Download China`s Economic Growth Model - Econ

China’s Economic Growth Model (1982-2005)
Daniela Slegrova, Kabyenad Tesfaye
Department of Economics, California State University East Bay
Economics 6896, Research Methods
Spring 2007
Abstract
The world noted China’s incredible growth to becoming one of the top economic
producers of the world in a relatively short amount of time. What is to explain this rapid
economic expansion? China’s GDP is 50 times bigger in 2005 (18,308.5 billion Yuan)
than in 1978 (365 billion Yuan). We approached this full-sized task in a multifaceted
manner as a pure research, consulted many scholars (peer and established), read many
scholarly journals and books, and aptly kept our ears open to the opinions of those who
had something to say about this subject matter. Time series analysis of annual data
between 1982 and 2005 was used to explain the trend of gross domestic product (GDP)
growth. The significant variables proved to be policy change after 1978 reflected in
economic freedom index—especially in the area of freedom to trade internationally and
foreign direct investment in fixed assets with further impact on employment of secondary
industry and township-village enterprises. Surprisingly, savings of China’s households
defined as a sum of time and demand deposits do not have positive effect on the GDP
growth.
Keywords: Economic Freedom Index; Economic Growth; National Bureau of Statistics of
China
JEL classification: F15; F21; F43; F59
1. Introduction
The Chinese economy recently surpassed many OECD countries such as Japan,
United Kingdom, South Korea, and France to become the second largest in the world
after the United States. (http://www.theodora.com/wfb2003/rankings/gdp_2003_0.html)
We note from the graph below that the Chinese economy began to take off near 1978,
when China began to put into practice a more liberalized and market oriented policies via
its government’s willingness to incrementally privatize its state owned enterprises.
Figure 1
China's GDP (100 million yuan) (1952-2005)
03
20
97
00
20
19
91
94
19
19
88
19
82
85
19
19
76
79
19
19
73
19
70
19
67
19
61
64
19
19
55
58
19
19
19
52
160,000
140,000
120,000
100,000
80,000
60,000
40,000
20,000
-
According to one estimate, China only needs a growth rate of 5.5% until 2015 to surpass
the United States. (http://www1.oecd.org/publications/observer/215/e-foy.htm) Table 1
show that China’s Gross Domestic Product (GDP hereafter) has been growing at an
average rate of 10% for the past quarter of a century. In view of that, using the rule of 72
we can expect the economy to double in size approximately every seven years. The rule
of 72 is not violated in China’s case because GDP numbers in 2004 (13,688 billion Yuan)
are 28 times what they were in 1981 (486 billion Yuan) which to some extent indicates a
doubling of GDP figures every six or seven years. More precisely, GDP figures roughly
tripled every six or seven years for most of the 90’s and more like doubled in the other
years. At one end of the extreme, GDP figures in 1996 (6,789) were 3.65 times bigger
than what they were in 1990 (1,855); whereas in 2002 GDP figures (10,517) were 1.55
times bigger than they were in 1996 (6,789). The impreciseness here most likely stems
from the change in population as well as from rapid change in policy during these times
where China became a lot more involved in the international markets. Furthermore, the
data from the bureau of statistics may suffer from uncertain accuracy. Hitherto on the
average, the Chinese economy was 2.18 bigger 5 years prior, 2.56 bigger 6 years prior,
and 3.02 times bigger seven years prior for the years 1981 to 2004.
Table 1
Year
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
5.2
9.1
10.9
15.2
13.5
8.8
11.6
11.3
4.1
3.8
9.2
14.2
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
14.0
13.1
10.9
10.0
9.3
7.8
7.6
8.4
8.3
9.1
10.0
10.1
%change
GDP (n/n-1)-1
Year
%change
GDP (n/n-1)-1
We should note here that we plan to look at what led to this apparent success.
Alternatively, many others attribute the opening up of China to be the main determinant
followed by foreign direct investment and household consumption. These are all
important aspects of economic growth that are intertwined.
2. Framework
The key question that we are trying to answer is: what are the key driving forces
of China’s economic growth—particularly after 1981. Leading Chinese Theorist George
Zhibin Gu was asked in a recent interview what in his mind were the key driving forces
behind China’s economic development today. His opinion was that 1) opening up of the
economy, 2) domestic consumption explosion, and 3) international involvement in three
areas—foreign direct investment, increase in manufacturing, and international trade were
the three most important explainers of China’s growth. He is not alone in this opinion.
Lawrence Summers also notes that ‘the most important determinant of every country’s
fortunes is the policy choices of its people and its government’.
(http://santiago.indymedia.org/news/2007/05/67313.php) Furthermore, in May 2006 we
asked Professor Gregory Christainsen, who currently lectures at California State
University East Bay, what in his opinion were the key determinants of a country’s
growth, he stated that opening up was the most important aspect of economic
development of a country, and ranked culture and rule of law to be next most important
variables. Although there may be a little disagreement on what are the second and third
most important variables, we should note that all of the variables mentioned above are
critical to economic development and that all three economists rank policy as the number
one variable of economic development.
Of course past empirical studies have alluded to the fact that China saves a lot and
credit this high savings rate to many favorable effects it has on the economy. In fact,
average time deposit’s as a share of GDP is near 37% for the last 25 years and even more
amazingly at approximately 50% in the last decade.
In comparison, similar to Japan, China certainly saves a lot more than the United
States, where many households spend more than they save. In fact, household savings
rate for US in January 2006 was -0.7% and in recent times close to 7% in Japan—far
below what it was in the mid-1970s at 20%. To add a few notes, Japan is certainly saving
less than it used to in the past. Nonetheless, the problem lies in China’s financial
intermediation process which has proven to be much less efficient than the latter two.
We should note here that there is no ‘purely private’ Chinese bank but rather state-owned
ones with much inefficiency.
According to the Solow growth model, savings rates play a crucial role in
explaining growth and China’s case seems to support this theory. The theory notes that
savings accelerate the economy to a higher level of production and later drop back to the
original rate of growth in the long run. In a sense, savings give a big boost to higher
production levels.
China is an appealing country to look at because it is incrementally moving away
from government or centrally planned operations to market driven operations; and such a
shift in economic systems will allow us to see the effects of the shift on the variables that
we are interested in observing. For example, we see clear shift in the data for foreign
direct investment and consumption along the same time period. Although it is not one of
the ‘freest’ economies in the world, China’s drive to get herself in to the World Trade
Organization has proven to be detrimental to her economy. Many countries around the
world have trepidations that they will not be able to compete with China because they
claim China has lots of labor with lower wages that allows it to produce more. They
further claim that China need to export less and consume more. In addition to these
aspects, we prepare to explore the degree of openness China has in terms of trade and in
comparison to other similar type of economies such as Japan, Germany and USA.
Certainly China wants to play a role in the global economy, but what kind of role it will
play seems to be still up in the air. Recently China was rated 95th. in economic freedom
in the index published in Economic Freedom of the World by the Fraser Institute. This
postulates that there is still room for improvement.
We found throughout our research that the Economic Freedom of the World index
composed by the Fraser Institute gives a good measure of policy because the institution
that provides the information seem to be highly reputable and was constructed under the
leadership of the late Nobel Laureate, Milton Friedman. Furthermore, this particular
freedom index is believed to be the most objective and accurate measure of economic
freedom published to date by any organization.. Policy is not an easy variable to
quantify. Nonetheless, the economic freedom index looks at five crucial categories of a
country’s activities and gives a rank of those categories, which are: 1) size of
government: expenditures, taxes, and enterprises, 2) legal structure and security of
property rights, 3) access to sound money, 4) freedom to trade internationally, and 5)
regulation of credit, labor, and business. These categories have many subcategories
which are averaged out to give a final summary index of a country. The graph below
illustrates how china compares to India, which has similar population to China, and USA,
the world’s most productive country.
Figure 2
Summary index of IEF
9.0
8.0
7.0
6.0
China
5.0
India
4.0
USA
3.0
2.0
1.0
0.0
1980
1985
1990
1995
2000
2001
2002
2003
2004
From the graph, we note that according to the freedom index, China has improved
her conduciveness to economic activities from near five to six. Clearly, the increase in
the ranking coincides with GDP growth in the early 90s, where China recorded GDP
growth rates in the middle teens. Moreover, we noted many variables such as
consumption and investment, which are components of GDP, proceed with GDP figures
as expected, which we will further discuss in detail later. From the graph below we note
that China has certainly improved in most aspects of economic freedom as measured by
the index, which is a real credit to her management of her country’s economy.
The graph below also illustrates China’s improvements in certain areas, especially
in monetary policy. There was a sharp increase in the sound money ratings of near six in
1995 to eight in 2000. The biggest threat to China’s current growth is inflation and the
monetary authorities have done a good job in containing it to a manageable level.
However, if this will be the case in the near future is open to discussion. China’s wages
have been increasing steadily in the past, and recently the Wall Street Journal reported
that wages on the average increased by 21% in 2006. The graph also indicates that the
legal system and property rights need improvement. In exception to that category, all
other indices have shown a general upward trend.
Figure 3
Economic Freedom Index
9.0
1 Size of
Government
8.0
7.0
2 Legal System &
Property Rights
6.0
3 Sound Money
5.0
4 Freedom to Trade
Internationally
4.0
3.0
5 Regulation
2.0
SUMMARY INDEX
1.0
0.0
1970
1975
1980
1985
1990
1995
2000
2001
2002
2003
2004
3. Methods
Our research tries to explain the major reasons of gross domestic product (GDP)
growth in post-communist countries, specifically in China. Time series analysis of annual
data between 1982 and 2005 was used to explain the trend of gross domestic product
growth. The significant variables proved to be policy change after 1978 reflected in
economic freedom index, especially in the area of freedom to trade internationally, and
foreign direct investment in fixed assets with further impact on employment of secondary
industry and township-village enterprises. Surprisingly, savings of China’s households
defined as a sum of time and demand deposits do not have positive effect on GDP
growth. This issue may be caused by the inaccuracy of the official data, multicollinearity
among more than two variables that is not shown in the correlation matrix or just by the
fact that China’s household savings do not positively contribute to GDP growth.
This single case study explains the major reasons that influenced trend of China’s
GDP between 1982 and 2005 using quantitative variables, multivariate linear regression
models and ordinary least squares method in the statistical program GRETL.
Our quantitative variables are related as follows. The policy changes from
communist to capitalist regime started in 1978. Considering time lag of this policy effect
and availability of official data, analysis from 1982 to 2005 seems appropriate. First
variables possibly influenced by the political change are economic freedom index and
foreign direct investment in fixed assets. Further impact could be seen in employment of
secondary industry and township-village enterprises. Logically, higher investment and
employment leads to increase in production, GDP.
Labor as a factor of production is being paid in form of wages/ salaries. After
income tax deduction, worker is left with disposable income that can be either consumed
or saved. Household consumption is part of GDP calculated by expenditure approach.
Household savings, as defined by National Bureau of Statistics of China, is the sum of
time and demand deposits that banks use as a source of funds for businesses loans and
that is supposed to contribute positively to higher investment and production, GDP.
This research presents five different models with possible explanations of the
China’s GDP growth rate in percent between 1982 and 2005. The first three models
consider percentage changes in savings, foreign direct investment, and freedom to trade
internationally, employment in secondary industry and township village enterprises. The
lagged GDP is used to deal with the autocorrelation issue. The forth and fifth model
represent further explanation of the GDP growth rate in percent. These two models
consider household consumption, domestic loans as source of funds for fixed assets and
lagged GDP. Since household consumption is highly correlated, 0.7, with lagged GDP
and foreign direct investment we were not able to use all of them in one model. More
details are demonstrated in Results, part 5.
The results of our regression models support the statement of leading Chinese
theorist George Zhibin Gu, who mentioned the key driving forces behind China’s
economic growth as 1) opening up of the economy, which we measured as economic
freedom index, respectively freedom to trade internationally, 2) domestic consumption
explosion, measured in our case as household consumption and 3) international
involvement in three areas—A) foreign direct investment, measured as foreign direct
investment in fixed assets, B) increase in manufacturing, reflected as employment of
secondary industry, C) and international trade, shown in freedom index to trade
internationally.
4. Data and Sample
All the data with exception of economic freedom index were drawn from the
Statistical Yearbook 2006, an official publication by National Bureau of Statistics (NBS)
of China. It is important to acknowledge uncertain accuracy of this data that were mostly
gathered as sample surveys with traditional bottom-up reporting system. In other words,
the data estimates were transferred from the level of village heads to townships, county
statistical offices, and provincial offices and finally aggregated by the National Bureau of
Statistics. The statistical impreciseness is always present and revisions happen either
during the economic census year or when more data is available. As a consequence, our
statistical models may not be accurate or regression coefficients may show a wrong sign
as in case of savings. Other possible explanation for negative coefficient of savings may
be multicollinearity among more than two variables that is not shown in the correlation
matrix.
Fred Gale compiles the NBS of China’s data for the purposes of Economic
Research Service of the United States department of agriculture. His opinion about
China’s official statistics in not optimistic. “The politicization of statistics, reliance on
bottom-up administrative reporting, use of nonstandard definitions, and parallel reporting
systems in multiple agencies often make Chinese statistics confusing and potentially
misleading. Many analysts believe that macroeconomic statistics overstate economic
growth and understate unemployment.”(China’s Statistics: Are They Reliable?)
Unfortunately, the official statistics was the only source providing us with
necessary data. Despite of these facts, National Bureau of Statistics has recently started
modernization of the old system of data collection and reporting that may bring positive
results in the future.
Our single case study explains the major reasons that influenced trend of China’s
GDP from 1982 to 2005 using quantitative variables and multivariate linear regression
functions in statistical program GRETL.
This research presents models with 24 annual data, expressed in form of
percentage annual changes. For example, foreign direct investment in 1982 is percentage
annual change between 1982 value in 100 million yuans, which is 60.5, and 1981 value in
100 million yuans, which is 36.4, indicating positive 66.4 percent change.
Another issue was incompleteness of the data in case of savings, township-village
enterprises employment, average real wage and annual per capita income of urban
household. We used linear extrapolation to obtain missing data necessary for our
research. From the line equation y = a x + b between two known values, we calculated
intercept (a) and slope (b). Since we knew years, variable x, it was easy to calculate
missing variable y, which is savings, township-village enterprises employment etc.
Despite of these limitations, the multivariate regression models provide us with
good explanation of the GDP growth trend from 1982 to 2005. We are aware of the fact
that increasing the sample size may be helpful for future research to improve significance
of the independent variables.
As already mentioned, our sample includes 24 annual observations expressed as
annual percentage changes to keep consistency with economic theory. Our dependent
variable is the growth rate of gross domestic product in percent. Independent variables
reflecting the impact of government policy change after 1978, also expressed as annual
percentage changes, are economic freedom of the world index (by Fraser Institute),
foreign direct investment in fixed assets, employment of secondary industry and
township-village enterprises. Other variables that are being considered are domestic loans
as another source of investment in fixed assets, average real wage, household savings and
consumption.
National Bureau of Statistics of China defines the gross domestic product (GDP)
as the final products at market prices produced by all resident units in a country (or a
region) during a certain period of time. (China’s Statistical Yearbook 2006, Chapter 3)
For the purposes of our research, we converted all the variables to annual percentage
changes.
“Foreign investment refers to foreign funds received during the reference period
for the construction and purchase of investment in fixed assets (covering equipment,
materials and technology), including foreign borrowings (loans from foreign
governments and international financial institutions, export credit, commercial loans from
foreign banks, issue of bonds and stocks overseas), foreign direct investment and other
foreign investment. Excluded in this category are capitals in foreign exchanges owned by
China (foreign exchanges owned by the central and local governments, foreign exchanges
retained by enterprises, foreign exchanges by enterprises through regulating mechanism,
loans in foreign exchanges issued by the Bank of China with its own fund, etc.).”
(China’s Statistical Yearbook 2006, Chapter 6) Consistently with the rest of our variables,
the foreign investment is also expressed in the form of percentage annual change.
“Economic freedom of the world index, published by Fraser Institute, measures
the degree to which the policies and institutions of countries are supportive of economic
freedom. The cornerstones of economic freedom are personal choice, voluntary
exchange, freedom to compete, and security of privately owned property. Thirty-eight
data points are used to construct a summary index and to measure the degree of economic
freedom in five areas: (1) size of government; (2) legal structure and security of property
rights; (3) access to sound money; (4) freedom to trade internationally; and (5) regulation
of credit, labour and business. The summary index is ranked between 0 and 10, meaning
0 % or 100 % economic freedom. ” (Fraser Institute Website) Like the rest of our
variables, the economic freedom index is also expressed in the form of percentage annual
change. The historical availability of the data was the main reason why we chose this
Fraser Institute freedom index to be included in our model.
National Bureau of Statistics does not present definition of savings. The only
information provided in Statistical Yearbook 2006 is that savings deposit in urban and
rural Areas are sum of time and demand deposits. (China’s Statistical Yearbook 2006,
Chapter 10) Savings are expressed as annual percentage changes.
“Households consumption expenditure refers to the total expenditure of resident
households on the final consumption of goods and services. In addition to the
consumption of goods and services bought by the households directly with money, the
households consumption expenditure also includes expenditure on goods and services
obtained by the households in other ways, i.e. the so-called imputed consumption
expenditure, which includes the following: (a) the goods and services provided to the
households by the employer in the form of payment in kind and transfer in kind; (b)
goods and services produced and consumed by the households themselves, in which the
services refer only to the owner-occupied housing and domestic and individual services
provided by the paid household workers; (c) financial intermediate services provided by
financial institutions; (d) insurance services provided by insurance companies.” (China’s
Statistical Yearbook 2006, Chapter 3) Consumption is expressed as annual percentage
change.
Township village enterprises employment in rural area is expressed as annual
percentage change in number of employed persons and is not defined in Statistical
Yearbook 2006.
Secondary industry employment refers to annual change in number of employed
persons in secondary industry, which refers to mining and quarrying, manufacturing,
production and supply of electricity, water and gas, and construction. (China’s Statistical
Yearbook 2006)
“Average real wage of staff and workers refers to the average wage of staff and
workers after removing the effects of the price changes. Average real wage indices of
staff and workers refers to the change of real wage, which reflects the relative increasing
or decreasing level of real wage of staff and workers, which is calculated as follows:
Average Real Wage Indices = Average Wage Indices of Staff and Workers at the
Reference Time / Urban Consumer Price Indices at Reference Time x 100%” (China’s
Statistical Yearbook 2006, Chapter 5) Similarly to the rest of the variables average real
wage is also converted into annual percentage changes.
“Domestic loans refer to loans of various forms borrowed by investing units from
banks and non-bank financial institutions during the reference period for the purpose of
investment in fixed assets, including loans issued by banks from their self-owned funds
and deposit, loans appropriated by higher responsible authorities, special loans by
government (including loan for substituting petroleum with coal, special loan for reformthrough-labour coal mines), loans arranged by local government from special funds,
domestic reserve loan, and working loan, etc.” (China’s Statistical Yearbook 2006,
Chapter 6) Similarly to the rest of the variables domestic loans are converted into annual
percentage changes.
Our quantitative variables are related as follows. The policy changes from
communist to capitalist regime started in 1978. Considering time lag of this policy effect
and availability of official data, analysis from 1982 to 2005 seems appropriate. First
variables possibly influenced by the political change are economic freedom index and
foreign direct investment in fixed assets. Further impact could be seen in employment of
secondary industry and township-village enterprises. Logically, higher investment and
employment leads to increase in production, GDP.
Labor as a factor of production is being paid in form of wages/ salaries. After
income tax deduction, worker is left with disposable income that can be either consumed
or saved. Household consumption is part of GDP calculated by expenditure approach.
Household savings, as defined by National Bureau of Statistics of China, is the sum of
time and demand deposits that banks use as a source of funds for businesses loans and
that is supposed to contribute positively to higher investment and production, GDP.
Domestic loans can be seen as another source of funds for investment in fixed assets.
Figure 4 - Annual Percentage Changes of China’s GDP, Savings, Foreign Direct
Investment (1982-2005)
Annual Percentage Changes of China's GDP, Savings, Foreign
Direct Investment (1982-2005)
120
100
80
GDP %
S%
FDI %
60
40
20
0
-20 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004
-40
Source: http://www.stats.gov.cn/tjsj/ndsj/2006/indexeh.htm
Figure 5 - Annual Percentage Changes of China’s GDP, Employment in Secondary
Industry and Township-village Enterprises (1982-2005)
Annual Percentage Changes of China's GDP, Employment in Secondary
Industry and Township-village Enterprises
(1982-2005)
25
GDP %
20
TVE %
15
ESI %
10
5
0
-5 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004
-10
Source: http://www.stats.gov.cn/tjsj/ndsj/2006/indexeh.htm
Figure 6 - Annual Percentage Changes of China’s GDP, Savings, Foreign Direct
Investment (1982-2005)
Annual Percentage Changes in China's GDP, Consumption,
Domestic Loans (1982-2005)
120
100
80
60
40
GDP %
C%
20
DL %
0
-20 1982
1985
1988
1991
1994
1997
2000
-40
Source: http://www.stats.gov.cn/tjsj/ndsj/2006/indexeh.htm
2003
5. Results
Our research tries to explain the major reasons that influenced trend of China’s
GDP from 1982 to 2005 using quantitative variables, multivariate linear regression
functions and ordinary least squares method in statistical program GRETL.
Of course, we checked for possible issues with our time series analysis. The
correlation matrix helped us with multicollinearity between two variables. DurbinWatson statistics measured the first order autocorrelation of our dependent variable,
which is annual percentage change in GDP. Further tests for normality and
heteroscedasticity were performed.
As mentioned in Methods, part 3, we used five different models with possible
explanation of the China’s GDP growth rate in percent between 1982 and 2005. The first
three models consider percentage changes in savings, foreign direct investment, freedom
to trade internationally, employment in secondary industry and township village
enterprises. The lagged GDP is used to deal with the autocorrelation issue. The forth and
fifth models represent alternative explanation of the GDP growth rate in percent. They
consider household consumption, domestic loans as source of funds for fixed assets and
lagged GDP. Since household consumption is highly correlated, 0.7, with lagged GDP
and foreign direct investment we were not able to use all of them in one model.
Table 2 - Ordinary Least Squares estimates using the 24 observations 1982-2005,
GRETL
Dependent Variable: GDP growth rate %. Independent variables in terms of annual %
change included below.
-0.137
p-value
<0.00001
***
0.01243 **
0.074
Model 1
10.332
Constant
Savings %
Foreign Direct
Investment %
Freedom to Trade
Internationally %
Secondary Industry
Empl. %
Township Village
Enterprises Empl. %
p-value
Model 2
10.003 <0.00001***
-0.099
0.04089 **
0.00028 ***
0.060
0.00367 ***
0.124
0.01679 **
0.094
0.06907 *
0.219
0.07983 *
0.105
0.173
Adjusted R2
0.474
F-statistic
6.189
0.002
5.495
0.004
Normality test
OK
0.234
NOT OK
0.075
Heteroscedasticity test
OK
0.192
OK
0.341
Durbin-Watson
Akaike information criterion
Schwarz Bayesian criterion
0.439
INCONCLUSIVE
INCONCLUSIVE
106.704
112.594
108.276
114.166
The explanatory variables in first two models differ only in the choice of
employment. As can be seen from above, the annual percentage change in secondary
industry employment has significant p-value and model 1 passes the test of normality as
compared to model 2, which does not pass the normality test and annual percentage
change in township-village employment, is not significant at the 10 % level.
Table 3 - Ordinary Least Squares estimates using the 24 observations
1982-2005, GRETL
Dependent Variable: GDP growth rate %. Independent variables in
terms of annual % change included below.
Constant
p-value
Model 1
10.332 <0.00001
***
-0.137
Savings %
0.074
Foreign Direct Investment %
Freedom to Trade
Internationally %
0.124
0.219
Secondary Industry Empl. %
0.01243
**
0.00028
***
0.01679
**
0.07983
*
p-value
Model 3
6.745 0.00028***
-0.152
0.0061***
0.055
0.00398
***
0.213
0.07676 *
0.488
0.00851
***
GDP t-1 %
Adjusted R2
F-statistic
Normality test
Heteroscedasticity test
Durbin-Watson
Akaike information criterion (AIC)
Schwarz Bayesian criterion (SBC)
0.474
6.189
OK
OK
INCONCLUSIVE
106.704
112.594
Figure 7 – Normality test of residual for model 3
0.002
0.234
0.192
0.507
6.924
OK
OK
105.143
111.033
0.001
0.509
0.226
0.25
uhat3
N(-5.181e-016,1.9739)
Test statistic for normality:
Chi-squared(2) = 1.350 pvalue = 0.50903
0.2
Density
0.15
0.1
0.05
0
-6
-4
-2
0
2
4
6
uhat3
Since the Durbin-Watson check for autocorrelation is inconclusive in model 1, we
implemented lagged GDP growth rate, which proved to be strongly significant at 10 %
level.
The p-values of normality and heteroscedasticity tests are higher than 0.1
indicating that residuals of model 3 are normally distributed and that variance is constant
at 10 % level. The last column gives the p-value for a two-tailed test for the null
hypothesis that the corresponding regression coefficient is zero. High p-value means that
the probability of a type I error in rejecting the null is high. Coefficients for all
explanatory variables in model 3 are significantly different from zero at 10 % level. The
overall F-test is also significant at 10 % level. The selected statistics Akaike Information
Criterion (AIC), Schwarz Bayesian criterion (SBC) are lower and therefore better in
model 3 compared to model 1.
We would therefore present model 3 as our “best” model with following
explanation. The adjusted R2 is 0.507, indicating that 50.7 % of the variance in annual
percentage change in GDP is explained collectively by the variables in model 3.
Explanation of coefficients in Model 3:

If household savings increase by 1 %, holding other variables constant, the growth
rate of GDP decreases by 0.152 %.

If foreign direct investment in fixed assets increases by 1 %, holding other
variables constant, the growth rate of GDP increases by 0.055 %.

If secondary industry employment increases by 1 %, holding other variables
constant, the growth rate of GDP increases by 0.213 %.

If GDP lag increases 1 %, holding other variables constant, the growth rate of GDP
increases by 0.488 %.
Other possible explanations were also taken in account. We chose different
variables than in previous models; their complete list is mentioned in the correlation
matrix. However, only annual percentage change in household consumption and
annual percentage change in domestic loans as investment source for fixed assets
proved to be significant at 10 % level. Since the Durbin-Watson autocorrelation test
for model 4 proved to be inconclusive, we added lagged GDP as explanatory variable
in model 5. Because the annual percentage change in household consumption is
highly correlated, 0.7, with lagged GDP, we did not include them in the same model.
Table 4 - Ordinary Least Squares estimates using the 24
observations 1982-2005, GRETL
Dependent Variable: GDP growth rate %. Independent variables in
terms of annual % change included below.
Model 4
6.157
Constant
0.171
Consumption %
Domestic Loans for Fixed
Assets %
0.052
p-value
<0.00001
***
0.0024 ***
Model 5
4.301
p-value
0.00539
***
0.00411
***
0.064
0.00073
***
0.00502
***
0.422
GDP t-1 %
Adjusted R2
F-statistic
Normality test
Heteroscedasticity test
Durbin-Watson
Akaike information criterion
Schwarz Bayesian criterion
0.569
16.206
OK
OK
0.0001
0.137
0.840
0.540
14.515
OK
OK
0.0001
0.402
0.120
INCONCLUSIVE
100.320
103.854
101.889
105.423
Similar to model 3, model 5 also passes the overall F-test, normality and
heteroscedasticity tests. The coefficients of all explanatory variables in model 5 are
significantly different from zero at 10 % level.
The adjusted R2 is 0.54, indicating that 54 % of the variance in annual percentage
change in GDP is explained collectively by the variables in model 5.
Explanation of coefficients in model 5:

If domestic loans as a source of investment in fixed assets increase by 1 %,
holding other variables constant, the growth rate of GDP increases by 0.064%.

If GDP lag increases 1 %, holding other variables constant, the growth rate of GDP
increases by 0.422 %.
Table 5 - Correlation matrix - Correlation coefficients, using the observations
1982 – 2005, 5% critical value (two-tailed) = 0.4044 for n = 24
GDP__
1.0000
S__
0.0673
0.4671
0.5199
0.5639
-0.5185
1.0000
GDP_t_1__
0.5183
1.0000
S_t_1__
-0.0245
-0.0812
0.2576
0.2032
-0.6207
0.6009
1.0000
C__
0.6424
0.6764
1.0000
PCI_AUH__
0.3942
0.5046
0.7342
1.0000
ARW__
-0.0383
-0.2814
-0.3998
-0.1895
1.0000
GDP__
GDP_t_1__
C__
PCI_AUH__
ARW__
FDI__
0.5464
0.4735
0.6815
0.7883
-0.1963
0.5320
0.2664
1.0000
TVE__
0.4580
0.1729
0.2835
0.0935
-0.2931
0.3371
0.3578
0.4429
1.0000
ESI__
0.1343
0.0520
0.1237
0.0034
-0.1120
0.4602
0.3518
0.1411
0.2662
1.0000
GDP__
GDP_t_1__
C__
PCI_AUH__
ARW__
S__
S_t_1__
FDI__
TVE__
ESI__
index__
0.2660
0.3952
0.3136
-0.1341
-0.0244
0.0632
-0.1430
-0.0540
0.1807
0.0046
1.0000
fti__
0.2915
0.4657
0.4377
0.0259
-0.0639
0.1521
-0.0350
-0.0013
0.1755
-0.0986
0.8778
1.0000
DL__
0.6197
0.1273
0.3133
0.0993
0.0821
0.0760
-0.0361
0.3826
0.3843
0.3274
0.4883
0.2950
1.0000
GDP__
GDP_t_1__
C__
PCI_AUH__
ARW__
S__
S_t_1__
FDI__
TVE__
ESI__
index__
fti__
DL__
Explanation of variables in correlation matrix:
GDP__ - GDP % - annual percentage change in GDP
GDP_t_1__ - GDP t-1 – annual % change in GDP lagged 1 period
C__ - C % - annual % change in households consumption
PCI_AUH__ - annual % change in per capita income of urban household
ARW__ - ARW % - annual % change in average real wage
S__- S % - annual % change in households savings
S_t_1__ - S t-1 – annual % change in household savings lagged 1 period
FDI__ - FDI % - annual % change in foreign direct investment in fixed assets
TVE__ - TVE % - annual % change in the township-village employment
ESI__ - ESI % - annual % change in secondary industry employment
index__ - INDEX % - annual % change in the summary index of economic freedom
fti__ - FTI % - annual % change in freedom to trade internationally, one area of
summary index
DL__ - DL % - annual % change in domestic loans as investment in fixed assets
Correlation coefficient values are between 0 and 1. In the regression models we
try to find combination of independent variables that are not correlated with each other
and are at the same time highly correlated with dependent variable, which is annual %
change in GDP.
Contrary to the economic theory, average real wage % is negatively correlated
with GDP. That may point to the issue of data inaccuracy. Per capita income of urban
household as annual % change is highly correlated with foreign direct investment and
therefore we cannot use both variables in the same regression model. Savings are
positively related with GDP % and have low correlation coefficients with the independent
variables, which is good. The possible reasons for negative sign of savings in regression
models are correlation between more than 2 variables that is not shown in correlation
matrix, data inaccuracy or just fact that China’s household savings have negative impact
on the GDP growth.
6. Summary
Our research tried to explain the major reasons of gross domestic product (GDP)
growth in post-communist countries, specifically in China. Time series analysis of annual
data between 1982 and 2005 was used to explain the trend of gross domestic product
growth. The significant variables at 10 % level using ordinary least squared method in
GRETL proved to be policy change after 1978 reflected in economic freedom index,
especially in the area of freedom to trade internationally, and foreign direct investment in
fixed assets with further impact on employment of secondary industry, township-village
enterprises and household consumption. Surprisingly, savings of China’s households
defined as a sum of time and demand deposits do not have positive effect on GDP
growth. This issue may be caused by the inaccuracy of the official data, multicollinearity
among more than two variables that is not shown in the correlation matrix or just by the
fact that China’s household savings do not positively contribute to GDP growth.
The results of our regression models support the statement of leading Chinese
theorist George Zhibin Gu, who mentioned the key driving forces behind China’s
economic growth as 1) opening up of the economy, which we measured as economic
freedom index, respectively freedom to trade internationally, 2) domestic consumption
explosion, measured in our case as household consumption and 3) international
involvement in three areas—A) foreign direct investment, measured as foreign direct
investment in fixed assets, B) increase in manufacturing, reflected as employment of
secondary industry, C) and international trade, shown in freedom index to trade
internationally.
References
China’s Statistical Yearbook 2006,
http://www.ers.usda.gov/publications/aib775/aib775r.pdf . Accessed June 6, 2007.
Fraser Institute Website, http://www.freetheworld.com/2006/0EFW2006frntXsum.pdf .
Accessed June 6, 2007.
http://www.oecd.org/countrieslist/0,3025,en_33873108_33844430_1_1_1_1_1,00.html
Accessed May25, 2007
http://santiago.indymedia.org/news/2007/05/67313.php) Accessed May17, 2007
http://www.theodora.com/wfb2003/rankings/gdp_2003_0.html,
Data source: 2003 CIA World Factbook, Accessed May15, 2007
Gale, F., China’s Statistics: Are They Reliable? ,
http://www.ers.usda.gov/publications/aib775/aib775r.pdf . Accessed June 6, 2007.