程晗蓓^,1,2, 刘于琪³, 田明⁴, 李志刚^,1,21.武汉大学城市设计学院,武汉 430072
2.湖北省人居环境工程技术研究中心,武汉 430072
3.香港大学城市规划与设计系,香港 999077
4.北京师范大学社会发展与公共政策学院,北京 100875

The effects of residential instability on migrants′ health in urban China

CHENG Hanbei^,1,2, LIU Yuqi³, TIAN Ming⁴, LI Zhigang^,1,21. School of Urban Design, Wuhan University, Wuhan 430072, China
2. Hubei Habitat Environment Engineering Research Center, Wuhan 430072, China
3. Faculty of Architecture, The University of Hong Kong, Hong Kong 999077, China
4. School of Social Development and Public Policy, Beijing Normal University, Beijing 100875, China

通讯作者: 李志刚（1976-）,男,湖北天门人,教授,博士生导师,研究方向为城市地理、城乡规划。E-mail: zhigangli@whu.edu.cn

收稿日期:2019-09-17接受日期:2020-02-17网络出版日期:2021-01-10

基金资助:

国家自然科学基金.41771167 国家自然科学基金.41371165 国家自然科学基金.41422103 国家社科基金重大项目.16ZDA026

Received:2019-09-17Accepted:2020-02-17Online:2021-01-10
作者简介 About authors
程晗蓓（1990-）,女,湖北武汉人,博士研究生,研究方向为人口迁移与流动、健康地理。E-mail: hanbei. cheng@whu.edu.cn






摘要
随着城镇化加速和人口“流动性”增强,中国大城市流动人口的健康问题备受关注。流动人口的居住具有典型不稳定特征,分析其对流动人口身心健康的影响具有重要意义。为此,本文采用北京、深圳、上海等9个大城市的实地调研数据,运用层级回归和分组回归,探讨影响流动人口身心健康的因素,关注“居住不稳定性”因素的影响。研究表明：城市内部多次迁居和城市间频繁流动对流动人口健康具有显著负面影响;住房因素中,自有住房和选择居住在本地人居多的邻里对其健康具有显著正面影响。另外,“性别”因素具有调节效应;居住不稳定性对主观幸福感和自评生理健康的影响程度因“性别”不同而有所差异,男性健康对时空因素更为敏感和脆弱,女性健康不佳多归因于住房和邻里。
关键词： 流动人口;居住不稳定性;健康;性别差异;影响因素

Abstract
Health research has long recognized the importance of residential instability. However, less attention has been paid to disclose their relationships against the background of frequent mobility in urban China. Thus, this study used multilevel linear models and grouping regression to explore the impacts of residential instability on migrants′ health based on the data collected from a questionnaire survey involving 9 cities and 2573 respondents. The results were as follows: (1) Migrants in urban China exhibited an ordinary level of subjective well-being (SWB) but reported preferable value in self-rated physical health (SRH). Notably, there were significant differences in health between genders. (2) Migrants′ health was affected by sociodemography, residential instability, as well as geographical factors. For instance, migrants working overtime and those living in cities with higher housing prices exhibited lower health outcomes. Married, well-educated, and high-income migrants were happier than the rest of the migrants. No evidence has shown that marital status, education level, and monthly income were significantly related to their physical health. (3) Residential instability had a statistical significance in health outcomes, and the strength of effect varied in different gender groups. In detail, interurban and intraurban mobilities were negatively related to migrants′ health in both subjective well-being and physical health. Importantly, these relationships were prominent in terms of males. In other words, frequent mobility can reduce their health. Besides, migrants moving with family members and owning purchased houses exhibited better health than those moving on their own and living in rented houses, especially in female groups. Moreover, migrants residing in neighborhoods with a higher proportion of locals had a higher probability of reporting better health. This result was also obvious in female migrants who concerned more on neighborhood safety. Finally, the number of friends was positively correlated with migrants′ health, which implied the health promotion of social capital in cities. All in all, this study confirmed that residential instability had a significant impact on migrants′ health and that gender played a moderating role in this relationship. Men were more sensitive and vulnerable to mobility factors, while women′s poor health was attributed to housing and neighborhood instability. Therefore, we call for the government to strengthen the housing attainment, education, medical services, etc. to increase migrants′ settlement intentions and reduce the frequency of mobility. On the other hand, community committees should encourage migrants to interact with locals to build a cohesive and supportive neighborhood for health promotion.
Keywords：migrants;residential instability;health;gender difference;determinants


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本文引用格式
程晗蓓, 刘于琪, 田明, 李志刚. “居住不稳定性”对中国大城市流动人口健康的影响研究. 地理研究[J], 2021, 40(1): 185-198 doi:10.11821/dlyj020190815
CHENG Hanbei, LIU Yuqi, TIAN Ming, LI Zhigang. The effects of residential instability on migrants′ health in urban China. Geographical Research[J], 2021, 40(1): 185-198 doi:10.11821/dlyj020190815

1 引言

进入新时代,伴随城镇化进程加速推进,流动人口及其健康问题愈加凸显。在此背景下,“健康中国”已被确立为国家战略,对流动人口的健康问题予以重点关注。因其“流动性”特征,流动人口的居住具有明显不稳定性^[1],分析其对流动人口身心健康的影响具有重要意义。居住不稳定性与公共健康问题密切相关。例如,根据“美国国家住房报告”数据显示,2015年美国约有56.5万人居无定所和无家可归（Displacement & Homelessness）,其中的40%患有慢性病（糖尿病、高血压和哮喘等）、35%患有重度抑郁、25%存在药物滥用等不良行为^[2]。目前,这一领域的研究主要集中在：① 探讨迁居过程的时空不稳定性对健康的影响。诸多****关注了迁居频率、居留时间、迁居距离、方向、性质（自愿或迫迁）和模式等因素的影响。例如,Vanhoutte等发现儿童时期频繁迁居与个体幸福感无关,成年早期频繁迁居则对个体幸福感有显著正面影响^[3]。Larson等发现长距离迁移与慢性疾病风险概率呈正相关^[4]。② 关注居住状态如住房和邻里不稳定性对健康的影响,包括住房产权、成本（租金/购房借贷）、拥挤率、住房质量、建筑年代及邻里犯罪率、失业人口比等。例如,Windle等发现,自有住房者的健康水平高于公共租赁房者和私人租赁房者^[5];Burgard等发现,租金变动、拖欠房租、止赎权等因素与健康不佳显著相关^[6]。其中,租房者多存在社交网络脆弱和敏感问题,更易出现“社会孤立”或“一次性”人际关系,不利于其身心健康^[7]。此外,房东的态度和行为也是影响租房者身心健康的关键变量^[8]51,[9]。

研究表明,不同“性别”的健康状况存在较大差异,其中女性移民在住房选择、住房支付能力、房源获取渠道和邻里社会资本等方面较为弱势,多重弱势因素交叠导致女性移民健康的不利发展。如Desmond发现,女性迁居概率显著高于男性,尤其是单亲母亲,其身心健康水平普遍较低^[8]196。Saito等证实,邻里社会排斥对女性死亡风险影响更显著,相对贫困则与男性死亡风险显著相关^[10]。Magdol围绕“压力脆弱性”（Stress Vulnerability）展开研究,认为压力因素如生活事件、地点的变化、社会关系的破坏等对健康有影响,并在不同性别间存在差异^[11]。例如,迁居的女性比男性更容易受到环境压力的负面影响^[11,12]。

国内研究多从个体、家庭、职业和收入等微观因素^[13,14,15]及邻里环境、城市建成环境、社会融合环境、公共医疗服务质量和城市化水平等中、宏观因素入手^{[16,17,18,19,20,21]},探讨流动人口健康问题,少量文献关注了居住不稳定性因素的影响^[22,23,24]。这些研究多基于全国性普查或专题调查数据,少量结合实地调研,关注特定城市或区域。例如,实证发现广州未成年人（<18岁）的频繁迁居对其心理和生理健康均有负面影响^[23];香港的公屋居民面临更高的过早死亡风险^[24];对12个典型城市的调查显示,租金负担、住房来源、设施指数、与他人同住等因素均与流动人口所感知的压力水平相关^[22]。不过,中国流动人口已发展为高度多样化（性别、户籍、职业阶层、收入水平和民族等）的异质性群体,亟待对群体多样性予以更多考察。例如,“性别”是不可忽视的内生性因素,从“性别”差异视角探讨对流动人口健康影响的研究偏少。总体上,此类研究尚处于起步阶段,需要更多基于中国背景的深入实证。

此外,已有研究对“居住不稳定性”（Residential Instability）并无明确定义,泛指个体或家庭居住地（在短时期或某一段时间内）的多次变化,或居住状态的非稳定性（如栖身公共空间、街道、不利住房条件、劣势邻里环境等）^[25,26]。本文的“居住不稳定性”指的是流动人口在从乡村到城市稳定居留中经历的时空变化及各种住房和邻里不稳定性,如城市内部迁居频率、城市间流动次数、居留时间、当前住房属性等,本文试图考察“居住不稳定性”的过程和状态两个维度。

后文计划以北京、上海、深圳等9个城市为对象,通过实地调研采集第一手数据,定量分析居住不稳定性对个体健康的影响。本文的贡献有以下三方面：① 从微观主体出发,基于实地调研一手数据,聚焦典型大城市,揭示流动人口主观幸福感和自评生理健康水平,拓展该领域的研究对象。② 重点关注居住不稳定性因素的影响,拓展该领域的分析维度。③ 为破解当前中国转型期“流动性”增强和“移民化”加深背景下的流动人口公共健康问题提供解决思路和政策建议。

2 数据与方法

2.1 案例地城市与数据来源

数据主要来自课题组于2016年9月至2017年6月在北京、上海、深圳、成都、武汉、郑州、西安、无锡和长春9个城市组织完成的“流动人口与家庭社会调查”问卷调查（表1）。案例城市的选取一方面考虑了超大城市、特大城市和大城市等不同城市级别,一方面结合了调查条件等实际情况,如合作机构、研究基础等。问卷对象为在该城市居留一个月及以上、非本市户籍的流动人口。在调查设计中每个城市设定350份样本,其中50份为备份样本。问卷共收集2662份,删除相关变量数据缺失的记录,有效问卷2573份,样本分布如表1。

Tab. 1
表1
表19市调查问卷构成
Tab. 1The number of respondents in 9 sample cities in this study

	北京	上海	深圳	成都	武汉	郑州	西安	无锡	长春	合计
数量（人）	314	287	291	309	313	334	235	216	274	2573
占比（%）	12.2	11.2	11.3	12.0	12.2	13.0	9.1	8.4	10.6	100.0

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2.2 变量测量与研究框架

因变量为流动人口的主观幸福感（SWB）和自评生理健康（SRH）。主观幸福感包括“情感幸福感”和“生活满意度”两个维度^[27,28],参考已有经验^[29],以“自我报告幸福感指数（1~7：很不幸福~很幸福）”和“Cantril生活满意度自我评估梯形量表（0~10：阶梯底端最差的生活~阶梯顶端最好的生活）”来测定,通过各自极大值标准化处理后等量加权求解（数值区间0~1）。生理健康以“自我评价等级”来测量（1~7：很不健康~很健康）,也以极大值标准化处理（数值区间0~1）。主观幸福感测度方法曾被用于不丹的《国家国民幸福总值社会调查》^[29];自评生理健康数据与死亡率等客观指标高度相关^[30]。这些指标和测量方法均在国内外相关研究中广泛采用^{[6,14,23,29,31]}。

流动人口健康受个体层级（个体因素、居住不稳定性）和城市层级（地方因素）共同影响（图1）。

图1

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图1研究框架

Fig. 1Research framework



就个体层级而言,个体因素包括性别、年龄、婚姻状况、受教育程度、个人收入、合同形式和工作强度7个指标。居住不稳定性因素包括城市内部迁居频率、城市间流动次数、本市居留时间、流动模式、住房属性、当地经常往来朋友数量和邻里人口构成7个指标;其中前4个变量反映了迁居的时空不稳定性,后3个变量反映了住房和邻里不稳定性,通过这7个变量共同考察居住不稳定性的“过程”和“状态”。

就城市层级而言,地方因素包括人均GDP、城市平均房价、城市外来人口占比、医疗卫生资源可及性和人均公园绿地面积。研究表明,各种经济、社会人文和环境质量对个体健康有直接影响^[16,32,33],人均GDP和城市平均房价折射了当前城市经济发展水平以及流动人口对城市住房的可负担性,城市外来人口比值能一定程度反映城市社会融合状况和人文环境的包容性,医疗卫生服务资源可及性衡量了医疗资源的公平性配置,人均公园绿地面积体现了城市自然环境舒适性和健康性。赵雪雁等^[16]、党晓云等^[32]证实上述因素对个体健康影响作用突出,值得重点关注。

个体因素、居住不稳定性和地方因素在对个体健康产生直接影响效应的同时（图1,路径a）,个体因素中的“性别”在居住不稳定性和健康之间也具有调节效应（图1,路径b）,即影响程度因调节变量“性别”不同而有所差异。不过,居住不稳定性除影响健康之外,也可能存在“选择性反馈”机制（图1,路径c）,即居住不稳定性（如低质量住房、过度拥挤等）导致部分移民群体（或流动人口）健康不佳,为改善健康状况出现再迁居决策,造成新的居住不稳定性。但这一反馈路径并非本文考察重点,将不予过多论述。本文重在关注居住不稳定性对流动人口健康的影响及“性别”的内在作用机制。

2.3 研究方法

（1）直接效应分析 本文涉及的自变量具有多层嵌套的数据结构。传统线性模型方法只针对单一层级（个体或地方）进行分析,忽视了多层级因素带来的影响^{[12], [34]27}。其次,传统线性模型中的样本多具有独立性,而同类群体健康具有交互传递作用（“同伴效应”）^[35]。因此,本文采用多层线性模型,以检验不同层级因素对因变量的解释贡献,并有效处理样本相关性,提升分析精度^[36]。具体而言,首先将个体因素和地方因素引入回归方程,见公式（1）;第二,将居住不稳定性因素加入,考察所有变量,通过系数变化和模型拟合程度分析居住不稳定性对流动人口主观幸福感和自评生理健康的影响,见公式（2）。模型如下^{[34] 28}：

（1）Yij=β0+β1X1+μZ+vi+eij
（2）Yij=β0+β1X1+β2X2+μZ+vi+eij
式中： Yij为调查地城市 j中受访者 i的两种健康结果; X1和 X2分别表示个体层级的个体因素和居住不稳性因素; Z为城市层级的地方因素; β0为模型总截距; β1、 β2和 μ分别表示各因素的回归系数; eij为模型的随机系数,代表不同城市 j中受访者 i的健康均值存在差异; vi为模型残差。

（2）调节效应分析 调节效应指在自变量X对因变量Y产生影响时,这种影响关系受到调节变量M的作用,影响程度因调节变量M不同而有所差异。效应检验方法包括建立乘积交互项（X×M）和分组回归两种^[37]。本文调节变量为“性别”,是二分变量,为识别不同性别主体受到的具体影响,故采用分组回归,并利用费舍尔组合检验验证组间系数差异是否显著,由此判定“性别”因素的调节效应。此类方法已被广泛用于验证性别、种族/族裔和代际差异的调节效应研究中^[12,38,39]。

2.4 样本信息

表2显示,全体样本平均年龄为32岁;在婚与未婚样本接近1:1;高中及专科占比最大（41.81%）。另外,绝大多数受访者为雇员（66.19%）,部分为自营劳动者（21.61%）,少数为雇主（4.54%）,7.66%为其他,包括帮工、流动性工作者及待业等;月收入均值约为4870元,工作强度较高（均值3.31）。在居住方面,62.10%的受访者经历过1次及以上的城市内部迁居（图2见第190页）,迁居频率在性别结构上无显著差异（表2）。城市间的平均流动次数为1.75次/人,5.90%的男性曾在3个及以上的城市有居住经历,这一比例在女性中仅为2.80%。流动人口在流入地居住呈长期化趋势（均值4.82a）,24.10%的样本居住10a及以上。48.49%的样本携亲迁移,女性家庭式流动（54.03%）比例显著高于男性（44.92%）。此外,67.89%的样本为租房,不到10%为自有住房。在社会网络方面,男性经常往来朋友数量（8.31）高于女性（7.16）,拥有更强的社会资本和人脉资源。绝大部分女性受访者选择居住在本地人较多或外地与本地人口结构相当的邻里（75.92%）,其比例高于男性。需要说明的是,样本的男女性别比为3:2,有别于社会调查中样本配额建议控制的性别结构比1:1,这与已有实证情况类似^[40],并不影响结论的有效性。

Tab. 2
表2
表2变量定义及统计描述
Tab. 2Definition and statistical description of variables in migrants among 9 cities

变量名称	变量定义		全体	女	男
			N=2573	N=999	N=1574
个体因素（人口学和社会经济）
性别	女	占比（%）	39.22	—	—
	男	占比（%）	60.78	—	—
年龄	调查时的实际数值（岁）	均值	32.36	32.21	32.46
		标准差	11.86	11.56	12.06
婚姻状况	在婚（已婚、离异和丧偶）	占比（%）	51.90	53.94	50.59
	未婚	占比（%）	48.10	46.06	49.41
受教育程度	初中及以下	占比（%）	33.86	34.28	33.59
	高中及专科	占比（%）	41.81	38.42	44.00
	本科及以上	占比（%）	24.33	27.30	22.41
个人收入	个人实际月收入（万元）	均值	0.49	0.41	0.53
		标准差	5.72	4.77	6.20
就业身份	雇员	占比（%）	66.19	69.28	64.23
	雇主	占比（%）	4.54	3.00	5.52
	自营劳动者	占比（%）	21.61	20.10	22.58
	其他（如帮工、非正规流动性工作及待业等）	占比（%）	7.66	7.62	7.67
工作强度	当前工作强度如何?（1~5：非常轻松~很累）	均值	3.31	3.27	3.33
		标准差	0.91	0.89	0.92
居住不稳定性
城市内部迁居频率	您在本市共更换住所次数	均值	1.46	1.43	1.47
		标准差	1.57	1.54	1.59
城市间流动次数	务工城市数量（居留半年及以上,包含当前城市）	均值	1.75	1.61	1.83
		标准差	0.70	0.83	1.04
本市居留时间	迁入本市至调查年份的时间（a）	均值	4.82	4.68	4.91
		标准差	6.31	6.09	6.46
流动模式	家庭式流动	占比（%）	48.49	54.03	44.92
	单独流动	占比（%）	51.51	45.97	55.08
住房属性	自有住房（商品房或政策保障房）	占比（%）	8.80	9.34	8.45
	租房（公租房、廉租房、单位付租宿舍、私人租房等）	占比（%）	67.89	65.57	69.39
	借住亲戚、朋友家及其他非正规住所等	占比（%）	23.31	25.09	22.16
当地经常往来朋友数量	在当地经常往来的朋友数量（人）	均值	7.86	7.16	8.31
		标准差	7.64	6.72	8.15
邻里人口构成	外地人居多	占比（%）	25.86	24.08	27.01
	本地人居多	占比（%）	30.85	33.14	29.21
	二者数量相当	占比（%）	43.29	42.78	43.78

注：“邻里人口构成”是分类变量,为受访者主观感知测评结果,而非客观人口统计,但仍能一定程度反映邻里人口结构大致现状。

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图2

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图2城市内部迁居频率和城市间流动次数样本比例分布

注：图2中城市间流动次数为不包含当前城市的统计结果,即与表2的描述性统计结果存在1个单位的量差。
Fig. 2Percentage of respondents in terms of intraurban mobility and interurban mobility

3 实证结果

3.1 流动人口的主观幸福感与自评生理健康

调查结果表明（表3）,全体样本的自评幸福指数均值为5.404（标准差1.278）（量表1~7）,生活满意度评价均值为5.008（标准差2.018）（量表0~10）,标准化等量加权后主观幸福感的综合测度均值为0.636,水平一般（0.535为中间临界值）。自评生理健康均值为5.807（标准差1.175）（量表1~7）,水平较高。对比不同性别主体,女性主观幸福感高于男性;而在自评生理健康上,则是男性高于女性。可见,就流动人口的主观幸福感与自评生理健康而言,男女均存在显著差异（SWB：T=3.485,P<0.01;SRH：T=-3.475,P<0.01）。

Tab. 3
表3
表3流动人口健康结果及其“性别”差异检验
Tab. 3Gender difference of migrants′ health based on T test

因变量	类别	均值（标准差） （标准化后）	中位数 （标准化后）	T值	Sig.	置信区间
主观幸福感（SWB）	全样本	0.636（0.159）	0.636	—	—	—
	女性（代码=0）	0.650（0.153）	0.657	3.485	0.001***	0.010~0.034
	男性（代码=1）	0.628（0.163）	0.629
自评生理健康（SRH）	全样本	0.830（0.168）	0.857	—	—	—
	女性（代码=0）	0.816（0.168）	0.857	-3.475	0.001***	-0.247~-0.069
	男性（代码=1）	0.838（0.167）	0.857

注：*、**、***分别代表P < 0.1、P < 0.05、P < 0.01。

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检验居住不稳定性与流动人口身心健康水平的相关性,发现城市内部迁居频率与主观幸福感和自评生理健康分别在10%和1%显著水平上相关;在城市内部多次迁居,流动人口的主观幸福感变化较小,但自评生理健康显著降低（图3a）。城市间流动次数仅与主观幸福感在1%显著水平上相关,超过3次城市间流动,流动人口的主观幸福感开始急剧下降（图3b）。本市居留时间仅与自评生理健康在1%显著水平上相关,流动人口在本市0~3年内自评生理健康较好（均值0.842）,居留时间超过3年开始下滑显著,随着居留时间的延长,生理健康状况开始趋于稳定,居留时间为4~9年的人口健康水平（均值0.808）与在本地超过10年以上群体（均值0.807）相差不大（图3c）。

图3

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图3主观幸福感和自评生理健康与居住不稳定性因素的相关性检验

注：为了实现多个独立样本相关性检验,此处将连续变量分组转化为分类变量。
Fig. 3The results of correlation test in terms of migrants′ health targeted on critical factors



可见,居住不稳定性的各关键因素与健康结果存在相关关系。后文进一步根据理论模型（图1）探究居住不稳定性对流动人口健康的影响。

3.2 验证居住不稳定性对流动人口健康的影响

首先,对各变量进行共线性检验,计算方差膨胀因子,发现自变量间无多重共线性问题（VIF≤5）。在主观幸福感和自评生理健康空模型中（即不纳入任何自变量）,得出城市层级标准偏差分别为0.096和0.102,说明9.6%的个体主观幸福感差异与10.2%的个体自评生理健康差异可以被城市层级的地方因素所解释,多层模型构建合理有效。对比模型1和模型2（主观幸福感）、模型3和模型4（自评生理健康）,发现加入居住不稳定性因素后,个体因素和地方因素估计系数和解释度都有所降低;根据似然比和AIC值,模型拟合程度显著提高,说明居住不稳定性因素对流动人口健康有显著影响（表4）。

Tab. 4
表4
表4回归模型结果（全样本）
Tab. 4The results of multilevel linear models on migrants’ health (all samples)

	模型1	模型2	模型3	模型4
	系数（标准误）	系数（标准误）	系数（标准误）	系数（标准误）
个体层级
性别（#女）	-0.079***(0.025)	-0.066***(0.025)	0.183***(0.046)	0.181***(0.046)
年龄	-0.001 (0.001)	-0.002 (0.001)	-0.017***(0.003)	-0.016***(0.003)
婚姻状况（#已婚）	-0.203***(0.033)	-0.119***(0.035)	-0.026 (0.062)	0.017 (0.066)
受教育程度（#初中及以下）
高中及专科	0.211***(0.030)	0.175***(0.030)	0.011 (0.056)	-0.017 (0.056)
本科及以上	0.396***(0.037)	0.330***(0.037)	0.091 (0.068)	0.011 (0.069)
个人月收入	0.007***(0.002)	0.006***(0.002)	0.003 (0.004)	0.002 (0.004)
工作强度	-0.056***(0.013)	-0.047***(0.013)	-0.109***(0.025)	-0.091***(0.025)
就业身份（#雇员）
雇主	0.157***(0.061)	0.099* (0.060)	0.032 (0.112)	0.002 (0.111)
自营劳动者	0.105***(0.032)	0.066**(0.031)	0.061 (0.059)	0.043 (0.058)
其他	0.052 (0.047)	0.032 (0.046)	0.187**(0.087)	0.172** (0.086)
城市内部迁居频率		-0.025***(0.008)		-0.049***(0.015)
城市间流动次数		-0.114** (0.055)		-0.141 (0.103)
本市居留时间		0.002 (0.002)		-0.002 (0.004)
流动模式（#家庭式流动）		-0.120***(0.028)		-0.045 (0.052)
住房属性（#自有住房）
租房		-0.209***(0.044)		0.019 (0.083)
借住或其他非正规居所		-0.152***(0.048)		0.066 (0.090)
当地经常往来朋友数量		0.005***(0.002)		0.014***(0.003)
邻里人口构成（#外地人居多）
本地人居多		0.131***(0.033)		0.213***(0.061)
二者数量相当		0.070** (0.031)		0.044 (0.058)
城市层级
城市外来人口占比	-0.268 (0.301)	-0.263 (0.269)	-0.003 (0.751)	0.037 (0.751)
人均GDP	0.016 (0.018)	0.017* (0.016)	-0.005 (0.044)	0.003 (0.044)
城市平均房价	-0.046* (0.033)	-0.049* (0.029)	-0.020* (0.081)	-0.002***(0.081)
医疗卫生资源可及性	0.012 (0.028)	0.018 (0.025)	0.018* (0.070)	0.034* (0.070)
人均公园绿地面积	0.245 (0.158)	0.305** (0.142)	-0.212 (0.392)	-0.091 (0.392)
截距项	2.437***(0.258)	2.528***(0.240)	6.698***(0.627)	6.315***(0.636)
似然比	-2369.1323	-2310.1353	-3952.4614	-3920.3379
AIC	4774.265	4678.271	7940.923	7898.676
Prob>F	0.000	0.000	0.000	0.000
样本量	2573	2573	2573	2573

注：① ^*、^**、^***分别代表P < 0.1、P < 0.05、P < 0.01,#表示参考组;② AIC数值越小,模型拟合程度越好;③ 地方因素中,城市外来人口占比和人均公园绿地面积数据来自各城市2018年统计年鉴;城市平均房价数据来自中国房价行情数据平台：http://www.creprice.cn/city/wh.html;医疗卫生资源可及性参照赵雪雁等^[16]以每千人卫生技术人员数、每千人医疗机构床位数、每万人医院数标准化后等量加权测度。

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具体而言,模型2和模型4结果显示,城市内部迁居频率对流动人口主观幸福感和自评生理健康均有显著负向预测作用,每迁居1次（单位）,将降低0.025个单位的幸福感值和0.049个单位的生理健康值。可见,城市内部频繁迁居造成社会资本断裂,产生身心“损耗”,对个体健康具有不利影响。类似的,城市间流动次数对流动人口主观幸福感也有显著负向预测作用,且更为强烈,即城市间每流动1次（单位）,将降低0.114个单位的幸福感值;但对自评生理健康影响并不显著。另外,流动模式与流动人口主观幸福感联系紧密,家庭式迁移的幸福感更强,说明随亲迁移可缓解流动人口的生存压力和负面情绪。

在住房和邻里不稳定性中,租房者和借住者的主观幸福感显著低于自有住房者,但住房属性与自评生理健康水平无直接关联,与既有研究一致^[41]。当地经常往来的朋友数量对两种健康结果均有显著正向预测作用,每增加1个单位的朋友数量,将提升0.005个单位的幸福感值和0.014个单位的生理健康值。可见,高度联结的社会资本加强了流动人口在本地的社会支持和抗风险能力,也降低了迁居概率,对健康有保护作用。另外,居住在本地人居多的邻里流动人口的幸福感和生理健康值更高,可推断“混居”一定程度上有助于提升流动人口的健康水平。值得关注的是,本市居留时间对两种健康结果均无显著影响（模型2和模型4）,但在多个独立样本非参数检验时,本市居留时间与自评生理健康显著相关（图3c）,主要原因在于模型估算中无法准确剥离年龄对健康的时间效应。总体上,本市居留时间对主观幸福感无影响,但对自评生理健康有显著负向预测作用。

此外,工作强度与两种健康结果均呈显著负相关;已婚、高文化程度和高收入的流动人口的主观幸福感更强;不过,婚姻状况、受教育程度和月收入与自评生理健康无直接关联;说明配偶的陪伴和家庭情感等因素可以积极调试流动人口在迁入地城市的不适,教育资本积淀的高收入回报可以增强幸福感。就业身份与主观幸福感存在一定联系,雇主和自营劳动者的主观幸福感显著高于雇员,但自评生理健康在不同就业身份中无显著差异。城市平均房价与两种健康结果均呈显著负相关,高房价对流动人口幸福感和生理有消极影响;人均公园绿地面积、医疗卫生资源可及性分别对主观幸福感和自评生理健康有积极影响,这与多数研究保持一致^[16,32]。

3.3 “性别”因素的调节效应

对比不同性别主体的分组回归模型,发现在控制个体因素和地方因素后,居住不稳定性对流动人口健康的影响存在性别差异,“性别”因素的调节效应显著（表5）。具体而言,城市内部多次迁居对男性生理健康有显著负向作用,与女性生理健康无关;城市间频繁流动也对男性主观幸福感有显著负向作用,与女性幸福感无关。这与诸多西方背景的研究结论相反。例如,针对美国密尔沃基地区的租户调查显示,女性在多次迁居中所受的影响程度大于男性^[8]196;对美国贫困社区的调查表明,多次迁居中的女性易怒、抑郁和焦虑概率更高^[42]。可能原因在于中国女性流动人口由乡村迁移至城市的城市化进程往往伴随着“赋权”,如参与城市活动和社区治理等,尽管频繁迁居对其健康具有不利影响,但社会地位提升和性别角色认同对健康也产生积极作用。就流动模式而言,家庭式流动可以提升流动人口的主观幸福感,且对女性健康的促增效应更明显;可见,随迁成员（如姊妹、配偶、子女等）的心理资源援助,如鼓励、安慰、帮助、互惠期望和情感支持等对女性样本作用更强。另外,“租房”或“借住”的女性流动人口的主观幸福感更低,说明住房获得对女性提升幸福感的影响更显著。同样,对女性而言,在当地经常往来的朋友数量越多,其主观幸福感越高,社会资本和社会互助对女性主观幸福感的提升效应更明显。此外,居住在本地人居多的邻里的女性的主观幸福感和自评生理健康值更高;这与美国和加拿大的调查研究结论一致^[11,43],但与英国苏格兰等地的实证结果不同^[44]。总体而言,“性别”作为调节变量的影响比较复杂,在不同国家背景的实证中尚未得出一致性结论。

Tab. 5
表5
表5居住不稳定性对健康影响的分组回归模型结果
Tab. 5The modelling results of gender difference in the impact of residential instability on migrants’ health

分组回归	主观幸福感				自评生理健康
	模型5 （女性）	模型6 （男性）	调节效应检验		模型7 （女性）	模型8 （男性）	调节效应检验
	系数 （标准误）	系数 （标准误）	组间系数差异		系数 （标准误）	系数 （标准误）	组间系数差异
个体层级
性别	—	—	—		—	—	—
其他	已控制	已控制	—		已控制	已控制	—
城市内部迁居频率	-0.031**(0.013)	-0.021**(0.010)	-0.010		-0.029 (0.025)	-0.058***(0.019)	0.029*
城市间流动次数	-0.092 (0.108)	-0.125* (0.065)	0.033*		0.005 (0.206)	-0.188 (0.119)	0.193
本市居留时间	0.002 (0.004)	0.003 (0.003)	-0.001		-0.009 (0.007)	0.001 (0.005)	-0.010
流动模式（#家庭式流动）	-0.123***(0.044)	-0.117***(0.037)	-0.006*		-0.039 (0.083)	-0.057 (0.067)	0.018
住房属性（#自有住房）
租房	-0.232***(0.069)	-0.209***(0.058)	-0.023*		-0.039 (0.131)	0.055 (0.106)	-0.094**
借住或其他非正规居所	-0.163** (0.074)	-0.153**(0.063)	-0.010		0.028 (0.142)	0.107 (0.115)	-0.079**
当地经常往来朋友数量	0.006** (0.003)	0.005**(0.002)	0.001*		0.012***(0.005)	0.018***(0.004)	0.006
邻里人口构成（#外地人居多）
本地人居多	0.178***(0.051)	0.104**(0.042)	0.074***		0.385***(0.098)	0.105 (0.077)	0.280***
二者数量相当	0.094* (0.049)	0.050 (0.040)	0.044*		0.206** (0.094)	-0.062 (0.073)	0.268***
城市层级
地方因素	已控制	已控制	—		已控制	已控制	—
截距项	2.772***(0.296)	2.296***(0.253)			6.736***(0.712)	6.251***(0.683)
似然比	-858.1077	-1441.0796			-1507.0369	-2391.5907
AIC	1772.215	2938.159			3070.074	4839.181
Prob>F	0.000	0.000			0.000	0.000
样本量	999	1574			999	1574

注：^*、^**、^***分别代表P<0.1、P<0.05、P<0.01;#表示参考组。

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4 结论与讨论

本文以城市流动人口为研究对象,基于中国9个大城市2573份样本的有效问卷调查,采用层级回归和分组回归,聚焦居住不稳定性对流动人口健康（主观幸福感和自评生理健康）的影响,并判别其“性别”差异,得出以下结论：

（1）总体而言,大城市流动人口的主观幸福感和自评生理健康在不同“性别”主体中存在显著差异。健康结果受个体层级的个体因素和居住不稳定性因素,以及城市层级的地方因素共同影响。个体因素中的性别和工作强度,以及地方因素中的城市平均房价等对流动人口的主观幸福感和自评生理健康影响较大。

（2）居住不稳定性对流动人口健康有显著影响。城市内部迁居频率和城市间流动次数与流动人口主观幸福感呈负相关;相比于独自流动、租房和借住者,家庭式流动、自有住房流动人口的主观幸福感更强。当地经常往来的朋友数量与两种健康结果均呈正相关;居住在本地人占比较高的邻里能有效促进流动人口的主观幸福感和自评生理健康。

（3）“性别”因素具有调节效应。城市内部多次迁居和城市间频繁流动对男性流动人口健康的负面影响程度大于女性;家庭式流动、自有住房和高度联结的社会资本对女性幸福感的提升效应更明显;居住在本地人居多的邻里对两种健康结果均有促进作用,且对女性的影响更大。总体而言,男性健康对时空因素更为敏感和脆弱,而女性健康不佳更多归因于住房和邻里不稳定性。

这些发现表明,频繁迁居不利于流动人口的主观幸福感和生理健康。在本次社会调查中,女性多为城市内部频繁迁居,主要原因包括住房质量、安全、便利设施（如公交站点、农贸市场、学校和商场等）需求,以及因工作变动满足家庭照顾等;而男性主要受经济驱动和职业调动在不同城市间辗转或处于“两栖”流动状态。因此,地方政府和规划部门应关注基础设施建设,以及邻里安全、互助和邻里守望等,降低搬迁次数水平。第二,家庭式流动可以提高流动人口的主观幸福感,且对女性的提升效应更明显。建议制定面向流动人口家庭的精准扶持战略,降低迁移成本,尤其关注流动儿童、随迁家属和老人等的需求,将公共住房、教育和医疗放在核心位置,减轻女性迁移心理负担。研究证实,自有住房和本地人居多的邻里环境对幸福感和生理健康有积极影响,对女性的影响大于男性。建议科学确定城市共有产权住房的租售比及住房价格,方便流动人口租购。对政策性保障房、廉租房或公租房等的区位布局予以重点考虑,使其更接近城区,或者采取“混居”模式,促进流动人口与本地居民交往,增强社会联系,提高社会资本。

由于数据与方法的限制,本文仍存在以下不足之处：① 本研究主要基于横截面数据,用“城市内部迁居频率”和“城市间流动次数”分析频繁迁居对健康影响的大致效应,忽视了每次迁居行为对健康影响的独立效应。未来研究可以尝试采用纵向追踪健康数据,利用时间序列模型或离散时间逻辑斯蒂模型进行更为全面的实证。② 受限于数据可及性,本研究用自我报告值测度生理健康,虽能一定程度反映个体生命质量,但也可能存在模型估计偏差。未来研究可运用多指标综合测定生理健康水平,或借助传感器实时监测体征数据,进行更加精准和客观的分析。

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Consumer-survivors (C/Ss) identify peer support as a resource that facilitates their recovery. However, little is known about the factors that influence or how the peer support relationship (PSR) develops/deteriorates. The purpose of the study was to explore and describe the PSR within the subculture of mental health. Using an ethnonursing method, the study focused on informants from two C/S organizations who received peer support (n = 14). Findings revealed that the PSRs may develop or deteriorate through three, overlapping phases. Contextual factors that influenced the development/deterioration of the PSR are discussed. Understanding the processes and factors that contribute to the development/deterioration of PSRs will enable clinicians and C/Ss to assess and promote the development of healthy, supportive PSRs in mental health.

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Several studies indicate that young people from certain ethnic minority groups in Britain have significant mental health advantages over their White majority counterparts, but the reasons for these differences have not been adequately explored. This work analyses the impact of neighbourhood characteristics, measured by socioeconomic deprivation; crime; living conditions; ethnic density and parenting behaviour on the mental health of young people. To determine the impact of these factors on mental health among young people, geocoded data from waves 1, 3 and 5 of the UK Household Longitudinal Study (UKHLS) are merged with small area statistics from the 2011 census, and multilevel linear regression models are fitted to the sample of 5513 (7302 observations) 10-15-year-olds of varying ethnicity residing in England and Wales. We find that mental health is generally poorer for White British youths, even after accounting for individual/family-level predictors, neighbourhood characteristics and parental behaviour than it is for minority youths. In keeping with results from studies of adult populations, neighbourhoods with high levels of deprivation are associated with poorer mental health. However, some aspects of parenting behaviour appear to have a more significant impact on the mental health of young people from ethnic minority backgrounds than on White British youths. Further research into factors that influence inter-ethnic disparities in mental health among young people is warranted, given that clear differences remain after the models in this study are fully adjusted.

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Using multilevel analysis we find that residents of

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