居民就业可达性的空间分布及对收入的影响——基于上海都市区的实证研究

doi:10.11821/dlyj201509012

摘要/Abstract

摘要：

基于第六次人口普查、第二次经济普查和问卷调查数据,定量描述和分析了上海都市区内部以街道为空间尺度的就业可达性及其空间格局,探讨了劳动者的工资收入与居住区位就业可达性的关系。结论表明：上海市的居民就业可达性呈现相对均衡的多中心分布格局,与北京就业可达性单中心的分布格局有明显的不同。同时,在控制个人经济社会属性和地区属性的条件下,上海的居民就业可达性对居民个人收入水平具有显著的正向影响。这一结论的政策含义是,地理区位较差的住房有损于居民收入,可能是吸引力不足的重要原因之一;政府通过提高地区的居民就业可达性,有助于改善就业机会,促进居民个人收入的增加。

关键词: 就业可达性, 空间格局, 收入水平, 上海都市区

Abstract:

The social effects of city spatial structure have long been an important issue in the field of urban geography and urban economics research. However, the impact of job-housing spatial relationship on income has received little attention in empirical analyses. Employing the Sixth Population Census and the Second Economic Census datasets, we apply a measure of job accessibility developed by Ong & Blumenberg to describe the spatial structure of employment opportunities and working people in Shanghai Metropolitan Area at sub-district level. Combining survey data, we empirically investigate the extent to which individual income may be influenced by job proximity. The results are the following. First, Shanghai has a polycentric job accessibility structure, indicating that job accessibility are very similar both in the inner city and in the sub-district. This does not apply to the case of Beijing. The main reason which can explain this phenomenon may be that Beijing and Shanghai have different industrial structures. Beijing strongly relies on services sector, which has caused most of job opportunities to be located in inner-city. As for Shanghai, manufacture still plays an important role in economic growth and many of jobs are located in suburban area of Shanghai. Second, the output of the regression shows that incomes are significantly and positively associated with job accessibility, indicating that personal income level increases along with job proximity increases correspondingly. In more detail, people who live in the sub-districts with richer job opportunities have higher income level. For people who live in job-poor sub-districts usually have lower incomes, which may be due to higher commuting expense and employment pressure. Since job proximity is closely related to income, those housing areas far from inner city and job opportunities may not only damage social efficiency but also drive the level of individual income down. This may be one of the reasons why people are not willing to live far from inner-city. It also inspires the local government that moderate measures on increasing job accessibilities are needed for improving employment situation and individuals’ income level. Job accessibility can be improved by creating more job opportunities which are suitable to the residents, and also by enabling the workers to travel easily to work sites.

Key words: job accessibility, spatial pattern, income level, Shanghai Metropolitan Area

吴江洁, 孙斌栋. 居民就业可达性的空间分布及对收入的影响——基于上海都市区的实证研究[J]. 地理研究, 2015, 34(9): 1744-1754.

Jiangjie WU, Bindong SUN. Job accessibility and its impact on income: Outcomes from Shanghai Metropolitan Area[J]. GEOGRAPHICAL RESEARCH, 2015, 34(9): 1744-1754.

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变量	定义	均值	标准差	最小值	最大值
就业可达性	就业可达性	0.6496	0.6139	0.0508	3.9545
家庭属性	家庭人口数（人）	2.6524	1.2247	1	8
人力资本	街道平均受教育年限（年）	11.5869	0.6652	9.2773	12.7848
产业结构	制造业比重（%）	0.1580	0.1630	0.0015	0.8115
人口规模	街道的人口数量（万人）	13.2194	8.5105	2.4487	37.1856
个人收入水平	2000元以下;2000~4000元;4000~6000元;6000~8000元;8000~10000元;10000元以上
年龄	小于20;20~30岁;30~40岁;40~50岁;50~60岁;大于60岁
教育程度	初中及以下;高中;本科;硕士及以上
职业类型	竞争性行业（生产、制造业,批发零售业,住宿、餐饮等服务业）;垄断性行业（交通运输、仓储,通讯、信息、软件行业,金融、房地产、设计及咨询）;事业性行业（科研教育,医务、社会保障行业,文化、体育、娱乐行业）;机关团体（国家机关政党机关和社会团体）

	样本属性	样本量（个）	比例（%）		样本属性	样本量（个）	比例（%）
年龄	小于20岁	19	2.46	个人收入水平	2000元以下	106	13.75
	20~30岁	420	54.47		2000~4000元	309	40.08
	30~40岁	250	32.43		4000~6000元	167	21.66
	40~50岁	60	7.78		6000~8000元	81	10.51
	50岁以上	20	2.85		8000~10000元	45	5.84
性别	男	481	62.39		10000元以上	63	8.17
性别	女	290	37.61	教育程度	初中及以下	32	4.15
职业类型	竞争性行业	145	18.81	教育程度	高中	168	21.79
	垄断性行业	184	23.87	本科		495	64.2
	事业性行业	334	43.32	硕士及以上		76	9.86
	机关团体	108	14.01

模型	y=1	y=2	y=3	y=4	y=5
就业可达性	0.335^***	0.335^***	0.335^***	0.335^***	0.335^***
	(0.126)	(0.126)	(0.126)	(0.126)	(0.126)
性别	-1.075^***	-1.075^***	-1.075^***	-1.075^***	-1.075^***
	(0.152)	(0.152)	(0.152)	(0.152)	(0.152)
年龄	4.031^***	3.960^***	3.031^***	5.182^***	1.548
	(0.761)	(0.673)	(0.710)	(1.142)	(1.447)
年龄的平方	-0.640^***	-0.543^***	-0.420^***	-0.792^***	-0.0576
	(0.121)	(0.107)	(0.113)	(0.197)	(0.248)
教育程度	1.879^***	1.997^***	1.455^***	1.473^***	0.728^**
	(0.198)	(0.186)	(0.193)	(0.231)	(0.297)
职业类型（竞争性行业作为参照）
垄断性行业	0.0606	0.0606	0.0606	0.0606	0.0606
	(0.221)	(0.221)	(0.221)	(0.221)	(0.221)
事业性行业	0.618^**	0.221	0.665^***	0.619^**	1.077^***
	(0.298)	(0.215)	(0.231)	(0.262)	(0.312)
机关团体	0.681^***	0.681^***	0.681^***	0.681^***	0.681^***
	(0.244)	(0.244)	(0.244)	(0.244)	(0.244)
家庭人口数	0.0871	0.0871	0.0871	0.0871	0.0871
	(0.0620)	(0.0620)	(0.0620)	(0.0620)	(0.0620)
街道平均受教育年限	0.0819	0.0819	0.0819	0.0819	0.0819
	(0.124)	(0.124)	(0.124)	(0.124)	(0.124)
街道制造业比重	-1.635^***	-1.635^***	-1.635^***	-1.635^***	-1.635^***
	(0.597)	(0.597)	(0.597)	(0.597)	(0.597)
街道人口	0.0399^***	0.0399^***	0.0399^***	0.0399^***	0.0399^***
	(0.0110)	(0.0110)	(0.0110)	(0.0110)	(0.0110)
常数项	-8.907^***	-12.48^***	-10.91^***	-14.74^***	-9.172^***
	(1.857)	(1.851)	(1.873)	(2.254)	(2.603)
样本数量	771
LR chi2(28)	452.77 0.0000 0.1861
Prob > chi2
伪R²