Download Growth Driven by Fast Variables and Slow Variables

Deconstructing the Transformation of Momentum: Growth
Driven by Fast Variables and Slow Variables
SHEN Minggao, RUAN Pengfei
Core notes
The slowdown of the economic growth is restructuring in nature. As the old growth
engine slows down and the momentum it generates accounts for a smaller share in the
economy, the share of new engine is bound to increase, even if the new engine does
not gain more speed. Such restructuring is not a substantial improvement in economic
structure. Rather, it is a shifting from fast variables to slow variables, a recessive
restructuring.
For China, slowdown in traditional growth engine, such as export and investment, is
fast variable, while the development of new growth engine, such as new economy,
innovation, consumption and service industry, is slow variable. The new engines are
not strong enough to outperform old engines in scale, speed and efficiency, which is
the major cause of economic slowdown. Only if we abandon the current GDP growth
target, or we are not able to keep steady growth without relying on and stimulating old
economy and old momentum, especially when new economy and new momentum are
still not strong enough to sustain growth by themselves.
Abstract
China’s economic growth is shifting from fast variables to slow variables
Calculated by nominal GDP, China’s service industry as a share of GDP has increased
from 33.6% in 1996 to 44.2% in 2011, while the share of secondary industry stood at
46%, which means that China is in non-agriculturalization period. The growth was
driven by relatively faster variables. In 2011-2015, the share of service industry
increased by six percentage points, while the share of secondary industry decreased
by 5.5 percentage points. This marked the beginning of de-industrialization period,
and the growth was driven by slow variables.
In the future decade, the increase in slow-variable-driven growth may drag GDP
growth rate down to 4%
As is shown by international practice, when the share of service industry exceeds 50%,
GDP growth rate stands at about 4-7%; when the share exceeds 60%, growth rate
stands at about 2.7%-4.4%. According to the estimation of Monita, China’s potential
GDP growth rate will be around 5% in 2025, but it may fall to around 4.0% if the low
productivity of service industry is considered.
The bottom of L-shaped growth may be 4-5.5%
Reform can increase potential GDP growth, by optimizing the allocation of resources,
especially capital, and promoting the shift from a growth model of fast variables and
heavy assets from a model of slow variables and light assets. If the reform measures
are actually implemented, potential GDP growth rate may reach 5.5% by 2025, and
this rate may be the bottom of “L-shaped growth with reform”.
It is the most uncertain of time; it is the most certain of time.
China’s economy has entered new normal. From the perspective of sustainability, this
new normal is still evolving in a dynamic process. In this process, it is normal for
industries and structures to become divided. Such division manifests as economic
slowdown in parallel with rising forces, alternation between liquidity overflow and
skyrocketing asset prices, and intertwinement of structural and periodical forces.
To deconstruct the transformation of China and to explicate the new normal, we
should not simply deduce the future with the past; instead, we should ravel out future
trend from new perspective and adopt new countermeasures.
Undeniably, China’s economy is following the former path of Japan. If we can
identify the forks ahead, we can get rid of “middle-income trap” in the mid run and
avoid repeating the mistakes of Japan in the long run. Time is limited.
During the evolvement from old normal to new normal, economic slowdown is fast
variable, and the future driving forces are mostly slow variables. Slowdown is faster
than going up, which is one of the basic landscapes of China’s economic slowdown in
transformation period. In a short-term, this is hard to change.
The major conclusion of this paper is: during the shift from fast variables to slow
variables, potential GDP growth rate driven by service industry may fall to 4% in the
future decade. Substantial reform, with the improvement of productivity as its core,
may raise potential GDP growth to 5.5%. Therefore, we suggest that China should
follow the major trend and give up the growth target of 6.5% to alleviate the sequela
of keeping steady growth, and should accelerate reform and prevent meltdown with
fiscal policies, so as to keep the growth rate above the red line of 4-5.5%.
I. Old and new momentum and shifting between fast and slow variables.
The slowdown of the economic growth is restructuring in nature. As the old growth
engine slows down and the momentum it generates accounts for a smaller share in the
economy, the share of new engine is bound to increase, even if the new engine does
not gain more speed. Such restructuring is not a substantial improvement in economic
structure. Rather, it is a shifting from fast variables to slow variables, a recessive
restructuring accompanied by economic slowdown.
On one hand, the economic slowdown in China is attributed to traditional sectors or
old economy, which has been a consensus. These traditional sectors include upstream
manufacturing based on resources and suffering from overcapacity, export sectors
mainly relying on overseas demand and real estate sector based on investment and
with a long industrial chain (Figure 1). Investment in infrastructures in also a
traditional growth momentum. Short-term growth is at the expense of higher leverage
ratio of local governments, so its investment efficiency and sustainability is doubtful.
Comprehensively, the core variable in slowdown of traditional sectors is export and
investment.
On the other hand, new economy, investment in innovation, consumption and the
development of service industry keeps a rapid growth, being the major force to sustain
current economic growth. Comparatively, the growth rate of consumer goods total
retailing and service industry has apparently become faster than that of export and
investment, but the growth rates have been slightly dragged down by the restructuring
of old economy (Figure 2).
Whether the two trends can offset each other will have direct impact on GDP growth
rate at sight and the prospect of economic growth.
Figure 1 Growth in Fixed Assets Investment and Export
Figure 2 Growth in consumer goods total retailing and nominal GDP of tertiary
industry
Currently, the basic logic for setting the growth target at a mid-high range above 6.5%
is that the drop in export can be compensated by domestic demand, insufficient
investment by consumption, and downward manufacturing industry by service
industry. The conclusion is that the growth basically keeps steady and the structure is
improved. To confirm that logic, we need to prove that the force of slowdown can be
entirely or mostly offset by the force of growth, and then we can conclude that the
economy can still keep a rapid growth despite the slowdown. To give an accurate
answer, we need to conduct quantitative analysis in scale, growth rate and efficiency.
(1) Scale
Can the scale of sectors with steady and rapid growth match that of sectors slowing
down? In terms of the proportion of primary, secondary and tertiary industry, the
share of tertiary industry in GDP exceeded 50% for the first time in the first quarter
last year, and reached 54.1% in the first half of this year. It seems that the service
industry is growing stronger to offset the drop of secondary industry, especially
manufacturing industry. However, the larger proportion of service industry results
from the slowdown of non-service industries. Service industry also grows at a slower
speed, but the decrease is smaller than other industries. Therefore, the larger
proportion of service industry synchronizes with the slowdown in GDP, or the former
is the result of the latter.
In terms of the perspective of old economy and new economy, MasterCard Caixin
BBD New Economy Index (NEI) attempts to measure the scale of new economy.
According to the calculation based on big data, as of September this year (translator’s
note: this paper was written in 2016), as much as 30% of total economic input can be
categorized as new economy. But this is only the increment. If calculated by stock, the
proportion of new economy will be much smaller. Even if this part of economy grows
at a higher speed, it is not able to entirely offset the slowdown of old economy.
Figure 3 MasterCard Caixin BBD New Economy Index
33
%
32.3
32
32.1
31
30
29.9
29
28
27
27.7
30.9
29.8
29.4
30.1
30.1 30.1
28.9
27.5
26
(2) Speed
More importantly, the traditional momentum represented by old economy, such as
export and investment, is the faster variable, which can even grow at 20-30% every
year in booming period; while the new momentum represented by new economy, such
as investment in innovation, consumption and service industry, is the slower variable,
which grows at 10-20% every year or even slower. Even if the two momentums have
equal scale, the new one is not able to supplement the old one in terms of speed. The
fast growing period of a country’s economy usually witness rapid growth of export
and investment. The closing of this period leads to economic slowdown. Unless the
trade in service are competitive enough to trigger rapid growth through export, the
growth rate of service industry cannot match the growth rate of export and investment
in the booming period. As China is in deficit in service trade, the majority of its
demand comes from within the country. Since 2003, the growth rate of urban
households’ disposable income kept downward on a year-on-year basis, forming
larger gap between the growth rate of service industry. This shows that the service
industry relying on domestic demand is confronted with downward pressure (Figure
4).
Figure 4 Growth rate of China’s service industry and per capita income
(3) Efficiency
Apart from scale and speed, efficiency also plays a role in deciding the input needed
for per unit of output. The real GDP generated by China’s second industry per unit of
employment has always been higher than that by tertiary industry, Besides, compared
with the productivity of secondary industry that is growing increasingly rapidly, the
productivity of tertiary industry tends to stagnate (Figure 5). It is noteworthy that the
proportion of employment in secondary industry kept falling since 2012, while that in
tertiary industry has risen at a fast speed. This means that labor force is shifting from
sectors with higher productivity to those with lower productivity, resulting in the
decrease in productivity of the macro economy as a whole. Therefore, as the share of
tertiary industry in the economy is enlarging, more input is needed for maintaining the
growth rate as high as before. In other words, the traditional input-output relationship
has also changed.
Figure 5 Comparison on efficiency of China’s secondary and tertiary industry (in
the price of 1978)
Aforementioned
analysis
shows
the
basic
features
of
China’s
economic
transformation: the momentum of economic growth is shifting from fast variables to
slow variables. Maybe the slow variables will keep growing; but due to obvious
mismatching in timing, that is, the fast variable slows down at sight but the slow
variable needs more time to get strong enough to lead the growth, the
slow-variable-driven growth will face ongoing downward pressure in a short-term. It
is hard to assert that the growth rate has bottomed out. Slowdown results from
adjustment of old economy. As new economy is still unable to sustain economic
growth alone, stimulus on traditional economy is a must for keeping steady growth.
II. How fast speed can slow-variable-driven growth reach?
In recent years, the momentum of China’s economic growth has been shifting from
fast variables to slow variables. Calculated by nominal GDP, the share of tertiary
industry in GDP began to grow from 33.6% in 1996, to 44.2% in 2011, but the share
of secondary industry stood at approximately 46%. This means that the larger share of
tertiary industry results from the shrinking share of primary industry, without
squeezing the share of secondary industry, which has grown rapidly (Figure 6). In
2011-2015, the share of tertiary industry rose by 6 percentage points, while that of
secondary industry dropped by 5.5 points. It seems that tertiary industry has become
the major driving force od economy, but in fact, this is the passive adjustment due to
the slowdown or even recession of secondary industry. The difference between the
former explanation and the latter one is: the shifting from primary industry to tertiary
industry is the result of non-agriculturalization, where the productivity increases
because the growth engine shifts from agriculture, the slower one, to service industry,
the faster one; but the shifting from secondary industry to tertiary industry is the result
of de-industrialization, where the productivity decreases because the growth engine
shifts from the faster one to the slower one. Calculated by the constant price of 1978,
the share of tertiary industry in 2015 remains 30%, which means that the slowdown in
secondary industry has a heavier impact on real GDP growth than tertiary industry
(Figure 7).
Figure 6 Share of China’s secondary and tertiary industry: nominal GDP
Figure 7 Share of China’s secondary and tertiary industry: real GDP
If the growth in tertiary industry represents slow variables, the question is: how fast
speed can slow-variable-driven growth reach? Experience of other countries shows
that when the share of tertiary industry in nominal GDP exceeded 50%, many
countries were faced with terraced slowdown in growth rate, forming a GDP growth
rate funnel in the transformation period (Figure 8). For example, in ROK and Brazil,
after the share of service industry exceeded 50%, their GDP growth rates touched the
bottom in that period.
Figure 8 GDP growth rate funnel in the transformation period (GDP growth rate
in the 10 years before and after the share of service industry in nominal GDP
reached 50% in other countries)
More experience shows that when service industry accounts for about 50% in nominal
GDP, the growth rate of real GDP is about 4-7%. Figure 9 illustrates the GDP growth
rate of major economies including China when their service industry surpassed 50%
of GDP. According to simple analysis on correlation, economic growth rate and the
share of service industry are in negative correlation. When the share of service
industry is 50%, GDP grows at an average rate of 6.8% annually; when the share is
60%, the growth rate falls to 4.4%. The exception is ROK. As an export-oriented
economy, it has GDP growth rate higher than global average for the same share of
industry. Further horizontal analysis found that similar negative correlation also
existed among G20 in 2015 (Figure 10). Generally speaking, when service industry
accounts for more than a half of GDP, the growth rate will be about 4%; for 60%, the
growth rate will further drop to 2.7%.
Figure 9 GDP growth rate of major economies when their service industry
accounted for about 50% of the economy
share of service industry in GDP (%)
Figure 10 The share of service industry and GDP growth rate in 2015: G20
Therefore, in the growth led by service industry, slowdown in GDP growth rate is the
new normal. Added by global economic slowdown and weak external demand, the
slow-variable-driven growth will be slower.
III. Estimation of potential GDP growth rate involving structure variable
Why the economy driven by service industry cannot keep a growth rate as high as
before? To answer this question, we need to involve a structure variable—the share of
service industry in GDP—in the analysis on GDP growth, so as to explain the impact
of structural changes on previous and potential GDP growth rate.
The estimation of GDP growth rate always involves only two major input factors:
capital stock and labor force. The residue apart from these two factors (namely the
unexplained part apart from the input factors) is the total factor productivity including
technological progress. In such a model, the economy is homogeneous, and structural
adjustments are neutral to economic growth. Per capita capital stock and its output
elasticity or efficiency are to key variables deciding economic growth, while
technological progress is invisible. Such model may be able to calculating the
potential GDP growth rate of a mature economy, but is not suitable for China’s
economy, which is standing at the key period of structural adjustment.
In practice, the share of service industry and GDP growth rate are in negative
correlation. To involve the structural variable, we make the following assumption and
form a “quadratic function structure model”. Relative methodology, data and
conclusion can be found in the annex: technical report of Monita research “Estimation
of China’s Potential GDP Growth Rate: A Structure Model”. The key points are
summarized as follows:
-
Structural adjustment will result in the inter-sector flow of production activities,
and then further lead to changes in efficiency of all factors. Therefore, it can be
assumed that the output elasticity of production factor is a function of the employment
mix of labor force. In other words, output efficiency of per capita capital changes with
the share of service industry employment.
-
Total factor productivity refers to the superposition of all factors except capital
and labor. It is also a function of industrial structure.
-
Experience of other countries show that enlarging the share of service industry is
an indispensable step towards developed economy. In this process, output efficiency
of per capita capital will decrease, but the decrease rate will be slower and slower as
the share of service industry employment grows. That is to say, enlarging share of
service industry employment has diminishing marginal impact on capital output
efficiency.
Empirical studies show: first, enlarging share of service industry employment has
obvious adverse effect on output efficiency of per capita capital, which means that
service industry values human capital more than material capital or assets. Within
rational range of the share of service industry, output efficiency of per capita capital
always drops as the share of service industry enlarges, but such drop will be gradually
slower (Figure 11). Second, enlarging share of service industry can improve total
factor productivity. In other words, with other conditions remaining unchanged,
economic scale enlarges as service industry employment accounts for a larger share.
Figure 11 Impact of enlarging share of service industry employment on output
efficiency: on the basis of “quadratic function structure model”
0.9
alpha
0.8
0.7
97, 0.39
0.6
0.5
0.4
s3(%)
0.3
20
30
40
50
60
70
80
90 100 110 120
Empirical estimation result shows that potential GDP growth of the year 2025
involving structure variable will be 1 percentage point lower. With other factors fixed
and future increase of the share of service industry employment excluded, China’s
potential GDP growth rate will be 5% by 2025; but based on the “quadratic function
structure model”, the number will be 4% (Figure 12). The “output gap” calculated on
the basis of potential GDP growth rate is highly positive correlated with CPI. That is
to say, positive output gap leads to inflation and negative output gap leads to deflation
(Figure 13 and 14). More importantly, the structure model involving the share of
service industry employment shows that output gap always appears prior to CPI for
about one year.
Figure 12 Comparison of estimation made by traditional production function
model and that by “quadratic function structure model” involving service
industry
Figure 13 The relationship between output gap and CPI without involving the
structure variable of service industry
Figure 14 The relationship between output gap and CPI involving the structure
variable of service industry
IV. Reform decides the floor of potential GDP growth rate
Apart from structural factors, reform factors should also be involved in the estimation
of potential GDP growth. Unlike other countries, when the share of service industry
surpasses the threshold of 50%, China’s most important variable in economy will be
reform. If reform measures are implemented, potential growth rate will be higher and
growth path will be reshaped. According to the analysis mentioned above, with other
conditions fixed, enlarging the share of service industry will accelerate the slowdown
of GDP growth. However, if other conditions change, the estimation of potential GDP
growth also needs adjustment.
There are many factors concerning reform having impact on input and output. Among
them, the reform to improve the efficiency of service industry has ample room, able to
improve per capita capital output efficiency by a large margin. Generally speaking,
major breakthroughs in China’s economic reform may manifest as optimization of
resource allocation, especially capital allocation. These results promote the shift from
a growth model of fast variables and heavy assets from a model of slow variables and
light assets: 1) reform on state-owned enterprises can raise the efficiency of
enterprises with heavier assets and higher leverage ratio; 2) opening-up and
competition in service industry can raise the low productivity in service sectors; 3)
urbanization can improve the efficiency of labor force market; 4) fiscal, financial and
land-related reform can improve the efficiency of resource allocation at the
macroscopic level; 5) IP protection can enhance incentives, encourage innovation and
improve the output efficiency at the microscopic level.
Reform can be fast variable and slow variable in the shift of growth momentum.
To find a baseline for reform results, we analyze changes in China’s per capita capital
output efficiency (namely capital output elasticity) between 2000 and 2010. During
this period, dividends of several reform measures, including SOE reform, China’s
accession to WTO and the following reform on banking system, were released, but the
per capita capital output efficiency we calculated decreased from 0.797 to 0.719,
down by 9.9%. Under the scenario that the potential GDP growth rate in 2025 is
estimated to be 4%, per capita capital output efficiency is estimated to decrease from
0.642 to 0.559 between 2015 and 2025, down by 12.9%. This estimation looks
pessimistic. If we take reform effects into consideration and assume the decline of per
capita capital output efficiency in 2025 compared with 2015 is similar to the decline
in 2000-2010, we can regard this scenario as a new baseline, or the baseline scenario
involving reform to estimate the potential GDP growth rate in the following ten years.
Among other countries’ experience, the share of service industry in GDP of ROK
exceeded 80% in 1980, and the country’s capital output elasticity decreased by more
than 15% in both 1970-1980 and 1980-1990. Compared with the real situation in
ROK, our estimation in China’s baseline scenario involving reform is optimistic.
According to the aforementioned judgment, if China’s reform dividend can be as
strong as that in 2000-2010, its potential GDP growth rate by 2025 may stay at around
5.5% (Figure 15). We summarize our estimation of potential GDP growth under
different scenarios as follows:
•
Under the baseline scenario without reform: the potential GDP growth rate by
2025 will be around 4%;
•
Under the baseline scenario involving reform: the potential GDP growth rate by
2025 will be around 5.5%.
These quantitative analysis shows that reform will decide China’s future growth path,
and that the bottom of “L-shape” growth may be 4-5.5%. The reform plays two roles:
accelerating the bottom-out of GDP growth rate and raising the floor of potential GDP
growth rate. Of course, the reform process is not as smooth and orderly as is shown in
Figure 15. In the preliminary stage, “reduction” (elimination of overcapacity and
deleverage) has a greater effect than “addition” (cultivating new driving force and
raising productivity), so the economy may undergo temporary rapid slowdown. But
the economic growth will see less uncertainty and brighter prospect. A step backward
is for a stronger leap over “middle-income trap” and a new path of sustainable
development.
Therefore, we suggest to give up current GDP growth target and tolerate a reasonable
slowdown in growth rate, so as to provide margin for overcapacity elimination and
deleverage. The following three to five years will be a crucial period of
transformation for the new normal, during which, the key is the quality and
sustainability of growth rather than the growth rate.
First, the government need to keep the goal of doubling per capita income in ten years,
so as to shift the policy targets from GDP growth to people’s wellbeing and the
emphasis of economic policy from investment to people’s livelihood. By doing so, the
government can avoid excessive reliance on old momentum including investment,
export and real estate sector and can boost new momentum, including new economy,
innovation, consumption and service industry, through cutting tax, raising social
security and encouraging old economy to deleverage.
Second, the government need to secure the floor of 4-5.5% with fiscal policy. Giving
up GDP growth target is not to give up the growth itself. Such a major strategic
adjustment is not an isolated policy, but should be supported by special fiscal policies
in transformation period. The government can raise the ratio of fiscal deficit to GDP
from 3% at present to 6%, so as to purchase toxic assets and address zombie
enterprises.
Third, the government need to replace “growth without reform” with “slowdown with
reform”. Promoting reform in key areas can improve the expectation of investors and
boost market confidence, so as to trigger a new round of long-term investment with
short-term economic adjustment.
Figure 15 The impact of reform on potential GDP growth rate in the future ten
years
GDP growth rate
Baseline estimation of potential GDP growth rate
GDP增速
Baseline estimation of potential GDP growth rate
潜在GDP增速基准预测
involving reform
有改革的潜在GDP增速基准预测
16%
14%
12%
2025, 5.5%
10%
8%
6%
4%
2%
2025, 4.0%
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