,1,2, 朱宇,3,4, 林李月1,3, 柯文前1,3, 肖宝玉1,3The intentions of migrants with respect to duration of residence: Spatial variation and determinants
LI Tingting,1,2, ZHU Yu,3,4, LIN Liyue1,3, KE Wenqian1,3, XIAO Baoyu1,3通讯作者:
收稿日期:2020-12-30修回日期:2021-08-18
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Received:2020-12-30Revised:2021-08-18
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李亭亭(1998-), 女, 河南驻马店人, 博士生, 主要从事人口与城乡发展研究。E-mail:
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李亭亭, 朱宇, 林李月, 柯文前, 肖宝玉. 流动人口居留时长意愿的空间分异及影响因素. 地理学报, 2021, 76(12): 2978-2992 doi:10.11821/dlxb202112008
LI Tingting, ZHU Yu, LIN Liyue, KE Wenqian, XIAO Baoyu.
1 引言
大规模的人口流动是中国改革开放以来经济社会发展的一个重要现象,由此产生的流动人口在城市的居留意愿也成为人口迁移流动研究中的重要课题。近年来,在推动新型城镇化战略的进程中,对这一问题的研究有着更为重要的意义[1]。流动人口是否愿意在流入城市长期居留对流入地和流出地的城镇化发展都具有重要影响[2];对认识中国人口迁移流动模式的复杂性,推动流动人口市民化也有着重要意义[1, 3]。在此背景下,近年来流动人口在城市居留意愿的研究在相关领域受到更为广泛而持续的关注并已产生了大量的研究成果。然而,应当看到迄今研究多局限于以单一指标来衡量流动人口的居留意愿[4,5],而从居留时间的维度进行考量,根据流动人口在流入地居留时长来划分不同类型居留意愿的文献还很少。在此基础上的研究成果往往混淆了具有不同类型居留意愿（短期、长期和永久居留意愿）的流动人口在迁移流动规律和对流入地及流出地城镇化进程影响上的重大差异,由此提出的流动人口服务管理的建议和决策也将缺乏针对性和合理性。因此有必要根据流动人口在流入地的预期居留时长,将流动人口的居留意愿划分为不同类型,并在此基础上对比分析流动人口不同类型居留意愿的空间格局及影响因素的差异。近年来对于流动人口居留意愿的研究多关注流动人口的长期居留意愿、户籍迁移意愿以及定居意愿,也有部分****分析和探讨了流动人口的回流意愿、返乡意愿[6,7,8,9,10,11]。研究发现,流动人口的居留意愿存在显著的空间分布差异。如蔚志新针对中国5城市的研究发现,不同地区的流动人口长期居留意愿有明显差异,流动人口更倾向于在中西部地区的省会级大城市长期居留[12];景晓芬基于中国各省份的研究发现,在经济发展水平越高的地区老年流动人口长期留居的可能性越高[13];古恒宇等研究发现东北三省流动人口的长期居留意愿在空间格局上呈现出北高南低、东高西低的特征,并且南北方向上的空间分布差异更大[4]。林李月等基于中国地级及以上城市的研究发现,中国流动人口户籍迁移意愿自东向西呈现出先下降后上升的“U”型特征,在东部沿海、中西部地区部分具有较好经济发展水平、交通区位与资源禀赋的城市流动人口户籍迁移意愿显著较强[7];王建顺等的研究进一步表明,在西部地区内部流动人口的户籍迁移意愿同样存在显著的地区差异,北疆地区流动人口户籍迁移意愿高于南疆地区[14]。有研究表明流动人口的定居意愿整体较低,倾向于在小城镇而非地级及以上城市定居,并且呈现出在东部地区高,中西部地区低的空间特征[15,16];郝璞等的研究也显示在社会经济发展水平较好的地区流动人口的定居意愿较高[17]。
对于流动人口居留意愿的影响因素,****们多基于劳动力市场分割理论、空间区位理论、推拉理论、新古典经济学理论、新迁移经济学理论等[7, 18-23],选取流动人口的个体特征、家庭特征、经济和就业特征、流动特征等作为解释变量进行综合考察。已有研究表明,流动人口中具有长期居留意愿、户籍迁移意愿和定居意愿者存在着一些相似的人口学特征,如年轻、非农业户口、女性、受教育程度较高;同时在流入地的家庭随迁成员越多、家庭规模越大以及家庭月均收入越高、省内流动并且流动次数越少、流动时间越长、就业和职业越稳定的流动人口,其长期、户籍和定居迁移意愿越强[24,25,26,27,28,29,30]。一些研究已注意到不同类型的流动人口居留意愿在个体特征方面存在差异,如经商的流动人口通常具有更高的长期居留意愿,但户籍迁移意愿较低[24, 29];不同于已婚流动人口具有较强的长期和户籍迁移意愿,未婚流动人口更倾向于永久定居[1, 31],也有****认为已婚流动人口在长三角地区三四线城市的定居意愿更高[32]。近年来****们还注意到不仅城市区位会影响流动人口的居留意愿选择,其他城市层面的因素也同样对流动人口的居留意愿具有显著影响,城市经济发展水平越高、基本公共服务越好,流动人口的长期、户籍迁移和定居意愿越强[33,34],流动人口在流入地的社会融入感越好,其居留意愿越强[35]。
上述研究为认识流动人口的迁移流动规律和居留意愿奠定了重要基础。但应当看到,迄今对流动人口居留意愿的考察较少涉及时间维度上的差异。国际上对移民居留意愿的研究多以5年为界,将打算在流入地居留5年及以上认定为永久迁移,而5年以下则被认定为暂时迁移[36,37,38];在中国,近年来随着流动人口动态监测调查数据的广泛使用,“是否愿意在流入地长期居住（5年以上）”也开始成为一项判断流动人口是否具有长期或永久居留意愿的常用指标[1]。在这些研究中,迁移流动人口的居留意愿往往以“是否打算长期居留5年及以上”为标准被处理成（0, 1）二元变量[24, 27, 39],从而忽视了流动人口在居留时长意愿上的内部差异。更为重要的是,上述以5年为界一分为二的分析方法,其内在的逻辑是流动人口“非留即走”的二元选择,没有将流动人口的居留意愿与其两栖或多栖生计策略联系在一起考察。这一不足使得迄今研究忽略了流动人口不同时长居留意愿在家庭生计策略中十分不同的作用和意义,以及不同时长居留意愿在影响因素方面的差异。
事实上,由于制度限制和自身能力不足等原因,流动人口的就业稳定性、获得的公共服务水平等都低于城市当地居民,因而多采取两栖或多栖的生计策略以增强家庭抵抗风险的能力[40,41],而这种两栖或多栖生计策略的不同则可能导致流动人口在流入地居留时长意愿的差异。因为两栖或多栖生计策略本质上是时空交互作用下的家庭生计策略。这种生计策略不仅通过家庭成员不同生计方式在空间上的分割和转移,而且通过不同生计方式在家庭成员生命历程不同阶段上的分割和转移来实现,而后者必然反映在流动人口在流入地城镇居留意愿的长短上。因此,两栖或多栖家庭生计策略是理解流动人口不同时长居留意愿及其影响因素的一个不可忽视的重要视角。另外,以往的研究较多使用最小二乘法（OLS）回归模型和逻辑斯蒂模型,这种做法忽略了数据的嵌套特性,导致研究结论缺乏科学性,需要采用分层模型来解决这一问题。
基于此,本文使用2018年中国流动人口动态监测数据,依据流动人口在流入地的居留时长意愿,划分出不同类型的居留意愿,接着描述地级及以上城市不同类型居留意愿的空间分布格局;在此基础上结合流动人口的两栖或多栖生计策略,利用分层模型探究流动人口个体特征和流入地的城市特征对不同类型居留意愿的影响作用及其差异。这一研究可深化和拓展对流动人口居留意愿的未来走势及其影响因素、以及中国人口迁移流动演变规律的认识。
2 数据来源与研究方法
2.1 研究区与数据来源
数据来源包括两个方面：① 2018年中国流动人口动态监测调查数据。数据获取时采用分层、多阶段、与规模成比例的PPS抽样方法,在中国31个省（区、市）和新疆生产建设兵团流动人口比较集中的地方,随机抽取在流入地居住1个月及以上,非本区（县、市）户口的年龄为15周岁及以上的流动人口作为调查对象。研究单元为作为行政单元的地级及以上城市,截至2018年末中国共有地级及以上城市298个。其中,昌都市、朝阳市、抚顺市、辽源市、山南市、阜新市、那曲市等7市未包括在本文所使用的数据库中;而且三沙市由于在统计年鉴中数据缺失较多予以剔除,因此研究区域为290个地级及以上城市（暂未包含港澳台地区）。此外,由于本文关注的是流动人口不同类型的居留意愿,因而剔除了数据库中没有回答关于居留意愿的问题或其回答为“没想好”的流动人口样本,最终得到样本的数量为88099份。② 社会经济数据,用于分析城市层面因素对流动人口居留意愿的影响,主要来源于2019年《中国城市统计年鉴》数据。2.2 居留意愿及其类型划分的方法
在2018年中国流动人口动态监测问卷调查中,涉及流动人口居留意愿的问题主要有两个：① 今后一段时间,您是否打算继续留在本地?② 如果您打算留在本地,您预计自己将在本地留多久?前者用来度量流动人口有无居留意愿;后者则关注流动人口对居留时间长短的预期,问题②共设5个选项,分别是0~4年、5~9年、10年以上、定居和没想好。本文在流动人口有居留意愿的基础上,剔除对居留时长预期的回答为“没想好”的样本,再根据居留时长预期,将流动人口居留意愿分为短期居留意愿、长期居留意愿、永久居留意愿3种类型。其中,短期居留意愿是指在流入地居住4年及以下,属于暂时性居留,也可视为一种即时性的迁移行为;长期居留意愿是指在流入地居留5年及以上（含5~9年和10年以上）,是一种长期居住的迁移行为;永久居留意愿也称为定居意愿,指的是流动人口打算在流入地定居的意愿,是一种永久性的迁居行为。3种类型的居留意愿构成本文居留意愿的值域。2.3 研究方法
2.3.1 趋势面分析 采用趋势面分析法,衡量中国地级及以上城市流动人口不同类型居留意愿空间格局的分异趋势。其计算公式如下：式中：Zi(xi, yi)为流动人口在第i个地级以上城市居留意愿的值,该值为居留意愿所占的百分比;xi、yi为平面空间坐标,其中i = 290;Ti(xi, yi)为趋势函数,反映城市流动人口居留意愿总变化趋势;εi为自相关随机误差,反映城市间流动人口居留意愿局部的变化特点[7]。本文采用二阶多项式计算趋势值,公式如下：
式中：β值代表根据样本数据估计的二阶多项式各项的估计值;Ti(xi, yi)同式（1）。
2.3.2 冷热点分析 采用冷热点分析（Getis-Ord Gi*）探讨中国流动人口不同类型居留意愿空间分布的热点区及冷点区。Getis-Ord Gi*统计量主要通过计算某区位上地理属性及其相邻区位上地理属性的相互关系,探测出各地理要素在空间上是否属于高值聚集或低值聚集模式[4]。冷热点分析计算公式如下：
式中：xi为空间要素j的属性值;Wi, j为要素i和j之间的空间权重,将其定义为相邻为1,不相邻为0;n为空间要素总数;
2.3.3 分层模型 由于运用流动人口个体层面和城市层面的数据分别作为自变量解释居留意愿因变量过程中,自变量数据具有嵌套结构,因此本文使用分层模型建立回归模型,先以个体层面的变量建立回归方程,在该层面所获回归模型基础上,加入城市层面的变量,建立随机截距模型[43]。模型表达式如下：
式中：Yij是城市j的流动人口i的居留意愿,使用居留意愿所占的百分比进行量化,其中,i = 88099,j = 290;βο是截距项;Xij是城市j的流动人口i的个体层次解释变量;Zj是城市j的地区层次解释变量;α和β分别为各层次解释变量的系数;μ是随机误差项。此外,利用组内相关系数（ICC）检验本文数据是否适合采用分层模型进行分析,并通过比较空模型和最后设定模型中随机方差的变化进行模型的拟合和检验[44]。
3 流动人口不同类型居留意愿的水平及其空间差异
本次调查数据结果显示,在中国地级及以上城市流动人口内部其居留意愿在时长上存在明显分异;在具有居留意愿的流动人口中,具有永久居留意愿的流动人口占36.93%,而具有短期和长期居留意愿的流动人口占比分别为29.06%和34.01%,合计比例为63.07%;也就是说虽然在具有居留意愿的流动人口中愿意在流入地永久居留的比例最高,但超过60%的流动人口仍倾向于只在流入地以短期和长期的形式暂时居留。3.1 流动人口不同类型居留意愿的区域分异
利用趋势面分析法分析中国地级及以上城市流动人口不同类型居留意愿的分异方向,结果如图1所示。流动人口短期居留意愿在东西方向上的分异并不明显,整体呈现出自东向西逐渐上升的趋势,而在南北方向上的分异比东西方向明显,呈现出南高北低的空间格局;与流动人口短期居留意愿的分异格局截然相反,流动人口永久居留意愿呈现出“东高西低,北高南低”的空间格局;流动人口长期居留意愿在东西和南北方向的分异呈现出自东向西、自北向南先上升后轻微下降的变化趋势,中西部地区的城市对流动人口长期居留意愿表现出不俗的吸引力。图1
新窗口打开|下载原图ZIP|生成PPT图12018年中国地级及以上城市流动人口不同类型居留意愿的趋势面分析
Fig. 1Trend surface analysis of different categories of residence intention for migrants in China's prefecture- and provincial-level cities, 2018
3.2 流动人口不同类型居留意愿的空间关联特征
利用冷热点分析法进一步识别流动人口不同类型居留意愿的热点区和冷点区,也即高值聚集区和低值聚集区。由图2可以看出,流动人口短期居留意愿在空间上呈现出显著的连块、连片集聚特征,低值聚集区主要集中在中国东北、华北、长江以北的华东和部分西北地区,共包括101个城市,短期居留意愿均值为9.68%;高值聚集区则主要集中在中国华南、中南、长江以南的华东和部分西南地区,共包括81个城市,短期居留意愿均值为32.32%。流动人口永久居留意愿在空间上同样具有明显的块状、成片集聚特征,但与短期居留意愿恰好相反,东北、华北和长江以北的华东和部分西北地区是永久居留意愿的高值聚集区,共包括94个城市,永久居留意愿均值为68.63%;而华南、中南、长江以南的华东和部分西南地区是永久居留意愿的低值聚集区,共包括85个城市,永久居留意愿均值为37.32%。与前述两类流动人口居留意愿的空间关联特征不同,流动人口长期居留意愿在空间上呈现出点状分布特征,仅东北和部分华北地区是显著的低值聚集区,共包括40个城市,长期居留意愿均值为17.82%;而部分西南、西北和华南地区是显著的高值聚集区,共包括32个城市,长期居留意愿均值为33.51%。图2
新窗口打开|下载原图ZIP|生成PPT图22018年中国地级及以上城市流动人口不同类型居留意愿的冷热点分析
注：基于自然资源部标准地图服务网站GS(2020)4632号的标准地图制作,底图边界无修改。
Fig. 2Hot spot analysis of different categories of residence intention for migrants in China's prefecture- and provincial-level cities, 2018
3.3 流动人口不同类型居留意愿的空间格局特征
利用ArcGIS 10.2软件对中国3种不同类型的流动人口居留意愿进行空间可视化,并利用自然断点法将其划分为5级,进一步对比其在中国东部、中部、西部、东北四大地理分区间的空间分布差异,结果如图3所示。流动人口的短期居留意愿在东部地区最高,为32.79%,其中东南沿海的长三角和珠三角地区流动人口的短期居留意愿较高,但东北地区流动人口的短期居留意愿最低,仅为8.36%;中部和西部地区流动人口的短期居留意愿大致相当,分别为17.51%和16.74%;而在西部地区内部流动人口的短期居留意愿存在着较大的分异,其中西北地区较低,西南地区较高。与流动人口短期居留意愿的空间分布格局恰好相反,东北地区流动人口的永久居留意愿显著高于其他地区,达72.31%,但东部地区特别是东南沿海地区流动人口的永久居留意愿较低;同样在西部地区内部流动人口的永久居留意愿存在显著的分异,西北地区较高,西南地区则明显偏低。流动人口的长期居留意愿除东北地区（8.36%）显著较低外,其他地区差异不大,值得注意的是,流动人口长期居留意愿在中西部地区最高,而不是社会经济水平相对发达的东部地区。图3
新窗口打开|下载原图ZIP|生成PPT图32018年中国地级及以上城市流动人口不同类型居留意愿的空间分布格局
注：基于自然资源部标准地图服务系统审图号为GS(2020)4632号的标准地图制作,底图边界无修改。
Fig. 3Spatial patterns of different categories of residence intention for migrants in China's prefecture- and provincial-level cities, 2018
4 流动人口不同类型居留意愿的影响因素分析
4.1 自变量选取
根据前文综述的目前国内外****对流动人口居留意愿的影响因素做出的实证研究,本文参考林李月等的做法,从流动人口特征和流入地特征两个层面分析不同类型居留意愿的影响因素[7]。在流动人口特征层面,本文选取了流动人口的个体特征（性别、年龄、户口性质、受教育程度、婚姻状况）、家庭特征（本地家庭规模、家庭月均收入对数）、流动经历（在外流动时间、本次流动范围、本次流动原因）、就业特征（就业身份、是否参与城镇职工医疗保险）等方面的12个解释变量;在流入地特征层面,参考前人的做法并考虑数据的可得性[10],选择分别代表城市经济发展水平（人均GDP）、就业收入水平（在岗职工平均工资）、城市公共服务水平（医院床位数、中小学教师数量）、城市等级（是否为直辖市、省会城市）、城市区位（是否位于东中西部地区）的共6个解释变量。针对上述自变量进行VIF共线性检验,发现VIF的值均小于3,说明自变量之间不存在多重共线性。变量的类型、名称及描述如表1所示。Tab. 1
表1
表1变量名称、赋值及描述
Tab. 1
| 变量名称 | 变量赋值 | 均值 | 标准差 |
|---|---|---|---|
| 因变量 | |||
| 是否短期居留 | 否=0;是=1 | 0.29 | 0.45 |
| 是否长期居留 | 否=0;是=1 | 0.34 | 0.47 |
| 是否永久居留 | 否=0;是=1 | 0.37 | 0.48 |
| 自变量 | |||
| 个体层面(N=88099) | |||
| 性别 | 女=0;男=1 | 0.53 | 0.50 |
| 年龄 | 连续变量 | 37.09 | 10.71 |
| 户口性质 | 农业=0;非农业=1 | 0.20 | 0.40 |
| 受教育年限 | 连续变量 | 10.66 | 3.43 |
| 婚姻状况 | 单身=0;在婚=1 | 0.85 | 0.36 |
| 本地家庭规模 | 连续变量 | 2.56 | 1.27 |
| 在外流动时间 | 连续变量 | 6.27 | 6.25 |
| 本次流动范围 | 跨省流动=0;省内流动=1 | 0.37 | 0.48 |
| 本次流动原因 | 其他=0;经商=1 | 0.20 | 0.40 |
| 是否参与城镇职工医疗保险 | 否=0;是=1 | 0.38 | 0.49 |
| 就业身份 | |||
| 是否为雇员 | 否=0;是=1 | 0.68 | 0.47 |
| 是否为雇主 | 否=0;是=1 | 0.09 | 0.29 |
| 是否为其他 | 否=0;是=1 | 0.23 | 0.42 |
| 家庭月均收入对数 | 加1取自然对数 | 3.90 | 0.31 |
| 城市层面(N=290) | |||
| 人均GDP | 连续变量 | 6.11 | 3.48 |
| 全市在岗职工平均工资 | 连续变量 | 7.20 | 1.47 |
| 每千人拥有的医院床位数 | 连续变量 | 4.80 | 1.95 |
| 每千人拥有的中小学教师数量 | 连续变量 | 8.74 | 4.15 |
| 是否为直辖市、省会城市 | 其他=0;直辖市、省会城市=1 | 0.11 | 0.31 |
| 城市区位 | |||
| 东 | 否=0;是=1 | 0.30 | 0.46 |
| 中 | 否=0;是=1 | 0.28 | 0.45 |
| 西 | 否=0;是=1 | 0.32 | 0.47 |
| 东北 | 否=0;是=1 | 0.10 | 0.31 |
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4.2 模型结果及实证分析
本文使用分层模型分析流动人口不同类型居留意愿的影响因素,其中流动人口特征作为第一层变量,流入地特征作为第二层变量。首先建立空模型,检验是否有必要采用分层模型。计算得到组内相关系数（ICC）分别为0.2254、0.0704、0.2367,结合已有文献的做法[44],由于ICC均大于0.059,说明本文建立分层模型解释流动人口不同类型居留意愿的影响因素是合理的。随后构建流动人口的短期、长期、永久3种不同类型居留意愿的随机截距模型,即在第一层变量回归分析结果的基础上加入第二层变量,考察流动人口特征和流入地特征对流动人口不同类型居留意愿的影响,分别为模型1、模型2、模型3,计算得到随机截距模型的各个层次变量可以进一步解释35.42%、28.00%、26.47%的随机方差变化,均大于常规认为的5.90%,并且模型的拟合程度提高,解释能力也有所增强。3类居留意愿的随机截距模型结果如表2所示。Tab. 2
表2
表22018年中国地级及以上城市流动人口不同类型居留意愿影响因素的回归结果
Tab. 2
| 模型1 | 模型2 | 模型3 | ||||||
|---|---|---|---|---|---|---|---|---|
| 系数 | 标准误 | 系数 | 标准误 | 系数 | 标准误 | |||
| 个体层面变量 | ||||||||
| 性别(女) | 0.095*** | 0.029 | 0.250*** | 0.020 | -0.355*** | 0.038 | ||
| 年龄 | -0.011*** | 0.003 | 0.011*** | 0.001 | -0.003 | 0.002 | ||
| 户口性质(农业) | -0.198*** | 0.054 | -0.177*** | 0.046 | 0.273*** | 0.074 | ||
| 受教育年限 | -0.073*** | 0.007 | -0.040*** | 0.005 | 0.103*** | 0.011 | ||
| 婚姻状况(单身) | -0.372*** | 0.041 | 0.152*** | 0.049 | 0.348*** | 0.051 | ||
| 本地家庭规模 | -0.279*** | 0.018 | 0.144*** | 0.011 | 0.063*** | 0.017 | ||
| 在外流动时间 | -0.107*** | 0.004 | -0.004 | 0.003 | 0.070*** | 0.003 | ||
| 本次流动范围(跨省流动) | -0.660*** | 0.046 | -0.225*** | 0.045 | 0.753*** | 0.071 | ||
| 本次流动原因(其他) | 0.119** | 0.056 | 0.222*** | 0.037 | -0.236*** | 0.047 | ||
| 是否参与城镇职工医保(否) | -0.546*** | 0.037 | 0.060* | 0.035 | 0.376*** | 0.034 | ||
| 就业身份(其他) | ||||||||
| 雇员 | 0.388*** | 0.053 | -0.110*** | 0.037 | -0.200*** | 0.047 | ||
| 雇主 | -0.392*** | 0.063 | -0.171*** | 0.040 | 0.330*** | 0.048 | ||
| 家庭月均收入对数 | -0.963*** | 0.087 | -0.413*** | 0.063 | 1.215*** | 0.100 | ||
| 城市层面变量 | ||||||||
| 人均GDP | 0.026 | 0.020 | 0.015 | 0.009 | -0.038** | 0.019 | ||
| 在岗职工平均工资 | 0.098** | 0.044 | 0.087*** | 0.020 | -0.170*** | 0.040 | ||
| 每千人拥有的医院床位数 | -0.172*** | 0.038 | -0.041* | 0.022 | 0.173*** | 0.040 | ||
| 每千人拥有的中小学教师数量 | 0.059*** | 0.020 | 0.006 | 0.013 | -0.054*** | 0.021 | ||
| 是否为直辖市、省会城市 | 0.067 | 0.139 | 0.023 | 0.071 | 0.026 | 0.143 | ||
| 区位(东北) | ||||||||
| 东 | 0.359** | 0.172 | 0.223* | 0.117 | -0.430** | 0.200 | ||
| 中 | 0.443** | 0.174 | 0.230* | 0.120 | -0.475** | 0.201 | ||
| 西 | 0.230 | 0.174 | 0.042 | 0.123 | -0.156 | 0.209 | ||
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由表2可见,那些倾向于短期居留意愿的流动人口表现出年轻、未婚、男性、受教育程度低、农业户口、就业身份为雇员、本地家庭规模小、家庭月均收入低、在外流动时间短、跨省流动、流动原因为经商、没有获得城镇职工医疗保险等特征,这些特征中的大部分使得他们在流入地城镇的生计来源和未来预期均不稳定,从而促使他们选择短期居留。倾向于短期居留的流动人口其流入地则具有工资收入水平较高、每千人拥有的中小学教师数量较多的特点,这符合他们在流入地以赚取工资收入为主要目的的生计目标,同时有利于解决其子女在其短期居留期间的受教育需要,但每千人拥有的医院床位数越多越不利于流动人口选择短期居留,则可能与在此条件下流动人口大都选择永久居留有关（见后文对模型3相关结果的解释）。模型1结果显示,相较于东北地区,在东部和中部地区的流动人口更倾向于短期居留。
倾向于长期居留的流动人口与倾向于短期居留的流动人口具有男性、农业户口、受教育程度低、家庭月均收入低、跨省流动和流动原因为经商等共同的特征,但不同于后者的是,前者表现出年纪较大、已婚、就业身份为自营劳动者（含其他）、本地家庭规模大、拥有城镇职工医疗保险的特征;显然,这些特征使得与倾向于短期居留的流动人口相比,倾向于长期居留的流动人口在流入地城镇有着较为稳定的生计需要和未来预期（源于如已婚和较大的家庭规模等因素）以及生计资源（源于如自营就业、拥有城镇职工医疗保险等因素）,从而为流动人口在流入地更长时间的居留创造了条件或需要。就流入地而言,与倾向于短期居留的流动人口相似,其流入地也具有收入水平较高的特点,这同样符合长期居留的流动人口在流入地以赚取工资收入为主要目的的生计目标;但每千人拥有的中小学教师数量这一变量不再显著。同时,相较于东北地区,在东部和中部地区的流动人口也更倾向于长期居留。
倾向于永久居留意愿的流动人口则具有已婚、女性、受教育程度高、非农业户口、就业身份为雇主、本地家庭规模大、家庭月均收入高、在省内流动、流动原因为其他（非经商）、在外流动时间长、拥有城镇职工医疗保险等特征;显然,这些特征（如已婚、本地家庭规模大、拥有城镇职工医疗保险）与倾向于长期居留的流动人口的特征有某些相似之处,但倾向于永久居留意愿的流动人口仍有着某些特有的特征,如女性、受教育程度高、就业身份为雇主、家庭月均收入高、在外流动时间长、在省内流动等。其中,拥有城镇职工医疗保险、受教育程度高、家庭月均收入高、在外流动时间长、在省内流动者显然在流入地城镇有着更好的生计资源和更为稳定的未来预期;而与男性多为务工经商等经济型流动不同,通过婚姻嫁娶等社会型流动的女性更容易实现永久居留,并且女性更多地在服务业工作,与男性相比工作相对稳定,也有利于其永久居留;自雇者（雇主身份）的就业更少受到劳动力市场的波动而表现出较强的就业稳定性,收入水平较好,因而在流入地的永久居留意愿也较强[1, 45];而已婚流动人口的家庭化迁移有利于降低流动人口融入新环境的成本以及规避风险,提高在城市生活的适应能力,从而对流动人口的永久居留产生激励作用[46]。但值得注意的是,模型3结果显示,流入城市的人均GDP、在岗职工平均工资、每千人拥有的中小学教师数量越高越不利于流动人口永久居留,这可能是对社会经济发展程度较高的城市对流动人口定居仍有着种种制度（如户籍）限制的一种反映。而每千人拥有的医院床位数越高越有利于流动人口具有永久居留意愿的结果,则与流动人口倾向于永久居留的城市虽然社会经济发展水平不高,但这一指标未必落后于其它城市有关①(①根据《中国城市统计年鉴2019》,哈尔滨、佳木斯、鹤岗、鸡西等东北地区流动人口永久居留意愿较高的城市,其人均GDP分别为6.61万元、3.93万元、2.89万元、3.07万元,远低于北京、上海、天津、广州等超大、特大城市的人均GDP分别为14.02万元、13.50万元、12.07万元、15.55万元。但这些永久居留意愿高的城市每千人拥有的医院床位数在6.46张及以上,高于北上广等城市每千人拥有的医院床位数（5.93张及以下）。)。与倾向于短期和长期居留的流动人口相反,相较于东北地区,在东部和中部地区的流动人口不倾向于永久居留。
上述模型结果所揭示的流动人口不同类型居留意愿的影响因素也在相当程度上解释了中国流动人口不同类型居留意愿的空间分布格局。东南沿海地区是中国社会经济最为发达的地区,吸纳了中国大部分流动人口;但从前面对流动人口不同类型居留意愿空间差异的分析中可见,这一地区流动人口的短期居留意愿较高,其中长三角和珠三角更是中国流动人口的短期居留意愿最高的地区,而永久居留意愿在东南沿海地区却较低。同时,流动人口的长期居留意愿在中国大部分地区大致相当,中西部地区的部分城市长期居留意愿甚至高于东南沿海地区经济较为发达的城市。上述空间格局与作为中国流动人口主要流入地的东南沿海地区长期以来劳动密集型行业占据重要地位,由此形成了对低技能、低工资劳动力的大量需求有着密切关系。这些低技能、低工资劳动力恰有着前述有利于形成短期居留意愿的人口学和社会经济特征,而东南沿海地区相对于内陆较高的工资水平和充沛的基础教育资源恰恰能够满足大量来自内陆地区的农业转移人口提高收入以及其子女接受义务教育的需求,由此吸引了大量流动人口流入。但长期以来东南沿海地区以劳动密集型行业为主的产业结构和流入这些地区的流动人口较低的收入水平使他们在流入地不具备稳定的生计资源和未来预期,他们还必须选择两栖甚至多栖的生计策略,从而降低在流入地的生活成本并充分利用流出地的家庭资源,这就导致这些地区的流动人口倾向于短期居留意愿,他们不仅要长期往返于流入地和流出地之间,而且可能继续流动到下一个城市找寻工作机会,而其最终目标是在赚到足够的收入后返回生活成本较低的家乡生活。因此这一地区不仅永久居留意愿在全国较低,甚至长期居留意愿也不突出。而流入东北地区的流动人口之所以永久居留意愿高,是由于东北地区较好的工业基础和一系列人口服务管理政策的支持,同时当地较高的基本公共服务水平,并且相对于东部地区而言,相对较低的房价和物价水平,能够为他们提供更为稳定的生计资源和未来预期,生活成本也大大降低,由此提高了他们的永久居留意愿。显然,今后中国流动人口不同类型居留意愿的空间分布格局将随着中国产业结构的变化和流动人口的生计特征和策略变化而改变,对此必须予以密切关注。
5 结论与讨论
本文利用2018年中国流动人口动态监测数据和相关统计年鉴数据,运用描述统计、GIS空间分析工具和分层模型,对中国290个地级及以上城市的流动人口不同类型居留意愿的空间分异和影响因素进行分析。得出结论如下：（1）中国地级及以上城市流动人口的居留意愿在时长上存在明显的内部分异,在具有居留意愿的流动人口中,具有短期、长期和永久居留意愿的流动人口比例分别为29.06%、34.01%和36.93%。也就是说,虽然流动人口在流入地具有永久居留意愿的比例最高,但超过60%的流动人口仍倾向于只在流入地以短期和长期的形式暂时居留。
（2）流动人口的短期、长期、永久居留意愿的空间分布格局也具有很大差异,其中短期和永久居留意愿在空间上具有截然相反的分布特征,短期居留意愿在东部地区最高,东北地区最低;永久居留意愿则在东部地区最低,东北地区最高;而流动人口的长期居留意愿除东北地区明显较低外,其他地区大致相当,中西部地区的部分城市长期居留意愿甚至更高于东部地区经济较为发达的城市。
（3）流动人口不同类型的居留意愿受到相对不同的内外部因素的影响。从流入地层面看,在人均GDP和在岗职工平均工资高的经济发展水平较好的城市,流动人口的短期和长期居留意愿较高,而永久居留意愿较低。从流动人口特征方面看,年轻、未婚、男性、受教育程度低、农业户口、雇员身份、本地家庭规模小、家庭月均收入低、在外流动时间短、流动原因为经商、跨省流动的流动人口短期居留意愿较高;年纪较大、已婚、男性、受教育程度高、农业户口、自营劳动者（含其他）身份、本地家庭规模大、家庭月均收入低、流动原因为经商、跨省流动的流动人口长期居留意愿较高;已婚、女性、受教育程度高、非农业户口、雇主身份、本地家庭规模大、家庭月均收入高、在外流动时间长、流动原因为其他（非经商）、省内流动的流动人口永久居留意愿较高。上述流动人口个人和流入地因素通过流动人口的生计特征和策略影响着流动人口居留意愿。
上述研究结果不仅在居留时间维度上细化了对流动人口居留意愿的认识,而且揭示了流动人口不同时长居留意愿的空间格局和影响机制、以及这些空间格局和影响机制间存在的显著差异,尤其是深入剖析了流动人口的生计策略对流动人口短期、长期、永久居留意愿具有的不同影响,拓展并深化了对流动人口居留意愿的未来走势及其影响因素的认识,也为更好地把握中国人口迁移流动的演变规律,在此基础上进一步改进人口流动相关政策,推动流动人口的市民化提供了重要科学依据。
流动人口居留意愿受流入地城市产业结构及其演变的影响。随着中国城镇化进入中后期阶段,流动人口流入地的产业结构将发生新的变化,流动人口的生计特征和策略也将随之改变,流动人口在流入地居留意愿将如何变化,仍值得进一步密切跟踪研究。随着城市间和城市内部的人口迁移流动逐渐取代乡城和区域间人口迁移流动,并成为人口迁移流动的主要形式,人口迁移流动的方向和趋势会发生怎样的变化,也是一个值得关注的研究课题。未来对这些问题的进一步深入探讨,将进一步深化对流动人口在流入地居留意愿变化趋势和影响机制以及人口迁移流动的复杂性的认识。
最后,由于样本量偏少和部分数据缺失等原因,本文剔除了部分地级行政单元（如自治区、州、盟等）的样本,故未能将研究范围覆盖至全部地级行政单元,因而在进行空间格局分析时可能存在因数据缺失而造成的误差。期待以后有更加丰富的数据,进一步完善和证实本文的研究。
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DOI:10.11821/dlxb201904009 [本文引用: 1]
In recent years, urbanization has been attached an increasing importance in China's overall development strategies. Migrants' settlement intention in cities has played an important role in affecting the urbanization trend in China. In such a context, both scholars and policy makers have increasingly attempted to understand the settlement intention of migrants in China. However, few studies so far have examined the role of migrants' access to urban public services in affecting their settlement intention from the perspective of different-sized cities. Based on the data from "the 2016 national dynamic monitoring survey of migrant population" in Shanghai Municipality and Shandong, Jiangsu, Zhejiang, Fujian, and Guangdong provinces, this paper aims to address this issue. We utilize a composite index consisting of three dimensions to measure migrants' settlement intention, namely the long-term residence intention, the hukou transfer intention, and the urban housing purchase intention. We divide urban public services into two types, namely employment-related public services and social (non-employment) public services. The paper then explores the differences in the supply of the two types of urban public services and their impacts on migrants' urban settlement intention of different-sized cities. The results show that the bigger the size of a city is, the greater the likelihood that migrants get access to urban pubic services and the higher their level of urban settlement intention is. Migrants with easier access to the urban public services are more likely to settle down in cities. Through the ordered logistic regression model analysis, we also find that, after controlling for the effects of individual characteristics, family features, and migration characteristics, the two types of urban public services provided by cities for migrants have played a critical role in affecting migrants' urban settlement intention. However, the impacts of access to urban public services on migrants' urban settlement intention are different among different-sized cities. This is reflected in the following facts. First, migrants with easier access to unemployment insurance, medical insurance and housing security are more likely to settle down in cities, but this positive effect is limited to large cities. Second, in all size cities, migrants who are more likely to be covered by resident health records and receive more health education are more likely to settle down in cities. Finally, in small cities, only those covered by resident health records and receiving more health education have great effects on urban settlement intention.
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DOI:10.13249/j.cnki.sgs.2020.02.011 [本文引用: 4]
In recent years, the three provinces of Northeast China (Liaoning, Heilongjiang, and Jilin) have suffered from economic decline and labor force loss. Supported by the China migrants dynamic survey in 2015, the present study aims to examine the spatial pattern and driving forces of the settlement intention of the floating migrants in the three provinces of Northeast China. Spatial autocorrelation analysis and trend analysis methods are applied to characterize the spatial pattern of the settlement intention at the city level, and a binary logistic model is constructed to detect the drivers of the settlement intention at the micro-level. According to the aboveanalyses, the main findings of our research are as follows: 1) The spatial distribution of the settlement intention of the floating population in three northeastern provinces presents a characteristic of “higher in the north and lower in the south”. Besides, the settlement intention has a more significant spatial variation in the north-south direction, yet the spatial variation degree is weaker in the east-west direction. 2) The spatial autocorrelation is insignificant in the spatial pattern of floating migrants’ settlement intention in the three provinces of Northeast China. Qiqihar City and Heihe City are detected as the High-Low cluster areas, while Haerbin City is detected as the High-High cluster area. With the increase in the size of cities, the settlement intention of the floating population shows the trend of first rising and then declining. From the perspective of the city level, the settlement intention of the floating population in sub-provincial cities is higher than that of ordinary prefecture-level cities in the three provinces of Northeast China. 3) Individual, economic, and social factors show significant effects on the settlement intention of floating migrants in the three provinces of Northeast China. In terms of individual factors, the model results indicate that migrants with agricultural hukou, migrants who are married, highly educated migrants, and ‘80s’ migrants have a stronger willingness to stay in destination cities. 4) For economic factors, income level has a significantly positive relationship with the settlement intention of the floating population, while housing expenditure has a negative effect. 5) Considering social factors, the results show that migrants with longer duration of staying, migrants whose occupation categories are professional or technical personnel and business service personnel, migrants whose employment status is the employer, and migrants participating in urban employee medical insurance have a stronger settlement intention.
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DOI:10.11821/dlxb201610003 [本文引用: 5]
Based on data from the 2012 national migrant population dynamic monitoring survey and related statistics, this article examines the spatial pattern and its determinants of migrants' intention of hukou transfer of China's 276 prefecture- and provincial-level cities, using GIS spatial analysis and statistical modelling. The results show that the overall level of migrants' hukou transfer intention of the cities is not high, and varies significantly among different cities. The intention of migrants' hukou transfer increases as the administrative level and/or the size of their destination cities increase. Meanwhile, migrants' hukou transfer intention is generally higher in coastal mega-city regions than in other cities, but it is also relatively high in some provincial capital cities and small and medium-sized cities in some inland regions with good transport location and resource endowment. The spatial pattern of migrants' intention of hukou transfer is shaped jointly by both the characteristics of the destination cities and migrants themselves characteristics, with the former exerting more influence than the latter. High level of socioeconomic development and good location of the destination cities can effectively promote their migrants' intention of hukou transfer; however, their level of basic public services does not have the same effect. The degree of migrants' social integration in the destination cities also exerts positive effects on their hukou transfer intention. However, having medical insurance, the concentration in the secondary labor market and higher household income are negatively related to such intention; furthermore, the individual and family characteristics of migrants do not have a significant impact on it. Finally, on the basis of the above findings, we put forward some suggestions for relevant policy making.
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DOI:10.11821/dlyj020180473 [本文引用: 2]
In China, population migration has an influence on the level of economic and social development in various regions. In 2016, the scale of migrant population reached 245 million, which became an important factor affecting population changes. From the perspective of migrant populations' destinations, with the control of population scale in some big cities in China, and the sustained economic growth in the central and western regions, the problem of population reflux is attracting more and more attention from the society. The spatial differentiation and influencing factors of migrant populations' return intention are vital issues which are urgent to be investigated. Based on data from the 2016 China Migrant Population Dynamic Survey (CMDS), this paper discusses the phenomenon about spatial differentiation of migrant populations' return intention in 279 prefecture and provincial level cities in China, using GIS spatial analysis methods such as Moran's I coefficient, Getis-Ord Gi *. Besides, we investigate the influencing factors of migrant populations' return intention. Study comes to the following conclusions: (1) Compared with the residence intention and hukou transfer intention, the return intention of migrant population in China is lower (6.17%), and the majority of migrants (74.05%) are eager to take their own home towns as refluxing destination. The scale of cities, the level of cities and the return intention of migrant population present an asymmetric "U"-shaped pattern. (2) The spatial distribution of return intention presents an aggregation pattern with marked spatial differentiation. The return intention of migrant population in Yangtze River Delta Urban Agglomeration, Beijing-Tianjin-Hebei Urban Agglomeration and Zhongyuan Urban Agglomeration is higher than that in Chengdu-Chongqing Urban Agglomeration and Harbin-Changchun Urban Agglomeration. Among the four main geographical divisions, the return intention of urban floating population in the eastern region is the highest while that of the northeast region is the lowest. South China, central-south China and part of East China are hot spots of return intention while Northeast and North China are in a weak corner. (3) The return intention is influenced by both internal factors of migrant population and external factors of in-flow area. Educational level and economic development are both internal and external factors that simultaneously play different roles in return intention. (4) Family connection, social networks, housing and economic factors of migrant population are main forces that shape the spatial pattern of return intention. Family scale in in-flow area and home-ownership rates inhibit return intention while variables such as family scale in non-in-flow area, migrant times and the family's income and expenditure proportion exert a positive influence on return intention. Based on the above conclusions, this paper proposes strategies for relevant departments on the management of migrant population.
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Migrants' hukou transfer intention of the western ethnic minority regions has great implications not only for the overall urbanization level, but also for national unity and harmony in the society. Based on the dynamic monitoring data in 2012 from Xinjiang Autonomous Region and using a binary logistic regression model, this study analyzed the similarities and differences between the willingness of migrants to migrate in different regions and ethnic minority groups and their influential factors. According to the results, migrants' hukou transfer intention reached a high level, and more than half of respondents were willing to transfer their hukou to cities where they reside. This proportion is slightly higher in northern Xinjiang than in the south, and the related value is higher in ethnic minorities than Han nationality. The modeling result shows that Xinjiang barely had any attraction to highly educated and economically well-to-do migrants. Ethnic type, time of residence, and social integration degree are found to be the core factors in the dynamic system concerning migrants' hukou transfer intention. As well, there are different factors in different regions and ethnic groups in Xinjiang, when migrants make transfer decisions. But there is no regional or inter-group difference concerning social integration factors. Elevated social integration significantly helped to heighten transfer intention.
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DOI:10.11821/dlyj201712008 [本文引用: 1]
China has experienced the unprecedented surge of rural-urban migration since the mid-1980s, which has led to rapid urban population growth. With the development of human-oriented new urbanization, the individual migration intentions attracted more attentions from scholars and governments in recent years. However, previous studies mainly examined the factors underpinning the peasant workers' intentions of settling down in various cities or returning rural hometowns, devoting insufficient attention to the peasants' migration intentions of leaving the countryside and moving to cities to settle down permanently. Based on the data of Chinese General Social Survey in 2010 (CGSS2010), the paper described the migration intentions of 4116 rural residents and then explained their migration intentions from three levels of individual, household and county with the multilevel Logistic model. The results are as follows. (1) The spatial heterogeneity makes the peasants who live in the same village have more similarity in migration intentions than those who live in different villages. Single level model ignores the spatial heterogeneity, and the inclusion of the spatial heterogeneity in the multilevel model can yield a better estimated result than single level model. (2) Most of the peasants do not intend to leave the countryside and move to cities in the next five years, and nearly 90% of peasants intend to stay in the countryside. 63.3% of peasants who intend to move to cities incline to moving to small cities and towns (counties, county-level cities and small towns), while 29.8% of peasants tend to move to prefecture-level cities, provincial capital cities and municipalities. (3) The peasants' migration decisions of leaving the countryside and moving to cities can be seen as a two-stage process. First, they decide whether or not to move to cities, and then they need to consider which city to settle down after they decide to leave the countryside. The empirical results show that the first-stage migration intentions are shaped by individual, household, and contextual factors jointly. Specifically, the peasants who have more human capitals (younger, with more education years, with migration experience), the peasants whose household has more children, higher economic status, more lands and more relationships with the city, and the peasants living in developed regions are more likely to move to cities. However, the second-stage migration decision is mainly shaped by regional economic development level and the residence locations. Specifically, the peasants living in undeveloped areas with their current residences not far from large cities have more willingness to move to large cities. (4) Finally, based on these findings above, some policy implications can be drawn. The governments should pay more attentions to improving the attractiveness of county-level cities and towns. And increasing peasants' human capitals and promoting the development of rural economy are the effective ways to promote the development of new urbanization.
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DOI:10.11821/dlxb202002003 [本文引用: 2]
It is demonstrated that the determinants of China's urban floating population's settlement intention are different among geographic units, which seems to be ignored by previous researches. Based on the data from the 2015 national migrant population dynamic monitoring survey (CMDS) and related statistics, this article uses the Semiparametric Geographically Weighted Regression (SGWR) model and k-means cluster method to examine the spatial variation of the factors influencing floating population's settlement intention in 282 prefecture- and provincial- level cites of China. Results provide the following conclusions. (1) The settlement intention of urban floating population is mainly influenced by the floating population characteristics instead of the destination characteristics. (2) Social and economic factors are closely related to the floating population's settlement intention. Meanwhile, the demographic, family and mobility factors exert a significant impact on such an intention. To be specific, there exists an inhibitory effect on floating population's settlement intention in factors such as income, marriage, and cross provincial mobility. However, housing expenditure, participation rate, number of children and other factors can effectively contribute to such intention. (3) Zonal spatial differentiation patterns of the influencing factors' coefficients are illustrated by the SGWR model, which can be further divided into four categories ("E-W", "N-S", "NE-SW" and "SE-NW"): The positive influences of ethnic and family factors are decreasing from the northern to southern regions, while the influence of employment ratio in the secondary industry is declining from the northwest to the southeast regions, and the impacts of factors such as the number of children and per capita GDP are diminishing from the northeast to the southwest regions. In eastern developed areas, the settlement intention of floating population with higher income is comparatively lower, while migrants with higher housing expenditure in southern China have a stronger intention to settle down. (4) Four influencing zones are detected by the k-means method: Floating population's settlement intention in North China, Central China and East China is significantly affected by multiple factors; In the northwest region and part of the southwest region, migrants' settlement intention is mainly influenced by demographic and social factors; The northeast region and the eastern part of Inner Mongolia's floating population's willingness to stay is mainly related to economic and family factors; Apart from housing expenditure, coefficients of other factors are relatively small in southern China and part of the central, eastern and southwestern regions. Additionally, this paper puts forward some suggestions on the service and management of the floating population in China.
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DOI:10.13249/j.cnki.sgs.2019.11.003 [本文引用: 3]
Based on data from migration dynamic micro survey 2016, this article first construct the network of interprovincial Hukou transfer intention in China. Then, with the use of some spatial analysis methods, including global Moran’s I coefficient and hot spot, we explore the spatial pattern of such network. Considering network autocorrelation in our data, Eigenvector Spatial Filtering Gravity Model (ESFGM) is applied for analyzing the driving factors. Main results reveal that: 1) The network shows a concentrating spatial pattern, and migrating flows with high ranks are mainly from the undeveloped areas to developed regions. That is to say, the willingness of transferring Hukou differs from regions to regions, showing heterogeneity. 2) Although there exists a random spatial pattern of Hukou attractiveness, such pattern of out-migration is concentrating. Furthermore, provinces in the west and northwest are the hot spot areas of Hukou emigration intention, while the cold spot areas are the mid-east regions in China. 3) From the macro perspective, population of destination shows a negative impact on Hukou transfer intention, while the size of population in an origin is not highly correlated; per capita GDP of both origin and destination, as well as export of foreign-invested firms, influence the intention positively and notably. But among all macro factors, the average wage of employees in urban areas leads to relatively higher impact. 4) From the micro perspective, generally speaking, migrants’ individual and family factors have strong ties with their Hukou transfer intention. A migrant, with higher education level, smaller age and larger scale of family in the destination, tends to transfer his (or her) Hukou to the immigration place. In the meantime, impacts of house condition and migration reason cannot be ignored: a migrant with lower intention of buying a local house or migrating for business, is likely to have a lower intention of transferring Hukou.
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In this paper, we take Beijing as a case study and employ the residential leasing parcel data from 2004 to 2015 within the Sixth Ring Road of Beijing metropolitan area. Also, we use the GIS data of Beijing's public facilities, such as bus stations, railway stations, park, hospital, primary school and so on. With the help of ArcGIS, GS+, Surfer and Geoda Software, we explore the spatial pattern of residential land parcels, residential land price and determinants of residential land price in Beijing. In the first place, we use the methods of Spatial Trend Analysis, Nearest Neighbor Index (NNI), Exploratory Spatial Data Analysis (ESDA) to explore the spatial pattern of residential land parcels and their price in Beijing. In the second place, we compare the spatial econometric models (SLM and SEM) with traditional OLS model to further explore the determinants of residential land price in Beijing. Based on the analysis, the main conclusions are drawn as follows. (1) The number of residential land leasing parcels is not balanced among years and ring roads. The residential land leasing parcels in the last 20 years are mainly concentrated between the fifth and the sixth ring roads in Beijing. (2) Residential land parcels are generally distributed along the main roads (such as Beijing-Shijiazhuang Expressway, Beijing-Kaifeng Expressway, Beijing-Shanghai Expressway and Beijing-Tibet Expressway) and the subway lines (such as Line 1, Line 5, Line 6, Line 15, Fangshan Line, Daxing Line and Yizhuang Line), which is more obvious in outer suburban areas. (3) Generally, there exists an inverted U-shaped curve trend, indicating that residential land price declines gradually from the city center to the city fringes as a whole, and spatial pattern of residential land price has turned from mono-centric structure to poly-centric structure. (4) Residential land price demonstrates a spatial cluster distribution pattern. There exists obvious spatial autocorrelation in residential land price and it is easy to distinguish "cold spots" from "hot spots". (5) In the model selection, we compare the spatial econometric model (SLM and SEM) with the traditional OLS model. The result shows that SLM is the best, followed by SEM, indicating that there indeed exist spatial spillover effects and spatial dependence in residential land price rather than error dependence. The residential land price is mainly affected by the surrounding residential land price, distance to bus station, distance to subway station, distance to key primary school, area of land parcel, FAR and the type of land leasing. However, in this paper, one drawback is that we fail to take macroeconomic policy factors into consideration, which may play a key role in the formation of residential land price. Also, we have not considered the subway's impact in different periods such as planning period, construction period and operation period on residential land price, which needs to be further studied.
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Based on a survey of 2 496 migrant workers in 9 cities of the eastern and central China, this article investigated the impact of local factors on social integration of migrant workers. A multiple-level regression model was applied to analyzing and revealing the differential impact of local factors, including level of economic development, intelligibility of local dialect, population share of migrants, and cost of rent. The cities, with more difficulties to join the local endowment insurance, the better the economic development, the greater gap between the earnings of the migrant workers and urban capita income, the higher rent, the more difficult urban dialect to master and the higher proportion of urban immigrant population, have a lower integration. The results find: 1) each city’s uniqueness produces some specific positive and negative effects. The positive effects can reduce the social distance between the migrant population and the urban residents. The sense of inclusion may become a source of pride in promoting the effective integration of migrants. However, the negative effects may serve to marginalize and alienate the migrant population. In cities that are not conducive to social integration, the migrants have a lower degree of recognition. 2) the differences between cities are selective in attracting migrant populations. Different cities attract different types of migrants, so that their migrant populations are structurally different. The abilities of the migrant populations to adapt and integrate are also different in different cities. Therefore, cities differ significantly in their levels of social integration. Recognition of these differences is an important basis for making urbanization policies that fit the local conditions. Given the large variety of cities, it was suggested that localized social policies are promoted to ease the integration of migrant workers in urban communities. To facilitate migrant workers’ social integration, priority should be given to promoting social integration within a province and at cities with high proportion of migrant population. Migrant workers should also be given the benefits of local social programs such as public housing rental assistance, especially in cities with a high level of rental cost.
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