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Impact of land expropriation on farmers’ livelihoods in the mountainous and
hilly regions of Sichuan, China
Article in Journal of Mountain Science · October 2019
DOI: 10.1007/s11629-018-5017-z
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J. Mt. Sci. (2019) 16(11): 2484-2501
https://doi.org/10.1007/s11629-018-5017-z
e-mail: jms@imde.ac.cn
http://jms.imde.ac.cn
Impact of land expropriation on farmers’ livelihoods in the
mountainous and hilly regions of Sichuan, China
GUO Shi-li1
https://orcid.org/0000-0002-3314-6386; e-mail: guoshili@swufe.edu.cn
LI Chun-jie2
https://orcid.org/0000-0003-0345-8749; e-mail: lichunjie08@126.com
WEI Ya-li3
https://orcid.org/0000-0002-7491-1558; e-mail: weiyali@sicau.edu.cn
ZHOU Kui1
https://orcid.org/0000-0002-2514-1454; e-mail: zhou@swufe.edu.cn
LIU Shao-quan4*
XU Ding-de5*
LI Qian-yu1
http://orcid.org/0000-0002-6853-0783;
http://orcid.org/0000-0001-6359-6540 ;
e-mail: liushq@imde.ac.cn
e-mail: dingdexu@sicau.edu.cn
https://orcid.org/0000-0002-6516-1417; e-mail: joyqili@163.com
*Corresponding author
1 China Western Economic Research Center, Southwestern University of Finance and Economics, Chengdu 610074, China
2 College of Environmental Science and Tourism, Nanyang Normal University, Nanyang 473061, China
3 College of Resources, Sihuan Agricultural University, Chengdu 611130, China
4 Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China
5 Sichuan Center for Rural Development Research, College of Management of Sichuan Agricultural University, Chengdu
611130, China
Citation: Guo SL, Li CJ, Wei YL (2019) Impact of land expropriation on farmers‟ livelihoods in the mountainous and hilly
regions of Sichuan, China. Journal of Mountain Science 16(11). https://doi.org/10.1007/s11629-018-5017-z
© Science Press, Institute of Mountain Hazards and Environment, CAS and Springer-Verlag GmbH Germany, part of Springer Nature 2019
Abstract: Research on the sustainable livelihoods of
rural households is of great significance to mitigating
rural poverty and reasonable land expropriation
policy helps to realize better livelihoods and
sustainable development. Scholars have conducted
considerable research on the relationships between
land expropriation and farmers‟ livelihoods. However,
few quantitative studies have used the characteristics
of villages as control variables to systematically
analyze the impact of land expropriation on farmers‟
livelihood capital and strategy in the mountainous
and hilly regions of China. This study uses the Partial
Least Squares-Structural Equation Model (PLS- SEM)
to systematically explain the impact of land
Received: 11-May-2018
Revised: 09-Aug-2019
Accepted: 03-Sep-2019
2484
expropriation on farmers‟ livelihoods in mountainous
and hilly regions of Sichuan in 2013, with the
characteristics of the village including income,
accessibility and terrain as control variables. The
analysis uses both representative sample data of 240
rural households and spatial data calculated using a
30 m Digital Elevation Model (DEM) by Geographic
Information System (GIS). Results are as follows: (1)
The land expropriation characteristics are negatively
affected by village accessibility. Villages with worse
accessibility tend to have fewer land parcels and less
land area expropriated. Additionally, land that is
expropriated from inaccessible villages tends to
receive less compensation. (2) Natural capital is
negatively affected by number and area of land
expropriation. Natural capital is not only directly
affected by village accessibility, but also indirectly
J. Mt. Sci. (2019) 16(11): 2484-2501
affected by village accessibility through the mediating
effect of the number and area of land parcels
expropriated. (3) Physical capital is positively affected
by compensation for land expropriation, and
negatively affected by village accessibility through
compensation for land expropriation. The worse a
village‟s
accessibility/location
is,
the
less
compensation it will receive for land expropriation,
resulting in lower physical capital. (4) Financial
capital is negatively affected by village accessibility
indirectly
through
compensation
for
land
expropriation. The better the village‟s accessibility is,
the greater is its compensation for land expropriation
and, hence, the greater is its financial capital. (5)
Social capital is directly and negatively affected by the
number and area of land parcels expropriated, and is
indirectly and positively affected by village
accessibility through the number and area of land
parcels expropriated. This study enhances our
understanding of the characteristics of land
expropriation and rural households‟ livelihood as well
as the impact of land expropriation on rural
households‟ livelihood. These findings provide
reference for the formulation of proper policies
related to land expropriation and the improvement of
rural households‟ livelihoods in the mountainous and
hilly regions of China.
Keywords:
Sustainable
livelihoods;
Land
expropriation; Partial least squares-structural
equation model; Mediating effects; Mountainous
regions
Introduction
Since the 1980s, China's economy has
developed rapidly. Especially in the past decade,
the pace of industrialization and urbanization has
accelerated. The urban population increased from
191 million in 1980 to 731 million in 2013, and over
the same period, the urbanization rate increased
from 19.40% to 53.73%. Gross domestic product
(GDP) also increased from 454.56 billion RMB in
1980 to 56,884.52 billion RMB in 2013, an average
annual increase of 15.8% (NBSC 2014). Due to the
construction of infrastructure, rapid urbanization
and economic development have led to the great
demand for land. As a result, many rural lands
have been expropriated for non- agricultural
purposes (Chan 2003; Grosjean, & Kontoleon 2011;
Hui and Bao 2013; Lai et al. 2014; Wu et al. 2012),
including
both
urban
construction
uses
(commercial and residential projects) to achieve
the goal of urban expansion (He, Liu, Webster, &
Wu 2009) and industrial purposes (Chen, Ye, Cai,
Xing, & Chen 2014) often driven by market forces
(Zhang 2000). Farmers are the most basic
recipients of land expropriation programs and their
livelihood is defined as their way of making a living
based on their capacity, assets and activities
(Chambers and Conway 1992). These land
expropriation projects lead to significant shifts in
the livelihoods of rural households in China
(Bhandari 2013; Huang et al., 2017; Liu et al. 2013;
Guo et al. 2014; Song, Wang, & Lei 2016; Xu et al.
2015; Wang, Yang, & Zhang 2011). Generally
speaking, the Chinese government provides an
assurance that it will improve farmers‟ livelihood
before carrying out rural land tenure reforms
(Huang et al. 2009). Changes in farmers‟ livelihood
can be used to assess effect of the rural land tenure
reform, and, further to formulate reasonable policy
(Li et al. 2015). In this context, the impact of land
expropriation on the livelihood of farmers has
become one of the focuses of current academic
research. The studies on the livelihood of landexpropriated farmers mainly reflect the actual
livelihood of farmers after land expropriation by
making a detailed field survey and analysis on the
living conditions of farmers (Zhang 2011; Zhou and
Fu 2012). They found that there were short-term
and long-term livelihood changes after land
expropriation. Short-term livelihoods are mainly
affected by the level of compensation for land
expropriation. Long-term livelihoods are closely
linked with employment and social security (Hu
and Su 2007; Liu et al. 2007).
In the short term, the farmers‟ livelihoods are
greatly affected by unreasonable compensation
standard for land expropriation and the single
mode of resettlement (Ghatak 2014; Huang 2017).
Rural residents continue to be compensated not for
the market value land will have once it becomes
part of urban real estate, but instead for lost
putative agricultural output; while in urban areas,
compensation is for the market value of the
buildings but not the land taken from urban
residents. Regardless of the by itself important
debate as to whether such standards are adequate,
research concluded that on average, the value of
land taken was 40 times the amount of
2485
J. Mt. Sci. (2019) 16(11): 2484-2501
compensation actually paid, not taking into
account the 40 percent of cases in which no
compensation at all was paid. (Landesa 2011).
Individual cases studies support such findings in
anecdotal ways (Pils 2014). The compensation
standard for rural land expropriation in China was
calculated based on the original use of land, that is,
agricultural benefits on the theoretical level. On the
practical level, the principle on low not to high
leads to low compensation standards for land
expropriation and low land compensation fees
don‟t help maintain the livelihoods and improve
the living standards of landless farmers (Eddie
2012). In many countries land is private property,
and can be publicly traded in the market. Therefore,
the compensation for land expropriation is
determined by the land owner and the land
expropriator based on the current market price and
is equivalent to the market value (Zhou 2011). In
the choice of resettlement mode, the land
expropriation department mainly pays the land
compensation fee, resettlement subsidy and young
crop compensation fee to the farmers in monetary
terms in one lump sum, and the land-lost farmers
lack long-term livelihood guarantee.
In the long run, on the one hand, land
expropriation can to some extent help farmers
optimize the livelihood structure, promote the
transformation of their livelihood model to nonagricultural mode, diversify their incomegenerating channels and decentralize the sources of
income (Hai 2016). Li et al. (2014) found that the
overall livelihood capital of the relocated
households was better than that of the nonrelocated ones in Southern Shaanxi. The relocation
brought the loss of natural capital to farmers, but
to a certain extent, increased the family's physical
requisition and most of the households, except the
elderly, the infirm and the day laborers, were able
to restore their livelihoods and live in better
conditions. Ding et al. (2016) found that the farmer
household's natural capital significantly decreased
and the physical capital and financial capital
significantly increased after land expropriation.
There was no obvious impact of land expropriation
on farmers' social capital. On the other hand, due
to problems such as the lack of perfect system of
land expropriation, the livelihood resources of the
farmers are broken and the livelihood of the
farmers may be difficult to be maintained and
2486
continue to be deteriorated. Land expropriation
leads to problems of farmers' livelihoods mainly
due to farmers‟ lack of employment ability (Eddie
2012; Wu 2013), ineffective maintenance of rights
and benefits (Husen 2017), imperfect social
security (Liu et al. 2007; Zhang et al. 2009; Liu
2014). Due to land expropriation, many villages
have suffered to different degrees of hollowing (Liu
and Li 2006; Chen et al. 2014; Long 2014; Zhang et
al. 2014; Liu et al. 2016; Long et al. 2016; Xie and
Jiang 2016; Yan et al. 2016), and “hollow villages”
are springing up (Liu et al. 2010; Long et al. 2012;
Chen et al. 2014; Gao et al. 2017; Li et al. 2017).
Land expropriation did not make farmers rich, and
caused a large number of "three-no-farmer
households" (no farmland, no work and no
subsistence allowances). The field research on land
expropriation in China found that unemployment
and low income are common among land- lost
farmers (Gan and Sun 2015), mainly due to their
low educational level and their lack of experience in
non-agricultural work (Chen 2013). Moses et al.
(2011) found that land expropriation led to the loss
of subsistence, interruption of economic activities,
psychological distress and land conflicts among
farmers whose land were expropriated (Lyoba
2006; Vilumba 2007). Hui et al. (2013) suggested
that the standard of compensation was low and the
social security system was not perfect, which made
the livelihood of land-lost farmers decline. Farmers
were not adapted to urban life, related rules,
regulation and excluded from the society. Li (2017)
found that there were property rights conflicts,
resource conflicts and development conflicts in the
process of land expropriation, resulting in a
decrease in the income of land-lost farmers.
In
short,
Scholars
have
conducted
considerable research on the impacts of land
expropriation on farmers‟ livelihoods. It has mainly
focused on existing issues, the influential factors of
land-lost farmers‟ livelihoods, and the changes of
livelihood capacity before and after land
expropriation. Few studies are quantitative and
have used the characteristics of villages as control
variables to systematically consider the impact of
land expropriation on farmers‟ livelihood capital
and strategy. Furthermore, few studies have
investigated these issues in the mountainous and
hilly regions of China. The results of previous
studies are not conducive to an in-depth
J. Mt. Sci. (2019) 16(11): 2484-2501
understanding of the impact mechanism of land
expropriation on farmers‟ livelihoods. However,
research on the sustainable livelihoods of farmers
is of great significance to mitigating rural poverty
and reasonable land expropriation policy helps to
realize better livelihoods and higher standard of
living. Therefore, what are the characteristic of
land expropriation and farmers‟ livelihood in the
mountainous regions of China? What is the
influence mechanism of the land expropriation on
farmers‟ livelihoods? Does the land expropriation
promote the improvement of farmers‟ livelihood
diversity? Does land expropriation promote the
improvement of livelihood capital? How does the
village characteristic affect the land expropriation
and farmers‟ livelihoods? To answer these
questions, this study employs the partial least
squares-structural equation model (PLS- SEM) to
systematically explain the impact of land
expropriation on the farmers‟ livelihood capital and
livelihood strategy. This study enhances our
understanding of the characteristics of land
expropriation and rural households‟ livelihood as
well as the impact of land expropriation on rural
households‟ livelihood. These findings provide
reference for the formulation of proper policies
related to land expropriation and the improvement
of rural households‟ livelihoods in the mountainous
and hilly regions of China.
1
Study Area and Sample Selection
1.1 Study area
With the rapid economic development since
the reform and opening up, especially in the past
decade, there has been accelerated pace of
industrialization, urbanization and new rural
construction. In rural China the rate of land
expropriation has improved, and the size and scope
of land expropriation has expanded gradually.
During the past 30 years, the area of construction
land has expanded nearly 5-fold and the average
annual growth rate (5.5%) is far beyond the average
global level (1.2%) (Meyer and Turner 2011). Largescale land expropriation has made a positive
contribution to the national economic development,
but has simultaneously resulted in a series of social
problems related to the welfare security of land-
expropriated farmers (Li et al. 2015). Land
expropriation compensation standards involve
various items such as standard measurement
methods, compensation scope, implementation of
standard and resolution of standard dispute.
China's rural land expropriation compensation
standards have no unified legislation, but are
scattered in various levels of documents such as
laws, administrative regulations, and judicial
interpretations. There are different levels of
legislative
loopholes.
For
example,
the
implementation of compensation standards lacks
institutional guarantee, and the resolution
mechanism of compensation standard dispute is
not perfect. In order to achieve reasonable
compensation for rural land expropriation, the
Land Administration Law is being revised and the
Rural
Land
Expropriation
Compensation
Regulations are being formulated in China.
In the south-western hilly and mountainous
areas, the location advantages are not obvious, and
the terrain conditions are harsh. The level of
economic and social development is still relatively
low in the south-western hilly and mountainous
areas of China, and farmers‟ livelihood problems
are more obvious. In such a special geographical
environment and economic condition, land
expropriation also has a special characteristic. With
the acceleration of industrialization, urbanization
and new rural development, the land expropriation
of Sichuan Province is typical in the southwest
mountainous and hilly regions of China. In a case
in Zigong, Sichuan (expropriation decision
announced in 2002), the ratio of the value of land
taken to the amount of compensation actually paid
was ca. 70:1 (Pils 2006; Pils 2010). In the 33
experimental counties of land expropriation system
reform announced in 2017 in China, there are two
experimental counties in Sichuan Province.
Located in south-western China, the Sichuan
Province is upstream of the Yangtze River and
covers 486,052 km2 with the population of 81.07
million in 2014 (SBS 2014). Most of the land is
covered by hilly terrain (90%), and only 5.30% of
the land is flat plains. More than 60% of the region
has an elevation of more than 1000 m (Xie et al.
2015) (Figure 1). The annual net income of farmers
in the Sichuan Province was 7,895 Yuan (USD
1,305), which was 12.67% less than the average
income of China in 2013 (SBS 2014). The total
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J. Mt. Sci. (2019) 16(11): 2484-2501
(a)
(b)
(c)
Figure 1 The elevation, slope and landform of Sichuan Province (Elevation is divided into five levels. The areas with
an altitude of more than 1400 m are mainly concentrated in the Northwest Sichuan and the Panxi Economic Zone.
More than 60% of the region has an elevation of more than 1000 m; Slop is divided into five levels. The areas with a
slop of more than 25 degree are mainly concentrated in the Northwest Sichuan and the Panxi Economic Zone.
Landform includes plain, platform, hills and mountain. 90% of the land is covered by hilly terrain. Only 5.30% of the
land is flat plains.)
labors of Sichuan in 2013 were 64.39 million and
consisted of 33.68 million rural labors (SBS 2014).
Many rural labors are employed in nonagricultural industries (Xie et al. 2015).
1.2 Data source
The data used in this study were collected from
a survey of rural livelihoods and land expropriation
in Sichuan Province by the authors and their
collaborators in April 2014. The team conducted
the data collection work in three counties, 6
townships, 12 villages, and 240 rural households.
Per capita gross value of industrial output is a good
predictor for living standards and development
potential of a region, and is often more reliable
than net rural per capita income (Rozelle 1990,
1996). All counties (districts) in Sichuan Province
were divided into three groups in descending order
of per capita gross value of industrial output, and a
county was randomly selected from each group and
three counties, including Shehong County
(affiliated to Suining City), Yantan District
(affiliated to Zigong City), and Yuanba District
(affiliated to Guangyuan City) were chosen. Using
the method cited above, two townships in each
county were randomly selected as samples, and two
villages in each sample townships were randomly
selected by drawing lots. As one of the sample
townships, Shuping Town in Yantan District of
Zigong City was placed under the jurisdiction of
2488
Ziliujing District in 2005. Altogether, 12 villages in
Sichuan were surveyed. Divided by landform types,
the 12 sample villages included 8 hilly villages and
4 mountainous villages. Finally, 20 households
were randomly selected from a list of farmer heads
in each sample village. Thus, a total sample of 240
rural households was acquired. The spatial
locations of the samples are shown in Figure 2.
1.3 Sample description
In 2013, the 240 farmer households had 1945
land parcels, 27% of which was slope land; the total
area was 1137.28 mu with an average area per block
of 0.58 mu. In terms of the land expropriation
characteristics, there were 101 farmers whose land
was expropriated, and the number and area of the
total expropriated land were 235 and 98.83 mu,
respectively; the number and area of the
expropriated agricultural land were 224 and 95.77
mu; the number and area of the expropriated
homestead were 11 and 3.06 mu.
With respect to mountainous villages, 80
farmer households had 684 land parcels and the
total area of agricultural land was 531.76 mu, with
an average area per village and per block of 132.94
mu and 0.78 mu, respectively. In terms of the
expropriation characteristics, there were 31
farmers whose land was expropriated, and the
number and area of the expropriated land parcels
were 82 and 234.96 mu, respectively.
J. Mt. Sci. (2019) 16(11): 2484-2501
be used to evaluate
complex
causal
relationships
among
variables (Williams et al.
2009). In SEM, variables
that cannot be directly
observed are set as latent
variables (LVs), indirectly
measured by the observed
variables (also often called
manifest variables). SEM
construction includes the
inner model (or structural
model) and outer model
(or measurement model),
which together constitute
the path model. The latent
variables acting only as
independent variables are
called exogenous latent
variables and are called
endogenous
latent
variables when they serve
only
as
dependent
variables, or as both
independent
and
dependent variables.
Figure 2 Distribution of sample villages (Three counties (districts), including
SEM analysis include
Shehong County in Suining City, Yantan District in Zigong City, and Yuanba District
in Guangyuan City were chosen. 12 villages in Sichuan were surveyed. 20 households
covariancebased
were randomly selected from a list of farmer heads in each sample village.)
structure analysis (CBSEM) and componentWith respect to hilly villages, the 160 farmers
based analysis using partial least square estimation
consisted of 1261 land parcels, and the total
(PLS- SEM). They are used for different research
agricultural land area was 605.51 mu, with an
objectives and sample characteristics (Haenlein
average area per village and per block of 75.69 mu
and Kaplan 2004; Astrachan et al. 2014). Due to
and 0.48 mu, respectively. In terms of the
the primary purpose of exploring the relationships
expropriation characteristics, there were 70
and investigating their directions and strengths,
farmers whose land was expropriated, and the
PLS- SEM is considered more appropriate for the
number and area of the expropriation blocks was
use in the social sciences (Hair et al. 2013; Gorai et
153 and 283.24 mu, respectively.
al. 2015; Peng et al. 2017). In this study, PLS- SEM
is employed to test the data and model developed,
and uses the two types of outer model including
2 Methodology
reflective and formative model.
The relationship between reflective latent
2.1 PLS- SEM
variables and manifest variables is as follows,
This paper performs the research on the
impact of land expropriation on the rural
households‟ livelihood. As a multivariate analytical
approach, the structural equation model (SEM) can
(1)
The relationship between formative latent
variables and manifest variables is as follows,
(2)
2489
J. Mt. Sci. (2019) 16(11): 2484-2501
Where x=vector of exogenous indicators,
y=vector of endogenous indicators, = vector of
exogenous latent variables,
= vector of
endogenous latent variables,
= matrix of
exogenous indicators on exogenous latent
variables, = matrix of endogenous indicators on
endogenous latent variables, ε = error term of
exogenous indicators,
= error term of
endogenous indicators. When there are seven
manifest variables, the measurement equation is as
follows,
samples, and four items (A7, A8, A9 and A10) with
insufficient reliability were deleted. Cronbach‟s
alpha of the farmers‟ livelihood scale was within
the acceptable range with the value of 0.735 and
was applicable for subsequent analysis. Cronbach‟s
alpha of land expropriation characteristic was
0.861 after seven insignificant items (B3, B4, B7,
B8, B9, B10 and B11) were discarded. Cronbach‟s
alpha of village characteristic was within the
acceptable range with the value of 0.690. The list of
variables is shown in Table 2.
2.3 Modeling hypotheses
{
=
{
} [
}+
[
]
The linear relationship
variables can be given by,
∑
∑
(3)
]
between
latent
(4)
Where 𝜁𝑗 = endogenous residual variables,
𝛽𝑗𝑖 ( 𝛶𝑗𝑖 ) = the coefficient connecting the
endogenous and exogenous latent variables. When
the correlation coefficient is equal to zero, there is
no correlation between the two. When j=3, the
structural equation is as follows,
{
𝛶
+[𝛶
𝛶
}=[
𝛶
𝛶
𝛶
]{
𝛶
𝛶 ]{
𝛶
}
𝜁
}+[𝜁 ]
𝜁
(5)
2.2 Selection and definition of the model
variable
With reference to the index of farmers‟
livelihood and land expropriation used by previous
studies (Bebbington 1999; Bhandari 2013;
Chambers and Conway 1992; Eddie 2012; Guo et al.
2014; Hai 2016; Huang 2017; Hui and Bao 2013;
Husen 2017; Lai et al. 2014; Liu et al. 2013; Wu et
al. 2012; Xu et al. 2015; Yan et al. 2016; Zhou and
Fu 2012), and combining the actual conditions of
the study area, we constructed the variables
according to the 36 item descriptions (A1–A11, B1B16, C1-C9, given in Table 1).
After finishing the data collection, we made
reliability analysis to validate the consistency of the
2490
After reviewing the literature and combined
with the situation of study area, we devise the
conceptual model shown in Figure 3 and the
following hypotheses.
H1, Number and area of land expropriation
(LV1) should be directly affected by village
accessibility (LV10) (Hui and Bao 2013; Lai et al.
2014).
H2, Compensation for land expropriation
(LV2) should be directly affected by village
accessibility (LV10) (Hui and Bao 2013; Lai et al.
2014).
H3, Livelihood strategy (LV3) should be
directly affected by number and area of land
expropriation (LV1) (H3a), human capital (LV7)
(H3b) and terrain of the village (LV11) (H3c)
(Bhandari 2013; Liu et al. 2013; Guo et al. 2014).
H4, Natural capital (LV4) should be directly
affected by number and area of land expropriation
(LV1) (H4a), village income (LV9) (H4b), village
accessibility (LV10) (H4c) (Xu et al. 2015).
H5, Physical capital (LV5) should be directly
affected by compensation for land expropriation
(LV2) (H5a), village income (LV9) (H5b), village
accessibility (LV10) (H5c) and terrain condition of
the village (LV11) (H5d) (Hu and Huang 2011).
H6, Financial capital (LV6) should be directly
affected by compensation for land expropriation
(LV2) (H6a), livelihood strategy (LV3) (H6b),
human capital (LV7) (H6c), village accessibility
(LV10) (H6d) and terrain condition of the village
(LV11) (H6e) (Guo et al. 2019; Hai 2016).
H7, Human capital (LV7) should be directly
affected by compensation for land expropriation
(LV2) (H7a) and terrain condition of the village
(LV11) (H7b) (Eddie 2012; Wu 2013).
H8, Social capital (LV8) should be directly
J. Mt. Sci. (2019) 16(11): 2484-2501
Table 1 Definitions and values for the variables (A1–A4: number and area of land expropriation. A5–A11:
compensation for land expropriation number and area of land expropriation. B1-B2: natural capitals. B3-B4: physical
capitals. B1-B2: natural capitals. B5-B9: financial capitals. B10-B12: human capitals. B13-B14: social capitals. B15B16: livelihood strategy. C1-C2: village income. C3-C6: village accessibility. C7-C9: terrain conditions.)
Variable
Unit
Definition and assignment
Land expropriation characteristic
Number and area of land expropriation
NUMA
block
A1: Number of expropriated agricultural land parcels
AREA
mua
A2: Area of expropriated agricultural land
NUMH
block
A3: Number of expropriated homestead units
AREH
mua
A4: Area of expropriated homestead
Compensation for land expropriation
104
A5: Funds compensation for agricultural land
COMA
yuanb
expropriation
A6: Number of the person becoming non-agricultural
COMR
person
resident due to agricultural land expropriation
A7: Number of the person acquiring the new rural
COMM
person
cooperative medical insurance due to agricultural land
expropriation
A8: Number of the person acquiring the new rural
COMP
person
social pension insurance due to agricultural land
expropriation
A9: Number of the person acquiring work due to
COMW
person
agricultural land expropriation
104
A10: Funds compensation for homestead
COMH
yuanb
expropriation
A11: Acquired homestead area due to homestead
2
COHH
m
expropriation
Farmers‟ livelihood capitals
Natural capital
ARA
mua
B1: Per capita arable land area
IRR
%
B2: Effective irrigation degree
Physical capital
HOU
m2
B3: Per capita housing area
104
B4: Per capita value of family assets
VFA
yuanb
Financial capital
104yua
B5: per capita annual income
INC
nb
NINC
%
B6: Proportion of non - agricultural income
B7: Proportion of the person participating in the new
MED
%
rural cooperative medical to the family population
B8: Proportion of the person participating in the new
PEN
%
rural social pension insurance to the family population
B9: Proportion of the person participating in other
OPEN
%
pension insurance to the family population
Human capital
LAB
%
B10: Proportion of labor to the family population
EDUL
year
B11: Average education level of labor
TIMN
month
B12: Time of non-agricultural technical training
Social capital
LEA
1/0
B13: Village leader of the family, LEA=1 for yes, and
LEA=0 for no
B14: Duty of leader, 0 = no, 1 = cadre residing in the
village, 2 = villager representative, 3 = female director,
DUT
4 = group leader, 5 = accountant, 6= deputy village
head, 7 = village head, 8 = deputy branch secretary, 9=
branch secretary
Min
Max
Mean
SD
0.00
0.00
0.00
0.00
8.00
5.50
1.00
220
0.93
0.40
0.05
1.76
1.55
0.86
0.21
19.15
0.00
10.12
0.65
1.78
0.00
5.00
0.27
0.78
0.00
5.00
0.05
0.38
0.00
3.00
0.15
0.48
0.00
1.00
0.11
0.32
0.00
51.60
0.54
4.64
180.00
2.07
17.66
0.00
0.00
4.13
100.00
0.85
48.34
0.68
36.92
0.00
160
52.87
32.49
0.00
32.03
1.24
2.42
0.05
10.61
1.41
1.26
0.00
100.00
68.49
30.36
0.00
100.00
91.96
18.51
0.00
100.00
38.70
35.87
0.00
100.00
14.24
25.92
0.00
0.00
0.00
100.00
21.00
232.00
64.41
6.93
9.35
24.98
3.20
26.92
0.00
1.00
0.15
0.36
0.00
10.00
0.91
2.32
0.00
Notes: a 1mu≈667m2. b During the study period, 1US dollar was equal to 6.6 Chinese yuan. For the TYPE of livelihood
strategy, when the proportion of agricultural income to the total household income >50%, the TYPE was assigned a
value of 1; when the proportion of non-agricultural income to the total household income >50%, the TYPE was
assigned a value of 2.
(-To be continued-)
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(-Continued-) Table 1 Definitions and values for the variables (A1–A4: number and area of land expropriation. A5–
A11: compensation for land expropriation number and area of land expropriation. B1-B2: natural capitals. B3-B4:
physical capitals. B1-B2: natural capitals. B5-B9: financial capitals. B10-B12: human capitals. B13-B14: social capitals.
B15-B16: livelihood strategy. C1-C2: village income. C3-C6: village accessibility. C7-C9: terrain conditions.)
Variable
Unit
Livelihood strategy
DIV
TYPE
-
Village characteristic
Village income
DEP
yuan/person
PCNI
104 yuanb
Village accessibility
DVCG
DFGG
DVTG
THTG
km
km
km
hour
Terrain conditions
ALT
m
SLO
degree
TER
m
Definition and assignment
Min
Max
Mean
SD
B15: Livelihood diversity index
B16: Livelihood strategy type, TYPE=1 for
agriculture – dependent farmer, and TYPE=2
for non- agriculture – dependent farmer
0.00
3.00
0.97
0.43
1.00.
4.00
2.69
0.77
C1: Deposit
C2: Per capita net income
0.00
0.23
66.60
1.06
19.83
0.63
25.38
0.28
12.90
94.10
29.36
23.03
1.00
11.00
4.35
3.26
0.00
15.00
4.52
4.04
0.10
1.00
0.31
0.24
312.00
0.00
32.00
800.00
19.93
250.00
487.42
7.89
119.00
169.26
6.43
63.97
C3: The distance from the farmer's village to
the county government
C4: The distance between the farthest two
groups
C5: The distance from the village committee
to the township government
C6: The time from the farmers‟ house to the
township government by the most commonly
used means of transport
C7: Altitude
C8: Slop
C9: Relief amplitude
Notes: a 1mu≈667m2. b During the study period, 1US dollar was equal to 6.6 Chinese yuan. For the TYPE of livelihood
strategy, when the proportion of agricultural income to the total household income >50%, the TYPE was assigned a
value of 1; when the proportion of non-agricultural income to the total household income >50%, the TYPE was
assigned a value of 2.
affected by number and area of land expropriation
(LV1) (H8a), village accessibility (H8b) and terrain
condition of the village (LV11) (H8c) (Zhang et al.
2009; Liu 2014).
3 Results
3.1 Descriptive statistics
In the surveyed samples, the number of
farmers whose land was expropriated was less than
that of farmers whose land was not expropriated.
For the farms whose land was expropriated, the
number of farmers whose agricultural land was
expropriated was more than that of farmer whose
homestead was expropriated. The proportions of
farmers in mountainous villages were 33.33% and
66.67%, respectively (Table 3).
Physical capital and financial capital of
farmers whose land was expropriated were
respectively higher than those that land was not
expropriated, while farmers whose land was not
2492
expropriated had higher natural capital and social
capital than those whose land was expropriated.
Farmers whose land was expropriated had higher
average education level of labor and were more
inclined to engage in non-agricultural livelihoods
than those whose land was not expropriated, while
farmers whose land was not expropriated had more
time of non-agricultural technical training than
those whose land was expropriated.
With respect to the terrain conditions, farmers in
the mountainous villages had greater physical
capital and less financial capital, human capital
and social capital than those in the hilly villages.
3.2 Reliability and validity of outer model
In PLS- SEM model, composite reliability
(C.R.) is used for a reliable assessment and
should be greater than 0.6 (Hair et al. 2014). We
established eight latent variables and their
composite reliability values can be seen in Table
2. The composite reliability values of the latent
variables were all greater than 0.7, indicating the
J. Mt. Sci. (2019) 16(11): 2484-2501
Scale questions for land expropriation and farmers‟ livelihood (Cronbach‟s alpha of the farmers‟ livelihood
scale was within the acceptable range with the value of 0.735 and was applicable for subsequent analysis. Cronbach‟s
alpha of land expropriation characteristic was 0.861. Cronbach‟s alpha of village characteristic was within the
acceptable range with the value of 0.690.)
Latent variable
Manifest variable
Number and area of land expropriation
NUMA
AREA
NUMHN.A
AREHN.A
Factor loading
T value
C.R．
AVE
COMA
COMR
COMMN.A
COMPN.A
COMWN.A
COMHN.A
COHHN.A
DIV
TYPE
ARA
IRR
HOU
VFA
INC
NINC
MEDN.A
PENN.A
OPENN.A
LABN.A
EDUL
TIMN
LEA
DUT
Village income
DEP
PCNI
Village accessibility
DVCG
DFGG
DVTG
THTG
Terrain condition
ALT
SLO
TER
Notes: N.A denotes not significance.
reasonable setting of the outer model.
Multiple questions should be used to
construct the outer model in PLS- SEM, which
helps to ensure the effective validity of result
(Dia- mantopoulos et al. 2012). The validity tests
of the outer model included convergent and
discriminant validity tests. The former is tested
by average variance extracted (AVE). According
to Fornell and Larcker (1981), an AVE value of
0.50 and higher means the latent variables can
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J. Mt. Sci. (2019) 16(11): 2484-2501
Figure 3 The conceptual model and hypotheses (H1, H2: land expropriation should be directly affected by village
accessibility. H3: Livelihood strategy should be directly affected by land expropriation, human capital and terrain of
the village. H4, H5, H6, H7, H8: livelihood capitals should be directly affected by land expropriation, livelihood
strategy and characteristics of the village.)
explain more than half of its variables‟ variances
and reflects a sufficient convergent validity.
Table 2 showed that the composite reliability
value of the latent variables was all higher than
0.5, indicating good quality of the model. In the
path model, discriminant validity explains the
extent to which a latent variable is empirically
different from others and the Fornell–Larcker
criterion is recommended to assess the
discriminant validity (Fornell and Larcker 1981).
A given discriminant validity is accepted since a
diagonal value (in bold) is higher than the value
in its row and column (Table 4).
3.3 Results of inner model testing
The path coefficient was used to interpret the
effects decomposed into direct and indirect effects
between latent variables in PLS- SEM model,
which does not require a normal sample
distribution assumption and provides a bootstrap
method to replace it. The significant path
relationships can be seen in Table 5. Path
relationships of hypotheses H1, H2, H3 (b), H4 (a,
b, c), H5 (a, b, d), H6 (a, b, c), H7 (b), H8 (a, c) are
2494
statistically significant. This suggests that the
hypotheses H1, H2, H3 (b), H4 (a, b, c), H5 (a, b, d),
H6 (a, b, c), H7 (b), H8 (a, c) are valid. The
bootstrap tests of the path coefficients (p<0.05)
also support the above conclusions.
3.4 Specific results and mediating effects
The path coefficients in the model were
decomposed into direct, indirect and mediating
effects as represented by the arrows in Figure 4.
For the land expropriation, village accessibility
had negative effect on the number and area of land
expropriation and compensation for land
expropriation, with path coefficient of -0.274 and
-0.299, respectively.
For the livelihood strategy, human capital had
a positive effect on it directly, with a path
coefficient of 0.416. Terrain conditions of the
village had an indirect effect on it through human
capital, with a path coefficient of (-0.140) ×0.416.
Natural capital had the following mediating
effect, village accessibility not only had a direct
effect on it with a path coefficient of 0.555 but also
had an indirect effect on it through the number and
J. Mt. Sci. (2019) 16(11): 2484-2501
Table 3 Mean comparison of land expropriation and terrain (Number of farmers whose agricultural land is
expropriated is 90. Number of farmers whose Homestead is expropriated is 11. Number of farmers whose land was
not expropriated is 139. Number of farmers in Mountainous villages is 80. Number of farmers in Hilly villages is 160.)
Variables
Unit
Land expropriation
Farmers whose land was expropriated
Agricultural
Homestead
Total
land
expropriation
(N=101)
expropriation
(N=11)
(N=90)
Livelihood capital
Natural capital
ARA
mua
0.68
0.68
IRR
%
47.34
47.15
Physical capital
HOU
m2
56.54
55.92
104yua
VFA
nb
1.56
1.53
Financial capital
104
INC
1.46
1.47
yuanb
NINC
%
73.65
73.58
Human capital
EDUL
year
7.20
7.22
TIMN
month
9.02
8.96
Social capital
LEA
1/0
0.12
0.12
DUT
0.61
0.63
Livelihood strategy
DIV
0.97
0.97
TYPE
2.80
2.80
Land expropriation characteristic
NUMA
block
2.22
2.24
AREA
mua
0.95
0.96
NUMH
block
0.11
0.10
AREH
mua
0.03
0.03
Compensation for land expropriation
COMA
104 yuanb 1.56
1.57
COMR
person
0.64
0.65
Terrain conditions
Farmers
whose land
was not
expropriated
(N=139)
Farmers in
mountainou
s villages
(N=80)
Farmers in
hilly villages
(N=160)
0.34
40.73
0.97
49.07
1.16
39.65
0.70
52.69
51.39
50.21
44.88
2.46
1.01
68.86
1.51
1.40
1.37
1.67
1.27
86.59
64.75
63.73
70.87
8.24
4.64
6.73
9.58
6.91
6.70
6.93
10.67
0.18
1.18
0.18
1.12
0.10
0.71
0.18
1.01
0.95
3.27
0.97
2.60
1.00
2.59
0.96
2.74
3.73
1.81
1.00
0.28
0.00
0.00
0.00
0.00
1.01
0.44
0.01
0.00
0.89
0.38
0.06
0.02
2.30
1.27
0.00
0.00
1.25
0.38
0.36
0.22
1.10
Notes: Definitions of variables are in Table 1.a 1mu≈667m2. b During the study period, 1US dollar was equal to 6.6
Chinese Yuan.
area of land expropriation with a mediating effect
value of (-0.274) ×(-0.194).
For the physical capital, compensation for
land expropriation, village income and terrain
conditions of the village had significant effects on it
directly, with the path being 0.371, 0.138 and 0.159,
respectively. Village accessibility, acting indirectly
through compensation for land expropriation, had
significant effect on natural capital.
Financial capital is negatively affected by
village
accessibility
indirectly
through
compensation for land expropriation. Financial
capital had the following mediating effect: human
capital not only had a direct effect on it with a path
coefficient of 0.124 but also had an indirect effect
on it through the livelihood strategy with a
mediating effect value of 0.416×0.731.
With respect to the human capital, terrain
condition of village had significant effect on it
directly, with the path coefficient being -0.140.
With respect to the social capital, the number
and area of land expropriation and terrain
condition of village had significant effects on it
directly, with the path coefficient being -0.116 and 0.141. Village accessibility, acting indirectly
through the number and area of land expropriation,
had significant effect on social capital.
4
Discussion and Conclusions
4.1 Discussion
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M Fornell–Larcker test for discriminant validity (A given discriminant validity is accepted since a
diagonal value (in bold) is higher than the value in its row and column.)
Table 5 Path Coefficients (PE) for the inner model (Path relationships of hypotheses H1, H2, H3 (b), H4 (a, b, c),
H5 (a, b, d), H6 (a, b, c), H7 (b), H8 (a, c) are statistically significant and the hypotheses H1, H2, H3 (b), H4 (a, b,
c), H5 (a, b, d), H6 (a, b, c), H7 (b), H8 (a, c) are valid.)
Path relationship
H1
LV10LV1
H2
LV10LV2
H3(b)
LV7LV3
H4(a, b, c)
LV1LV4
LV9LV4
LV10LV4
H5(a, b, d)
LV2 LV5
LV9LV5
LV11LV5
H6(a, b, c)
LV2LV6
LV3LV6
LV7LV6
H7(b)
LV11LV7
H8(a, c)
LV1LV8
LV11LV8
PE
T statistics bootstrap
P values
-0.274
8.133
0.000***
-0.299
10.987
0.000***
0.416
6.167
0.000***
-0.194
0.126
0.555
3.71
1.918
6.76
0.000***
0.028**
0.000***
0.371
0.138
0.259
3.604
1.813
3.784
0.000***
0.035**
0.000***
0.102
0.731
0.124
2.678
21.601
3.238
0.004***
0.000***
0.001***
-0.140
2.149
0.016**
-0.116
-0.141
2.321
2.055
0.010**
0.020**
Villages with worse accessibility tend to have
fewer land parcels and less land area expropriated.
Additionally, land that is expropriated from
inaccessible villages tends to receive less
compensation. The space and the location of land
are of utmost importance to its usefulness. J.R.
Commons, the American economist, once
emphasized that “the land is valuable because it
provides the place and the location…and that is the
original and irreversible power of the land” (Erien
1940). This has provided a strong theoretical basis
for the choice of land use locations under the
conditions of market economy. Regions with better
accessibility attract greater competition for its use
2496
(Ong 2014). High-density land use will lead to high
agglomeration benefits and high rents, resulting in
geographical and economic benefits (Liu 2003).
Therefore, in the context of urbanization, the more
accessible rural areas are, the more land will be
expropriated and the more land compensation will
be paid.
Natural capital is negatively affected by
number and area of land expropriation. Natural
capital is not only directly affected by village
accessibility, but also indirectly affected by village
accessibility through the mediating effect of the
number and area of land parcels expropriated. The
worse the location of the village is, the greater is
J. Mt. Sci. (2019) 16(11): 2484-2501
Figure 4 Path model and PLS-SEM estimates (The path coefficients in the model were decomposed into direct,
indirect and mediating effects as represented by the arrows.)
the natural capital. Villages with poor accessibility
have lower numbers and lesser areas of land
parcels expropriated, and the greater is their
natural capital. Better accessible areas attain
greater economic benefits due to their locations.
Township enterprises and the diversified
management of rural areas have facilitated rapid
development, such that rural economies are no
longer solely dependent on agricultural production.
The level of non- agricultural industries increases
rapidly. As a result, many lands have been
expropriated and natural capital has decreased.
Social security provided by cultivated land not only
guarantees farmers a living, but also functions as a
form of unemployment insurance (Hodge 1984).
Medical care, education, and old-age security of
land- lost farmers after changing household
registration from a rural area to a city require
further improvement to compensate for the gaps
with those of urban residents.
Physical capital is positively affected by
compensation for land expropriation, village
income, and the terrain conditions of the village,
and negatively affected by village accessibility
through compensation for land expropriation. This
shows that compensation for land expropriation is
mainly used for the improvement of physical
capital. The higher the per capita net income /
deposit of the village is, the greater is its physical
capital. This may be due to the fact that rural areas
with better economic conditions have greater
ability to provide work. Workers then remain in
local employment and are more inclined to
upgrade the physical capital. The worse the terrain
conditions of a village are, the greater is its physical
capital. This may be due to the fact that it is more
difficult
to
achieve
non-agricultural
and
diversification of livelihood strategies in areas with
poor terrain conditions. With the development of
economy, people‟s living demands now exceed
satisfaction of basic needs, with more value placed
on esthetical enjoyment of a house and the comfort
it can provide (Taehoon et al. 2012; Li et al. 2015).
Peasant workers prefer to remain in local
agricultural production activities and are more
inclined to upgrade the physical capital.
Financial capital is negatively affected by
village
accessibility
indirectly
through
compensation for land expropriation, and by the
terrain conditions of the village indirectly through
human capital (and livelihood strategy). Further,
financial capital is not only affected by human
capital directly but also affected by human capital
indirectly through the mediating effect of
livelihood strategy. This shows that compensation
for land expropriation plays a significant role in
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J. Mt. Sci. (2019) 16(11): 2484-2501
improving the financial capital of farmers, which is
recognized as one of the most important factors
influencing welfare (Kawanaka 2014). The worse
the terrain conditions of the village are, the lower is
its human capital, and the lower is the nonagricultural degree/ the diversification of farmer's
livelihood strategy, leading to less financial capital.
This may be due to the fact that the worse the
topographic conditions are, the smaller is the
probability that nine-year compulsory education
and training in agricultural technology and related
skills are implemented. This results in the lower
human capital which does not meet the demand for
the development of non-agricultural industries.
Therefore, the choice of livelihood strategies is
more passive and it becomes difficult to diversify
livelihood strategies, resulting in less financial
capital. After land expropriation, the principle
reason for the financially improved livelihoods is
these farmers have been able to integrate into
urban markets and engage in a range of non-farm
sectors for livelihood restorations (Bebbington
1999; Gu & Ma 2013; Mcdowell & De haan 1997 ;
Wilmsen, Webber, & Yuefang 2011).
Social capital is directly and negatively
affected by the terrain conditions of the village and
the number and area of land parcels expropriated,
and is indirectly and positively affected by village
accessibility through the number and area of land
parcels expropriated. This may be due to the fact
that the family of the village cadres accompanying
the survey have been the samples of the study,
while the sample size of hilly villages is double of
that of the mountainous villages. The better the
village‟s accessibility is, the more is the number
and area of land expropriation, and the less is the
social capital. The more accessible the village is, the
more land is expropriated. The farmers become
more inclined to go out to participate in nonagricultural activity which reduces the possibility of
them becoming village cadres.
This study contributes to the literature by
improving our understanding of the impact of land
expropriation on farmers‟ livelihoods in rural
China. The PLS- SEM approach used here is more
suitable to the study, as it can consider and deal
with multiple dependent variables in a model, can
make analysis of the relationship between multicausal and latent variables, and can simulate the
internal logic of multiple factors. Moreover, the
2498
research combines the direct, indirect and
mediating effects to reveal the influencing
mechanism of multiple latent variables. All of these
complement the existing literature.
In addition, this study highlights several
avenues for further research. First, the paper is
relevant only to mountainous and hilly areas. Thus,
research should be undertaken to examine
differences in the impacts between mountainous,
hilly areas and plain regions. Second, to fully
capture the nuances of the impact of land
expropriation on farmers‟ livelihood, it is
imperative that the dynamics be studied over time.
Due to a lack of comparable time-series data, this is
not possible in the present study. Only by
addressing these research shortcomings can we
obtain in-depth knowledge and understanding of
the impact of land expropriation on farmers‟
livelihoods.
4.2 Conclusions
This paper has investigated the influential
factors of farmers‟ livelihood based on land
expropriation. By use of 240 households data from
a representative survey of rural Sichuan in China
and spatial data calculated using a 30 m DEM by
GIS, this analysis has explored the characteristic of
farmers‟ livelihoods under different situation of
land expropriation, analyzed the characteristic of
farmers‟ livelihood and land expropriation under
different types of terrain, assessed the impact of
land expropriation and village characteristic on
farmers‟ livelihoods, the effects between livelihood
capital and livelihood strategy, and the mutual
influences of farmers‟ livelihood capitals.
The analysis of land expropriation and terrain
classification reveals that effects differ strongly
among land expropriation, livelihood strategies,
and livelihood capital in the study area. (1) The
land expropriation characteristics are negatively
affected by village accessibility. Villages with worse
accessibility tend to have fewer land parcels and
less land area expropriated. Additionally, land that
is expropriated from inaccessible villages tends to
receive less compensation. (2) Natural capital is
negatively affected by number and area of land
expropriation. Natural capital is not only directly
affected by village accessibility, but also indirectly
affected by village accessibility through the
J. Mt. Sci. (2019) 16(11): 2484-2501
mediating effect of the number and area of land
parcels expropriated. (3) Physical capital is
positively affected by compensation for land
expropriation, and negatively affected by village
accessibility through compensation for land
expropriation.
The
worse
a
village‟s
accessibility/location is, the less compensation it
will receive for land expropriation, resulting in
lower physical capital. (4) Financial capital is
negatively affected by village accessibility indirectly
through compensation for land expropriation. The
better the village‟s accessibility is, the greater is its
compensation for land expropriation and, hence,
the greater is its financial capital. (5) Social capital
is directly and negatively affected by the number
and area of land parcels expropriated, and is
indirectly and positively affected by village
accessibility through the number and area of land
parcels expropriated.
The conclusions of this study have important
policy implications. (1) To improve the
compensation mechanism for land expropriation.
Compensation criterion should be made according
to the purpose of land expropriation. Monetary and
non-monetary compensation should be organically
combined, and non-monetary compensation for
land expropriation should include social security,
such as pension insurance, medical insurance and
unemployment insurance, and vocational skill
training (especially that in mountainous area with
poor terrain conditions), to improve the living
security, enhance the employment competitiveness,
improve the level of physical capital, human capital,
financial capital and non-agricultural level of
livelihood strategy/ livelihood diversity level. (2)
To change the form of rural land organization. In
the process of land expropriation, by obtaining the
relevant policies farmers whose land has been
expropriated can share the benefits of enterprises
those have expropriated the land in the form of
putting in land-expropriation compensation and
land-use rights. At the same time, farmers can get
wage for their work in the enterprises, which will
greatly raise the level of agricultural production,
achieve the sustainable development of the
collective economy, increase the financial and
physical capital of farmers, and achieve the
diversity of livelihood strategies by giving full play
to the advantages of land, capital and labour. (3) To
vigorously promoted the land transfer. After land
expropriation, farmers' natural capital is reduced
and the land is fragmented. Farmers who do not
want to grow their land are reluctant to transfer
their land because they want the land to be
expropriated, resulting to the inefficient land use or
abandoned land. Farmers who want to grow their
land face the inability to use modern production
modes because of the small size of land, resulting
to the inefficient land use. Therefore, land
expropriation can be taken as an opportunity to
vigorously promote land transfer, and contiguous
production and scale management should be
conducted to break through the traditional mode of
agricultural production and management, and to
improve the quality and efficiency of natural capital.
Acknowledgement
This study was funded by the National Natural
Science Foundation of China (grant number 41601
614, 41571527, 41771194) and supported by the Fun
damental Research Funds for the Central Universit
ies (grant number JBK1902059).
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