impact of farmer households’ livelihood assets on their options of economic compensation patterns...

J. Geogr. Sci. 2014, 24(2): 331-348

DOI: 10.1007/s11442-014-1091-5

© 2014 Science Press Springer-Verlag

Received: 2013-04-23 Accepted: 2013-08-20 Foundation: National Natural Science Foundation of China, No.41371177 Author: Li Guangdong, PhD Candidate, specialized land use/cover change, urban and regional development, and urban

geography. E-mail: [email protected] *Corresponding author: Fang Chuanglin (1966–), PhD and Professor, E-mail: [email protected]

www.geogsci.com www.springerlink.com/content/1009-637x

Impact of farmer households’ livelihood assets on their options of economic compensation patterns for cultivated land protection

LI Guangdong1,2, *FANG Chuanglin1, QIU Daochi3, WANG Liping4

1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China; 2. University of Chinese Academy of Sciences, Beijing 100049, China; 3. School of Geographical Sciences, Southwest University, Chongqing 400715, China; 4. Collage of Public Administration, Zhejiang University, Hangzhou 310029, China

Abstract: With rapid urbanization and the socio-economic transformation, cultivated land protection has gradually become a major concern in China. The economic compensation plays a crucial role in promoting cultivated land protection and improving the utilization ratio of cultivated land. Farmer household’s satisfaction has a great influence on the effectiveness of compensation. Therefore, households’ willingness to select the economic compensation pattern for cultivated land protection has been considered and re-examined. By employing Participatory Rural Appraisal method (PRA), 3 villages and 392 households were investigated and sampled in mesa and hilly areas of Chongqing. Then a quantitative analysis framework of household livelihood hexagon has been developed to quantify the livelihood assets of differ-ent farmer households. Finally, the Gray Relation Model and Probit Regression Model have been employed to explore the coupling relationship between the household livelihood assets and their compensation pattern options. The results show that there are both qualitative and spatial heterogeneity in household livelihood assets. We found that the inequality of livelihood assets is evident for five household types. There is a spatial trend that the higher the eleva-tion, the less livelihood assets are. In addition, their options of economic compensation pat-tern vary from Chengdu Pattern to Foshan Pattern due to their difference in livelihood assets and difference in location. In detail, there is a coupling relationship between household live-lihood assets and their compensation pattern; negative correlation is observed between natural assets value and household pattern options, while the other livelihood assets have positive impacts on compensation pattern in varying degrees, which from the top are psy-chological assets, human assets, physical assets, financial assets, and social assets respec-tively. A conceptual compensation pattern system has been designed to meet the demands for farmer households mainly according to their shortage in livelihood assets. In addition, compensation method, compensation standard, the basis of compensation and the source of compensation funds have been proposed accordingly.

332 Journal of Geographical Sciences

Keywords: livelihood assets; cultivated land protection; economic compensation patterns; farmer household; Chongqing

1 Introduction

Livelihood portfolio of household, in developing countries, is widely recognized as a core factor that influences the famer household economy (Block and Webb, 2001; Cinner and Bodin, 2010). The comprehensive quantitative analysis of household livelihood diversifica-tion, especially their livelihood assets (Chen et al., 2013; Li et al., 2007; Sharp, 2003), the description of livelihood assets dynamic change (Ulrich et al., 2012), particularly the avail-ability of natural asset (Nawrotzki et al., 2012) and the quantitative expression of social as-set (Islam et al., 2011), and the inner differentiation at the micro-level of village and com-munity have become the major concerns of household research. Furthermore, the dynamic relationships between household livelihoods and their frangibility and poverty, especially the constraints from livelihood assets shortage to village poverty, have caught more and more attention in recent studies (Foster et al., 2011; Kristjanson et al., 2005; Mwakubo and Obare, 2009).

In terms of methodology, the Sustainable Livelihoods Framework provides feasible way for the above analysis and anti-poverty (DFID, 2000; Frankenberger et al., 2000; Krantz, 2001). This framework, developed by the UK Department for International Development (DFID), is the most widely used sustainable livelihood framework, and has become the clas-sical paradigm in household livelihood research (Carney, 1998; DFID, 2000; Ellis, 2000). The conditions of households’ livelihood assets, the core of DFID framework, are not only the basis of household livelihood strategy, but also the safeguard mechanism of their ability to respond to risk and frangibility (Bebbington, 1999; Giddens, 1979; Habermas, 1971). The differentiation of household livelihood assets influences both their decision-making behavior for production and their cultivated land utilization decision (Soini, 2005; Vista et al., 2012; Zhang et al., 2009). Previous studies have focused less attention on the interactions between household livelihood and land use (McCusker and Carr, 2006; McLennan and Garvin, 2012). And only 5% of total 216 papers on household livelihood research mentioned the back-ground of land use. There have been relatively few studies on the relationship between household livelihood and land use policy.

Cultivated land, a natural asset type, is the core of household livelihood. Cultivated land protection is not only the final safeguard to sustainable household livelihood, but also the objective requirement of food security in China. Given the double failure of administrative mechanism and market mechanism, the economic compensation mechanism of cultivated land protection has become an efficient solution to control cultivated land decrease in terms of household rights and interests (Chen et al., 2010; Qu and Zhu, 2008; Sun and Zhang, 2006; Wang, 2007; Wang et al., 2009; Zhang et al., 2007; Zhu and Qu, 2008). The basic economic compensation framework has been investigated (Wang, 2007; Wang et al., 2009; Zhang et al., 2007; Zhu and Qu, 2008), including the compensation standard (Niu and Zhang, 2009; Wu and Liu, 2009; Zhang et al., 2008), the management and operation of compensa-tion funds, etc. (Wang et al., 2009; Wu and Liu, 2009; Zhang et al., 2007; Zhu and Qu, 2008). However, relatively few studies have been made on the specific compensation pattern especially the practical pattern in relevant regions. Household is the direct and main partici-

LI Guangdong et al.: Impact of farmer households’ livelihood assets on their options of economic compensation 333

pants of cultivated land protection. So the study at household level should be the core of economic compensation pattern designing for cultivated land protection. Although re-searches at household level have gain more and more attention, the application of household theory and practical research still need to be strengthened.

By sampling three villages in different regions of Chongqing, the household livelihood diversification, especially the livelihood assets differentiation, has been quantitatively ana-lyzed at first. And then the cultivated land protection willingness and economic compensa-tion pattern choice of household with different livelihood assets have been investigated, by taking the influence of household livelihood on economic compensation pattern selection for cultivated land protection (hereinafter referred to as “pattern selection”) into consideration. Finally, differing economic compensation patterns of cultivated land protection (hereinafter referred to as “compensation pattern”) have been designed, based on the previous analysis of households’ willingness and selection. This paper tries to improve the household sustainable livelihood system, especially the household livelihood conditions, and investigates empiri-cally the co-production relationship between household livelihood and land-use policy.

2 Data and methods

2.1 Study area

Tongnan County, the study area, is located in the center of mesa and hilly areas, Chongqing of western China (105º31´–106º01´E and 29º47´–30º26´N) (Figure 1). It has an area of 1582.89 km2. The northeast and southwest parts of this county mainly consist of mid-dle-high mesa, while middle part is valley and low hills. With total annual precipitation of 970–1100 mm, Tongnan is good for agricultural production because of its richness in heat and fertile in soil, combining with abundant water condition. At the end of 2010, the total population was 935,200, of whom 791,900 were farm people and 143,300 off-farm residents. The population density was 572 persons per square kilometer. In 2010, per capita net income of rural resident was 5889 yuan RMB, lower than the national average level. The total culti-vated land area was 75,313.30 hm2, of them 65,266.70 hm2 were prime farmland protection areas. The cultivation rate reaches to 46.86% and per unit area yield of cultivated land was 5609.77 kg/hm2. For its high cultivated land productivity, Tongnan has become an important grain and vegetable producing center in Chongqing and even in western China.

2.2 Household type classification

Many studies have analyzed the classification of household types (Chen and Fang, 1999; Chen and Wang, 2007; Ouyang et al., 2004; Zhang et al., 2009). In general, three household types are identified, including pure-agricultural households, part-time agricultural house-holds and non-agricultural households, according to the source of their income. Actually, the marketization of agricultural products is an important indicator to distinguish market econ-omy from completely small peasant economy. Therefore, this paper subdivides pure household based on household market behavior. Given the income sources of household, labor investment direction, livelihood strategy, and allocation pattern of product and labor, five household types are identified, Self-supporting pure agricultural households (A), mar-ket-oriented agricultural households (B), part-time agricultural households I (C), part-time


agricultural households II (D), and non-agricultural households (E) (Table 1).

2.3 Economic compensation patterns for cultivated land protection

Many regions have attempted to develop an innovative compensation pattern. Based on

Figure 1 Sketch map of the study area

Table 1 Household type and classification standard

Type Household income

source (%) Main direction of labor

input Livelihood strat-

egy Allocation pattern of product or labor

Samples

A. Self-supporting pure agricultural households

Agricultural income >95

Crop farming Planting Self-production

and sales 36

B. Market-oriented pure agricultural households

Agricultural income >90

Crop farming, livestock raising

Planting, culti-vating

Marketing 70

C. Part-time agricultural households I

Agricultural >50Crop farming, livestock

raising, non- agricultural activities

Planting, seasonal working outside

Marketing, self- production and

sales 114

D. Part-time agricultural households II

Non-agricultural income >50

Livestock raising, crop farming, non-

agricultural activities

Seasonal working outside, planting

Marketing, self- production and

sales 90

E. Non-agricultural households

Non-agricultural income >90

Non-agricultural activities

Work outside (the whole year)

Marketing 82


comparison analysis, the major patterns at present include “Chengdu Pattern” and “Foshan Pattern”. These two patterns have similarities in terms of compensation purposes, compen-sation subject, compensation object, operational effects, and so on (Table 2). However, there are still differences between them and these differences not only affect the household choices about whether protecting cultivated land, but also greatly influence the livelihood of household.

Table 2 Comparison of economic compensation patterns of cultivated land protection

Pattern Subjects Targets Objects Basis Standard Methods Funds re-sources

“Chengdu Pattern”

Municipal government

Rural collec-tive economic organization and farmers

Cultivated land

Quality

Prime farmland: 6000 yuan/hm2;General culti-

vated land: 4500 yuan/hm2

10% of the fund is used as agricultural insurance allowance; 90% of the fund is used as endow-

ment insurance allowance;

Land for new construction land use fees

and land-trans-ferring fees

“Foshan Pattern”

Municipal government

Rural collec-tive economic organization and farmers

Prime farmland

Location

Economic de-veloped areas:

7500 yuan/hm2; Less developed

areas: 3000 yuan/hm2

20% of the fund is used as special funds of agricul-tural infrastructure con-

struction; 80% of the fund is used by farmers them-

selves

District and county level

revenue

2.4 Data collection

Employing PRA (Cramb et al., 2004; Kangalawe and Liwenga, 2005), we have surveyed typical villages of mesa and hilly areas in Chongqing in September 2010. The interviewees are chosen through following steps: firstly, three typical towns are selected, based on the quantity and quality of cultivated land of each town and their socio-economic development, including Guilin sub-district offices in valley area, Bozi town in low mountains and hilly area and Wugui town in middle-high mesa area; secondly, three specific sample villages of the selected towns are chosen according to their economic development, including the rela-tively developed Shuangba village, the moderately developed Donglin village and the less developed Fangpo; thirdly, 126, 142 and 124 households respectively from the three sam-pled villages are investigated according to stratified random sampling method. Survey methods include questionnaire investigation, semi-structure interview, mini-type small meetings. The collected information includes the type, labor allocation, education level, age, family generation, livelihood strategy, the recognition of cultivated land protection and eco-nomic compensation, option willingness of compensation pattern, livelihood assets of each farm household. Each household investigation interview lasts about 1 to 1.5 hours.

2.5 Analytical method

Traditional quantification of livelihood assets, based on DFID sustainable livelihood framework, focused on five types of assets and neglected psychological and mental states of household. Therefore, this work tends to improve the quantitative analytical method, and to integrate psychological assets into analysis. Our livelihood asset framework extends the pentagon of DFID to hexagon (DFID, 2000). In livelihood assets quantification, the weight of measuring indicator is an important factor affecting the quantification results. However, previous studies have focused less attention on this issue (Li et al., 2007; Sharp, 2003; Yan


et al., 2010). In order to enhance the reliability of the quantification results, this paper tries to determine the weight from both subjective and objective point of view. In detail, this pa-per adjusts the indicator weights proposed by previous studies through interviewing house-hold about the importance of specific indicator firstly, and then captures the weight value of investigation data through the Principal Component Analysis (PCA). Finally, the weight is determined as the weighted mean of the two values (Table 3). Table 3 Indicators and the calculation of livelihood assets

Types of assets

Indexes SymbolsTraditional index for-

mula

Adjusted weight with investigation data (WSUR)

Adjusted weight with principal com-

ponent analysis (W*

PCA)

Final weight (WULT)

Total family labor capacity HU1 0.500 0.320 0.410

Adult male labor HU2 0.250 0.510 0.380 Human assets (HU)

Education level of adult labor HU3

HU1/2+ HU2/4+ HU3/4

0.250 0.170 0.210

Cultivated land area per capita NA1 0.300 0.270 0.285

Woodland area per capita NA2 0.100 0.070 0.085

Garden land area per capita NA3 0.100 0.150 0.125

Average land quality grade NA4 0.300 0.350 0.325

Natural assets (NA)

Land use structure ratio NA5

(NA1+ NA2

+NA3)/3

0.200 0.160 0.180

Housing condition PH1 0.500 0.460 0.480

Family Assets PH2 0.300 0.390 0.345 Physical

assets (PH)

Perfect degree of rural infrastructure PH3

PH1×0.6+ PH2×0.4

0.200 0.150 0.175

Opportunity of getting cash credit FI1 0.250 0.310 0.280

Opportunity of getting cash support FI2 0.250 0.310 0.280 Financial

assets (FI)

Family cash debit FI3

FI1/4+ FI2/4+ FI3/2

0.500 0.380 0.440

Relative and friend relation net SO1 0.300 0.410 0.355

Funds assistance SO2 0.300 0.190 0.245

Labor assistance SO3 0.300 0.290 0.295

Social assets (SO)

Perfect degree of rural public services SO4

(SO1+ SO2

+ SO3)/3

0.100 0.110 0.105

Confidence index PS1 0.230 0.100 0.170

Life improving expectation index PS2 0.230 0.250 0.240

Happiness index PS3 0.230 0.250 0.240

Psycho-logical assets (PS)

Toughness index PS4

—

0.300 0.400 0.350

Note: The calculation and selection of the first five assets indexes in traditional index formula refers to the result of Sharp (2003), Li et al. (2007) and Yan et al. (2010). NA4, NA5, PH3 and SO4 are improved indexes and are not included in the assets indicators.

(1) Standardization Method of Standard Deviation equation:

-

( 1,2,..., ; 1,2,..., )ij jij

j

x xZ i m j n

s (1)

where 1

1 m

j iji

x xm

,1

1( )

m

j ij ji

s x xm

;

(2) PCA as a calculation method of the objective weight, the calculation equation is:


PCA

MW

N (2)

where M is component score coefficient, and N is characteristic roots. Given that the weight is determined independently on each type of assets, W*

PCA is normalized value calculated by WPCA.

This paper uses the Grey Correlation Analysis to test the existence of coupling relation-ship between household livelihood asset and compensation pattern selection, and further analyzes the influence of different combination of livelihood assets on the options of household compensation pattern.

(3) Grey Correlation Analysis equation:

min min ( ) ( ) max max ( ) ( )

( )( ) ( ) max max ( ) ( )

i j i ji j i j

ij

i j i ji j

x t x t x t x tt

x t x t x t x t

(3)

where ξij (t) is the correlation coefficient of factor xj on xi at time t (the correlation coefficient of different household assets on the option of compensation pattern); is resolution coeffi-

cient and generally equals to 0.5. The influence-response degree is calculated by the Probit Regression Model. Firstly, this

paper considers the household compensation option willingness as dependent variable (“Chengdu Pattern” equals to 0, and “Foshan Pattern” equals to 1). Secondly, based on im-proved livelihood asset framework, the first level independent variable can be divided into six categories and specific measuring indicators in each category are considered as second level independent variable, which are 22 in total, and then all the data in different levels are standardized. Thirdly, both the first and second level dependent variables are calculated. Finally, different compensation patterns are designed based on the above results.

(4) Probit Regression Model is as follows:

0 1 2 3 4 5 6( )TP HU NA PH FI SO PS (4)

where PT is the probability of overall pattern option of households. Ф is cumulative standard normal distribution function, HU, NA, PH, FI, SO and PS are the values of household live-lihood assets respectively.

0 1 1 2 2 22 4( ... )DP HU HU PS

(5)

where PD refers to specific pattern selection, HU1, HU2, …, PS4 are the livelihood asset in-dex value respectively.

3 Results

3.1 Household livelihood assets

The investigation results show that household livelihood assets have both qualitative (Figure 2) and spatial differences (Figure 3). It is obvious that different types of household differ greatly in their livelihood assets. There is linear correlation between the types of household and their real value of six livelihood assets.

(1) Total livelihood assets value shows an increasing trend from type A to E. The average livelihood assets value of type E reaches to 3.362 which is 1.57 times of type A’s (Table 4).


Spatially, the livelihood assets become lower and lower with the increase of the elevation, which is in consistence with previous study (Yan et al., 2010). All five types of assets except social asset show this trend (Table 5).

Figure 2 The hexagon of farmer households’ livelihood assets of various types

Figure 3 The hexagon of household livelihood assets in various areas

Table 4 Indicators values of various types’ household livelihood assets in sample villages

Types of household Human assets

Natural assets

Physical assets

Financial assets

Social assets

Psychological assets

Total assets

A. Self-supporting pure agricultural households 0.432 0.279 0.586 0.148 0.233 0.468 2.147

B. Market-oriented pure agricultural household 0.463 0.283 0.642 0.185 0.312 0.543 2.366

C. Part-time agricultural households I 0.459 0.249 0.660 0.248 0.346 0.654 2.616

D. Part-time agricultural households II 0.519 0.237 0.718 0.350 0.378 0.707 2.911

E. Non-agricultural households 0.631 0.203 0.805 0.526 0.446 0.751 3.362


Table 5 Index value of households’ livelihood assets in different sampling villages

Sample villages Human assets

Natural assets

Physical assets

Financial assets

Social assets

Psychological assets

Total assets

Valley and plain dam area (Shuangba) 0.581 0.348 0.774 0.468 0.369 0.682 3.222

Low mountains and hilly area (Donglin) 0.489 0.311 0.683 0.391 0.317 0.613 2.804

Middle-high mesa area (Fangpo) 0.398 0.213 0.620 0.282 0.426 0.599 2.538

(2) Human assets. The human assets differ greatly in different types. Type A has the high-est human assets while type E has the lowest value. The human assets of pure household are beyond the expectation and market-oriented household has relatively more human assets than self-supporting household. Besides rice planting, the cultivation of vegetables is main livelihood strategy with most of the pure households in sampling area. The reasons may lie in that vegetable planting, a labor-intensive producing activity, requires much more labor and working hours than grain-growing activity. Moreover, vegetable planting needs con-tinuous work and then constraints labor into agricultural activities. Most of the households involving in part-time agricultural (type C) work outside seasonally. However, because of the shortage of agricultural labors, part-time agricultural households cannot meet their live-lihood by migrant working outside. Hence, migrant livelihood strategy becomes the best choice for this type of household. Large number of high quality surplus labors not only pro-vide precondition for non-agricultural activities of both part-time agricultural households II (type D) and non-agricultural households (type E) but also lay a good foundation for work-ing outside.

In fact, the higher the elevation, the less human assets are. It may be caused by many reasons like the spatial differences in population density and population quality. In valley and plain dam area both the cultivated land quality and non-agricultural employment oppor-tunities are higher than those of other areas. Therefore, the endogenous labor demand in this area is high. In low mountains and hilly area, most of the households are engaged in grain growing activities and so they have plenty of time working outside. Actually, the chronic poverty, caused by their lower education and lower agricultural earning, has led to the de-creasing of the households scale in this area. Considering the topographic constraints, the areas of cultivated land in middle-high mesa regions are limited and then result in most of the households choose to work outside (about 73.25%). However, the overall human assets are lower than other areas because of the high illiteracy rate of the households.

(3) Natural assets. In contrast to human assets, the overall distribution of natural assets shows a gradually increase trend from type A to E. Specifically, market-oriented pure agri-cultural households (type B) have the maximum value of natural assets (0.283) while non-agricultural households (type E) have the lowest assets (with the average value of 0.203). This indicates that natural asset is the precondition of household livelihood diversity. Natural assets have crucial effect on the early differentiation of household livelihood for the lack of natural assets accelerates the non-agricultural-transformation of household at first. However, with the increase of non-agricultural rate, the natural assets effect will gradually decrease. Due to the natural conditions, natural assets value in plain area is always higher than the value of the mesa and hilly area.

(4) Physical assets. Physical assets value of type A is averagely 0.586 and that of type E


is 0.805. There is small difference in housing conditions of household in sampling villages. Most of the houses are brick structured and have many rooms. The agricultural mechaniza-tion rate is low because of topographic constraints. Hence, only about 2.35% of the whole households use tractors in production. Because the village is far from town, the main trans-portation tool is motorcycle. The cultivating radius is short in this area, so cultivating activ-ity mainly relies on labor not livestock. The infrastructure construction is poor; some vil-lages have no water diversion system; some of high hill areas even have no electricity. Therefore, the physical assets decrease with the increase of elevation in general.

(5) Financial assets. Households generally have little financial assets. There are huge dif-ferences among different types of household, for instance, the value of non-agricultural households is 3.55 times higher than of self-supporting pure households. Most of the pure agricultural households believe it is difficult to borrow low-interest or interest-free loan through financial institutions, which may be the major constraint of household livelihood development. In addition, the lack of rural financial products and the low entrance to social financial credit (usury) make that social financial credit rate is much higher than financial institution. Hence, the financial risks are high for borrower especially for poor households.

(6) Social Assets. Although household social assets have little differences, it shows an obvious spatial differentiation. Middle-high mesa areas have relative high social assets be-cause of familial factor. In general, large family and community can widen and intensify the relations between relatives and friends, and then increase their opportunity of getting funds and labor assistance. The overall level of rural public services, including medical care con-dition and education, is generally low and varies in different rural areas due to population and economic development level.

(7) Psychological assets. The psychological assets of household are higher than expecta-tion. Nevertheless, the psychological endurance of household is poor when facing great changes. The capacity to resist risk is weak because of the shortage in other livelihood assets or the improper assets combination. Few households feel lack of confidence on life. They have poor psychological flexibility and have extremely low expectation on improving future living condition. As a result, their psychological assets have turned into “psychological li-abilities” for this type household. In total, there is little difference of household psychologi-cal assets in different sample villages. Topographic factor has no significant influence on this kind of assets.

3.2 Willingness of economic compensation pattern selection for cultivated land pro-tection

Investigation results show that there is a significant differentiation in option willingness of compensation pattern. With the increase of livelihood assets, households’ choice of com-pensation pattern changes from “Chengdu Pattern” to “Foshan Pattern”. Generally, 143 households, accounting for 36.57%, favor the “Chengdu Pattern” while 249 households, ac-counting for 63.43%, choose the “Foshan Pattern” (Table 6). The results also indicate that the pure agricultural households pay more attention to the social protection function of cul-tivated land than other part-time agricultural households and non-agricultural households. There is also spatial differentiation of pattern selection of household in different areas (Table 7).


Households in developed area tend to choose “Foshan Pattern” while those of the less de-veloped area are likely to choose the “Chengdu Pattern”.

Table 6 The option matrix of different type households’ economic compensation pattern for cultivated land protection

Compensation pattern selection probability (%) Types of household

“Chengdu Pattern” (Household) “Foshan Pattern” (Household)

A. Self-supporting pure agricultural households 83.26 (30) 16.74 (6)

B. Market-oriented pure agricultural household 78.01 (55) 21.99 (15)

C. Part-time agricultural households I 34.63 (39) 65.37 (75)

D. Part-time agricultural households II 15.29 (14) 84.71 (76)

E. Non-agricultural households 6.76 (6) 93.24 (76)

Table 7 The option matrix of different sample village of households’ economic compensation pattern for culti-vated land protection

Compensation pattern option probability (%) Sample villages

“Chengdu Pattern” (Household) “Foshan Pattern” ( Household)

Valley and plain dam area (Shuangba) 28.03(35) 71.97(91)

Low mountains and hilly area (Donglin) 36.47(52) 63.53(90)

Middle-high mesa area (Fangpo) 45.22(56) 54.78(68)

3.3 The impact of livelihood assets on cultivated land economic compensation pattern selection

This paper employs the Grey Correlation Analysis Model in DPS software to explore the coupling relationship between livelihood assets and pattern selection by calculating the value of economic compensation pattern selection (by setting “Chengdu Pattern” as zero and “Foshan Pattern” as one), household livelihood assets value, and each specific measuring indicator value. Results show that there is a strong-coupled relationship between household livelihood assets and compensation pattern selection (Table 8). The correlation coefficient between total household livelihood assets and pattern selection willingness is 0.833. Only natural assets have no significant correlation with pattern selection willingness, while the other five types of assets have strong correlation with pattern selection willingness (R>75%). Seventeen out of 22 specific indicators are strongly related to selection willingness.

The Grey Correlation Analysis Model has explained the coupling relationship between household livelihood assets and selection willingness of compensation pattern properly. Then we use the Probit Regression Model to measure the degree of impact-response between household livelihood assets and compensation pattern selection. We implement the Probit Regression Model using Eviews 6. Table 9 shows the regression result. The likelihood ratio test statistics—χ 2 is 93.45 and pseudo R2 is 0.194, which indicate the model has high good-ness of fit. The significance probabilities of Z-test values of explanatory variable and con-stant term are less than 0.05 except social assets. Among all the six assets, only natural as-sets are significantly negative correlated to household selection willingness for compensa-tion patterns. This also demonstrates the negative relation between natural assets and


non-agricultural level of households. Social assets have little influence on pattern selection of household. The reason may lie in that household rarely take this kind of assets into con-sideration in their decision-making process. Psychological assets, however, have the most significant influence on pattern option. Therefore, the state of households’ psychology is a determining factor on their decision-making at the micro level. Besides external influence factors, psychological status and appeal of household are much more important. It is unwise to neglect psychological assets. Both human assets and physical assets significantly affect the decision-making of households. Given the vulnerability of rural finance, households

Table 8 The correlation coefficient between household livelihood assets and pattern option willingness

Asset type Correlation coefficient (R) Indicators Correlation coefficient (R)

Human Assets 0.8374 Total family labor capacity 0.8280

Adult male labor 0.8165

Education level of adult labor 0.8613

Natural Assets 0.6163 Cultivated land area per capita 0.7212

Woodland area per capita 0.5583

Garden land area per capita 0.5819

Average land quality grade 0.6534

Land use structure ratio 0.7002

Financial Assets 0.8113 Housing condition 0.8186

Family assets 0.7932

Perfect degree of rural infrastructure 0.8248

Physical Assets 0.8391 Opportunity of getting cash credit 0.8389

Opportunity of getting cash support 0.8563

Family cash debit 0.8101

Social Assets 0.8252 Relative and friend relation net 0.8207

Funds assistance 0.8528

Labor assistance 0.7970

Perfect degree of rural public services 0.8012

Psychological Assets 0.8395 Confidence index 0.8984

Life improving expectation index 0.8889

Happiness index 0.8776

Toughness index 0.8091

Table 9 The estimates result of household livelihood assets impact on the option of compensation pattern

Variables Coefficients Standard variation Z-statistics Sig.

C(Constant) –1.2971 0.1126 –11.5174 0.0000

Human Assets 0.6351 0.1272 4.9931 0.0000

Natural Assets –0.2204 0.1544 –2.4273 0.0343

Physical Assets 0.6077 0.1408 4.3170 0.0000

Financial Assets 0.3462 0.1574 2.1988 0.0285

Social Assets 0.0576 0.1823 0.3162 0.7520

Psychological Assets 1.7556 0.1627 10.7879 0.0000


have little financial assets. Hence, financial assets do not influence significantly on the be-havior of pattern selection.

Table 10 shows the correlation between household livelihood asset indicators and house-hold pattern selection willingness calculated by the Probit Regression Model. The model fit well as the Likelihood Ratio Statistics χ2 is 125.37 and Pseudo R2 is 0.171. Eighteen out of 22 specific indicators are significant at 0.05 levels, while four indicators, land area per capita, living condition, labor assistance condition, and happiness index, cannot pass the signifi-cance test. Six indicators, including cultivated land area per capita, woodland area per capita, garden land area per capita, average level of soil quality, opportunity of getting fund and labor assistance, have negative influence on household selection willingness for compensa-tion pattern. These six factors induce household to choose “Chengdu Pattern”. Other 16 in-dicators have positive effect on household selection willingness for compensation pattern and lead household to select “Foshan Pattern”. Most of the regression results are close to what we have expected.

Table 10 The estimated result of household livelihood assets indicators impact on compensation pattern option

Variables Coefficients Standard variation Z-statistics Sig.

C (Constant) –0.2189 0.1494 –2.1588 0.0000

Total family labor capacity 0.4879 0.2941 1.6588 0.0292

Adult male labor 0.1039 0.1666 0.6238 0.0354

Education level of adult labor 0.0990 0.2998 0.3971 0.0242

Cultivated land area per capita –0.5551 0.3391 –1.6369 0.0303

Woodland area per capita –0.2642 0.3002 –0.8803 0.0980

Garden land area per capita –0.5048 0.2528 –1.9967 0.0158

Average land quality grade –0.6512 0.2112 –1.8019 0.0025

Land use structure ratio 0.3216 0.3428 0.9725 0.0345

Housing condition 0.0585 0.1191 0.4913 0.1575

Family assets 0.6008 0.3093 1.9427 0.0175

Perfect degree of rural infrastructure 0.4125 0.2346 1.1264 0.0328

Opportunity of getting cash credit 0.2544 0.3901 1.1649 0.0353

Opportunity of getting cash support –0.3438 0.3025 –1.1980 0.0228

Family cash debit 0.4759 0.2266 1.7765 0.0122

Relative and friend relation net 0.1791 0.1849 0.9688 0.0168

Funds assistance 0.2177 0.3507 0.3463 0.0144

Labor assistance –0.1848 0.2502 –0.7388 0.1177

Perfect degree of rural public services 0.2012 0.1783 0.3142 0.0416

Confidence index 0.7371 0.4807 2.5335 0.0062

Life improving expectation index 0.2141 0.7324 0.2923 0.0235

Happiness index 0.0410 0.6548 0.0626 0.2377

Toughness index 0.3223 0.2804 1.1494 0.0468

Among all indicators of human assets, the total family labor capacity has the most sig-

nificant influence on compensation pattern selection that implies that labor capacity plays a


fundamental role in household decision-making. Nevertheless, we also find that households tend to rely on labor quantity while neglect labor quality in their decision-making. Four in-dicators in natural assets have negative impacts on compensation pattern selection. Specifi-cally, cultivated land area per capita and garden land area per capita have significant influ-ence on pattern selection. This means that the situation of natural assets not only affect household differentiation but also influence their micro decision-making. For most of the households, natural assets are the last safeguard of living in their old ages. The effects of housing condition, one indicator of physical assets, are lower than the expected values. The history inertia of house building, the number of children, and children’s marriage status all influence housing conditions and lead to the unbalance between housing condition and in-come level. Opportunities of getting cash assistance, one of the financial assets indicators, have negative effect on household pattern selection. This means the households prefer to choose cash compensation pattern if the opportunities to get cash assistance are lack. Among all social assets indicators, labor assistance has negative influence and this is in consistence with the investigation result. Labor assistance comes from the relatives and friends at the micro level and most of the labor assistance is for agricultural work. Therefore, labor assis-tance has negative influence on households’ non-agriculturization. All the four indicators of psychological assets have positive relation with compensation pattern selection. Confidence index has the most significant influence, followed are toughness index, life improving ex-pectation index and happiness index from high to low respectively. The underlying reason is that happiness is affected by various factors apart from economic income. Household type is not linear correlated to happiness.

3.4 Differentiated economic compensation patterns system design for cultivated land protection based on household livelihood assets

The above-mentioned analysis provides a fundamental framework for designing a differenti-ated compensation pattern system in terms of household differentiation, livelihood assets spatial heterogeneity, and the correlation between livelihood assets and pattern selection. Although the existing two compensation patterns perform well in practice in some regions, they are just suitable to these regions. The applicability of these two patterns for different types of households and different regions still needs to be surveyed further.

As the basis of decision-making, household livelihood assets play a key role in the proc-ess of pattern selection. Therefore, designing a livelihood assets-oriented compensation pat-tern system not only appropriate to local conditions, but also represent the willingness and aspirations of household. Our study tends to break through the limitations of existing pat-terns and design a conceptual compensation pattern system for different types of household livelihood assets. Table 11 shows a conceptual compensation pattern system for seven types of households following the principle of “finding specific way to solve problems” and alter-native principle.

Aiming at making up for the shortage of household livelihood assets, seven types of spe-cific compensation pattern have been designed and several compensation methods have been proposed here. For example, the lack of human assets is a common phenomenon for most of the self-supporting pure agricultural households. This kind of household prefers to get social security and improve their education level. Based on their specific need, the compensation


Table 11 Various compensation patterns are design for different types of household

Types of household

Compensation patterns Compensation methods Compensa-tion basis

Compensation standard

Compensation funds resources

Lack of human assets

Endowment insurance / Cash compensation+

Education compensation

Give priority to the endowment in-surance compensation, optional cash compensation, continuation educa-tion and agro-technical consultation

Lack of natural assets

Cash compensation / Endowment insurance + Grain ration compensa-

tion

Give priority to the cash compensa-tion, optional endowment insurance support, and provide 200kg per per-son grain ration support each year

Lack of physical

assets

Endowment insurance / Cash compensation + Housing or household electrical appliances

compensation

Optional support in cash and en-dowment insurance, allowance of

new housing building and electrical appliances purchasing

Lack of financial

assets

Endowment insurance / Cash compensation + Financing policy com-

pensation

Optional support in cash and en-dowment insurance, enjoy preferen-tial policy of micro-credit in finan-

cial institutions

Lack of social as-

sets

Endowment insurance / Cash compensation + Social organization

admittance

Optional support in cash and en-dowment insurance, recommenda-

tion of joining household economic cooperation organization

Lack of psycho-logical assets

Endowment insurance / Cash compensation +

Psychological consulta-tion

Optional support in cash and en-dowment insurance, enjoy regular

psychological consultation services

Lack of various assets

Comprehensive com-pensation

Optional support in cash and en-dowment insurance with compre-

hensive supports

Cultivated land

Quality

100l

AIC

(yuan/per household, per year)

Establishing cultivated land protection fund

based on the central and pro-vincial financial, supplemented by

general fiscal income from

district and town (basic ratio is 8:2)

Note: compensation objects are all in forms of cultivated land.

pattern focusing on continuing education and agro-technical guidance are designed. Other compensation patterns are designed along with this idea. Compensation basis is determined by the quality of cultivated land and can be calculated through the gradation of agricultural land. Compared with traditional unchangeable compensation standard, the conceptual com-pensation standard proposed here is dynamic, varying with the regional economic develop-ment especially the average net income change of households. The compensation standard model is as follows:

100l

AIC

(6)

where Cl is standard line of compensation, and α is transforming coefficient whose values are 1.5, 3 and 4.5, indicating low, medium, and high standards respectively. The coefficients are determined by cultivated land quality. In practical, government will make minute ad-justment according to the volume of fund. μ is the smoothed price index and is normally determined by CPI. A is the total area of household cultivated land and I is the annual aver-age net income of local households. For example, given the condition that one household had 0.26 hm2 low quality cultivated land at the end of 2010, the CPI at that time was 101.55 (the CPI level of Chongqing in 2010) and annual average net income was 5244 yuan, then Cl

equaled to 2076.86 yuan. In order to improve the stability of compensation fund resources, the compensation pattern refers to “Foshan Pattern” to establish cultivated land protection


fund that mainly comes from state and provincial financing investment and secondarily from the fiscal revenue of district and town.

4 Discussion and conclusions

4.1 Discussion

(1) The feasibility of introducing household livelihood assets into compensation pattern study will need to verify. Because this paper only analyzes western mesa and hilly area in Chongqing, this work still has many unsolved problems that need to use empirical cases in other regions to prove the general applicability. In addition, as it is the first attempt to intro-duce psychological assets into the quantitative analysis of livelihood assets, the enactment of psychological indexes and weight need to be further analyzed.

(2) This paper focuses on analyzing the impact of livelihood assets on household com-pensation pattern selection. As there is interactive relationship between these two sides, how to bring both of them into an integrally uniform analysis framework, especially livelihood assets influence on household compensation pattern selection, still need to be further re-searched. So further study should be carried out to monitor and evaluate the effect on liveli-hood improvement of different compensation pattern.

(3) Nationally, the design of compensation pattern is still in “experimental stage”. Differ-ent compensation patterns will be brought out with the increasingly uphill task of cultivated land protection. Therefore, during the implementation of different patterns, the potential risks should be predicted and prevented under the precondition of effective cultivated land protection by taking households’ rights and interests into consideration. The design of com-pensation pattern should focus on the sustainable development of household.

4.2 Conclusions

(1) Households are divided into five types including self-supporting pure agricultural households, market-oriented pure agricultural households, part-time agricultural households I, part-time agricultural households II, and non-agricultural households. Psychological assets are introduced into the quantitative analysis framework of livelihood assets for the first time. Moreover, the livelihood assets indicator weight is determined through an integrated evalua-tion of both subjective household investigation and objective PCA. The survey results show household livelihood assets have both qualitative differentiation and spatial heterogeneity. The overall household livelihood assets gradually increase from self-supporting household to non-agricultural household. The spatial distribution of livelihood assets shows a trend that the higher the elevation, the less human assets are. Physical assets, financial assets, social assets and psychological assets all follow this trend.

(2) This paper has compared the “Chengdu Pattern” with the “Foshan Pattern”. House-holds have quite different opinions on two compensation patterns. Households’ willingness about compensation pattern selection varies from “Chengdu Pattern” to “Foshan Pattern” as the diversification of their livelihood assets. The social security function of cultivated land is more effective for pure household than that for other part-time household and non-household. Although most of households prefer “Foshan Pattern” in general, there is significant differ-


ence among the three sample villages because of their difference in economic development. (3) The result of Grey Correlation Analysis indicates a strong correlating relationship

between household livelihood assets and willingness of compensation patterns selection. This paper specifically estimates the impact-response degree of household livelihood assets and compensation patterns. The results show that natural assets are negatively correlated to pattern selection willingness and social assets have the least influence. In addition, financial assets have a relatively weak influence on pattern selection willingness. Both human assets and physical assets have significantly influence. Psychological assets have the most signifi-cant influence on pattern option willingness. Each specific livelihood indicator has quite different impact on decision-making of household.

(4) A conceptual compensation pattern system for seven types of households has been de-signed based on household livelihood assets and pattern selection willingness by following the principle of “finding specific way to solve problems” and alternative principle. Specifi-cally, this system has constructed compensation pattern responsively aiming at making up for the shortage of household livelihood assets. It also explores the specific methods, basis, standards and fund sources of economic compensation pattern.

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