Sense of community and residential well-being among rural-urban migrants in China

The study examines how living conditions, neighborhood/community factors, and socioeconomic characteristics shape sense of community (SoC) and residential well-being (RWB) among China’s rural-urban migrants (RUMs). Against a backdrop of rapid urbanization and the enduring Hukou system, RUMs often face discrimination, constrained access to services, and poor housing in marginalized urban villages, all of which may affect their subjective well-being. Prior work links migration to mental health and subjective well-being outcomes and highlights institutional barriers for RUMs. However, little empirical research has jointly assessed how living arrangements and neighborhood conditions influence both SoC and RWB among RUMs while addressing endogeneity, simultaneity, and heteroskedasticity. This study aims to fill that gap by modeling SoC and RWB together and identifying key determinants, with attention to Hukou status and other socioeconomic factors.

The review situates SoC and RWB within broader subjective well-being (SWB) research. It outlines SoC concepts (membership, influence, integration/need fulfillment, shared emotional connection) and empirical links to life satisfaction, social support, quality of life, participation, and community cohesion. Socioeconomic correlates of SoC (age, ethnicity, marital status, length of residence, group participation) are discussed, though income effects are mixed. RWB is framed as satisfaction with living arrangements that enhance well-being, with influences spanning dwelling conditions, neighborhood context, community services, accessibility, safety, sanitation, amenities, and natural environment. For migrants, psychological well-being relates to stressors, health behaviors, and job mobility, with Chinese evidence showing housing environments and community satisfaction matter. Table 1 synthesizes SoC components (e.g., cohesion, values, participation, neighborhood environment) and RWB components (e.g., living space, housing quality, services, facilities, law and order, sanitation, natural environment). The review identifies a gap: few studies analyze living arrangements and neighborhood conditions as determinants of both SoC and RWB for Chinese RUMs, and fewer still address endogeneity, simultaneity, confounding, multicollinearity, and heteroskedasticity. The study poses two questions: (a) Which primary socioeconomic, living, and neighborhood factors affect SoC and RWB separately? (b) Which factors affect SoC and RWB simultaneously?

Data source: The study uses the 2010 face-to-face survey “Development of Migrant Villages under China’s Rapid Urbanization” (DMVCRU; UK Data Service study 850682) led by Cardiff University. Urban villages were sampled in Beijing, Shanghai, and Guangzhou. From lists of villages, 20 urban villages were randomly selected per city, and 20 households per village via random starts, yielding 1208 valid questionnaires. Measures: SoC is measured by 12 items on neighborhood/community perspectives; RWB by 14 items on housing and environmental status. Likert responses range 1–5. SoC total score ranges 5–60; RWB 5–70. Internal consistency: SoC alpha = 0.8775; RWB alpha = 0.8150. Normality tests (Shapiro–Wilk, Shapiro–Francia, skewness/kurtosis) support approximate normality, justifying OLS. Independent variables: Socioeconomic—age (years), gender (0=female,1=male), marital status (0=single/divorced/widowed,1=married), educational attainment (0=junior high and below,1=senior middle and above), Hukou (0=agricultural,1=non-agricultural), household monthly income (CNY). Living conditions—home ownership (0=shared accommodation,1=own home), number of bedrooms, number of living rooms, number of housing facilities (sum of separate kitchen, separate toilet, shower, LPG, gas pipeline, AC, heating, internet). Community/neighborhood—number of problem-solving channels (sum of old friends/neighbors, neighborhood/village committees, property/developer, owners’ committee, government departments, media, others), converted residences (0=no,1=yes). Models: Separate linear regressions for RWB and SoC include all covariates. Diagnostics assess multicollinearity (VIF<10 threshold), heteroskedasticity (Breusch–Pagan/Cook–Weisberg; Cameron–Trivedi IM-test). Heteroskedastic linear regressions model variance with selected predictors (e.g., age, housing facilities, income, problem-solving channels). To address confounding/endogeneity, instrumental-variable quantile treatment effects (ivqte, τ=0.5) estimate effects of Hukou (instrumented by ownership) and ownership (instrumented by Hukou) on SoC and RWB, controlling for all covariates. Additional OLS without constant examines sub-samples (shared vs own housing). For simultaneity between SoC and RWB, three-stage least squares (reg3) estimates joint equations across sub-samples and total sample. Joint models also use seemingly unrelated regression (sureg), multivariate regression (mvreg), and mixed-process regression (cmp) to estimate the correlation between equations (rho).

Sample characteristics: Among 900 heads of household, 74.49% married and 84.53% male; age 15–80 (mean 35.18, SD 12.54). 86.09% believed residences would be converted. Ownership: 14.06% own homes; 85.94% shared accommodation. Hukou: 74.01% agricultural. Education: 55.17% junior high or below. Many had two housing facilities, one bedroom, and no living room. Diagnostics: VIFs <1.80 for all regressors, indicating low multicollinearity. Heteroskedasticity present by IM-tests. Heteroskedastic linear regression (Table 5): Positive associations with both SoC and RWB for gender, marital status, ownership, number of bedrooms, number of living rooms, converted residences, and number of problem-solving channels. Educational attainment positively related to SoC. Variance modeling: number of problem-solving channels significantly increased variance (Lnσ²) for both outcomes. IV-QTE (median, Table 6): Hukou status significantly increased RWB score (coef ≈ 7.40, p<0.01) but had no significant effect on SoC. Home ownership had no significant effect on either outcome after controls. OLS by tenure and total (Table 7): Across shared accommodation and total samples, age and number of problem-solving channels positively associated with SoC and RWB; gender, number of housing facilities, income, number of living rooms, and converted residences also showed positive associations. In the total sample, ownership positively associated with both outcomes. Some sub-sample coefficients showed negative associations for number of bedrooms and living rooms in certain specifications. Three-stage least squares (Table 8) and simultaneous equations (Table 9): Consistent predictors of higher SoC and RWB included age, male gender, higher educational attainment, home ownership, more housing facilities, higher household income (for RWB), more problem-solving channels, and expectation of converted residences. Hukou status negatively predicted RWB (around −2.0, p<0.05) but not SoC. Number of living rooms negatively predicted both outcomes (approximately −1.8 to −2.3, p<0.05), while bedrooms tended to be positive for SoC and sometimes for RWB. Estimated correlation between SoC and RWB equations was moderate (rho ≈ 0.57), indicating a moderately strong positive relationship between the two constructs. City comparisons: Median RWB was highest in Guangzhou, then Shanghai and Beijing; interquartile range of SoC was largest in Guangzhou. Overall: Institutional (Hukou), socioeconomic (age, gender, education, income), living (ownership, facilities, rooms), and neighborhood/community (problem-solving channels, conversion expectations) factors jointly shape SoC and RWB. Hukou notably affects RWB more than SoC.

The study addresses the research questions by jointly modeling SoC and RWB and identifying common and distinct determinants. Socioeconomic factors (age, gender, education, income) and living/neighborhood conditions (ownership, housing facilities, rooms, problem-solving channels, conversion expectations) significantly relate to both outcomes, with patterns differing by tenure and in joint models. Crucially, Hukou status exerts a significant effect on RWB but not SoC, underscoring institutional constraints on residential well-being beyond community integration. The moderate correlation between SoC and RWB suggests interdependence: improvements in living conditions and neighborhood problem-solving capacity are associated with higher community attachment and residential satisfaction. Negative associations for number of living rooms alongside positive associations for bedrooms likely reflect overcrowded, low-quality configurations in urban villages where additional ‘living rooms’ may proxy cramped subdivisions, whereas bedrooms indicate improved privacy and space utilization. The findings align with prior literature linking housing quality and community factors to well-being among migrants and suggest that enhancing both physical dwellings and neighborhood support structures can improve outcomes.

Living conditions and community/neighborhood conditions significantly influence both SoC and RWB among rural-urban migrants in China, and SoC and RWB are moderately correlated. Institutional factors, particularly Hukou status, affect RWB more than SoC. Policy levers that increase housing assets (e.g., ownership, facilities) and support neighborhood problem-solving and planned residential conversion are likely to improve both community integration and residential well-being. Future research should use longitudinal and comparative designs (including urban and rural peers) to strengthen causal inference, incorporate richer neighborhood built/social/natural environment measures, and examine the role of social support networks.

The study is cross-sectional, limiting causal inference. It lacks comparative samples of urban and rural non-migrant peers, precluding direct benchmarking. Some relevant neighborhood environmental variables were not available in the dataset. Findings are based on data collected in 2010, which may limit temporal generalizability despite suggested contemporary relevance.