Welfare migration or migrant selection? Social insurance participation and rural migrants’ intentions to seek permanent urban settlement in China

Abstract

Chinese

The welfare system can be a crucial factor in the urban settlement of rural migrants, but its effects are difficult to determine because to do so one must distinguish the effect of welfare entitlement from the effect of ‘migrant selectivity bias’, which widely exists in cities in developing countries. Using survey data from 15 Chinese cities, this study examines the ways in which social insurance – the most critical social welfare package in China – affects rural migrants’ urban-settlement intentions. The results show that there is a ‘migrant selectivity bias’ in Chinese cities, that is, rural migrants who are better off socio-economically are more inclined to participate in social insurance and are more inclined to pursue permanent urban settlement. Meanwhile, social insurance participation is significantly and positively related to rural migrants’ urban-settlement intentions, and this positive relation remains even after we discount the effect of ‘migrant selectivity bias’ in the analysis. We argue that, for rural migrants in China, the effect of social insurance participation on urban settlement intentions closely resembles that experienced by those who move to migration-managed regimes. We conclude that the Chinese government should establish a more inclusive social welfare system to enable rural migrants to permanently settle in cities.

Keywords

migrant selectivity bias rural migrant social insurance urban settlement intention welfare migration

Introduction

Circular migration has been the primary model used to explain rural migration in China over the past few decades. Rural migrants, whose numbers exceeded 288 million in 2018 (National Bureau of Statistics of China (NBS), 2019), have been mainly regarded as temporary residents who seek job opportunities and earn money in cities to finance the education and housing of their families in the countryside. When those family needs are met, rural migrants go back to the countryside. This strategy of circular migration is predicted to give way to permanent settlement of rural migrants in cities when the urbanisation ratio reaches a certain point (Skeldon, 1990), and that ratio has now been reached: the urbanisation ratio has increased from less than 20% in 1980 to nearly 59% in 2017 (NBS, 2018). Permanent urban residence has become the first choice of the majority of rural migrants.

A number of theoretical approaches have been developed to explain the urban settlement of rural migrants, including the human-capital approach (Borjas, 1989; Khoo et al., 2008; Simmons and Cardona, 1972), the success-or-failure framework (Borjas and Bratsberg, 1996; Gibson and McKenzie, 2011), the social capital perspective (Haug, 2008) and the health selection perspective (Abraído-Lanza et al., 1999; Franzini et al., 2001). These approaches have been applied to the case of Chinese rural migrants and have proved to be more or less relevant (Cao et al., 2014; Chen and Liu, 2016; Fan, 2011; Huang et al., 2018; Liu et al., 2017; Sheng et al., 2019; Xie and Chen, 2018; Xie et al., 2017; Yang and Guo, 2018; Zhu and Chen, 2010).

Welfare is another crucial factor in the settlement of migrants. ‘Welfare migration’ refers to the phenomenon of migration to places with more generous welfare benefits (Blank, 1988; Borjas, 1999; De Giorgi and Pellizzari, 2009; Levine and Zimmerman, 1999; McKinnish, 2005, 2007; Sabates-Wheeler and Feldman, 2011; Sinn, 2004). This phenomenon has been observed in places such as the EU, the US and Australia, among both natives and immigrants. However, the effect of the welfare system on the urban settlement of Chinese internal migrants has received scant attention.

The effect in China, however, is different from that in developed economies. The welfare entitlements of people in industrialised countries are usually bound to the place where they live, at the national level, the local level or both. People can freely choose to migrate to places where they can maximise their welfare benefits. This is not the case in China. At its inception, Chinese welfare fell into two categories. The welfare entitlement of people was determined by the status of their household registration (hukou): people who have urban hukou possess more welfare benefits than those who have rural hukou. In recent years, the Chinese government has been pursuing a universal welfare system to encourage urban–rural harmonisation. However, Shi (2012) suggests that this effort has only led to a new division: ‘within–without’ standards rather than rural and urban hukou. Scholars have labelled this ‘local citizenship’: social welfare entitlements are only granted to citizens with the local hukou status (Smart and Smart, 2001). Non-local hukou holders are excluded from the local welfare system. Even though rural migrants work and reside in cities, they do not necessarily have access to welfare benefits there because they are not local hukou holders. Many of them are regarded as sojourners in cities and are excluded from the urban welfare system. Without the protection offered by the urban welfare system, many rural migrants are compelled to return to rural areas when they cannot continue working in cities due to ageing, sickness or unemployment.

The association between the welfare benefits and the urban residence of rural migrants in China, therefore, does not fit the usual pattern of welfare migration. A significant positive association may lead to two seemingly contradictory conclusions. On the one hand, such a relationship could be viewed as strong evidence that social welfare programmes protect rural migrants and enable them to reside permanently in cities and, therefore, to contribute to sustainable urbanisation in China. On the other hand, the association could be seen as an example of a selection process in social insurance participation (Jiang et al., 2018), which is caused by ‘migrant selectivity bias’ in cities (Williamson, 1988a, 1988b). That is, urban governments use the social welfare system as an instrument to control the scale of the urban population: only preferred migrants are included in the urban welfare system and can settle in cities, while migrants without welfare coverage are compelled to return to rural areas. Not surprisingly, rural migrants who have social insurance are more likely to want to stay in the city.

Against this theoretical and political background, this article examines how social insurance – the most crucial form of social welfare in China – affects rural migrants’ intentions to pursue permanent urban residence. Intentions are the product of a complicated psychological process, and determining the factors that shape them will provide important insights (Piotrowski and Tong, 2013). The challenge is how to separate the effect of social insurance from the effect of ‘migrant selectivity bias’. Resolving this issue will illuminate the links between the welfare system and the urban settlement of Chinese domestic migrants, which have not yet been explored. The findings of this study have significant theoretical and political implications for the welfare situation of domestic migrants in developing countries.

Literature review

Welfare, migration and settlement

Welfare is a significant factor in settlement decisions. That is, the decision about where to settle involves a consideration of the generosity and inclusiveness of the welfare system. There are at least four main types of welfare migration: two apply to welfare migration of internal migrants, and the other two to welfare migration of international migrants.

The first type is generosity-oriented migration within a country, which occurs when citizens migrate to a place with more generous welfare provisions. This type of migration is prevalent in the US and Europe. In the US, welfare recipients, particularly single-parent families, are more likely to migrate to states where the welfare benefits are better (Blank, 1988; Enchautegui, 1997). Other studies show similar findings that the generosity of the welfare system significantly affects individuals’ migration decisions (Gramlich and Laren, 1984; McKinnish, 2005, 2007).

The second type is so-called ‘counter-urban’ migration within a country, which occurs when welfare recipients and those with low incomes move out of major cities to suburban and rural areas. This type of migration is prevalent in Australia, where low-income groups and welfare recipients tend to move away from municipalities instead of towards them. A study conducted by Wulff and Bell (1997) reveals that the movement away from cities is not, as previously thought, a phenomenon applying only to wealthy people; instead, it is the result of the fact that a large proportion of the poor, the unemployed and welfare recipients move to escape the high cost of cities. In a later study, Hugo and Bell (1998) further developed a welfare theory of counter-urbanisation.

Internal welfare migration patterns in the US, the EU and Australia are similar in some respects: low-income groups have a higher mobility rate than wealthy groups because these vulnerable people’s living conditions are more likely to be affected by welfare benefits. The main difference in these national patterns is that the directions of welfare migration are not the same. One is generosity-oriented and the other is counter-urban. This difference is a consequence of differences in welfare systems. In the US, welfare benefits are delivered largely by the state, so migration can be motivated by differences in benefits and taxes between one state and another. In Australia, in contrast, benefits are more or less uniform across the nation, so it is other factors that influence internal migration. Instead, the poor and unemployed in Australia tend to move to places where the cost of living cost is lower (Wulff and Bell, 1997).

The third type of welfare migration is international immigration to free-migration regimes. Based on the finding that immigrants who are welfare recipients in the US are more inclined to settle in high-benefit states, Borjas (1999) put forward the welfare magnet hypothesis: immigrants are drawn to a generous welfare state. Other studies have supported this hypothesis. Boeri (2010) notes that unskilled immigrants in the EU are net recipients of non-contributory benefits. This is particularly applicable to immigrants who have the freedom to choose their destination. The most generous welfare states are often the most popular destinations of immigrants (Boeri, 2010; Nannestad, 2007).

The fourth type of welfare migration – immigration in managed-migration regimes – is very different. Razin and Wahba (2015) point out that the welfare magnet hypothesis is only effective under free-migration regimes where immigrants can choose where to settle down. Free-migration regimes usually attract more unskilled than skilled immigrants. In managed-migration regimes, immigrants are expected to be net contributors to the welfare system. Welfare states prefer to select highly skilled immigrants (Giulietti and Wahba, 2013). Blanchflower and Lawton (2009) reveal that in the UK, immigrants are more likely to be employed and less likely to be welfare recipients than those who were born there.

The findings of the above studies confirm that the influence of welfare systems on migration is moderated by the type of welfare regime. In the case of internal migration, nationwide social welfare leads to counter-urban migration, while a diversified welfare system leads to cross-region mobility. In the case of international migration, free-migration regimes usually attract unskilled immigrants, whereas managed-migration regimes are more likely to attract skilled immigrants. Such variations may be the reason that studies on welfare migration and settlement do not always reach consistent conclusions.

In addition to the two types of internal migration mentioned above, there is a type that is closely associated with the welfare system but has not received much research attention. This is the large-scale migration of rural citizens in developing nations such as China, Indonesia and India. Usually, the living conditions of rural migrants in these developing countries are poor. The migration they undertake is the opposite of the ‘counter-urban’ migration characteristic of Australia. Instead, cities are viewed as offering enough of an increase in social welfare to outweigh the potential risks of a move. The welfare system, in this case, promotes urban settlement. Urban governments, however, may use welfare entitlements as an instrument to control the scale of rural migration (Sabates-Wheeler and Feldman, 2011). For instance, MacAuslan (2011) notes that urban governments in India use the welfare system to regulate migration. Their migrant selection system is very similar to that employed in many Chinese cities.

The urban-settlement intention of rural migrants in China

Many studies have been dedicated to explaining the urban-settlement behaviours of rural migrants in China, with particular attention paid to urban-settlement intention. The hukou-centred approach has been the dominant approach used (Zhu, 2007). This holds that the hukou system restricts rural migrants’ access to decent jobs and public services in urban areas, and that rural migrants are therefore regarded as temporary sojourners in cities (Du et al., 2018; Tang et al., 2016). Meanwhile, other studies suggest that in addition to the important role played by the hukou system, attention should also be paid to rural migrants’ livelihoods and well-being in cities (Chen and Fan, 2016; Zhu, 2007). In line with this argument, the majority of rural migrants express little or no intention of converting their rural hukou to urban hukou; rather, they only wish to permanently reside in cities (Chen and Fan, 2016). Such a finding suggests that studies on rural–urban migration in China must go beyond the hukou-centred approach and pay more attention to other determinants of the urban-settlement intention of rural migrants.

Human capital is a factor that is often considered in studies on the urban-settlement intention of rural migrants. Many studies have found that highly skilled rural migrants often express a stronger desire for permanent urban settlement than their low-skilled counterparts (Hao and Tang, 2015; Liu and Wang, 2014; Zhu and Chen, 2010), suggesting that there might be a ‘migrant selectivity bias’ in Chinese cities. In addition, the human-capital perspective is also embedded in policy practice in urban China. In recent years, many Chinese cities, such as Shanghai, Guangzhou and Beijing, have implemented a points system to determine which migrants will be granted urban hukou, allowing them to access the urban public services that were once only provided for local residents. Zhang (2012) suggests that this system is an instrument for urban governments to attract highly skilled migrants and exclude the low-skilled. Using this system, they prioritise human resources to advance the economic prosperity of their cities.

Further, a large set of studies have focused on how socio-economic factors may affect the urban-settlement intentions of rural migrants. Many studies have shown that rural migrants with a higher income and better occupational status have a stronger urban-settlement intention (Hu et al., 2011; Wang and Fan, 2006). These studies suggest that economic capital also matters in determining the urban settlement of rural migrants. They show that in addition to the significant effect of the hukou system, rural migrants’ livelihoods and well-being should not be neglected when analysing their urban-settlement decisions.

Other studies have paid attention to the role of social capital in determining the urban-settlement intentions of rural migrants. Generally, these studies find that there are mixed effects of social capital on the urban-settlement intention of rural migrants. While there is a positive relationship between connections with urban locals and the urban settlement intentions of rural migrants, the intra-group relationship within the migrant population shows no such effect (Chen and Liu, 2016; Huang et al., 2018). These studies reflect that building relationships with urban locals is very important for the urban settlement of rural migrants.

Finally, some studies have also noticed the importance of health in influencing the urban-settlement intentions of rural migrants. The health selection perspective is an extension of the healthy migrant phenomenon, which refers to the hypothesis that migrants usually show better health conditions than local residents, despite their lower socio-economic status in the host society (Abraído-Lanza et al., 1999; Franzini et al., 2001). Relevant studies of rural migrants in China have found evidence to support the health selection perspective (Chen, 2011). Because rural migrants in China face difficulties in accessing medical services in urban areas, they are forced to evaluate their health status when considering settling in cities (Xie et al., 2017). This situation may intensify the link between health and the urban-settlement intention of rural migrants.

The social insurance participation of rural migrants in China

The lack of social protection for rural migrants is well documented (Nielsen and Smyth, 2008). Incorporating rural migrants into the urban social welfare system is a crucial step in protecting them from potential risks. Social insurance is the primary component of the Chinese welfare system. It includes pensions, work injury insurance, unemployment insurance, maternity insurance and medical insurance. These five insurance programmes, plus the housing provident fund, are known as the ‘five insurances plus one fund’ in China, representing a crucial social welfare package. In this study, we consider the housing provident fund as a part of social insurance. While the pension, unemployment insurance, medical insurance and housing provident fund receive contributions from both employees and employers, work injury insurance and maternity insurance receive contributions from employers only and are determined according to the employee’s wage. The Social Insurance Law states that rural migrants can partake in social insurance regardless of their hukou status (Government of the People’s Republic of China, 2011).

Labour contracts are a prominent determinant of social insurance participation among rural migrants (Gao et al., 2012; Meng, 2017; Li and Freeman, 2015; Wu and Xiao, 2018). The 2008 Labour Contract Law states that employers should share the costs of employees’ social insurance. If an employer refuses to pay social insurance contributions, employees have the right to end the labour contract and to claim financial compensation. Therefore, the social insurance coverage of rural migrants has been greatly improved since the enactment of this law (Li and Freeman, 2015). Even with the implementation of the law, however, the proportion of rural migrants covered by social insurance programmes is still quite low. According to NBS (2010), of all five social insurance programmes, work injury insurance is the most common among rural migrants, but its coverage was still less than 25% in 2009. The percentage of rural migrants with maternity insurance was only around 2%. These statistics indicate that rural migrants still confront many barriers to participation in social insurance.

Research has demonstrated the positive effect of social insurance participation on rural migrants’ well-being. Those covered by medical insurance tend to have more health-seeking behaviours and health-service utilisation (Mou et al., 2009; Peng et al., 2010; Qin et al., 2014). Social insurance participation is also positively linked to rural migrants’ subjective well-being (Fang and Sakellariou, 2016). Such findings suggest that social insurance is effective in enhancing rural migrants’ livelihoods and well-being in urban areas. This background will help us to explore the potential link between social insurance participation and rural migrants’ desire to settle permanently in the city.

Linking social insurance participation to the urban-settlement intention of rural migrants

The above analysis suggests that, theoretically, social insurance participation and the urban-settlement of rural migrants should be closely related. However, since there might be a ‘migrant selectivity bias’ in Chinese cities, the real effect of social insurance participation on the urban settlement of rural migrants should be carefully analysed. Figure 1 illustrates the effect of social insurance participation on the urban-settlement intention of rural migrants, taking into consideration the ‘migrant selectivity bias’ in Chinese cities.

Figure 1.

The effect of social insurance participation on the urban-settlement intention of rural migrants.

Social insurance participation is a primary criterion for distinguishing formal employment from informal employment (Gallagher et al., 2015). Since urban governments do not provide the same services to rural migrants as they do to local citizens, rural migrants have suffered very serious discrimination in terms of social welfare entitlements (Solinger, 1999). On the other hand, those rural migrants who participate in social insurance are protected by the urban welfare system and feel more settled in the city. Hence, we propose the first hypothesis of this article:

Hypothesis 1: There is a significant positive link between social insurance participation and rural migrants’ intention to remain in the city.

However, the potential positive association between social insurance participation and urban settlement does not necessarily mean that social insurance is beneficial to rural migrants. It can also be viewed as a form of ‘migrant selectivity bias’ used to select preferred migrants (Williamson, 1988a, 1988b). Rural migrants who are better off socio-economically are more inclined to participate in social insurance (Gao et al., 2012); consequently, they are more likely to settle in cities. Since economic development is often viewed as the first priority of local governments in China (Li and Zhou, 2005), we could expect that urban governments prefer younger and highly skilled rural migrants to permanently stay in cities. Based on the above analysis, we propose our second hypothesis:

Hypothesis 2: A ‘migrant selectivity bias’ exists in Chinese cities. Rural migrants who have the potential to contribute more to the economic development of the city have a higher probability of permanent urban settlement.

What is the real effect of social insurance on the urban-settlement intention of rural migrants? In order to tackle this question, the effect of social insurance on rural migrants’ intention to remain in the city, then, must be distinguished from the effect of the ‘migrant selectivity bias’ in the analysis. Previous studies have shown that social insurance is beneficial to improving rural migrants’ livelihoods and well-being in the city, such as protecting health and improving subjective well-being (Fang and Sakellariou, 2016; Mou et al., 2009; Peng et al., 2010; Qin et al., 2014). Thus, we can expect that after excluding the ‘migrant selectivity bias’, social insurance participation should still be positively related to the urban-settlement intention of rural migrants. Based on the above analysis, we propose the third hypothesis of this study:

Hypothesis 3: After eliminating the ‘migrant selectivity bias’, social insurance participation still has a positive effect on the urban settlement intention of rural migrants.

Data and methods

Data

The data collected by the survey project – Rural–Urban Migration in China (RUMiC) – are the basis of this study. The RUMiC is a large survey project that investigates the living conditions of Chinese rural migrants. The project was designed by a group of international scholars from China, Australia and Germany. Due to the high number and frequent mobility of rural migrants, it is very difficult to obtain a sampling frame that contains a representative sample (Kong, 2010). To overcome this issue, the research team selected 15 cities, all popular migrant destinations, from nine provinces throughout the eastern, central and western areas of China. The cities were divided into blocks, and the research team randomly selected a few blocks in each city. A list of all rural migrants in the selected areas was collected. Finally, a group of rural migrants was selected for face-to-face interview from all listed migrants. Kong (2010) describes the survey design and implementation in detail. The dataset is arguably the best available record of rural-to-urban migration in China (Gao et al., 2012; Meng, 2017).

The survey team interviewed 8446 migrants from 5000 households in 2008, and another 5426 migrants from 5000 households in 2009. The sample distribution in 15 cities is presented in Table 1. We merged the two rounds of the survey to create a pooled cross-sectional dataset. We deleted any observations with missing values. Since this study examines the intentions of rural migrants, we excluded urban–urban migrants from the sample. We ended up with a dataset with 10,510 respondents for the analysis.

Table 1.

Sample distribution in 15 survey cities.

Region	Province/municipality	Survey year	2008		2009
		City	Frequency	Percentage	Frequency	Percentage
Eastern area	Shanghai	Shanghai	852	10.09	471	8.68
	Jiangsu	Nanjing	611	7.23	458	8.44
		Wuxi	331	3.92	254	4.68
	Zhejiang	Hangzhou	639	7.57	580	10.69
		Ningbo	331	3.92	230	4.24
	Guangdong	Guangzhou	617	7.31	491	9.05
		Dongguan	469	5.55	280	5.16
		Shenzhen	365	4.32	265	4.88
Central area	Henan	Zhengzhou	658	7.79	339	6.25
		Luoyang	366	4.33	204	3.76
	Anhui	Hefei	705	8.35	295	5.44
		Bengbu	428	5.07	247	4.55
	Hubei	Wuhan	692	8.19	515	9.49
Western areas	Chongqing	Chongqing	682	8.07	414	7.63
	Sichuan	Chengdu	700	8.29	383	7.06
		Total	8446	100.00	5426	100.00

Measures

The RUMiC survey measured rural migrants’ urban settlement intentions by rating their answer to the following question: ‘How long would you like to reside in the city if government policy allowed you to do so?’. The respondents could choose from the following answers: ‘less than 1 year’, ‘1–3 years’, ‘more than 3 years’, ‘permanently’ and ‘not sure’. Because this study focuses on the intention to pursue permanent settlement, we coded the responses according to two types (1 = permanent; 0 = temporary).

Social insurance participation was determined by asking the rural migrants whether they were included in the pension fund; the medical, work injury and unemployment insurance schemes; and the housing provident fund in their city of residence. Maternity insurance was not included in the RUMiC survey. China has also implemented the New Rural Cooperative Medical Scheme and New Rural Pension Scheme for rural residents, and many migrants with rural hukou participate in these programmes as well; however, due to lack of relevant information on these programmes, we were unable to include them in our dataset and analysis.¹ All the variables of insurance participation are dichotomously coded (1 = participated; 0 = did not participate).

In addition to the above-mentioned key independent variables, we also controlled for rural migrants’ demographic attributes, socio-economic characteristics, years of urban residence, hukou status (1 = local hukou; 0 = non-local hukou) and rural assets.²

Socio-economic traits include educational attainment, salary, labour contract status, occupational status and work industry. Educational attainment was a binary variable (1 = university and above; 0 = other). Salary was divided into four categories based on rural migrants’ monthly wage. Labour contract status was a binary variable (1 = having a labour contract; 0 = having no labour contract). Occupational status was divided into five categories: professionals and managers; routine non-manual workers; small property owners; supervisors and skilled workers; and semi-skilled and unskilled workers. In line with NBS (2018), we classified the working industries into seven types. Rural assets include rural migrants’ residences and farmland in the countryside. Both variables were operated as dummy variables.

Analytical strategy

The challenge of this research was to separate the effects of social insurance on urban settlement intentions from the effects of self-selection in social insurance participation caused by ‘migrant selectivity bias’. We used three steps to overcome this problem. First, we used the logistic regression model to see if there is a ‘migrant selectivity bias’ in the city by examining whether well-educated, highly skilled and wealthier rural migrants are more likely to pursue permanent urban residence.

Second, we tested how social insurance participation is associated with urban settlement intentions. The stepwise strategy was adopted to add different social insurance programmes to the regression model separately. Since there is a high correlation in participation in various social programmes (i.e. a person enrolled in one programme is likely to be enrolled in others, as shown in Supplemental Table S1), there is a multicollinearity problem when all the social insurance programmes are added to the regression model simultaneously. In order to overcome this problem, we adopted two measures. First, we used the principal component analysis (PCA) method to extract common factors from the five items of social insurance participation. The scree plot of eigenvalues indicates that extracting one common factor fits the data best. We named the common factor ‘the PCA index’. Second, we aggregated the total number of social insurance programmes available to rural migrants. We named this indicator ‘the sum index’. We separately added the PCA index and the sum index to the regression model to test the link between social insurance participation and urban settlement intentions.

Third, we used the propensity score matching (PSM) method to estimate the real effect of social insurance on the urban settlement intentions of rural migrants, which might otherwise be mixed with the effect of migrant selection. The PSM method is effective in dealing with the imbalance of covariates distributed in the treated and comparison groups. It is a statistical approach that estimates the effects of policy interventions by matching subjects in a treatment group to others in control groups that have similar characteristics (Imbens, 2000; Rosenbaum and Rubin, 1983, 1984, 1985). Since we assume that rural migrants who are better off socio-economically are more likely to participate in social insurance programmes, we used the PSM method to estimate the propensity for social insurance participation among rural migrants. Then, to strike a balance of covariates between treatment and comparison groups inside each block of the propensity score, the balancing property of the propensity score was examined. After the balancing property was satisfied, we estimated the real effect of social insurance participation on urban settlement intentions.

Results

Descriptive statistics

Table 2 displays the descriptive statistics. In general, the majority of rural migrants intend to stay permanently in cities. The percentages of rural migrants covered by pensions, medical insurance, work injury insurance, unemployment insurance and the housing provident fund are 19.40%, 13.49%, 17.10%, 11.91% and 6.83%, respectively. These figures indicate that the percentage of rural migrants who are covered by social insurance is still extremely low: they have very little social protection. Rural migrants are relatively young, with an average age of 31. They also have relatively low educational attainments and salaries. Less than 6% have a university education, and most have a salary level of less than 2000 yuan per month. About half of the rural migrants have labour contracts. On average, the length of rural migrants’ residence in the city is about eight years, and about 18% are local migrants. Approximately 90% of rural migrants have farmland and 86% have a residence in their rural place of origin.

Table 2.

Descriptive statistics of independent variables.

Variables	Mean/percentage
Settlement intention
Permanent	60.53
Temporary	39.47
Social insurance participation
Medical insurance	19.40
Pension	13.49
Work injury	17.10
Unemployment	11.91
Housing provident fund	6.83
Gender (%)
Male	59.93
Female	40.07
Age (SD)	31.09 (10.00)
Marital status (%)
Married	62.13
Unmarried	37.87
University education (%)	5.57
Salary (%)
Less than 1000 yuan	17.26
1000–2000 yuan	54.92
2000–3000 yuan	18.09
More than 3000 yuan	9.73
Labour contract status (%)
Having labour contracts	50.12
No labour contract	49.88
Work industry (%)
Construction	13.18
Manufacturing	20.12
Transportation, warehouse and postal	23.26
Wholesale and retail	2.46
Accommodation and catering	26.61
Service industry	8.09
Others	6.27
Occupational status
Professionals and managers	2.65
Routine non-manual workers	12.86
Small property owners	16.07
Supervisors and skilled workers	35.93
Semi-skilled and unskilled workers	32.49
Hukou status (%)
Local hukou	17.52
Non-local hukou	82.48
Length of urban residence (SD)	7.84 (6.43)
Residence at hometown	86.01
Farmland at hometown	89.53

Notes: SD represents standard deviation.

Regression results

Table 3 presents the coefficient estimates based on the logistic regression model in order to determine whether there is a ‘migrant selectivity bias’ in Chinese cities. The results of Model 1 suggest that the association between age and urban settlement intentions can be mapped as an inverted U-shaped curve. As rural migrants age, their desire to permanently settle in the city will increase and then decrease. In other words, rural migrants who are in their prime working years are more likely to intend to stay in the city. Socio-economic status is also positively linked to rural migrants’ urban settlement intentions. Those with university education, labour contracts, higher salaries and higher occupational status are more likely to desire permanent urban settlement. Rural assets, however, show no significant association with the urban settlement intentions of rural migrants. Housing and farmland in their place of origin are not significantly linked to rural migrants’ settlement intentions.

Table 3.

Logistic regression on the effect of social insurance participation on rural–urban migrants’ desire to stay permanently.

Independent variables	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8
Hukou status (ref: local hukou)	−0.275***(0.070)	−0.275***(0.070)	−0.262***(0.070)	−0.272***(0.070)	−0.268***(0.070)	−0.268***(0.070)	−0.260***(0.070)	−0.259***(0.070)
Length of urban residence	0.029***(0.004)	0.029***(0.004)	0.028***(0.004)	0.028***(0.004)	0.028***(0.004)	0.029***(0.004)	0.028***(0.004)	0.028***(0.004)
Gender (ref: male)	0.040(0.046)	0.040(0.046)	0.043(0.046)	0.046(0.046)	0.041(0.046)	0.042(0.046)	0.046(0.046)	0.046(0.046)
Age	0.049**(0.017)	0.049**(0.017)	0.045**(0.017)	0.046**(0.017)	0.045**(0.017)	0.045**(0.017)	0.041*(0.017)	0.041*(0.017)
Age squared	−0.001**(0.000)	−0.001**(0.000)	−0.001**(0.000)	−0.001**(0.000)	−0.001**(0.000)	−0.001**(0.000)	−0.001*(0.000)	−0.001*(0.000)
Marital status (ref: married)	−0.067(0.070)	−0.067(0.070)	−0.074(0.070)	−0.071(0.070)	−0.070(0.070)	−0.083(0.070)	−0.078(0.070)	−0.077(0.070)
University degree (ref: no university degree)	0.294**(0.097)	0.294**(0.097)	0.274**(0.097)	0.280**(0.097)	0.274**(0.097)	0.275**(0.097)	0.255**(0.097)	0.254**(0.097)
Salary (ref: less than 1000 yuan)
1000–2000 yuan	0.170**(0.063)	0.170**(0.063)	0.161*(0.063)	0.159*(0.063)	0.155*(0.063)	0.157*(0.063)	0.147*(0.063)	0.147*(0.063)
2000–3000 yuan	0.205**(0.078)	0.205**(0.078)	0.185*(0.079)	0.185*(0.079)	0.185*(0.079)	0.175*(0.079)	0.162*(0.079)	0.162*(0.079)
More than 3000 yuan	0.449***(0.096)	0.449***(0.096)	0.428***(0.097)	0.426***(0.097)	0.428***(0.097)	0.419***(0.097)	0.403***(0.097)	0.402***(0.097)
Occupational status (ref: semi-skilled and unskilled workers)
Professionals and managers	0.240(0.137)	0.240(0.137)	0.236(0.137)	0.233(0.137)	0.224(0.137)	0.237(0.137)	0.225(0.137)	0.226(0.137)
Routine non-manual workers	0.294**(0.100)	0.294**(0.100)	0.299**(0.100)	0.306**(0.100)	0.304**(0.100)	0.299**(0.100)	0.312**(0.101)	0.312**(0.101)
Small property owners	0.541***(0.096)	0.541***(0.096)	0.551***(0.096)	0.553***(0.096)	0.549***(0.096)	0.546***(0.096)	0.559***(0.096)	0.559***(0.096)
Supervisors and skilled workers	0.183**(0.060)	0.183**(0.060)	0.191**(0.060)	0.185**(0.060)	0.183**(0.060)	0.177**(0.060)	0.187**(0.060)	0.188**(0.060)
Work industry (ref: construction)
Manufacturing	0.311***(0.080)	0.311***(0.080)	0.275***(0.080)	0.273***(0.080)	0.252**(0.081)	0.236**(0.081)	0.209**(0.081)	0.211**(0.081)
Transportation, warehouse and postal	0.412***(0.083)	0.412***(0.083)	0.402***(0.083)	0.414***(0.083)	0.395***(0.083)	0.394***(0.083)	0.392***(0.083)	0.392***(0.083)
Wholesale and retail	0.370*(0.149)	0.370*(0.149)	0.353*(0.150)	0.354*(0.150)	0.349*(0.150)	0.350*(0.151)	0.338*(0.151)	0.338*(0.151)
Accommodation and catering	0.414***(0.105)	0.414***(0.105)	0.404***(0.105)	0.408***(0.105)	0.392***(0.105)	0.391***(0.105)	0.386***(0.105)	0.387***(0.105)
Service industry	0.468***(0.097)	0.468***(0.097)	0.457***(0.097)	0.472***(0.097)	0.456***(0.097)	0.454***(0.097)	0.452***(0.097)	0.453***(0.097)
Others	0.444***(0.103)	0.444***(0.103)	0.428***(0.103)	0.439***(0.103)	0.425***(0.103)	0.429***(0.104)	0.414***(0.104)	0.413***(0.104)
Labour contract status (ref: no labour contract)	0.246***(0.049)	0.246***(0.049)	0.199***(0.050)	0.204***(0.051)	0.202***(0.050)	0.197***(0.050)	0.154**(0.051)	0.153**(0.051)
Residence at hometown (ref: no residence at hometown)	0.019(0.063)	0.019(0.063)	0.020(0.063)	0.017(0.063)	0.016(0.063)	0.014(0.063)	0.016(0.063)	0.016(0.063)
Farmland at hometown (ref: no farmland at hometown)	−0.043(0.069)	−0.043(0.069)	−0.043(0.070)	−0.039(0.070)	−0.040(0.070)	−0.038(0.069)	−0.039(0.070)	−0.040(0.070)
Pension		0.388***(0.061)
Medical insurance			0.321***(0.066)
Injury insurance				0.212***(0.063)
Unemployment insurance					0.306***(0.073)
Housing provident fund						0.558***(0.098)
The PCA index							0.160***(0.025)
The sum index								0.118***(0.018)
Constant	−0.952**(0.341)	−0.952**(0.341)	−0.886**(0.341)	−0.925**(0.341)	−0.893**(0.342)	−0.885**(0.341)	−0.764*(0.343)	−0.842*(0.342)
City-fixed effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Survey year	Yes	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Observations	10,510	10,510	10,510	10,510	10,510	10,510	10,510	10,510
Wald chi2	777.39	811.13	792.87	786.50	794.16	802.26	811.32	811.33
Pseudo R2	0.0612	0.0641	0.0629	0.0620	0.0625	0.0638	0.0642	0.0642

Notes: Robust standard errors in parentheses. ***p < 0.001, **p < 0.01, *p < 0.05.

Overall, the results of Model 1 suggest that there is, indeed, a ‘migrant selectivity bias’ in Chinese cities. Rural migrants in their prime working years, with higher educational attainments, salaries and occupational status, are more likely to show a stronger desire to remain in the city. Chan (2018) also notes that China’s new urbanisation strategy gives priority to well-educated, highly skilled and relatively well-to-do rural migrants. This supports the findings of Zhang and Wang (2010), who demonstrated that the likelihood of rural migrants’ gaining urban hukou is generally based on their contribution to the host city.

Models 2 to 6 in Table 3 show the coefficient estimates after social insurance participation variables were separately added to the benchmark model. The results suggest that social insurance participation has a significantly positive association with the urban settlement intentions of rural migrants. More specifically, if rural migrants have a pension, medical insurance, injury insurance, unemployment insurance or access to the housing provident fund, the predicted odds for their permanent residence in the city are 1.47 (e^0.388), 1.38 (e^0.321), 1.24 (e^0.212), 1.36 (e^0.306), and 1.75 (e^0.558) times higher, respectively, than those of migrants who do not have the benefit. Model 7 suggests that for every one-unit increase in the PCA index, rural migrants’ desire to permanently reside in the city increases 1.17 (e^0.160) times. Moreover, Model 8 suggests that for every one-unit increase in the sum index, the urban-settlement intentions of rural migrants increase 1.13 (e^0.118) times. The above results certify the strong correlation between social insurance participation and rural migrants’ urban-settlement intentions.

Since rural migrants who are better off socio-economically are more likely to partake in social insurance programmes, the results of the logistic regression model might be skewed. To correct the potential bias, we used the PSM method to estimate the real effect of social insurance participation on urban settlement intentions. The results of the PSM estimation, shown in Table 4, indicate that, after excluding the effect of ‘migrant selectivity bias’, social insurance participation still shows a positive relationship with urban settlement intentions, although the coefficients are smaller than those reported in Models 2 to 6 of Table 3. For instance, the results of the logistic regression in Model 2 of Table 3 suggest that rural migrants who have a pension show a 47% higher probability of permanent residence in the city. In the PSM estimation, however, this value drops to only about 9%. The decrease of the value of the coefficients demonstrates that better-off rural migrants are more inclined to participate in social insurance and therefore are more determined to stay in the city, which supports the migrant selectivity bias. Still, there remains a positive relationship between social insurance participation and urban settlement intentions (as shown in the PSM model in Table 4), which supports the welfare migration hypothesis (i.e. that social insurance protection attracts rural migrants and encourages them to permanently settle in cities). Incorporating rural migrants in the urban social welfare system, then, will lead to an increase in permanent residents in cities, which will contribute to sustainable urbanisation in China.

Table 4.

Propensity score matching estimation on the effect of social insurance participation on the permanent urban settlement intentions of rural–urban migrants.

	ATT	Std. Err.	T-value	Numbers of treated	Numbers of controls
Pension	0.092	0.016	5.759	1975	1409
Medical	0.053	0.021	2.563	1376	1233
Injury	0.062	0.026	2.378	1747	1269
Unemployment	0.105	0.018	5.868	1217	1096
Housing provident fund	0.198	0.020	9.928	697	681

Notes: The estimation method is ATT estimation with Nearest Neighbour Matching method. ATT represents average treatment on the treated. The standard errors are bootstrapped standard errors. The numbers of treated and controls refer to actual nearest neighbour matches.

Discussion and conclusion

Welfare is a crucial factor in determining the habitation decisions of international migrants and internal migrants in industrialised economies. However, the role of welfare benefits in determining rural migrants’ urban settlement intentions in developing countries has received scant attention. The main challenge in calculating the role of these benefits is that their effect is combined with the effect of ‘migrant selectivity bias’. This research resolves this issue by applying the PSM method. Using the RUMiC 2008–2009 data, we examined the effect of social insurance participation on rural migrants’ urban settlement intentions in China, and our findings confirm both the ‘migrant selectivity bias’ and the welfare migration hypothesis.

First, our study reveals that there is a ‘migrant selectivity bias’ in Chinese cities. That is, those who can contribute to the economic prosperity of the city have a higher probability of being selected as a resident. Migration is selective (Lee, 1966). Urban governments in China expect rural migrants to be net contributors to the urban welfare system rather than net beneficiaries. That is why many large cities in China have designed a points system to determine which migrants will be granted urban hukou and the consequent welfare entitlements. This system allows urban governments to attract highly skilled migrants who will benefit the city, and to exclude unskilled ones. The rejected migrants, without support from the government, are more likely to eventually return to the countryside.

Second, our study indicates that social insurance participation has a positive effect on urban settlement intentions. Rural migrants who have social insurance coverage show a stronger inclination to take up permanent residence in the city. This result was still upheld after we used the PSM method to exclude the ‘migrant selectivity bias’. Social insurance participation benefits rural migrants in a number of ways, including contributing to their health and subjective well-being (Fang and Sakellariou, 2016; Qin et al., 2014). More importantly, social insurance provides long-term security to rural migrants. For instance, pensions provide economic support in old age, so it is not surprising that rural migrants with pensions are more likely to wish to remain in the city.

Third, our study reveals that, even after discounting the ‘migrant selectivity bias’, social insurance participation still has a positive effect on the urban settlement intentions of rural migrants. This finding confirms the positive role of social welfare in Chinese urbanisation. Urban settlement follows a different migration pattern from circular migration. When rural migrants adopt the strategy of circular migration, they have families in the countryside who act as a safety net, protecting them from the potential risks of the city. In contrast, rural migrants who permanently settle in cities must rely on social welfare to protect them from risks because urban settlement can loosen family bonds. The findings of this study suggest that more attention should be paid to the protection offered by social insurance to rural migrants in cities.

Fourth, our study also finds that social insurance participation among rural migrants in China is still low. There are still many flaws in the social insurance system. Jiang et al. (2018) note that the present social insurance system offers little protection to workers without full-time contracts, which is the status of nearly half of rural migrants. Rural migrants are often highly mobile, but transferring social insurance from one city or province to another is very difficult (Gallagher et al., 2015). When their social insurance is not portable, rural migrants may not try to regain it in their new place of business. The Chinese social insurance system needs to better accommodate the occupational characteristics of rural migrants.

The coexistence of the ‘migrant selectivity bias’ and the positive effect of social insurance on rural migrants’ urban settlement intentions suggests that rural–urban migration in China is not like domestic welfare migration in western countries, which is characterised by the free migration of low-income groups (Blank, 1988; Cushing, 1993; Enchautegui, 1997); instead, it resembles immigration in migration-managed regimes, as described by Razin and Wahba (2015), and is the product of negotiation between rural migrants and urban welfare regimes. Freeman’s (1986) theory – that welfare states are, by nature, closed and, therefore, are not open to the free movement of labour – more or less describes the urban welfare regime in China today. Urban governments adopt migration-management measures to regulate rural migration. Although most rural migrants do wish to remain in cities, urban welfare regimes only want to encourage those who can contribute to the urban welfare system and the city’s economic prosperity. As a result, rural migrants who are not in their prime working years, who are unskilled or who are poor are likely to be excluded.

The above discussion leads us to rethink the theoretical relationship between welfare and the settlement behaviours of internal migrants. Previous studies have highlighted that rural–urban migration leads to an improvement in the welfare condition of both migrant households and the remaining households in the countryside (Nguyen et al., 2015). Our results, however, suggest that the improvement in the welfare condition of rural migrants might only be partial, since urban governments tend to grant welfare entitlements to those rural migrants who substantially contribute to the economic development of the city. Thus, our study challenges Tiebout’s ‘voting with one’s feet’ hypothesis that citizens always choose to move to places with better local public services, which leads to the efficient allocation of local public goods (Dowding et al., 1994; Tiebout, 1956). In the case of China, the association between social welfare and the urban settlement of rural migrants is intervened by the ‘migrant selectivity bias’ of urban governments. As a consequence, rural migrants do not have the power of ‘voting with one’s feet’, and local public goods do not reach optimal allocation in the context of rural–urban migration. Our study thus enlightens that it is very important to consider the preference of local governments when analysing how social welfare might affect the settlement behaviours of internal migrants.

Although the findings of this study were based on data collected in China, they have significant implications for studies on rural–urban migration in other developing countries. The coexistence of the ‘welfare magnet effect’ and the ‘migrant selectivity bias’ in cities means that social welfare is not only a carrot that attracts rural migrants to the city but also a stick that pushes them to return to the countryside. Whether the carrot or the stick will hold sway depends very largely on the social setting and has significant implications for the process of urbanisation. In order to better understand rural–urban migration in developing countries, it is vital to consider the role of the social welfare system, especially in studies that undertake cross-national comparisons.

A few limitations of the present study should be noted. The RUMiC dataset includes only cross-sectional data; therefore, there are limits to the causal inferences that can be made on the social insurance participation and urban settlement intentions of rural migrants. Even though the current study employs the PSM method to take into account the fact that some rural migrants have social insurance and others do not, this method does not resolve the problem of omitted variables, which could cause bias. Some factors such as social expenditure at the city level and rural migrants’ participation in social insurance in their place of origin might affect both their social insurance participation in cities and their urban settlement intentions. In order to determine pure causality between social insurance participation and urban settlement intentions, alternative methods such as the instrumental variable (IV) could be used. However, we found it difficult to obtain a good IV for social insurance participation using the RUMiC data, and, given that the main purpose of this article is to separate the effect of welfare from the effect of ‘migrant selectivity bias’, PSM offered a better method. It would be beneficial for future research to adopt a longitudinal research design in order to pursue a more robust causal link between social insurance participation and the urban settlement intentions of rural migrants.

Supplemental Material

USJ936153_Supplemental_Table – Supplemental material for Welfare migration or migrant selection? Social insurance participation and rural migrants’ intentions to seek permanent urban settlement in China

Supplemental material, USJ936153_Supplemental_Table for Welfare migration or migrant selection? Social insurance participation and rural migrants’ intentions to seek permanent urban settlement in China by Shenghua Xie, Juan Chen, Veli-Matti Ritakallio and Xiangming Leng in Urban Studies

Footnotes

Acknowledgements

The authors would like to thank Professor Martin Bell for his insightful comments on this article.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the MOE (Ministry of Education in China) Project of Humanities and Social Sciences (No. 19YJC840049) and the self-determined research funds of CCNU from the colleges’ basic research and operation of MOE (No. CCNU19 TD005, No. CCNU19A03009).

ORCID iD

Shenghua Xie

Supplemental material

Supplemental material for this article is available online.

Notes

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