The Analysis of Rural Income in Heilongjiang Province Was
Analyzed by The Surplus Labor Transfer Perspective
Shuli Song
School of Economic Management, Heilongjiang Bayi Agricultural University Daqing 163319
Keywords: Rural, Partial least squares, Transfer of surplus labor force, Rural income.
Abstract: Based on the view of increasing rural income by transfer of surplus labor force, this study took partial
elements as independent variable to build partial least-squares regression (PLS) model. According to the
statistic data from 2001 to 2015 in Heilongjiang province, we analyzed the prechose variable about route of
rural income increased by PLS method using matab 7.0 to analysis the data. The research suggested that all
chosen variables have positive effect, such as value of agriculture production, value of forestry animal
husbandry and fishery, number of rural workers, number of rural migrant workers, the total power of
agricultural machinery, agricultural production investment, non-agricultural income and rural per capita net
income. Based on analysis of result, this study proposed the effective transfer of surplus of labor force is the
policy suggestion for realizing rapid growth in rural income.
1 INTRODUCTION
China is a typical country with surplus labor force.
The ‘Three agriculture-related issues’ is the most
prominent problem during the process of
modernization and coordinated development of
urban-rural economy. However, the core to solve the
‘Three agriculture-related issues’ is to solve the
issue of increasing the rural income well. Based on
the No.1 document of central government in year
2015, China will be rich in the near future, and
farmer must be rich. To increase the rural income, it
must promote farmer transfer employment and
entrepreneurship. Domestic research showed that
rural poverty problem must take reduction rural
labor as the main strategic objectives, transferring
rural surplus labor could promote to increase rural
family income (Zhishui Piao, 2003), at the same
time, which is the precondition of food production
scale (Tianlong Jiang, 2012) and has become a new
growth point of underdeveloped rural areas’ income
(zhong-dong Ma, etc., 2004).It has a positive
correlation between farmers' income and the number
of labor force engaged in the secondary industry
(Shanjun Wang, 2006). The degree of transferring
rural labor force makes an important contribution to
society economic development, while it also brings
the negative impact on development of agricultural
production (Shilan Qi, etc., 2009). For example, a
large number of transferring rural labor would make
the land derelict, which woud be serious detrimental
to agricultural sustainable development, and even
affect food security (Qing-song Ruan, 2010). The
importance of agricultural income from the income
of farmers is gradually weakened, while the
importance of non-agricultural income from the
income of farmers is increased (Shuli Song, 2014).
Through the literature review, it was found that
more studies are qualitative research on the
relationship between surplus labor transfer and
farmers' income, and most of them are intuition
judgment and general description, while relatively
few systematic research are from a quantitative view.
In addition, many of them are from national
macro-level, and less research on micro-level of
single provinces and cities, where especially the
study of Heilongjiang province is quite few.
2 MODEL BUILT AND ANALYSIS
2.1 Variables and variable description
Peasant income consists of agriculture part and
non-agriculture part. According to the impact factors
of these two parts, it combined income source with
components of Heilongjiang province peasant,
According to expert's point of view who is devoted
Song, S.
The Analysis of Rural Income in Heilongjiang Province Was Analyzed by The Surplus Labor Transfer Perspective.
In 3rd Inter national Conference on Electromechanical Control Technology and Transportation (ICECTT 2018), pages 5-8
ISBN: 978-989-758-312-4
Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
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to research transfer of surplus labor force and
peasant income, it was selected parts of variable as
researching index.variable type, variable symbol
(variable name, variable unit, variable
description) such as：Dependent variable y(Rural per
capita net income, Yuan, From Statistical yearbook
indicators),Independent variable x1(Agriculture
output, Thousand Yuan, Total value-Agriculture
output),x2(Forestry and fishery output, Ten
Thousand Yuan, Total valu- Forestry and fishery
output),x3(The number of people entering farming,
Ten Thousand Person, The number of people
performing farming at current year ),x4(The number
of rural migrant workers, Ten Thousand Person,
Rural labor force leaving countryside and living on
wage income),x5(Total power of agricultural
machinary, Ten Tousand KW, From Statistical
yearbook indicators),x6(Agricultural production
investment, Yuan, Original value of fixed assets per
capita),x7(The average non-agricultural income per
capita ,Yuan, Rural migrant workers income in the
employ of enterprise or individual household, based
on wage income of statistical yearbook).
2.2 Principle of Model
We assumed
y
as dependent variable, p{x
1
,x
2
,…x
p
}
as Independent variable, n as sample quantities, and
X=[x
1
,x
2
,…x
p
]
n×p
as independent variable and
Y=[y]
n×1
as dependent variable For the
requirement of regression analysis as extracting
components.After extracting t
1
, u
1
, we performed the
regression of X vs t
1
and Y vs u
1
by the method of
PLS. If the regression equation get a satisfied
accuracy, and the operation be terminated, otherwise
we will perform the second operation using residual
X, Y to be resolved by t
1
. Looping computing
fuction until we get the satisfied accuracy(Wang
huiwen, Wu zaibin etc. 2006).
2.3 PLS Analysis
2.3.1 Data statistics, Multiple correlation test
and Data normalization processing
This paper collected Time-series data influencing
rural income from《Heilongjiang province statistical
yearbook》, 《China Statistical Yearbook》2000-2016,
multiple correlation test use Variance Inflating
Factor.Using maltab7.0 to make the analysis to
original data,showed serious multiple correlations
are existed both among dependent variables and
among independent variables.In order to remove
adverse effects due to differences between test units
and ensuring eaching variable own the same
expression, we used maltab7.0 to make
normalization treatment for dependent variables and
independent variables and got normalization data.
2.3.2 Extracting principal component
1) Extracting the first principal component:
W
1
*
=(-0.3936,-0.3949,-0.3466,0.3207,-0.3943,-0
.3912,-0.3970)
P
1
=(-0.3804，-0.3911，-0.3673，0.3490，-0.3907，
-0.3818，-0.3860)
t
1
=E
0
W
1
*
=-0.3936E
1
-0.3949E
2
-0.3466E
3
+0.3207
E
4
-0.3943E
5
-0.3912E
6
-0.3970E
7
Regression Equation F
0
to t
1
:
F
0
=r
1
t
1
=-0.3878t
1
=0.1526E
1
+0.1531E
2
+0.1344E
3
-0.1244E
4
+0.1529E
5
+0.1517E
6
+0.1540E
7
(1)
Cross validation judgement:
Q
1
2
=0.9576>(1-0.95
2
)=0.0975, it was demonstrated
that adding component is valid to improve modeling
quality and continue to extract principal component.
2) Extracting the second principal component:
W
2
*
=(0.3251,0.0956,-0.5096,0.6990,0.0899,0.2
351,0.2711)
P
2
=(0.3228,0.0849,0.5238,-0.4849,0.0815,0.26
28,0.2662)
t
2
=E
0
W
2
*
=0.3251E
1
+0.0956E
2
-0.5096E
3
+0.699
0E
4
+0.0899E
5
+0.2351E
6
+0.2711E
7
Regression Equation F
0
to t
1
, t
2
:
F
0
=r
1
t
1
+r
2
t
2
=-0.3878t
1
+0.2354t
2
=0.2291E
1
+0.1756E
2
+0.0144E
3
+0.2889E
4
+0.17
39E
5
+0.2070E
6
+0.2178E
7
(2)
Cross validation judgement:
Q
2
2
=0.2341>(1-0.95
2
)=0.0975,which
demonstrateadded component is valid to improve
modeling
quality and continue to extract principal component.
3) Extracting the third principal component
W
3
*
=(0.0634,0.2894,0.3812,0.3790,0.2261,-0.74
48,0.1322)
P
3
=(0.0807,0.2279,0.2742,0.2935,0.3423,-0.821
4,0.1654)
t
3
=E
0
w
3
*
=0.0634E
1
+0.2894E
2
+0.3812E
3
+0.3790
E
4
+0.2261E
5
-0.7448E
6
+0.1322E
7
(3)
Cross validation judgement:
Q
3
2
=-0.4573<(1-0.95
2
)=0.0975, it was
demonstrated that added component is not valid to
improve modeling quality and terminate extracting
principal component.
2.3.3 Building PLS Regression equation
As above calculation and ratiocination showed, it is
only to extract two principal components for
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satisfying modeling quality, and the regression
equation for normalization variable as follows:
F
0
=r
1
t
1
+r
2
t
2
=-0.3878t
1
+0.2354t
2
=0.2291E
1
+0.1756E
2
+0.0144E
3
+0.2889E
4
+0.1739E
5
+0.2070E
6
+0.2178E
7
(2)
Regression equation y to x={x
1
，x
2
，…,x
p
}:
y=0.8345x
1
+0.8342x
2
+6.4500x
3
+10.6437x
4
+0.1
147x
5
+0.0326x
6
+1.6716x
7
-8325.1270 (4)
As we can see from the equation(4), that x
j
is
negative to showed x
j
and y are negative correlation,
that x
j
is positive showed that x
j
and y are positive
correlation. For x
1
,x
2
,x
3
,x
4
,x
5
,x
6
,x
7
, the degree of
positive correlation in a descending order as
x
4
>x
3
>x
7
>x
1
>x
2
>x
5
>x
6
, That x
4
is maximum
indicated x
4
is the biggest influence for y.
2.3.4 Results analysis
From the PLS model regression equation (4) and
figure 1, it was concluded that each variable xj's has
influence on per capita net income of farmers y:
through the establishment of the regression equation,
it showed that the larger independent variable
regression coefficient absolute value, and the greater
this variable’s influence on per capita net income of
farmers. The results showed that the agricultural
output value and value of forestry, animal husbandry
and fishery havethe same influence on per capita net
income of farmers to some degree. The reason is that
all output values should deduct production costs of
farming, forestry, animal husbandry and fishery,
then the farmers' income. Agricultural
mechanization level and the investment of
agricultural production both affect per capita net
income of farmers, but the impact is not significant.
Non-farm income plays obvious promoting effect on
increasing of per capita net income of farmers. In
thisstudy, In order to emphasize the relationship
between the surplus labor transfer and the farmers'
income, we took non-agricultural income which
only take salary income earned in the form of
engaging in non-agricultural industries after labor
transfer to work as an example. In fact, the farmers'
income of non-agricultural income partly also
include the incomes from operating of the second
and third industry and property transfer income and
so on, so when we took the salary income as
example to explain the influence of non-agricultural
income from per capita net income of the farmers,
and there is also gearing effect by property income
and transfer income of the, etc. together with salary
income.
Among these, how much the number of migrant
rural labor directly shows the transfer of rural
surplus labor, and non-agricultural income is the
manifestation of earned income by engaging in
non-agricultural industries in form of wages after the
rural surplus labor transfer. Therefore, the research
results showed that with the improvement of modern
agricultural mechanization, agricultural output value
increased at the same time and also produced a large
number of surplus labor force. Retention of surplus
labor force in agriculture will cause a decline in
agricultural labor productivity, so agricultural
production is improving but it does not affect much
on the increase in per capita net income of farmers.
At the same time, in the context of fixed land
acreage and improving degree of agricultural
mechanization, the effect to promoting farmers'
income by increasing the number of workers for the
agricultural will be limited.
3 CONCLUSIONS AND
SUGGESTIONS
Through previous results analysis, we can reach the
following conclusions: (1) Adoption the PLS model
can make the results more intuitively and it
accurately reflect the influence of various influence
factors to farmers' income, according to this, it was
found that the smoothly and orderly transfer of rural
surplus labor is the realization of the effective ways
to increase farmers' income. (2) on the basis of the
limited planting land area, it allocates rationally the
agricultural workforce and the proportion of the
number of rural migrant workers, which further
increases the non-agricultural income and improves
output value of forestry, animal husbandry and
fishery industry to realize per capita net income of
farmers and effective way.
For increasing farmers' income, it combined with
the present situation of the rural population in China
and surplus labor transfer long-term characteristic,
the following policy suggestions have been put
forward: (1) To accurately position the government
in the labor transfer work, actively promote
institutional innovation, change from directly
management to indirectly control the economic,
unblock external environment of rural labor force
transfer. (2) To improve the training ability of
training institutions and government should
strengthen the support to training institutions, and
increase training for farmers to enhance working
skills and cultural quality of rural labor force,
therefore. (3) To guide the establishment of rural
labor transfer institutions or organizations,it
The Analysis of Rural Income in Heilongjiang Province Was Analyzed by The Surplus Labor Transfer Perspective
7

improves the rural labor transfer information service
and gradually establish the coordinated development
of urban and rural labor market.
ACKNOWLEDGMENT
Fund project: (1)research project of philosophy and
social science planning of daqing city, heilongjiang
province (project no:DSGB2015012). ⑵
Heilongjiang university students innovation and
entrepreneurship training program (project no:
201710223033)
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