Acta Geographica Sinica ›› 2020, Vol. 75 ›› Issue (12): 2716-2729.doi: 10.11821/dlxb202012012
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LIU Tao1,2(), ZHUO Yunxia1,2, WANG Jiejing3(
)
Received:
2020-03-14
Revised:
2020-07-27
Online:
2020-12-25
Published:
2021-02-25
Contact:
WANG Jiejing
E-mail:liutao@pku.edu.cn;wangjiejing@ruc.edu.cn
Supported by:
LIU Tao, ZHUO Yunxia, WANG Jiejing. How multi-proximity affects destination choice in onward migration: A nested logit model[J].Acta Geographica Sinica, 2020, 75(12): 2716-2729.
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Tab. 1
Variables and statistical description
类型 | 变量名称 | 变量说明 | 样本量 | 均值 | 标准差 | |
---|---|---|---|---|---|---|
自变量 (邻近性) | 认知邻近性 | Education | 潜在目的地与该个体相同学历人口占6岁及以上常住人口比例/该学历人口占全国6岁及以上常住人口比例 | 1027007 | 0.98 | 0.35 |
地理邻近性 | Distance_F | 潜在目的地与首次流入地的最短铁路距离(km) | 1027007 | 1434.23 | 781.71 | |
Distance_H | 潜在目的地与户籍地的最短铁路距离(km) | 1027007 | 1546.25 | 823.44 | ||
制度邻近性 | Province_F | 潜在目的地与首次流入地是否属于同一省份(否= 0,是= 1) | 1027007 | 0.04 | 0.20 | |
Province_H | 潜在目的地与户籍地是否属于同一省份(否= 0,是= 1) | 1027007 | 0.04 | 0.20 | ||
信息邻近性 | Search_F | 首次流入地对潜在目的地城市名称的日均百度搜索指数 | 1027007 | 44.80 | 43.26 | |
Search_H | 户籍地对潜在目的地城市名称的日均百度搜索指数 | 1027007 | 6.88 | 15.41 | ||
社会邻近性 | Migrants_F | 首次流入地到潜在目的地流动人口/首次流入地总流出人口 | 1027007 | 0.00 | 0.02 | |
Migrants_H | 户籍地到潜在目的地的流动人口/户籍地总流出人口 | 1027007 | 0.00 | 0.02 | ||
控制变量 (城市特征) | 劳动力市场 | Employment | 城镇单位从业人员与私营个体从业人员之和(万人) | 285 | 130.17 | 191.98 |
Wage | 城镇在岗职工平均工资(元/人) | 285 | 54004.08 | 12188.42 | ||
舒适物 | Public | 每万名小学生、每万名中学生拥有的教师、学校数,每万人拥有的医院和卫生院数、医院和卫生院床位数、医生数主成分分析综合得分值 | 285 | 0.00 | 1.00 | |
PM2.5 | PM2.5年平均浓度(μg/m3) | 285 | 37.31 | 18.01 | ||
抽样比 | Samplingratio | CMDS2017中的样本量与2010年全市流动人口之比(%) | 285 | 0.07 | 0.07 | |
调节变量 (个体特征) | 性别 | Male | 女性= 0,男性= 1 | 3629 | 0.55 | 0.50 |
代际 | Gen | 老一代= 0,新生代= 1 | 3629 | 0.77 | 0.42 | |
学历 | Higher | 高中及以下= 0,大专及以上= 1 | 3629 | 0.25 | 0.44 |
Tab. 2
The trajectory and distance of primary and secondary migration in 2017
序号 | 流动轨迹 | 人数(人) | 占比(%) | 首次流入地与户籍地 平均距离(km) | 现住地与首次流入地 平均距离(km) | 现住地与户籍地 平均距离(km) |
---|---|---|---|---|---|---|
1 | 一直省内流动 | 506 | 13.94 | 316.67 | 210.07 | 284.67 |
2 | 从省内到省际 | 408 | 11.24 | 259.72 | 943.56 | 956.72 |
3 | 从省际回省内 | 655 | 18.05 | 1000.96 | 978.74 | 226.23 |
4 | 从省际到流入省内 | 524 | 14.44 | 1012.17 | 173.49 | 1011.39 |
5 | 从省际到其他省份 | 1536 | 42.33 | 1177.95 | 1156.63 | 1139.50 |
Tab. 3
Results of nested logit regression model
模型1 | 模型2 | 模型3 | ||||||
---|---|---|---|---|---|---|---|---|
系数 | 标准误 | 系数 | 标准误 | 系数 | 标准误 | |||
Education | 0.386*** | 0.040 | 0.242*** | 0.032 | 0.144*** | 0.030 | ||
Distance(ln)_F | -0.446*** | 0.046 | -0.343*** | 0.033 | -0.357*** | 0.044 | ||
Distance(ln)_H | -0.091*** | 0.021 | -0.232*** | 0.032 | ||||
Province_F | -0.305*** | 0.047 | -0.099** | 0.041 | 0.036 | 0.042 | ||
Province_H | 0.031 | 0.040 | 0.374*** | 0.061 | ||||
Search(ln)_F | 0.616*** | 0.063 | 0.333*** | 0.037 | 0.273*** | 0.039 | ||
Search(ln)_H | 0.346*** | 0.036 | 0.171*** | 0.031 | ||||
Migrants_F | 2.456*** | 0.275 | 0.744*** | 0.229 | -0.207 | 0.232 | ||
Migrants_H | 4.598*** | 0.359 | 3.224*** | 0.319 | ||||
Employment(ln) | 0.319*** | 0.040 | ||||||
Wage(ln) | 0.462*** | 0.078 | ||||||
Public | 0.047*** | 0.010 | ||||||
PM2.5(ln) | -0.247*** | 0.038 | ||||||
Samplingratio | 2.890*** | 0.369 | ||||||
λ1 | 0.764*** | 0.073 | 0.676*** | 0.055 | 0.587*** | 0.061 | ||
λ2 | 0.514*** | 0.050 | 0.500*** | 0.043 | 0.527*** | 0.062 | ||
Loglikelihood | -15401.962 | -13615.199 | -13272.273 | |||||
chi2 | 135.76 | 178.31 | 127.57 | |||||
Prob>chi2 | 0.000 | 0.000 | 0.000 | |||||
IIA检验 | 0.000 | 0.000 | 0.000 |
Tab. 4
Results of regression for different migrants
模型4:性别差异 | 模型5:代际差异 | 模型6:学历差异 | ||||||
---|---|---|---|---|---|---|---|---|
系数 | 标准误 | 系数 | 标准误 | 系数 | 标准误 | |||
Education | 0.162*** | 0.040 | 0.183** | 0.077 | 0.240*** | 0.070 | ||
Distance(ln)_F | -0.342*** | 0.046 | -0.354*** | 0.052 | -0.398*** | 0.049 | ||
Distance(ln)_H | -0.228*** | 0.039 | -0.291*** | 0.050 | -0.253*** | 0.037 | ||
Province_F | 0.041 | 0.060 | -0.041 | 0.080 | -0.031 | 0.050 | ||
Province_H | 0.343*** | 0.074 | 0.364*** | 0.092 | 0.377*** | 0.068 | ||
Search(ln)_F | 0.316*** | 0.052 | 0.326*** | 0.059 | 0.310*** | 0.044 | ||
Search(ln)_H | 0.148*** | 0.035 | 0.058 | 0.040 | 0.152*** | 0.031 | ||
Migrants_F | -0.378 | 0.338 | -0.511 | 0.457 | -0.646** | 0.307 | ||
Migrants_H | 3.690*** | 0.395 | 2.681*** | 0.408 | 3.538*** | 0.356 | ||
Attribute×Education | -0.035 | 0.047 | -0.059 | 0.079 | -0.115 | 0.072 | ||
Attribute×Distance(ln)_F | -0.023 | 0.032 | 0.004 | 0.036 | 0.065 | 0.042 | ||
Attribute×Distance(ln)_H | -0.007 | 0.040 | 0.086* | 0.046 | 0.025 | 0.049 | ||
Attribute×Province_F | -0.010 | 0.079 | 0.094 | 0.092 | 0.245** | 0.107 | ||
Attribute×Province_H | 0.046 | 0.078 | 0.004 | 0.090 | 0.014 | 0.100 | ||
Attribute×Search(ln)_F | -0.075 | 0.052 | -0.082 | 0.056 | -0.121* | 0.066 | ||
Attribute×Search(ln)_H | 0.040 | 0.038 | 0.144*** | 0.045 | 0.160*** | 0.055 | ||
Attribute×Migrants_F | 0.294 | 0.434 | 0.405 | 0.505 | 1.561*** | 0.552 | ||
Attribute×Migrants_H | -0.811*** | 0.306 | 0.597* | 0.357 | -0.798** | 0.373 | ||
ControlVariables | Yes | Yes | Yes | |||||
λ1 | 0.585*** | 0.061 | 0.574*** | 0.059 | 0.622*** | 0.063 | ||
λ2 | 0.525*** | 0.062 | 0.514*** | 0.061 | 0.561*** | 0.064 | ||
Loglikelihood | -13266.152 | -13255.493 | -13253.974 | |||||
chi2 | 129.02 | 128.93 | 135.98 | |||||
Prob>chi2 | 0.000 | 0.000 | 0.000 | |||||
IIA检验 | 0.000 | 0.000 | 0.000 |
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