EMPIRICAL ANALYSIS ON PSYCHOLOGICAL HEALTH OF FARMERS IN POVERTY-STRICKEN MOUNTAINOUS AREAS FROM THE PERSPECTIVE OF SOCIAL CAPITAL

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Revista Argentina de Clínica Psicológica 2020, Vol. XXIX, N°1, 838-844

DOI: 10.24205/03276716.2020.114 838

E

MPIRICAL

A

NALYSIS ON

P

SYCHOLOGICAL

H

EALTH OF

F

ARMERS IN

P

OVERTY

-S

TRICKEN

M

OUNTAINOUS

A

REAS FROM THE

P

ERSPECTIVE

OF

S

OCIAL

C

APITAL

Jing Zhang

1,2

_{, Yuchun Zhu}

1

_*

Abstract

The psychological health of the impoverished Chinese farmers in mountainous areas has received less attention than it deserves. From the perspective of social capital, this paper empirically analyzes the psychological health of farmers in poverty-stricken mountainous areas. First, a 2 month-long questionnaire survey was conducted among farmers in the impoverished areas of Qinba Mountains, China. Based on the data of 680 farmers, the social capital was measured from three dimensions: social network, social trust and social support. In addition, the structural equation model was adopted to analyze the impacts of social capital on the psychological health of these farmers. The results show that social capital greatly promotes the psychological health of farmers in poverty-stricken mountainous areas; each dimension of social capital has a significant positive effect on the psychological health of these farmers; the three dimensions are ranked as social trust > social support > social network by the degree of impact. The research results provide an important guide for psychological health of the impoverished Chinese farmers in mountainous areas.

Key words: Social Capital, Poverty-Stricken Mountainous Areas, Farmers, Psychological Health.

Received: 26-03-19 | Accepted: 17-09-19

INTRODUCTION

Poverty is a worldwide problem, and poverty alleviation is a common task facing mankind. In early 2014, the Chinese government successfully launched a targeted poverty alleviation strategy, making decisive progress in combating poverty. Many scholars have made extensive research on the targeted poverty alleviation strategy implemented in China, but few studies have been done on the psychological health problems of farmers in poverty-stricken mountainous areas. For a long time, sociological research has found that there is a close relationship among social network, social support and health, but in

1_{College of Economics and Management, Northwest}

Agriculture and Forestry University, Yangling 712100, China.

2_{Six Industrial Research Institute, Northwest Agriculture and}

Forestry University, Yangling 712100, China. E-Mail: [email protected]

recent years, the significant relationship between social capital and psychological health has been proved by many researchers.

The concept of social capital was first proposed by Bourdieu (1983), a French sociologist, who believed that social capital is an actual or potential collection of resources and these resources are inextricably linked to the possession of a network of institutionalized

relationships. Then many scholars have

explained social capital from different angles, including Coleman's (1986) view of social capital function, Putnam's (1994) view of social capital community and Lin's (2000) view of social capital resources, while the more recognized is Lin's definition of social capital. He believes that social capital is a resource embedded in a social structure that can be acquired or mobilized in purposeful activities.

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JING ZHANG,YUCHUN ZHU

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psychological health started from the study on the relationship between social capital and mortality. Durkheim, Spaulding, & Simpson (1951) found that social capital was closely

related to mortality, that’s, the higher the

amount of social capital is, the lower the mortality is. There are also studies showing that social capital (trust, reciprocity, and network) has an important impact on longevity, infant mortality, cardiovascular disease, psychological health, and subjective health, even if the income variable is controlled (Harpham, Grant, & Thomas, 2002). Subsequently, there are more and more researches directly on social capital and psychological health. Rose's (2000) study of Russia found that the two predictors of human capital and social capital generally account for 19.3% of the variance of the dependent variable psychological health, with specific types of social capital networks helping to reduce depression, provide emotional support and ultimately improve the level of psychological health. Studies by Hyyppä & Mäki (2001) show that the number of friends, trust and organizational participation of individuals are directly related to their psychological health after controlling several variables such as gender, age and household income. Phongsavan, Chey, Bauman et al. (2006) have studied social capital at the individual level, and the results suggest that people with higher levels of trust, security, social participation and social reciprocity have better self-perceived health. Miller, Scheffler, Lam et al. (2006) using data from Indonesia, found that an increase in the number of community organizations could boost psychological health. Abundant social capital plays an important role in improving the quality of life of urban and rural residents and maintaining their good health.

However, how to calculate the social capital of farmers in poverty-stricken mountainous areas in China? What are the effects on psychological health? The answers to these questions not only provide theoretical support for the effect of social capital on the psychological health of farmers in poverty-stricken mountainous areas, but also help to improve the psychological health level of farmers in poverty-stricken mountainous areas. In view of this, this study uses the survey data of 680 farmers in the poverty-stricken areas of Qinba Mountain Area in China to explore the impact of different dimensions of social capital on the psychological health of farmers in

poverty-stricken mountainous areas by empirical analysis, which has a certain theoretical value and practical significance.

CONCEPTUAL FRAMEWORK AND RESEARCH HYPOTHESES

Social capital is the sum of resources that the members of the social network have a trust relationship with each other because of long-term communication and mutual benefit, and then form social prestige and get social support. Network resources are the operational basis of social capital, and trust, participation and support are the core elements of social capital. According to the three dimensions of social network, social trust and social support, this study constructs a social capital indicator system, and puts social capital and psychological health into an analysis framework, as shown in Figure 1. This study analyzes the influence mechanism of social capital on the psychological

health of farmers in poverty-stricken

mountainous areas.

Figure 1

.

Influence mechanism diagram of

social capital on the psychological health of

farmers in poverty-stricken mountainous

areas

Influence of social network on the psychological health of farmers in poverty-stricken mountainous areas

The richer the social network of farmers in poverty-stricken mountainous areas is, the easier it is to get more information about psychological health through the social network between neighbors. Social network can promote the psychological health of farmers in poverty-stricken mountainous areas by promoting the

dissemination of psychological health

information, cultivating behavior rules in compliance with psychological health, and

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EMPIRICAL ANALYSIS ON PSYCHOLOGICAL HEALTH OF FARMERS IN POVERTY-STRICKEN MOUNTAINOUS AREAS FROM THE PERSPECTIVE OF SOCIAL CAPITAL 840

effectively controlling behaviors deviating from psychological health. Social networks can provide perceptual and practical social identity; social networks impose social norms that help safeguard the psychological health of farmers in

poverty-stricken mountainous areas

(Brechwald& Prinstein, 2011); the extensive social connection means the increase of social activities and social participation, so as to promote the farmer's psychological health; social networks enable individuals to gain access to more psychological health resources and information (Berkman & Glass, 2000). In view of this, this study proposes the following research hypothesis:

H1: Social network has a positive effect on the

psychological health of farmers in poverty-stricken mountainous areas

Influence of social trust on the psychological

health of farmers in poverty-stricken

mountainous areas

Social trust is not only beneficial to the interaction and communication among farmers in poverty-stricken mountainous areas, but also can enhance the activity and enthusiasm of farmers to participate in social groups, effectively raise their chances of contact with pleasant events, and thus improve the psychological health level of farmers. Contacts and trust with family, relatives, friends and neighbors, and access to internal reciprocal norms and cooperative practices (Helliwell, 2006), make it easier for farmers to obtain better jobs and higher incomes, and improve their economic status, thus indirectly affecting the

farmers’ psychological health level. In view of this, this study proposes the following research hypothesis:

H2: Social trust has a positive effect on the

Influence of social support on the psychological health of farmers in poverty-stricken mountainous areas

Social support enables farmers in poverty-stricken mountainous areas to obtain more emotional support, enhances mutual trust and mutual benefit of farmers, and has a positive impact on individual psychological health by enhancing self-esteem and encouraging mutual

assistance, thereby promoting healthy

behaviors, creating a healthy environment and

effectively relieving psychological and social stress. When an individual has more social support resources, the negative effects of adverse life events and difficulties are greatly reduced and the negative effects of social risk factors on the psychological state of an individual in tension are offset. Social support also has an impact on individual psychological health by providing psychological emotional

support, improving self-esteem, and

encouraging mutual assistance. In view of this, this study proposes the following research hypothesis:

H3: Social support has a positive effect on the

MATERIALS AND METHODS

Sampling and data collection

Qinba Mountain Area, as the main battlefield for poverty alleviation in China at present, is one of the 14 concentrated contiguous poverty-stricken areas in China, integrating old revolutionary areas, large reservoir areas and areas prone to natural disasters. Poverty is characterized by deep degree and complex structure. In 2015, the total poor population reached 3.46 million, accounting for 12.03% of the population in all concentrated contiguous poverty-stricken areas, and the incidence of poverty was 12.3%. The geographical range of Qinba Mountain Area refers to the Daba Mountain in the upper reaches of the Hanshui River and its adjacent areas, including 75 counties in Shaanxi, Gansu, Sichuan, Chongqing, Henan and Hubei. In Qinba mountain area, that total number of poverty counties at the state level is 60, accounting for up to 80%, of which there are 34 poverty counties in Shaanxi and Sichuan, account for 56.7% of the total, which are the two most concentrated provinces of poverty-stricken counties in the mountainous areas. Therefore, two provinces of Shaanxi and Sichuan in Qinba Mountain Area are selected as the research area, and a two-month field questionnaire survey was conducted from September to October 2016. The method of stratified random sampling was used in the investigation, one city was selected in each province, and three poor counties were selected in each city, and three to five towns were

randomly selected in each county for

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841

questionnaires were obtained, and 680 farmer questionnaires were finally obtained after excluding the invalid samples. The validity rate of the samples is 99.56%.

Variable measurements

The design of this questionnaire refers to the relevant literature at home and abroad, and combined with the pre-investigation of many cases made by the research group in Longnan area of Qinba Mountain Area in the early stage, this study designs a large sample questionnaire. All the data in this study are designed with Likert's five-grade scale. Social capital includes three dimensions: social network, social trust and social support. According to the actual situation of rural areas in China, the Self-rated

Health Measurement Scale (SRHMS) is

developed to describe the psychological health level of farmers in poverty-stricken mountainous areas. See Table 1 for specific variables and subject items.

Research method

Structural equation model is a research method based on statistical analysis technology. It is mainly used to solve multivariable problems in social science research and to deal with the exploration and analysis of complex multivariate research data. The structural equation can

estimate and verify the abstract concepts, and can simultaneously estimate the latent variables and the parameters of the complex variable model. The structural equation model includes two parts: measurement equation and structural equation. The measurement equation is mainly used to describe the relationship between the observation variables and the latent variables, and the structural equation is mainly used to describe the relationship among the latent variables, and the specific form is as follows:

Measurement equation

X=Λxξ+δ, yΛyη+ε (1)

Structural equation

Η=Bη+Гξ+ζ (2)

where, η and ξ are endogenous latent variables

and exogenous latent variables respectively, y is

endogenous index, x is exogenous index, Λx and

Λy are load matrix, Β and Гare path coefficient

matrix, δ and ε are error terms of index, ζ is

residual term of structural equation, reflecting

the part of η that cannot be explained in the

equation.

In this study, SPSS 22.0 and AMOS 20.0 software are used to analyze and manage the data.

Table 1.

Main variables and measurement indicators

Item Description Measurement Variables

SN1 How many relatives work in government departments

5-point Likert scale (less

to more) (Social Network)

SN2 SN2

How many contacts in the head of the household's mobile phone SN3

SN3

How many people are contacted during the holidays

SN4

SN4 How much does the family spend on gifts

ST1 Trust in family

5-point Likert scale

(Distrust to trust) (Social Trust)

ST2 Trust in neighbors

ST3 Trust in friends

ST4 Trust in government staff

SS1 Get support from family when you need help

(Disagree to agree) (Social Support) SS2 Get support from the neighborhood when

you need help

SS3 Get support from the village when you need help

SS4 Your opinions are considered for village affairs

PH1 Are you satisfied with your present living conditions?

(Disagree to agree) (Psychological Health)

PH2 Do you feel in a bad mood?

PH3 Do you feel lonely when you are with others?

PH4 Do you think most people are better off than you?

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Firstly, the reliability, validity and other descriptive statistical analysis of the data are performed by using SPSS software, and the data of abnormal distribution are tested and corrected by using AMOS software and Bollen-Stine method. The maximum likelihood method is used to analyze the structural equation model, and the parameter estimation, path analysis and significant test are carried out to verify the positive influence of the three dimensions of social capital on the psychological health of the users in poverty-stricken mountainous areas.

RESULTS AND DISCUSSION

Reliability and validity of the measurement model

Before the structural equation analysis, in order to ensure the standardization of the data, the reliability and validity of the measurement model are tested in this study. The Cronbach's Alpha coefficient is used to test the structural consistency of each latent variable. The convergence validity of the model is tested by combined reliability (CR) and average variance extraction (AVE). Generally, it is acceptable when the Cronbach coefficient exceeds 0.6, and it is ideal when it is more than 0.8. When the normalized factor load is more than 0.5 and the average variation extraction amount is more

than 0.5, it is considered that the apparent variable is effective. As shown in Table 2, the results indicate that the factor load of all apparent variables has reached the acceptance standard of more than 0.5, and most of them have reached the ideal standard of more than 0.8. In addition, the combined reliability and the average variation extraction amount of the model also meet the ideal requirement, and meet the criterion of convergence validity, which indicates that the convergence validity of the measurement model passes the test.

Fitting degree of structural model

The index values, such as absolute fitness index and increment fitness index, are generally used to test the goodness of fit of structural equation models. The higher the fitness of the model is, the higher the availability of the model is, and the more meaningful the parameter estimation is. The fitness indices mainly adopt

the chi-square freedom ratio χ2_{/df in the}

absolute fitness index, the Root Mean Square Error Approximation (RMSEA) and the Goodness of Fit Index (GFI), the Adjusted Goodness of Fit Index (AGFI), the Comparative Fit Index (CFI), the Normed Fit Index (NFI) and the Incremental Fit Index (IFI) in the value-added fitness index. The estimated value and reference standard of the goodness of fit of the model are shown in Table 3.

Table 2.

Factor loadings, average variance extracted (AVE) and construct reliability (CR) in

CFA

Variables Item Factor loadings Cronbach′s Alpha AVE CR

Social Network

SN1 0.894a

0.912 0.730 0.915

SN2 0.874***

SN3 0.704***

SN4 0.928***

Social Trust

ST1 0.862a

0.892 0.738 0.918

ST2 0.888***

ST3 0.739***

ST4 0.935***

Social Support

SS1 0.885a

0.900 0.688 0.897

SS2 0.830***

SS3 0.673***

SS4 0.910***

Psychological Health

PH1 0.708a

0.898 0.750 0.922

PH2 0.933***

PH3 0.947***

PH4 0.855***

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843

Table 3.

The goodness of fit indices of structural equation model

Fitness index of the model Fitness criteria or

thresholds Model index value Fitness of the model Ratio of chi-square value to

degree of freedom χ2 /df <3.00 1.431 Acceptable

Root Mean Square Error

Approximation (RMSEA) <0.06 0.046 Acceptable

Goodness of Fit Index (GFI) >0.90 0.918 Acceptable

Adjusted Goodness of Fit Index

(AGFI) >0.90 0.939 Acceptable

Comparative Fit Index (CFI) >0.90 0.915 Acceptable

Normed Fit Index (NFI) >0.90 0.948 Acceptable

Incremental Fit Index (IFI) >0.90 0.916 Acceptable

The results show that the overall fitting effect of the structural equation model is good, the ratio of chi-square value to degree of freedom

(χ2/df = 1.431), the fitness index (GFI = 0.918, CFI

= 0.915), and the residual root mean square (RMSEA = 0.046), all fitness indices are better than the critical standard (Chan, Lee, Lee et al., 2007), indicating that the model can be adapted to the data and the fitness of the model is acceptable. The results show that the theoretical model built herein and the actual data are well fitted and the hypothesis can be tested further.

Estimate results and hypotheses testing

Estimated results

After the model passes the goodness of fit test, the path analysis of the model can be carried out. After the estimation operation, the detailed path analysis is shown in Figure 2, and

the detailed parameter estimation and

hypothesis test results are shown in Table 4.

Figure 2

.

Results of the structural model

Note: → significant path coefficient

Table 4.

Standardized coefficient estimates

of the structural model

Path Estimates S.E. t-Value

H1: SN→PH 0.170 0.045 2.409**

H2: ST→PH 0.297 0.050 4.181***

H3: SS→PH 0.214 0.044 3.051***

Note: **significant at 5% and *** significant at 1%.

Analysis of estimation results

Social network has a significant positive impact on the psychological health of farmers in

poverty-stricken mountainous areas. The

standardized path coefficient of social network for the psychological health of farmers in poverty-stricken mountainous areas is 0.170, which passes the significance test at the 95%

confidence interval, and the hypothesis H1

stands. Social trust has a significant positive impact on the psychological health of farmers in

standardized path coefficient of social trust on the psychological health of farmers in poverty-stricken mountainous areas is 0.297, which passes the significance test at the 99%

confidence interval, and the hypothesis H2 is

valid. Social support has a significant positive impact on the psychological health of farmers in

standardized path coefficient of social support for the psychological health of farmers in poverty-stricken mountainous areas is 0.214, which passes the significance test at the 99%

confidence interval, and the hypothesis H3 holds.

The empirical results suggest that improving the psychological health level of farmers in poverty-stricken mountainous areas not only requires economic assistance and assistance from the government and society, but also pays attention

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to the influence of social capital on the

psychological health of farmers. The

construction of activity rooms and cultural activities creates good conditions for the social communication and social participation of farmers in poverty-stricken mountainous areas, and promotes the level of psychological health of farmers in poverty-stricken mountainous areas by improving their social capital.

CONCLUSIONS

In this study, based on the data of 680 farmers in the poverty-stricken areas of Qinba Mountains, the social capital of farmers is measured from three dimensions: social network, social trust and social support, and the structural equation model is adopted to analyze the impact of social capital on the psychological

mountainous areas. The results show that each dimension of social capital has a significant positive effect on the psychological health of farmers in poverty-stricken mountainous areas, and social capital plays an important role in promoting the psychological health of farmers in poverty-stricken mountainous areas. The effects of social capital on the psychological health of farmers in poverty-stricken mountainous areas

are as follows in order: social trust > social

support > social network. The main contribution

of this study is to explore the micro-mechanism of social capital acting on the psychological

mountainous areas, and to test the hypotheses of social capital acting on the psychological

mountainous areas with sample data.

Acknowledgments

We are grateful to the Natural Science Foundation of China (71573211) and the research program of humanities and social sciences of Ministry of Education of China (17YJA790102) for funding the research. We appreciate the constructive comments and suggestions made by the anonymous referees and editors of this journal.

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