Multidimensional Poverty in Rural China: Human Capital vs Social Capital
Abstract
Occupational stratification is the comprehensive division and classification of various occupations undertaken by members of society according to specific standards and methods. Based on China Family Panel Studies data, we use the Alkire–Foster method to calculate the rural multidimensional poverty index and empirically examine the impact of human capital, social capital, and occupational stratification on rural multidimensional poverty reduction. The results show that the improvement of human capital and social capital can affect the occupational stratification of rural household members, thereby promoting the growth of household income and reducing multidimensional poverty in the household; occupational stratification is an intermediator in the poverty reduction effect of human capital and social capital; compared to social capital, human capital has a more substantial impact on occupational stratification and rural multidimensional poverty; human capital has a long-term dynamic impact on household multidimensional poverty. On the other hand, social capital has a short-term impact on household multidimensional poverty. At the same time, occupational stratification has a long-term dynamic impact on household multidimensional poverty and is also a long-term poverty reduction mechanism. We delve into the long-term mechanisms for addressing multidimensional poverty through the lens of occupational stratification. Furthermore, we compare the contributions of social and human capital to occupational stratification and the reduction of multidimensional poverty in Chinese rural areas. This analysis enriches the existing literature on poverty studies.
1 Introduction
This article will explore how rural households can achieve poverty reduction directly by accumulating human and social capital, but equally how human and social capital affect career options and how career options affect poverty in a two-step mechanism. This will be represented by three equations (one the direct effect on poverty and two representing the indirect mechanism transmitting social and human capital through occupational choice to poverty levels). Measurements will be taken at the household level. The innovation of this article is we compare social and human capital’s role in occupational stratification and multidimensional poverty reduction in Chinese rural areas. Using Chinese data from 2010 to 2018, we estimate our equations correcting for endogeneity and find that social capital contributes very little to either occupational choice or poverty reduction. Rather, human capital composed of both health quality and educational level drives both direct and indirect effects. As human capital is highly correlated across periods, we find a long-term effect as well, although we do not find a long-term effect of social capital accumulation.
The main deviation of our results from the existing literature is that we find that social capital plays a relatively small role in rural multidimensional poverty reduction relative to human capital. Although there are few articles comparing the importance of human capital and social capital in poverty reduction, a large number of literatures show that social capital is an important influencing factor in poverty reduction. We speculate that the poverty reduction effect of social capital may be related to marketization and institutional environment. We compared China’s coastal areas with inland areas, large cities, and small cities and found that the effect of social capital in coastal areas is weaker than that in inland areas and the effect of social capital in large cities is weaker than that in small cities. In addition, we interact social capital with market-oriented scores and find that the interaction coefficients are positive, that is, the poverty alleviation effect of social capital is weaker in provinces with a good market-oriented environment. We find that the results of the current literature on the significant effect of social capital on poverty reduction are mostly found in the disadvantaged areas of developing and developed countries, and combined with the weaknesses of social capital pointed out in some literature, our results can actually be coupled with the conclusions in the literature, which is a useful supplement to the research on human capital and social capital.
2 Background
Occupational stratification is the comprehensive division and classification of various occupations undertaken by members of society according to specific standards and methods. It is one of the most critical components of social stratification research (Treiman, 1977). In the context of China’s urban labor market, there is a wealth of evidence indicating significant occupational segregation, which significantly contributes to wage disparities among urban residents and migrant workers. This segregation is fostered by various institutional arrangements, including the hukou registration system, enterprise hiring practices, and social welfare provisions, which collectively give rise to an urban dual labor market structure. Contrary to Piore’s dual labor market theory, which attributes market segmentation to market dynamics (Doeringer & Piore, 1971), China’s labor market segmentation is predominantly shaped by institutional legacies arising from its economic transition. The distinctive feature of China’s labor market lies in its institutional barriers to labor mobility, particularly the constraints on rural-to-urban migration imposed by the hukou system. Migrant workers experience discrimination and occupational segregation within the labor market, which exhibits a high degree of institutional segmentation. Migrant workers encounter substantial barriers to urban employment, coupled with lower incomes, in stark contrast to the more favorable opportunities and benefits enjoyed by urban residents (Tsui, 2005). State-owned enterprises are known for their provision of stable employment and higher welfare benefits, whereas the non-state sector, while offering greater flexibility, is often less secure (Zhang & Song, 2003). Internal labor markets, comprising state and large non-state enterprises, have the autonomy to set labor prices independently of external market forces (Bai, 2009). This situation is detrimental to the formation of a harmonious social-occupational hierarchy and leads to a substantial occupational wage gap among migrant workers and urban residents (Meng & Zhang, 2001). Moreover, the cost of transitioning between occupations is substantial (Artuc & McLaren, 2015), and occupational stratification is constrained by institutional factors such as career trajectory, occupational identity, and job availability. Consequently, occupational stratification exerts a significant influence on long-term career progression (King, 2005). Even when individuals switch careers, low-income workers often gravitate toward new occupations with lower average wages (Fane et al., 2015). Therefore, occupational stratification may exert a sustained and long-term impact on the multidimensional poverty experienced by families.
The data of the third national agriculture census in Table 1 showed that in 2016 owners of large farms[1] with education at junior high school level and above were 65.8%, 9.1 percentage points higher than all farmers,[2] and the proportion of agriculture firm/organization[3] with a junior high school education or above is 74.6%, 17.9 percentage points higher than that of farmers.[4] The 2016 migrant workers[5] monitoring survey report released by the National Bureau of Statistics shows that the proportion of migrant workers with junior high school education or above is 85.8%, 29.1 percentage points higher than that of farmers in the same period. Unlike farmers, they are employed in manufacturing (30.5%), construction (19.7%), wholesale and retail (12.3%), and resident service industries (11.1%).[6] With the improvement of rural residents’ human capital, their careers are more diversified than farmers. In 2016, rural residents’ per capita disposable income reached 12,363 yuan, while the average income of migrant workers was 39,300 yuan in the same period, indicating that with the improvement of rural residents’ human capital, they have more employment choices and income. The practice has shown that rural laborers with higher human capital have more careers to choose from; that is, they have more diverse occupational stratification (Figure 1).
Indicator system and weight setting for multidimensional poverty measurement
Comparison of education of farmer and migrant workers.
Since 2013, China has pursued a targeted poverty alleviation strategy, initially conducting comprehensive surveys to identify impoverished areas and households, and establishing a national poverty database for accurate targeting in 2013–2015. Subsequently, a range of measures including financial aid, infrastructure development, and industrial growth were implemented to enhance the income and living conditions of the targeted households in 2016–2018. From 2019 to 2020, support for impoverished areas was intensified, alongside strengthened monitoring and evaluation to prevent poverty recurrence. These efforts have yielded significant results: the poverty headcount ratio decreased from 10.2% in 2012 to 0.6% in 2019,[7] and by the end of 2020, all poverty-stricken counties and regions in China had escaped poverty, achieving the scheduled goal of poverty eradication. With the effective implementation of China’s targeted poverty alleviation policy, rural poverty alleviation has entered the “high-quality poverty alleviation” stage, which requires the Chinese government not only to win the battle against poverty resolutely but also to establish a long-term mechanism to solve relative poverty.[8] Xi Jinping emphasized that rural intelligence support is the core of a long-term poverty reduction mechanism and that basic educational conditions for weak schools in compulsory education should be continuously improved and high-quality educational resources should be distributed to schools in weak areas to promote educational equity and cut off intergenerational transmission of poverty.[9] Then, under the background of sustained high growth in national education poverty alleviation investment, can the improvement of human capital obtained by the rural population through education provide endogenous motivation for solving the problem of multidimensional poverty?
Much literature currently focuses on the impact of human capital accumulation on poverty reduction. For example, Schultz (1961) used human capital to explain the sustained growth of national income in the United States in the first half of the twentieth century and believed that the lack of human capital and the neglect of human capital investment in less developed countries were the reasons for their poverty. Becker (2009) found by comparing the return of human capital with that of physical capital that education can increase personal income and promote the improvement of other people’s production efficiency through spillover effects. Sen (1982) analyzed the causes of poverty and found that people experiencing poverty are poor because of their insufficient human capital; that is, the low level of education and health leads to the deprivation of the ability or opportunity to create income. In the past 20 years, researchers have applied random field experiments to anti-poverty research, and the effects of health and education in developing countries have become the focus of attention (Banerjee et al., 2007, 2010; Duflo, 2001) and believe that the impact of human capital on the income of migrant workers mainly includes the following aspects: first, the improvement of human capital can make farmers easier to adopt new technologies, carry out independent innovation, improve labor productivity, and thus increase personal income (López & Valdés, 2000; Parman, 2012). Second, human capital, including education, vocational training, health, and working experience, plays a vital role in narrowing the relative wage gap by expanding non-agricultural employment opportunities for farmers (Démurger & Li, 2013; Kurosaki & Khan, 2001; Teulings, 2005).
Although human capital is essential for farmers to overcome poverty, its full play depends on the social environment. Social capital is one of the sociological concepts that refers to the social network, trust, and norms that can improve economic efficiency through coordinated actions (Putnam, 1993). For individuals, social capital refers to the ability to acquire and use resources embedded in social networks in actions (Lin et al., 2001). Social capital has also been studied in poverty research (Abdul-Hakim et al., 2010; Grootaert et al., 2002). Social capital is “the norms and networks that enable people to act collectively,” which can reduce rural poverty and improve the welfare of poor households by lowering transaction costs (e.g., trust can lower the negotiation time and cost) and promoting cooperation (Woolcock & Narayan, 2000). Much of the literature discusses the role of social capital in the labor market, such as facilitating information exchange (Granovetter, 1974; Waldinger, 1996), reducing transaction costs (Abraham & Medoff, 1983; Eccles & Crane, 1988; Waldinger & Bozorgmehr, 1996), and improving efficiency (Burt, 1997). China is a traditional relational society (Bian, 1997; Yang, 1994), and social capital, as an informal institution, has largely shaped the reciprocal norms of Chinese society (Hwang, 1987). Social capital helps promote employment (Montgomery, 1991; Munshi, 2006) and increase income (Granovetter, 1999; Narayan & Pritchett, 1999). Bian et al. (2015) demonstrated the causal effect of social capital on job opportunities and wage income in the labor market from the perspective of information and preferences. Due to information transmission (social ties offer inside information about jobs and qualifications of job seekers, leading to desirable job assignments) and resource mobilization (ties to government officials are the key channels to acquire economic resources), social capital is essential in China’s economic and social activities and labor market.
Social capital is an important factor in poverty research (Knight & Yueh, 2008). Asadi et al. (2008) suggest that developing countries improve social capital to integrate the environment and people to alleviate poverty and receive sustainable development. Hassan and Birungi (2011) access to social capital defined in terms of membership in social organizations positively affects household income and reduces poverty in Uganda. Saracostti (2007) found social capital contributes to poverty reduction in most Latin American countries. Andriani and Karyampas (2015) social capital contributes to the poverty transition in Italy. The effect of social capital is also verified in other countries, such as Indonesia Rustiadi and Nasution (2017) and Brazil (Marques, 2012). The poverty reduction effects of social capital are also verified in China. Zhang et al. (2017) found social capital, such as business ties, political ties, and appropriable social organizations can contribute significantly to poverty reduction in western China. Hong et al. (2019) social capital shows a significant association with the absence of poverty in border regions of China. However, social capital is not necessarily the perfect solution. Neira et al. (2009) found social capital does not guarantee growth in itself, but rather serves as a lever on the fulcrum of social capital to stimulate economic progress and there may be negative forms of social capital at a given moment in time in a particular society. Méreiné Berki (2017) finds bonding ties as tools for everyday survival easily overwrite system integration efforts for poverty alleviation. Callahan (2005) relates corruption and vote buying to social capital in the research on the reform in Thailand.
Most existing literature focuses on the poverty effect of human capital and social capital accumulation and provides valuable insights for this study, but it also has some limitations. Wang et al. (2023) examined the role of social capital on poverty, but without controlling for human capital factors, which may lead to an overestimation of the poverty reduction effect of social capital. Fan et al. (2023) simultaneously investigated the impact of human capital and social capital on the re-poverty risk of rural residents, but the sample was limited to a single county in Henan Province and did not directly compare the magnitude of the effects of human and social capital. Emran et al. (2023) explored the intergenerational impacts of occupational stratification and human capital but overlooked the effect of social capital. It needs to include the impact of human capital and social capital accumulation on career choice and its poverty reduction effect, which cannot help us fully understand the relationship between human capital and social capital accumulation and poverty reduction.
After achieving the phased goals of poverty alleviation, China will focus on solving the problems of relative poverty alleviation, which requires building long-term poverty reduction mechanisms to achieve short- and long-term goals. However, the current dilemma confronting poor rural households in China is that their family members need higher vocational and technical skills, while simultaneously facing employment discrimination in the non-agricultural labor market. This restricts their career options significantly. Notably, existing research has insufficiently emphasized the role of career choice.
3 Multidimensional Poverty State Identification and Poverty Measurement
3.1 Measurement of Multidimensional Poverty
There have been many definitions of poverty. Oppenheim and Harker (1996) defined poverty based on the perspective of “lack”; the World Bank (2001) defined poverty based on the view of “social exclusion”; Sen (2006) defined poverty based on the lack of feasible capabilities. This article’s poverty measurement is mainly based on the perspective of “minimum living standard.” Combined with this connotation of poverty, Alkire and Foster (2011) proposed a multidimensional poverty measurement method, the Alkire–Foster (AF) method. This article will use this method to measure the multidimensional poverty of Chinese rural residents.
Firstly, following Alkire and Shen (2017) and combining the availability of variables in the 5 years from 2010 to 2018 in the China Family Panel Studies (CFPS) data, we set the indicators of rural multidimensional poverty as five dimensions: income and consumption, education, health, living standards, and assets. Each dimension has 1–3 deprivation indicators, and the number of indicators under dimension j is labeled d j . The indicator system and weight setting are shown in Table 1.
In the identification of multidimensional poverty and the calculation of the multidimensional poverty index, we denote the dimension of multidimensional poverty as j = 1, 2, …, 5, and the corresponding deprivation status of variable k under dimension j as α jk , where α jk = 1 represents the deprivation status of the household at this level, and α jk = 0 represents the non-deprivation status of the household at this level. Then, the deprivation status of different indicators under different dimensions is summed up according to their weights, and the comprehensive deprivation index of household h is obtained:
Since the deprivation index reflects the overall level of deprivation in different dimensions of a household, we can use this indicator to identify the multidimensional poverty status of households. Specifically, we consider households with a deprivation index above a certain threshold to be in a state of multidimensional poverty. Following the World Bank’s approach, we set the threshold at 0.3, which means the definition of multidimensional poverty status for households is as follows:
where I() is an indicator function, taking the value 1 if the inequality in the parentheses holds and 0 otherwise. During the multidimensional poverty status identification, weight changes will have unpredictable effects on the poverty status. Therefore, to ensure the robustness of the empirical results, we have set two types of weights for the indicator system, resulting in two types of multidimensional poverty status and poverty index measurement. Method 1 is the default equal weight method in the AF method, in which the weight of each dimension is 0.2, and the weight of each indicator under each dimension is equal. Method 2 is the entropy weight method,[10] which calculates the weight based on the change in data, with larger weights for indicators with greater dispersion. Compared to the equal weighting method, the entropy weight method utilizes the information contained within the data, representing a more effective approach to leveraging information for assigning weights. In Table 1, the entropy weighting allocates more weight to income and consumption, which aligns with our intuitive understanding of poverty. Therefore, we use the multidimensional poverty status of the entropy weighting as the main explanatory variable, while we use the multidimensional poverty status of equal weighting in robustness checks.
In addition, to compare the results of multidimensional poverty with those of income poverty and conduct a robustness test on multidimensional poverty, we defined three income poverty statuses, represented by poverty1, poverty2, and poverty3. Among them, poverty1 refers to the income poverty status identified using the World Bank’s low standard (1.9 US dollars/day) as the poverty line, poverty2 refers to the income poverty status identified using the World Bank’s high standard (3.1 US dollars/day)[11] as the poverty line, and poverty3 refers to the income poverty status identified using China’s official poverty standard as the poverty line.
Based on the multidimensional poverty status of families, a group’s multidimensional poverty headcount ratio can be calculated. That is, for a group with n families, the headcount ratio of multidimensional poverty in a group can be represented by the H index provided by the AF algorithm:
However, this indicator does not meet the strict monotonicity requirement for poverty measurement indicators. For example, for a family h that is already in poverty, if the family’s situation deteriorates and its deprivation index increases, the H index will not increase. Therefore, the H index can only measure the proportion of households in a group in multidimensional poverty and cannot reflect the severity of multidimensional poverty. Thus, the AF algorithm also provides a similar index to measure poverty depth:
In this way, the H index can be adjusted to satisfy the strict monotonicity of poverty measurement, namely the MHA index:
This index increases with the proportion of multidimensional poor households and the degree of deprivation of poor households.
3.2 Data Sources
We use data from the CFPS from 2010 to 2018 for empirical analysis. CFPS data provides information on household income and expenditure, as well as personal education and work information, which can effectively identify the education information of rural residents. In the individual samples, CFPS data provides detailed job information, which helps identify career choices. Among them, the CFPS data has detailed occupation codes and can calculate and match the occupational prestige scores (ISEI) for each Occupation, which facilitates the construction of measurement for occupational stratification. In addition, CFPS provides rich information on rural household income, assets, consumption, and living conditions, and its tracking data advantage can reflect the dynamic changes of multidimensional poverty in rural China. The above characteristics provide data support for the empirical analysis. The sample size of each year in the original data of the CFPS is as follows: 14,798 households in 2010, 13,315 households in 2012, 13,946 households in 2014, 14,019 households in 2016, and 14,241 households in 2018. This article studies the multidimensional poverty of rural families, so only the sample of rural residents is used. We define multidimensional poverty status and social capital on the household level. For a family’s per capita human capital and occupational stratification, we selected family members aged 16–60. We averaged their human capital and occupational stratification to obtain the family’s human capital and occupational stratification. While processing the CFPS data, we calculated the multidimensional poverty status, human capital, social capital, occupational stratification indicators, and various control variables required in the regression. After deleting samples with missing values in income and consumption, education, health, living standards, and assets variables, the final sample contains 27,594 households. However, when conducting regression analysis on the binary variable of household multidimensional poverty status, we use the conditional logit model (clogit) with year-fixed effects to control for household fixed effects. Because the clogit model cannot handle samples whose status of the dependent variable does not change across periods, the actual samples used in the regression are those whose poverty status changes across periods. The total sample size used in the regression is 11,306.
3.3 Poverty Measurement Results
Using the AF method, the results of multidimensional poverty measurement with two types of weights are shown in Tables 2–4. The figure in each table indicates that the level of multidimensional poverty in rural areas varies significantly under different weights. Still, the degree of multidimensional poverty under different weights decreases over time. Table 2 shows that from 2010 to 2018, the multidimensional poverty index MHA under the two weightings decreased by 0.95 (MHA decreased from 9.2 in 2010 to 1.6 in 2018) and 0.575% annually, indicating that China’s poverty reduction policies have achieved significant success. Comparing Tables 3 and 4 shows that the measurement of different weights shows significant differences in the Head Count ratio of multidimensional poverty H and the depth of poverty A. The incidence of poverty has been reduced. From 2010 to 2018, the average Head Count ratio of poverty decreased by 2.413 and 1.375 percentage points yearly. Further, we used different poverty lines to compare the Head Count ratio of multidimensional poverty with income poverty. We found that the Head Count ratio of multidimensional poverty was lower than the income poverty in all three categories, and compared with the equal weight method, the entropy weight method calculated multidimensional poverty Head Count ratio is relatively low. The above result may be because when using the entropy weight method to calculate the deprivation index s, greater weight is assigned to income, education, and health.
Rural poverty measurement MHA (%)
| Years | Equal weight | Entropy weight |
|---|---|---|
| 2010 | 9.2 [8.7, 9.6] | 5.3 [5.0, 5.7] |
| 2012 | 6.0 [5.6, 6.4] | 3.2 [2.9, 3.5] |
| 2014 | 3.5 [3.3, 3.8] | 1.8 [1.6, 2.0] |
| 2016 | 1.6 [1.4, 1.8] | 0.9 [0.7, 1.0] |
| 2018 | 1.6 [1.4, 1.8] | 0.7 [0.6, 0.9] |
Rural poverty measurement H (%)
| Years | Multidimensional poverty | Income poverty | |||
|---|---|---|---|---|---|
| Equal weight | Entropy weight | 1 | 2 | 3 | |
| 2010 | 24.0 [23.0, 25.1] | 13.0 [12.1, 13.8] | 31.6 | 47.8 | 30.5 |
| 2012 | 16.6 [15.6, 17.6] | 8.5 [7.7, 9.3] | 26.9 | 38.4 | 26.2 |
| 2014 | 10.0 [9.2, 10.7] | 4.7 [4.1, 5.2] | 19.8 | 28.7 | 18.8 |
| 2016 | 4.6 [4.1, 5.2] | 2.4 [2.0, 2.8] | 10.6 | 28.7 | 10.8 |
| 2018 | 4.7 [4.1, 5.2] | 2.0 [1.6, 2.4] | 7.5 | 15.6 | 9.4 |
Rural poverty measurement A (%)
| Years | Equal weight | Entropy weight |
|---|---|---|
| 2010 | 38.1 [37.6, 38.6] | 41.1 [40.4, 41.8] |
| 2012 | 36.2 [35.7, 36.8] | 38.2 [37.5, 38.9] |
| 2014 | 35.6 [35.0, 36.2] | 38.1 [37.1, 39.0] |
| 2016 | 35.0 [34.3, 35.8] | 36.3 [35.2, 37.5] |
| 2018 | 35.0 [34.2, 35.7] | 36.1 [34.9, 37.3] |
In this article, when empirically testing the relationship between household human capital, social capital, occupational stratification, and multidimensional poverty, we mainly use the multidimensional poverty status mpoverty2 identified using the entropy weight while using the poverty status mpoverty1 identified using the equal weight and the income poverty poverty1, poverty2, poverty3 for robustness testing, to verify the robustness of the conclusions of this article for poverty identification methods.
4 Empirical Model and Variable Selection
4.1 Empirical Model
The key question of this study is whether human capital and social capital affect rural poverty reduction through its impact on occupational stratification. We use a classic three-step regression (Baron & Kenny, 1986) to test the mediating effect of occupational stratification in the relationship between human capital, social capital, and poverty. The three-step method for discussing mediating effects involves three models, represented by equations (6)–(8). In equation (6), the coefficients β 11 and β 12 represent the total effect of human capital and social capital on poverty. In equation (7), β 21 and β 22 represent the effect of human capital and social capital on the mediating variable occupational stratification. In equation (8), β 33 represents the effect of occupational stratification on poverty, controlling for the influence of human capital and social capital. Under the path of “human capital and social capital → occupational stratification → poverty,” the mediating effect is estimated as the product of the path coefficients in the first half (β 11, β 12) and the second half (β 33) of the mediating path.
where mpoverty is the multidimensional poverty status of the household; career is the occupational stratification of the household, including the proportion of migrant workers in the household (mig_rate), average occupational prestige in the household (isei_mean); h is the per capita human capital, including knowledge capital and health capital, including per capita years of education and health level; sc is the social capital of the household; X represents control variables such as household characteristics and community characteristics; μ i and ν t represent household fixed effects and year fixed effects, respectively, and ε represents the error term. Since the multidimensional poverty status of households is a 0–1 variable, we use the conditional logit model (clogit) to control for households.
4.2 Variable Selection
4.2.1 Human Capital and Social Capital
Human capital is the knowledge, abilities, skills, and experiences in economic activities. Knowledge and health are the two most concerned aspects in the study of human capital. For knowledge, we use the years of education of the household labor force to represent the household labor force’s human capital level. For health indicators, we use the self-reported health of the respondents in the CFPS personal questionnaire as a proxy variable for the health capital of the household labor force. Correspondingly, at the household level, we average the years of education of the household labor force within the household and use the per capita years of schooling (feduuyear) to represent the level of knowledge and human capital of the household. At the same time, the health status of the household’s labor force is averaged within the household, with the per capita health (fhealth) representing the level of health human capital in a household. We measure social capital at the household level. Social networks are an essential component of social capital, including the size and density of social or relational networks (Burt, 1997; Wasserman & Faust, 1994). We use a total indicator that comprehensively reflects the size and density of social or relational networks: the total amount of cash gifts received and paid by a household to measure the social capital of the household. Besides, we use a binary indicating related to party member of local manager as another proxy for social capital.
Due to the two-way causality between human capital and household poverty status and the possible simultaneous influence of certain omitted variables on household human capital and household poverty status, endogenous problems may cause estimation bias. To address this issue, we construct instrumental variables for household human capital to correct the estimation bias caused by endogeneity. First, we use the implementation of the Compulsory Education Law to construct an instrumental variable for the number of years of education received. The logic of using this event to construct the instrumental variable is that it satisfies the conditions of being correlated with the endogenous variable and is uncorrelated with the error term. First, implementing the Compulsory Education Law can affect the years of education of family members. Second, as an exogenous event, implementing the Compulsory Education Law does not affect family occupational stratification and income in other ways. The construction of the instrumental variable is as follows: first, we use the year in which each province implemented the Implementation Rules of Compulsory Education Law as the year in which the province implemented the Compulsory Education Law. When the provinces began to implement the Compulsory Education Law, if the age of the family member was greater than 16, it was considered that they had completed compulsory education and their education level was not affected by the Compulsory Education Law. If the age was less than 16 at the time, we assume their education was affected by the implementation of the Compulsory Education Law. We regress the years of schooling of family members on whether they were affected by the Compulsory Education Law and average the resulting fitted values across family labor to obtain the household’s per capita years of education. Second, for the health capital of family members, we use the average health status of family members evaluated by interviewers in the questionnaire as the instrumental variable of family members’ health capital. Finally, like human capital, there may be two-way causality and omitted variable problems between social capital and household poverty status. Following Bentolila et al. (2010), we construct the average social capital of all households in the same community except for the household as the instrumental variable of the household’s social capital.
4.2.2 Occupational Stratification
Previous literature has discussed occupational stratification from the perspective of occupational prestige or the economic status of occupations. The essential criteria for determining occupational stratification are multidimensional and subjective to some extent. The ranking order of different occupations not only stems from corresponding educational requirements and income mobility but also depends on the general evaluation of relative occupational status (occupational prestige) and the social attitudes towards the occupation. There are various views on the classification of occupational prestige. For example, Treiman (1977) used empirical data from 60 countries and cultural groups, including agricultural and post-industrial societies, to construct the Standard International Occupational Prestige Scale by averaging the prestige scores of these individuals. Ganzeboom (2008) proposed a hierarchical model of occupational prestige based on the social structure of a country and constructed the International Socioeconomic Index (ISEI). Since the CFPS data provide household members’ occupation codes and ISEI, we use the ISEI to measure occupational stratification. Many studies have shown that migrant workers improve rural labor skills and income (Du et al., 2005; Liu, 2008). Therefore, we use whether or not the household labor force migrates as an important aspect to reflect occupational stratification.
In addition to the explanatory variables, this article adds other control variables, including household and community characteristics. Household characteristics mainly include family size (familysize) and dependency ratio (dependrate). In estimating the impact of human and social capital on occupational stratification and multidimensional poverty, it is also necessary to control the overall human capital status of the community to control the spillover effect of human capital. We use the village per capita education years (ceduyear) as a proxy for the community’s overall human capital. In addition, due to specific trends and fluctuations in economic development in different years, we need to control the fixed effects of the year. Variable names, definitions, and statistical descriptions are shown in Table 5.
Variable definition and summary statistics
| Variables | Variable definition | Obs | Mean | Std. error | Min | Max | |
|---|---|---|---|---|---|---|---|
| Poverty | poverty 1 | Poverty status defined according to the World Bank’s low standard (US$1.9/day) poverty line | 33,179 | 0.195 | 0.396 | 0 | 1 |
| poverty 2 | Poverty status defined according to the World Bank’s high-standard (US$3.1/day) poverty line | 33,179 | 0.312 | 0.463 | 0 | 1 | |
| poverty 3 | Poverty status based on the Chinese official poverty line | 33,179 | 0.202 | 0.401 | 0 | 1 | |
| mpoverty1 | Multidimensional poverty status identified using the AK method (equal weight) | 28,836 | 0.266 | 0.442 | 0 | 1 | |
| mpoverty2 | Multidimensional poverty status identified using the AK method (entropy weight) | 28,836 | 0.077 | 0.266 | 0 | 1 | |
| Human capital | fhealth | Household average health level | 29,310 | 3.325 | 1.040 | 1 | 5 |
| feduyear | Household average years of education | 28,690 | 6.655 | 3.563 | 0 | 19 | |
| feduex | Household average years of education based on the impact of the implementation of the Compulsory Education Law | 35,855 | 5.848 | 1.246 | 4.56 | 8.85 | |
| fhealth_ev | The average health status of the household evaluated by the interviewer | 32,737 | 5.175 | 1.162 | 1 | 7 | |
| Social capital | gc | The total amount of cash gifts received and paid by a household | 36,238 | 3.487 | 3.907 | 0 | 13.710 |
| pc | Related to party member of local manager | 36,238 | 0.121 | 0.543 | 0 | 1 | |
| gc_n | Average gift change of all households in the same community except for the household | 36,238 | 3.542 | 1.545 | 1.434 | 12.231 | |
| pc_n | Average political relation of all households in the same community | 36,238 | 0.123 | 0.276 | 0.004 | 0.643 | |
| Occupational stratification | mig_rate | Percentage of household labor force who work outside of their resident county | 36,238 | 0.115 | 0.241 | 0 | 1 |
| isei_mean | Family.Occupation per labor reputation score | 36,238 | 12.068 | 10.293 | 0 | 88 | |
| Family and community characteristic | Familysize | Number of family members | 35,421 | 4.108 | 1.909 | 1 | 26 |
| dependrate | Dependency ratio | 35,065 | 0.376 | 0.328 | 0 | 1 | |
| ceduyear | The average years of education per capita in the village | 36,105 | 5.298 | 1.567 | 0 | 17.5 |
5 Estimation Results
5.1 Human Capital, Social Capital, Occupational Stratification, and Multidimensional Poverty Reduction: Benchmark Model
This section uses econometric models (6)–(8) to examine the impact of human capital, social capital, and occupational stratification on rural poverty reduction and the mediating impact mechanism. The results are shown in Table 6.
Human capital, occupational stratification, and rural multidimensional poverty
| (1) FE | (2) FE | (3) clogit | (4) clogit | |
|---|---|---|---|---|
| mig_rate | isei_mean | mpoverty2 | mpoverty2 | |
| fhealth | 0.009*** | 0.340*** | −0.010*** | −0.002 |
| (0.002) | (0.070) | (0.001) | (0.001) | |
| feduyear | 0.009*** | 0.450*** | −0.005*** | −0.002*** |
| (0.001) | (0.029) | (0.000) | (0.000) | |
| gc | 0.001*** | 0.030** | −0.003*** | −0.002*** |
| (0.000) | (0.014) | (0.000) | (0.000) | |
| pc | 0.002*** | 0.021** | −0.002*** | −0.001*** |
| (0.000) | (0.000) | (0.000) | (0.000) | |
| mig_rate | −0.032*** | |||
| (0.007) | ||||
| isei_mean | −0.002*** | |||
| (0.000) | ||||
| familysize | 0.005*** | −0.141*** | 0.009*** | 0.008*** |
| (0.001) | (0.050) | (0.001) | (0.001) | |
| dependrate | 0.024** | −3.887*** | 0.065*** | 0.061*** |
| (0.010) | (0.326) | (0.006) | (0.007) | |
| ceduyear | −0.003 | 0.455*** | −0.017*** | −0.016*** |
| (0.002) | (0.075) | (0.001) | (0.001) | |
| constant | 0.024 | 8.614*** | ||
| (0.015) | (0.506) | |||
| N | 27,594 | 27,594 | 11,306 | 11,306 |
| R 2 | 0.433 | 0.571 | ||
| Ll | −1,799.454 | −1,887.765 |
Note: The reported values in the table are marginal effects, and the numbers in parentheses are standard errors. ***, **, and * indicate significance at the 1, 5, and 10% levels, respectively.
Columns (1) and (2) use occupational stratification as the explained variable to verify human and social capital’s impact on occupational stratification. The results show that both human capital and social capital have a significant promoting effect on the occupational stratification of family members. Among them, improving human and social capital has encouraged rural residents to migrate for work and significantly enhanced the occupational prestige of family members. This result means that improving human and social capital will benefit the occupational stratification of rural family members, consistent with multiple studies, such as Bian et al. (2015), which demonstrate that social capital provides individuals with more job opportunities in the labor market.
Column (3) in Table 6 studies the impact of human and social capital on multidimensional poverty in rural households. The results show that the coefficients of human capital, including health and knowledge, are significantly positive at the 1% level, indicating that human capital significantly reduces the probability of multidimensional poverty in rural households, similar to the findings of Chen and Wang (2001), which verify that human capital, including health, has a positive effect on income, thus making the accumulation of human capital significantly reduce the multidimensional poverty of rural households. Like human capital, the coefficient of social capital is also significantly positive at the 1% level, indicating that improving social capital level significantly reduces multidimensional poverty in rural households, consistent with the results of Bian et al. (2015), which suggest that social capital provides individuals with more job opportunities in the labor market, increases the wage income of family members, and thus reduces the risk of multidimensional poverty in rural households. But compared to Wang et al. (2023), our coefficient of social capital is much lower, indicating social capital and human capital are positively correlated. After controling human capital, the importance of social capital has decayed.
Based on column (3), we control the occupational stratification variables in column (4). The results show that the proportion of migrant workers and the per capita occupation prestige in the household has a significant multidimensional poverty reduction effect. At the same time, after controlling for occupational stratification, the impact of human capital and social capital on multidimensional poverty in rural households decreases, among which the magnitude of the marginal effect of health on multidimensional poverty in rural households decreases from 0.010 to 0.002 and is no longer significant; the magnitude of knowledge on multidimensional poverty in rural households decreases from 0.005 to 0.002; the magnitude of the marginal effect of gift change on multidimensional poverty in rural households decreases from 0.003 to 0.002. Following Preacher and Hayes (2008), we do the Sobel test by calculating Sobel, Aroian, and Goodman statistics.[12] The results of the test show that among the effects of human capital and social capital on household multidimensional poverty, various Sobel statistics from different channels are significant at the 5% level, indicating that occupational stratification plays a mediating role in the effects of human capital and social capital on household multidimensional poverty. In addition, as shown in Table 6, some household and community characteristics also significantly impact the probability of household multidimensional poverty. Among them, the family dependency ratio significantly positively impacts household multidimensional poverty.
Dominance analysis, as technically detailed by Sfakianakis et al. (2021), involves sequentially adding independent variables to assess their relative importance while mitigating the effects of correlations among them, through an extension of the Shapley decomposition method. We have utilized this approach on the models presented in Table 6 to determine the average contributions of each variable to the explanation of the dependent variable. This allows us to evaluate the relative importance of human and social capital in influencing occupational stratification and multidimensional poverty.
The results of the dominance analysis show that, among the influences of human capital and social capital on occupational stratification, the importance of human capital and social capital for the proportion of migrant workers in the family labor force (mig_rate) are 95.01 and 4.99%, respectively; Among the influences of human capital and social capital on occupational prestige, the importance of human capital and social capital are 98.21 and 1.79%, respectively. Therefore, for occupational stratification, human capital is the most critical factor, indicating that human capital is far more important than social capital in determining occupational stratification, and the impact of social capital on occupational stratification is relatively weak. For multidimensional poverty, the importance of human capital and social capital is 98.00 and 2.00%, respectively, indicating that for the impact of multidimensional poverty in rural areas, the importance of human capital is far greater than social capital, and the impact of social capital on multidimensional poverty in rural areas is relatively weak. Using column (4) in Table 7 for dominance analysis, the results show that for multidimensional poverty, the importance of human capital, social capital, and occupational stratification are 53.06, 2.13, and 44.81%, respectively, indicating that considering occupational stratification, the importance of human and social capital on multidimensional poverty decreases, indicating that human capital and social capital reduce multidimensional poverty in rural households by affecting the occupational stratification of rural household members.
Robustness test using instrumental variables
| (1) IV-FE | (2) IV-FE | (3) IVprobit | (4) IVprobit | |
|---|---|---|---|---|
| mig_rate | isei_mean | mpoverty2 | mpoverty2 | |
| fhealth | 0.009*** | 0.321*** | −0.457*** | −0.488*** |
| (0.001) | (0.065) | (0.076) | (0.042) | |
| feduyear | 0.006*** | 0.487*** | −0.132*** | −0.145*** |
| (0.002) | (0.021) | (0.003) | (0.005) | |
| gc | 0.001** | 0.032** | −0.020*** | −0.012*** |
| (0.000) | (0.032) | (0.004) | (0.001) | |
| pc | 0.000** | 0.030** | −0.030*** | −0.010*** |
| (0.000) | (0.031) | (0.002) | (0.000) | |
| mig_rate | −0.201*** | |||
| (0.000) | ||||
| isei_mea | −0.009*** | |||
| (0.001) | ||||
| familysize | 0.004*** | −0.121** | 0.110*** | 0.095*** |
| (0.000) | (0.050) | (0.010) | (0.009) | |
| dependrate | 0.021*** | −3.780*** | 0.684*** | 0.532*** |
| (0.010) | (0.300) | (0.071) | (0.045) | |
| ceduyear | −0.000 | 0.433*** | −0.132*** | −0.160*** |
| (0.001) | (0.002) | (0.011) | (0.011) | |
| N | 26,576 | 26,576 | 11,306 | 11,306 |
| R 2 | 0.011 | 0.032 | ||
| Endogeneity test | 0.986 | 1.564 | 5.432 | 7.543 |
| Underidentification test | 44.990 | 76.454 | ||
| Weak IV test | 18.543 | 19.743 | 343.462 | 358.321 |
Note: In (1) and (2), the endogeneity test reports a C-statistic, the underidentification test reports Anderson canon. corr. LM statistic, and the weak IV test reports Cragg-Donald Wald F statistic. In (3) and (4), the endogeneity test reports the Wald test of exogeneity, and the weak IV test reports the Anderson-Rubin (AR) test statistic. ***, **, and * indicate significance at the 1, 5, and 10% levels, respectively.
5.2 Robustness Test
Table 6 examines the relationship between human capital, occupational stratification, and multidimensional poverty. This section mainly conducts a robustness test on the above results from two aspects. First, we address the endogeneity between human capital and social capital, occupational stratification, and household income using instrumental variables of human capital and social capital. Second, we replace the measure of poverty, including using income-defined poverty status and multidimensional poverty status constructed using the equal-weighting method.
First, we used the instrumental variable of human capital and social capital described in Section 4 to perform instrumental variable regression on each column in Table 7. For columns (1) and (2), we used panel GMM estimation, and for columns (3) and (4), we used an IV-probit model with household fixed effects and calculated the marginal effects of each coefficient. Due to the insufficient development of instrumental variable methods for logit models, we use the instrumental variable method of the probit model to address endogeneity issues. Since all binary models report marginal effects, the results of the probit model and the logit model are comparable. The results in Table 8 are consistent with those in Table 7, indicating that both human and social capital significantly improve the occupational stratification of family members, thereby reducing the probability of multidimensional poverty in the family. It is worth noting that after using instrumental variables to avoid endogenous bias, the magnitude of human capital, social capital, and occupational stratification’s coefficients in each regression significantly increased, possibly due to local treatment effects. In addition, the underidentification and weak identification test showed that the instrumental variables strongly correlated with the endogenous variables. The endogenous test results showed that there were endogenous problems in the regression. The above results indicate that the selection of instrumental variables is reasonable.
Robust test using substitution variable and instrumental variable
| (1) clogit | (2) clogit | (3) clogit | (4) clogit | (5) clogit | (6) clogit | (7) clogit | (8) clogit | |
|---|---|---|---|---|---|---|---|---|
| poverty1 | poverty1 | poverty2 | poverty2 | poverty3 | poverty3 | mpoverty1 | mpoverty1 | |
| fhealth | −0.018*** | −0.003 | −0.032*** | −0.005* | −0.018*** | −0.004* | −0.017*** | −0.002 |
| (0.002) | (0.002) | (0.003) | (0.002) | (0.002) | (0.002) | (0.002) | (0.002) | |
| feduyear | −0.012*** | −0.005*** | −0.018*** | −0.005*** | −0.013*** | −0.005*** | −0.008*** | −0.003*** |
| (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | |
| gc | −0.009*** | −0.007*** | −0.009*** | −0.006*** | −0.009*** | −0.007*** | −0.005*** | −0.003*** |
| (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | |
| pc | −0.003*** | −0.002*** | −0.003*** | −0.002*** | −0.003*** | −0.002*** | −0.003*** | −0.002*** |
| (0.000) | (0.000) | (0.000) | (0.000) | (0.001) | (0.000) | (0.000) | (0.000) | |
| mig_rate | −0.281*** | −0.315*** | −0.273*** | −0.053*** | ||||
| (0.013) | (0.013) | (0.013) | (0.009) | |||||
| isei_mean | −0.006*** | −0.008*** | −0.006*** | −0.002*** | ||||
| (0.000) | (0.000) | (0.000) | (0.000) | |||||
| N | 11,306 | 11,306 | 11,306 | 11,306 | 11,306 | 11,306 | 11,306 | 11,306 |
| (9) IVprobit | (10) IVprobit | (11) IVprobit | (12) IVprobit | (13) IVprobit | (14) IVprobit | (15) IVprobit | (16) IVprobit | |
|---|---|---|---|---|---|---|---|---|
| poverty1 | poverty1 | poverty2 | poverty2 | poverty3 | poverty3 | mpoverty1 | mpoverty1 | |
| fhealth | −0.170*** | −0.103*** | −0.156*** | −0.117*** | −0.193*** | −0.100*** | −0.372*** | −0.348*** |
| (0.041) | (0.046) | (0.039) | (0.041) | (0.041) | (0.045) | (0.051) | (0.045) | |
| feduyear | −0.190*** | −0.106*** | −0.191*** | −0.100*** | −0.179*** | −0.009*** | −0.164*** | −0.108*** |
| (0.010) | (0.012) | (0.010) | (0.010) | (0.011) | (0.013) | (0.016) | (0.013) | |
| gc | −0.033*** | −0.029*** | −0.028*** | −0.019*** | −0.034*** | −0.030*** | −0.026*** | −0.014*** |
| (0.003) | (0.003) | (0.002) | (0.002) | (0.003) | (0.003) | (0.004) | (0.003) | |
| pc | −0.022*** | −0.019*** | −0.024*** | −0.018*** | −0.023*** | −0.019*** | −0.019*** | −0.017*** |
| (0.002) | (0.002) | (0.003) | (0.003) | (0.002) | (0.002) | (0.003) | (0.002) | |
| mig_rate | −1.057*** | −0.951*** | −1.039*** | −0.204*** | ||||
| (0.059) | (0.049) | (0.058) | (0.050) | |||||
| isei_mean | −0.024*** | −0.025*** | −0.025*** | −0.007*** | ||||
| (0.002) | (0.001) | (0.002) | (0.002) | |||||
| N | 11,306 | 11,306 | 11,306 | 11,306 | 11,306 | 11,306 | 11,306 | 11,306 |
| Endogeneity test | 5.11 | 6.43 | 5.63 | 9.76 | 4.56 | 4.98 | 9.07 | 8.55 |
| Weak IV test | 145.32 | 165.98 | 145.11 | 134.54 | 143.86 | 176.87 | 197.32 | 189.06 |
Note: All columns control the fixed effects of family size, depend rate, ceduyear, and family and year, and the rest are the same as above. ***, **, and * indicate significance at the 1, 5, and 10% levels, respectively.
The three income poverty states poverty1, poverty2, and poverty3, as well as the equal-weighted multidimensional poverty state mpoverty1, were used to clogit estimation and IV estimation on models (7) and (8) for columns (1)–(12) in Table 8, and we did Sobel tests. The results were consistent with the results of the benchmark model. Both human capital and occupational stratification had significant poverty reduction effects, and occupational stratification was a mediator in the impact of human capital on poverty.
5.3 Compare the Effect of Social and Human Capital in Different Areas
The main deviation of our results from the existing literature is that we find that social capital plays a relatively small role in rural multidimensional poverty reduction relative to human capital. We compared China’s coastal areas with inland areas, large cities, and small cities, and found that the effect of social capital in coastal areas is weaker than that in inland areas, and the effect of social capital in large cities is weaker than that in small cities. In addition, we interact social capital with market-oriented scores and find that the interaction coefficients are positive, that is, the poverty alleviation effect of social capital is weaker in provinces with a good market-oriented environment (Table 9).
Compare the effect of social and human capital in different areas
| Small city | Big city | Inland areas | Coastal areas | Marketization | |
|---|---|---|---|---|---|
| (1) clogit | (2) clogit | (3) clogit | (4) clogit | (5) clogit | |
| mpoverty2 | mpoverty2 | mpoverty2 | mpoverty2 | mpoverty2 | |
| fhealth | −0.005*** | −0.012*** | −0.007*** | −0.013*** | −0.010*** |
| (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | |
| feduyear | −0.003*** | −0.009*** | −0.004*** | −0.008*** | −0.005*** |
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |
| gc | −0.003*** | −0.001*** | −0.004*** | −0.002*** | −0.003*** |
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |
| pc | −0.003*** | −0.002*** | −0.004*** | −0.001*** | −0.003*** |
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |
| gc_mark | 0.000*** | 0.001*** | 0.001*** | 0.000*** | 0.001*** |
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |
| pc_matk | 0.001*** | 0.000*** | 0.001*** | 0.000*** | 0.001*** |
| (0.000) | (0.000) | (0.000) | (0.000) | (0.000) | |
| N | 3,391 | 10,967 | 3,957 | 7,349 | 11,306 |
| ll | −598.874 | −1,283.656 | −731.685 | −1,097.545 | −1,798.454 |
Note: All columns control the fixed effects of family size, depend rate, ceduyear, and family and year, and the rest are the same as above. ***, **, and * indicate significance at the 1, 5, and 10% levels, respectively.
5.4 Analysis of the Long-Term Impact of Human and Social Capital and Occupational Stratification on Multidimensional Poverty in Rural Areas
Establishing a long-term mechanism for sustainable poverty alleviation is a crucial task of poverty alleviation in the post-2020 period.[13] Due to the multidimensional poverty reduction effects of human capital and social capital through occupational stratification in rural households, we need to examine the long-term dynamics of human capital, social capital, and occupational stratification on multidimensional poverty to explore the long-term mechanism of multidimensional poverty reduction in rural areas. We examine the correlation between human capital, social capital, and occupational stratification in different periods. Table 10 shows that the correlation between different periods in variables of human and social capital, and occupational stratification was positive, while education exhibits the highest intertemporal correlation. The above results indicate that human and social capital and occupational stratification have a certain persistence in the long run. As human capital has significant poverty reduction effects, sustained human capital investment and long-term human capital accumulation have important implications for household poverty. Current household human capital may affect future household human capital accumulation through education investment, thereby affecting poverty’s long-term persistence. Social capital and occupational stratification may also have similar long-term mechanisms.
The correlation between values in 2012–2018 and in 2010 of human capital, social capital, and occupational stratification
| Values in 2012–2018 | Human capital | sc | Occupational stratification | ||
|---|---|---|---|---|---|
| Education | Health | mig_rate | isi_mean | ||
| 2012 | 0.78 | 0.31 | 0.1 | 0.18 | 0.42 |
| 2014 | 0.72 | 0.30 | 0.1 | 0.10 | 0.29 |
| 2016 | 0.62 | 0.24 | 0.09 | 0.11 | 0.20 |
| 2018 | 0.56 | 0.22 | 0.07 | 0.10 | 0.20 |
In Table 11, we added the lag of human capital, social capital, and occupational stratification to the regression of household multidimensional poverty status to examine the long-term impact of human capital, social capital, and occupational stratification on household multidimensional poverty. In column (1), we added the lag of human capital and social capital (2 years ago), and the results showed that human capital had a significant negative impact on the current household multidimensional poverty status, indicating that human capital has a long-term dynamic impact on household multidimensional poverty. On the other hand, the lagged value of social capital had no significant impact on the household multidimensional poverty status, indicating that social capital has a short-term impact on household multidimensional poverty. In column (2), we added the lagged value of human capital and social capital (2 and 4 years ago), which once again confirmed the results of column (1). The coefficients of the lagged value of human capital were significantly negative, while the coefficients of the lag of social capital were not significant. Social capital is less sustainable in reducing household multidimensional poverty than human capital. In columns (3) and (4), we gradually added the lag of occupational stratification, and the results showed that the lag of isi_mean in occupational stratification was significantly negative in both lags of 2 and 4 years ago, indicating that occupational stratification has a long-term dynamic impact on household multidimensional poverty.
Analysis of the long-term impact of human and social capital and occupational stratification on multidimensional poverty in rural areas
| (1) clogit | (2) clogit | (3) clogit | (4) clogit | |
|---|---|---|---|---|
| mpoverty2 | mpoverty2 | mpoverty2 | mpoverty2 | |
| fhealth | −0.002 | −0.001 | −0.001 | −0.001 |
| (0.002) | (0.002) | (0.002) | (0.002) | |
| feduyear | −0.001* | −0.000 | −0.001 | −0.000 |
| (0.001) | (0.001) | (0.001) | (0.001) | |
| sc | −0.002*** | −0.002** | −0.001*** | −0.001* |
| (0.000) | (0.000) | (0.000) | (0.000) | |
| fhealth t–2 | −0.007*** | −0.003* | −0.003*** | −0.001* |
| (0.002) | (0.002) | (0.002) | (0.002) | |
| feduyear t–2 | −0.002*** | −0.001* | −0.001*** | −0.001 |
| (0.001) | (0.001) | (0.001) | (0.001) | |
| sc t–2 | −0.001 | −0.000 | −0.001 | −0.000 |
| (0.000) | (0.000) | (0.000) | (0.000) | |
| fhealth t–4 | −0.005*** | −0.002*** | ||
| (0.002) | (0.002) | |||
| feduyear t–4 | −0.001 | −0.001 | ||
| (0.001) | (0.001) | |||
| sct–4 | −0.001 | −0.001 | ||
| (0.000) | (0.000) | |||
| mig_rate | −0.019*** | −0.023*** | ||
| (0.007) | (0.008) | |||
| isei_mean | −0.001** | −0.000 | ||
| (0.000) | (0.000) | |||
| mig_rate t–2 | −0.003 | 0.008 | ||
| (0.007) | (0.007) | |||
| isei_mean t–2 | −0.001*** | −0.001*** | ||
| (0.000) | (0.000) | |||
| mig_rate t–4 | −0.005 | |||
| (0.008) | ||||
| isei_mean t–4 | −0.000* | |||
| (0.000) | ||||
| N | 9,674 | 8,014 | 9,674 | 8,014 |
***, **, and * indicate significance at the 1, 5, and 10% levels, respectively.
6 Conclusion
Rural intellectual assistance is vital for China to implement targeted poverty alleviation strategies and strategies to address long-term relative poverty. Based on the CFPS data, this article uses the AF method to calculate the rural multidimensional poverty index, empirically examines the impact of human capital and social capital and occupational stratification on rural multidimensional poverty reduction, and discusses the long-term characteristics of the above impacts. The main conclusions are as follows: the improvement of human capital level and social capital can affect the occupational stratification of rural household members, thereby promoting the growth of household income and reducing the probability of multidimensional poverty in the household; occupational stratification is the intermediator in the poverty alleviation effect of human and social capital; compared to social capital, human capital has a more substantial impact on occupational stratification and rural multidimensional poverty; human capital has a long-term dynamic impact on household multidimensional poverty, which is a long-term poverty alleviation mechanism. On the other hand, social capital has a short-term impact on household multidimensional poverty. At the same time, occupational stratification has a long-term dynamic impact on household multidimensional poverty, which is also a long-term poverty alleviation mechanism.
We propose the following suggestions based on the findings and considering the current situation in China and the need to address relative poverty in the future.
Our research confirms that human capital accumulation significantly contributes to multidimensional poverty reduction in rural areas. Despite the narrowing educational gap between urban and rural China, disparities in educational development persist, with rural education quality remaining a concern. For instance, in 2021, the national average teacher-to-class ratio in primary schools was 2.02:1, but rural areas only had 1.88:1, indicating a shortage of full-time teachers (Guo & Li, 2024). Additionally, the aging of rural teachers is a serious issue, with the proportion of teachers over 55 years old being significantly higher in rural areas than in urban areas (Shi & Sercombe, 2020). Additionally, the growth rate of investment in rural compulsory education is below the national average. Revitalizing rural education requires addressing these challenges. Specific measures include: strengthening the rural teacher workforce through professional development and improved compensation (Li et al. 2023); promoting curriculum and teaching reforms in rural schools, and facilitating partnerships with urban schools to share resources; enhancing rural school infrastructure and investing in educational informatization; developing teaching materials and resources tailored to rural students’ needs in collaboration with educational publishers and higher education institutions; and establishing a diverse curriculum system that includes courses related to rural career development. The government should encourage social participation in rural education and form a collaborative investment mechanism. These measures will help cultivate rural talents, promote rural development, and support long-term poverty reduction efforts.
The 50–50 division in high school admissions has exacerbated the educational divide between urban and rural regions, a consequence of the initial disparities in educational quality. Since 2016, the Ministry of Education has mandated that enrollment in general and vocational education at the high school level should be approximately equal.[14] This policy, in effect, results in a 50–50 split in high school admissions, which exacerbates the educational quality gap between urban and rural areas, leading to a higher proportion of rural students being directed into vocational education. However, this policy, coupled with the urban-rural educational quality gap, directs more rural students into vocational education, depriving them of educational choices and causing widespread anxiety. This aligns with Johannes Giesinger’s assertion that “it is unreasonable to prematurely close the doors of future choices for children without necessity. what we need is to promote the development of students, not to simply judge their talents and abilities (Giesinger, 2017).” Therefore, it is recommended that the restrictions on high school admissions be relaxed, and enrollment quotas be determined based on actual teaching resources and demand. Additionally, vocational education grapples with challenges such as low social recognition, parental and student resistance, misalignment with industry needs, talent–employer mismatch, and graduate dissatisfaction (Chen, 2020). Insufficient infrastructure, a weak teaching staff, and funding disparities further impede its progress. To bolster vocational education, it is crucial to legislatively elevate its status within the national education system, align professional settings and training with industry demands (Zhe, 2023), strengthen school–enterprise cooperation, enhance teaching quality and career prospects, and increase funding and optimize resource allocation, particularly in western regions.
Our research confirms that occupational stratification significantly contributes to multidimensional poverty reduction in rural areas. To enhance rural vocational education and non-agricultural training, it is crucial to address issues such as the disconnect between training and practice, monotonous content, insufficient qualified teachers, and low education levels among farmers. Strategies include refining training content and methods to align with practical needs, improving the training system to increase diversity and effectiveness, strengthening teacher team development with a focus on practical experience, emphasizing agricultural and information technology education to uplift farmers’ education levels, and promoting rural vocational and adult education to enhance employability. Implementing a lifelong vocational skill training system and enhancing employment guidance and entrepreneurship support are also vital. These measures will effectively advance rural vocational education, improve farmers’ professional skills and employability, elevate the occupational stratification of rural residents, and provide a robust talent foundation for sustained rural poverty reduction.
Although social capital reduces the multidimensional poverty of households through occupational stratification, some literature (Bentolila et al., 2010) also points out that social capital may cause negative externalities by distorting the labor market and reducing overall productivity besides its benefit to individuals’ career choices. The distribution and role of social capital widen the income gap among rural households and need to be more conducive to equal opportunity. Therefore, the government should not encourage residents to improve their occupational stratification fully through social capital. In fact, according to empirical results, the role of social capital in occupational stratification and poverty reduction is far less than that of human capital. We should build formal institutions to promote information exchange, reduce transaction costs, and improve the employment efficiency of rural residents, leveraging the development of state and social institutions to partially replace the role of family social capital in occupational stratification to inclusively improve the occupational stratification of rural households and reduce the multidimensional poverty of rural households.
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Funding information: The author states no funding is involved.
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Author contributions: The author confirms the sole responsibility for the conception of the study, presented results, and manuscript preparation.
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Conflict of interest: The author states no conflict of interest.
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Data availability statement: The data supporting this study’s findings are available from the Institute of Social Science Survey of Peking University. Restrictions apply to the availability of these data, which were used under license for this study. Data are available from the author or applied from the Institute of Social Science Survey of Peking University at http://isss.pku.edu.cn/cfps/index.htm with the permission of the Institute of Social Science Survey of Peking University.
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Article note: As part of the open assessment, reviews and the original submission are available as supplementary files on our website.
Use the logit model and remove education and health-related variables in poverty measure
| Use logit model | Remove education and health-related variables in poverty measure | |||
|---|---|---|---|---|
| (1) logit | (2) logit | (3) clogit | (4) clogit | |
| mpoverty2 | mpoverty2 | mpoverty3 | mpoverty3 | |
| fhealth | −0.020*** | −0.001 | −0.011*** | −0.001 |
| (0.000) | (0.001) | (0.000) | (0.002) | |
| feduyear | −0.004*** | −0.003*** | −0.006*** | −0.003*** |
| (0.000) | (0.000) | (0.000) | (0.000) | |
| gc | −0.004*** | −0.001*** | −0.004*** | −0.002*** |
| (0.000) | (0.000) | (0.000) | (0.000) | |
| pc | −0.001*** | −0.000*** | −0.002*** | −0.002*** |
| (0.000) | (0.000) | (0.000) | (0.000) | |
| mig_rate | −0.043*** | −0.045*** | ||
| (0.009) | (0.008) | |||
| isei_mean | −0.004*** | −0.001*** | ||
| (0.000) | (0.000) | |||
| N | 29,395 | 29,395 | 11,306 | 11,306 |
| ll | −4,494.087 | −4,756.065 | −1,518.001 | −1,678.032 |
Note: All columns control the fixed effects of family size, depend rate, ceduyear, and family and year, and the rest are the same as above. ***, **, and * indicate significance at the 1, 5, and 10% levels, respectively.
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