line from below in Y –Y /e , which is disposed to overstate country D ’s economic growth rate. If the real exchange rate does not adjust monotonically, but overshoots following such a time path as b in Y –Y , Y /e will grow at a slower pace than Y , or even demonstrate negative growth at the initial stage of economic development, as b in Y –Y /e demonstrates.10

85

Theoretical System of China's Macroeconomic Analysis

An Econometric Model Based on International Panel Data The compound growth model Table 6.2 presents data on the gross national income (GNI) and gross national income at PPP (PPP GNI) of 136 nations and regions as provided in the World Development Indicators (WDI) database of 2003 and 2004, based on which the dynamic PPP theory is to be tested and simulated. GNI per capita (Y ) indicates the degree of domestic economic development, while the GNI-to-PPP GNI ratio (Y /Y PPP) measures the real exchange rate relative to the PPP level.11 Table 6.2

Pertinent indicators from the WDI database

Variable

Definition

Unit

Year(s)

Y

GNI per capita

current USD

2004, 2003

Y PPP

PPP GNI per capita

current USD

2004, 2003

GROWTH

GDP growth rates

%

2004

TRADE

Merchandise trade/GDP

%

2004

Fig. 6.2 is a scatter plot of Y /Y PPP (labeled FX ) versus lnY for 136 nations and regions in 2004. It manifests the international trend of relative exchange rates appreciating alongside national income and real exchange rates toward the PPP level in developing countries, which is consistent with the PPP theory. 12 The scatter plot for 2003 illustrates a similar trend. Fig. 6.2

Scatter plot of relative exchange rates vs. GNI 1.4 1.2

FX

0.8 0.4 0.0

86

1.9

2.4

2.9

3.4 InY

3.9

4.4

4.9

The Dynamic Purchasing Power Parity Theory: Conception, Evidence, and Prediction

Table 6.3 estimates the structural equation of the dynamic PPP theory Y / Y = C 0 + C 1 • Y – C 2/Y by OLS based on individual samples from 136 nations and regions in 2004 (sample dataset I), 2003 (sample dataset II), as well as the combined 272 samples from both years (sample dataset III), which yields the respective results of I, II, and III.13 PPP

Table 6.3

Estimation of the structural equation of the dynamic PPP theory I

Y /Y PPP = C 0 + C 1 • Y – C 2/Y II

III

C0

0.375 798 (21.172 83)

0.350801 (21.652 86)

0.363211 (30.362 91)

C1

2.11E – 05 (20.362 10)

2.15E – 05 (19.829 27)

2.13E – 05 (28.643 16)

C2

29.029 58 (4.481 569)

25.378 87 (4.618 239)

27.183 74 (6.460756)

0.820 288

0.815 578

0.8181 73

0.817 586

0.812 804

0.816 821

0.130 683

0.118 055

C3 R

2

Adj. R

2

Samples

2004 (136)

2003 (136)

0.124 168 2003–2004 (272)

Under the assumptions that x = lnY and z = Y /Y PPP, the structural function Y / Y PPP = C 0 + C 1 • Y – C 2/Y can be transformed into the classical binary exponential growth function z = a • exp(α • x ) + b • exp(–β • x ), which comprises both positive (exp(α • x )) and negative (exp(–β • x )) growth elements. Based on the regression equation in Table 6.3, relative exchange rates (Y /Y PPP) and national incomes (Y ) coevolve across nations in compound — instead of logistic — growth, and can thus accommodate the opposing effects of θ, ρ, and σ versus λ on real exchange rates.

Structural stability and the representativeness of variables Designating the equation estimated with sample dataset I as the basic dynamic PPP econometric model, we now test its structural stability and the representativeness of the national income variable by re-estimating the equation Y /Y PPP = C 0 + C 1 • Y – C 2/Y with the addition of time trends and other explanatory variables. Introduce the GROWTH and TRADE variables for GDP growth rates and the merchandise trade to GDP ratio, respectively, as listed in Table 6.2, to reflect countries and regions’ pace of economic growth and dependence on trade. Moreover, define the dummy variable DRIFT as 0 in 2004 and 1 in 2003.

87

Theoretical System of China's Macroeconomic Analysis

Establish alternative hypotheses on the basic econometric model, namely, Ha, Hb, Hc, and Hd, to test whether it is intertemporally stable from 2004 to 2003, and whether national income is sufficiently representative as a single explanatory variable in the model:14 (1) Null hypothesis Ha: Y /Y PPP = C 0 + C 1 • Y – C 2/Y + C 3 • DRIFT for structural instability, assuming that the intercept term of the basic econometric model (C 0) has drifted over the years. It has explanatory power with DRIFT attached to the intercept term; the coefficient C 3 is of statistical significance. (2) Null hypothesis Hb: Y /Y PPP = C 0 + C 1 • (1 + C 4 • DRIFT ) • Y – C 2 • (1 + C 5 • DRIFT )/Y for structural instability, assuming that the coefficients C 1 and C 2 have drifted over the years. It has explanatory power with DRIFT attached to C 1 and C 2; C 4 and C 5 are of statistical significance. (3) Null hypothesis Hc: Y /Y PPP = C 0 + C 2 • Y – C 3/Y + C 6 • GROWTH for the unrepresentativeness of variables, assuming that the basic econometric model has omitted the explanatory variable of economic growth rate. It has explanatory power with the addition of the GROWTH variable; C 6 is of statistical significance. (4) Null hypothesis Hd: Y /Y PPP = C 0 + C 1 • Y – C 2/Y + C 7 • TRADE for the unrepresentativeness of variables, assuming that the basic econometric model has omitted the explanatory variable of dependence on trade. It has explanatory power with the addition of the TRADE variable; C 7 is of statistical significance. Estimate Ha and Hb by OLS with sample dataset III for testing the statistical significance of time trends, and then Hc and Hd with sample dataset I for the statistical significance of the explanatory variables. The results of the tests, which show whether the structure and fitness of the basic dynamic PPP econometric model are (significantly) sensitive to time trends and/or the added explanatory variables, are displayed in Table 6.4. Table 6.4

Single equation test of the dynamic PPP theory

Y /Y PPP = C 0 + C 1 • (1 + C 4 • DRIFT ) • Y – C 2 • (1 + C 5 • DRIFT )/Y + C 3 • DRIFT + C 6 • GROWTH + C 7 • TRADE Ha

Hb

Hc

Hd

C0

0.371 740 (26.196 82)

0.363 429 (30.227 40)

0.405 287 (15.271 16)

0.392 696 (13.801 61)

C1

2.13E–05 (28.596 53)

2.15E–05 (24.384 54)

2.07E–05 (19.539 32)

2.11E–05 (20.175 71)

C2

27.117 38 (6.447 291)

26.029 25 (4.769 808)

28.493 97 (4.412 111)

30.041 40 (4.530 743)

88

The Dynamic Purchasing Power Parity Theory: Conception, Evidence, and Prediction

(Cont'd)

Y /Y PPP = C 0 + C 1 • (1 + C 4 • DRIFT ) • Y – C 2 • (1 + C 5 • DRIFT )/Y + C 3 • DRIFT + C 6 • GROWTH + C 7 • TRADE Ha

Hb

Hc

Hd

—

—

—

C4

–0.016 822 (–1.116 379) —

—

—

C5

—

—

—

C6

—

–0.025 618 (–0.476 632) 0.085 826 (0.338 215) —

—

C7

—

—

–0.004 988 (–1.489 142) —

R2

0.819 015

0.818 418

0.823 257

–0.000 222 (–0.761 122) 0.820 008

Adj. R 2

0.816 989

0.815 697

0.819 241

0.815 886

Sample

2003–2004 (272)

2003–2004 (272)

2004 (136)

2004 (135)*

C3

SE

0.124 111

0.124 548

0.130 089

0.131 134

* Data on Algerian merchandise trade unavailable.

In comparison with the basic econometric model illustrated in Table 6.3, the results in Table 6.4 demonstrate only weak improvement in overall fitness for each null hypothesis. Changes in the regression coefficient on the national income variable are unobvious, whereas the coefficients on time trends, economic growth rates, and the dependence on trade are statistically insignificant. Rejecting all null hypotheses, the single equation test in Table 6.4 justifies the intertemporal stability of the basic structural function Y /Y PPP = C 0 + C 1 • Y – C 2/Y and proves that the variable of GNI per capita is sufficient to represent the explanatory power of economic growth rates and the degree of dependence on trade.15

A Scenario-Based Forecast of the RMB Real Exchange Rate The scenario-based forecasting approach and a computable econometric model According to the basic dynamic PPP econometric model, exclude from sample dataset I four outliers that fall above one standard error away from the mean to

89

Theoretical System of China's Macroeconomic Analysis

form a refined sample dataset for 2004, S W132_2004, which covers 132 nations and regions. Construct a new, computable econometric model that is applicable for forecasting by another OLS estimation of the structural function Y /Y PPP = C 0 + C 1 • Y – C 2/Y using the subsample data S W132_2004:

Y W132/

= 0.349 328 + 2.19E – 05 • Y – 25.039 78 • (1/Y ) (26.686 27) (29.006 31) (5.312 966)

R 2 = 0.902 361, adj. R 2 = 0.900 848, SE = 0.094 662.

(6–19)

Fig. 6.3 depicts a scatter plot of Y /Y PPP (labeled FX ) versus lnY based on this set of data and its regression line corresponding to the computable dynamic PPP model: Fig. 6.3

Regression line of the computable dynamic PPP model 1.6 1.2

FX 0.8 0.4 0.0

1.9

2.4

2.9

3.4

InY

3.9

4.4

4.9

Based on the computable dynamic PPP model, the actual value of the RMB relative exchange rate (Y /Y PPP) for 2004 was 0.254 669, fitted value 0.365 424, and regression residual –0.110 755; in mathematical terms, (a) Y CN_2004/ (b) Y W132/

= 0.254 668 93; | Y W132 = Y CN_2004 = 0.36542406;

(6–20) (6–21)

| Y W132 = Y CN_2004 – Y CN_2004/ = 0.110 755 13. (c) CN_2004 = Y W132/ (6–22) The strctural function Y W132/Y = C 0 + C 1 • Y – C 2/Y predicts a double mechanism in the appreciation of the RMB real exchange rate in the future: on one hand, national income growth (ΔY CN) will induce a marginal increase in Y CN/ through the mechanism of economic growth along international trends:

90

The Dynamic Purchasing Power Parity Theory: Conception, Evidence, and Prediction

Δ(Y CN/

) = C 1 • ΔY CN – C 2/ΔY CN; (6–24)

on the other, the decrease in deviations from the regression line ( CN ) may cause Y CN/Y to increase toward the international trend by the selfequilibrating process of Δ ( CN ) • CN < 0:

Y W132/

| Y W132 = Y CN. (6–25)

Meanwhile, set up a scenario-based forecast that predicts the appreciation trends of the RMB real exchange rate under both the fast and slow growth scenarios in terms of national income, and in the complete disappearance as well as total retention of deviations from the international trend, as illustrated in Fig. 6.4. From the starting point F , China’s national income per capita (Y CN) increases from Y CN_2004 to Y CN_2010; the deviations of its relative exchange rate (Y CN/ ) diminish from Y W132/ | Y W132 = Y CN to zero, so CN_2010 = 0, or = , throughout the 11th Five-Year remains unchanged, i.e., CN_2010 CN_2004 Plan of 2006 to 2010. With the line FAB paralleling Y W132/ , and the distances of both AC and BD equaling that of CN_2004, the RMB relative exchange rate in 2010 would be confined within four extreme points: point A, featuring low national income and unchanged deviations; point B, featuring high national income and unchanged deviations; point C, featuring low national income and no deviation; and point D, featuring high national income and no deviation. Fig. 6.4

Scenario-based forecasting approach to RMB appreciation

Y / Y PPP D

YW_132 / Y PPP W132 | YW_132 = YCN_2010

YW132 / Y PPP W132

C

YW_132 / Y PPP W132 | YW_132 = YCN_2004 DVCN_2004 YCN_2004 / Y PPP CN_2004

0

B A

F

YCN_2004

YCN_2010

Y

91

Theoretical System of China's Macroeconomic Analysis

Appreciation trends of the RMB real exchange rate Table 6.5 illustrates the procedures of a scenario-based forecast on China’s national

income in 2010. The floor value of the annual growth rate of GDP per capita during 2006 to 2010 is set at 6.6% according to the official target stated in the Outline of the

11th Five-Year Plan for National Economic and Social Development of the People’s Republic of China , based on which 2% is added to set up the ceiling value, with reference to the difference between the target (7.0%) and actual (9.5%) GDP growth rates during the preceding 10th Five-Year Plan of 2001 to 2005.16 Table 6.5

Growth scenarios of China’s national income [1] China’s GDP per capita in 2005 (2004 RMB): 14,061.925

Preliminary indicators

[2] RMB nominal exchange rate to USD in 2005 (RMB/USD): 8.194 [3] U.S. GDP deflator in 2004 (2000 = 100): 109.418 5 [4] U.S. GDP deflator in 2005 (2000 = 100): 112.728 3

Intermediary indicators

Scenarios

Predicted indicators

[5] China’s GDP per capita in 2005 (2004 RMB) (Y CN_2005): 1,665.737

(= ([1]/[2])/([4]/[3]))

[6] Growth rate of China’s GDP per capita from 2006 to 2010 by floor assumption (gY CN): 6.6 (%) [7] Growth rate of China’s GDP per capita in from 2006 to 2010 by ceiling assumption (gY CN): 8.6 (%) [8] China’s GDP per capita in 2010 - by floor assumption (2004 USD) (Y CN_2010): 2,292.939 (= [5] • (1 + [6]/100)5) [9] China’s GDP per capita in 2010 - by ceiling assumption (Y CN_2010): 2,516.261 (2004 USD) (= [5] • (1 + [7]/100)5)

According to the dynamic PPP function, the equation that estimates the range of

GDP per capita that would correspond to the international trend for China in 2010 is given by Y W132/

| Y W132 = Y CN_2010 = C 0 + C 1 • Y CN_2010 – C 2/Y CN_2010. The lower

upper and lower limits of the results, 0.394 380 96 and 0.388 530 08, respectively, correspond to the ceiling and floor values of the scenario forecast. If trends of time series data on China’s economy between 2006 and 2010 follow international

patterns in 2004, according to the computable dynamic PPP theory, we can calculate the ratio of China’s GDP per capita to GDP (PPP) per capita in 2010 by the formula

Y CN_2010/ = Y W132/ | Y W132 = Y CN_2010 – CN_2010, situating the extreme scenarios of A, B, C, and D by the upper and lower limits on national income

92

The Dynamic Purchasing Power Parity Theory: Conception, Evidence, and Prediction

growth as well as the extreme deviation conditions where CN_2010 = CN_2004 and CN_2010 = 0. Hence, the RMB real exchange rate in 2010 in terms of the (domestic) purchasing power of 2004 USD can be predicted by a cross-period comparison between Y CN_2010/ and Y CN_2004/ . Table 6.6 displays a scenario-based forecast on the appreciation trends of the RMB real exchange rate during 2006 to 2010 that includes both the year-end levels and annual appreciation rates. It presents the exchange rate as indexed by the GDP deflator (real exchange rate I) and the CPI (real exchange rate II). Table 6.6

Scenario-based forecast of RMB real exchange rates CN_2010

Y CN_2010/Y

=

CN_2004

CN_2010

=0

gY CN = 6.6%

gY CN = 8.6%

gY CN = 6.6%

gY CN = 8.6%

A

B

C

D

0.277 775

0.283 626

0.388 530

0.394 381

Real exchange rate I Level in 2010 (2004 = 100)

91.681 75

89.790 46

65.546 77

64.574 35

6.361 219

8.292 883

33.054 07

34.047 25

Annual appreciation in 2006–2010 (%)

1.334 549*

1.754 473

7.907 442

8.191 241

Level in 2010 (2004 = 100)

91.681 75

89.790 4

65.546 77

64.574 35

Cumulative appreciation in 2006–2010 (%)

Real exchange rate II

Cumulative appreciation in 2006–2010 (%)

Annual appreciation in 2006–2010 (%)

8.363 049

10.253 42

34.485 25

35.457 21

1.730 669

2.139 274

8.1053 67

8.379 456**

* Lowest annual appreciation rate in 2006 to 2010. ** Highest annual appreciation rate in 2006 to 2010.

Policy Targets for China’s Demand Management The dynamic PPP theory accommodates the departures of the RMB exchange rate ≠ 1, as well as differences between from the conventional PPP level, where Y CN/ the deviations of the RMB exchange rate from PPP and the international trend of ≠ Y W132/ | Y W132 = Y CN. Since 2004, the RMB has deviations, where Y CN/ been under great pressure of revaluation. While the large and expanding trade surplus, as well as the resultant quick accumulation of foreign reserves, could

93

Theoretical System of China's Macroeconomic Analysis

be ascribed to diverse factors including the transformation of investment, trade, and supply chains at home and abroad, it did reflect the structural problems of the existence of large surplus savings and currency undervaluation (Zhou 2006).17 Nevertheless, unlike the short-run deviations in other error correction models for time series data on RMB real exchange rates, deviations in the basic dynamic PPP model have no propensity for mean reversion.18 To condense the appreciation trends of RMB real exchange rates into a single number, we now assume the probable bias of the scenario-based forecast toward the fast growth scenario with the complete disappearance of deviations from the regression line. Averaging the forecasted exchange rates with the weights of 2/3:1/3 for the extreme scenarios where gY CN = 8.6% / gY CN = 6.6% and CN_2010 = 0 / CN_2010 = CN_2004, the predicted cumulative appreciation rate of the RMB between 2006 and 2010 becomes 25%, at an annual rate of close to 6%.19 In the context where the RMB appreciates at an annual rate of 6%, or /e = –6%, China’s demand management policy should adhere to the targets quantified by the following equations for the dynamic adjustments of wages, prices, and exchange rates during 2006 to 2010: (1) Inflation target: With reference to the price stability policies of the European Central Bank and the Taylor rule, which aim to keep the annual inflation rate close to 2%, set the target for China to be 4%. Thus, there is a 2% difference between China’s inflation rate and the international level; πN – πW = 2%, where πW = 2% and πN = 4%. (2) Exchange rate target: Aim for a 4% annual appreciation in the nominal exchange rate and hence a cumulative increase of 20%. Therefore, gE = /e + (πN – πW) = –4%. (3) Real wage target: Make the targeted annual growth rate 10% and cumulative growth rate 60%, at the same pace as in the preceding Five-Year Plan period (2001– 2005), to match with the progress of labor productivity growth, i.e., /w = 10%. (4) Nominal wage target: From the formula gW = /w + πN = 14%, set the nominal wage target to be 14%, meaning a cumulative growth of 90%. Accordingly, on account of the annual inflation rate of 4% and GDP growth rate of 9% to 10%, the share of wages in the national income should be stable in the period.

Data Appendix Indicators for 2003 and 2004 in Table 6.2 are derived from World Development Indicators 2005 and World Development Indicators 2006 , respectively. Table 6.7 enumerates 136 nations and regions in the WDI database, whose GNI and

94

The Dynamic Purchasing Power Parity Theory: Conception, Evidence, and Prediction

PPP GNI for 2003 and 2004 are available and constitute the sample datasets I, II, and III. After excluding four outliers according to the basic dynamic PPP econometric model, sample I is refined into subsample S W132_2004 for estimating the computable dynamic PPP function. Table 6.7

Nations and regions in the WDI database

[1] Albania; [2] Algeria; [3] Angola; [4] Argentina; [5] Armenia; [6] Australia [7] Austria; [8] Azerbaijan; [9] Bangladesh; [10] Belarus; [11] Belgium; [12] Benin; [13] Bolivia; [14] Bosnia

and Herzegovina; [15] Botswana; [16] Brazil; [17] Bulgaria; [18] Burkina Faso; [19] Burundi; [20] Cambodia; [21] Cameroon; [22] Canada; [23] Central African Republic; [24] Chad; [25] Chile; [26] China; [27] Colombia; [28] Congo, Dem. Rep.; [29] Congo, Rep. *; [30] Costa

Rica; [31] Côte d’Ivoire; [32] Croatia; [33] Czech Republic; [34] Denmark; [35] Dominican

Republic; [36] Ecuador; [37] Egypt, Arab Rep.; [38] El Salvador; [39] Eritrea; [40] Estonia; [41] Ethiopia; [42] Finland; [43] France; [44] Gabon; [45] Gambia; [46] Georgia; [47] Germany; [48] Ghana; [49] Greece; [50] Guatemala; [51] Guinea; [52] Guinea-Bissau; [53]Honduras;

[54] Hong Kong, China; [55] Hungary; [56] India; [57] Indonesia; [58 ] Iran, Islamic Rep.; [59] Ireland; [60] Israel; [61] Italy; [62] Jamaica *; [63] Japan; [64] Jordan; [65] Kazakhstan; [66]

Kenya; [67] Korea, Rep.; [68] Kuwait; [69] Kyrgyz Republic; [70] Lao PDR; [71] Latvia; [72]

Lebanon *; [73] Lesotho; [74] Lithuania; [75] Macedonia, FYR; [76] Madagascar; [77]Malawi; [78] Malaysia; [79] Mali; [80] Mauritania; [81] Mauritius; [82] Mexico; [83] Moldova; [84] Mongolia; [85] Morocco; [86] Mozambique; [87] Namibia; [88] Nepal; [89] Netherlands;

[90] New Zealand; [91] Nicaragua; [92] Niger; [93] Nigeria; [94] Norway; [95] Pakistan; [96] Panama; [97] Papua New Guinea; [98] Paraguay; [99] Peru; [100] Philippines; [101] Poland;

[102] Portugal; [103] Romania; [104] Russian Federation; [105] Rwanda; [106] Saudi Arabia;

[107] Senegal; [108] Sierra Leone; [109] Singapore; [110] Slovak Republic; [111] Slovenia; [112] South Africa; [113] Spain; [114] Sri Lanka; [115] Sudan; [116] Swaziland; [117] Sweden; [118] Switzerland; [119] Syrian Arab Republic; [120] Tajikistan; [121] Tanzania; [122] Thailand; [123] Togo; [124] Trinidad and Tobago; [125] Tunisia; [126] Turkey; [127] Uganda; [128]

Ukraine; [129] United Kingdom; [130] United States; [131] Uruguay; [132] Uzbekistan; [133] Venezuela, RB; [134] Vietnam; [135] Yemen, Rep. *; [136] Zambia * Outlier 29, 62, 72 and 135.

The FRED database provides monthly data on the RMB to USD exchange rate, quarterly data on U.S. chained GDP deflators, and quarterly data on U.S. chained PCEPI, which are labeled EXCHUS , GDPCTPI , and PCECTPI , respectively. Annual data of China’s CPI, nominal GDP, and real GDP are available in the National Bureau of Statistics’ China Statistical Yearbook 2006 , Statistical Communiqué on the 2006 National Economic and Social

95

Theoretical System of China's Macroeconomic Analysis

Development and Bulletin on Preliminary Verified GDP Data in 2005 . For the sake of consistency, annual data on nominal RMB exchange rates, China’s GDP deflators and CPI, and U.S. GDP deflators and CPI are calculated through seasonal averaging and using price deflators. Table 6.7 shows the nominal and RMB real exchange rates to the USD in 2003 to 2005, with the real exchange rates being indexed to the GDP deflators and CPI. Table 6.8

RMB nominal and real exchange rates

Nominal exchange rate (RMB/USD) Real exchange rate I (GDP-deflatorindexed) Real exchange rate II (CPI-indexed)

2003

2004

2005

8.277 2

8.276 8

8.194 0

104.151 9

100

97.910 0

101.258 0

100

100.048 9

Renewed Estimation and Prediction Appendix Based on data on the GNI per capita and PPP GNI per capita of 161 nations and regions in 2009 provided by World Development Indicators 2010 , the structural function of the dynamic PPP theory Y /Y PPP = C 0 + C 1 • Y – C 2 /lnY is reestimated with primary sample dataset S W161_2009 and then subsample dataset S W 143_2009 and S W 120_2009, as shown in Table 6.9. S W 143_2009 excludes 18 outliers that are above one standard error from the mean, while S W 120_2009 excludes 41 outliers, drawing the line of deviation at 1.5. By refining the sample dataset, the ) becomes 0.547 529, and estimated RMB relative exchange rate (Y CN_2009/ its deviation from the regression line ( CN_2009) changes from a small positive value into a small negative value. After constant appreciation since 2005, the RMB real exchange rate would approach the dynamic PPP level in 2009, in sync with the predicted international trend. From 2010 onward, it would be national income growth that constitutes the fundamental driver of the appreciation of the RMB real exchange rate, rather than the equilibration of the undervalued exchange rate toward the dynamic PPP level. Table 6.9

Re-estimation of the structural equation of the dynamic of PPP theory

Y /Y PPP = C 0 + C 1 • lnY + C 2/lnY C0

96

S W161_2009

S W143_2009

S W120_2009

–5.267 912 (–10.120 20)

–5.640249 (–12.82156)

–5.530 817 (–13.632 78)

The Dynamic Purchasing Power Parity Theory: Conception, Evidence, and Prediction

(Cont'd)

Y /Y PPP = C 0 + C 1 • lnY + C 2/lnY S W161_2009

S W143_2009

S W120_2009

C1

0.424 304 (13.072 41)

0.447 219 (16.288 62)

0.440 351 (17.519 20)

C1

19.131 42 (9.382 729)

20.554 83 (11.933 31)

20.122 74 (12.562 32)

0.737 418

0.830 482

0.884 630

0.734 094

0.828 061

0.882 657

SE

0.125 139

0.093 189

0.074 621

DW

1.820 508

2.143 183

1.896 454

–0.000 881

0.009 912

0.009 507

R2 Adj. R

2

CN_2009

Subsample data S W120_2009 is designated as the basis of the computable dynamic PPP econometric model for predicting the appreciation trends of the RMB real exchange rate during the 12th Five-Year Plan. Fig. 6.5 depicts a scatter plot of Y /Y PPP (labeled FX ) versus lnY for subsample dataset S W120_2009, and the corresponding dynamic PPP regression line.20 Conforming with the scenariobased forecasting approach of Fig. 6.4, Table 6.10 presents the four extreme points that confine the predicted relative exchange rate of the RMB in 2015 calculated from the scenarios of fast national income growth, slow national income growth, complete disappearance of deviations, and complete retention of deviations. Fig. 6.5

Renewed dynamic PPP regression line 1.6 1.2 FX 0.8 0.4 0.0

2.1

2.6

3.1

3.6

InY

4.1

4.6

5.1

97

Theoretical System of China's Macroeconomic Analysis

Assume that in the fast growth scenario, China’s GDP per capita grows at the mean rate between 1991 and 2009 in the period of 2010 to 2015, i.e., g * = 9.578 440%, while in the slow growth scenario, the growth rate is the mean minus one standard error, i.e., σ g = 2.122 536%. Assign weights to the scenarios of gY CN = g */gY CN = g * – σ g and CN_2015 = 0 / CN_2015 = CN_2009 by their probability of occurrence, 3/4:1/4, and average the forecast results laid out in = 0.623 990. Predictably, the RMB real exchange Table 6.10, so Y CN_2015/ rate will undergo cumulative appreciation of 13.964 82% at an annual rate of 2.022 567%. Table 6.10 Scenario-based forecast of the RMB real exchange rate in 2015

Y CN_2013/

CN_2015

=0

CN_2015

=

(Prob)

98

CN_2009

gY CN = g *

gY CN = g * – σg

(Prob)

0.626 765

0.606 158

(0.75)

0.636 272

0.615 665

(0.25)

(0.75)

(0.25)

Part IV

Demand Management

7

Chapter

The Macroeconomic Analysis and Forecasting Model of China (CMAFM)

Theoretical System of China's Macroeconomic Analysis

The Theoretical Core of the IS–LM–AS Model Macroeconomics has been enriched by vibrant development since the 1970s. Revolutions have risen up, challenged by counter–revolutions, and then reconciled in syntheses, resulting in groundbreaking theoretical frameworks. Following the split of the neoclassical synthesis by the rational-expectations revolution was the confrontation, convergence, and mutual transformation between new classical and new Keynesian economics. Gradually, they were integrated to provide a sound theoretical foundation for research on major macroeconomic issues, demonstrating operational significance (Blanchard 2000; Snowdon and Vane 2005; Mankiw 2006). New classical economics adopted the Walrasian general equilibrium under the optimal criterion, principally interpreting and predicting the consequences of structural departures, if any, from the perfect market, perfect information, and other assumptions of the general equilibrium, which later found echoes in the new Keynesian approach. A primary success of new Classical economics in the intertemporal optimization of the Walrasian equilibrium based on the representative-agent assumption, the dynamic stochastic general equilibrium (DSGE) modeling approach was popular within the realm of academia and pedagogy; however, the model and its calibration failed to penetrate into applied macroeconomics, hardly shattering the dominance of structural macroeconometric models, especially in real-time forecasts and simulations. Out of the devastating Lucas critique, large-scale macroeconometric models, as epitomized by the FRB/US model, were born with improved configurations of expectations and optimization along new classical directions. Yet, their fundamental structures still retain the theoretical core of the MIT–Penn–Social Science Research Council (MPS) model, i.e. the combination of IS–LM with the Phillips curve after the fashion of the neoclassical synthesis.1 Since economics has not developed — and is not likely to develop — to such sophistication as to infer aggregate behavior directly from simply gathering individual choices, such positive methodologies as Marshallian partial equilibrium analysis as well as Keynesian structural modeling have remained indispensable for macroeconomics. In line with international traditions of applied macroeconomics, macroeconomic research on China can be structured around the theoretical framework of the IS–LM–AS model, and then executed with the pragmatic selection of variables and configurations of equations. It is convenient to elaborate and formalize, for example, the mix of an expansionary demand-side policy with the related proactive fiscal policy and prudent monetary policy when coping with the Asian financial crisis in 1998 as a Keynesian case of the IS–LM model (CCER 1998).

102

The Macroeconomic Analysis and Forecasting Model of China (CMAFM)

The Institute of Quantitative and Technical Economics of the Chinese Academy of Social Sciences and the State Information Center of the National Development and Reform Commission have been building their own annual macroeconometric models of China, standing at the academic frontier of China’s macroeconomic modeling as the models are integrated into the international Project LINK (Wang, Li, and Li 1999). Nevertheless, China’s structural macroeconometric modeling has traditionally been inconsistent with the IS–LM–AS model: The national income identity is formulated from the perspective of the value-added of sectors instead of the types of expenditure, which implies the lack of standard aggregate demand analysis as adopted by the IS–LM model. Nor does aggregate analysis apply, as price formation is formulated directly based on related costs without interpreting inflation dynamics in light of output gaps. Although large-scale macroeconometric models with such defects are capable of portraying China’s macroeconomic development step-by-step, they fail to trace the transmission mechanisms of its fiscal and monetary policies, and therefore cannot meet the economy’s practical needs of demand management. Following the IS–LM–AS approach, this chapter aims to build an IS–LM model of China by estimating the consumption demand function, investment demand function, net export demand function, and money demand function; establish a Lucas aggregate supply function for China by fitting the expectations-augmented Phillips curve; and develop policy rules for taxation and money supply in China, eventually completing the Macroeconomic Analysis and Forecasting Model of China, a.k.a. the CMAFM. A compact annual structural macroeconometric model oriented toward demand management, the CMAFM serves the purposes of macroeconomic analysis and forecasting. With its effectiveness in simulating China’s macroeconomy and policies, it can also assist with the design and selection of fiscal and monetary policies.

Structural Specifications and Variables of the CMAFM Configurations of aggregate supply and demand The Lucas supply function y – y * = f (π – π E) is able to synthesize traditional aggregate supply functions pro forma through discretionary selection of the mechanism of inflation expectation formation, and its equivalent Phillips curve with expectations augmented π = f –(y – y *) + π E is identifiable under

103

Theoretical System of China's Macroeconomic Analysis

specific assumptions of inflation expectations. The aggregate supply side of the CMAFM is built upon the expectations﹣augmented Phillips curve, and it selects the inflation expectation formation mechanism by regression analyses of the Phillips curve. The aggregate demand side of the model adopts the standard structure of the IS–LM model, as illustrated in Fig. 7.1. In the figure, “government expenditure,” “money supply,” and “exchange rate” represent the variables of the fiscal policy, monetary policy, and exchange rate policy, respectively, while “household dispensable income” and “interest rate” are determined by the taxation policy and money demand function, respectively. Resembling the standard IS–LM model, the CMAFM introduces government expenditure and narrow money supply as inputs from the demand management policy. Table 7.1

Aggregate demand structure of the CMAFM Goverment expenditure

GDP

Fixed investment

Net exports

Exchange rate

Inventory investment

Interest rate

Money supply

Endogenous variables

Consumption

Household income

Tax

Exogenous variables

Sources of data and the system of variables Relevant variables are derived from the primitive variables listed in Table 7.1 (a), as displayed in Table 7.1 (b), and then indexed by a GDP deflator, forming a system of variables for the CMAFM (see Table 7.1 (c)). The sources of data include China Statistical Yearbook published by the National Bureau of Statistics of China (NBS), the IMF “International Financial Statistics” (IFS), and the WTO’s online statistics database.

104

The Macroeconomic Analysis and Forecasting Model of China (CMAFM)

Table 7.1 (a) Primitive variables of the CMAFM Variable

BFCI

Definition

Unit

Fixed investment within state budget

100 million, current RMB

NBS

GFCI

Formation of gross fixed capital

100 million, current RMB

NBS

GOVC

Government consumption

100 million, current RMB

NBS

National government expenditure

100 million, current RMB

NBS

National government revenue

100 million, current RMB

NBS

Inventory accumulation

100 million, current RMB

NBS

M 1S

Year-end balance of narrow money supply

100 million, current RMB

IFS

M 2S

Year-end balance of broad money supply

100 million, current RMB

IFS

Net exports

100 million, current RMB

NBS

GDP (by expenditure approach)

100 million, current RMB

NBS

Household consumption

100 million, current RMB

NBS

Official interest rate of three-year deposits

%

NBS

Year-end SDR to RMB exchange rate

RMB/SDR

IFS

Average USD to RMB exchange rate

RMB/USD

NBS

World merchandise exports

Million, current USD

WTO

World merchandise imports

Million, current USD

WTO

World service exports

Million, current USD

WTO

World service imports

Million, current USD

WTO

Real GDP

1978 = 100

NBS

GOVEX

GOVRE INV

NEX

NGDP

PRIVC R 3O SDRES USDE WME WMI WSE WSI Y

Source

Table 7.1 (b) Designation of variables for the CMAFM Variable

Definition

Formula

FCI

Fixed investment

FCI t = GFCI t – BFCI t

G

Government expenditure

G t = GOVC t + BFCI t

GTX

Comprehensive government revenue

PDY

Household disposable income

PDY t = GDP t – GTX t

SDRE

Average SDR to RMB exchange rate

SDRE t = (SDRES t + SDRES t – 1)/2

WT M1 M2

Total world trade

GTX t = GOVRE t – (GOVEXt – G t)

WT t = (WME t + WMI t + WSE t + WSI t)/100

Average balance of narrow money M1 = ((M1S + M 1S – 1)/2) • 10 t t t supply Average balance of broad money supply

M2 t = ((M2S t + M 2S t – 1)/2) • 10

105

Theoretical System of China's Macroeconomic Analysis

Table 7.1 (c)  CMAFM’s system of variables Variable

Definition

Formula

Unit

P

Price index

P t = (NGDP t/Y t)/(NGDP 1981/ Y 1981)

1981 = 1.00

INFL

Inflation rate

INFL t = (P t/P t – 1 – 1) • 100

%

YR

Real national income

YR t = NGDP t/P t

100 million, 1981 RMB

PDYR

Real household disposable PDYR t = PDY t/P t income

100 million, 1981 RMB

GTXR

Real consolidated government revenue

GTXR t = GTX t/P t

100 million, 1981 RMB

PRIVCR

Real household consumption

PRIVCR t = PRIVC t/P t

100 million, 1981 RMB

FCIR

Real fixed investment

FCIR t = FCI t/P t

100 million, 1981 RMB

INVR

Real inventory accumulation

INVR t = INV t/P t

100 million, 1981 RMB

NEXR

Real net exports

NEXR t = NEX t/P t

100 million, 1981 RMB

GR

Real government expenditure

GR t = G t/P t

100 million, 1981 RMB

M 1R

Real money supply M1

M 1R t = M 1t/P t

100 million, 1981 RMB

M 2R

Real money supply M2

M 2R t = M 2t/P t

100 million, 1981 RMB

R3

Average interest rate of three-year deposits

R 3t = Σi{R 3Oti • Δt ti}/Σi {Δt ti}

%

Single Equation Estimation of the CMAFM Generate an econometric CMAFM for 1981 to 2009 by single-equation

estimation, setting the time variable T to be 1 in 1981 and 29 in 2009.

Furthermore, assign the institutional dummy variable DUM to be 1 from 1988 to 1994, between the two peak years of inflation, and 0 in other times so as to reflect China’s swift transition from a planned economy to a market economy during the period. Below are the structural equations of the model: (1) The defining equation of household dispensable income:

PDYR t = YR t – GTXR t

106

The Macroeconomic Analysis and Forecasting Model of China (CMAFM)

(2) The household consumption demand function: logPRIVCR t = 0.543 831 + 0.472 901 • logPRIVCR t – 1 + 0.441 511 • logPDYR t (4.509 097) (3.946 191) (4.314 550) + [MA(1) = 0.648 198] (3.977 748)

R 2 = 0.999 077, adj. R 2 = 0.998 966, SE = 0.022 544, DW = 1.901 459. (3) The fixed investment demand function: logFCIR t = –3.260 735 – 0.007 016 • (R 3t – INFL t) + 1.221 933 • logYR t – 1 (–10.468 65) (– 3.989 385) (38.800 27) + [MA (1) = 1.364 935, MA (2) = 0.381 336] (7.216 150) (2.070 341)

R 2 = 0.997904, adj. R 2 = 0.997554, SE = 0.049492, DW = 1.780012. (4) The inventory investement deamnd fucntion: INVR t/YR t = 0.100 130 + 0.831 488 • (INVR t – 1/YR t – 1) – 0.850 686 • DUM (5.049 216) (10.119 22) (– 4.804 046) • (INVR t – 2/YR t – 2) – 0.002 175 • (R 3t – INFL t ) + 0.002 972 • DUM (– 5.691 864) (5.315 968) • (R 3t – INFL t) – 0.009 065 • logYR t + 0.007 030 • DUM • logYR t – 1 (– 5.291 635) (4.491 883) + [MA (1) = – 0.948 434] (– 9.610 639)

R 2 = 0.923 654, adj. R 2 = 0.896 933, SE = 0.008 560, DW = 2.188 931. (5) The net export demand function: NEXR t/YR t = – 0.758 279 + 0.384 595 • (NEXR t – 1/YR t – 1) – 0.195 080 • ΔlogYR t (–7.462 042) (2.880 984) (– 1.962 876) – 0.075 514 • logYR t – 1 – 0.109 504 • log(SDRE t/P t) + 0.132203 (– 4.347 409) (– 5.490 525) (5.818 157) • log(WT t • USDE t/P t) + [MA (2) = – 0.893 484] (– 7.537 378)

R 2 = 0.915 993, adj. R 2 = 0.893 082, SE = 0.009 601, DW = 2.022 837. (6) The national income equilibrium equation: YR t = PRIVCR t + FCIR t + INVR t + NEXR t + GR t

107

Theoretical System of China's Macroeconomic Analysis

(7) The money demand function: R 3t – INFL t = 3.852 006 – 0.874 882 • INFL t + 0.284 096 • INFL t – 1 (6.446 896) (– 16.340 14) (4.710 104) – 3.913 752 • log(M 2R t/YR t) + [MA (1) = 0.951538, MA (2) (– 4.131 582) (7.108 380) = 0.285 162] (2.403 122)

R 2 = 0.946 740, adj. R 2 = 0.935 161, SE = 1.002 751, DW = 1.811 355. (8) The Phillips curve: ΔINFL t = –2.939 776 – 0.537 436 • DUM • INFL t – 1 – 0.488 674 • INFL t – 2 (–2.736 606) (– 2.683 318) (–11.059 52) + 48.250 29 • ΔlogYR t + 55.678 33 • DUM • ΔlogYR t – 1 + [MA (1) (4.203 558) (11.283 64) = – 0.921 838] (– 13.115 75)

R 2 = 0.806 800, adj. R 2 = 0.764 800, SE = 2.040 781, DW = 2.011 310. (9) The taxation regime: GTXR t/PDYR t = 0.316 815 – 0.024 708 • T + 0.001 315 • T 2 – 2.17E – 05 • T 3 (38.31567) (– 12.524 81) (9.528 417) (– 7.466 515) 2 + 0.127 810 • Δ logYR t + [MA (2) = –0.979 981] (2.335 379) (–3,375.690)

R 2 = 0.943 592, adj. R 2 = 0.931 329, SE = 0.007 498, DW = 1.584 325. (10) The monetary transmission mechanism: log(M 2R t/YR t) = 0.029 628 + 0.975 974 • log(M 2R t – 1/YR t – 1) + 0.631 738 (3.658 340) (66.176 33) (6.936 903) • Δlog(M 1R t/YR t) + [MA (1) = 0.754 948] (6.547 322)

R 2 = 0.998 125, adj. R 2 = 0.997 900, SE = 0.021 487, DW = 2.048 745. Table 7.2 presents the computable equation system of the CMAFM without undergoing the ARMA process, which consists of the four blocks of the IS curve, LM curve, AS function, and policy rule. The structural parameters are all assigned a positive value.

108

The Macroeconomic Analysis and Forecasting Model of China (CMAFM)

Table 7.2 Structural equation system of the CMAFM Block

Structural equation

[1] PDYR t = YR t – GTXR t

[2] logPRIVCR t = C 10 + C 11 • logPRIVCR t – 1 + C 12 • logPDYR t [3] logFCIR t = –C 20 – C 21 • (R 3t – INFL t) + C 22 • logYR t – 1

[4] INVR t/YR t = C 30 + C 31 • (INVR t – 1/YR t – 1) – C 32 • DUM • (INVR t – 2/YR t – 2) – C 33• (R 3t – INFL t) + C 34 • DUM • (R 3t – INFL t) – C 35 • logYR t + C 36 • DUM • logYR t – 1

IS curve

[5] NEXR t/YR t = C 40 + C 41 • (NEXR t – 1/YR t – 1) – C 42 • ΔlogYR t – C 43 • logYR t – 1 – C 44 • log(SDRE t/P t) + C 45 • log(WT t • USDE t/P t)

[6] YR t = PRIVCR t + FCIR t + INVR t + NEXR t + GR t

[7] R 3t – INFL t = C 50 – C51 • INFL t + C 52 • INFL t – 1 – C 53 • log(M 2R t/YR t)

LM curve

[8] ΔINFL t = – C 60 – C 61 • DUM • INFL t – 1 – C 62 • INFL t – 2 + C 63 • ΔlogYR t + C 64 • DUM • ΔlogYR t – 1

AS function

[9] GTXR t/PDYR t = C 70 – C 71 • T + C 72 • T 2 – C 73 • T 3 + C 74 • Δ2logYR t

Policy rule

[10] log(M 2R t/YR t) = C 80 + C 81 • log(M 2R t – 1/YR t – 1) + C 82 • Δlog(M 1R t/YR t)

The Accuracy of Historical Simulation and Dynamic Multiplier Analysis With the computable equation system of the CMAFM, conduct static and dynamic forecasts using the endogenous variables from 1981 to 2009. The errors in the historical simulation of the model are presented in Table 7.3. Then, carry out dynamic simulations of the fiscal and monetary multipliers during the 29year period. Table 7.3

Errors in the CMAFM’s historical simulation

Variable Static simulation logYR

logPRIVCR

Variable

Relative error

Absolute error

Mean (%)

RMSE (%)

Mean

RMSE

–0.002 073

0.194 508

—

—

–0.003 019

0.302 346

Relative error

Mean (%)

RMSE (%)

—

Absolute error

Mean

—

RMSE

109

Theoretical System of China's Macroeconomic Analysis

(Cont'd) Static simulation logFCIR

INVR

–0.003 056

0.547 197 —

–5.734 739

200.378 8

—

—

1.447 466

227.626 0

—

NEXR

—

—

INFL

—

—

–0.227 318

1.817 445

R3

—

—

–0.032 361

0.811 411

logYR

0.041 149

0.361 294

—

—

logFCIR

0.200 437

1.171 818

Dynamic simulation logPRIVCR

0.001 045

INVR

0.513 741

—

NEXR

—

—

INFL

—

—

R3

—

—

—

— —

–8.810 008 –38.180 84 0.137 612

0.025 184

— —

397.668 5 328.332 4 3.62918 0

1.45931 6

As demonstrated in Fig. 7.2, the time paths ΔYR t/ΔGR t – i and ΔYR t/ΔM 1R t – i trace the multipliers of China government expenditure and narrow money supply, respectively, as national income responds to their perpetual increase. Within the adjustment interval, the dynamic multiplier of demand management exhibits a convergence tendency after a short period of over-shooting, evincing the nonneutrality of the monetary policy. Fig. 7.2

Dynamic multipliers of government expenditure and money supply

0.8

0.015

0.6

0.010

0.4

0.005

0.2

0.000

0.0

0

2

4

6

8

10 12 14 16 18 20 22 24 26 28 i

∆YR/∆GR(–i)

110

∆YR/∆M1R(–i)

–0.005

The Macroeconomic Analysis and Forecasting Model of China (CMAFM)

Appendix on the Original Econometric CMAFM The original econometric model for 2002 is generated by a single-equation estimation of the CMAFM between 1981 and 2001, in which time the variable T is 1 in 1980 and 20 in 2001, and the dummy variable DUM is 1 from 1988 to 1994 and 0 in the rest of the years. The original CMADM model has the same data sources and system of variables as the renewed 2009 model explicated in the section “Structural Specifications and Variables of the CMAFM,” except for the nominal GDP that provides the NGDP variable, which is measured by the production approach, and the consolidated government revenue that provides the GTX variable, which is equal to total government revenue minus fiscal transfers spent on principal and interest payments on domestic and foreign loans, price subsidies, and pension, social welfare, and relief. 2 The structural equations of the model are as follows: (1) The defining equation of household dispensable income: PDYR t = YR t – GTXR t (2) The household consumption function:

logPRIVCR t = 0.375 375 + 0.630 277 • logPRIVCR t – 1 + 0.314 191 • logPDYR t

(5.688 347) (9.258 999)



(– 4.848 378)



(5.401 941)

– 0.004 319 • DUM • logPRIVCR t – 1 + [MA (2) = –0.510 285]

(–2.242 748)

R 2 = 0.999 118, adj. R 2 = 0.998 897, F = 4,529.222, DW = 2.539 886. Household consumption behavior in China demonstrates a relatively low propensity to consume out of national income, exhibiting conventional partial adjustment dynamics. (3) The fixed investment function:

logFCIR t = –4.003 477 + 1.291 883 • logYR t – 0.009 541 • (R 3t – INFL t)



(– 47.591 92) (145.956 8)

+ [MA (2) = –0.979 165]

(– 3.096 062)

(–4,276.416)

R 2 = 0.996 164, adj. R 2 = 0.995 487, tSE = 1,471.395, DW = 0.815 668. China’s fixed investment adopts a semi-log demand function, demonstrating normal positive income elasticity and negative interest rate elasticity.

111

Theoretical System of China's Macroeconomic Analysis

(4) The inventory investement function: INVR t = – 442.440 2 – 0.848 715 • INVR t – 1 + 0.496 371• INVR t –2 – 0.280 996 (–6.628 827) (– 7.883 5760) (4.577 101) (– 4.818 460) • ΔPDYR t + 0.200 579 • ΔPDYR t – 1+ 93.591 40 • R 3t (3.234 297) (8.347 061) + [MA (1) = 1.709 665] (3.916 906)

R 2 = 0.970 398, adj. R 2 = 0.957 712, SE = 76.490 82, DW = 1.798 962. Inventory investment in China exhibits characteristics of both partial adjustment and error correction dynamics. The positive nominal interest rate coefficient reveals a speculative motive in inventory accumulation. (5) The net export function: ΔNEXR t = 97.821 18 – 0.815 416 • NEXR t – 1 – 0.272 051 • ΔYR t + 0.004 852 (0.679 469) (– 7.931 892) (– 2.265 151) (6.375 042) • (WT t • USDE t/P t) – 214.756 9 • (SDRE t – 1/P t – 1) (– 4.240 833) + [MA (2) = –0.903 923] (–12.442 81)

R 2 = 0.782 512, adj. R 2 = 0.710 016, F = 10.793 87, DW = 1.260 933. When the RMB to SDR exchange rate approximates the RMB effective exchange rate, and the nominal RMB to USD exchange rate is deflated into a real exchange rate, the lagged positive real effective exchange rate coefficient verifies the J-curve effect in the short run.3 (6) The national income equilibrium equation: YR t = PRIVCR t + FCIR t + INVR t + NEXR t + GR t (7) The money demand function: R 3t = 1.314 383 + 0.235 059 • INFL t – 0.000 751 • M 2R t + 0.001 359 • YR t (3.171 980) (6.538 891) (– 11.537 55) (12.320 21) – 0.003 004 • YR t + [MA (1) = –0.948952] (– 6.442 615) (–18.926 67)

R 2 = 0.955 063, adj. R 2 = 0.940 084, F = 63.760 37, DW = 1.521 126. China’s nominal interest rate decreases with money supply and increases with national income in the normal fashion, but markedly underreacts to the inflation rate, violating the Taylor rule in broad money supply.

112

The Macroeconomic Analysis and Forecasting Model of China (CMAFM)

(8) The Phillips curve: ΔINFL t = –5.365 558 + 0.541 867 • INFL t – 1 + 67.443 68 • ΔlogYR t + (–3.166 121) (5.820 425) (– 3.883 681) 55.900 83 • DUM • ΔlogYR t – 1 (5.037 907)

R 2 = 0.879 14, adj. R 2 = 0.857 820, F = 41.222 31, DW = 1.944 641. China’s Philips curve includes the annual growth rate of national income instead of the output gap. 4 If potential national income (YR *) grows intertemporally at a constant rate, the Δlog(YR /YR *) variable will be functionally equivalent to the ΔlogYR variable in the estimation of the Philips curve. While the setting of this particular explanatory variable does not involve estimating the potential national income, it does implicitly assume its intertemporal growth. (9) The taxation regime: GTXR t/YR t = 0.017 408 + 1.426293 • (GTXRt – 1/YRt – 1) – 0.576 646 (1.442 174) (4.953 599) (– 2.082 378) • (GTXR t – 2/YR t – 2) + [AR (1) = 0.893 294, MA (1) = –1.296 632] (4.847 705) (–3,375.690)

R 2 = 0.971 257, adj. R 2 = 0.964 072, F = 135.166 2, DW = 1.979 664. Rather than providing a historical narrative or econometric analysis of the adjustment and reform of China’s taxation regime, the CMAFM model indicates the average effective tax rate using the consolidated government revenue to GDP ratio, and then depicts the taxation regime by a second-order autoregressive progress. (10) The monetary transmission mechanism: log(M 2R t/YR t) = 0.040 153 8 + 0.988 617 • log(M 2R t – 1/YR t – 1) + 0.634 178 (2.688 767) (41.324 03) (4.400 906) • Δlog(M 1R t/YR t) + [MA (1) = 0.847 433] (7.497 118)

R 2 = 0.997 149, adj. R 2 = 0.996 646, F = 1,981.994, DW = 2.336 196. Money supply management in China takes place through multi-stratum recursion and gradual of transmission from the monetary base to narrow money and broad money (Zheng, Yu, and Teng 2000). The CMAFM takes narrow money supply as the monetary policy control variable. In view of the near-unitroot estimate of its lagged variable, the observed partial adjustment mechanism

113

Theoretical System of China's Macroeconomic Analysis

of the coefficient on broad money holdings is almost rooted in its non-stationary time series. After making the model stationary by time differencing, narrow money supply will be scattered into broad money supply, thereby decelerating immediately. For the purpose of evaluating its performance in historical simulation, the CMAFM is computed to dynamically forecast the endogenous variables during 1981 to 2001 based on exogenous variables from historical data before 2001 and endogenous variables from historical data before 1981. The actual and forecast time paths of basic endogenous variables, including national income, household consumption, fixed investment, inventory investment, net exports, inflation rates, and interest rates, are illustrated in Fig. 7.3, where the suffix “_DF ” denotes the forecast values, while the means and rooted mean squares of the errors in historical simulation are presented in Table 7.4.5 Fig. 7.3

Historical simulations of endogenous variables

(a)

Real GDP: YR 0.15 0.12 0.09 0.04 0.03

1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 ∆log(YR_DF)

∆log(YR)

Real household consumption: PRIVCR

(b) 0.16 0.12 0.08 0.04 0.00

1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 ∆log(PRIVCR_DF)

114

∆log(PRIVCR)

The Macroeconomic Analysis and Forecasting Model of China (CMAFM)

Real fixed investment: FCIR

(c) 9.60 8.90 8.20 7.50 6.80

1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 ∆log(FCIR_DF)

∆log(FCIR)

Real inventory investment: INVR

(d) 1400 900 400 –100 –600

1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 INVR_DF

INVR

Real net exports: NEXR

(e) 1000 650 300 –50 –400

1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 NEXR_DF

NEXR

115

Theoretical System of China's Macroeconomic Analysis

Inflation rates: INFL

(f) 20.0 14.0 8.0 –2.0 –4.0

1981

1983

1985 1987

1989

1991

INFL_DF

1993 1995

1997 1999

2001

1997 1999

2001

INFL

Nominal interest rates: R 3

(g) 13.2 9.9 6.6 3.3 0.0

1981 1983

1985 1987

1989

1991

R3

Table 7.4 Variable logYR logPRIVCR logFCIR INVR

NEXR INFL R3

116

1993 1995

R3_DF

Errors in the original CMAFM’s dynamic historical simulations Relative error

Absolute error

Mean (%) –0.144 008 0.054 161 –0.227 523 — —

RMSE (%) 0.333 738 0.420 688 1.265 945 — —

Mean — — — –64.699 13 –23.863 31

RMSE — — — 192.205 1 178.818 0

—

—

0.056 356

3.330 860

—

—

–0.281 190

1.334 952

The Macroeconomic Analysis and Forecasting Model of China (CMAFM)

The CMAFM dynamically simulates the intertemporal response of national income to the transitory increase in government expenditure and narrow money supply, from which we can compute the dynamic multipliers of government expenditure and narrow money supply within the 21-year adjustment interval. Their time paths, ΔYR /ΔGR and ΔYR /ΔM 1R , respectively, are depicted in Fig. 7.4. As observed, the impulse–response function of China’s national income to fiscal and monetary policies follows a normal inverted U﹣shaped, front﹣loaded pattern.6 Fig. 7.4

(Original) dynamic multipliers of government expenditure and money supply

1.30

0.15

1.10

0.11

0.90

0.07

0.70

0.03

0.50

0

2

4

6

8

10

∆YR/∆GR(–i)

i

12

14

16

18

20

–0.01

∆YR/∆MIR(–i)

117

118

8

Chapter

Hysteresis, Multiple Equilibriums, and Reverse Soft Landing: China’s Experience in Demand Management

Theoretical System of China's Macroeconomic Analysis

From Historical Experience to Theoretical Hypothesis China’s economic growth miracle can be interpreted ex post from the point of view of economics. From the 1980s onward, the economy made great progress in the “triple transition” of industrialization, marketization, and economic opening as large-scale capital accumulation and labor mobilization took place, laying a solid ground for rapid, sustainable growth backed by abundant resources, advanced technology, and sound institutions. Nevertheless, ex ante prediction of economic growth for the expansionary phase of China’s (autonomous) business cycle before the subprime crisis was characterized by undervaluation and pessimism bias. During 2003 to 2006, its actual GDP growth rates continuously surpassed the forecast values; the predicted inflection point toward negative growth, too, was delayed until the subprime crisis, as illustrated in Fig. 8.1 (a). At the same time, the expansionary phase witnessed widespread concern and warnings over the development of rapid economic growth into overheating and high commodity prices into general inflation. However, as it turned out, although economic growth followed an uninterrupted, accelerating path throughout the period, inflation was moderated by constraints on basic supplies such as raw materials, energy, and transportation. As expressed in the inflation dynamics illustrated in Fig. 8.1 (b), price levels descended in the order of the PPI of raw materials, fuel, and power (RMFPPI), the general PPI, and CPI. Fig. 8.1 (a) Real-time forecast errors of China’s GDP growth rates 1.2 0.6 % 0.0 –0.6 –1.2

2003 2004 2005 2006 Absolute error Absolute acceleration rate

120

2003 2004 2005 2006 Foreasted acceleration rate

Hysteresis, Multiple Equilibriums, and Reverse Soft Landing: China’s Experience in Demand Management

Fig. 8.1 (b) China PPI, CPI, and GDP deflator inflation 12.0 9.0 % 6.0 3.0 0.0

2003 2004 2005 2006

2003 2004 2005 2006

RMFPPI (Raw materials, fuel and power)

CPI

PPI (All industrial products)

GDP deflator

In times of drastic changes of the economic structure and economic conditions, such as during the late 1990s and early 2000s following the Asian financial crisis, undervaluation of China’s potential economic growth rates seems unavoidable. This may lead to the adoption of undesirable contractionary demand management and hinder economic expansion so far as to cause irrational economic depression. 1 With the potential continuous expansion of aggregate supply, which cannot be accurately determined in real time, China should iterate the positive trial-and-error process of “increasing aggregate demand on a small scale → monitoring inflation → increasing aggregate demand on a small scale (in the absence of accelerated inflation) / decreasing aggregate demand on a small scale (in the case of accelerated inflation),” exploring its future direction for aggregate supply through intermittent augmentation of aggregate demand; i.e., by “fine-tapping” instead of fine-tuning. In view of the large gap between the growth of actual and potential aggregate supply, a certain degree of tension between supply and effective demand should be maintained provided the prevention of economic overheating. In practical terms, the reverse soft landing of potential aggregate supply on actual aggregate demand should be realized via an upward equilibrating mechanism, where potential aggregate supply increases alongside demand for investment.2 Although pragmatic syntheses of new Keynesian and new classical macroeconomics tend to verify Keynes’ law in the short run but Say’s law in

121

Theoretical System of China's Macroeconomic Analysis

the long run, Keynes’ law holds both in the short run and long run if aggregate supply responds sensitively to aggregate demand and actual aggregate supply can be sufficiently converted into potential aggregate supply. By investigating into the growth model, Phillips curve, and policy preference of China, this chapter seeks to conceptualize the macroeconomic significance of China’s demand management experience during its economic expansion before the subprime crisis. Through interactions between the Phillips curve, which accommodates hysteresis effects on potential national income, and China’s policy preference, which sought maximum sustainable growth under price stability, as the constraint and objective of China’s demand management, respectively, the fine-tapping approach and the resulting reverse soft landing process shall be verified within the conventional theoretical framework of macroeconomic analysis. Positive research is laid out in three main sections: First, a quasi-AK model and its associated vintage investment model are built in consideration of China’s dualistic economy, endowing economic growth with the investmentdriven nature and actual national income with the influence of hysteresis. The second section examines the prudent demand management policy, which is inclined to generate multiple actual national income equilibriums and subject to the low-level equilibrium trap, after augmenting the Phillips curve with hysteresis for China. The third section turns to proactive demand management, which has the potential for generating a unique equilibrium of actual national income through fine-tapping and hence achieving the reverse landing of actual national income on the upper limit of potential national income. The chapter concludes by depicting the inherent equilibration of the domestic investment gap and the undervalued exchange rate in China’s economic stabilization mechanism in light of the Keynesian approach, which is based on the chain reaction among fixed investment, trade surplus, and banking liquidity.

Potential National Income with Hysteresis Effects The quasi-AK growth model revisited Let’s revisit the quasi-AK growth model introduced in Chapter 3. Under a dual economy, China possesses a nearly unlimited supply of surplus labor in the traditional agricultural sector. As the Lewis model in Fig. 8.2 demonstrates, in the modern industrial sector, the labor supply curve (L S) is asymptotic to the labor force (L max) and perfectly elastic below the Lewis turning point (L LTP), while the actual wage rate is at the subsistence wage level ( ). According to the labor

122

Hysteresis, Multiple Equilibriums, and Reverse Soft Landing: China’s Experience in Demand Management

demand equilibrium condition, the marginal product of labor (MPL ) is equal to , and the relative surplus labor is L LTP – L * when the actual employment is L *.3 Fig. 8.2

Labor supply, labor demand, and employment w L

0

D

L*

L

L

LTP

S

L

max

L

According to the Cobb-Douglas aggregate production function Y = A • K α • L 1 – α, MPL = (1 – α) • A • (K /L )α, and (K /L )* = ( /(A • (1 – α)))1/α , so Y = A • K • (( /A )/(1 – α))(α – 1)/α. Define the time function φ(t) such that it describes the intertemporal determination of the equilibrium capital–labor ratio (K /L )* by the time-varying parameters of subsistence wage ( ) and technology (A ): φ(t ) = A • ((

/A )/(1 – α))(α – 1)/α = (1 – α)(1 – α)/α• (A /

1 – α 1/α

)

.

(8–1)

Hence, China’s aggregate production function is given by Y = φ(t ) • K , and the marginal capital product MPK = φ(t ) with non-diminishing returns to capital, which is fixed in the short run but variable in the long run. Functionally analogous to the AK model, China’s economy is capable of endogenous growth, driven by capital accumulation. Without data on the initial capital stock, the general form of China’s aggregate production function is immeasurable in principle. The time differencing of the aggregate production function Y = φ(t ) • K results in

Y ’ = φ • K ’ + (φ’/φ) • Y.

(8–2)

Subject to the coefficient constraint α = β’/β, we can estimate the implicit difference form of China’s aggregate production function as ΔY t = α • Y t – 1 + β • ΔK t – 1, if investment only forms capital stock in a single period.

The vintage investment model of endogenous growth Assume that investment forms capital stock in many periods and capital has its own life cycle, establish a vintage investment model of endogenous growth of

123

Theoretical System of China's Macroeconomic Analysis

China in congruence with Solow’s vintage capital approach. Within k periods of the capital life cycle, assume that investment at time t (I t) contributes to capital stock at time t + i (K t + i) by capital formation (I t • w (i )) according to the probability density w (i ); therefore,

K t = ∫0 → k (I t – i • w (i ))di ,

(8–3)

where ∫0 → kw (i )di = 1. Based on the aggregate production function Y = φ(t ) • K t and investment–saving function I t = s • Y t,

Y t = s • φ(t ) • ∫0 → k (I t – i • w (i ))di .

(8–4)

On the basis of Kaldor ’ stylized facts (Barro and Sala-i-Martin 2003), suppose that China’s capital–output ratio (K /Y ) is stable over time, and degenerate the time function φ(t ) into a constant coefficient, that is to suggest that the subsistence wage ( ) grows at 1/(1 – α) times the pace of the technological level (A ). After detrending the size variables K , Y , and I using the natural growth rate δ and omitting the constant coefficient terms, we obtain the autoregressive equations of China’s national income in the discrete form:

Y t = ∏1 → k {(Y t – i • (1 + δ)i)w(i)},

(8–5)

and logY t = ∑1 → k {w (i ) • (logY t – i + i • log(1 + δ))}.

(8–6)

China’s potential national income growth reacts to actual national income growth with a time lag, reflecting the hysteresis effect through capital formation. The potential GDP growth rate is fixed in the long run (i.e., growing at δ) but variable in the short run (i.e., departing from δ).4 Dependent on the probability density w (i ), the productivity of vintage investments in various periods may differ with respect to capital stock formation. This is contrary to vintage capital models, of which capital stock has no difference in productivity in terms of national income across periods once corrected with the natural growth rate (δ).

Potential national income and the output gap With the accepted time lag of k = 5, which is about half the length of the Juglar cycle, estimate the autoregressive model for China’s actual GDP in 1978 to 2006

124

Hysteresis, Multiple Equilibriums, and Reverse Soft Landing: China’s Experience in Demand Management

using OLS, as given by the exponential product equation Y t = (∏1 → 5Y t – i)1/5 • (1 + δ)3 under an uniformly-distributed linear-weighted probability density, and the log-linear equation logY t = ∑1 → 5{w (i ) • (logY t – i + i • log(1 + δ))} under a front-loaded cosine-weighted probability density.5 The linear weight function is defined as w (i ) = 1/k = 1/5, whereas the cosine weight function w (i ) = cos((i – 1)•(π/2k )) = cos((i – 1)•(π/10)), which is assigned with the normalized values listed in Fig. 8.3 under the constraint that ∑1 → 5w (i ) = 1. Hence:

Y t = (∏1 → 5Y t – i)1/5 • (1 + 0.095 895)3 (40.542 86) R 2 = 0.996 476, SE = 20.739 75, DW = 0.376 479;

(8–7)

logY t = ∑1 → 5 {w (i ) • logY t – i}+ log(1 + 0.099 421) •∑1 → 5 { i • w (i )} (26.678 86)

R 2 = 0.995 977, SE = 0.041 890, DW = 0.560 367. Fig. 8.3

Cosine weight function: w (i ) = cos((i – 1) • (π/10))

0.3

w(i)

(8–8)

0.273457

0.260074 0.221232

0.2

0.160734

0.1 0.0

0.084503

1

2

3

4

5

Produce a static forecast of China’s actual GDP from the estimation of the

autoregressive exponential product equation, and then static and dynamic

forecasts of the same index from the autoregressive estimation of the exponential product equation, for the purpose of generating their respective

time series of China’s potential GDP during 1983 to 2006. Then, compute the

time series of China’s relative output gap by comparing the actual and potential national income of all three forecasts, as plotted in Fig. 8.4.

125

Theoretical System of China's Macroeconomic Analysis

Fig. 8.4

China relative GDP gaps

10 5

%

0 –5 –10 1983

1985

1987

1989

1991

Linear/Static

1993

1995

1997

Cosine/Static

1999

2001

2003

2005

Cosine/Dynamic

China’s natural rate of potential GDP growth (δ) was 9.589 5% in the

adoption of linear weights, and 9.942 1% when calculated with cosine weights. As the linear weights are evenly distributed over time but the cosine weights

are tilted toward the current time, the fact that a higher δ is yielded from the calculation with cosine weights reveals an accelerating trend of economic

growth, and correspondingly, the cosine-weighted output gap indicates heavier

economic contractions and more moderate economic expansion. Moreover, since the dynamic forecast simulates historical actual GDP growth rates during 1983 to 2006 based on historical data before 1982, preserving historical inertia

from before 1982 while omitting developments after 1983, it predicts a longer

lag in economic contraction and milder economic recession when compared to the static forecast.

Multiple National Income Equilibriums under Prudent Demand Management Classical demand management The classical Phillips curve π = –α • (u – u *) + L [π] assumes a constant natural

growth rate of potential national income without considering the effect of hysteresis. Accordingly, aggregate supply takes the form of the Lucas supply function:

126

Hysteresis, Multiple Equilibriums, and Reverse Soft Landing: China’s Experience in Demand Management

y – y * = λ • (π – L [π]),

(8–9)

in which L is the lag operator, and adaptive inflation expectations are defined

as πE = L [π].

Incarnate the preference for a prudent policy regarding the national income

target (y T) and inflation target (πT) by the quadratic loss function V = θ • (y –

y T)2 + (π – πT)2. The stability-oriented demand management policy can then be expressed by static optimization:6 min V = θ • (y – y T)2 + (π – πT)2 s.t. y – y * = λ • (π – L [π]).

(8–10)

On the plane y –π in Fig. 8.5, prudent demand management explores the

smallest loss ellipse V = θ • (y – y T) 2 + (π – π T) 2, which is tangent to the AS

curve y – y * = λ • (π – L [π]), while the long-run AS curve is the vertical line

LRAS : y = y *. With the discretionary but predetermined inflation target πT = π*,

if the national income target is rightly set so y T = y *, the inflation and national

income targets of prudent demand management (y *, π*) will be simultaneously realized in the long run, independent from the coefficients θ and λ, which

denote the policy preference for price stability and full employment, and the sensitivity of short-run aggregate supply to unexpected inflation, respectively.

Real-time forecast errors of potential national income are unavoidable

(Mishkin 2007). Nevertheless, even if an improper national income target is set so y T ≠ y *, and consequently, V = θ • (y – y T)2 > 0, the loss ellipse V will still be

tangent to the line LRAS at the point (π*, y *) in the long-run equilibrium. As

the center (y T, π*) is attracted toward the long point (y *, π*) through gradual

learning from real-time potential national income forecasts, V shrinks and

finally condenses into (y *, π*). Fig. 8.5 shows two loss ellipses centered at A

and B , which, respectively, correspond to scenarios where potential national

income is underestimated and overestimated. They shrink toward the right and left alongside the correction of the national income target while remaining

tangent to the line LRAS at the point (π*, y *) throughout the process. Under prudent demand management, the value of potential national income is always

fixed, while the national income target is intertemporally adjusted according

to the qualitative rule Δy T • (y T – y *) < 0, resulting in the unique equilibrium position (y *, π*).

127

Theoretical System of China's Macroeconomic Analysis

Fig. 8.5

Unique equilibrium under prudent demand management LRAS

π

π* A

0

B

y*

y

Estimation and appraisal of China’s Phillips curve Thanks to the challenges made by Edmund Phelps and Milton Friedman, the original Phillips curve π = –α • (u – u *) took on an (adaptive inflation) expectations-augmented, accelerationist form: π = –α • (u – u *) + L [π].

(8–11)

Further augmented by the factor of supply shocks, it subsequently evolved into the triangle model: π = –α • (u – u *) + L [π] + z ,

(8–12)

in which (u – u *) stands for the output gap, L [π] represents inflation inertia, and z refers to supply shocks.7 Based on the triangle model, build a Phillips curve for China by directly assuming the output gap (y – y *) and omitting the supply shock term (z ): π = α • (y – y *) + L [π].

(8–13)

Then, refine it by abandoning the natural growth hypothesis on potential national income and introducing the hysteresis effect from actual national income such that y * = L [y ]: π = α • (y – L [y ]) + L [y ].

128

(8–14)

Hysteresis, Multiple Equilibriums, and Reverse Soft Landing: China’s Experience in Demand Management

As the time series of potential GDP that reflects the hysteresis effect is accessible, calculate the inflation rates using the GDP deflator, and then estimate China’s Phillips curve π = α • (y – L [y ]) + L [y ] for the period of 1984 to 2006 by OLS both under the hysteresis and natural growth hypotheses. The structural equation is formulated as πt = c + ∑1 → 2 {ρi • πt – i} + α • (y t – y *), as illustrated in t Table 8.1. Table 8.1

Estimation of China’s Phillips curve ΔlogP t = C 0 + C 1 • logP t – 1 + C 2 • logP t – 2 + C 3 • Δlog(Y t/Y t*) + [MA (2) = C 4] Linear/static

Hysteresis: logY * = L [logY ] Cosine/static

Cosine/dynamic

Natural Growth: ΔlogY * = const.

C0

0.224 046 (4.471 886)

0.202 027 (4.216 622)

0.264 154 (4.255 682)

0.145 682 (2.565 042)

C1

0.584 128 (6.052 765)

0.639 814 (6.822 612)

0.412 482 (3.372 834)

0.426 688 (3.515 701)

C2

–0.620 081 (–6.850 799)

–0.672 492 (–7.621 784)

–0.453 429 (–3.958 508)

–0.466 571 (–4.100 425)

C3

0.816 652 (4.653 028)

0.830 682 (4.497 807)

1.206 017 (4.154 909)

1.176 983 (4.091 436)

C4

–0.920 963 (–23.603 28)

–0.903 343 (–17.213 02)

–0.958 001 (–31.432 48)

–0.953 140 (–29.072 97)

R2

0.863 771

0.860 665

0.830 726

0.832 185

0.833 498

0.829 702

0.793 109

0.794 893

0.020 174

0.020 402

0.022 488

0.022 391

Adj. R 2

SE

DW

2.370 973

2.333 612

1.942 421

1.908 382

In comparison with the natural growth hypothesis, the fitness and overall significance of the OLS estimation greatly improves under the hysteresis hypothesis. The estimation coefficients C 0 and C 1 increase while C 2 and C 3 decrease, resulting in a nearly invarible sum of (C 1 + C 2) and a smaller ratio of (C 1 + C 2)/C 3. When the output gap is zero in the intertemporal equilibrium, the autoregressive process of the equilibrium inflation rate πt = c + ∑1 → 2 {ρi • πt – i} is stable under the coefficient constraint that (ρ1 + ρ2) < 1, and has a steady solution: π = c /(1 – ρ1 – ρ2), so the inflation target inherently determines the constant coefficient such that C 0 = π* • (1 – C 1 – C 2). Hence, as potential national income takes into account the hysteresis effect in relation to actual national income, the autoregressive process for China’s inflation becomes more volatile intertemporally and less sensitive to the output gap with a similar sensitivity to the inflation inertia, while the steady-state inflation rate is inclined to rise.8

129

Theoretical System of China's Macroeconomic Analysis

The low-level and high-level equilibrium traps In conformity to the refined Phillips curve π = α • (y – y *) + L [π], China’s aggregate supply function takes the form of

y – L [y ] = λ • (π – L [π]),

(8–15)

from which we can derive the long-run AS curve LRAS : y = L [y ]. Suppose the lag distribution function L [y ] is first-order homogeneous, so the fixedpoint equation of potential national income y * = L [y ] has multiple equilibrium solutions within the technically feasible interval of [y m* in, y m* ax]. Except in the special case where the national income target is equal to potential national income, the realized equilibrium of actual national income is critically dependent on the initial national income target.9 As illustrated in Fig. 8.6 (a), when the national income target is moderately set, * < y T < ymax * , the loss ellipse V , which is initially centered at point A , for an so ymin underestimation of potential national income, and point B , for an overestimation of potential national income, is tangent to the line LRAS at the point (y *, π*), and then gradually shrinks around its two center points as potential national income decreases or increases and the line LRAS moves correspondingly to the left or right, until finally degenerating into the point (y T, π*). While the national income target is constant, potential national income is adjusted intertemporally according to the qualitative rule Δy T • (y T – y *) < 0. The equilibrium position of actual national income is stable, and it is randomly distributed within the interval [ym* in, ym* ax] depending on the initial national income target. Fig. 8.6 (b) illustrates the scenarios when the national income target is extremely * or y T > ymax * . As in Fig 8.6 (a), the loss ellipse V is initially centered set, so y T < ymin at point A (for an underestimated potential national income) or point B (for an overestimated potential national income) and tangent to the line LRAS at point (y *, π*), before gradually shrinking around the respective centers as potential national income decreases or increases and the LRAS line moves leftward or rightward. Once the LRAS line arrives at the limit positions, y = ym* in or y = ym* ax, and stops moving, the center points at (y T, π*) start moving toward (ym* in, π*) or (ym* ax , π*), * , π*) while V shrinks around the moving centers, until finally degenerating into (ymin * , π*). The equilibrium of actual national income is stable, and it is positioned or (ymax * ) or upper technical limit (ymax * ) depending on either at the lower technical limit (ymin T T * or y > ymax * . During the early the initial national income target, i.e., whether y < ymin adjustment period, the national income target is fixed, while potential national income is adjusted intertemporally according to the qualitative rule Δy T • (y T – y *) < 0. In the late adjustment period, potential national income is fixed at either of

130

Hysteresis, Multiple Equilibriums, and Reverse Soft Landing: China’s Experience in Demand Management

the technical limits, while the national income target is adjusted intertemporally according to the same qualitative rule. Fig. 8.6 (a) Multiple equilibriums under prudent demand management: y m* in < y T < y m* ax LRAS

π

π*

A

0

B

y*

y*min

y* max

y

Fig. 8.6 (b) Multiple equilibriums under prudent demand management: y T < y m* in or y T > y m* ax LRAS

π

π*

B

A

0

y*min

y*

y* max

y

Fine-Tapping and Reverse Soft Landing Meanwhile, let’s shift our study of stability-oriented demand management to an exploration of the dynamic optimization of the parabolic loss function V = –θ • y + (π – πT)2, which represents a proactive resource utilization and price stabilization policy and is tangent to the AS curve y – L [y ] = λ • (π – L [π]):

131

Theoretical System of China's Macroeconomic Analysis

min V = –θ • y + (π – πT)2 s.t. y – L [y ] = λ • (π – L [π])

(8–16)

As Fig. 8.7 illustrates, given the inflation rate target π T = π* and the LRAS curve y = L [y ], proactive demand management is capable of realizing the highest sustainable growth rate of actual national income at the upper technical limit of potential national income (y m* ax), with a stable and unique final equilibrium position of national income at the point (y m* ax, π*). Initially tangent to the LRAS curve at the original potential national income level (y *), the loss parabola V leads the LRAS curve to move rightward until reaching the upper limit position, i.e., when y = ym* ax, increasing the size of potential national income. Correspondingly, the tangent point (y *, π*) moves rightward along the horizontal axis π = π*, arriving at the final equilibrium position (ym* ax, π*).10 Fig. 8.7

Unique equilibrium under proactive demand management LRAS

π

π*

0

y*min

y*

y*max

y

Fig. 8.9 shows that proactive demand management is capable of realizing

the reverse soft landing of potential national income on actual national income through fine-tapping operations during economic expansion, as epitomized by

the rise of actual and potential national income from the historical equilibrium

level (y’ ) to the upper technical limit (ym* ax). From the time t 1 onward, aggregate

demand expands and thereby elevates the inflation rate and results in an

increase in actual national income, which is then brought down to potential

national income by hysteresis. Then, following t 2, aggregate demand remains

stable while potential national income continues expanding; therefore the output gap is narrowed and inflation is decelerated. At t 3 , the output gap

132

Hysteresis, Multiple Equilibriums, and Reverse Soft Landing: China’s Experience in Demand Management

disappears altogether as actual and potential national incomes arrive at the upper technical limit (ym* ax), and the actual inflation rate returns to the desired level (π*).11 Fig. 8.8

Reverse soft landing of national income and inflation y*max

y, y* y y* t

π*

π 0

t1

t2

t3

t

Assume that the maximum affordable inflation rate is the target inflation rate plus the increment, i.e., (π* + ), and the maximum speed of adjustment of the lag distribution function y = L [y ] is ν. According to the aggregate supply function y – y * = λ • (π – L [π]), Fig. 8.8 linearly approximates the fastest path of national income growth from y’ to ym* ax. The following properties are observed: (1) From t 1 to t 2, the actual inflation rate is equal to (π* + ), and the vertical distance between the time paths of actual national income and potential national income is (λ • ). (2) The time span from t 1 to t 3 is (ym* ax – y’ )/ν, and the time span from t 2 to t 3 is (λ • )/ν, so the time span from t 1 to t 2 is equal to (ym* ax – y’ – λ • )/ν. (3) From t 2 to t 3, the inflation rate decelerates at the speed of (ν/λ).12

The Keynesian Approach to Equilibration Analysis on the formation and circulation mechanisms of China’s “macroeconomic trinity” of large fixed investment, large trade surplus, and large banking liquidity has conventionally been done complementarily from the structuralist and monetarist perspectives.13 Structuralism postulates that local governments and state-owned enterprises tend to overinvest with non-economic motives,

133

Theoretical System of China's Macroeconomic Analysis

while overinvestment by private enterprises involves irrationality. Monetarism deals with the undervaluation of RMB exchange rates, attributing this to the objective of achieving external balance. As Fig. 8.9 illustrates, structuralism and monetarism, respectively, advocate the policies of containing fixed investment and appreciating RMB exchange rates following their own lines of argument, although both schools agree that the closed loop of interaction among fixed investment, trade surplus, and banking liquidity gives rise to positive feedback, resulting in a destabilizing mechanism in economic circulation. Starting with the presumption of overinvestment, structuralism delineates the following reaction chain for China’s economy: increase of fixed investment → expansion of the domestic supply capacity (beyond domestic absorption) → increase of net exports (to absorb the domestic supply capacity) → increase of foreign reserves → increase of money supply and domestic credits → increase of fixed investment. The reaction chain put forward by monetarism, which starts with the presumption of currency undervaluation, is as follows: increase of net exports → increase of foreign reserves → increase of money supply and domestic credits → increase of fixed investment → expansion of the domestic supply capacity (beyond domestic absorption) → increase of net exports (to absorb the domestic supply capacity). Fig. 8.9

Structuralist and monetarist views of economic instability Monetarism: Underevaluation

Structuralism: Overinvestment

Fixed investment

+

+ Credit balance

Supply capacity

+

+ +

Money supply

Trade surplus +

+

Foreign reserves

The high propensity to save out of national income in China’s economy is reasonable, in the sense that it can be explained by multiple causes, such as households’ deployment of capital accumulation during economic transition, the current life-cycle model featuring a young population and a growing economy, and the prevalence of the precautionary saving motive induced by the yet imperfect

134

Hysteresis, Multiple Equilibriums, and Reverse Soft Landing: China’s Experience in Demand Management

social security measures. Moreover, the presence of a large and increasing trade surplus, also ascribable to many factors, reveals the problems of savings surplus and currency undervaluation. 14 The Keynesian approach to analyzing China’s economic circulation uncovers an equilibrating mechanism in the chain reaction of fixed investment, trade surplus, and banking liquidity, which hinges on the reduction of domestic savings surplus and the appreciation of RMB exchange rates, as illustrated by Fig. 8.10. The adjustment of domestic savings surplus starts with the presumption of underinvestment, seeking to boost investment in the following process: deficiency of domestic investment (below domestic savings) → deficiency of domestic absorption (below the domestic supply capacity) → trade surplus (to absorb the domestic supply capacity) → increase of foreign reserves → increase of money supply and domestic credits → increase of fixed investment → contraction of domestic savings surplus (and the domestic investment gap). As for the appreciation of RMB exchange rates, the mechanism begins with the presumption of currency undervaluation and acts through the enhancement of inflation as such: undervaluation of exchange rates → increase of net exports (to absorb domestic capacity) → increase of foreign reserves → increase of money supply and domestic credits → increase of fixed investment and aggregate demand → rise of the inflation rate → appreciation of (real) exchange rates. Fig. 8.10

Keynesian view of economic stabilization

Trade surplus

+

+ Foreign reserves

Underinvestment

− Fixed investment

+

+

−

+ Money supply

+

Undervaluation

−

− Inflation

+

Credit balance

135

Theoretical System of China's Macroeconomic Analysis

Data Appendix The Chinese Academy of Social Sciences’ annual Blue Book of China’s Economy contains a summary of forecasts on the economic growth rate of the succeeding year that are conducted by government, university, and other academic forecasting institutes apart from the Academy’s own contribution, all submitted for its “Fall Forum of Analyses and Forecasts on China Economy.” Based on the summaries in the 2002 to 2007 Blue Book of China’s Economy , Table 8.2 demonstrates the real-time forecast errors regarding China’s economic growth rates during 2002 to 2006. Under the column “Current forecast” are the results from forecasts done in the current year, while “Prior forecast” shows the forecast results from the year before. Table. 8.2 Year

Real-time forecast of China economic growth rates (%) Actual GDP growth rate

Forecasted GDP growth rate

Statistical Communique

Statistical Yearbook

Acceleration rate

Current forecast

Prior forecast

Acceleration rate

Absolute error

2002

—

8.0

—

7.8

—

—

—

2003

9.1

9.3

1.1

8.2–8.6

7.4–8.3

0.05

–0.70

2004

9.5

9.5

0.2

9.0–9.5

8.0–9.5

0.35

–0.25

2005

9.9

10.2

0.4

9.4

8.0–9.0

–0.75

–0.50

2006

10.7

—

0.5

10.3–10.6

8.9

–0.50

–0.25

At

Bt

At – Bt – 1

Dt

Et

Et – Dt – 1

Dt – At

Regarding the annual actual GDP growth rates, the National Bureau of Statistics routinely publishes official primary statistics in its Statistical Communique in March of the succeeding year, and then authenticated statistics in its Statistical Yearbook in the following October, which are further revised to be included in every year ’s Statistical Yearbook thereafter. As the “Fall Forum of Analyses and Forecasts on China Economy” is scheduled for October every year, when measuring the real-time (rather than ex post ) forecast errors in the annual Blue Book of China’s Economy , we can assume that the forecast information set for GDP growth prediction included the Statistical Yearbook of the current year, while the actual growth rate was to be realized in the Statistical Communique of the succeeding year. As such, Table 8.2 takes the primary GDP growth rate that appeared in the Statistical Communique of the succeeding year as the actual GDP growth rate, and uses the absolute difference between data published in the Statistical Communique of the succeeding year

136

Hysteresis, Multiple Equilibriums, and Reverse Soft Landing: China’s Experience in Demand Management

and the Statistical Yearbook of the current year as the acceleration rate of GDP growth. Based on basic data from China Statistical Yearbook 2007 , Table 8.3 calculates China’s GDP deflator, and then its real GDP and real capital formation in 2006 RMB according to the formulas P t = (GDPt/Y ’t )/(GDP1978/Y ’1978) •100, Y t = Y ’t • (P 2006/P 1978) • 100 and ΔK t = CF t • (P 2006/P 1978). Table 8.4 that follows presents the time series of China’s potential GDP from 1983 to 2006, which is statically forecasted based on the linear-weighted exponential product autoregressive equation of China’s actual GDP, statically and dynamically forecasted based on the cosine-weighted log-linear autoregressive equation of China’s actual GDP, and then measured in 2006 RMB. It also presents China’s relative GDP gap during 1983 to 2006 across scenarios, which is obtained by comparing the potential GDP values to the actual GDP figures displayed in Table 8.3 Table 8.3

China national income, inflation, and capital formation GDP

Year

RMB billion (current price) GDP t

1978=100 Y ’t

Capital formation GDP deflator RMB billion RMB billion 1981=100 RMB billion (current (2006 price) (2006 price) Pt price) Yt ΔK t CF t

1978

364.52

100.0

1,581.05

100.0

137.79

597.64

1979

406.26

107.6

1,701.21

103.6

147.89

619.29

1980

454.56

116.0

1,834.01

107.5

159.97

645.43

1981

489.16

122.1

1,930.46

109.9

163.02

643.35

1982

532.34

133.1

2,104.37

109.7

178.42

705.30

1983

596.27

147.6

2,333.62

110.8

203.90

798.00

1984

720.81

170.0

2,687.78

116.3

251.51

937.83

1985

901.60

192.9

3,049.84

128.2

345.75

1,169.57

1986

1,027.52

210.0

3,320.20

134.2

394.19

1,273.74

1987

1,205.86

234.3

3,704.39

141.2

446.20

1,370.72

1988

1,504.28

260.7

4,12179

158.3

570.02

1,561.88

1989

1,699.23

271.3

4,289.38

171.8

633.27

1,598.57

1990

1,866.78

281.7

4,453.81

181.8

674.70

1,609.71

1991

2,178.15

307.6

4,863.30

194.3

786.80

1,756.74

1992

2,692.35

351.4

5,555.79

210.2

1,008.63

2,081.36

137

Theoretical System of China's Macroeconomic Analysis

(Cont'd) GDP

Capital formation GDP deflator RMB billion RMB billion 1981=100 RMB billion (current (2006 price) (2006 price) Pt price) Yt ΔK t CF t

RMB billion (current price) GDP t

1978=100 Y ’t

1993

3,533.39

400.4

6,330.51

242.1

1,571.77

2,816.02

1994

4,819.79

452.8

7,158.97

292.0

2,034.11

3,021.32

1995

6,079.37

502.3

7,941.59

332.0

2,547.01

3,327.21

1996

7,117.66

552.6

8,736.86

353.3

2,878.49

3,533.32

1997

7,897.30

603.9

9,547.93

358.8

2,996.80

3,623.17

1998

8,440.23

651.2

10,295.77

355.6

3,131.42

3,819.85

1999

8,967.71

700.9

11,081.55

351.0

3,295.15

4,071.87

2000

9,921.46

759.9

12,014.37

358.2

3,484.28

4,219.28

2001

10,965.52

823.0

13,012.01

365.5

3,976.94

4,719.15

2002

12,033.27

897.8

14,194.63

367.7

4,556.50

5,374.92

2003

13,582.28

987.8

15,617.57

377.2

5,596.30

6,434.90

2004

15,987.83

1087.4

17,192.29

403.3

6,916.84

7,437.93

2005

18,386.79

1200.8

18,980.29

420.1

8,064.63

8,324.94

2006

21,087.10

1334.0

21,087.10

433.7

9,410.32

9,410.32

Year

Table 8.4 Year

China potential national income and output gap Potential GDP (RMB billion, 2006 price) Y *t

Linear weights

138

Cosine weights

Static

Static

Dynamic

1983

2,396.63

2,391.06

1984

2,590.71

1985

Linear weights

Output gap (%) (Y t – Y *t)/Y *t

Cosine weights

Static

Static

Dynamic

2,391.06

–2.647 7

–2.420 9

–2.420 9

2,591.04

2,608.46

3.726 9

3.713 7

3.021 0

2,838.87

2,861.26

2,856.21

7.410 7

6.570 3

6.758 6

1986

3,142.83

3,201.56

3,137.53

5.623 3

3.685 6

5.801 7

1987

3,502.85

3,578.34

3,455.00

5.733 3

3.502 7

7.197 6

1988

3,922.29

4,000.17

3,801.83

5.065 9

3.020 4

8.395 0

Hysteresis, Multiple Equilibriums, and Reverse Soft Landing: China’s Experience in Demand Management

(Cont'd) Year

Potential GDP (RMB billion, 2006 price) Y *t Linear weights

Cosine weights

Output gap (%) (Y t – Y *t)/Y *t Linear weights

Cosine weights

Static

Static

Dynamic

Static

Static

Dynamic

1989

4,394.92

4,459.58

4,176.32

–2.420 2

–3.834 9

2.687 4

1990

4,825.60

4,866.01

4590.96

–7.722 3

–8.488 7

–3.006 2

1991

5,205.26

5,213.88

5,048.05

–6.587 5

–6.741 9

–3.678 4

1992

5,618.18

5,586.71

5,550.46

–1.129 4

–0.572 5

0.077 0

1993

6,092.58

6,070.38

6,102.11

3.885 2

4.265 2

3.723 0

1994

6,638.53

6,703.39

6,708.49

7.819 0

6.775 9

6.694 7

1995

7,354.67

7,515.46

7,375.53

7.959 5

5.649 8

7.654 2

1996

8,256.55

8,457.28

8,108.91

5.797 0

3.285 9

7.723 3

1997

9,282.90

9,464.50

8,915.09

2.835 4

0.862 2

7.078 0

1998

10,344.66

10,489.72

9,801.40

–0.491 7

–1.867 8

5.023 7

1999

11,401.46

11,499.30

10,775.85

–2.824 5

–3.651 4

2.817 2

2000

12,442.58

12,500.35

11847.21

–3.460 0

–3.906 2

1.391 5

2001

13,516.65

13,544.80

13,025.07

–3.751 9

–3.952 0

–0.119 4

2002

14,637.49

14,654.24

14,320.02

–3.044 2

–3.154 9

–0.894 7

2003

15,845.63

15,884.41

15,743.72

–1.458 2

–1.698 7

–0.820 3

2004

17,222.67

17,304.03

17,308.97

–0.195 6

–0.664 8

–0.693 2

2005

18,803.87

18,933.85

19,029.84

0.944 9

0.2519

–0.253 7

2006

20,605.83

20,799.31

20,921.79

2.335 6

1.383 7

0.790 1

Renewed Estimation and Simulation Appendix In Table 8.5, the log-linear equation of China’s actual GDP autoregressive model logY t = ∑1 → 5{w (i ) • (logY t – i + i • log(1 + δ))}is estimated for 1978 to 2009 with geometric weights and cosine weights using OLS, the time lag (k ) set as 5. The geometric weight function w (i ) = q i, which has a more fronted-loaded probability density than the cosine weight function, is subject to the constraint that ∑1 → 5w (i ) = 1, and the assignment of its normalized values is portrayed in Fig. 8.11. Finally, Fig. 12 shows the time series of China’s potential GDP, and the associated absolute and relative GDP gaps across scenarios as derived from a dynamic forecast of the real GDP index over the years.

139

Theoretical System of China's Macroeconomic Analysis

Table 8.5

Estimation of China’s potential national income {w (i ) • (logY t – i + i • log(1 + δ))}

logY t =

qi

cos((i – 1) • (π/2k ))

0.100 980 (25.743 74)

0.100 524 (28.519 27)

R2

0.997 789

0.996 709

SE

0.033 770

0.041 198

w (i )

δ

Geometric weight function: w (i ) = q i

Fig 8.11

w(i)

0.6

0.508660

0.4 0.258735

0.2 0.0

100 million current RMB

Fig. 8.12

0.131608

1

2

3

4

0.034052

5

China absolute and relative GDP gaps

16000

12

8000

6

0

0

–8000

–16000

–6

1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Absolute / Geometric Absolute / Cosine

140

0.066944

Relative / Geometric Relative / Cosine

–12

%

9

Chapter

China’s Monetary Policy Rules and the Effects of Monetary Policy

Theoretical System of China's Macroeconomic Analysis

The Effects of Money Supply in Economic Stabilization The “discretion versus rules” debate over demand management reveals possible dynamic inconsistency in Keynesian monetary policy and the resulting consequence of inflation. While monetarist and new classical economics approve of mechanical rules such as Friedman’s k % rule of constant money growth and seem to deny any discretion to monetary authorities, we can formulate Keynesian monetary policy into a special reaction function analogous with the Taylor rule, and classify it as a form of constrained discretion. Table 9.1 presents a system of money supply policy rules especially designed for China based on the equation of exchange of the quantity theory of money MV = PY . It consists of the Friedman/monetarist rule of constant growth, Lucas’s rule of monetary neutrality, and the Keynesian rule of countercyclical policy. Table 9.1

Money supply targeting policy rules

Monetarist constant growth rule

gM Monetarism = π* + gY * – gV *

Lucas neutrality rule

gM Lucas = πE + gY * – gV E

Keynesian countercyclical policy rule

gM Keynesianism = πT + gY T – gV E

π* + gY * – gV *

Anticipated / optimal / target inflation rate

YE / Y*

Anticipated / potential national income

VE / V*

Anticipated / trends of velocity of money

Under the assumption of extreme liberalism, the real economy operates in continuous equilibrium, independent of monetary shocks, whereas extreme monetarism assumes that the velocity of money is intertemporarily stable, independent of the monetary policy. Theoretically, the monetarist rule gM Monetarism = π* + gY * – gV * should be optimal both in the short run and long run, enabling full employment in the context of price stability. However, in practice, it can only serve as a long-term reference for money supply management, and be considered solely as a forward-looking policy rule with no memory of the history of money supply; otherwise, money supply will be prone to over-deflation after overheating or over-inflation after depression, leading to deflation or inflation of the economy. The new classical neutrality rule gM Lucas = π E + gY * – gV E is defined

142

China’s Monetary Policy Rules and the Effects of Monetary Policy

under the rational expectations and perfect market hypotheses, with which the Lucas aggregate supply function Y /Y * = f (P /P E) implies that gY = gY * when π = π E. In other words, the neutrality rule will not suffice for the full employment equilibrium under partial rational expectations and an imperfect market. Relating the monetarist and Lucas rules, (gM Lucas – gM Monetarism) • ((π E – π*) – (gV E – gV *)) ≥ 0. It is difficult to compare their effectiveness during economic depression, as the procyclicality of inflation expectations and the velocity of money have opposing effects on the economy. Yet in times of economic overheating, the Lucas neutrality rule is inclined toward being overly accommodative, favoring a higher money supply target than the monetarist rule. An equivalent of the Lucas aggregate supply function Y /Y * = f (P /P E), the expectation-augmented Phillips curve π = πE + β • (gY – gY *) can be modified into a Keynesian form: π = πT + β • (gY – gY T) (9–1) when the growth target is set correctly and the inflation target is realized in the long run, i.e., gY * = gY T and π E = π T, thereby defining the Keynesian countercyclical policy rule gM Keynesianism = πT + gY T – gV E. Without errors in the potential national income forecast, i.e., gY * = gY T, (gM Keynesianism – gM Lucas) • (πT – πE) ≥ 0, and (gM Keynesianism – gM Monetarism) • ((πT – π*) – (gV E – gV *)) ≥ 0. Thus, compared to the Lucas rule, the Keynesian rule is more contractionary during overheating and more expansionary during depression; compared to the monetarist rule, it is also likely to be more expansionary during overheating.1

The effects of income and price stabilization Fig. 9.1 shows the time paths of China’s M1 and M2 growth targets during 1992 to 2009, which are computed according to the system of policy rules laid out in Table 9.1. Calculate potential national income by the equation logY t = ∑ 1 → 5{w (i ) • (logY t – i + i • log(1 + δ))}, where hysteresis from actual income is factored in. Its growth rate is the mean of the natural growth rate (δ) in 1983 to 2009 based on the geometric weight function w (i ) = q i and cosine weight function w (i ) = cos((i – 1) • (π/2k )): gY * = 10.192 60%. Then, using the long-run cointegration relationship between the two indexes between 1991 and 2009, i.e., Δln(GDPPI) = 2.494 578 + 0.680 511 • Δln(CPI), map the 2% CPI inflation target of the Taylor rule onto the 3.855 601% GDP deflator inflation target. Next, find the exponential growth trends (β) of the M1 and M2 velocity of money by an OLS estimation of the log-linear function lnV = α + β • t : gV 1* = –2.776 415% and gV 2* = –4.062 905%. Finally,

143

Theoretical System of China's Macroeconomic Analysis

adopting the rational expectations hypothesis and the two-year business cycle of Keynesian economics, λ = 0.5, gY T = gY – λ • (gY – gY *), and πT = π – λ • (π – π*). Table 9.2 presents a correlation analysis between the absolute deviation of actual money supply, the Lucas neutrality rule, and the Keynesian countercyclical policy rule from the monetarist constant growth rule, i.e., (gM – gM Monetarism), (gM Lucas – gM Monetarism), and (gM Keynesianism – gM Monetarism), as well as the absolute deviation of the actual economic growth rates and inflation rates, respectively, from the national income targets and price stabilization targets, i.e., (gY – gY T) and (π – πT), of 1992 to 2009. Fig. 9.1 (a) Actual and target growth rates of China M1 money supply

40

Actual

30

Monetarist

%

Lucas 20

10

Keynesian

1992 1994 1996 1998 2000 2002 2004 2006 2008

Fig. 9.1 (b) Actual and target growth rates of China M2 money supply

40

Actual

30

Monetarist

%

Lucas 20

10

144

Keynesian

1992 1994 1996 1998 2000 2002 2004 2006 2008

China’s Monetary Policy Rules and the Effects of Monetary Policy

Table 9.2

Cyclical correlation of China’s money supply targets: ρ(x , z ) Z

M2

1997–2009

gY – gY T

π – πT

gY – gY T

π – πT

gM – gM Monetarism

0.651 484

0.696 282

0.080 290

–0.155 762

gM Lucas – gM Monetarism

0.367 648

0.547 783

–0.571 071

–0.568 034

gM Keynesianism – gM Monetarism

0.145 953

0.107 584

–0.565 482

–0.755 455

gM – gM Monetarism

0.557 310

0.792 462

0.007 167

–0.180 063

gM Lucas – gM Monetarism

0.261 659

0.645693

–0.588 859

–0.554 474

gM Keynesianism – gM Monetarism

0.060 244

0.296 683

–0.584 310

–0.744 384

X

M1

1992–2009

As Table 9.2 illustrates, the Lucas rule and Keynesian rule were procyclical in 1992 to 2009, but strongly leaned against economic fluctuations and inflation during the period of 1997 to 2009. China’s stability-oriented monetary policy since 1997 can be summarized as an ascension from the Lucas neutral money rule to the Keynesian countercyclical policy rule, from an income stabilization objective to a price stabilization one, and from M2 to M1.2

Interest Rates, Exchange Rates, and Endogenous Economic Fluctuations Monetary interaction among interest rates, exchange rates, and inflation rates Denote the nominal exchange rate and its equilibrium value by e and e *, the domestic and international inflation rates by πN and πW, and the domestic and international nominal interest rates by R N and R W. Then, the extent of exchange rate overvaluation can be measured by (e *t – e t), and the expected arbitrage returns from international capital inflows by ((R N,t – R W,t) – E [e t + 1 – e t]), while e *t + 1 = e *t + (πN,t – πW,t). Construct the inflation response function to describe the monetary channel of balance of payments (BoP) through which the domestic inflation rate is influenced by the exchange rate disequilibrium and international capital arbitrage, where π 1 < 0 and π 2 > 0: πN,t = π(e *t – e t, (R N,t – R W,t) – E [e t + 1 – e t]). (9–2)

145

Theoretical System of China's Macroeconomic Analysis

Next, establish the interest rate policy rule to signify the comovement between the domestic interest rate and domestic inflation rate, where 0 < R ’ < 1, which does not conform to the Taylor principle:

R N,t = R (πN,t). (9–3) .

Then, set the partial adjustment equation to describe the exchange rate equilibrating mechanism, where 0 ≤ A ’ ≤ 1:

e t + 1 – e t = A (e *t – e t). (9–4) Hence, we can model the process of interaction among China’s interest rate, exchange rate, and inflation rate into a dynamic system [I] and its first-order linear approximation system [II], where > 0, > 0, 0 < b > πW > R . As for the equation e + 1 = e + a • ((e * + πN – πW) – e ), when a = 0, it will be reduced to e + 1 = e , which represents a fixed exchange rate regime; when a = 1, it will be reduced into e + 1 – e = πN – πW, which represents a floating regime (from the historical starting point of e = e *); and when a increases in the interval (0, 1), the exchange rate regime evolves from the spectrum of fixed exchange rates to one of floating exchange rates. Meanwhile, abandon the general solution where 0 ≤ a ≤ 1 and examine only the static equilibrium under the extreme condition where a = 1, and the dynamic equilibrium under the extreme condition where a = 0.4 When a = 1, system II can be combined into system III:

146

China’s Monetary Policy Rules and the Effects of Monetary Policy

R N = πN =

+ b • max{πN – , 0} + l • ((R N – R W) – (πN – πW)),

[III]

and its equilibrium solution is illustrated in Fig. 9.2. On the plane π N – R N, the straight line PP : πN = + l • ((R N – R W) – (πN – πW)) is intersected with the upward segment of the kinked line RR : R N = + b • max{πN – , 0} at point G , which determines the equilibrium solution (π N*, R N*) to system III. Since d(ΔπN)/ dπN < 0, the equilibrium solution (π N*, R N*) is globally stable.5 Fig. 9.2

Static equilibrium solution for floating regimes RN

nac

R*N

H

PP

RR

G

– R RW – πW 0

45˚

– R

π*N

πN

– RW – πW – π/1

In Fig. 9.2, the auxiliary line nac : R N = πN + (R W – πW) is drawn to define the no-arbitrage condition for international capital inflows. Corresponding to the equilibrium point G , the vector HG is equal to the expected arbitrage returns from international capital inflows, and HG = (R N* – π N*) – (R W – πW) < 0, manifesting positive returns from international capital outflows. As system III enters the stage where πN > , the partial adjustment of the interest rate following the inflation rate will have the effect of increasing the positive returns from international capital outflows. It is due to the deflating effect of international capital outflows that π N* < .6 In the case of an internal inflation shock, which causes to rise, the line PP is shifted downward, while point G moves upward and rightward along the line RR . With an autonomous hike of the domestic interest, as denoted by the rise of , the line RR is shifted upward, while point G moves rightward and upward along the line PP . Hence, d(π N*, R N*)/d( , ) > 0. Because the vector HG changes in opposite directions to and , respectively, dHG /d < 0, and dHG /d > 0. Moreover, as dπ N*/d = 1/(1 – l • (1 – b )) < 1, when brings shocks to system III, international capital inflows will counteract by going up or down, thereby dampening inflation rate fluctuations.

147

Theoretical System of China's Macroeconomic Analysis

Endogenous economic fluctuations under managed-float regimes Assume that the exchange rate target of an adjustable-peg regime is (e * – e ) [–ξ –, ξ +], implying the adoption of a one-sided state-dependent policy rule amid domestic inflation (i.e., when πN > πW): e + 1 = e when e * – e ≤ ξ+; e + 1 = e + ξ– + ξ+ when e * – e > ξ+ .7 If –ξ– < e * – e < ξ+ , system II can be combined into system IV under the extreme condition where a = 0:

R N = πN =

+ b • max{πN – , 0} – k • (e * – e ) + l • (R N – R W).

[IV]

Similar to Fig. 9.2, Fig. 9.3 depicts a diagrammatic analysis of system IV, in which the straight line PP: – k • (e * – e ) + l • (R N – R W) is located at PP *, PP ’, and PP ’’, respectively, when e * – e = 0, e * – e < ξ +, and e * – e < –ξ –, and distributed in parallel between PP ’ and PP ’’. It is also intersected with the upward segment of the kinked line RR : RN = + b • max{π N – , 0} at point G , where the equilibrium solution (π N*, R N*) is determined. The no-arbitrage condition for international capital inflows is denoted by the horizontal line nac : R N = R W, while the expected returns from international capital inflows equal the vector HG , with HG = R N* – R W > 0. Hence, d(πN*, R N*)/d( , ) > 0, dHG /d( , ) > 0, and dπN*/d = 1/(1 – lb ) > 1. Fig. 9.3

Dynamic equilibrium solution for managed-float regimes RN

PP’ PP* PP’’ G’

– R RW

0 – + (kξ–)/1 RW – π/1 – RW – π/1 – – (kξ–)/1 RW – π/1

148

G*

G’’

RR

nac – R

π*N’

π*N’’

πN

China’s Monetary Policy Rules and the Effects of Monetary Policy

The lines PP *, PP ’, and PP ’’ cross with the line RR at points G *, G ’, and G ’’, respectively. At point G *, where e * = e , the equilibrium is transient. Since πN* >

πW, e * and (e * – e ) grow intertemporarily at the speed of (πN* –πW), shifting PP

to the left from PP * to PP ’. Accordingly, the equilibrium position of system IV moves along the line RR to the left from G * to G ’. As the line PP arrives at PP ’

and (e * – e ) reaches the upper bound of the exchange rate target interval, i.e., ξ+, e is depreciated to (e – 1 + ξ – + ξ+), causing PP to jump to the right to PP ’’. Meanwhile, e * – e = –ξ–, and e * grows at a new speed of (πN* – πW), shifting PP

leftward to PP *, where preemptive depreciation is triggered off once again.

Thus, system IV cycles between points G ’ and G ’’, forming the exchange rate and inflation rate time paths illustrated in Fig. 9.4, in which time t 1 and t 2

mark the critical moments when PP jumps rightward from PP ’ to PP ’’. On account of the linear relationship between R N* and πN* (when πN > ), the vector

HG fluctuates in the same phase as R N*, and both R N* and international capital inflows follow a time path that is analogous to that of πN*. Fig. 9.4

Exchange rate and inflation rate time paths

e* + δ

e*

–

e

e* – δ+ 0

t πN, RN

0

t1

t2

t

Fig. 9.4 stipulates that international capital does not anticipate preemptive

depreciation before (e * – e ) advances near the upper bound of the targeted

interval. If international capital is able to anticipate preemptive depreciation so that E [e + 1 – e ] = ξ– + ξ+, expected returns from international capital inflows

will be equal to ((R N – R W) – (ξ– + ξ+)) instead of (R N – R W), leading international

capital inflows to scale down or even reverse. Therefore, shortly before

149

Theoretical System of China's Macroeconomic Analysis

preemptive depreciation, the line PP jumps upward to the line πN = – k ξ+ + l • (R N + ξ– + ξ+) (above PP ’); when preemptive depreciation happens, PP jumps rightward to PP ’’, inevitably intensifying system IV’s volatility at t 1 and t 2.

A capital asset pricing model for exchange rates For China’s economy, assume that capital assets comply with the riskless no-arbitrage condition P t = (D t + P t + 1)/(1 + R N), where P and D denote the asset price and asset returns, respectively, and that there are fixed returns on production and variable returns on investment, the latter being proportional + κ • P t + 1. Furthermore, assume that uncovered to the asset price, so D t = interest rate parity holds (IRP), so that 1 + R N = (1 + R W) • (1 + Δe ).8 Hence, the riskless no-arbitrage equation for asset pricing is given by + (1 + κ) • P t + 1)/((1 + R W) • (1 + Δe )),

Pt = (

(9–5)

and the fundamental equilibrium solution to the asset price /((1 + R W)) • (1 + Δe ) – (1 + κ)) ≈

P* =

/(R W + Δe – κ). (9–6)

In the primitive case where Δe = 0 and κ = 0, P * = /R W, and P */ = 1/R W. In the practical case where Δe < 0 and κ > 0, P * = /(R W + Δe – κ) > /R W, and D* = + κ • P* = • ((R W + Δe )/(R W + Δe – κ)). Thus, for the price–return ratio in the practical case, when measured simply in terms of the returns on production, it is expressed as

P */

= 1/(R W + Δe • (1 + R W) – κ) ≈ 1/(R W + Δe – κ),

with P */ counted,

P */(

(9–7)

> 1/R W; when the returns on both production and investment are + κ • P *) = 1/(R W + Δe • (1 + R W)) ≈ 1/(R W + Δe ),

(9–8)

with P */ > P */D > 1/R W. On the plane P t + 1 – P t in Fig. 9.5, the 45° line crosses with the line P t = ( +P t + 1)/(1 + R N), determining the equilibrium solution to the asset price in the primitive case, and also with the line P t = ( + (1 + κ) • P t + 1)/(1 + R N) • (1 + Δe )), which gives the equilibrium solution in the practical case. Confronted with a shock, i.e., when Δe < 0 and κ > 0, the asset price jumps upward from the position of the primitive equilibrium solution /R W to meet the line P t = ( + (1 + κ) • P t + 1)/(1 + R N) • (1 + Δe )), along which it moves toward the final equilibrium solution /(R W + Δe – κ). If Δe and κ are so small

150

China’s Monetary Policy Rules and the Effects of Monetary Policy

Fig. 9.5

Fundamental equilibrium solutions to the capital asset price – Pt = ( D+(1+K) • Pt+1 )/((1+RW) • (+ ∆e))

Pt

– Pt = ( D+Pt+1 )/(1+RW ) – D/(1 + RW + ∆ e) – D/(1 + RW )

0

45˚

– D/RW

– D/(RW + ∆e – K)

Pt + 1

that (1 + κ)/((1 + R W) • (1 + Δe )) < 1, the asset price will be globally stable when it converges forward along P t = ( + (1 + κ) • P t + 1)/(1 + R N) • (1 + Δe )). Predictably, the dynamic adjustment of China’s capital asset price will take the shape of the time paths P t and P t/D t in Fig. 9.6. At t 1, exchange rates start to appreciate and returns on investment begin to emerge, while t 3 signifies the end of the dynamic adjustment. When P t + 1 = /R W, D t = • (1 + (1 + κ)/R W), so P t = • (1 + (1 + κ)/R W)/((1 + R W) • (1 + Δe )) ≈ ( /R W) • (1 – Δe + κ), and P t/D t ≈ 1/ (R W + κ). Fig. 9.6

Time paths of the capital asset price and price–return ratio

– D/(RW +∆e – K) – D/(RW + K )

Pt

– ( D/RW ) • (1 –∆e + K) – D/RW

0 Pt /Dt

1/(RW + ∆e)

t

1/RW

1/RW +K) 0

t1

t2

t3

t

151

Theoretical System of China's Macroeconomic Analysis

As Fig. 9.6 illustrates, the time paths P t and P t/D t originate, respectively, from the primitive equilibrium solutions /R W and 1/R W. Then, at t 1, P t jumps upward to the point ( /R W) • (1 – Δe + κ), while P t/D t descends to 1/(R W + κ). Both time paths increase monotonically afterwards, arriving at their respective final equilibrium solutions /(R W + Δe – κ) and 1/(R W + Δe ) at t 3. With the equation P t/D t = 1/R W determining the critical price value /(R W – κ) at t 2, P t/D t reverts to 1/R W from the point 1/(R W + κ), while P t steps up to /(R W – κ). During the transition period from t 1 to t 2, the asset price continues increasing, while the price–return ratio stays below the primitive equilibrium level, contributing to a desirable P t and P t/D t combination that creates a “golden age” for China’s capital market.

Data Appendix Table 9.3 computes the growth rate target of China’s M1 and M2 money supply during 1992 to 2009 for the system of policy rules presented by Table 9.1 according to the growth rate version of the equation of exchange, i.e., gM + gV = π + gY . Although the Keynesian money supply growth and inflation targets have memory, all other monetary policy rules adopt money supply growth targets with no memory. Therefore, as a rolling forecast, the target balance of current money supply is extrapolated from the actual money supply balance of the preceding year, according to the current money supply growth target. Table 9.3

Actual and target growth rates of China M1 and M2 money supply gM

1992

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

152

30.677 62

37.550 58

31.448 06

20.930 12

17.908 14

20.645 35

16.480 45

14.923 96

16.739 41

14.178 40

15.691 73

18.544 07

gM Monetarism Average M1 balance (%) 17.709 29

17.709 29

17.709 29

17.709 29

17.709 29

17.709 29

17.709 29

17.709 29

17.709 29

17.709 29

17.709 29

17.709 29

gM Lucas 26.048 66

33.021 71

28.083 82

20.124 10

18.099 62

21.649 09

19.029 91

17.657 97

18.650 50

16.169 73

16.862 44

18.725 18

gM Keynesianism 25.763 99

28.668 86

20.754 62

15.291 00

16.573 71

22.544 98

20.555 19

19.300 99

18.729 19

16.191 36

18.167 87

19.353 86

China’s Monetary Policy Rules and the Effects of Monetary Policy

(Cont'd) gM 2004

2005

2006

2007

2008

16.185 70

12.860 47

14.792 43

19.407 89

14.423 08

2009

21.156 28

1992

29.179 67

1993

1994

1995

1996

1997

1998

1999

2000

2001

2002

2003

34.702 06

35.701 04

31.625 50

27.092 33

22.101 44

16.998 96

14.784 41

13.419 02

15.089 49

17.205 39

18.327 78

2004

17.010 08

2006

16.549 61

2005 2007

2008

2009

16.311 39

16.246 60

17.297 07

23.079 99

gM Monetarism Average M 1 balance (%) 17.709 29

17.709 29

17.709 29

17.709 29

17.709 29

gM Lucas 16.301 36 11.726 12 12.26488

15.251 75

15.006 17

gM Keynesianism 14.581 67 11.797 06

13.006 67

14.671 35

12.616 75

17.709 29

22.358 91

24.498 01

19.287 73

24.603 77

24.322 36

Average M2 balance (%) 19.287 73

19.287 73

19.287 73

30.266 98

32.227 95

30.748 19

19.287 73

27.298 73

19.287 73

19.559 77

19.287 73

19.287 73

19.287 73

19.287 73

19.287 73

19.287 73

23.117 29 17.51510

19.713 93

18.508 56

13.983 36

19.287 73

21.091 84

19.156 12

18.391 43

19.287 73

19.287 73

24.024 00

15.352 20

17.126 56

19.287 73

25.487 64

25.653 97

15.275 75

17.096 72

19.287 73

19.287 73

26.004 27

24.661 61

15.142 35

12.200 50

17.894 81

24.301 71

17.118 52

19.136 10

15.394 67

15.215 46

14.736 50 11.635 47

15.445 38

26.474 77

153

Theoretical System of China's Macroeconomic Analysis

Appendix: The Empirical and Theoretical Foundation of China’s Macroeconomic Policy Orientation* Stylized Facts on the Pre-crisis Macroeconomy An unsustainable economic miracle Having gone through two entire trough-to-trough cycles during the periods of 1982 to 1990 and 1991 to 2001, China’s economy started to expand in 2002, reached a new peak in 2007, and then entered another phase of contraction under the heavy impact of the global financial crisis, as illustrated in Appendix Fig. 1. Appendix Fig. 1

Economic growth and inflation in China

25 20 15 % 10 5 0 –5

1978 1980 1982 1984 1986 1988 1990 19921994 19961998 2000 2002 2004 2006 2008

GDP

CPI

RPI

* This appendix, originally titled “Experimental and Theoretical Foundation of China’s Macroeconomic Analysis,” was first delivered at the international conference “China and the Great Recession: The Global Financial Crisis and China’s Development,” which was jointly sponsored by the University of Chicago Center in Beijing and the Renmin University’s School of Economics and held in Beijing, China on July 30 and 31, 2010.

154

Appendix

The 10.014 6% average annual GDP growth rate during the three decades from 1978 to 2007, especially in the period of 2002 to 2007, when rapid growth was combined with low inflation, had once led to the commendation that China was forging a new economic miracle. However, even before the outbreak of the international financial crisis, doubts had emerged over the sustainability of the miracle, which was on the verge of being refuted when the nation’s GDP growth plunged in 2008. Thereafter, its government struggled to guarantee an annual growth of 8%.

Unstable economic circulation Before the financial crisis, China’s economy was characterized by a large fixed investment, a large trade surplus, and large banking liquidity. Part of mainstream macroeconomics, structuralism and monetarism started investigating the “macroeconomic trinity” of fixed investment, trade surplus, and banking liquidity by highlighting the problems of overinvestment and undervaluation in China’s economic structure, respectively, concluding that China’s economic circulation was unstable per se, as illustrated in Appendix Fig. 2. Appendix Fig. 2

Structuralist and monetarist views of economic instability Monetarism: Underevaluation

Structuralism: Overinvestment

Fixed investment

+

+ Credit balance

Supply capacity

+

+ +

Money supply

Trade surplus +

+

Foreign reserves

Structuralism attributed the large fixed investment to the expansion

instinct of state-owned enterprises and local governments as well as the irrational behavior of private enterprises, identifying it as an overinvestment

in domestic demand. As for monetarism, it ascribed large trade surplus mainly

to the undervalued RMB exchange rate. Therefore, without necessary policy intervention, positive feedback in China’s economic circulation tended to result in internal overheating and external imbalance.

155

Theoretical System of China's Macroeconomic Analysis

A decoupled business cycle Since the 1980s, China’s economy has been increasingly integrated into the world economy. After its accession to the WTO, the nation’s economic growth accelerated. Accompanying it was the simultaneous surge of its dependence on trade and the size of its trade surplus, leading some to regard China and the U.S. as the twin engines of the world economy at the turn of the century. As Appendix Fig. 3 illustrates, in the early stage of this last expansion phase, China’s economic conditions coincided with those of the U.S., but into the latter half of the phase, different pathways are observed: while China’s economy continued expanding, the U.S. economy started to contract. This explains the pre-crisis proposition that China’s economy had decoupled from the U.S. economy, becoming a unique engine of the world economy that would be immune to global financial crises. Appendix Fig. 3

China GDP growth and U.S. GDP gaps

14

8

12

4

% 10

0 –4

8 6

%

1Q00 1Q01 1Q02 1Q03 1Q04 1Q05 1Q06 1Q07 1Q08 1Q09 China GDP (5QMA)

–8

U.S. GDP gap

The Mode of Economic Growth and Price Stabilization Mechanism A quasi-AK model of economic growth Despite its progress in industrialization, China’s economy still falls within the category of dual economies, that is to say, its labor supply has yet to pass the critical Lewis turning point in spite of the rising (subsistence) wage level, as illustrated by Appendix Fig. 4.

156

Appendix

Appendix Fig. 4

The revised Lewis model w

D L’

LD

L

S

w*’ w 0 w

L

D

LD’

L

L

LTP

L*’ L

L

max

Beyond the turning point

S

w 0

L*

Classical model

L*

L*’

L

LTP

L

max

Subsistence wage up

L w

L

D

D L’

L’ S

w’

L

S

w

0

L* L*’

L

LTP

L

max

L

China’s economy takes the form of the Cobb-Douglas production function

Y = A • K α • L 1 – α, and its equilibrium capital-labor ratio is given by (K /L )* = (( /A )/(1 – α))1/α, where is the subsistence wage level. Therefore, Y = A • K • (( /A )/(1 – α))(α – 1)/α = φ(t) • K , in which the time function is defined as φ(t ) A (t ) • (( (t )/A (t ))/(1 – α))(α – 1)/α, summarizing the time-varying parameters of and A . Because MPK = φ(t ), which implies the non-diminishing returns to capital accumulation, China’s economic growth follows the endogenous classical AK model. Through time differencing, dY /dt = φ • (dK /dt ) + (dφ/dt ) • K, so dY /dt = φ(t ) • (dK /dt ) + (dφ/dt ) • (Y /φ). If fixed investment produces capital stock only in one single period, it is feasible to estimate the first-order temporal difference of China’s production function Y = φ(t ) • K such that ΔY t = α • Y t – 1 + β • ΔK t – 1 without data on the initial capital stock, being subject to the coefficient constraint that α = Δβ/ β. Appendix Table 1 presents an OLS estimation of China’s production function ΔY t = α • Y t – 1 + β • ΔK t – 1 in 1981 to 2009, in which φ(t ) = exp(C 1 • T ) = e –0.035 414 • T

157

Theoretical System of China's Macroeconomic Analysis

and K /Y = 1/φ(t ) = e 0.035 414 • T. Hence, China’s economic growth is driven by capital accumulation, and is undergoing capital deepening. Appendix Table 1

Estimation of China’s production function

ΔYt = C 1 • Y t – 1 + exp (C 1 • T ) • ΔK t – 1 + C 2 + [AR (1) = C 3]

Coefficient

Estimate

C2

–869.758 1

C1 C3

–0.035 414 0.519 796

SE

t -stat

P > |t |

–2.985 820

0.006 1

0.001 837

–19.279 73

0.175 586

2.960 353

291.296 3

0.000 0

0.006 5

R = 0.940 985, adj. R = 0.936 445, SE = 511.485 7, DW = 1.543 096. 2

2

Economic growth forecast and growth accounting As Appendix Fig. 5 illustrates, the forecast of China’s economic development from 2008 to 2032 predicts economic fluctuations in three possible scenarios before 2012, and then economic growth over two consecutive periods after 2013 on account of the effect of the political business cycle as revealed by the CPC’s18th National Congress in 2012. For the period of 2008 to 2012, the scenario-based forecast produces an S -curve that signifies mild recovery followed by stable expansion, an inverted J -curve that denotes slow recovery followed by strong expansion, and an inverted-U curve that suggests rapid recovery followed by heavy decline in predicting the short-run response to the global financial crisis. For 2013 to 2032, when the impact of the global financial crisis will have faded out, the lines labeled mean , max , and min are presented to denote the average, upper limit, and lower limit of the average annual growth rate of potential national income. Appendix Fig. 5 InY

Horizon and envisioning of China’s economic development J

max mean min

S

U

0

158

2008

2012

2013

2022

max mean min 2023

2032

Appendix

Appendix Table 2 and Appendix Table 3 successively present China’s forecasted GDP growth rates and a growth accounting of its economy. Although economic growth will inevitably slow down in the long run, the economy still has significant potential for high savings, high investment, and high growth in the future decades. Considering the factor of technological process, capital accumulation shall contribute to China’s economic growth far more than Table A.3 suggests. Appendix Table 2

S J

U

2009

2012

2008–2012

11.01

9.54

10.29

10.32

9.50

10.94

9.67

9.06

8.00

9.39

2013–2022

10.34

8.82 8.54

min

8.26

Appendix Table 3

GDP L K A

2011

10.07

mean

GDP L K A

2010

8.74

max

GDP L K A

China GDP annual growth rates (%)

2023–2032

9.68 9.63

7.65 7.48 7.31

Growth accounting of China (%)

S /mean J /max U /min Growth Contribution Growth Contribution Growth Contribution rate rate rate 2008–2012 9.68 100.00 9.54 100.00 9.63 100.00 1.22 5.04 1.10 4.61 1.15 4.78 11.07 68.62 11.02 69.29 11.10 69.16 2.55 26.34 2.49 26.10 2.51 26.06 2013–2022 8.82 100.00 8.54 100.00 8.26 100.00 1.02 5.97 1.05 5.95 1.00 6.05 10.36 60.66 10.51 59.58 10.12 61.26 2.85 33.37 3.04 34.47 2.70 32.69 2023–2032 7.48 100.00 7.65 100.00 7.31 100.00 0.92 7.38 0.94 7.37 0.91 7.46 9.45 50.52 9.62 50.29 9.27 50.70 3.15 42.10 3.24 42.34 3.06 41.84

159

Theoretical System of China's Macroeconomic Analysis

The forecasted growth rates during 2008 to 2032 are decomposed from the supply-side perspective into primary, secondary, and tertiary industries, as well as from the demand-side perspective into consumption, investment, and net exports, as illustrated in Appendix Fig. 6. In line with the PettyClark Law, China will eventually transform from an industrial economy to a service economy, and domestic demand will substitute for net exports whereas consumption demand will substitute for investment demand, resulting in the gradual rebalancing of the economic growth drivers. Nevertheless, for a long time in the future, the industrial sector will continue to dominate over the service sector, as will investment demand over consumption demand. Appendix Fig. 6 (a) Supply structure of China’s economic growth 10.0 7.5

4.00

3.98

3.96 3.72

3.59

3.47

4.83

4.69

4.53

0.27

0.26

0.26

3.48

3.36

3.29

4.01

3.97

3.87

0.16

0.16

0.15

% 5.0 2.5 0.0

5.27

5.14

5.26

0.41

0.42

0.41

08-12 08-12 08-12 (S) (J) (U)

13-22 13-22 13-22 (max) (mean) (min)

Agriculture

Industry

23-32 23-32 23-32 (max) (mean) (min) Service

Appendix Fig. 6 (b) Demand structure of China’s economic growth 10.0

0.97

0.95

0.91

7.5 % 5.0 2.5 0.0

0.88 4.23

4.00

3.71

3.40

4.75

4.60

4.82

4.07

3.95

3.99

3.91

3.61

08-12 08-12 08-12 (S) (J) (U) Consumption

160

0.86

0.86

13-22 13-22 13-22 (max) (mean) (min) Investment

0.56

0.57

3.41

3.56

3.31

3.51

3.52

3.46

0.53

23-32 23-32 23-32 (max) (mean) (min) Net Exports

Appendix

Historical trends of price stability Along with the institutional transition from a planned economy toward a market economy, China’s inflation mechanism has been transformed from one with a high inflation pattern to its current state with a low inflation pattern. Accordingly, Appendix Table 4 makes a (conceptual) moderation hypothesis on the evolution of China’s historical stationary inflation in three stages. China’s GDP deflator annual inflation rates from 1983 to 2009 are simulated using the AR (2) process π t = C (0) + ∑1 → 2 {C (i ) • π t–i} + DUM , so the steadystate inflation rate is expressed as π * = (C (0) + DUM t)/(1 – C (1) – C (2)). As Appendix Fig. 7 illustrates, the time path of China’s GDP deflator verifies the moderation hypothesis, in which π * = 8.967 705% in 1983 to 1997, and π * = 2.826 585% in 1998 to 2009. Appendix Table 4

Evolution of China’s inflation trends

Phase I: mid-to-late1980s

Pattern

Phase II: early-to-mid 1990s

Phase III: post-late 1990s

High inflation

High inflation

Driver

Repressed inflation

Labor compensation Competitive market

Expression

Product price liberalization

Increased nominal wage

Mechanisms

Low inflation

Increased productivity

Background Goods market reform Labor market reform Market Integration Appendix Fig. 7   China steady-state GDP deflator inflation rates 25 20 15 % 10 5 0 –5

1984

1986

1988

Actual

1990

1992

1994

1996

Regressed

1998

2000

2002

2004

2006

2008

Steady

Resource and commodity prices are prone to rise, pushing up final goods prices solely by domestic factors, such as a heavy-industry-dominated industrial structure, strict environmental protection regulations, and improved public ownership. However, technological progress and productivity growth with the

161

Theoretical System of China's Macroeconomic Analysis

support of domestic and international competition have the effect of absorbing such cost-push forces along the production chain in stages, containing the pass-through effect from upstream to downstream goods. In 1993 to 2009, China’s CPI was not sensitive to the PPI, nor was its PPI sensitive to the purchasing price index of raw materials, fuel, and power (RMFPPI). The indexes are related by co-integrated long-run equilibrium relationships defined as such: πCPI = 0.801 095 1 • πPPI – 0.219 014 • T + 6.221 351, and πCPI = 0.658 271 • πRMFPPI – 0.317 252 • T + 7.774 349. China’s economy has maintained, and will continue to demonstrate a descending structure of dynamic stationary inflation rates beginning with the RMFPPI, followed by the PPI and the GDP deflator, and ending with a moderate CPI inflation rate.

The Equilibration and Coupling of Economic Fluctuations The Keynesian approach to equilibration The disequilibrium school did not revive along with the post-crisis renaissance of Keynesianism, but the taxonomy of non-Walrasian equilibriums, mainly incarnated in the Barro–Grossman–Malinvaud (BGM) model, inspired broad Keynesian recognition of China’s macroeconomic equilibrium. Based on the BGM model, China’s economy is framed both from the vertical perspective of structure and cyclicity as well as the horizontal perspective of the planned economy and market economy, as illustrated in Appendix Table 5. On top of the perpetuation of classical unemployment typical of dual economies is the alternation between underconsumption and Keynesian unemployment according to the business cycle, after departing from the repressed inflation of the planned economy era. For a growing economy with a relatively young population, the life cycle theory of consumption and savings predicts a high propensity to save out of national income. In China’s economy, the large trade surplus is not only derived from the undervalued RMB exchange rate, but also mirrors the investment gap in the national income identity S – I = X – M , which is analogous to the two-gap model. Considering the economy’s high saving rate, the Keynesian approach to macroeconomic analysis and policies regarding China is centered on enhancing domestic investment for the sake of absorbing the saving surplus, instead of expanding the consumption demand. Because of strong international demand and the resultant large trade surplus, the paradoxical coexistence of the saving glut and labor shortage is possible in the domestic market.

162

Appendix

Appendix Table 5 Spectrum Cyclical

Non-Walrasian equilibriums in China’s economy Market economy

Planned economy

Depression

Prosperity

Keynesian unemployment

Under-consumption

Structural

Repressed inflation

Classical unemployment Goods market

Labor market

YD < YS

LD < LS

Under-consumption

YD < YS

LD > LS

Classical unemployment

YD > YS

LD < LS

Repressed inflation

YD > YS

LD > LS

Keynesian unemployment

Keynesianism starts probing into the problems of underinvestment and undervaluation in China’s economy, aiming to uncover an internal equilibration mechanism, as illustrated in Appendix Fig. 8. The autonomous chain interactions among fixed investment, trade surplus, and banking liquidity are designed to diminish the investment gap through enhancing investment and to appreciate RMB exchange rates through enhancing inflation, so as to implant double passive feedback in China’s economic circulation. Appendix Fig. 8

Keynesian view of economic stabilization Trade surplus

+

+ Foreign reserves

Underinvestment

− Fixed investment

+

+

−

+ Money supply

+

Undervaluation

−

− Inflation

+

Credit balance

163

Theoretical System of China's Macroeconomic Analysis

Demand drivers and international coupling National income can be defined by Y = C + I + (X – M ), in which C = C (Y ), I = + u , X = + v , M = M (Y ), and u and v carry the variances σ u2 and σ v2, respectively. The covariance between national income and net exports is given by cov(X – M , Y ) = (–m • σu2 + (1 – c ) • σ v2)/(1 – c + m ), and ρ(Y , X – M )

= cov(Y , X – M )/(σu2 • σ v2)1/2, where c = dC /dY and m = dM /dY . Thus, ρ(Y , X – M ) < 0 when σu2 > 0 and σ v2 = 0, while ρ(Y , X – M ) > 0 when σu2 = 0 and σ v2 > 0.

Consequently, the demand driver of economic fluctuations can be identified by ρ(Y , X – M ) as follows: if ρ(Y , X – M ) < 0, economic fluctuations are driven by external demand shocks; if ρ(Y , X – M ) > 0, economic fluctuations are driven by domestic demand shocks.

Appendix Fig. 9 (a) plots the time paths of the U.S. GDP gap and U.S. net

exports in terms of GDP share, and Appendix Fig. 9 (b) plots the time paths of

China’s GDP growth rates and its net exports in terms of GDP share. The U.S. trade deficit was procyclical during 1981 to 2009, demonstrating an expansion

after the mid-1990s, while China’s trade surplus evolved from a con-growth pattern to a pro-growth one toward 1994, becoming more pro-growth as the

country adopted a managed-float exchange rate regime. Accordingly, Appendix Table 6 presents the cyclical correlations between China’s national income and net exports, in which data on net exports have been detrended by time

differencing. In a nutshell, U.S. economic fluctuations were domestic-demand driven in 1981 to 2009, while China’s were domestic-demand driven during 1981 to 1996 but external-demand-driven from 1997 to 2009. Appendix Fig. 9 (a) U.S. GDP gaps and net exports 8

2

4

0

% 0

–2 %

–4

–4

–8

–6

1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 U.S. GDP

164

U.S. net exports

Appendix

Appendix Fig. 9 (b) China GDP growth and net exports

%

20

10

15

5

10

0

5

–5

0

1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 China GDP

Appendix Table 6

%

–10

China net exports

Cyclical correlations between China and U.S. GDP and net exports 1981–1996

1997–2009 China

ρ(ΔY /Y , (X – M )/Y ) ρ(ΔY /Y , (X – M )/Y )

0.751 034 –0.493 689 U.S.

ρ((Y – Y *)/Y , (X – M )/Y ) ρ((Y – Y *)/Y , Δ((X – M )/Y ))

–0.4855 60 –0.713404

As Appendix Fig. 10 illustrates, U.S. economic fluctuations are propagated onto China’s economy through the direct channel of U.S. trade deficits with China and the indirect channel of U.S. trade dominance in the world economy, within an international structure that comprises China’s external-demand-driven business cycle and the U.S.’s internal-demand-driven business cycle. In conclusion, a coupling relationship that contains a substitution effect of exchange rates and a time lag in the propagation process is established between the national income of China and the U.S. through the international trade channel. Unfortunately, due to misrecognition of the two countries’ economic fluctuations as being dis-synchronized before the global financial crisis and during its early phase, there was an overstating of the independence of China’s economy, which prevented it from preempting the spillover effect of the American subprime crisis.

165

Theoretical System of China's Macroeconomic Analysis

Appendix Fig. 10

International propagation of U.S. economic fluctuations onto China

eUS XUS + MUS

f (XUS + MUS , NXUS , eUS)

eCN

YUS NXUS

f (NXUS–CN , EXW , eCN)

EXW f (NXUS , eUS–CN)

NXUS–CN

NXCN YCN

eUS–CN

Post-Crisis Proactive Demand Management Potential national income affected by hysteresis Applying Solow’s vintage approach to capital stock on capital formation in China, investment (I t) produces capital (K t + i) according to the distribution probability w (i ) during a life cycle of k periods, i.e., K t = ∫ 0 → k (I t – i • w (i )) di , where ∫ 0 → kw (i )di = 1. Based on the production function Y = φ(t ) • K and the investment–saving function I t = s • Y t, national income takes the form of Y t = s • φ(t ) • ∫ 0 → k(I t – i • w (i ))di . Therefore, potential national income can be expressed as Y t* = ∏ 0 → k{(Y t – i • (1 + δ) i) w(i)}, or logY t* = ∑ 0 → k{w (i ) • (logY t – i + i • log(1 + δ))}, the size variables K , Y , and I detrended using the natural growth rate δ. Thus, in China’s economy, actual national income is capable of permanently promoting potential national income, demonstrating a hysteresis effect. Appendix Table 7 shows an OLS estimation of the autoregressive model of China’s real GDP index logY t = ∑0 → k{w (i ) • (logY t – i + i • log(1 + δ))} for 1978 to 2009, in which k = 5, i.e. about half the length of the Juglar cycle, and w (i ) takes the representative weights of the geometric series q i and cosine series cos((i – 1) • (π/2k )) in sequence. China’s potential GDP is built on the basis of a dynamic forecast of China’s actual GDP, and the corresponding absolute and relative GDP gaps are demonstrated in Appendix Fig. 11. Because the geometrical series is more front-loaded than the cosine series, the forecasted natural growth rate (δ) of China’s economy is slightly higher in the case where the geometric

166

Appendix

weight is adopted than otherwise. Appendix Table 7

Estimation of China’s potential national income

logY t = w (i )

{w (i ) • (logY t – i + i • log(1 + δ))}

qi

cos((i – 1) • (π /2k ))

0.100 980 (25.743 74)

0.100 524 (28.519 27)

R2

0.997 789

0.996 709

SE

0.033 770

0.041 198

δ

100 million current RMB

Appendix Fig. 11

China absolute and relative GDP gaps

16000

12

8000

6

0

0

–8000

–16000

%

–6

1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009

Absolute / Geometric Absolute / Cosine

–12

Relative / Geometric Relative / Cosine

Multiple equilibrium traps in prudent demand management Augmenting the Phillips curve π = –α • (u – u *) with adaptive inflation expectations (L [π ]) and supply shocks (z ) produces the triangle model π = –α • (u – u *) + L [π] + z . Conventional demand management can be formalized by minimizing the quadratic loss function V = θ • (y – y T) 2 + (π – πT)2, which is subject to the triangle model, i.e., π = –α • (u – u *) + L [y ] + z , and the policy goal (y T, πT) can be assumed as attaining the potential national income level of y * and desirable inflation rate of π*. Without hysteresis effects, even if the national income target is misspecified, i.e., y T ≠ y * ex ante , the loss ellipse V can be condensed into the unique equilibrium (y *, π *) by fine-tuning operations, obeying such tâtonnement rules as Δy T • (y T – y *) < 0, as illustrated in Appendix Fig.12.

167

Theoretical System of China's Macroeconomic Analysis

Appendix Fig. 12

Unique equilibrium under prudent demand management LRAS

π

π* B

A

0

y*

y

Refining the Phillips curve π = –α • (u – u *) + L [π ] + z to accommodate the

hysteresis effect, which is expressed as y = L [y ], we can generate the following aggregate supply function for China: y – L [y ] = λ • (π – L [π ]). Because of the

multiple solutions to the fixed-point equation y * = L [y ], prudent demand

management tends to produce multiple national income equilibriums, as illustrated in Appendix Fig. 13.

The loss ellipse V is centered at points A and B , which respectively represent

an optimistic and pessimistic view toward potential national income. In

Appendix Fig. 13 (a), where potential national income is contained within the feasible interval [y *min, y *max], the policy rule of Δy * • (y * – y T) < 0 shrinks V to points A and B , to which the long-run AS curve is attracted. By the same policy

rule, the loss ellipse is shrunk to the point of being tangent to the long-run

AS curve, centered around points A and B , at the lower limit (y *min) and upper limit (y *max), when potential national income falls beyond the feasible interval, as illustrated in Appendix Fig. 13 (b). Hence, the multiple national income

equilibriums are stable and randomly distributed within the feasible interval [y *min, y *max], contingent on the target national income.

168

Appendix

Appendix Fig. 13 (a) Multiple equilibriums under prudent demand management: y*min < yT < y*max LRAS

π

π*

A

0

B

y*

y*min

y* max

y

Appendix Fig. 13 (b) Multiple equilibriums under prudent demand management: yT < y*min or yT > y*max LRAS

π

π*

A

0

B

y*min

y*

y* max

y

169

Theoretical System of China's Macroeconomic Analysis

Fine-tapping operations and reverse soft-landing Subject to the aggregate supply function y – L [y ] = λ • (π – L [π ]), proactive demand management explores the development of maximum potential national income under uncertainty by minimizing the parabolic loss function V = –θ • y + (π – π T) 2. The loss parabola V touches the long-run AS curve, and then induces it to shift rightward to the upper limit of potential national income, as illustrated in Appendix Fig.14. Whether the target national income lies primarily within the feasible interval [y * min , y * max ] or not, the optimal equilibrium (y *max, π *) is unique and globally stable. Appendix Fig.14

Unique equilibrium under proactive demand management LRAS

π

π*

0

y*min

y*

y*max

y

Fine-tapping operations of the proactive demand management approach prove successful in reverse soft-landing, capable of increasing actual national income from the historical equilibrium (y ') to the upper limit of potential national income (y * max ), as illustrated in Appendix Fig. 15. Given that the ceiling rate of tolerate inflation is (π * + ) and the speed of adjustment of the autoregressive process y = L [y ] is denoted by ν, the dynamic properties of reverse soft-landing include: (1) π = π * + , and y – y * = λ • , from t 1 to t 2 ; (2) t 3 – t 1 = (y *max – y ')/ν, t 3 – t 2 = (λ • )/ν, and t 2 – t 1 = (y *max – y ' – λ • )/ν; and (3) Δ(π ) = ν/λ. China’s economy went on to expand following its strong recovery during 2002 and 2003, which led to high inflation in 2004. In 2005, reverse soft

170

Appendix

landing was achieved as accelerated GDP growth coincided with CPI and PPI disinflation, continuing until the outbreak of the American subprime crisis, as illustrated by Appendix Fig. 16. Appendix Fig. 15

Reverse soft landing of national income and inflation

y*max

y, y* y y* t

π*

π 0

t1

t2

t3

t

Appendix Fig. 16 (a) Economic expansion and moderation of inflation in China

%

15

10

14

8

13

6

12

4

11

2

10

1Q04 2Q04 1Q04 3Q04 4Q04 2Q05 3Q05 4Q05 1Q06 2Q06 3Q06 4Q06 GDP

CPI(3MMA)

%

0

PPI(MMA)

171

Theoretical System of China's Macroeconomic Analysis

Appendix Fig. 16 (b) Reverse soft landing of China’s economy AD

AS 0

t

CPI 0

t

Concluding from China’s experience, for reverse soft landing to take place, potential aggregate supply must increase in response to the aggregate demand-supply gap more promptly than inflation expectations adapt to actual inflation. Despite heavy cost-push pressures passing from upstream industries to downstream industries in 2004 to 2005, CPI disinflation preceded PPI disinflation in China, affirming that inflation expectations were wellanchored in the face of a large aggregate demand-supply gap at least in the short run. Hence, Keynes’ law holds in China’s economy, where aggregate supply responds sensitively to aggregate demand and actual aggregate supply is sufficiently converted into potential aggregate supply.

172

Notes Chapter 1 1.

Adaptive expectations learn unanticipated shocks and make incomplete

adjustments to them at once, so they can be approximated with the continuation of fixed expectations in high frequency (the integer multiple of the current frequency). The qualitative conclusion under a fixed PE is

2.

applicable to adaptive expectations.

Branson (1997) has built the labor supply function N = N d(W /P ) = N s(W /P E), and P E = E (P ), assuming that the map O corresponds to the pure Keynesian

model, the general Keynesian model, or the classical model when dE /dP = 0, 0 < dE /dP < 1, or dE /dP = 1, respectively. Nevertheless, the general map O

by Branson is only a new classical aggregate supply function under different mechanisms for the formation of expectations, while the Keynesian and

new Keynesian map O tends to be constructed under the assumption of an uncleared labor market.

3.

Rational expectations can only realize E (P t | I t) = P in an average ex ante

4.

The labor supply functions of traditional aggregate supply functions also

forecast.

assume representative households. In comparison with the representative

agent, the concept of “macroeconomic individual ” proposed by Fan, Zhang, and Yang (1990) may be more vivid and accurate in describing the classical

and new classical approaches to (or inattentiveness of) aggregation. See the “representative agent model” in Snowdon and Vane (2002).

5.

Proving that s b/d b ≤ 1 by contradiction, if s b/d b > 1, s i/d i > 1, ∑s i > ∑d i, which

6.

At the change of Y d, the general AS curve not only shifts vertically due to

contradicts the budget constraint ∑s i = ∑d i = 1.

the augmentation of inflation expectations and demand as traditional AS

curves predict, but also horizontally due to changes of effective potential aggregate supply. The traditional paradigm of the aggregate supply curve is still reasonable if potential aggregate supply does not react to changes of

aggregate demand despite its own structural slackness. Moreover, if Y E is

invariably proportional to Y M, whether the AS curve is asymptotic to the lines

Y = Y or Y = Y M is theoretically equivalent.

173

Notes

7.

Commending the prototypical general aggregate supply function in Zheng (1999) for breaking through the traditional dichotomy between aggregate supply and demand, Lin (2000) also holds that the demand elasticity of every good, including bottleneck products, should not be exogenous or fixed in the long

run and in an open perspective. While this did point to a future direction for

the expansion and refinement of the general aggregate supply function, it is important to note that the real aggregate demand structure is still rigid under the hypothesis of fixed demand elasticity in the (Marshallian) short run. For any Y d,

8.

9.

10.

174

Y E can only be (s b/d b) • Y M, rather than Σi(min{d i • Y d, s i •Y M }), as Lin proposes. The aggregate demand structure resulting from (min{d i • Y d, s i •Y M}) is apt to depart from [d i]. Financial repression theories examine the economic origins of government regulations of the real interest rate below the equilibrium interest rate and the attendant negative effects from the perspective of resource allocation. On the contrary, Hellmann, Murdock, and Stiglitz (2000) argue for financial constraints, for example, the necessary substitution of capital requirements for interest rate regulations during financial liberalization, and, to some extent, justifies the cause of government regulations on the real interest rate below the marketclearing interest rate and the resultant positive effect on the grounds of economic stabilization. At the abstract level of the IS–LM model, the demand for loans is equivalent to bond supply as a financing instrument. Here, the credit market plays the role of the bond market in the basic IS–LM model, complementing the goods and money markets. The deficiency of effective demand stems from a low equilibrium level of national income in comparison to full employment, which is compatible with excess investment demand over the supply of savings ex ante given a specific level of national income. Hence, the rationing equilibrium of the IS–LM model is applicable to China’s economy both in times of overheating and depression. Under China’s financial arrangements, the functions of money are clearly divided between M1 and M2, with M1 being dominated by transactions and speculative motives and M2, after deducting M1, being the main choices of household financial investments, such as assets with fixed returns. Zhang (1999) conducts joint estimation regarding the basic IS–LM model, modifying it for China by accommodating a negatively titling LM curve. However, as the real interest rate is the expected return on M2 instead of its opportunity cost, the usage of the real interest rate, as opposed to its nominal counterpart, for estimating the moneydemand function constitutes an incorrect definition of the interest rate variables, contributing to an abnormal demand for money with a positive interest elasticity. See Laidler (1993) and Zheng, Yu, and Teng (2000).

Notes

11.

An OLS estimation of fixed asset investment on the national income in 1981 to 2009 will affirm the national income elasticity of China’s investment demand. In the formula, I refers to the amount of off-budget capital formation and Y is GDP by the production approach, both deflated by a GDP deflator that uses 1981 as the base year.

log I t = –3.470 390 + 1.229 576 • log Y t + [AR (1) = 1.000 647, AR (2) = –0.684 212]

(–20.381 08) (71.912 14)

(6.337 948)

(–4.310597)

R 2 = 0.997 936, adj. R 2 = 0.997 667, SE = 0.044 596, DW = 2.168 765.

Chapter 2 1.

2. 3.

4.

U n d e r t h e t r a d i t i o n a l p a r a d i g m o f t h e a g g re g a t e s u p p l y f u n c t i o n

Y s = f (P /P E) •Y M, supply-side economics advocates that the shift of the AD curve leads to the horizontal shift of the AS curve in the long run on account of the variations of Y M, as opposed to aggregate demand variations. For example, Fig. 2.1(a) and Fig. 2.1(b) show the time path EEm with a negative slope.

d(ΔA )/de φ = –2λ • (Y N/P *) • e D • (λ • (Y N/P *) • (e f + (1 – e φ) •e D) – θ). When

e φ = thr3, d(ΔA )/de φ = 0, and d2(ΔA )/d(e φ)2 = (λ • (Y N/P *) • e D)2 > 0, so ΔA reaches its minimum value 4(λ • θ) • (Y N/P *) • e f. The necessary condition for the global asymptotic stability of the dynamic

AD-AS model is e φ ≤ 1, as illustrated in Table 2.2. Hitherto, it is still

impossible to determine the positive effect of aggregate demand on effective potential aggregate supply, i.e. e φ > 0, purely on the basis of Samuelson’s

correspondence principle.

5.

The dynamic mirror of Fig. 2.5 will reflect the procyclicality of economic

6.

In line with the Austrian proposition on liquidation, traditional aggregate

efficiency, a.k.a. the economies of speed, in China since the 1980s.

supply functions tend to predict some procyclicality in the total structural

imbalance, asserting that austerity (as opposed to stimulus) during economic

depression or recession uncovers structural imbalance developed in times of prosperity or economic expansion and moderates structural imbalance

by fostering economic equilibrium through inducing severe competition in the market.

Chapter 3 1.

Even the labor force’s seeming market power to adjust the wage level in pace with the rise of the cost of living is insufficient to cause the labor supply

175

Notes

to approach or surpass the Lewis turning point. As the Iron Law of Wages

proposes, the impact of the cost of living on the wage level is only caused by

the fact that the subsistence wage is no longer able to accommodate the cost 2.

of living.

Without data on the capital stock of the initial year, the time series of China’s

capital stock has to be tentatively extrapolated under specific assumptions,

such as a perpetual inventory system, although data on annual capital formation is accessible. At the same time, because China’s System of National

Accounts (SNA) does not provide for the income method of measuring

national income, the parameter of factor contribution in the aggregate production function cannot be calculated out of the factor distribution of

national income, but can only be assigned by empirical knowledge and subjective judgment. Therefore, the general form of China’s aggregate 3.

production function is inestimable in principle.

Although the RMB is still inconvertible under the capital account, the

increasing conformity of its exchange rates to the interest rate parity (IRP) condition reveals the openness of China’s capital market. With universal market expectations of RMB appreciation, speculative capital must evade

domestic regulations on international capital inflows, and delivery dates in offshore forward markets must be extended. However, since the adoption

of the managed-float regime, expectations of RMB appreciation in nondeliverable forward (NDP) transactions have been converging with the 4.

5.

interest rate gap between the domestic and international markets.

With the global saving glut, the domestic interest rate may be refrained by international interest rates from following the domestic marginal product of

capital. Therefore, (δ – r ) > 0, offsetting the negative effect of the decline of η on the aggregate saving rate.

There is evidence that American advancement after the information technology revolution has depended a lot on large-scale capital accumulation.

Generally speaking, international capital inflows have financed trade deficits and effectively loosened the budget constraint of deficient domestic savings

on domestic investment demand. Specifically, after becoming a service

economy in the 1970s, and then a knowledge economy in the 1990s, human

capital accumulation has become the main form of capital accumulation. Yet,

in the current American SNA, personal educational and medical expenses, as well as public expenditure on education, science, and health care remain consumption entries although they are important vehicles of human

investment. Arguably, this, together with twin deficits and low household saving rates, has contributed to an illusion of underinvestment.

176

Notes

Chapter 4 1.

The historical adjustment of stationary inflation from high to low levels need not be the structural cause of deflation in China in the late 1990s. Without

the negative shock of the Asian financial crisis, China could have gradually eliminated the high inflation of the mid-1990s without experiencing such 2.

“overshooting” disinflation from inflation to deflation.

National economy is dichotomized into a progressive sector versus a conservative

sector in the Hicks–Tobin model, and an exposed sector versus a sheltered sector in the Scandinavian model. According to these models, productivity grows

faster in the progressive/exposed sector than in the conservative/sheltered sector. Moreover, when wages and productivity grow at the same speed in the progressive/exposed sector, the same rate of wage growth surpasses that of 3.

productivity growth in the conservative/sheltered sector.

In China’s economy, while the price equilibration did create structural inflation, a

speed ceiling on price equilibration was imposed by a social tolerance threshold for inflation. With the persistence of disequilibrium during price equilibration, the

4.

downward stickiness of absolute prices can induce real stickiness in relative prices.

Table 4.2 does not take into consideration the possible shifts in preferences for production technology or consumption, which will lead to changes in the

equilibrium price, but mainly demonstrates the operational measurement of

structural inflation rates. There is an innate dilemma between the periods of inflation assessment and price equilibration: the assumption of fixed supply is

reasonable if a short inflation assessment period is adopted, but the equilibration of relative prices in a short time span is unrealistic; yet if a long inflation assessment period is applied in favor of a longer period for price equilibration, 5.

the assumption of fixed supply will become unreasonable.

It is customary and convenient to divide the complete adjustment of the monetary wage into three progressive levels: Level 1, where the monetary

wage adjusts completely to compensate the difference in the cost of living, so that the real wage is fixed when the cost of living changes; Level 2, where the

monetary wage adjusts completely both to compensate the difference in the cost

of living and to accommodate changes in labor productivity, so that the share of wages in the national income is fixed despite changes in the cost of living and

labor productivity; and Level 3, where the monetary wage adjusts completely to compensate the difference in the cost of living, accommodate changes in

labor productivity, and benefit from the redistribution of national income, i.e.,

gW = gC + g ρ + gy , so as to ensure that wage adjustments will not grow into pressure on inflation.

177

Notes

6.

7.

8.

When gW = gy , (k 2 – k 1l 1) • g ρ + (k 3 – k 1l 1) • gy + k 1l 2 = 0, and π = –g ρ. A wage adjustment at the traditional Level 1 is not sufficient for attaining price stability,

as it will only be non-inflationary in the special case where g ρ = 0 and gy = (k 1l 2)/ (k 3 – 1). The contrast between the experience of economic development of North and South America reveals that exceedingly cheap labor might induce the reverse substitution of labor input for capital input in the long run, hindering capital deepening in industrial production. Constrained by structural food inflation, China should measure its attainment of price stability against the core CPI in its demand management in the future. Both theories and empirical evidence indicate that a core inflation target with price stability not only can accommodate structural food price increase, but also detach the impact of the transitory fluctuations of food prices to avoid possible overreaction of the monetary policy. When most supply and demand shocks are absorbed by flexible prices and sticky prices are correspondently stable, relative price twists and inefficient resource allocation will be reduced.

Chapter 5 1.

2.

3.

4.

178

In China, there is no official or authorized business cycle dating comparable to the potential national income forecasts by the Congress Budget Office (CBO) or the dating of the peaks and troughs of the business cycle by the National Bureau of Economic Research (NBER). Therefore, we can only deduce in general from the five-quarter moving average of the accumulative seasonal GDP growth rates presented in Fig. 5.1 whether China’s business cycle is experiencing expansion or contraction depending on the difference between the real and potential GDP growth rates, where expansion and contraction do not necessarily denote prosperity and depression, respectively. Similarly, the method of time series analysis is only capable of distinguishing between aggregate demand fluctuations caused by internal and external demand shocks, but fails to identify the primary source (or first cause) of the shockpropagation mechanism. Under effective supply constraints, the equilibrium national income is defined as

Y = + w , where w refers to supply shocks. On the effective demand side, Y = C (Y ) + ( + u ) + ( + v ) – M (Y ), and X – M = + v – M (Y ), but the demand shocks u and v have no effect on the national income. At present, dY /dw > 0, d(X – M )/ dw < 0, and cov(X – M , Y ) = –m • σw2 < 0, similar to conditions under internaldemand-driven economic fluctuations. See Zheng (1997). Instead of using a specific theory to determine China’s potential national income, the more simple natural growth assumption and H–P filter are opted for to avoid

Notes

5.

6.

7.

theory bias. (In Zheng (2009) and Chapter 8 of this book, for example, a special process of capital formation has to be adopted to accommodate the hysteresis effect of real national income on potential national income.) From 1981 to 2008, the time paths of China’s GDP gaps under the natural growth assumption and the H–P filter nearly always coincided, except for the separation at the beginning and end of the period. As illustrated in Table 5.1, the two measurements of GDP gaps make no structural difference in determining the time trend of China’s net exports. As shown in Table 5.1, the estimating equation includes both the present level and future growth rates of the RMB REER, whose regression coefficients are both positive. The positive regression coefficient on the present REER level can be attributed to the appreciation of the real RMB exchange rates, as pointed out in Zheng, Zhu, and Zhang (2007), and the export orientation of China’s economic development. The future growth rates are related to the market timing hypothesis that low expectations of future investment returns promote current investment. A form of foreign investment out of domestic savings, net exports yield returns in a direction opposite to real exchange rate movements, thereby positively reacting to future REER growth rates. Just as that for China’s net exports, the equation for estimating the time trends of U.S. net exports yields a positive regression coefficient for the future growth rates of the USD REER. However, being the currency of a developed economy, the USD gets a negative coefficient for the present REER level. c c c ; nx CN, nx US; nxCN , nxUS , it is easy to build VAR models With the dataset y CN, y US ; yUS with varying, detrended or undetrended indicators of economic fluctuations, for testing the coupling of the Chinese and U.S. business cycles. An example is to build a center country model that regresses China’s GDP gaps on the lagged GDP gaps of China and the U.S. (Bordo and Helbling 2010). However, even in the coupling of economic fluctuations and when the time lag of the propagation mechanism is taken into account, the business cycles of the two economies can hardly synchronize completely. Without a priori theories of economic fluctuations, statistics showing the lack of synchrony in international business cycles may blur the intrinsic coupling mechanism of international economic fluctuations while highlighting extrinsic decoupling phenomena.

Chapter 6 1.

Modern exchange rate theories are largely framed around the PPP theory, and they involve vehement debates on the applicability of PPP in the

adjustments of international exchange rates. Examples include the alignment and equilibration of exchange rates after World Wars I and II and through the

179

Notes

Bretton Woods System. Any contemporary research on exchange rate theories and policies must investigate whether equilibrium exchange rates should be

defined by PPP, and whether and how real exchange rates can be reverted to the PPP level. See Engel (2000), Obstfeld and Kenneth Rogoff (1994), and 2.

Taylor and Taylor (2004).

See Engel and Rogers (1996), Lothina and Taylor (2005), and Lu and Han

(2006). In addition, the accumulated balance of current accounts and government expenditure, which Rogoff (1996) identifies as modern factors

that determine the equilibrium exchange rate, also serve to modify the classical PPP theory. However, both factors work on the equilibrium exchange

rate in indeterminate directions, and therefore cannot be determined as long3.

run and structural.

Edison (1987) rejects the PPP theory by statistical studies on the long-run adjustment of exchange rates between the GBP and USD, where he observes a half-life of as long as five years in the process, providing one of the important empirical grounds on which Rogoff (1996) puts forward the PPP puzzle.

However, re-estimations of the half-life by improved econometrics with

considerations of the HBS effect and nonlinear correction mechanism have found the duration remarkably shortened, for example, to about 2.5 years in Lothina and Taylor (2000).



Since the 1970s, many statistical studies on the PPP theory have turned to

unit root tests and cointegration analyses on the mean reversion of exchange rates, as well as tests for threshold values and reaction intervals in nonlinear exchange rate adjustments. Studies in China, where the RMB has attained full current account convertibility, too, have yielded rich results, such as Yi and

4.

Fan (1997) and Yu (2000).

There is a classical dichotomy between absolute and relative PPP; see Rogoff

(1996). Some so-called dynamic PPP theories are in fact based on relative PPP; they tackle the formation of growth rates, and have little dynamic sense. As Pippenger (2004) points out, while relative PPP is more convenient in

application, as a necessary but not sufficient condition for absolute PPP, it is not equivalent to the latter.



Thus, Chapter 6 sets up a dynamic PPP theory and carries out corresponding

econometric analyses on its basis. An additional note is that the interest rate

5.

parity theory has been excluded because of its short-run characteristic.

The developing country is supposed to be a small country; therefore, in the

presence of international trade barriers, the prices of its tradable goods are marked up on those of tradable goods at the PPP level in the developed country.

180

Notes

6.

Since the denominator equals ( ρD + (1 – ρD ) • ηD ), and ηD equals 1/( λ •

(1/ θ D – 1) + 1), both of them are decreasing functions of λ and increasing

functions of θD. When the absolute value of (1 – λ) • (1 – θD) is small, ηD is

7.

close to θD, and ηD < θw , even though ηD > θD. Exposed to international competition, the tradable sector is comparatively

efficient with more rapid technical progress, so ρ is inclined to increase whether in the developing or developed country. As reviewed in Chapter 4,

the structural inflation model for open economies assumes the tradable sector

8.

to be more efficient than the non-tradable sector; see Frisch (1984).

In view of economic globalization, the IMF (2006) puts up an extensive study on the low and moderate inflation since the 1990s, identifying it as a global phenomenon while highlighting the important role of market-oriented

reforms and economic opening in the enhancement of efficiency and price stability. See Kohn (2006), which explicates the Federal Reserve Board’s 9.

theoretical interpretation and policy stance on economic globalization.

Theoretical explanations on the continuous appreciation of real exchange

rates during economic development are dichotomized by Bergstrand (1991) into supply- and demand-oriented hypotheses: the former includes the HBS

effect, which is based on differences in productivity, and the Heckscher–Olin model, which is established on the grounds of factor endowment, while the latter includes his own research based on non-homothetic tastes. According

to his classification, the dynamic PPP theory belongs to the supply-oriented 10.



category.

In Fig. 6.1, the nonlinear time path e –y /e incarnates the complicated

mechanism in the determination of real exchange rates by the structural parameters and their correlation with the national income.

Japanese economists have borrowed the philosophical concept of “parallax” to describe the different, or even opposing, results in the measurement of economic performance based on the domestic and foreign currencies; see

Yang (1999). Indeed, China’s economy concurrently recorded positive growth in the RMB and negative growth in the USD during the late 1980s and early 11.

1990s.

In their classical study on the correlation of exchange rates with national

income, which is primarily built on the HBS effect while employing the World Bank Atlas method, Kravis and Lipsey (1983, 1988) defines the national price



level (PL ) as equivalent to Y /Y PPP per se.

To illustrate the calculation of national price levels using developing countries as an example, because the nominal exchange rates of developed countries are normalized against their PPP levels according to the Atlas method, the

181

Notes

PPP level of a developing country’s nominal exchange rates is equivalent to

the ratio of the domestic price levels of a developed country to a developing country, i.e. P W/P D. After the price level indicator of developed countries

(PL W) normalized as 1, the price level indicator of developing countries (PL D)

12.

13.

14.



is correspondingly fixed to (P D/P W)/E , i.e., PL D = (P D/P W)/E . Since Y = (P D • Y D)/E and Y PPP = (P D • Y D)/(P D /P W) in the Atlas method, it can be verified that Y /Y PPP = (P D /P W)/E = PLD. The WDI data on China has been rectified with reference to China Statistical Yearbook 2006 . Moreover, because the WDI database uses the 2004 USD for calculating GNI, the U.S. Y /Y PPP indictor is necessarily 1 in Fig. 6.2. It does not mean that the actual USD exchange rate was at the PPP level in 2004. In order to eliminate the difference in the intertemporal purchasing power of the USD in 2003 and 2004, Table 6.3 and 6.4 multiply the variable Y by the purchasing power factor (usd ). Therefore, the indicator usd • Y is adopted in the place of Y in the econometric analysis. It is inversely proportional to the U.S. GDP deflator, and normalized into 1.000 00 for 2004 and 1.028 40 for 2003. The domestic purchasing power of any currency can be investigated in the broader view that a currency’s internal value is ultimately supported by national wealth, instead of national income, where the national income level and national income growth rate constitute the estimates of the first- and second-order moments of national wealth, respectively. When the GROWTH variable is added on top of Y for testing the latter’s representativeness, what is really tested is the comparative efficiency of the first- and second-order moments in representing measurable and immeasurable national wealth, respectively. Kravis and Lipsey (1983, 1988) test the HBS effect by such equations as ln (Y /Y PPP)= C 0 + C 1 • lnY and ln (Y /Y PPP)= C 0 + C 1 • TRADE and interpret the effect using the latter equation, verifying that the national income level and the trade-to-GDP ratio are powerful in explaining the national price level.

15.

16.

17.

182

Therefore, the test on the representativeness of variables here appends the TRADE variable to the basic econometric model. The structural stability of the basic econometric model is also justified by the negligible difference between the results of I and those of II and III in Table 6.3 in terms of fitness, statistical significance, and regression coefficients. To make use of the computable dynamic PPP econometric model, the currency unit of China’s GDP per capita in 2010 in Table 6.5 has to be converted from 2005 RMB to 2004 USD. Domestic investment absorbs national savings and reduces trade surplus, and as a result enhances domestic inflation. To reduce its high saving rate, China

Notes

should channel more national savings into domestic investment in line with

the Keynesian approach as explicated in Chapter 3, which will lead to rapid

economic growth and balanced trade. In this way, the RMB will undergo real appreciation, or appreciate even with differences in international inflation 18.

rates taken into consideration, instead of mere nominal appreciation.

S t a t i s t i c a l i n f e re n c e t h a t t h e R M B re a l e x c h a n g e r a t e i n 2 0 0 4 w a s undervalued by 75% (≈ 1 – Y CN_2004/ Y CN_2004/

(Y CN_2004/Y

) or 43% (≈

CN _2004

/

)) makes no sense. The practice of inferring the equilibrium

exchange rate from the prices of specific goods is even less reliable. This can be verified by comparing the Big Mac Index and Tall Latte Index, both

published in The Economist in July 2004, with the former suggesting that the RMB exchange rate to the USD was undervalued by 56%, and the latter

19.

20.

only 1%.

Regarding the annual appreciation rate of the RMB, (1/3 • (1/3 • A + 2/3 • B ) + 2/3 • (1/3 • C + 2/3 • D )) amounts to 5.935 927% in the case of real

exchange rate I, and 6.193 086% in the case of real exchange rate II.

On the basis of the 2009 WDI database, the structural dynamic PPP function takes the new form of Y /Y PPP = C 0 + C 1 • Y – C 2 /lnY instead of the old

form of Y /Y PPP = C 0 + C 1 • Y – C 2/Y . With the logarithmic transformation of the national income variable, the new function allows for real exchange

rate depreciation, not only appreciation, at the primary stage of economic

development, as demonstrated by the regression line in Fig. 6.5, in contrast to the monotonic pattern of development displayed in Fig. 6.3.

Chapter 7 1.

With the large divide between theories and application in macroeconomics,

we cannot be content with the unilateral progress in theoretical invention, but should make an effort to realize the applicability and practicality of theories.

Post-revolution macroeconomic theories should be shifted away from the

destructive orientation to a constructive one, aiming for synthesis rather than invention, so as to contribute to a new theoretical framework that is open

to modifications by experiences, supportive of policy designs, and ready to

interact with competitive theoretical paradigms.

The common argument that application necessarily falls behind theory, as Mankiw (1991) points out, reveals the presence of unexploited profits in the

market of macroeconomics, which is contrary to the equilibrium assumption of new classical economics: if a theory is useful in practice, it is applied; if a theory is not applied, it is useless in practice, at least up till the present.

183

Notes

2.

Since the right-hand side of the behavioral equations includes endogenous

variables correlated with the error terms, which are correlated across equations, estimating the CMAFM with the OLS method instead of the 2SLS method, or with the single-equation approach rather than the 3SLS method is inclined to be biased and inconsistent. Nevertheless, using the OLS method

can avoid possible specification errors that stem from the subjective selection of instrumental variables in the case of the 2SLS method, while the single-

equation method is adopted to avoid the spread of specification errors from

individual equations to the others as in the case of the 3SLS method. With respect to the rooted-mean-square errors (RMSE), the OLS method is superior to the 2SLS method, whereas the single equation method is preferred to the 3SLS method (Greene, 2007).



An initial estimation of the IS–LM–AS model of China, the CMAFM’s

structure and equations are designed and specified almost without practical reference, making single-equation OLS estimation more preferable. Moreover, the model alternatively employs endogenous variables in the forms of finite

difference, ratio, and logarithm when estimating the behavioral equations in

order to eliminate serial correlations between the explanatory variables and 3.

error terms, as well as among error terms across equations.

With only a one-period lag of the effective exchange rate, the depreciation of

the RMB is predicted to have a negative effect on net exports in the long run, which is obviously contrary to practical experience. Considering the reality, a dynamic multiplier analysis has not been conducted for the exchange rate

4.

policy, although the exchange rate is an exogenous variable of the CMAFM.

By introducing the depreciation rate of the real effective exchange rate, Δlog(SDRE /P ), to the Phillips curve, it will take into account imported

inflation, and its structural parameters will be more stable. While the revised

Phillips curve will improve in statistical significance, it will impair the CMAFM’s performance in historical simulation:



INFL t = –4.826 862 + 0.500 204 • INFL t – 1 + 58.492 30 • ΔlogYR t – 1 (–3.100 673) (5.776 900) (3.592 548)

+ 65.895 89 • DUM • ΔlogYR t – 1 + 8.743 522 • Δlog(SDRE t – 1/P t – 1)





 (5.951 455)           (2.162 468)

R 2 = 0.906 480, adj. R 2 = 0.883 100, F = 38.77157, DW = 1.917807. 5.

184

As Table 7.4 demonstrates, historical simulation using the CMAFM produces

acceptable forecast errors. In Fig. 7.3 (a) and 7.3 (c), for example, the

Notes

actual and forecast time series values of national income and household consumption are too close to be differentiated in display, so the time paths

have to be depicted in natural logarithm in the form of finite difference

instead of the original natural log.

As the textbook paradigm reported in Pindyck and Rubinfeld (1997), the

small macroeconomic model of the U.S. by Michael Donihue is also structured in line with the standard IS–LM–AS model. However, unlike the CMAFM, it

adopts the single-equation estimation approach with the 2SLS method, and is thus inferior to the CMAFM in the accuracy of historical simulation within 6.

the sample interval.

As illustrated in Fig. 7.4, the dynamic multiplier of government expenditure attenuated slowly, without evident inclination to converge toward zero over

the 21-year period. The CMAFM might have intrinsically extended the time horizon of Keynes’ law, so that demand could create its own supply in the long run as well.

Chapter 8 1.

As a counterexample, an ex post interpretation of the policy cause of the Great

Inflation is that the Federal Reserve ignored or undervalued the slowing trend in labor supply and productivity in the 1970s, and mistakenly adopted an inappropriate expansionary monetary policy due to an overvaluation of

2.

the U.S. potential economic growth rate. See Orphanides (2002).

Meyer (2004) applauds the U.S. monetary policy in the Greenspan era, comparing its successful demand management to a runway (potential

aggregate supply) being lifted to receive a plane (actual aggregate supply), as opposed to a plane being plunged to hit a runway. This explicates the coined

concept of “reverse soft landing” in this chapter.

Meyer used the reverse soft landing analogy in a speech in 2000 as part of the four scenarios of U.S. economic growth which he observed, the others

being “soft landing,” “growth of potential [output],” and “hard landing.” The reverse soft landing scenario is but a moderate variant of soft landing,

while the growth of potential scenario was depicted as the best case where “supply meets demand,” or in the governor ’s own words, “the growth of potential increases just as output threatens to push beyond potential.”

However, the historical preconditions that actual aggregate supply be below

potential aggregate supply as well as actual aggregate supply growth be above potential aggregate supply growth as he assumed prove abnormal

for economic landings (despite being normal preconditions for economic

185

Notes

recovery): economic landings are only necessary only if actual supply goes 3.

beyond potential aggregate supply.

Based on the rising trend of rural migrant workers’ wages since 2004, Cai

(2007) conjectures that China’s labor supply may approach the Lewis turning

point or enter the Lewis turning interval in the late 2000s. Along the same

line, the CCER (2007) studies the effects of labor supply on economic growth

and the transformation of development pathways after China bypasses the Lewis turning point.



Nevertheless, because the notion of subsistence incorporates both natural and social needs, the absolute level of the subsistence wage necessarily increases with economic development and social advancement. Thus, it is impossible to

position the Lewis turning point of China’s labor supply only on the basis of

wage growth or accelerating wage growth. Neither a rising wage rate nor the

market power of the labor force in adjusting the wage level in pace with the cost of living is a sufficient condition for labor supply to approach or bypass the Lewis turning point.



Even in the early expressions of the Iron Law of Wages mercantilism already

pointed out that any rise in the cost of living, for example, a levy of tax on the

wage income, has to be fully passed onto the wage level, which tends toward 4.

the minimum level of subsistence living. See Brue and Grant (2006).

As Snowdon and Vane (2005) reviewed, the term “hysteresis” was first borrowed by Phelps (1972) from physics to describe the (historical) path dependence of the equilibrium unemployment rate on the actual

unemployment rate. Since the 1970s, the main European economies have

been afflicted with high unemployment, coinciding with an obvious rise in their equilibrium unemployment rates, which are measured in terms of

the NAIRU (non-accelerating inflation rate of unemployment). Among the theoretical interpretations of the European “unemployment disease,” the

hysteresis theory emerged as an alternative hypothesis to the (fixed) natural rate hypothesis, and has come to receive general support from empirical data

from European and other OECD economies. See Blanchard and Summers (1986), Gordon (1989), and Ball (1996).



Classical studies on hysteresis such as Sachs (1986), and Blanchard and

Summers (1987, 1988) focus on the labor market, modeling possible

differences between the insiders and outsiders, as well as short-term and long-term unemployment in terms of their influence in wage bargaining. Although Dumas (1989) takes into consideration the capital channel of the

hysteresis effect, it only involves marketing capital related to costs and risk in the market access for goods, but not production capital.

186

Notes

5.

Since the 1980s, China’s economic fluctuations have successively gone

through three complete trough-to-trough cycles during 1982 to 1990, 1991 to 2001, and 2002 to 2009, and the length of each cycle fell within the seven-to-



eleven year standard of the Juglar cycle.

Whether performed using the exponential product equation or the loglinear equation, the OLS estimation of the autoregressive process for China’s national income should generate residuals that indicate the cyclical

components of actual national income. Hence, it is impossible to evaluate or select the alternative process of China’s potential national income growth,

including the appropriate lag order (k ) and probability density (w (i )) only

on the basis of the traditional criteria of data fitness and significance of an

6.

OLS model.

Woodford (2003) has presented a theoretical construction of quadratic loss functions based on a utility function. With the policy objective defined as

the minimized loss function ∑0 → ∞{βi • V i}, the frequency of decision making in demand management can be extended from a single period into multiple periods, exhibiting dynamic characteristics of a repeated game; see Walsh

(2010). If decision making is not affected by intergenerational discrimination, demand management in multiple periods is equivalent to one-period demand management over a time horizon long enough to entail no inflation 7.

forecast errors.

The Lucas critique has challenged the accelerationist Phillips curve’s interpretation of the comovement between inflation and unemployment by explaining it on the basis of an equilibrium labor market, instead of a

disequilibrium labor market. However, the new classical rational expectations

hypothesis and equilibrium labor market perspective lack empirical evidence, but rather, prove contradictory to most historical experiences during

economic overheating and depression.

The new Keynesian Phillips curve as represented by that developed in Gali and Gertler (1999) adopts a forward-looking assumption of inflation

expectations, and is built on a strong microfoundation of imperfect labor and

goods markets.

While the econometric test in Gali, Gertler, and Lopei-Salido (2005) is supportive of the New Keynesian Phillips curve, especially in affirming the

statistic robustness of its forward-looking inflation expectations. However, as Meyer (2004) suggests, new Keynesianism has exerted limited influence on the U.S. fiscal and monetary policies, and the Phillips curve of the FRB/US model does not include a term for inflation expectations. Moreover, among

others, Rudd and Whelan (2007) have expressed doubts over the practical

187

Notes

applicability of the new Keynesian Phillips curve, stating that it is inferior to the Federal Reserve Board’s existing triangle model, at least with respect to 8.

policy analysis and forecast.

Table 8.1 presents a sketchy estimation equation for China’s Phillips curve,

in order to demonstrate the practical applicability of the refined Phillips

curve which is augmented by hysteresis, thereby indirectly testing whether potential national income has reflected the hysteresis effect from actual

national income. If there existed an acknowledged phase chronology of

China’s business cycles comparable to that of the NBER Business Cycle Dating Committee determined for the U.S., it would provide a basis for selecting the right time path of China’s output gap in Fig. 8.4, and hence

determining the lag order and probability density of the autoregressive

process of China’s potential national income. Then, Table 8.1 would give a more powerful verification to the goodness-of-fit test regarding the existence of hysteresis without the uncertainty of formalization.



As the representative theoretical and empirical research of China’s Phillips curve, Liu (1997) provides comprehensive explication of the structural

mutation of the classical Phillips curve in China’s economy. As for the flattening of the Phillips curve which became a worldwide phenomenon

at the turn of the 2000s and its possible intrinsic relationship with the restructuring of monetary policy and economic globalization, see Ball (2006), 9.

IMF (2006), and Mishkin (2005).

In addition to the conventional presumption that inflation expectations adjust more slowly than actual inflation and potential national income adjusts more slowly than actual national income, we can further assume that the national

income target adjusts more slowly than potential national income. Hence,

the speed of adjustment in China’s economy descends in the order of actual 10.

national income, potential national income, and the national income target.

When Barro and Gordon (1983) introduced the parabolic loss function, it

was assumed that such a one-way target of economic growth would provide excess welfare incentives to economic expansion, which would generate

dynamic inconsistency when combined with the classical Phillips curve. However, in long-run demand management, after excluding the possible economic stimuli of unexpected inflation, the use of a parabolic loss function

can eliminate the technical difficulty in setting the national income target that 11.

would arise from the real-time forecast of potential national income.

The price stability objective is not equivalent to zero-inflation targeting. Both the Taylor rule and the European Central Bank have set the price stability

objective of their monetary policies at 2% CPI inflation. Likewise, in striving

188

Notes

for price stability, China’s monetary policy should target an appropriate,

positive CPI inflation rate instead of a zero one.

Current CPI statistics are prone to understating the extent of quality improvement in goods and services, and thereby overstating the inflation

rate. For an economy undergoing rapid technological changes like China, the overstatement of inflation can be a serious problem; very often, an official

report of a zero, or close-to-zero inflation rate may in fact reflect heavy

deflation rather than price stability. According to the Boskin Commission, the CPI statistics of the U.S. Bureau of Labor overstated inflation by about 1.1% in the 1990s and then 0.8% in the early 2000s, despite the nation’s mature

technological development (Boskin 2005). Moreover, a zero inflation rate may

fall to a negative value under adverse demand shocks, which will pose a real challenge to monetary easing. China should draw lessons from Japan’s Lost

Decades, and target at a positive inflation rate in order to avert the fat-tail 12.

risk of deflation traps.

Inflation expectations tend to react to one-off acceleration of inflation weakly in the short run but completely in the long run. Nevertheless, during the

Great Moderation, inflation expectations are efficiently anchored whether

due to currency competition or improvements in monetary policy, thereby reacting weakly to one-off acceleration of inflation also in the long run. At

the Federal Reserve System’s FOMC (Federal Open Market Committee) meeting in October 2007, board members and bank presidents carried out independent, individual projections of the U.S. inflation rates during

2007 to 2010. As it turned out, although their projections for 2007 were widely dispersed, reflecting significant differences in terms of ideology and

knowledge, their (long-run) projections were inclined to converge further into

the forecast horizon. See Fed (2007).

Even in the case of adaptive inflation expectations, the time span from t 1 to t 3 can be defined as a necessary spread during which inflation expectations have fully adjusted to a one-off acceleration of inflation ( ), and the maximum expansion of potential national income from t 1 to t 3 can be computed as

(y max * – y ’ )/n , on the basis of the aggregate supply function y – y * = λ •

( π – L [ π ]), so that potential national income will expand from y ’ to y max * through repeating the dynamic adjustment process of Fig. 8.8 by n times.

Thus, the time path of actual national income is still linear, but that of

potential national income becomes sawtooth.

Moreover, the Lucas supply function y – y * = λ • (π – π*) can be presented in the horizontal form of Y /Y * = λ • (P /P *), and the technically feasible

interval [ymin * , ymax * ] can be imposed upon the potential national income level.

189

Notes

To interpret Fig. 8.8 in terms of national income and price levels, a one-off

growth in the price level leads only to one-off acceleration of inflation at the

initial stage. Under the assumption of adaptive inflation expectations, the succeeding actual inflation rate may decelerate continuously with a rising, stable, or even declining price level. During China’s economic expansion

before the subprime crisis, the price levels of its basic industries, which were characterized by intensive and procyclical fixed investment, first rose due to the increase of demand, and then declined upon the increase of supply.

Eventually, following the deflation, supply and demand are balanced with an 13.

elevated production capacity.

Simon (2006) treats excess liquidity as the accounting concept of the assetdriven expansion of a central bank’s balance sheet, suggesting that it can

and should be sterilized in the short run by a central bank. In contrast, Warsh (2007) examines excess liquidity from a subjective point of view, and concludes that the overflow or shortage of liquidity is mainly rooted in

optimistic or pessimistic market sentiments. Hardly had the subprime crisis

broken out when the U.S.’s liquidity overflow was reversed to a liquidity 14.

shortage, (partially) verifying Warsh’s insight.

Regarding global economic imbalances and their relationship to China’s economic structure, see Bernanke (2007)’s authoritative outline. For the extent of the undervaluation of RMB exchange rates, see Zheng, Zhu, and Zhang (2007)’s direct measurement using the dynamic PPP theory.

Chapter 9 1.

Adopting the instrumentalist “as-if” methodology, the monetary policy rule of the People’s Bank of China (PBC) can be formulated as gM PBC = π E + gY E – gV

E

, in which the inflation rate target is the forecasted inflation rate instead

of the publically declared one. Since the money market is efficient and always clearing, even if both the actual economic growth and inflation rates are correctly anticipated, and the actual money supply is equal to its target value, the PBC rule cannot be verified as neutral; rather, it remains a rule that accommodates the natural business cycle. In times of deflation,

gM PBC < gM Lucas < gM Keynesianism, and gM Keynesianism > gM Monetarism. Therefore, a countercyclical monetary policy will only be expansionary if actual money supply surpasses the critical value of gM Lucas; otherwise, the natural inclination of China’s economic contraction will be reinforced. Furthermore,

with reference to the full employment criterion of economic stability, the countercyclical disposition of monetary policy will only suffice to

190

Notes

countercheck the natural inclination of economic contraction if actual money



supply surpasses the critical value of gM Keynesianism. The PBC has resorted to the percentage gap between money supply growth

and the sum of GDP growth plus CPI inflation, which approximates

(gM – gY – π ), to ex post evaluate the effect of monetary policy in economic

stabilization, for example, in times of deflation following the Asian financial

crisis. In effect, the (gM – gY – π ) indicator measures the negative growth rate of the velocity of money (–gV ). Its procyclical increment not only signals economic recession and the resulting necessity of money easing, but also

possibly attests that (relative) tight money supply has aggravated economic recession, which contradicts the PBC’s explanation of easy and appropriate 2.

money supply.

To cope with the negative shocks of the American subprime crisis, China expanded its money supply and credits boldly in 2008 and 2009. This

withnessed the process of its expansionary monetary policy in shifting, as the

Federal Reserve advocates, from quantitative easing (QE) to credit easing (CE), averting the risk of high inflation.



Different form the standard function M /P = L (R , Y ), China’s money demand

3.

Y ), and R = φ(M2/P , Y ). Therefore, M1 should be a more appropriate money supply indicator than M2 in reflecting the stability orientation of China’s monetary policy. If international capital flows are completely free and the adjustment of exchange

exhibits multi-stratum recursion from M1 to M2. For example, M2/Y = f (M1/

rates is partially equilibrated, (R N,t – R W,t) – E [e t + 1 – e t]] = 0, and e t + 1 – e t = a •

(e t*+ 1 – e t) (0 < a < 1), so R N,t – R W,t = a • (e t*+ 1 – e t). Nevertheless, positive causation between the interest rate and exchange rate as such is not universal. To infer their

4.

positive comovement, structuralism introduces the assumption that R N,t = a + β • R W,t (0 < β < 1) on top of R N,t – R W,t = a • (e t*+ 1 – e t). System II when a = 0 approximates an adjustable-peg regime. Starting the study of system II with a floating regime will provide a basic reference for, as well as reduce the analytic difficulty in, studying it under an adjustablepeg regime. When 0 < a < 1, the system can be transformed into a first-order difference equation of π N or R N. The dynamics of the solution will depend purely on the mixture of parameters, and the structural change on πN = bear little empirical significance.

will

5.

Regarding the line PP , d(Δ π N)/d π N = 1 + 1/l > 1, and R N =

6.

dπN = b < 1. Therefore, PP crosses with the upper segment of RR form below. Whether imposing strict capital control to increase the expected costs of

l – ( π W – R W)