Download The Researches on China’s Economic Growth: Implications of the Test Results

The Researches on China’s Economic Growth: Implications of the
Test Results
DING Hua
School of Economics and Trade, Henan University of Technology, P.R.China , 450052
Abstract This paper investigates China’s economic growth by performing multiple-break unit root
tests on the data of national and sectoral output and output per worker to identify their steady-state and
transitional growth paths. The evidence generated suggests that the growth behaviour of the Chinese
economy is consistent with endogenous growth theory. A learning-by-doing model in the China setting
is then used to explain the results of multiple-break unit root tests, based on the historical observations
about how the evolution of economic environment causes changes in some key policy parameters and
hence in the steady-state growth rate of GDP per worker.
Key words
China, Economic growth, Structural breaks, Steady state, Learning-by-doing
1 Introduction
Neoclassical growth theory predicts that long-run or steady-state growth is constant over time, as
the underlying technological progress is exogenously determined and constant. Endogenous growth
theory, on the other hand, maintains that steady-state growth rates could be changed by government
policies, and economies could transit to new steady-state growth paths, steeper or flatter than the old
ones, following the policy shocks. Since the mid-1990s, a number of studies have examined the question
of which of theses two predictions is more representative of the evidence. See, for example, Ben-David
and Papell (1995), Ben-David and Papell (1998), Ben-David and Papell (2000), and Ben-David et al
(2003). In particular, Ben-David et al (2003) reveals that, of the 16 industrialised countries under
investigation, some can be characterised by the neoclassical growth model, some by the endogenous
growth model, and some by both.
Interest in studying China’s economic growth has recently been growing, and among a few studies
that utilise formal growth theories, most of them have been conducted within the framework of the
neoclassical growth model. See, for example, Rebelo (1998), Yang fan (1999), and Cai Fang, Du Yang
and Wang Meiyan (2002), Wang Xiaoguang(2006). One exception is Shu Yuan and Xu
xianxiang(2002) . By empirically testing the Solow-Swan model, the R&D model and the AK model
using Chinese data, the authors find that only the AK model seems to be reasonably consistent with the
stylised facts of China’s economic growth, while the Solow-Swan and R&D models are strongly
rejected by the data. These findings suggest that China’s economic growth is better explained by some
endogenous growth theory than neoclassical growth theory. To confirm that China’s economic growth is
characterised by some endogenous growth models, it is necessary to further investigate whether there
have been significant changes in the slope of the steady-state growth path of the Chinese economy; and
if so, what have been the root causes. Such investigations should be informative for further
understanding the features of China’s economic growth and for Chinese policymakers to promote
growth. Therefore, the present paper plans to accomplish two main tasks. The first is to identify the
steady-state and transitional growth paths of the Chinese economy using multiple-break unit toot tests
that treat the break dates as unknown a priori and estimate them from data. The second is to build a
learning-by-doing model based on Shu Yuan and Xu xianxiang (2002). But our purpose is to see if the
predictions of the learning-by-doing model are consistent with the test results concerning changes in the
steady-state growth rate of China’s GDP per worker. We will provide detailed historical narrative to
help account for changes in some key model parameters that determine the steady-state growth rate.
China should provide a better case for evaluating these different views. China underwent not just
changes in social and economic policies but also institutional changes – from a centrally planned
economy to a market economy, plus many other political upheavals in the past half a century. That the
Chinese economy has experienced a larger number of cataclysmic shocks over a relatively shorter
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historical period than other countries would presumably increase the likelihood of detecting the presence
of growth effects as evidence in support of endogenous growth theory.
This paper adopts analytic approach of the real example, describes multiple-break tests and reports
their results, provides discussions on the growth implications of the results for China, points out that the
learning-by-doing model is developed and used together with historical narrative to explain changes in
the steady-state growth path of the Chinese economy, obtains conclude: the trend-stationarity alternative
hypothesis allows for two or three structural breaks ,the growth implication of the test results for China
is the policy can be considered a good guide to Chinese policy-markers.
2 Multiple-break unit root tests
In this paper, we use output and output per worker to study China’s national and sectoral growth
performance. The variables to be tested are defined as follows:
Y0 ≡ log of real GDP of the national economy, 1952-2005;
Y1 ≡ log of real output of primary industry, 1952-2005;
Y2 ≡ log of real output of secondary industry, 1952-2005;
Y3 ≡ log of real output of tertiary industry, 1952-2005.
X0 ≡ log of real GDP per worker of the national economy, 1952-2005;
X1 ≡ log of real output per worker of primary industry, 1952-2005;
X2 ≡ log of real output per worker of secondary industry, 1952-2005;
X3 ≡ log of real output per worker of tertiary industry, 1952-2005.
Following Perron (1997) but allowing for more than one break, the unit-root test for ρ = 0 is
performed for the eight series in the following regression:
n
n
n
k
j =1
j =1
j =1
s =1
∆y t = α + βt + ρy t −1 + ∑ φ j DU jt + ∑ δ j D(TB j ) t + ∑ γ j DT jt + ∑ θ s ∆y t − s + e t (1)
with et assumed to satisfy the assumptions as given in Lumsdaine and Papell (1997) or in the Appendix
of Be-David et al (2003). The break dummy variables take the following values:
1, if t ≥ TB j
,
DU jt = 
0,
otherwise

t - TB j + 1, if t ≥ TB j
, D TB j
DT jt = 
0,
otherwise

( )
t
1, if t = TB j
,
=
0,
otherwise

Equation (1) allows simultaneous changes in both the intercept and slope, assuming all the break
parameters, φj and γj, are statistically significant. It is known as Model CC, as opposed to Model AA
where only φj are significant, and to Model CA where at least one, but not all, of γj is insignificant. But it
is also possible to have models where one or more of φj are zero. These models distinguish from each
other with regard to where breaks occur in terms of the intercept and the slope and how many, and we
let data determine which model should be chosen. The n break points TBj are unknown a priori, and are
to be estimated from data such that their associated t-statistic for ρ is the least favourable to the unit root
null (i.e., is the most negative). Note that many similar studies, such as Ben-David et al (2003), do not
introduce the additional one-time dummy variables D(TBj) in the regression equation (1). Since these
regressors are asymptotically negligible, this makes little difference for moderate to large samples in the
case where the break dates are treated as unknown. But our sample is small, and so we choose to include
them if the following two conditions are all met: (1) they are significant and (2) their inclusions make
the ADF(ρ) statistics more negative.The multiple-break tests start with the two-break case (i.e., n = 2 in
equation (1)). The choice among the aforesaid different models is made in the following way: starting
from the estimation of the general model (1), if all φj and γj are significant, stop; if one or more of them
are insignificant, proceed to the estimation of the model with the insignificant coefficient dropped. The
criterion for significance of individual break coefficients is the 5% value of the t-statistic, 1.96,
following Ben-David and Papell (1998). Regarding the determination of the truncation lag length k, we
employ the "t-sig" method described in details in Perron (1994) and commonly used in many empirical
studies dealing with trend-break unit root tests. For the reasons why the t-sig method is superior to those
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based on some information criteria, see Perron (1997). Briefly speaking, the t-sig procedure is as follows:
start with k = kmax and then reduce k from kmax by 1 at a time, till the absolute value of the t-statistic on θk
in equation (1) is greater than or equal to 1.6, the 10% value of the asymptotic normal distribution. kmax
is set at 4 in this paper.To be able to make statistical inference, we computed the exact finite-sample
distributions of the test statistics for the eight series through Monte Carlo simulations, following the
bootstrap method as suggested in Zivot and Andrews (1992). The critical values so obtained are
presented in Table 1.
Series
Y0
Y1
Y2
Y3
X0
X1
X2
X3
1.0%
-8.15
-7.80
-7.91
-8.73
-7.80
-7.16
-8.15
-7.26
2.5%
-7.67
-7.11
-7.42
-8.24
-7.40
-6.82
-7.65
-6.75
X2
X3
-9.74
-8.84
-9.14
-8.44
Table 1 Finite-sample Distributions
Two breaks
5.0% 10.0% 50.0% 90.0% 95.0%
-7.30 -6.85
-5.48 -4.48
-4.24
-6.70 -6.32
-5.11 -4.16
-3.91
-7.06 -6.65
-5.40 -4.42
-4.19
-7.75 -7.23
-5.76 -4.70 -4.43
-7.00 -6.64
-5.31 -4.42
-4.22
-6.48 -6.14
-5.06 -4.18 -3.94
-7.43 -7.03
-5.77 -4.75
-4.50
-6.47 -6.18
-5.10 -4.24
-4.03
Three breaks
-8.68 -8.10 -6.83 -5.71 -5.43
-8.05 -7.65 -6.43 -5.39 -5.18
97.5% 99.0%
-4.07
-3.86
-3.72
-3.48
-4.03
-3.82
-4.21
-3.91
-3.97
-3.75
-3.78
-3.56
-4.35
-4.15
-3.85
-3.69
-5.20
-4.96
-4.90
-4.77
Source of the materials: Percentage points are based on Chinese Statistical Yearbooks,
arrangement.
3 Implications of the test results for growth
3.1 Computing growth rates
Let r = 0 denote the regime before the first shift occurs, r =1 the regime after the first but before the
second shift occurs, and so on. Then, for each regime r = 0, 1,…, n, the steady-state growth rates of a
series, GSr, are obtained as:
r
b+
∑d
j
j =0
G Sr =
r = 0, 1,…, n; and d0 ≡ 0.
g
1−
(2)
∑h
i
i =1
where the values of the coefficients b, dj (j > 0) and hi come from the following regression equation for
the series:
n
n
n
g
j =1
j =1
j =1
i =1
y t = a + bt + ∑ c j DU jt + ∑ δ j D(TB j ) t + ∑ d j DT jt + ∑ hi y t − i + v t (3)
In equation (3), the number of breaks n, the break dates TBj for DUjt and DTjt, and which of the
coefficients cj, δj and dj should be imposed a priori a zero restriction are all determined with reference to
the results of unit root tests obtained in the preceding section. The truncation lag parameter g is selected
also using the t-sig method as described in the preceding section, with gmax again set at 4. The results are:
g = 3 for Y0, 2 for X0, 4 for Y1, 3 for X1, 2 for Y2, 2 for X2, 2 for Y3, and 3 for X3. Note that equation (2)
only gives long-run growth rates, i.e., growth rates when an economy is in a steady state. It does not and
cannot say anything about transitional growth rates, i.e., growth rates when an economy is in a
transitional period following a break-causing shock. To be able to see how the Chinese economy transits
from one steady-state path to another or returns to the original one, we next compute the fitted trend
function (Trendt) for each series that captures transitional dynamics. As suggested by Perron (1994), this
can be done using, again, the estimates of equation (3) but in the following manner:
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
−1
Trend t = τ 0 + τ 1t + A(L ) 


n
∑
n
c j DU jt +
j =1
∑ d DT
j
j =1
jt




(4)
g
a−b
∑ ih
i =1
g
where τ o =
1-
∑h
i =1
i
i
g
b
, τ1 =
and
g
1-
∑h
A(L ) = 1 − ∑ hi Li
i =1
i
i =1
Equation (4) can then be used to calculate the growth rates of potential output/output per worker
during a transitional period, simply by differencing Trendt once:
GTt ≡ Trendt – Trendt-1
(5)
We will label GTt as “transitional growth rates”.As for the growth rates of actual output/output per
worker, we use the first difference of its logarithm:
GAt ≡ yt – yt-1
(6)
which we shall label as “actual growth rates”.
3.2 A historical narrative of the growth pattern
According to Chinese Statistical Yearbooks and Table 1, the long-run growth effects of some
historical events on the economy can be best seen by looking at changes in steady-state growth rates. Let
us focus on output growth for the moment. GDP, secondary and tertiary industries’ outputs all
experienced their first steady-state growth changes in 1961 (Y0 and Y2) or 1960 (Y3), as the aftermath of
the Great Leap Forward launched in 1958. The changes were devastating, in that the steady-state growth
rate of potential output dropped by 2.41 percentage points for the national economy, 11.29 for secondary
industry, and 6.49 for tertiary industry. Nevertheless, primary industry’s potential output did not
undergo a steady-state growth slowdown at that time, as the growth rate remained constant at 3.78%
until 1975. Meanwhile, these four output variables all suffered an adverse level effect. Potential output
of national and sectoral economies would otherwise have been, respectively, 55.38% (=
35.64%/(100%-35.64%)), 27.98% (= 21.86%/(100%-21.86%)), 143.96% (= 59.01%/(100%-59.01%))
and 46.54% (= 31.76%/(100%-31.76%)) greater than in their second regimes. Since the first break date
1961, secondary industry’s output has never had a chance to move to a higher steady-state growth path,
but only experienced a rise in its level in 1993 following Deng Xiaoping's exhortation in 1992 to urge
the country to invest and grow. Primary industry’s output embarked on a higher steady-state growth path
at the end of the Cultural Revolution in 1976, albeit suffering a drop in its potential level. This might
have been due to the death of three highest leaders and a natural disaster (earthquake) that killed more
than 260,000 people, all happening in 1976. Thus, economic reforms uplifted national labour
productivity, which in turn became the main driving force of the increased steady-state growth rate of
GDP throughout the 1980s, 1990s and early 2000s.
It is also informative to examine the dynamics of national and sectoral economies following a
shock. When large shocks occur with or without growth effects, fluctuations in potential output/output
per worker could be quite drastic, resulting in transitional growth rates overshooting steady-state growth
rates. Accordingly, not just growth of actual output per worker, but also growth of potential output per
worker, can change in the short run. Since new reform policies aimed at changing economic systems
come one after another, they are likely to cause more trend breaks leading to more fluctuations in
potential output in the Chinese economy than in a developed economy. In other words, growth of
potential output in China may be often along a transitional path; or that the level of potential output is
volatile may be often a normal state of the Chinese economy. Therefore, there is a trade-off between
economic stability and growth. Drastic reform measures may be able to move the economy to a higher
steady-state growth path, but overshooting of potential output would become more severe, rendering
China’s short-run stabilisation more difficult. On the other hand, perfunctory reforms are unlikely to
generate significant long-run growth effects, although potential output would become less volatile. The
transitional dynamics of our testing models explains why the balance between economic stability and
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growth has always been the main consideration of the Chinese policymakers when they come to the
decisions on stabilisation and reform policies.
4 Explaining changes in steady-state growth using the learning-by-doing model
We observed from section 3 that some historical episodic events served as large shocks causing
changes in the slope of the steady-state growth path of the Chinese economy. These observations are
consistent with the prediction of endogenous growth theory. However, endogenous growth theory shares
one view with neoclassical growth theory that technological progress is the engine of long-run economic
growth. It assumes that changes in government policies affect steady-state growth through affecting
technological progress. We do not believe that this is largely the case in China. Rather, we believe that
large shocks like the Great Leap Forward must have changed the evolutionary behaviour of some
institutional variable in the Chinese context, which in turn alters the steady-state growth rate. In this
section, we ask what the institutional variable is, what institutional/policy parameters it depends on and
how historical episodic events changed these parameters. We seek answers to these questions within the
framework of the learning-by-doing model, as the R&D model has been found not to fit the Chinese
data in Shu Yuan and Xu xianxiang (2002). But the model we specify here is slightly different from
theirs to serve our purposes. Suppose that China’s aggregate production function is of the form:
Y(t) = K(t)α {[ψA(t)]L(t)}1-α,
0 < α < 1 and 0 < ψ ≤ 0
(7)
or in per-worker term,
y(t) = k(t)α [ψA(t)]1-α,
with y(t) ≡ Y(t)/L(t) and k(t) ≡ K(t)/L(t)
(8)
where Y, K and L are output, capital and labour respectively. However, A does not measure the level of
technology, or the level of knowledge about production methods. Rather, it measures the level of
knowledge about development strategy, resource allocation system, and microeconomic management
institution for carrying out economic construction nation-wide. The combination of these three elements
is termed as “the trinity of economic system” by Lin Yifu, Cai Fang and Li Zhou(1995). It is assumed
that such knowledge is accumulated during the practical process of “exploring various roads to
economic construction to find one that better suits China’s national conditions” (Shu Yuan and Xu
xianxiang, 2002). The accumulation of knowledge about economic construction is the source of what we
term as “the evolution of economic environment”. Based on historical observations, it appears that
different kinds of “the trinity of economic system” do cause different attitudes of individual workers
towards their work and hence different levels of the efficiency of labour. Accordingly, ψAL in the China
setting can be interpreted as the amount of effective labour affected not by technological progress but by
the evolution of economic environment.
The accumulation of knowledge about economic construction also occurs in practice, and thus as a
side effect of the production of new capital (Romer, 2001, page 120). But exploring or learning is a
continuous process, continuing from what has already been learnt and practiced. These two
considerations put together make China’s knowledge accumulation equation look different from the
standard learning-by-doing model, as follows:
A& (t ) = Bk (t ) β A(t )1− β ,
(9)
B > 0 and 0 < β <1
Equation (9) says that knowledge accumulation is a function of the stock of capital per worker and
the stock of existing knowledge, assumed to exhibit constant returns to scale. B is a parameter that may
reflect how keen the government is in pursuing economic construction. The implied growth rate of
knowledge is then:
−β
 A(t ) 
A& (t )
= B

A(t )
 k (t ) 
(10)
which, in steady sate, becomes a constant gA. This requires that A(t) and k(t) grow at the same rate in
steady state: gA = gk. It immediately follows from equation (8) that output per worker y must also grow
at this rate: gy = gA, in steady state. Based on the familiar capital accumulation equation
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k&(t ) = sy(t ) − (n + δ )k (t ) (where s, n and δ denote the investment rate, the growth rate of labour or
population, and the depreciation rate of capital respectively), it is ready to show the steady-state growth
rate of capital per worker or knowledge as
gk = g A = ψ
1−α
 A(t ) 
s

 k (t ) 
1−α
− (n + δ )
(11)
Substituting away the endogenous variable A(t)/k(t) using equation (10) and rearranging, one gets
g 1A+(1−α ) / β + (n + δ ) g A(1−α ) / β −ψ 1−α sB (1−α ) / β = 0
(12)
Although it is difficult to find an expression for gA as an explicit function of all other exogenous
parameters in (12), we can still take partial derivatives to reveal how gA responds to a change in each of
these parameters. A rise in the enthusiasm of the government in pursuing economic construction, or in
the investment rate, will raise the steady-state growth rate of knowledge and hence of output per worker.
These results should provide the Chinese government with rich information on policy implications for
promoting growth.
5 Cconclusions
This paper investigates China’s economic growth by performing multiple-break unit root tests on
the data of national and sectoral output and output per worker to identify their steady-state and
transitional growth paths. 10 sets of the exact finite-sample distributions of the test statistics are
bootstrapped through Monte Carlo simulations, and their critical values then used for inference. Based
on the implied steady-state and transitional growth rates for national and sectoral output and labour
productivity, one will be able to gain an idea what precise long-run growth rates of the economy and its
three sectors were over each regime and how exactly their potential output and labour productivity
growth evolved over time following each break-causing shock. From the results provided in this paper,
one can derive a rich set of information about the growth performance of China. In addition, we have
shown that it normally takes more than 10 years for transitional growth rates to converge to steady-state
growth rates after a shock dissipates, and the former can exhibit overshooting at the initial adjustment
stages. An understanding of this should be conducive to the design and implementation of stabilisation
and reform programs. If demand-type policies mainly act on actual output while supply-type policies
on potential output, then the timing of implementing, and the way of combining, they are an important
issue.
Our result of the changing steady-state growth rate of GDP per worker provides empirical evidence
in support of endogenous growth theory, and thus is explained within the framework of an endogenous
growth model-the learning-by-doing model. However, learning-by-doing is endowed with a different
meaning from that in standard economics textbooks: it is the source not of technological progress but of
the evolution of economic system/environment in the China setting, and it takes the form of the
government exploring various roads to economic construction to find one that better suits China’s
national conditions. Modified in this way, the learning-by-doing model seems to have served as a good
theory in helping us to understand the results of multiple-break unit root tests, based on the historical
observations about how the “trinity of economic system” evolves over time in China. It is then
suggested that the policy implications of this model can be considered a good guide to Chinese
policy-markers.
References
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Evidence from Two Structural Breaks. Empirical Economics, 2003, 28(2):303 319
[2] Shu Yuan,Xu xianxiang.The Specification of China’s Economic Growth Model: 1952-1998. Economic
Research Journal, 2002(11):3 11 (in Chinese)
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[3]Cai Fang,Du Yang and Wang Meiyan.Regional disparity and economic growth in China: the impact of labour
market distortions. China Economic Review, 2002(13) 197 212 (in Chinese)
[4]Wang Xiaoguang. The macroeconomic Retrospect in 2005 and prospect in 2006. Econommics Trends, 2006
(1)5 11 (in Chinese)
The author can be contacted from e-mail: [email protected]
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