Download Economic Growth and Employment Elasticity Problems of Heilongjiang

Economic Growth and Employment Elasticity Problems of
Heilongjiang
WANG Qiuju 1,2 WANG Lijie 1
1. School of Management, University of Mining Technology, Beijing, China, 100083;
2. School of Management, Heilongjiang institute of Technology, Harbin, China, 150008
[email protected]
＆
Abstract: this paper is based upon the method of dual-logarithm model estimating employment
elasticity of GDP, in light of the dates of employment and GDP between 1992 and 2006 in Heilongjiang
province. Through testing the stability of the model with chow, the idea that employment function has
significant differences in 1996, 1999, and 2003 before and after the period is put forward. Meanwhile
this article believes that Heilongjiang Province is experiencing the dramatic changes of economic
structure and industrial structure, that the relationship of employment and economic growth showed the
law of random Changes. To describe this relationship, employment elasticity of GDP is estimated
through state-space models. In the end of article, the author draws a conclusion that economic growth in
Heilongjiang Province plays a limited role in employment growth to some degree.
Keywords: state-space models, Kalman filtering; employment elasticity
1 Introduction
Economic growth has a close connection with employment growth. It has been the focus of
macroeconomic research in the field that how many impetuses economic growth give to employment
growth. In fact, the essence of the problem is how to estimate employment elasticity accurately and
reasonably. At present, there are three main methods of estimating employment elasticity in domestic
and foreign research: (1) estimating employment elasticity according to its definition. This method can
obtain employment elasticity not only every year, but also a longer period. This method is simple, but
the lack of accuracy, because this prerequisite that the other factors of economic growth remain
unchanged in the flexible definition, which is often difficult to meet. (2) Estimating employment
elasticity of GDP by economic growth model, which is an indirect method of calculation. The
advantages of this method lie in considering economic growth and technological progress, capital
investment and labor inputs and other factors. The disadvantages lie in regarding employment variables
as independent variables, and regarding GDP variables as dependent variables. According to the
presupposition of the model, independent variables should be Non-random variables, and attributive
variable should be random variables. So the method confuses the cause and effect of the fact. (3)
Estimating employment elasticity by dual-logarithm model. [1] This method is simple and intuitive, and
the assumption accords with the causal relationship, regarding GDP as independent variables and seeing
employment as dependent variables. Dates is accessible, and all the factors that do not be considered,
which are included in the constants. Employment elasticity estimating by this method is a constant. the
model assumes that the economic structure for a period of time is not unchanged. But the assumption is
usually difficult to meet.
Considering the superiority of the dual-logarithm model, this article is based upon the method to
estimate employment elasticity, using the dates of employment and GDP during 1992-2006 in
Heilongjiang Province, and to test the stability of the model with chow. On this basis, state-space model
is established to estimate employment elasticity of Heilongjiang Province, and put forward
corresponding employment policies.
2 dual-logarithm model and stability test
According to the law of dual-logarithm model, dual-logarithm model may be expressed:
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LN(EMP)t =Ct+B t× LN(GDP) t +ui
(1)
In equation: Ct as the constant of a model, Bt as employment elasticity, ui as random errors.
This article selects the dates of employment and GDP during 1992-2006 to test the stability of model.
With taking advantage of Eviews5.0, estimating results of the model may be expressed:
LN(EMP) t = 9.532783506 + 0.1457055417×LN(GDP) t
(2)
59.35430
6.187892
Adjusted R-squared =0.727042 Durbin-Watson stat=0.767505
Although the equation passes variable significance test, regression equation exists self-related. To
remove the autocorrelation, the article uses generalized least squares, and introduces AR (1), AR (2).
The regression equation after adjustment can be expressed
LN(EMP) t = 6.72877+ 0.08250×LN(GDP) t +[AR(1)=1.0645 AR(2)=-0.80161]
(3)
(48.63833)
(4.767731)
(4.592994) (-3.208950)
To examine the stability of the model parameters, the article uses Chow Breakpoint Test. The test results
can be shown in table 1. We can be seen from Table 1, the model did not pass stability tests.
Dual-logarithm model exists a few notable differences, In 1996 before and after the period. Model
parameters have significant changes.
（
）
（
）
，
Table 1 stability tests of the model
Chow Breakpoint Test: 1996
F-statistic
Log likelihood ratio
3.698928
11.55616
Probability
Probability
0.032418
0.009069
With the same method, Dual-logarithm model exists a few notable differences in 1999 and 2003 too. So
the establishment of dual-logarithm model for estimating constant employment elasticity is not reliable.
3 Estimating employment elasticity
Heilongjiang Province is undergoing a accelerative macroeconomic the transition. employment and
economic growth showed dramatic changes in the law. to describe this relationship, we use a variable
parameter model combined with Kalman filtering method, and estimate employment elasticity by
state-space models.
3.1 state-space model
In the 1980s, state-space model has become a modeling tool. Many time-series models, including the
typical linear regression model and ARIMA model can be written in the form of state space as a special
case, and estimate the parameters of the models. In the econometric literature, the state-space models are
used to estimate the time variables not be observed, such as rational expectations, measurement errors,
long-term income, and other factors not be observed (trend and circulation elements).
The Use of state-space dynamic system forms the main advantages: First, the state-space models allow
variables (state variables) not be observed, which be incorporated into the observation model, and they
get the estimated results together with the observation model. The second, state-space model is
estimated with Kalman Filtering. Kalman filter can estimate single-and multi-variable model, variables
ARMA, Markov switching model, and Variable parameter model and so on. [2]
2.2 Employment growth model expressed by state space model
The relationship between employment growth and economic growth can be expressed by state-space
model, shown as follows.
LN(EMP)t =C(1)+SV1 t ×LN(GDP) t +ui
(4)
SV1t=c(3) ×SVt(-1)+gt
(5)
In equation: LN(EMP)t as the logarithm of employment dates; LN(GDP)t as the logarithm of GDP; SV1t
as employment elasticity, that is estimated parameters; ut~N 0 σ1 gt~N 0 σ2 as a smooth sequence,
（ ， ）、 （ ， ）
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that is, the Stochastic errors.
The model is composed of the measurement equation (4) and the state equation (5). equation
(4)describes the combined influence of economic growth on employment growth. Equation (5) describes
the formation of state variables. The article assumes: Variable parameters are subject to random walk
process. ut and gt are independent, and subject to the assumption that mean is zero, the variance is a
normal distribution of constant. [3]
3.3 estimating the state-space model
Using Eviews to estimate the state-space model, the sentences of state-space model are expressed
@signal LNGDP = c(1) + sv1×LNEMP + [var = exp(c(2))]
@state sv1 = c(3)×sv1(-1) + [var = exp(c(4))]
@param c(1) 6.72 c(2) -3.47 c(3) 0.96 c(4) -15
super variable c(1) initialize 6.72 c(2) initialize -3.47 c(3) initialize 0.96 c(4) initialize -15.
The state-space model describing the relationship between employment growth and economic
growth can be obtained by estimating the parameters.
LNGDP = 6.76607 + SV1×LNEMP + [VAR = EXP(-7.33077)]
SV1 = 0.99996×SV1(-1) + [VAR = EXP(-14.64930)]
，
，
，
Table 2 estimating results of state-space model
Coefficient
Std. Error
z-Statistic
Prob.
C(1)
C(2)
6.766074
-7.330773
0.184413
0.673720
36.68983
-10.88103
0.0000
0.0000
C(3)
C(4)
0.999966
-14.64930
0.000829
3.200648
1206.636
-4.576981
0.0000
0.0000
Final State
Root MSE
z-Statistic
Prob.
0.078675
27.80929
0.001489
Akaike info criterion
52.83696
0.0000
-3.174572
SV1
Log likelihood
Parameters
4
Schwarz criterion
-2.985758
Diffuse priors
0
Hannan-Quinn criter.
-3.176583
The estimating results of the state-space model are shown in Table 2. Super variable c(1) is equal to
6.766074, C(2) to -7.330773 C(3) to 0.999966 and C(4) to -14.64930. all parameters pass t-test. AIC
and SC of the model all are small, so they meet the criteria and guidelines for Schwarz. Thus, the model
fits good effect.
sv1t is the series of variable parameter reflecting the de facto employment elasticity each
year(Figure 1). Employment elasticity of Heilongjiang province changes between 0.0762 and 0.0794
during the period.
，
，
.0795
.0790
.0785
.0780
.0775
.0770
.0765
.0760
92 93 94 95 96 97 98 99 00 01 02 03 04 05 06
SV1F
Figure 1 the curve of employment elasticity
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With the impact of national economic reform and Restructuring, the influence of economic growth
on employment plays down, and the changes of employment elasticity in Heilongjiang Province show a
downward trend. 96 percent of state-owned enterprises completed restructuring after 1996, and at the
same time enterprises in Heilongjiang Province had put the labor contract system into practice. All types
of workers of enterprises became contract workers, that is their legitimate rights protected equally. Thus,
those measures greatly promote Employment growth. After 1999, employment growth slowed down
with the impact of agricultural development lags. Since 2003, the Sunshine Project in Heilongjiang
Province, speed up training of farmers, and actively promote the transfer of rural laborers, farmers
greatly promote the growth of employment, making employment elasticity rose to 0.0786 from 0.0765.
3.4 results analysis
Estimating employment elasticity of Heilongjiang Province can be seen:
Although economic growth of Heilongjiang promotes employment growth to some degree, the influence
is so limited. Employment elasticity changes between 0.0762 and 0.0794. Main reasons are: (1) There
are seven resource-based cities such as Daqing, Shuangyashan, Jixi, Hegang, Qitaihe, Yichun and so on
in 13 cities of Heilongjiang Province. The formation of these cities originates from the mass investment
of key projects by the state in the planned economy period. the industrial structure is single, which is not
conducive to the expansion of employment channels.(2) With the impact of the transformation of
economic growth mode in Heilongjiang Province, the economic growth mode is changing from
extensive to intensive growth. Economic growth will rely more on technological progress, and
technological advances will increase labor productivity and organic composition of capital. Thus, labour
demands will be reduced, and the growth of employment elasticity will be affected. (3) The economic
structure of Heilongjiang Province is not reasonable. Of the output structure in a region, the greater the
proportion of labor-intensive products, the higher the value of employment elasticity. On the contrary,
the greater the proportion of capital-intensive, technology-intensive products, the lower the value of
employment elasticity. [4] Of some cities in Heilongjiang Province, the proportion of secondary industry
is generally high, and the proportion of tertiary industry is generally low. The slow development of
tertiary industry restricts the level of employment elasticity.
Therefore, to promote employment growth in Heilongjiang Province, in addition to maintaining rapid
economic growth, we must also take the following measures:
First, we should change the economic growth mode and properly handle the capital-intensive
growth mode and labor-intensive growth mode. Heilongjiang Province should vigorously develop
intensive industries which need more labor and less capital. Second, Heilongjiang Province should
optimize the industrial structure, by the structural transition from the unitary structure to the
diversification, and vigorously develop the tertiary industry, to reach the balance of labor in the three
industries. Third, the value of the employment elasticity is influenced not only by the impact of
economic growth, but also by the adjustments of government's macroeconomic policies. The inflexions
of the employment elasticity curve are the key points of the policy adjustments. Therefore,
Heilongjiang’s macroeconomic targets should be to expand jobs and lower the unemployment rate, and
establish corresponding policies.
4 conclusion
(1) When estimating employment elasticity with dual-logarithm model, the stability of the model should
be tested firstly. Passed the stability test, they can direct estimate employment elasticity of GDP. Instead,
the factors influencing employment elasticity are very complex. There are no fixed parameters, but there
are change parameters of the function. Employment elasticity should be estimated by state-space models.
(2) Although economic growth of Heilongjiang promotes employment growth to some degree, the
influence is so limited. Employment elasticity changes between 0.0762 and 0.0794.
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References
[1]Xu XiuChuan. On the chaoice of the methods for estimating employment elasticity and its positive
analysis in china. Journal of southwest agricultural university, 9(2005),P58~60(in Chinese)
[2]Gao TieMei. . Econometric analysis methods and modeling. Beijing: Tsing Hua University Press,
2006,P352~385(in Chinese)
[3]Chen Yang. The Time-varying Parameter Model of Liaoning Province on the Relation of the
Employment and the. Statistics & Information Forum, 9(2007),P77~80(in Chinese)
[4]Fortin, Pierre. Macroeconomic Unemployment and Structural Unemployment. Canadian Public Policy.
(4)2000,P125
Author in brief: Wang QiuJu, female, doctor, associate professor, majoring in labor economics and
human resource management.
Telephone: 13313651517 Email:
[email protected]
Address 999 Hongqi street, Harbin 150008
：
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