Are mixed neighborhoods more socially cohesive? Evidence from Nanjing, China

Figures & data

Figure 1. Locations of sampled neighborhoods in Nanjing, China.

Figure 2. Distribution of neighborhood cohesion across different types of sampled neighborhoods.

Figure 3. Standardized heterogeneity scores across different types of sampled neighborhoods.

Table 1. Summary statistics

					Frequencies (Binary variables only)
Variables	Mean or %	Standard Deviation	Min	Max	0	1
Dependent variables
Neighborly ties	94.16	207.84	0	2000	-	-
Community participation	-	-	0	1	325	576
Neighborhood attachment	3.72	0.78	1	5	-	-
Independent variables
Hukou heterogeneity	0.22	0.15	0	0.51	-	-
Tenure heterogeneity	0.36	0.21	0.07	0.95	-	-
Income heterogeneity	11.00	8.17	4.79	56.30	-	-
Occupational heterogeneity	2.56	0.30	1.86	3.07	-	-
Educational heterogeneity	0.04	0.02	0.02	0.10	-	-
Control variables
Age	50.25	16.69	16	96
Gender (ref=male)	-	-	0	1	415	497
Local hukou (ref=non-local)	-	-	0	1	126	786
Urban hukou (ref=rural)	-	-	0	1	127	780
Ownership (ref=tenant)	-	-	0	1	208	697
Length of residence	11.52	10.21	0	75	-	-
Marital status (ref=single)	-	-	0	1	74	836
Year of schooling	12.96	3.82	6	19	-	-
Household annual income (ln)	2.50	0.77	0	5.30	-	-
Subjective evaluation of built environment	3.15	0.91	1	5	-	-
Objective evaluation of built environment	1.30	1.01	0	4	-	-
Population density (per 10,000 people per km^2)	2.11	1.88	0.15	8	-	-
Percentage of migrants	0.22	0.22	0	0.77	-	-
Neighborhood type (ref=traditional neighborhoods)
Privatized work units	0.19	-	-	-	-	-
Commodity neighborhoods	0.37	-	-	-	-	-
Affordable neighborhoods	0.22	-	-	-	-	-

Table 2. Multilevel regression results predicting neighborhood cohesion in Nanjing

	Model 1 Multilevel negative binomial model predicting neighborly ties Incidence rate ratios	Model 2 Multilevel logit model predicting community participation Odds ratios	Model 3 Multilevel linear model predicting neighborhood attachment Coefficient
Neighborhood mix measures
Hukou heterogeneity	0.89 (0.10)	0.73 (0.17)	−0.16** (0.05)
Tenure heterogeneity	1.36** (0.14)	0.87 (0.19)	−0.07 (0.05)
Income heterogeneity	0.84* (0.07)	0.59*** (0.12)	−0.02 (0.05)
Occupational heterogeneity	0.91 (0.07)	0.92 (0.16)	−0.05 (0.04)
Educational heterogeneity	1.10 (0.09)	1.42† (0.27)	0.03 (0.04)
Individual characteristics
Age	1.01*** (0.00)	1.01 (0.01)	0.01* (0.00)
Female	1.08 (0.10)	1.31 (0.27)	0.09† (0.05)
Local hukou	1.35† (0.22)	2.18* (0.76)	0.00 (0.09)
Urban hukou	1.28 (0.22)	1.21 (0.42)	0.01 (0.10)
Homeowner	0.90 (0.13)	2.32** (0.71)	−0.08 (0.08)
Length of residence	1.04*** (0.01)	1.01 (0.02)	0.00 (0.00)
Married	1.42† (0.28)	1.59 (0.67)	−0.06 (0.11)
Years of schooling	0.95** (0.02)	1.05 (0.04)	0.01 (0.01)
Household income (ln)	1.02 (0.08)	0.77 (0.14)	0.01 (0.04)
Neighborhood characteristics
Subjective evaluation	1.08 (0.06)	1.36* (0.18)	0.30*** (0.03)
Objective evaluation	1.01 (0.10)	1.52* (0.32)	0.19*** (0.05)
Population density	0.83*** (0.04)	0.85† (0.08)	−0.02 (0.02)
Percentage of migrants	0.76 (0.41)	0.97 (1.09)	1.03*** (0.26)
Neighborhood type (ref=traditional neighborhoods)
Privatized work units	0.68† (0.14)	1.11 (0.51)	−0.09 (0.11)
Commodity housing	0.86 (0.20)	0.87 (0.43)	−0.16 (0.12)
Affordable housing	3.57*** (0.87)	0.54 (0.27)	−0.28* (0.12)
Constant	16.66*** (7.99)	0.06** (0.06)	2.07*** (0.26)
Observations (Neighborhoods)	887 (32)	824 (32)	903 (32)
Intra-neighborhood correlation	0.25	0.18	0.19
AIC (Akaike’s Information Criteria)	5951.65	689.86	1279.30
BIC (Bayesian Information Criteria)	6015.71	793.97	1385.73
Deviance	5903.65	646.86	1231.30
PRE (%) (Proportional reduction of error)	36.33	28.06	31.06

Notes: *** p < 0.001, ** p < 0.01, * p < 0.05, † p < 0.1. Standard errors are in parentheses.