中国城市流动人口住房质量的空间分异与影响因素

doi:10.11821/dlxb202112006

Abstract

Abstract:

Based on data from the 1% National Population Sample Survey 2015 and the 2014 China Migrant Population Dynamic Survey (CMDS), the paper selects four indicators to measure housing quality of the migrant population including floor area, housing facilities, construction period and living communities (urban or rural) and discusses the phenomenon about spatial differentiation of migrant populations' housing quality in 310 prefecture- and provincial-level cities in China, using GIS spatial analysis methods such as Moran's I coefficient and Getis-Ord G_i^*. Besides, we investigate the influencing factors of migrant populations' housing quality. Some conclusions can be drawn as follows: (1) Compared with the local residents, the housing quality of the migrants is not that worse. (2) The spatial distribution of housing quality of migrant population presents marked spatial differentiation on cities of different levels and scales, population sizes and geographical divisions. (3) The results of spatial autocorrelation analysis demonstrate that there is a significant positive spatial correlation in the indicators of the migrant population's housing quality on a national scale, and identify the phenomenon of their spatial clustering and the centers of such spatial clustering. (4) The housing quality is influenced by both internal factors of migrant population and external factors of in-flow cities. (5) Population, economic development and the housing market play different roles in the housing quality of migrant population.

Key words: China, cities, migrant population, housing quality, spatial differentiation, influencing factor

WANG Yufan, CHAI Kangni, ZHUO Yunxia, FENG Changchun. Spatial variation of migrant population's housing quality and its determinants in China's prefecture-level cities[J].Acta Geographica Sinica, 2021, 76(12): 2944-2963.

Figures/Tables 10

Tab. 1

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Tab. 2

Fig. 5

Tab. 3

Descriptive statistics of variables

类型	变量名称	变量描述	均值(标准差)
房屋面积	Area	城市流动人口平均家庭住房建筑面积(m²)	97.179(24.853)
设施条件	Facility	住房内有独立使用的厨房的流动人口占比与住房内有独立使用的厕所的流动人口占比的乘积	0.717(0.191)
建筑年代	Buildage	住房建成年代在2000年后的流动人口占比	0.585(0.118)
社区性质	Community	社区性质为居委会的流动人口占比	0.749(0.224)
户籍制度	Crossprov	流入地跨省迁移流动人口与总流动人口之比	0.389(0.239)
	Rural	流入地乡—城流动人口与总流动人口之比	0.844(0.095)
家庭人口	Age	流动人口平均年龄(岁)	34.556(1.823)
	Single	从未结过婚的流动人口占比	0.196(0.091)
	Child	流动人口平均子女数量(个)	1.137(0.218)
	Family	流动人口本地家庭成员数量与家庭总规模之比	0.852(0.084)
流动属性	Purpose	迁移目的为务工经商的流动人口占比	0.872(0.096)
	Length	流动人口平均停留时间(年)	4.482(1.658)
	Stay	拥有在流入地停留5年以上意愿的流动人口占比	0.563(0.158)
教育经济	Education	受教育水平在大专及以上的流动人口占比	0.117(0.073)
	Job	雇员流动人口占比	0.453(0.181)
	Security	有社保的流动人口占比	0.209(0.130)
	lnIncome	流动人口平均家庭月总收入(取对数)	8.572(0.227)
住房特征	Ownership	流动人口住房自有率	0.194(0.168)
	lnRentexp	流动人口租户平均月租金支出(取对数)	6.588(0.399)
人口	lnPopulation	流入地总人口(取对数)	15.046(0.761)
	Migrant	流入地流动人口与总人口之比	0.091(0.119)
经济发展	lnGDP	流入地人均GDP(取对数)	10.662(0.544)
	Sector2	流入地第二产业占比	0.491(0.098)
	Sector3	流入地第三产业占比	0.386(0.091)
	lnPublic	流入地人均公共财政支出(取对数)	8.957(0.422)
房地产市场	lnPrice	流入地商品住宅平均售价(取对数)	8.586(0.435)
	lnRent	流入地商品住宅平均租金(取对数)	5.289(0.319)
	Land	流入地居住用地面积与城镇建设用地总面积之比	0.322(0.085)
	lnInvest	流入地地均商品住宅开发投资完成额(取对数)	18.596(1.335)

Tab. 3

Tab. 4

Overall results of regression models of migrant population's housing quality in China

				(5) 房屋面积		(6) 设施条件		(7) 建筑年代		(8) 社区性质		(9) 房屋面积		(10) 设施条件		(11) 建筑年代		(12) 社区性质
Crossprov				-16.481^***		-0.184^***		-0.078^**		-0.328^***		-21.724^***		-0.156^**		-0.072^*		-0.318^***
				(5.682)		(0.054)		(0.036)		(0.063)		(6.707)		(0.062)		(0.042)		(0.069)
Rural				-38.488^***		0.014		-0.057		-0.588^***		-13.292		0.058		0.002		-0.534^***
				(13.526)		(0.121)		(0.087)		(0.154)		(18.198)		(0.157)		(0.118)		(0.193)
Age				-3.837^***		0.016^**		-0.015^***		-0.005		-2.573^***		0.017^**		-0.010		0.020^**
				(0.796)		(0.007)		(0.005)		(0.009)		(0.921)		(0.008)		(0.006)		(0.010)
Single				2.077		0.119		0.064		-0.171		23.641		0.168		0.060		0.229
				(18.310)		(0.180)		(0.117)		(0.209)		(20.245)		(0.200)		(0.130)		(0.214)
Child				19.612^**		-0.178^**		0.014		-0.057		23.803^***		-0.182^**		-0.005		-0.176^**
				(8.177)		(0.084)		(0.052)		(0.093)		(8.395)		(0.088)		(0.054)		(0.088)
Family				-77.287^***		0.098		-0.060		0.214		-57.521^***		0.090		0.000		0.133
				(15.481)		(0.141)		(0.098)		(0.175)		(18.480)		(0.162)		(0.119)		(0.196)
Purpose												25.092^*		0.229^*		0.202^**		0.128
												(13.989)		(0.126)		(0.090)		(0.148)
Length												-1.110		-0.010		0.002		-0.015
												(0.913)		(0.008)		(0.006)		(0.010)
Stay												-5.528		0.074		0.034		0.248^**
												(11.386)		(0.096)		(0.074)		(0.121)
Education												26.006		0.014		0.176		-0.066
												(24.383)		(0.213)		(0.157)		(0.259)
Job												-12.609		-0.142^*		-0.002		-0.287^***
												(9.666)		(0.086)		(0.062)		(0.102)
Security												26.831^**		0.110		-0.076		0.066
												(13.346)		(0.115)		(0.086)		(0.141)
lnIncome												12.505^*		-0.024		-0.013		0.094
												(6.847)		(0.059)		(0.044)		(0.072)
Ownership												0.415		0.053		-0.028		-0.212^*
												(11.222)		(0.100)		(0.073)		(0.119)
	(1) 房屋面积	(2) 设施条件	(3) 建筑年代	(4) 社区性质	(5) 房屋面积		(6) 设施条件		(7) 建筑年代		(8) 社区性质		(9) 房屋面积		(10) 设施条件		(11) 建筑年代		(12) 社区性质
lnRentexp													-3.425		-0.047		0.042^*		0.122^***
													(3.791)		(0.034)		(0.024)		(0.040)
lnPopulation	-2.112	-0.014	0.011	0.032	-2.516		-0.022		0.000		-0.001		-3.689^*		-0.025		-0.009		-0.032
	(2.190)	(0.019)	(0.013)	(0.025)	(2.094)		(0.018)		(0.014)		(0.023)		(2.091)		(0.019)		(0.014)		(0.022)
Migrant	-35.533^**	-0.092	0.149^*	-0.045	-50.559^***		-0.015		0.112		0.179		-63.468^***		-0.051		0.154		0.244
	(15.047)	(0.149)	(0.09)	(0.173)	(14.983)		(0.145)		(0.095)		(0.169)		(16.088)		(0.153)		(0.102)		(0.168)
lnGDP	-1.734	0.085^**	0.011	0.000	6.963^*		0.053		0.027		-0.060		8.401^*		0.072^*		0.020		-0.021
	(4.188)	(0.041)	(0.026)	(0.049)	(4.199)		(0.040)		(0.027)		(0.048)		(4.288)		(0.041)		(0.028)		(0.045)
Sector2	-38.857	-0.061	-0.057	-0.404	-72.770^***		0.031		-0.161		-0.158		-80.689^***		-0.039		-0.101		-0.172
	(24.96)	(0.242)	(0.153)	(0.292)	(23.891)		(0.235)		(0.153)		(0.273)		(23.891)		(0.240)		(0.154)		(0.254)
Sector3	-52.515^*	0.072	0.145	-0.711^**	-58.003^**		0.190		0.118		-0.407		-62.054^**		0.170		0.164		-0.385
	(27.429)	(0.262)	(0.168)	(0.320)	(25.718)		(0.252)		(0.165)		(0.293)		(25.433)		(0.254)		(0.164)		(0.269)
lnPublic	-15.334^***	-0.110^***	0.012	0.075	-12.295^***		-0.115^***		0.017		0.020		-12.582^***		-0.118^***		0.006		-0.047
	(4.119)	(0.038)	(0.025)	(0.048)	(3.917)		(0.037)		(0.025)		(0.045)		(3.964)		(0.038)		(0.026)		(0.042)
lnPrice	-3.200	-0.005	-0.093^***	-0.047	-7.678		0.017		-0.095^***		0.009		-7.853		0.016		-0.102^***		0.009
	(5.495)	(0.053)	(0.034)	(0.064)	(5.324)		(0.051)		(0.034)		(0.061)		(5.342)		(0.050)		(0.034)		(0.057)
lnRent	10.639	0.090	0.000	-0.066	9.276		0.078		0.003		-0.019		9.246		0.085		0.010		-0.037
	(7.010)	(0.068)	(0.043)	(0.082)	(6.892)		(0.066)		(0.044)		(0.079)		(6.923)		(0.066)		(0.045)		(0.073)
Land	4.642	0.078	-0.001	0.122	5.211		0.082		0.029		0.096		9.896		0.049		0.035		0.098
	(12.894)	(0.107)	(0.079)	(0.151)	(12.163)		(0.105)		(0.078)		(0.139)		(12.001)		(0.105)		(0.077)		(0.127)
lnInvest	0.519	0.004	0.001	-0.005	0.142		0.004		-0.001		-0.005		0.019		0.005		-0.002		-0.009*
	(0.466)	(0.004)	(0.003)	(0.005)	(0.439)		(0.004)		(0.003)		(0.005)		(0.437)		(0.004)		(0.003)		(0.005)
λ	0.339^***		0.177^***		0.252^***				0.154^**				0.203^***				0.150^**
	(0.057)		(0.062)		(0.057)				(0.062)				(0.057)				(0.061)
ρ		0.597^***					0.578^***								0.580^***
		(0.057)					(0.058)								(0.058)
N	275	275	275	275	275		275		275		275		275		275		275		275
R²	0.451	0.388	0.094	0.109	0.535		0.446		0.155		0.298		0.564		0.461		0.200		0.452

Tab. 4

Tab. 5

Overall results of regression models of migrant population's housing quality in China: Heterogeneity between cities of different scales

	房屋面积			设施条件			建筑年代			社区性质
	(1) 中小城市	(2) 大城市	组间系数差异 SUR检验	(3) 中小城市	(4) 大城市	组间系数差异 SUR检验	(5) 中小城市	(6) 大城市	组间系数差异 SUR检验	(7) 中小城市	(8) 大城市	组间系数差异 SUR检验
Crossprov	-37.026^***	-7.796	-29.23^**	-0.242^***	0.010	-0.253^**	-0.078	-0.054	-0.024	-0.320^***	-0.197	-0.123
	(9.585)	(8.133)		(0.086)	(0.114)		(0.059)	(0.072)		(0.085)	(0.139)
Rural	-5.825	-10.092	4.267	0.010	-0.649	0.659	-0.070	0.295	-0.365	-0.554^**	-1.977^***	1.423^***
	(23.908)	(37.235)		(0.215)	(0.523)		(0.148)	(0.329)		(0.213)	(0.638)
Age	-2.637^**	-2.101	-0.536	0.022^**	0.002	0.021	-0.013^*	0.017	-0.030^**	0.023^**	-0.020	0.043^*
	(1.196)	(1.607)		(0.011)	(0.023)		(0.007)	(0.014)		(0.011)	(0.028)
Single	33.420	36.898	-3.478	-0.006	0.138	-0.144	0.020	0.159	-0.139	0.334	-0.100	0.434
	(27.795)	(29.840)		(0.250)	(0.419)		(0.172)	(0.263)		(0.247)	(0.511)
Child	28.469^***	31.617^*	-3.149	-0.223^**	0.339	-0.562^***	0.010	0.004	0.006	-0.138	-0.242	0.104
	(10.792)	(15.950)		(0.097)	(0.224)		(0.067)	(0.141)		(0.096)	(0.273)
Family	-62.556^***	14.608	-77.164	-0.049	1.571^***	-1.620^***	-0.060	0.705^*	-0.764^**	0.066	-0.149	0.216
	(23.944)	(39.558)		(0.215)	(0.555)		(0.148)	(0.349)		(0.213)	(0.677)
Purpose	21.805	17.101	4.704	0.214	0.951^**	-0.737^**	0.208^*	0.261	-0.053	0.228	0.163	0.064
	(17.970)	(32.769)		(0.161)	(0.460)		(0.111)	(0.289)		(0.160)	(0.561)
Length	-1.388	-2.148	0.760	-0.008	-0.067^**	0.058^**	0.002	-0.017	0.019	-0.016	-0.041	0.025
	(1.165)	(1.875)		(0.010)	(0.026)		(0.007)	(0.017)		(0.010)	(0.032)
Stay	-0.392	-31.367	30.975	0.024	-0.477	0.500	0.081	-0.307	0.388^**	0.214	0.979^**	-0.764^**
	(14.618)	(25.310)		(0.131)	(0.355)		(0.091)	(0.223)		(0.130)	(0.433)
Education	18.592	27.207	-8.615	0.173	-0.058	0.231	0.109	0.053	0.056	-0.097	-1.217^*	1.12^*
	(33.377)	(38.674)		(0.300)	(0.543)		(0.207)	(0.341)		(0.297)	(0.662)
Job	-7.035	-30.292	23.256	-0.111	-0.349	0.238	0.049	-0.089	0.138	-0.283^**	-0.448	0.165
	(12.759)	(19.829)		(0.115)	(0.278)		(0.079)	(0.175)		(0.114)	(0.340)
Security	41.291^**	9.107	32.184	0.098	0.729^***	-0.631^***	-0.103	0.144	-0.248	-0.012	0.122	-0.134
	(19.311)	(17.439)		(0.173)	(0.245)		(0.120)	(0.154)		(0.172)	(0.299)
lnIncome	18.173^**	7.406	10.767	0.029	-0.627^***	0.656^***	0.028	-0.164	0.192^*	0.095	-0.065	0.16
	(8.734)	(14.507)		(0.078)	(0.204)		(0.054)	(0.128)		(0.078)	(0.248)

Tab. 5

References 40

[1]	Fang Xiaodan. Building a moderately prosperous society in all respects from the perspective of residents' income and expense. People's Daily, 2020-07-27.
	[方晓丹. 从居民收支看全面建成小康社会成就. 人民日报, 2020-07-27. http://paper.people.com.cn/rmrb/html/2020-07/27/nw.D110000renmrb_20200727_1-10.htm.]
[2]	Jiang Leiwen, Pang Lihua, Zhang Zhiming. Living conditions of floating population in urban China. Population Research, 2005, 29(4): 16-27.
	[蒋耒文, 庞丽华, 张志明. 中国城镇流动人口的住房状况研究. 人口研究, 2005, 29(4): 16-27.]
[3]	He Zhaohua, Yang Juhua. Settling down or lodging in cities? A comparative study of living conditions among internal migrants in China. Population Research, 2013, 37(6): 17-34.
	[何炤华, 杨菊华. 安居还是寄居?不同户籍身份流动人口居住状况研究. 人口研究, 2013, 37(6): 17-34.]
[4]	Lin Liyue, Zhu Yu, Liang Pengfei, et al. The spatial patterns of housing conditions of the floating population in China based on the sixth census data. Geographical Research, 2014, 33(5): 887-898. doi: 10.11821/dlyj201405008
	[林李月, 朱宇, 梁鹏飞, 等. 基于六普数据的中国流动人口住房状况的空间格局. 地理研究, 2014, 33(5): 887-898.]
[5]	Zou Jing, Chen Jie, Wang Hongwei. How does social integration affect the residential choice of floating population? Based on the monitoring data of floating population in China in 2014. Journal of Shanghai University of Finance and Economics, 2017, 19(5): 64-79.
	[邹静, 陈杰, 王洪卫. 社会融合如何影响流动人口的居住选择: 基于2014年全国流动人口监测数据的研究. 上海财经大学学报, 2017, 19(5): 64-79.]
[6]	Yang X, Miao S, Chinmoy S, et al. Exploring the impacts of housing condition on migrants' mental health in Nanxiang, Shanghai: A structural equation modelling approach. International Journal of Environmental Research & Public Health, 2018, 15(2): 225. DOI: 10.3390/ijerph15020225. doi: 10.3390/ijerph15020225
[7]	Chen Y, Dang Y X, Dong G P. An investigation of migrants' residential satisfaction in Beijing. Urban Studies, 2020, 57(3): 563-582. doi: 10.1177/0042098019836918
[8]	Liang Tukun. An analysis of influencing factors of settling willingness of floating population. Population and Society, 2016, 32(2): 63-74.
	[梁土坤. 流动人口定居意愿影响因素分析. 人口与社会, 2016, 32(2): 63-74.]
[9]	Qiu Hong, Zhou Wenjian. A study on migrants' settling willingness and its influencing factors. Population Journal, 2019, 41(5): 91-100.
	[邱红, 周文剑. 流动人口的落户意愿及影响因素分析. 人口学刊, 2019, 41(5): 91-100.]
[10]	Gui Jing. Study on migrants' stratification factor and residential choices: Based on survey in 7 cities in 2013[D]. Shanghai: East China University of Science and Technology, 2015: 13-17.
	[桂静. 流动人口的分层因素与居住选择的相关研究: 基于2013年我国七城市流动人口调查数据[D]. 上海: 华东理工大学, 2015: 13-17.]
[11]	Hou Huili, Li Chunhua. Gradient urbanization: Community type and residential housing of migrants. Population Research, 2013, 37(2): 83-92.
	[侯慧丽, 李春华. 梯度城市化: 不同社区类型下的流动人口居住模式和住房状况. 人口研究, 2013, 37(2): 83-92.]
[12]	Li Zhigang. Housing conditions, patterns and mechanisms of second generation migrants in urban China: A case study of six large cities. Acta Geographica Sinica, 2012, 67(2): 189-200. doi: 10.11821/xb201202005
	[李志刚. 中国大都市新移民的住房模式与影响机制. 地理学报, 2012, 67(2): 189-200.]
[13]	Feng Changchun, Li Tianjiao, Cao Guangzhong, et al. Housing outcomes of family migrants at the place of destination. Geographical Research, 2017, 36(4): 633-646. doi: 10.11821/dlyj201704003
	[冯长春, 李天娇, 曹广忠, 等. 家庭式迁移的流动人口住房状况. 地理研究, 2017, 36(4): 633-646.]
[14]	Niu G, Zhao G C. Living condition among China's rural-urban migrants: Recent dynamics and the inland-coastal differential. Housing Studies, 2018, 33(3): 476-493. doi: 10.1080/02673037.2017.1351924
[15]	Liu Tao, Wei Changchuan, Tong De. Human capital, social support and social assimilation of floating population: A case study of Beijing. Population and Development, 2020, 26(2): 11-22.
	[刘涛, 韦长传, 仝德. 人力资本、社会支持与流动人口社会融入: 以北京市为例. 人口与发展, 2020, 26(2): 11-22.]
[16]	Zhao Yeqin, Meng Zhaomin. Floating population: Social stratification and housing quality: Evidence from the six census data of Changning District in Shanghai. Population and Development, 2012, 18(5): 59-66, 75.
	[赵晔琴, 孟兆敏. 流动人口的社会分层与居住质量: 基于上海市长宁区“六普”数据的分析. 人口与发展, 2012, 18(5): 59-66, 75.]
[17]	Liu Zuyun, Mao Xiaoping. Housing stratification in urban China: A study based on a Guangzhou household questionnaire survey. Social Sciences in China, 2012(2): 94-109, 206-207.
	[刘祖云, 毛小平. 中国城市住房分层: 基于2010年广州市千户问卷调查. 中国社会科学, 2012(2): 94-109, 206-207.]
[18]	National Health and Family Planning Commission of the People's Republic of China. 2015 Report on China's Migrant Population Development. Beijing: China Population Publishing House, 2016: 84-91.
	[国家卫生健康委员会. 中国流动人口发展报告2015. 北京: 中国人口出版社, 2016: 84-91.]
[19]	Li Xiangfen. Study on the housing area differences' influencing factors of urban residents in China[D]. Shanghai: Shanghai Normal University, 2016: 18-20.
	[李香粉. 我国城镇居民住房地区差异的影响因素研究[D]. 上海: 上海师范大学, 2016: 18-20.]
[20]	Wang Xinxian. The analysis of evolution of spatial pattern of China's floating population and its influencing factors[D]. Shanghai: East China Normal University, 2017: 25-37.
	[王新贤. 中国流动人口空间格局演变及影响因素分析[D]. 上海: 华东师范大学, 2017: 25-37.]
[21]	Liu Tao, Qi Yuanjing, Cao Guangzhong. China's floating population in the 21st century: Uneven landscape, influencing factors, and effects on urbanization. Acta Geographica Sinica, 2015, 70(4): 567-581. doi: 10.11821/dlxb201504005
	[刘涛, 齐元静, 曹广忠. 中国流动人口空间格局演变机制及城镇化效应: 基于2000和2010年人口普查分县数据的分析. 地理学报, 2015, 70(4): 567-581.]
[22]	Zhang Haifeng, Zhang Jiazi, Yao Xianguo. Spatial evolution of housing cost and its effect on labor migration in China. Economic Geography, 2019, 39(7): 31-38.
	[张海峰, 张家滋, 姚先国. 我国住房成本的空间演化与劳动力流动决策影响. 经济地理, 2019, 39(7): 31-38.]
[23]	Yang Juhua, Chen Chuanbo. Characteristics of family of migrants at the place of destination. Population Journal, 2013, 35(5): 48-62.
	[杨菊华, 陈传波. 流动家庭的现状与特征分析. 人口学刊, 2013, 35(5): 48-62.]
[24]	Liu Chao. The convenience of urban residents' housing in China. The World of Survey and Research, 2012(11): 13-18.
	[刘超. 我国城镇居民家庭住房方便程度分析. 调研世界, 2012(11): 13-18.]
[25]	Ma Xiaohong, Duan Chengrong, Guo Jing. The comparisons of four types of floating population. China Journal of Population Science, 2014(5): 36-46.
	[马小红, 段成荣, 郭静. 四类流动人口的比较研究. 中国人口科学, 2014(5): 36-46.]
[26]	Lin Liyue, Zhu Yu. Housing conditions of floating population under the status of circulation: A case study of Fujian province. Population Research, 2008, 32(3): 48-56.
	[林李月, 朱宇. 两栖状态下流动人口的居住状态及其制约因素: 以福建省为例. 人口研究, 2008, 32(3): 48-56.]
[27]	Li Chen. Study on assessment China's urban human settlements[D]. Shanghai: East China Normal University, 2015: 113-118.
	[李陈. 中国城市人居环境评价研究[D]. 上海: 华东师范大学, 2015: 113-118.]
[28]	Liu Tingting, Li Hanwei, Gao Kai. A study of housing selection of the floating population with family migration and its influencing factors: A case of Shanghai. South China Population, 2014, 29(3): 17-27.
	[刘婷婷, 李含伟, 高凯. 家庭随迁流动人口住房选择及其影响因素分析: 以上海市为例. 南方人口, 2014, 29(3): 17-27.]
[29]	Lin Liyue, Zhu Yu. Spatial variation and its determinants of migrants' Hukou transfer intention of China's prefecture- and provincial-level cities: Evidence from the 2012 national migrant population dynamic monitoring survey. Acta Geographica Sinica, 2016, 71(10): 1696-1709. doi: 10.11821/dlxb201610003
	[林李月, 朱宇. 中国城市流动人口户籍迁移意愿的空间格局及影响因素: 基于2012年全国流动人口动态监测调查数据. 地理学报, 2016, 71(10): 1696-1709.]
[30]	Yang Juhua. Migration and living apart in China: Economic rationality vs. institutional barriers. Population Journal, 2015, 37(1): 26-40.
	[杨菊华. 人口流动与居住分离: 经济理性抑或制度制约. 人口学刊, 2015, 37(1): 26-40.]
[31]	Gu Hengyu, Qin Xiaoling, Shen Tiyan. Spatial variation of migrant population's return intention and its determinants in China's prefecture and provincial level cities. Geographical Research, 2019, 38(8): 1877-1890. doi: 10.11821/dlyj020180473
	[古恒宇, 覃小玲, 沈体雁. 中国城市流动人口回流意愿的空间分异及影响因素. 地理研究, 2019, 38(8): 1877-1890.]
[32]	Gu Hengyu, Meng Xin, Shen Tiyan, et al. Spatial variation of the determinants of China's urban floating population's settlement intention. Acta Geographica Sinica, 2020, 75(2): 240-254. doi: 10.11821/dlxb202002003
	[古恒宇, 孟鑫, 沈体雁, 等. 中国城市流动人口居留意愿影响因素的空间分异特征. 地理学报, 2020, 75(2): 240-254.]
[33]	Zhang Zhanxin, Gao Wenshu, Hou Huili. Urban-rural divide, regional segmentation and insufficient access of urban floating population to social security entitlements: Evidence from Shanghai and other four Chinese cities. Chinese Journal of Population Science, 2007(6): 33-41, 95.
	[张展新, 高文书, 侯慧丽. 城乡分割、区域分割与城市外来人口社会保障缺失: 来自上海等五城市的证据. 中国人口科学, 2007(6): 33-41, 95.]
[34]	Liu Yi, Guo Xinran, Gao Chuansheng. A study on factors affecting migratory intentions of rural-urban and urban-urban floating population. Journal of Nanjing Institute of Technology (Social Science Edition), 2019, 19(1): 31-37.
	[刘艺, 郭馨冉, 高传胜. 乡—城、城—城流动人口居留意愿的影响因素研究. 南京工程学院学报(社会科学版), 2019, 19(1): 31-37.]
[35]	Chen X F. Why do migrant households consume so little? China Economic Review, 2018, 49: 197-209. doi: 10.1016/j.chieco.2017.11.005
[36]	Wang L L, Sun T S. Capitalization of legal title: Evidence from small-property-rights houses in Beijing. Habitat International, 2014, 44: 306-313. doi: 10.1016/j.habitatint.2014.07.013
[37]	Zhu Xiangbo, Tan Shukui, Wang Siliang, et al. Housing choice of migrant population in China: Facts and explanation. South China Population, 2015, 30(3): 35-44.
	[朱祥波, 谭术魁, 王斯亮, 等. 城市流动人口的住房选择: 事实与解释. 南方人口, 2015, 30(3): 35-44.]
[38]	Xie Dixiang, Chang Jiang. Literature review of gentrifi cation in China. Planners, 2015, 31(9): 73-77, 96.
	[谢涤湘, 常江. 我国城市更新中的绅士化研究述评. 规划师, 2015, 31(9): 73-77, 96.]
[39]	Lian Yujun, Liao Junping. How to test the coefficient difference between groups after group regression? Journal of Zhengzhou University of Aeronautics, 2017, 35(6): 97-109.
	[连玉君, 廖俊平. 如何检验分组回归后的组间系数差异? 郑州航空工业管理学院学报, 2017, 35(6): 97-109.]
[40]	Qi Huifeng, Wang Weiqiang. An analysis of the housing security system based on population flow. City Planning Review, 2015, 39(2): 31-37.
	[齐慧峰, 王伟强. 基于人口流动的住房保障制度改善. 城市规划, 2015, 39(2): 31-37.]

类别	影响因素	研究理论基础	选取指标
流动人口特征	户籍制度因素	制度文化理论	户籍所在地、户籍性质
	家庭人口因素	生命周期理论效用经济学理论	性别、年龄、婚姻、子女数量、家庭规模、家庭迁移比例、迁移目的
	流动因素	社会融合理论	在流入地的停留时间、定居落户意愿、社会资本、居住社区
	教育经济因素	社会分层理论人力资本理论效用经济学理论	受教育程度、就业身份、职业类型、单位性质、社保、住房自有率、流出地资产、流出地收入、流入地收入、流入地支出
流入地特征	人口因素	制度文化理论住房市场供需理论效用经济学理论	人口规模、人口结构
	经济因素		经济水平、产业结构、公共服务
	房地产市场因素		房价、房租、住房存量、新增住房供给、住房限购政策

Spatial variation of migrant population's housing quality and its determinants in China's prefecture-level cities

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Figures/Tables 10

References 40

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	Moran's I	P值	Z值
住房质量综合指数	0.435	0.000	11.825
房屋面积	0.401	0.000	10.319
设施条件	0.422	0.000	10.856
建筑年代	0.132	0.001	3.448
社区性质	0.302	0.000	7.790

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