Labor mobility is a crucial aspect of economic development and labor market dynamics, particularly significant in developing nations experiencing globalization and urbanization. China, with its rapid urbanization and reforms to the Hukou system (household registration system), provides a compelling case study. Millions of rural laborers have migrated to urban areas seeking better employment opportunities and higher incomes, becoming "rural migrants." This paper focuses on the impact of a unique aspect of rural-urban migration in China: the decision of rural migrants to move with fellow townsmen (individuals from the same county). This practice is deeply rooted in the Chinese cultural emphasis on social networks and 'human kindness' and can significantly influence the migrant's ability to navigate the urban labor market. While some argue that such co-migration enhances wages through information sharing and social support, others suggest it may lead to negative economic outcomes due to potential exploitation or limited opportunities. This research seeks to empirically investigate this relationship, addressing the gap in current literature on the causal effect of co-migration with townsmen on the wages of Chinese rural migrants. The study uses the CMDS 2017 data, a large-scale national survey, to explore this issue, employing rigorous econometric methods like PSM to address potential endogeneity concerns. The findings contribute significantly to understanding the complexities of rural-urban migration, labor market dynamics in China, and have wider implications for similar migration patterns in East and Southeast Asia.

The literature on migrant assimilation offers two main theoretical frameworks: straight-line assimilation and segmented assimilation. Straight-line assimilation suggests a gradual integration into the host society, while segmented assimilation acknowledges the possibility of diverse assimilation paths, potentially leading to integration into either the middle/upper or lower classes. The study of ethnic enclaves or agglomerations highlights the role of social networks in mitigating disadvantages migrants face in the urban labor market. Research shows that these networks, often based on ethnicity or shared origin, can improve access to jobs, enhance wage bargaining power, and increase earnings. However, existing studies on Chinese rural migrants often focus on individual, family, or environmental factors, neglecting the specific impact of co-migration with townsmen. This study bridges this gap by investigating the influence of informal networks formed by fellow townsmen, which can provide significant social capital.

This study utilizes data from the 2017 China Migrants Dynamic Survey (CMDS), a large-scale national survey of the migrant population. The CMDS data include rich information on demographic characteristics, migration history, employment details, and social networks, making it suitable for examining the research question. The sample is restricted to rural migrants aged 16-60 who have been living outside their hometown county for over six months. The dependent variable is the logarithm of monthly wages, while the independent variable is a binary indicator of whether the migrant moved with fellow townsmen during their first migration. Several control variables are included to account for individual characteristics (years of education, work experience, gender, marital status, CCP membership) and job characteristics (industry, occupation, enterprise ownership, inflow region). The initial analysis uses Ordinary Least Squares (OLS) regression. To address potential sample selection bias—a key concern when studying the impact of co-migration choices—the study employs propensity score matching (PSM). Three different PSM methods—nearest neighbor matching, radius matching, and kernel matching—are implemented to ensure robustness. Additional robustness checks are performed by excluding migrants whose parents had prior migration experience, and by focusing on subsets of migrants with more recent migration dates to minimize the temporal gap between migration and wage observation. Mediation analysis is used to investigate the mechanisms through which co-migration with townsmen impacts wages, focusing on information search and wage negotiation. This is achieved by incorporating additional variables representing access to hometown networks and participation in hometown association activities.

Descriptive statistics reveal that rural migrants who moved with fellow townsmen earned significantly higher wages (mean log wage difference of 0.152) than those who migrated alone. OLS regression results show a statistically significant positive relationship between co-migration with townsmen and wages, even after controlling for various individual and job characteristics. This positive relationship remains robust after applying PSM, with the Average Treatment Effect on the Treated (ATT) consistently positive and statistically significant across all three matching methods. Robustness tests, which address concerns about parental migration influence and temporal misalignment between migration and wage data, confirm the positive effect of co-migration. Mediation analysis demonstrates that co-migration with townsmen positively affects wages through two key mechanisms: (1) improved information search, as migrants have better access to job-related information; and (2) strengthened wage bargaining power, resulting from participation in hometown associations and greater social support. Heterogeneity analysis reveals that the positive impact of co-migration is stronger for: rural migrants in business services, consumer services, and blue-collar occupations; those working in state-owned and private enterprises (particularly private); and older migrants. Interestingly, migrants who moved with family members showed no statistically significant wage increase, possibly due to a prioritization of job security over higher salaries.

The findings strongly support the hypothesis that co-migration with fellow townsmen significantly enhances the wages of rural migrants in China. This is explained by the improved access to information and the increased collective bargaining power facilitated by the strong social ties within these migration groups. These findings highlight the importance of informal social networks in mitigating the disadvantages rural migrants often face in urban labor markets. The heterogeneity analysis adds further nuance to our understanding, showing that the benefits of co-migration are not uniformly distributed across all migrants and job sectors. This has significant implications for policymakers. While emphasizing the economic advantages of co-migration, the research also highlights the need for policy interventions to support family well-being, improve formal labor market protections, and strengthen social safety nets for vulnerable migrants.

This study provides robust evidence that co-migration with fellow townsmen significantly improves the wages of rural migrants in China. This effect operates through mechanisms of enhanced information access and stronger wage negotiation capabilities. However, the benefits are not uniform across all migrant groups or sectors. Future research could explore the long-term effects of co-migration, investigate the dynamics of social networks within migrant communities, and analyze the potential for policy interventions to leverage the positive aspects of these networks while addressing potential inequalities.

The study's primary limitation is its reliance on cross-sectional data from the 2017 CMDS. This limits the ability to fully analyze dynamic effects and potential causal pathways over time. Another limitation is the potential for unobserved confounders despite the use of PSM. Finally, the study focuses specifically on China, limiting the direct generalizability to other contexts, although the findings have broader relevance to migration patterns in culturally similar regions.