Download Pattern and Sustainability of China`s Economic Growth

Pattern and Sustainability of China’s Economic Growth
towards 2020∗
Xiaolu Wang, Gang Fan, and Peng Liu
I. Introduction
In this paper, we attempt to examine evidence and influential factors of changing
economic growth pattern in China, and examine future growth sustainability towards
2020.
China has maintained a high economic growth rate for nearly three decades, since the
beginning of its economic reform in 1978. The average GDP growth rate during the
period of 1979-2000 was 9.2%, and further accelerated to 10.1% from 2001 to 2006.
GDP in real term by the end of 2006 has expanded to 13 times of that in 1978, reached
US$ 2.62 trillion.
However, economic growth in China has been described as “unsustainable” by
economists (e.g., Paul Krugman, 1994) or “extensive pattern of growth” by Chinese
government leaders (Premier Wen Jiabao, 2006), both meaning growth driven by massive
input with limited technological progress or total factor productivity (TFP) growth.
This pattern of economic growth in China can be characterized by very high rates of
savings and investment, massive transfer of unskilled labor from the agricultural to urban
non-agricultural sectors (which was the main source of TFP growth at the economy level),
cheap labor cost, low level of labor education, low level of technical innovation, large
income inequality, heavy dependency on external demand, inefficient energy
consumption, heavy environment pollution, and so on.
∗
This paper is an outcome of the research project “Pattern and Sustainability of China’s Economic
Growth”. The authors thank Mastcard for its financial support.
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Nevertheless, some sighs of changing growth pattern was observed in recent years.
Demand for skilled labor and professional workers is increasing, and supply of unskilled
rural labor in some source regions is exhausted, both causing increases in labor costs.
Workers’ education level is getting higher. There are shifts towards more capital
intensive production. Some authors suggest that China’s economy development is now at
the turning point (see e.g., Garnaut, 2006).
Investment in R&D is still low, but its ratio to GDP doubled in the past decade, and is 1.3
percent in 2005. Both values of annual technical transactions in markets (in real term) and
granted patents nearly quintupled during the same period. Some studies have identified
higher growth of TFP or capital productivity at either the national or firm levels in recent
years (see, e.g., Jefferson, Rawski and Zhang, 2007).
Evidence shows that large, and increasing, income inequality is a main reason for relative
weak domestic consumption growth and high savings rate. Some new policies has been
adopted since 2004 to encourage rural income and consumption growth and to reduce
poverty, such as abolition of agricultural tax, exemption of school fees in rural nine-year
education, and improvement in rural and urban social security systems.
It will be interesting to find out whether or how growth pattern in China is changing, and
what will be the impact on growth in the future. To do so, we carry out empirical tests in
this paper to examine possible effect of a number of potentially influential factors on
growth, and use these results to forecast future growth in China towards 2020.
In 2006, some modified historical GDP data, based on the new economic census, were
published by the National Bureau of Statistics (NBS, 2006). GDP in 2004 were upward
modified by 16.8%. GDP and GDP growth rate in earlier period were also adjusted
accordingly. This enables us to get a more accurate growth accounting result.
In section II, we review the roles of a number of influential factors in economic growth
and their recent changes. Section III specifies a growth model for empirical tests and
reports the results. Section IV carries out growth accounting to calculate contribution of
factors to growth, and to forecast future economic growth towards 2020. Finally, Section
V is the conclusion.
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II. What Contributed to Growth and Do These Show Any Chang in Growth Pattern?
Capital stock
There has been an increasing rate of capital formation in the past, resulting from high
savings and massive capital inflow. The average rate of capital formation rose from
35.2% of GDP in the 1980s to 40.2% in 2001-2005. Our calculated growth rate of fixed
capital stock was 9.2% in average for the pre-reform period of 1952-1978, 10.6% from
1979 to 1998, and 12.8% from 1999 to 2005. In 2005-06 it was 15.6%. Rapid capital
formation has played a major role in China’s economic growth. Because the savings rate
remains high, capital growth rate is unlikely to slow down in the near future. We assume
an annual rate of 15% in 2006-2010, and 14% in 2011-2020. Improvement in social
security systems and reduction in income inequality can help to resume consumption and
to moderate the rate of capital formation, but this depends on the government policies.
Details of our calculation of capital stock, human capital stock and other variables can be
seen in the notes of Table A1 in the Appendix.
Labor and Human capital
The increasing supply of unskilled cheap labor to industrial and service sector in the past
two to three decades is an important source of China’s rapid economic growth. However,
there has been some evidence showing that the trend is slowing down, and the labor cost
is increasing. Meanwhile, the roles of human capital in economic growth are getting
more important. While workers’ education level is increasing, skilled labor and
professional workers are in short supply.
Following Lucas (1988), human capital in this study is defined as effective labor that
enhanced by their year of schooling. According to our calculation, average year of
schooling of labor force is increasing. It was 6.1 years in 1980, 7.1 years in 1990, 8.6 in
2000, and 9.2 in 2005 (calculated from NBS data, various years; same below for unsourced data). However, average growth rate of human capital stock was diminishing,
7.3% in 1979-1988, 3.2% in 1989-1998, and 2.5% in 1999-2005. This was mainly caused
by diminishes of labor growth rate, as the result of one-child policy. We expect fast
increases in workers’ year of schooling in coming years due to expansion of tertiary and
secondary vocational education, and improvement in rural nine-year education. Therefore
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the current growth rate of human capital (around 2.6%) in 2006-2010 can be continued,
and it may be slightly lowered to around 2% in 2011-2020.
Marketzation
Many evidences indicate that high economic growth during the reform period was
benefited from the market oriented institutional changes, especially development of the
non-state enterprise sector (private sector). Due to lack of overall historical data, shares of
the non-state enterprise sector in industrial output are used in this study as a proxy of
marketization. Data between 1996 and 1998 are found overstated, and data between 1999
and 2003 are not completed (small enterprises with annual sales below five million Yuan
were missing). They are therefore modified according to data from the two industrial
censuses in 1995 and 2004. According to the modified data, the shares of non-state
enterprises were 22.4% in 1978, 43.2% in 1988, 63.8% in 1998, and 69.2% in 2005.
Because many SOEs have already been privatized, further increase in the non-state share
will be minor. We assume a three percentage point increase by year 2010, and another
four percentage point increase by year 2020.
Urbanization
As one of the most important result of market oriented reform, urbanization was
accelerated during the reform period, especially since the 1990s. The urbanization ratio,
that is, urban share in total population, increased from 18% in 1978 to 43% in 2005,
roughly meaning 300 million rural residents have transferred to urban areas. This has
sustained increasing labor supply to the rapid growing industrial and services sectors.
Meanwhile, reallocation of labor from sectors with low productivity to those with higher
productivity became a major source of China’s productivity growth. In the past five years,
the urbanization ratio increased at 1.4 percentage point per year. We expect same speed
of increasing urbanization ratio from 2006 to 2010 to reach 50%, and possibly another ten
percentage point or higher increase from 2011 to 2020 to reach at least 60%.
Trade
One of the most significant characteristics of China’s past growth pattern was export
orientation. The trade dependency ratio, i.e., total value of exports and imports as a
proportion of GDP, increased dramatically from 9.7% in 1978 to 63.9% in 2005.
Economic theories state that trade contribute to productivity growth via exploitation of an
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economy’s comparative advantages, may lead technological transfers among countries,
and lead to efficiency increases via international competition. However, given the high
achievement, we expect a slower increase in this ratio by annually two percentage points
in 2006-2010, and then remain unchanged in 2011-2020.
Foreign direct investment
FDI has been another source of capital formation in China. It remains at the level of 5060 billion US dollars annually in recent years, making China one of the largest FDI
recipient countries in the world. However, the growth rate of foreign capital diminished
from above 30% in the 1990s to only 6% in 2005. In addition, compared with the huge
domestic investment, FDI only accounts for a small part in China’s capital formation. Our
calculation shows a nine percent or lower foreign share in total fixed capital stock in
20051. In this study, we assume an average 0.5 percentage point decrease of foreign share
in total fixed capital stock between 2006 and 2010, and then a 0.3 percentage point
decrease between 2011 and 2020.
Foreign capital may contribute to TFP growth if it had a higher productivity than
domestic capital, or transferred new technology to the economy, although an earlier study
using foreign shares in total investment in fixed assets did not find significant
contribution of FDI to TFP (Wang, 2006). To further test these effects, we calculate
foreign capital stock and use the foreign share in total capital stock in the model. A
positive and significant estimate indicates a higher productivity of foreign capital than
total capital, thus a contribution to TFP growth.
Infrastructure
Better infrastructure makes more efficient use of other factors. In the past decade, there
was a rapid improvement in infrastructure conditions, especially the highway system. The
length of highway increased from 1157 thousand (1995) to 1930 thousand km (2005).
The quality of the road system also much improved. Of the total length of highway,
freeway increased from 2 to 41 thousand km. To make the data comparable, we
converted different grade of road length into a grade II equivalent highway length
according to the road capacity of transport volumes. In this study, it is called standard
1
There should be a deduction of the part invested for working capital from total FDI. However, data are
unavailable. Therefore the share of nine percent is an upper limit.
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highway length. The standard highway length increased dramatically in the past decade,
from 275 thousand (1995) to 830 thousand km (2005).
Infrastructure investment forms part of total capital stock, and contribute to economic
growth via growth of capital. Meanwhile, improvement in infrastructure may also
generate externalities to the economy and therefore contribute to TFP growth. To test this
effect, we calculated a ratio of standard highway to population. This ratio was 23 km per
thousand persons in 1995, 44 km in 2000, and 64 km in 2005. In the future, growth of
natural length of highway will slow down, but quality improvement in the road system
will continue. We assume another 15 km increase in this ratio in 2006-2010, and 20 km
further increase in 2011-2020.
Research, development and technical innovation
R&D in China was government-leading and generally at a low level in the earlier stage
even during the reform period. No significant contribution of R&D on growth was found.
However, the recent trend shows increases in fund raising for science and technology
activities, mainly led by enterprises. The funds raised by enterprise accounted for 44% of
the total in 1995, increased to 66% in 2005. During the same period, R&D expenses as a
proportion to GDP increased from 0.60% to 1.34%, and total patents granted increased
from 45 to 214 thousand items per year (data are from NBS, 2006 and 2005b). The
increase in enterprise spending is a clear evidence for positive market returns to R&D.
One can expect that the ratio of R&D expenses to GDP will continue to increase in the
future until achieve a reasonable level, possibly around 3%. This implies an accelerated
growth of R&D expenses until the middle of 2020s.
Structural bias
Although high saving and investment have contributed largely to economic growth, the
continued decreases in the share of final consumption in GDP have drawn much concern
from economists and the government. From 1980 to 2005, the share of final consumption
in GDP dropped from 65.5% to 51.9%; and the share of private consumption dropped
from 50.8% to 38.0%. A major drop of final consumption, by ten percentage points,
occurred in recent years from 2001 to 2005.
Due to relative weak consumption growth, the rapid increasing production capacity relies
more and more on investment demand and net export to be utilized, and leading more
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trade disputes with other countries. Net export is increasing, accounting for 6.7% of GDP
in 2006. Foreign exchange reserves, resulted from both trade surplus and capital inflow,
mounting up to 1.066 trillion US dollars by the end of 2006; a large part of which was
used to buy US treasure bonds. Balance of bank deposit is huger, reached 33.54 trillion
Chinese Yuan, or 1.6 times of GDP by the end of 2006. The ratio of total bank loans to
bank deposit dropped from 93.8% in 1995 to 67.8% in 2005. All these indicates
inefficient uses of resources.
A number of factors are responsible for weaker consumption growth. Firstly, income
inequality is getting larger. The Gini coefficient was around 0.30 in early 1980s, then
0.45 in 2001. Larger income gap caused more savings and less consumption because the
saving rates are uneven between rich and poor. Secondly, public services on education,
healthcare and housing were largely withdrawn during the reform period, and social
security systems are incomplete, causing heavy burden and future uncertainties to, and
forced savings of, middle and low income people. Finally, due to incompletion of the
taxation system, enterprise savings, from undistributed profits, were built up to a huge
amount. In short, the continued decreases in final consumption ratio are results of
institutional defects, calling for public sector reforms.
Recently, the government adopted a number of policies to restore consumption growth,
including abolition of agricultural tax, exemption of school fees for rural nine year
education, more government expenses on poverty reduction, and improvement in social
security systems. These will certainly have a positive impact on final consumption.
However, whether these are enough to turn back the decreasing trend of consumption
ratio is still uncertain. Improvement in public sector governance is more basic.
In this study, we define the ratio of final consumption in GDP as a structure variable, and
hypothesize that it had a negative effect on economic growth when dropped below a
certain level. This hypothesis will be empirically tested.
Government administration cost
As a result of inefficient use of public resources and possible corruption, the government
administration expenses, as a part of fiscal expenditure, is increasing. It accounted for
1.35% of GDP in 1978, 1.80% in 2000, then increased to 2.63% in 2005. This figure
exclude the operating cost of government departments and their subordinates in the areas
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such as agriculture, industry, transport, communication, commerce, culture activities,
publication, education, healthcare, public medication, sports, sciences, social sciences,
government and party training, and superannuation of retired administrative persons.
With these included, it mounts to 1.35 trillion Yuan in 2005, accounting for 7.4% of GDP.
Inefficient and inadequate use of public resources may form some deduction from
economic growth and TFP changes. In this study, fiscal expenses on government
administration (the narrow sensed) as a ratio to GDP is used to test this possible effect.
The increases in administration cost may be treated as a reflection of a wider range of
government activities with low efficiency, including government investment and
government distribution of other public resources.
In this study, the same trend of increases in the share of administrative cost in GDP is
assumed to continue in the future till 2010, and slightly slower increases in 2011-2020, as
a result of possible improvement in government administration, is assumed. Different
scenarios are also simulated.
III. Empirical Tests
In an earlier study, Wang (2006) estimated contribution of a number of factors to
economic growth. A similar method is used in this study with modifications of the model
to examine the roles of more factors played in China’s economic growth.
A Lucas typed growth model is employed for the study. We use time series data at the
national level from 1952 to 2005. Most of the data, except those otherwise referenced, are
calculated from China Statistical Yearbook (NBS, various years) and China Compendium
of Statistics 1949-2004 (NBS, 2005b). Original input and output data used in this study
are shown in Table A1 in Appendix at the end of the paper.
The basic empirical model is specified as follows:
lnY(t)=C+a1lnK(t)+a2lnH(t-3)+a3Ha(t)+R(t)
(1)
where Y(t) is GDP in 1978 constant price at year t; K(t) is fixed capital stock in 1978
constant price at year t; H is human capital stock or effective labor that is defined as total
labor force enhanced by their year of schooling; Ha is works’ average year of schooling
for possible spillover effect of human capital on economic growth, C is the intercept term,
and R(t) is the residual term, which contains both unexplained TFP changes and random
8
errors (thus, without inclusion of other related variables, there should be an
autocorrelation problem).
This is much the same as Lucas (1988) model except a few modifications. First, lnHa is
replaced by Ha so that the effect of workers’ average year of schooling can be explicitly
measured. Second, the residual R(t) is not simply defined as a random error term, since,
obviously, Ha can not catch the entire TFP change. Third, human capital H takes a threeyear lag in this model. This is because our preliminary study using a distributed lag
model found a largest and most significant coefficient of lnH with three-year lag. This is
reasonable since H is only education-enhanced human capital without considering the
learning-by-doing effect. Experiences tell us that school graduates usually become more
productive a few years after they being employed. It is therefore reasonable to believe
that the three-year lag of lnH is a best representative of human capital stock in our case.
To test the possible effects of technical innovation, marketization, urbanization, foreign
investment, foreign trade, and government administration cost on TFP growth, above
model is expanded into the following form:
lnY(t)=C+a1lnK(t)+a2lnH(-3t)+a3Ha(t)+a4DlnRK(t)+a5m(t)+a6u(t)+a7fk(t)+a8td(t)
+a9ga(t)+a10hw(t)+ε1(t)
(2)
where RK is a research capital stock that is accumulated by R&D investment. Due to
insignificance of lnRK in a preliminary estimation, we take its first difference, i.e., DlnRK,
in the model. According to this specification, a significant estimate of it meaning that
only an accelerating growing RK contributes to economic growth. m is the share of nonstate sector in industrial output as a proxy of marketization; u is the urbanization ratio,
that is, the share of urban population in total; fk is the share of foreign capital in total
capital stock; td is the trade dependency ratio; ga is the government administration cost as
a proportion to GDP; hw is the highway-population ratio; and ε1 is a random error.
In an alternatively model, we hypothesize that economic growth was restricted by a
structural bias, i.e., too low final consumption and too high saving and investment results
in overcapacity of production. This may work as a deduction of TFP. To test this
hypothesis, a structure variable fc, i.e., the ratio of final consumption in GDP, and its
quadratic term, is included in Model 3:
lnY(t)=C+a1lnK(t)+a2lnH(-3t)+a3Ha(t)+a4DlnRK(t)+a5m(t)+a6u(t)+a7fk(t)+a8td(t)
9
+a9ga(t)+a10hw(t)+a11fc(t)+a12fc2(t)+ε2(t)
(3)
To impose a restriction for constant-returns-to-scale hypothesis (a2=1-a1), both Y(t) and
K(t) are divided by H(-3t), so Model 3 can be transformed into the following version:
lny(t)=C+a1lnk(t)+a3Ha(t)+a4DlnRK(t)+a5m(t)+a6u(t)+a7fk(t)+a8td(t)
+a9ga(t)+a10hw(t)+a11fc(t)+a12fc2(t)+ε3(t)
(3’)
where lny(t)=lnY(t)-lnH(-3t), and lnk(t)=lnK(t)-lnH(-3t).
We also want to investigate possible changes in productivity of capital and human capital.
For this purpose, both the capital and human capital variables are multiplied respectively
by three dummy variables for the first and second decades of the reform periods and the
most recent period (1979-1988, 1989-1998, and 1999-2005). Below is the modified
Model:
lnY(t)=C+a1lnK(t)+a1’lnK1979-88(t)+a1’’lnK1989-98(t)+a1’’’lnK1999-2005(t)+a2lnH(-3t)
+a2’lnH1979-88(-3t)+a2’’lnH1989-98(-3t)+a2’’’lnH1999-2005(-3t)+a3Ha(t)+a4DlnRK(t)
+a5m(t)+a6u(t)+a7fk(t)+a8td(t)+a9ga(t)+a10hw(t)+a11fc(t)+a12fc2(t)+ε4(t)
(4)
where, for instance, lnK1979-88(t)=lnK(t) for years from 1979 to 1988, and lnK1979-88(t)=0 for
other years. The coefficient a1’, for instance, indicates changes of a1 in the period 19791988. Therefore for this period, the elasticity of capital should be a1+a1’.
In Table 1, empirical results of the models defined above are reported, which are obtained
from Prais-Winsten AR(1) Regressions.
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Table 1. Estimation result: Prais-Winsten Regression AR(1)
lnK(t)
Model 1
0.6045
(4.89**)
Model 2
0.3950
(4.00**)
Model 3
0.2815
(4.61**)
Model 3’
0.2722
(3.88**)
0.8323
(3.28**)
0.6615
(4.65**)
0.5893
(6.75**)
-0.1015
(-1.38)
0.0282
(0.72)
0.7419
(9.25**)
0.3331
(2.37*)
1.1125
(1.32)
0.2214
(0.27)
0.1773
(0.88)
-22.237
(-4.71**)
0.0291
(1.27)
-6.5602
(-2.95**)
0.964
0.323
-3.5624
(3.24**)
0.996
1.643
0.0619
(3.10**)
0.3584
(4.43**)
0.3249
(3.48**)
0.9824
(2.06*)
1.1780
(2.33*)
0.2906
(1.99’)
-11.491
(3.26**)
0.0294
(2.34*)
4.1182
(2.54*)
-3.8605
(3.25**)
-3.0078
(-4.00**)
0.999
1.792
0.0251
(1.73’)
0.3973
(5.08**)
0.2821
(2.98**)
0.9460
(1.77’)
1.3280
(2.43*)
0.2043
(1.35)
-10.292
(-3.03**)
0.0322
(2.23*)
2.6788
(1.71’)
-2.7047
(-2.37*)
-3.9580
(-7.37**)
0.995
1.563
Model 4
0.3229
(4.95**)
0.0111
(2.99**)
0.0121
(2.93**)
0.0167
(3.05**)
0.5897
(6.18**)
0.0025
(0.98)
0.0072
(2.08*)
0.0131
(2.67*)
-0.0015
(-0.05)
0.3100
(3.02**)
0.3849
(4.28**)
0.4832
(0.94)
0.8634
(1.52)
0.1896
(1.32)
-15.193
(3.75**)
0.0340
(1.75’)
4.6895
(2.93**)
-4.2725
(-3.68**)
-3.1308
(-3.87**)
0.999
2.075
1.059
1.734
1.847
1.694
2.067
51
51
51
51
51
lnK1979-88(t)
lnK1989-98(t)
lnK1999-05(t)
lnH(t-3)
lnH1979-88(t-3)
lnH1989-98(t-3)
lnH1999-05(t-3)
Ha(t)
DlnRK(t)
m(t)
u(t)
fk(t)
td(t)
ga(t)
hw(t)
fc(t)
fc2(t)
C
Adj. R2
DW (original)
DW(transform
ed)
n
Note: figures in parentheses are t-ratios. Those with ’ are significant at the 10% level,
with * are at the 5% level, and with ** are at the 1% level.
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In Model 1, both capital and human capital show significant contribution to economic
growth, and the estimated spillover effect of human capital is negative and insignificant.
As expected, the Durbin-Watson statistic indicates existence of autocorrelation, therefore
unreliable estimates.
Model 2 produces better results after including a number of relevant variables. Estimates
of capital, human capital, marketization, difference of research capital, and government
administration cost (negative) are significant. Others are insignificant; they are spillover
effect of human capital, the urbanization ratio, foreign capital share, trade dependency
ratio, and highway-population ratio. Durbin-Watson statistic is much improved from
Model 1. It is in an inconclusive interval but close to dU.
Model 3 rejects the null hypotheses and confirms the negative impact of the structural
bias. The estimate of final consumption ratio, fc, is positive and significant; and the
estimate of its quadratic term is negative and significant. This indicates an invert U-shape
curve for effect of final consumption ratio on economic growth: positive before a certain
critical point and negative after that point. Figure 1 illustrates this effect using the
estimated coefficients of fc and fc2, for an interval of fc between 90% to 10%. Shown by
the simulated curve, the critical point of final consumption in GDP is around 55%.
According to this result, the current final consumption ratio, 51.9% (year 2005), already
has a negative effect on economic growth; and the current trend of changing fc indicates a
greater negative effect on growth in the future.
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Figure 1. Simulating the negative effect of structural bias on growth
Demand effect
1.2
1.0
0.8
0.6
0.4
0.2
90%
80%
70%
60%
50%
40%
30%
20%
10%
Final consumption ratio
Source
：Data are from estimates of the model.
Model 3 also shows further improvement in Durbin-Watson statistic. As a result, the
spillover effect of human capital, urbanization, share of foreign capital, trade dependency,
and per capital standard highway all become significant at least at the 10% level (only for
the trade dependency ratio). All the other estimates are significant at either 1% or 5%
levels.
The restricted version of the model for constant returns to scale (Model 3’) and the model
with periodical dummies on capital and human capital (Model 4) obtained very similar
estimates to Model 3, except that the spillover effect of human capital is smaller or
becomes negative and insignificant (indifference from zero in Model 4), due to that more
contribution is attributed to capital and the own effect of human capital, and the DurbinWatson statistic for Model 3’ is lower.
Results of Model 4 show significant increases in returns to both capital and human capital
during the reform period. Returns to capital increased by 0.0167 in the 1999-2005 period
compared with the average of pre-reform period, and returns to human capital increased
by 0.0131 in the 1999-2005 period. These equal to a five-percent increase in marginal
productivity of capital and two-percent increase in marginal productivity of human
capital.
Using statistical data and estimates of Models 2, 3, 3’ and 4 to simulate economic growth
for the past periods, all the three models provide generally good simulations, but Model 3
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provides the closest ones compared with the actual growth rates. Especially, the
simulated average GDP growth rate for the recent two periods, i.e., 1989-1998, and 19992005, are only -0.05 and -0.2 percentage points away from the actual growth rates. The
estimated parameters from Model 3 are therefore used in growth accounting in the next
section. Table 2 compares different simulation results. Statistics for growth of inputs and
contributing factors in different periods can be seen in Table A2 in Appendix.
Table 2. Simulating GDP growth rates for the past periods (average growth rate, %)
1953-1978
1979-1988
Actual rates
6.15
10.06
Simulated rate
Model 2
1989-1998
9.59
1999-2005
9.11
7.29
10.19
9.33
8.72
Model 3
6.57
9.56
9.54
8.90
Model 3’
6.72
9.64
9.49
8.67
Model 4
6.48
8.56
8.62
7.32
Model 2
1.15
0.13
-0.26
-0.39
Model 3
0.43
-0.49
-0.05
-0.21
Model 3’
0.58
-0.41
-0.10
-0.43
Model 4
0.34
-1.50
-0.96
-1.79
Error
：
Source Data are from the author’s simulation based on estimates of the models and statistical data, NBS
(various years).
IV. Growth Accounting
Based on the estimates of above and statistical data, the author carries out growth
accounting to decompose economic growth rate into different sources for different period.
Table 3 shows the result. Contribution of inputs and productivity changes to economic
growth are calculated.
The result indicates that capital and human capital growth (or, input-driven growth) have
played a crucial role in driven economic growth in the past, both pre-reform and reform
periods. It contributed 5-6 percentage points in most periods in the past.
TFP growth contributed a neglected 0.3 percentage point to growth in the pre-reform
period, increased significantly to 3.3 percentage points in the early stage of the reform
period, and then between 3.7 - 4.4 percentage points in the later stage of the reform
period until 2005.
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Most important sources of TFP growth during the whole reform period was marketization
and urbanization, they together made average 1.5 – 1.7 percentage point contribution to
TFP growth. The market-oriented institutional changes lead to increasing efficiencies via
factor reallocation and better incentive system, and urbanization also leads to reallocation
of labor and other resources from the low-productivity agricultural sector to higherproductivity urban non-agricultural sectors. The results show that, while the effect of
marketization diminishing, the urbanization effect increased to 1.3 percentage point in
recent years.
The effects of foreign investment and foreign trade, via spillover of technology and
management skills, together contributed only 0.6 percentage point to TFP growth in the
earlier reform period, then increased to 1.0 - 1.3 in the 1990s and recent years. While the
foreign share in total capital stock is decreasing in recent years, the effect of foreign trade
is playing a more important role in TFP growth, made 1.3 percentage point contribution
to TFP. Nevertheless, one can hardly separate the demand effect that driven by export
growth from the effect of trade-sourced productivity growth.
Spillover effect of human capital was also found to make important contribution to TFP
growth, at 0.8-1.0 percentage point.
Infrastructural improvement, reflected by growth of per capital highway, made little, but
increasing, contribution to TFP growth in the 1980s and 90s. It became important source
of TFP growth in recent years, i.e., 1.3 percentage point.
While research capital (i.e., accumulated R&D investment that calculated by Perpetual
Inventory Method) was not found to have significant impact on growth, its annual
difference is significant. This suggests that the faster accumulation of research capital has
lifted up economic growth rate to some extent. The effect is positive but very small in the
1990s, and then increased to 0.5 percentage point in recent years. Considering the fact
that either the research capital stock or the annual R&D expenses is still fairly small, this
is likely an indicator for growth effect of technical innovation in the future.
The high administration cost made an increasing negative impact on economic growth. It
reduced TFP growth by 0.1 percentage points in the 1980s and 1990s, but this effect
jumped to -1.7 percentage points in recent years, heavily reduced TFP growth.
15
One should note that, the negative effect of the structural bias on growth and TFP
changes only exists when the final consumption ratio in GDP falls below the estimated
critical point of 55%, because in this situation it reduce economic efficiencies. However,
when it varies in the range above 55%, its impact on growth is actually via the effect of
changing savings and investment, and therefore should be reclassified as a part of
contribution by growth of capital. In Table 3, this was added back to input-driven growth
as a part of capital contribution.
The final consumption ratio dropped dramatically from 62.3% in 2000 to 54.3% in 2004,
then to 51.8% in 2005. This made negative contribution to economic growth in 2005, but
not explicitly reflected in the average contribution in the period of 1999-2005. A
continuation of the current trend will lead to further drop of the ratio in the coming a few
years, although will be moderated by the government policy reinforcing income
redistribution and poverty reduction.
There is a small part of unexplained TFP growth for different periods, i.e., differences
between the actual and simulated growth rates. It was negative in the pre-reform period,
indicating a technical deterioration, half percent in the earlier reform period (which was
likely a unexplained allocative effect leading by rapid rural industrialization in that
period), diminished to a neglectable level in the 1989-1998 period, and then increased to
0.21% in recent years. The recent change may be considered as a result of technical
progress that was not indicated by the effect of research capital growth.
Table 3. Growth accounting: input-driven and TFP growth (annual growth rate, %)
1953-78
1979-88
1989-98
1999-05
Economic growth rate
6.15
10.06
9.59
9.11
Input-driven growth
5.83
6.70
5.16
5.36
By capital
2.59
2.58
2.70
3.59
By human capital
2.39
4.26
2.19
1.56
0.74
2.86
4.37
3.53
Spillover effect of human capital
0.40
1.02
0.84
0.79
Increasing R&D expenses
0.11
-0.18
0.16
0.47
Marketization
-0.45
0.68
0.92
0.32
Urbanization
0.21
0.78
0.74
1.35
Foreign capital effect
0.00
0.16
1.15
-0.35
TFP growth – explained
16
Foreign trade effect
0.00
0.46
0.19
1.33
Government administration cost
0.35
-0.14
-0.12
-1.73
Infrastructure effect
0.11
0.10
0.49
1.35
Effect of structural bias
0.86
-0.14
0.28
0.21
TFP growth - unexplained
-0.43
0.49
0.05
0.21
Source
：Same as Table 2.
The above result rejects the judgment on China’s economic growth as “input-driven
growth without productivity changes” (see, e.g., A. Yang, 2000; P. Krugman, 1994). It
clearly indicates a 3-4 percentage point TFP growth during the reform period. Meanwhile,
it also shows that one-third or half of the TFP growth was from “allocative effect”, i.e.,
productivity growth induced by improvement in factor allocation. This effect is reflected
by contribution of marketization and urbanization. Economic theories have proved that
reallocation of economic resources is a short run effect which does not sustain a high
growth rate in the long run, although urbanization effect in China is unlikely to diminish
in the next 10-15 years. Similar role may be attributed to contribution of infrastructural
improvement. It generates positive externalities to the economy, may continue to push up
economic growth in the coming decades or so, but the effect on growth rate may not be
sustainable in the ‘long run’ that defined in growth theories.
Another important part of TFP growth, at least one percentage point, was identified as
spillovers of technology and management skills from foreign investment and foreign
trade. This is partially a sustainable source of growth since it injects new technology into
the economy. However, sustainability of these effects on growth is discounted to some
extent because the source of TFP is not endogenously generated.
A new finding in this study is that the increasing R&D expenses and the spillover effect
of human capital made important contribution to TFP growth, accounting for 1.3
percentage points in 1999-2005. With inclusion of the unidentified TFP growth, which
should be considered as normal technical innovations that did not reflected from the
effect of increasing research capital, their total contribution to TFP was 1.5 percentage
points. This is a clear signal to indicate high possibility of changing growth pattern
towards a more sustainable way in the future.
17
While optimistic results were obtained, the outcome of growth accounting also delivers
some pessimistic massages on economic growth. The first one is the significant negative
effect of government administration cost on economic growth, which made a 1.7
percentage point deduction to growth and productivity changes during the 1999-2005
period. This indicates that government inefficiency and corruption is becoming a serious
threat to sustainability of economic growth. The second one is insufficient domestic
consumption, which already made deductions to economic growth in 2005, and likely to
further restrict economic growth in the near future. These two effects make uncertainties
to future economic growth.
Based on these results, economic growth in 2006-2010 and 2011-2020 is projected in the
following in two scenarios. The basic scenario is obtained generally based on the current
trend of changing contributing factors. The alternative scenario makes two optimistic
assumptions on changing government efficiency and increasing final consumption, as
results of government reform, with other conditions the same as in first scenario. For the
2011-2020 period, the effect of increasing R&D is assumed to be zero, and a same effect
is attributed to a common technical progress and added to the item: TFP from
unidentified factors. The two scenarios are compared in Table 4. Predictions and
assumptions on changing contributing factors can be seen in Table A2 in the appendix.
Table 4. Growth forecasts: different scenarios (annual growth rate, %)
Basic Scenario (I)
Alternative Scenario (II)
2006-10
2011-20
2006-10
2011-20
5.84
5.12
5.84
5.12
By capital
4.31
3.94
4.31
3.94
By human capital
1.53
1.18
1.53
1.18
1.58
0.10
3.64
3.34
Spillover effect of human capital
0.74
0.74
0.74
0.74
Increasing R&D expenses
0.47
0.00
0.47
0.00
Marketization
0.19
0.13
0.19
0.13
Urbanization
1.35
1.08
1.35
1.08
-0.59
-0.35
-0.59
-0.35
0.58
0.00
0.58
0.00
-1.73
-1.73
0.00
1.15
0.88
0.59
0.88
0.59
Input-driven growth
TFP from identified factors
Foreign capital effect
Foreign trade effect
Government administration cost
Infrastructure effect
18
Effect of structural bias
-0.31
-0.35
0.00
-
TFP from unidentified factors
0.40
0.80
0.40
0.80
Projected growth rate
7.82
5.92
9.88
9.26
Source
：Same as Table 2.
There are two differences in assumptions used for the two scenarios:
First, in the basic scenario, the author assumes that government administration cost as a
share in GDP will continue to expand in 2006-2010 and 2011-2020 at the same rate as in
1999-2005, i.e., increase by 0.15 percentage point per year, whereas in the alternative
scenario, it is assumed to stop expansion in 2006-2010, and decrease by 0.1 percentage
point per year in 2011-2020 as a result of possible government reform and therefore
increasing government efficiency.
Second, the author assumes that, in the basic scenario, the share of final consumption in
GDP will continue to decrease, resulting from further expansion of income inequality in
initial income distribution, but at a slower rate, due to increases in government transfer
payment. In 2000-2005, this ratio actually dropped by 10.5 percentage points. It is
assumed to further decrease by 5.0 percentage points in 2006-2010, and another 5.0
percentage points in 2011-2020. In the alternative scenario, the consumption ratio is
assumed to be stable in 2006-2010, and increase to 55% in 2011-2020. This is considered
as a result of a series policy adjustment toward a more healthy income distribution and
improvement in social security systems and public services.
The results of the two scenarios are very different. In the first scenario, economic growth
rate will drop from average 9.5% (2000-2005) to 7.8% (2006-2010), then to average
5.9% (2011-2020). The rapid growth period since 1978 will end in the 2020s. In the
second scenario, economic growth rate will be sustained at 9.9% in 2006-2010, and 9.3%
in 2011-2020. A continued rapid growth in longer term can be expected.
V. Conclusion
In this study, the author examines China’s economic growth pattern in the past and future
and growth sustainability towards 2020. Empirical study using a Lucas typed growth
model and data after statistic revisions identifies a 3-4 percentage point TFP growth
19
during the past period of economic reform from 1978 to 2005. Of which, 1.5-1.7 was
contributed by marketization and urbanization during the whole reform period, mainly
via improvement in factor allocation; 1.0-1.3 was contributed by spillover effect of
international trade and foreign direct investment since the 1990s; 1.3 was contributed by
externalities from improvement in infrastructure in most recent years. Increases in R&D
expenses and spillover effect of human capital together contributed 0.8 percentage point
in the 1980s, 1.0 in the 1990s, and 1.3 in most recent years, indicating an increasing trend
of technological progress.
Two negative impacts on TFP growth are identified. One negative impact is the
increasing government administration cost, representing effect of government
inefficiencies and corruptions, which was found to make a rapid increasing deduction to
TFP growth, accounts for -1.7 percentage points in recent years. Another is a structural
bias, i.e., continued drop of the share of final consumption in GDP, which started to
generate negative impact on GDP growth in the immediate past years.
The effect of the structural variable is found to be non-linear. The effect of diminishing
consumption ratio is positive above a critical value around 55%, and turns into negative
after dropping below this point.
Based the result of growth accounting and analysis on changes in various contributing
factors, economic growth rate is projected for the periods of 2006-2010 and 2011-2020. It
suggests a continued trend of growth driven by inputs in the two coming periods with
minor decreases, a diminishing contribution of marketization but a continued strong
contribution of urbanization and infrastructure improvement, and a continued, if not
further increasing, contribution of TFP growth by R&D, human capital spillover, and
other sources of technical innovation.
Changes in the government administration cost and final consumption are found to be
crucial determinants for future growth. With the current trends of increasing government
administration cost and decreasing final consumption, economic growth rate will be
around 7.8% in average of the 2006-2010 period, and then drop to an average level of
5.9% in 2011-2020. The rapid growth period since 1978 will end in the 2020s. However,
with possible increases in government efficiency via government reforms, and recovery
of domestic consumption that can be induced by improvement in public services, social
20
security systems, and more equity of income distribution, economic growth can be
sustained at above 9% level in both the 2006-2010 and 2011-2020 periods. A more
sustainable and continued rapid growth in longer period can be expected.
21
References:
Chow, G. C. 1993, “Capital Formation and Economic Growth in China”, The Quarterly
Journal of Economics, August, pp. 809—842.
Garnaut, R. 2006, “The Turning Point in China’s Economic Development”, in Garnau
and Song (eds), The Turning Point in China’s Economic Development, Asia Pacific Press
at the Australian National University, Canberra.
Jefferson, G, T. Rawski, and Y. Zhang, 2007, “Productivity Growth and Convergence
Across China’s Industrial Economy”, paper presented in International Workshop on
Chinese Productivity 2007, Tsinghua University.
Krugman, P. 1994, “The Myth of Asia's Miracle”, Foreign Affairs, November/ December
1994, Vol 73, Number 6.
Lucas, R. E., 1988, “On the Mechanics of Economic Development”, Journal of Monetary
Economics, 22, 3-42.
National Bureau of Statistics (NBS), various years, China Statistical Yearbook, China
Statistics Press, Beijing.
_______
, 2005b, China Compendium Statistics 1949-2004, China Statistics Press, Beijing.
Wen Jiaobao, 2006, “Government Work Report”, at the Fourth Session of the Tenth
National People’s Congress, Xinhuanet, http://news.xinhuanet.com.
Wang Xiaolu, 2006, “Growth Accounting after Statistical Revisions”, in Garnaut and
Song (eds), The Turning Point in China’s Economic Development, 35-52, ANU E Press,
Australian National University, Canberra.
Young, A., 2000, “Gold into Base Metals: Productivity Growth in the Peoples Republic
of China during the Reform Period”, NBER Working Paper W7856, National Bureau of
Economic Research, Cambridge.
Zhang Jun, et al., 2007, “Estimation of Capital Stock for Chinese Provinces”, paper
presented in International Workshop on Chinese Productivity 2007, Tsinghua University.
22
Appendix
Table A1. Input-output data for China’s economic growth
Year
GDP
RMB 100 mil,
1978 price
1952
773
Total
Capital
Foreign
Research
employment
stock
capital
Capital
10000
RMB 100 mil, RMB 100 mil, RMB 100 mil,
persons
1978 price
1978 price
1978 price
20729
700
0.0
3.0
Human
capital
10000
person year
74727
Year of
schooling
Year per
laborer
3.605
1953
894
21364
852
0.0
3.3
75047
3.513
1954
931
21832
1020
0.0
4.3
75905
3.477
1955
995
22328
1158
0.0
6.2
77949
3.491
1956
1144
23018
1388
0.0
11.4
79341
3.447
1957
1202
23771
1602
0.0
16.0
81834
3.443
1958
1458
26600
1939
0.0
26.6
83362
3.134
1959
1587
25173
2350
0.0
44.2
86470
3.435
1960
1582
25880
2809
0.0
75.1
88827
3.432
1961
1150
25590
2888
0.0
87.2
93728
3.663
1962
1086
25910
2858
0.0
92.5
100338
3.873
1963
1196
26640
2862
0.0
102.1
106922
4.014
1964
1415
27736
2931
0.0
116.5
112839
4.068
1965
1656
28670
3071
0.0
133.5
117526
4.099
1966
1833
29805
3235
0.0
147.4
128377
4.307
1967
1729
30814
3307
0.0
150.8
136632
4.434
1968
1658
31915
3337
0.0
153.5
146365
4.586
1969
1938
33225
3496
0.0
165.9
152306
4.584
1970
2314
34432
3806
0.0
183.5
155017
4.502
1971
2476
35620
4160
0.0
207.7
158376
4.446
1972
2570
35854
4483
0.0
228.2
165296
4.610
1973
2773
36652
4822
0.0
245.5
172877
4.717
1974
2837
37369
5176
0.0
261.4
179460
4.802
1975
3084
38168
5609
0.0
281.7
186501
4.886
1976
3034
38834
5989
0.0
299.1
191801
4.939
1977
3265
39377
6371
0.0
317.1
198498
5.041
1978
3645
40152
6878
0.0
344.6
212410
5.290
1979
3922
41850
7358
1.7
377.0
236568
5.653
1980
4228
43850
7811
6.4
406.6
267700
6.105
1981
4451
45950
8234
13.6
429.8
293978
6.398
1982
4852
48150
8829
26.7
453.7
318083
6.606
1983
5380
50100
9509
40.1
486.8
338705
6.761
1984
6197
52450
10408
64.1
528.7
357099
6.808
1985
7032
54800
11670
103.3
567.0
374509
6.834
1986
7655
57050
13114
150.5
605.3
392615
6.882
1987
8541
59300
14766
196.5
635.4
410318
6.919
23
1988
9503
61650
16526
251.2
655.7
427752
6.938
1989
9889
63450
17669
299.9
668.2
445292
7.018
1990
10269
64749
18590
361.3
676.5
462533
7.143
1991
11213
65491
19697
441.3
692.6
478822
7.311
1992
12809
66152
21343
658.7
712.5
494272
7.472
1993
14595
66808
23714
1131.6
727.3
509303
7.623
1994
16505
67455
26657
1914.2
735.8
524132
7.770
1995
18310
68065
29887
2636.6
768.4
539186
7.922
1996
20143
68950
33390
3348.2
807.5
553964
8.034
1997
22013
69820
37004
4052.6
868.5
569558
8.158
1998
23738
70637
41234
4694.7
938.4
585766
8.293
1999
25549
71394
45488
5162.2
1040.0
601840
8.430
2000
27700
72085
50077
5583.8
1188.3
617415
8.565
2001
30000
73025
55355
6099.3
1361.6
632901
8.667
2002
32727
73740
61805
6694.4
1587.3
647730
8.784
2003
36007
74432
70583
7224.2
1852.4
663166
8.910
2004
39638
75200
81484
7789.4
2177.7
679444
9.035
2005
43695
75825
95576
8261.5
2578.9
696327
9.183
Note:
1. Modifications were made to employment data, because there was originally a large gap between the 1989 and 1990
statistics on total employment, equals to 17% of the 1989 employment. This was a result of data inaccuracy between
the two national population censuses in 1982 and 1990. Data for the period of 1983-1989 are modified accordingly.
2. Fixed capital stock is calculated using historical data of total investment in fixed assets and price index for fixed
investment, using a Perpetual Inventory Method (see Wang, 2006). To avoid shortage of data on various categories of
capital stock, an overall depreciation rate of 5% is used for the pre-reform period. Some studies found faster capital
depreciation during the reform period. In this study, we adopt a final 9.6% rate that recommended by Zhang Jun (2007),
but assume it was gradually achieved during the 1979-1992 period with a 0.3 percentage point change per year. For the
initial fixed capital stock in 1952, we take RMB 70 billion (in 1978 price), mainly based on Chow (1993). He
calculated that capital stock in the non-agricultural sectors was 58.3 billion Yuan in 1952 (1952 price), of which fixed
capital was 31.6 billion, and capital stock in the agricultural sector was 45 billion including non-fixed capital. We
assume 70% of total agricultural capital being fixed capital, and upward modify the total fixed capital by 10% with
consideration that Chow’s capital stock maybe more or less underestimated due to data incompletion, we get a total
69.4 billion fixed capital stock in 1952 at 1952 price (see Wang, 2006).
3. Foreign direct investment data are used for calculation of foreign capital stock. They are converted to Chinese Yuan
at the official exchange rates and deflated using Fixed Asset Investment Price Index of the NBS. A depreciation rate of
9.6% is assumed.
4. The research capital is defined as an accumulation of knowledge and technology, and approximately measured by the
accumulated expenses on R&D at constant prices. Because of data incompletion, and considering that enterprise
expenses on R&D were rare in earlier period, we use fiscal expenses on science and technology for the period before
1990. Data are deflated into 1978 prices using a GDP deflator. The invisible depreciation rate is assumed to be 8%.
5. Annual formation of human capital from 1952 to 2005 is calculated from graduation and enrollment data (with one
education-period lag) of all kinds of schools, from primary to postgraduate education. The calculation referenced the
information from the four national population censuses in 1964, 1982, 1990 and 2000. Unfinished school education (i.e.,
the differences between graduation and period lagged enrollment) is assumed to have an average 50% length of the
corresponding education period. Vocational education, adult education, special education, overseas study, and informal
training programs are also included. Human capital depreciation is calculated based on the death rate of the population
and the calculated average year of schooling of population (not of labor force) with time lag. The initial human capital
stock in 1952 is projected as average 1.3 year of schooling of the total population. This is based on 1964 national
census data on education and detailed education data between 1952 and 1964.
6. Workers’ average year of schooling is calculated using human capital and employment data.
24
Table A1. continued
1952
9031
Urbanization ratio
Urban/total
population
0.1246
1953
10049
0.1331
0.5700
1954
11004
0.1369
0.5290
1955
12930
0.1348
1956
17946
1957
20700
1958
Year
Standard
highway
km
Non-state
share
In industrial
output
0.5850
Non-state
share (adj)
In industrial
output
0.5850
Trade ratio
To GDP
Administration cost
To GDP
Final
consumption
To GDP
0.0951
0.0214
0.7892
0.5700
0.0982
0.0213
0.7723
0.5290
0.0986
0.0213
0.7447
0.4870
0.4870
0.1207
0.0206
0.7726
0.1462
0.4550
0.4550
0.1057
0.0235
0.7471
0.1539
0.4620
0.4620
0.0978
0.0203
0.7409
35134
0.1625
0.1080
0.1080
0.0985
0.0165
0.6603
1959
43319
0.1841
0.1140
0.1140
0.1038
0.0185
0.5660
1960
45347
0.1975
0.0940
0.0940
0.0881
0.0192
0.6184
1961
42626
0.1929
0.1150
0.1150
0.0743
0.0219
0.7803
1962
42316
0.1733
0.1220
0.1220
0.0704
0.0189
0.8379
1963
44323
0.1684
0.1070
0.1070
0.0695
0.0191
0.7844
1964
45662
0.1837
0.1050
0.1050
0.0671
0.0173
0.7481
1965
50054
0.1798
0.0990
0.0990
0.0632
0.0148
0.7111
1966
53977
0.1786
0.1000
0.1000
0.0680
0.0139
0.6848
1967
56471
0.1774
0.1150
0.1150
0.0633
0.0129
0.7470
1968
59054
0.1762
0.1160
0.1160
0.0630
0.0133
0.7429
1969
63243
0.1750
0.1130
0.1130
0.0552
0.0128
0.7318
1970
68311
0.1738
0.1240
0.1240
0.0501
0.0112
0.6614
1971
73820
0.1726
0.1410
0.1410
0.0498
0.0127
0.6512
1972
77895
0.1713
0.1510
0.1510
0.0583
0.0138
0.6701
1973
81078
0.1720
0.1600
0.1600
0.0810
0.0131
0.6560
1974
85082
0.1716
0.1760
0.1760
0.1047
0.0132
0.6608
1975
91919
0.1734
0.1890
0.1890
0.0969
0.0130
0.6397
1976
98232
0.1744
0.2170
0.2170
0.0897
0.0139
0.6635
1977
103777
0.1755
0.2300
0.2300
0.0851
0.0135
0.6500
1978
109763
0.1792
0.2240
0.2240
0.0974
0.0135
0.6210
1979
109734
0.1896
0.2150
0.2150
0.1119
0.0141
0.6435
1980
112613
0.1939
0.2402
0.2402
0.1254
0.0147
0.6549
1981
116457
0.2016
0.2520
0.2520
0.1503
0.0145
0.6711
1982
119813
0.2113
0.2560
0.2560
0.1449
0.0153
0.6645
1983
123364
0.2162
0.2660
0.2660
0.1442
0.0171
0.6638
1984
127735
0.2301
0.3090
0.3090
0.1666
0.0174
0.6582
1985
133571
0.2371
0.3514
0.3514
0.2292
0.0145
0.6595
1986
141596
0.2452
0.3770
0.3770
0.2511
0.0164
0.6492
1987
152505
0.2532
0.4030
0.4030
0.2558
0.0149
0.6357
1988
164196
0.2581
0.4320
0.4320
0.2541
0.0147
0.6394
1989
173701
0.2621
0.4390
0.4390
0.2446
0.0154
0.6449
25
1990
184691
0.2641
0.4539
0.4539
0.2978
0.0162
0.6249
1991
193751
0.2694
0.4383
0.4383
0.3317
0.0158
0.6242
1992
206101
0.2746
0.4848
0.4848
0.3387
0.0158
0.6241
1993
224837
0.2799
0.5305
0.5305
0.3190
0.0152
0.5929
1994
245657
0.2851
0.6266
0.6266
0.4229
0.0151
0.5823
1995
274513
0.2904
0.6603
0.6603
0.3866
0.0144
0.5813
1996
304256
0.3048
0.6368
0.6217
0.3391
0.0146
0.5922
1997
444028
0.3191
0.6838
0.6487
0.3415
0.0144
0.5895
1998
391918
0.3335
0.7176
0.6379
0.3181
0.0157
0.5962
1999
439258
0.3478
0.5108
0.6376
0.3334
0.0170
0.6116
2000
554138
0.3622
0.5267
0.6359
0.3958
0.0180
0.6230
2001
579256
0.3766
0.5557
0.6446
0.3847
0.0200
0.6137
2002
639305
0.3909
0.5922
0.6602
0.4270
0.0248
0.5957
2003
693469
0.4053
0.6246
0.6736
0.5189
0.0253
0.5678
2004
756825
0.4176
0.6519
0.6841
0.5976
0.0254
0.5430
2005
831022
0.4299
0.6672
0.6919
0.6386
0.0263
0.5186
Note (continued):
7. Standard highway is calculated from natural highway length to grade II highway equivalent according to .the road
capacity on transport volumes.
8. There are data inconsistencies in unadjusted non-state share in industry after late 1990s. Data are adjusted according
to two national censuses on industry in 1995 and 2004.
Source: Calculated from NBS (various year); NBS (2005b).
26
Table A2. Contributing factors in different period:
Average growth rate and changing percentage point (%)
19531978
19791988
19891998
19992005
Scenario I
200620112010
2020
Scenario II
200620112010
2020
Capital
9.19
9.16
9.57
12.76
15.30
14.00
15.30
14.00
Human capital
4.06
7.22
3.71
2.65
2.60
2.00
2.60
2.00
6.48
16.48
13.54
12.72
12.00
12.00
12.00
12.00
0.32
-0.52
0.46
1.32
1.32
0.00
1.32
0.00
Marketization
-1.39
2.08
2.83
0.99
0.60
0.40
0.60
0.40
Urbanization
0.21
0.79
0.75
1.38
1.38
1.10
1.38
1.10
Foreign capital
effect
0.00
0.14
0.98
-0.29
-0.50
-0.30
-0.50
-0.30
Foreign trade effect
0.01
1.57
0.64
4.58
2.00
0.00
2.00
0.00
Government
administration cost
-0.03
0.01
0.01
0.15
0.00
-0.10
Infrastructure effect
3.78
3.39
16.62
45.92
30.00
20.00
70.89
65.22
60.96
58.37
51.90
55.00
Spillover effect of
human capital
Increasing R&D
expenses
Effect of structural
bias
TFP from
unidentified factors
Source: Calculated from NBS (various year); NBS (2005b).
27