基于企业大数据的京津冀制造业集聚的影响因素
黄宇金, 盛科荣, 孙威

Influencing factors of manufacturing agglomeration in the Beijing-Tianjin-Hebei region based on enterprise big data
HUANG Yujin, SHENG Kerong, SUN Wei
表5 Hurdle模型第一阶段Probit回归结果
Tab. 5 Probit regression results of the first stage in the Hurdle model
变量 2004—2008年 2004—2013年
模型(1) 模型(2) 模型(3) 模型(4) 模型(5) 模型(6) 模型(7) 模型(8)
50 km 100 km 150 km 194 km 50 km 100 km 150 km 194 km
RES_AGR -3.813**
(1.695)		 -4.153**
(1.734)		 -4.106**
(1.709)		 -3.703**
(1.694)		 -3.042**
(1.467)		 -3.158**
(1.488)		 -3.119**
(1.489)		 -2.837*
(1.509)
RES_MIN 1.325
(2.039)		 1.262
(2.099)		 1.201
(2.067)		 0.915
(1.995)		 0.249
(1.412)		 0.162
(1.432)		 0.153
(1.424)		 0.202
(1.413)
RES_ENE -13.264
(8.470)		 -14.357*
(8.597)		 -13.884
(8.643)		 -10.649
(8.524)		 6.624
(5.391)		 7.071
(5.520)		 7.579
(5.511)		 7.808
(5.437)
AGG_EMP 0.287***
(0.086)		 0.321***
(0.083)		 0.328***
(0.084)		 0.373***
(0.085)		 0.240***
(0.058)		 0.252***
(0.057)		 0.258***
(0.057)		 0.311***
(0.058)
AGG_INI 1.086
(1.706)		 1.716
(1.701)		 1.720
(1.690)		 1.459
(1.698)		 -0.637
(1.278)		 -0.345
(1.293)		 -0.428
(1.298)		 -0.570
(1.318)
AGG_INT 0.206
(1.609)		 0.516
(1.632)		 0.395
(1.608)		 0.570
(1.611)		 0.929
(1.377)		 1.133
(1.396)		 1.119
(1.399)		 1.268
(1.433)
AGG_TEC -0.014
(0.077)		 -0.006
(0.084)		 -0.028
(0.084)		 -0.044
(0.094)		 -0.061
(0.041)		 -0.060
(0.042)		 -0.069*
(0.042)		 -0.067
(0.043)
GOV_NAT 0.651
(3.807)		 0.756
(4.088)		 0.224
(4.056)		 0.883
(3.903)		 -1.233
(2.271)		 -1.382
(2.284)		 -1.634
(2.299)		 -0.937
(2.266)
GOV_LEV -0.007
(0.006)		 -0.010
(0.006)		 -0.006
(0.006)		 -0.002
(0.007)		 -0.001
(0.003)		 -0.002
(0.003)		 -0.001
(0.003)		 -0.001
(0.003)
GLO_EXP 0.611
(0.585)		 0.551
(0.627)		 0.608
(0.621)		 0.456
(0.594)
GLO_ FOR 5.552**
(2.780)		 6.333**
(3.052)		 6.367**
(3.089)		 4.989*
(2.979)		 4.690**
(2.003)		 4.830**
(2.132)		 4.891**
(2.150)		 3.434*
(2.034)
SPA_BJ 2.568***
(0.672)		 2.583***
(0.712)		 2.592***
(0.713)		 2.204***
(0.706)		 2.501***
(0.517)		 2.491***
(0.533)		 2.515***
(0.534)		 2.357***
(0.539)
SPA_TJ 1.751**
(0.694)		 1.945**
(0.756)		 1.658**
(0.735)		 1.118
(0.718)		 2.580***
(0.562)		 2.769***
(0.583)		 2.623***
(0.579)		 2.316***
(0.583)
RES_TRA -13.759
(8.473)		 -15.439*
(8.735)		 -15.359*
(8.670)		 -13.606
(8.390)		 -12.833**
(6.412)		 -13.414**
(6.547)		 -13.757**
(6.547)		 -12.867**
(6.558)
常数项 -2.321**
(1.118)		 -2.652**
(1.100)		 -2.656**
(1.092)		 -2.771**
(1.092)		 -2.218**
(0.894)		 -2.353***
(0.896)		 -2.342***
(0.897)		 -2.525***
(0.910)
年份固定效应 是 是 是 是 是 是 是 是
样本量 324 324 324 324 492 492 492 492
Pseudo R2 0.224 0.247 0.241 0.219 0.223 0.237 0.236 0.229