Understanding different levels of segregation in urban China: a comparative study among 21 cities in Guangdong province

Figures & data

Table 1. Description of the dependent variable and selected independent variables

Variable	Mean	Std.Dev.	Min	Max	Average segregation degrees by group
					1	2	3	4
Segregation degree	0.45	0.08	0.26	0.61
Per Capita GDP (Yuan)	38,409	26,081	14,554	94,296	0.49	0.50	0.44	0.37
Percentage of Agriculture GDP (%)	10.70	7.94	0.07	25.12	0.37	0.49	0.45	0.52
Percentage of Manufacturing GDP (%)	45.52	8.59	33.91	60.5	0.47	0.49	0.47	0.38
Per Capita FDI (Dollar per person)	177.63	189.23	5.24	783.5	0.53	0.46	0.40	0.42
Proportion of Unit Head (%)	0.02	0.01	0.01	0.05	0.52	0.45	0.48	0.36
Proportion of Technician (%)	0.07	0.02	0.04	0.12	0.51	0.46	0.46	0.37
Proportion of Clerk (%)	0.06	0.03	0.02	0.11	0.50	0.49	0.42	0.39
Proportion of Agriculture Related Employee (%)	0.35	0.24	0.00	0.68	0.37	0.49	0.45	0.52
Proportion of Production Related Employee (%)	0.32	0.15	0.11	0.63	0.48	0.47	0.46	0.39
Proportion of Public Housing (%)	0.05	0.03	0.01	0.13	0.49	0.51	0.46	0.36

Note: Groups 1 to 4 are obtained through ranking and dividing the 21 cities into 4 groups based on each variable. In general, group 1 includes the first 6 cities, with the lowest percentages or values of the variables; group 2 includes the following 5 cities, with the secondly lowest percentage or values of the variables, and so on. Std.Dev stands for standard deviation.

Figure 1. The social spatial structures of subdistricts in Guangdong province in terms of educational attainment

Notes: the Equal Count Mode is used to display the social spatial structures of the subdistricts in QGIS. The subdistricts are divided into five groups that contain an equal number of units.

Figure 2. Segregation degrees of the 21 cities in Guangdong province in 2010

Table 2. Results of separate quadratic Beta regression

Independent variables	Linear term	Quadratic term	Overall significance	Average marginal effect	Tipping point
Per Capita GDP (Yuan)	−0.342***	0.136*	***	−0.085***	71,194***
	(0.098)	(0.080)		(0.023)
Percentage of Agriculture GDP (%)	0.202***	−0.063	**	0.049***	23.48
	(0.062)	(0.075)		(0.014)
Percentage of Manufacturing GDP (%)	−0.155**	−0.155*	**	−0.037***	41.22
	(0.060)	(0.089)		(0.013)
Per Capita FDI (Dollar/per person)	−0.298***	0.086**	***	−0.074***	507***
	(0.087)	(0.040)		(0.023)
Proportion of Unit Head (%)	−0.204***	−0.006	***	−0.050***	−0.21
	(0.071)	(0.067)		(0.019)
Proportion of Technician (%)	−0.242***	0.073	**	−0.060**	0.10
	(0.087)	(0.061)		(0.025)
Proportion of Clerk (%)	−0.234***	0.077	***	−0.058***	0.1
	(0.071)	(0.076)		(0.018)
Proportion of Agriculture Related Employee (%)	0.183***	−0.057	**	0.045***	0.73
	(0.061)	(0.087)		(0.015)
Proportion of Public Housing	−0.310***	0.095	***	−0.077***	0.10**
	(0.096)	(0.061)		0.021

Notes: Each row is a separate model, with both the linear term and quadratic term of the variable added into the model simultaneously; the equation is Y = a*X2 + b*X + c. The constant and the error term are not shown. The overall significance of the model is based on the likelihood-ratio test; it tests whether the model significantly improves the model fit compared to the intercept-only model. if it is significant, we can conclude that it is unlikely that all of the coefficients of the model equal to zero. Only models passing the overall significance test (P < 0.05) are reported. The average marginal effect reports the average change in the segregation degree for a unit of standard deviation change in the explanatory variables. The tipping point is also reported (the calculation is -b/2a, in which a and b are coefficients in the equation); however, due to the relatively small sample and relatively large standard error, we should interpret these with caution. The tipping points in the models of the proportion of unit head, the agriculture related employee as well as the production related employees, do not fall into the sample value range of the variable. Numbers in the parenthesis are standard errors. *** P < 0.01, ** P < 0.05, * P < 0.1.

Figure 3. Quadratic Beta regressions between segregation and economic development variables

Notes: Only the relationships passing the overall significance test (P < 0.05) are shown; all independent variables are standardized in the figures.

Figure 4. Quadratic Beta regressions between segregation and globalization (1), social structure (2–5) and housing regime (6) related factors

Notes: Only the relationships passing the overall significance test (P < 0.05) are shown; all independent variables are standardized in the figures.

Figure 5. Economic and occupational structure and restructuring processes by city groups

Notes: the numbers are the group average.

Table

							Local-nonlocal		Education
City	Population (10,000)	GDP (0.1 billion Yuan)	Per Capita GDP (Yuan)	FDI (10,000 Dollars)	Per Capita FDI (Dollars)	Urbanization rate (%)	Local	Nonlocal	Low	Middle	High
Shenzhen	1037	9582	94,296	429,724	414	100	17%	83%	56%	35%	8%
Guangzhou	1271	10,748	87,458	397,862	313	83.78	51%	49%	55%	34%	10%
Foshan	720	5652	80,313	196,749	273	94.09	46%	54%	69%	27%	4%
Zhuhai	156	1209	77,888	122,351	784	87.65	52%	48%	54%	35%	10%
Zhongshan	312	1851	60,797	72,964	234	87.82	43%	57%	69%	28%	3%
Dongguan	822	4246	52,798	273,171	332	88.46	19%	81%	71%	26%	2%
Huizhou	460	1730	38,650	143,761	313	61.84	54%	46%	76%	22%	2%
Jiangmen	445	1570	35,622	110,849	249	62.3	73%	27%	72%	26%	2%
Qingyuan	370	1088	29,487	32,017	87	47.54	79%	21%	83%	16%	1%
Zhaoqing	392	1086	27,987	93,388	238	42.39	82%	18%	82%	17%	2%
Yangjiang	243	640	26,676	20,751	85	46.81	85%	15%	79%	20%	1%
Maoming	583	1492	25,496	3099	5	35.06	90%	10%	80%	19%	1%
Shaoguan	283	683	24,050	21,236	75	52.53	79%	21%	74%	23%	2%
Shantou	540	1209	22,776	25,553	47	68.46	83%	17%	80%	19%	1%
Chaozhou	267	559	21,107	11,233	42	62.75	83%	17%	81%	17%	1%
Zhanjiang	700	1405	20,161	3672	5	36.68	87%	13%	80%	18%	2%
Jieyang	588	1010	17,264	14,857	25	47.31	93%	7%	86%	14%	0%
Yunfu	236	401	17,074	7929	34	36.96	89%	11%	84%	15%	1%
Heyuan	296	475	16,301	16,846	57	40.04	85%	15%	79%	20%	1%
Shanwei	294	465	15,845	28,450	97	54.18	90%	10%	86%	14%	1%
Meizhou	424	613	14,554	8959	21	43.01	87%	13%	77%	22%	1%

Source: Yearbook of Guangdong province in 2011; the sixth Census of the cities.

Table

	High-Other	Middle-Other	Low-Other	High-Middle	High-Low	Low-Middle
High-Other	1.00
Middle-Other	0.73	1.00
Low-Other	0.74	0.94	1.00
High-Middle	0.68	0.12	0.24	1.00
High-Low	0.98	0.76	0.81	0.67	1.00
Low-Middle	0.73	0.98	0.98	0.16	0.79	1.00

Source: based on calculation of the authors.

  Note: A kernel density plot of the dependent variable is shown as above; Statistically, the mean of the high-other segregation degree between the highly educated and other people is 0.45, medium being 0.45 (the mean equals to the medium value in a normal distribution), skewness −0.302 (usually the absolute value less than −0.5 indicates approximate symmetry) and Kurtosis 3.16 (the Kurtosis is 3 for a normal distribution). We can in general conclude that the dependent variable is normally distributed

Table

Industrial sectors	2010	Industrial sectors	2000
Agriculture, forestry, farming and fishery	0.4	Agriculture, forestry, farming and fishery	0.5
Accommodation, catering industry	0.6
Construction industry	0.7	Construction industry	0.8
Resident services and other services	0.7	Social service industry	1.2
Manufacturing	0.8	Manufacturing	0.9
Water, environmental and public facilities management	0.8	Geological survey water conservancy management industry	0.8
Real estate industry	0.9	Real estate industry	1.3
Wholesale and retail trade	0.9	Wholesale and retail trade	0.8
Leasing and business services	1.0
Education	1.1	Education, Culture, Radio, Film and Television Industry	1.0
Culture, sports and entertainment	1.2	Health and sports welfare industry	1.1
Mining industry	1.2	Mining industry	0.6
Transportation, warehousing and postal services	1.2	Transportation, warehousing, postal and telecommunications	1.4
Health, social security and social welfare	1.3	Health and sports welfare industry	1.1
Public and social organization administration	1.3	Organization and social groups	1.1
Electricity, gas and water production and supply	1.4	Electricity gas and water production supply industry	1.3
Information transmission, computer services and software industry	1.7
Scientific research, technical services and geological exploration	1.7	Scientific research integrated technology service industry	1.5
Financial industry	2.2	Financial insurance industry	1.5

Notes: the data is from the Guangdong province Yearbook in 2011; the numbers in the table are ratios of annual income of each industrial sector to the average income of all the industrial sectors in 2000 and 2010; as there are different industries in each year, we list the sectors separately.