No.62-Internal Migration with Social Networks in China
Zhou, Jin
Published: 2017/3/29 23:01:59    Updated time: 2017/3/29 23:09:01
Abstract: Numerous empirical studies have documented a strong association between social networks and individuals' migration decisions. Few papers formally analyse how social networks affect both migration decisions that affect the evolution of social networks overtime, and labor market outcomes. In order to understand these relationships, I develop and estimate a dynamic model with return and repeated migration, social network investment decisions and labor market transitions. The model distinguishes between two channels through which social networks may affect migration decisions: (1) a direct effect on migration costs and (2) an indirect effect on labor market outcomes through the job arrival rate. I use the model to study one of the largest ongoing internal migrations in human history: rural-urban migration in China. To estimate the model, I use panel data from the Chinese Household Income Project (2007-2009). The estimation results show that social networks affect both channels significantly. Individuals with networks have 40% higher job arrival rate than those without networks on average. In addition, social networks reduce average migration costs by 7%. I also show that policies that directly lower migration costs may be more cost effective at increasing rural-urban migration in China. These policy experiments also show that without considering the impact of network investment, the government has to spend more to offset the effect of no investment in social networks.
Keywords: Internal Migration, Search, Social Networks


Zhou, Jin (Center of Economics for Human Development, University of Chicago)



A strong association between social networks and migration decisions has been consistently documented in numerous empirical studies. In most economic models, migration decisions are based on potential labor market outcomes. Social networks are often viewed as an important non-market institution through which individuals reduce market frictions and affect labor market outcomes. However, there are conflicting findings about the quantitative effects of social networks on labor market outcomes. For example, social networks may provide access to better jobs ((Munshi, 2003); (Edin, Fredriksson, and ? Aslund, 2003)) or to less desirable ones ((Borjas, 2000); (Barry R. Chiswick and Miller, 2005)). Although some researchers point out that individuals with social networks in destination places are more likely to migrate (eg., Munshi, 2003), there are not many papers which formally analyse how social networks affect individuals' migration behavior and their labor market outcomes.

In this paper, I construct a dynamic model with return and repeated migration, unemployment, and social network investment decisions that affect the evolution of social networks overtime. The existing migration literature suggests two alternative mechanisms through which social networks may affect migration decisions and migrants’ labor market outcomes. First, social networks may reduce migration costs (e.g., Carrington, Detragiache, and Vishwanath, 1996; Munshi, 2003), decreasing individuals' migration reservation values causing individuals with networks to be more likely to migrate. Second, social networks provide information about labor markets and then increase the probability of getting job offers in the destinations (e.g., Montgomery, 1991; Kono, 2006; Goel and Lang, 2016; Buchinsky, Gotlibovski, and Lifshitz, 2014). Under both of these mechanisms, individuals with social networks are more likely to migrate.

Although both of these mechanisms can explain why individuals with networks are more likely to migrate, they have different implications about migrants’ earnings. Individuals with social networks have lower migration costs which cause lower reservation earnings. This means that migrants with networks are more likely to have lower earnings compared to similar individuals without networks. However, if social networks reduce search frictions, for example, by increasing the job arrival rate, then migrants with networks will have higher earnings than similar individuals without networks. These different implications for migrants’ earnings may be one reason why some papers find positive earnings’ effects while others find the opposite. The goal of this paper is to quantify the different roles that social networks may play with regard to labor market outcomes.

One issue concerning social networks is that they are unlikely to arise independently of individuals’ labor market prospects. That is, individuals make investment choices in  their social networks by comparing the loss from the payment of network investment to the benefit from increasing the probability of having a social network. In the literature, the common approach has been to look for natural or quasi-natural experiments as an attempt to deal with this problem.2 In contrast, in this paper, I account for this possibility by formally modelling the social network investment decisions made by individuals. Modelling social networks with network investment decisions aids in our understanding of how individuals respond to market frictions through their social networks. Considering social network investment decisions also helps to evaluate potential government migration policies. The effects of government policies on market frictions and migration costs are likely to result in differential responses by individuals in terms of their social investment decisions and, ultimately, their migration outcomes. Failure to account for these feedback effects may lead to inaccurate policy evaluation.

Understanding different channels through which social networks operate is crucial for accurately designing migration policies. For example, the Chinese government aims to increase the urbanization rate to 60% by 2020, which means that an additional 100 million rural people will need to migrate to urban areas.3 Whether social networks are substitutes or complements to government policies aimed to increase migration may greatly affect their cost effectiveness. Besides accounting for the impact of social networks, the model I use in this paper also contains a number of mechanisms through which individuals’ migration decisions are affected. First, I allow individuals to accumulate human capital within a search framework. Individuals’ earnings reflect both their observed characteristics  e.g., education), their location-specific human capital accumulation (i.e., urban and rural), and unobserved endowment (i.e., ability).

Second, individuals’ earnings are also affected by frictions in the urban labor market. Individuals do not automatically have a job if they migrate. Instead, they need to search for one. Depending on the outcome of the search process, individuals may choose to stay in urban areas or return to rural areas. This setting incorporates one of the main features of rural-urban migration in China: most people do not migrate permanently. 

To study the role of social networks, this paper examines one of the largest migration episodes of the 20th century: rural to urban migration in China. The current internal rural-urban migration in China provides an ideal setting to examine the role of social networks in a labor market with frictions. Hare and Zhao (2000), Meng (2000) and Zhao (2003) show that social networks are strongly correlated with rural-urban migration in China. Zhang and Zhao (2015) find that social networks also affect migrants’ subsequent labor market outcomes. However, these papers do not distinguish the social network effects through the two different channels discussed above.

The panel data I use for this study come from the Chinese Household Income Project (CHIP, 2007-2009). It is well suited to examine the effects of social networks on migration decisions and labor market transitions in China. First, the data cover most provinces of rural-urban migration in China. Second, the data contain enough information on social networks and labor market outcomes across different locations to identify the effect of social networks through migration costs and the job arrival rate. Finally, the data contain information on individuals’ social network investment.

I estimate the model using the CHIP (Rumic) data. The estimation results show that social networks both significantly reduce migration costs and increase the job arrival rate. The job arrival rate for individuals with networks is 40% higher compared to those without. Social networks reduce migration costs by 7% on average. To analyse the importance of these two channels, I simulate the model and show that migration decisions are affected more by the impact of social networks on reducing search frictions than by their impact on reducing migration costs. If I shut down the effects of social networks on both channels, only 15% of rural people migrate. Allowing social networks to only affect migration costs leads to 17% of rural people migrating. If social networks only increase the job arrival rate, 27% of rural people will migrate, compared to 29% in the data.

The simulation results also illustrate how individuals respond to the impact of social networks through network  investment. When social networks affect both channels, 63% of individuals invest in their social networks. If social networks only lower migration costs, the fraction of individuals who invest decreases to 47%. When social networks only affect the job arrival rate, 56% of individuals invest in their social networks. The results also show that most individuals who invest in their social networks are the ones living in rural areas and the ones unemployed in urban areas.

I simulate three different policies to achieve the stated Chinese government’s goal of a 60% urbanization rate: an unconditional lump sum subsidy for rural individuals who migrate, the provision of unemployment benefits for rural migrants in urban areas, and a migration cost subsidy for rural people, but only for those who have social networks in urban areas. The simulation results show that the policy of conditional lump-sum  transfers for migrants will cost less than the other two policies. I also compare the effects of the policies to those obtained in a model estimated under the restriction that individuals do not invest in their social networks. I find that the government has to spend substantially more if individuals cannot invest in their social networks. Since individuals cannot use network investment to increase or keep their network status, if they migrate, they have to pay more migration costs or face more search frictions in urban areas. As a consequence, to achieve the same urbanization rate, the government will have to spend more to offset the effect of no investment in social networks.

The rest of the paper is organized as follows. Section 2 provides a review of the relevant literature. Section 3 presents a background on rural-urban migration in China, describes the data in detail, and provides a preliminary empirical examination of the key mechanisms in the model. In Section 4, the model is described, identification conditions are provided, and I also describe the estimation procedure, including challenges and solutions. Estimation results and counter-factual simulations are presented in Section 5. Section 6 concludes.


 - CIIDWPNo.62-Zhou Jin
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