中国人才分布的学历梯度分异性：时空格局及影响机理

doi:10.18306/dlkxjz.2023.05.001

地理科学进展 ›› 2023, Vol. 42 ›› Issue (5): 821-836.doi: 10.18306/dlkxjz.2023.05.001

• 研究论文 • 下一篇

中国人才分布的学历梯度分异性：时空格局及影响机理

齐宏纲¹(), 戚伟², 刘振², 赵美风¹^,^*()

1.天津师范大学地理与环境科学学院，天津 300387
2.中国科学院地理科学与资源研究所，区域可持续发展分析与模拟重点实验室，北京 100101

收稿日期:2022-09-19 修回日期:2022-11-10 出版日期:2023-05-28 发布日期:2023-05-24
通讯作者: *赵美风(1986— )，女，河北鹿泉人，博士，副教授，主要从事城市地理与人口地理研究。E-mail: zhaomeifeng@foxmail.com
作者简介:齐宏纲(1992— )，男，河北唐山人，博士，讲师，主要从事人口地理与城市地理研究。E-mail: qihg192@163.com
基金资助:
国家自然科学基金项目(42101237);江苏省社会科学基金青年项目(22SHC003);江苏省高校自然科学面上项目(21KJB170015)

Heterogeneity of educational attainment of talents in China: Spatial and temporal patterns and driving factors

QI Honggang¹(), QI Wei², LIU Zhen², ZHAO Meifeng¹^,^*()

1. School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin 300387, China
2. Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China

Received:2022-09-19 Revised:2022-11-10 Online:2023-05-28 Published:2023-05-24
Supported by:
National Natural Science Foundation of China(42101237);Social Science Foundation of Jiangsu Province(22SHC003);University Natural Science Research Project of Jiangsu Province(21KJB170015)

摘要/Abstract

摘要：

人才内部存在着受教育程度由低到高的学历梯度，关注不同学历人才分布影响机理的差异性，对于制定因类而异的人才政策和优化城市体系的经济高质量发展格局具有重要意义。论文利用中国2005、2010和2015年人口抽样调查数据资料，从地级尺度上揭示了2005—2015年中国大专、本科及研究生学历人才分布的时空格局特征差异，采用空间计量模型解释了不同学历人才时空分布差异的影响机理。结果发现：① 中国不同学历人才的空间分布高度不均衡，大专、本科及研究生学历人才均主要集聚在直辖市、省会城市和计划单列市，而本科及大专学历人才在内蒙古西部和新疆北部等西北地区资源型城市也具有一定的集聚优势，人才集聚的不均衡度随着学历提升而依次递增。人才集聚的不均衡程度均随时间而有所缓解，但缓解程度随学历提升而依次递减。② 人才动态集聚呈现出初期人才比重越高、期间人才比重增幅越大的马太效应，人才动态集聚的马太效应强度随学历提升而逐渐增强。③ 中国大专、本科及研究生学历人才集聚均主要为经济主导型，薪资待遇是不同学历人才集聚的最重要影响因素，中国区域间薪资水平差距的缩小是不同学历人才分布不均衡态势缓解的主要原因。服务业快速增长，中学教育、医疗条件和交通出行等社会舒适性也会促进不同学历人才的集聚，海拔高度及绿化休憩等环境舒适性整体上并未显著推动中国人才的集聚。④ 经济及社会舒适性因素对人才集聚的促进作用均呈现出随学历提升而依次增加的趋势。

关键词: 人才分布, 研究生, 本科, 大专, 时空格局, 影响机理

Abstract:

Educational attainments of talents are different, and exploring the spatiotemporal distribution of talents with different educational attainments and its driving factors is of key significance for formulating different kinds of talent policies and optimizing the high-quality economic development pattern of the urban system. Using data of the 2005, 2010 and 2015 population sample surveys of China, this study examined the differences in the spatial and temporal patterns of China's talents with college, undergraduate, and graduate degrees from 2005 to 2015 at the prefecture level, and used a spatial econometric model to explain the driving factors of these differences. The results show that: 1) The spatial distribution of China's talents with different educational attainments was highly uneven, and talents with college, undergraduate, and graduate degrees were mainly concentrated in the municipalities, provincial capitals, and independent plan cities. There was also some concentration of talents with college and undergraduate degrees in resource-based cities of the northwestern region, such as western Inner Mongolia and northern Xinjiang. The level of uneven distribution of talents increased with the increment of educational attainments and the unevenness was alleviated through time, but the degree of its alleviation decreased with the increase of educational attainment. 2) The dynamic agglomeration of talents showed the Matthew effect—the higher the proportion of talents at the beginning of the study period, the greater the increase of the proportion of talents during the period. The intensity of the Matthew effect of dynamic agglomeration of talents gradually increased with the increase of educational attainment. 3) The concentration of China's talents with college, undergraduate, and graduate degrees was mainly economic driven, and salary played the most important role in influencing the concentration of talents with different educational attainments. The reduction of regional salary gaps in China had caused a decrease in the level of uneven distribution of talents with different educational attainments. The rapid growth of service industry and improvements in social amenities such as secondary education, medical service, and transportation had also promoted the agglomeration of talents with different educational attainments. Nevertheless, environmental factors such as elevation and green leisure space did not significantly boost the agglomeration of talents in China. The role of both economic development and social amenities in promoting talent agglomeration showed a tendency to increase with increasing educational attainments.

Key words: talent distribution, graduate, undergraduate, college, spatial and temporal patterns, driving factors

齐宏纲, 戚伟, 刘振, 赵美风. 中国人才分布的学历梯度分异性：时空格局及影响机理[J]. 地理科学进展, 2023, 42(5): 821-836.

QI Honggang, QI Wei, LIU Zhen, ZHAO Meifeng. Heterogeneity of educational attainment of talents in China: Spatial and temporal patterns and driving factors[J]. PROGRESS IN GEOGRAPHY, 2023, 42(5): 821-836.

图/表 8

图1

图2

图3

图4

表1

表2

表3

回归结果

变量	研究生			本科			大专
变量	OLS	SAR	SEM	OLS	SAR	SEM	OLS	SAR	SEM
tez	0.529^***	0.529^***	0.533^***	0.253^***	0.247^***	0.270^***	0.141^***	0.137^***	0.156^***
tez	(4.439)	(4.451)	(4.452)	(3.788)	(3.719)	(4.028)	(2.841)	(2.778)	(3.149)
wag	0.607^***	0.605^***	0.701^***	0.476^***	0.468^***	0.557^***	0.160^***	0.160^***	0.220^***
wag	-4.884	(4.775)	(5.426)	(6.834)	(6.671)	(7.637)	(3.093)	(3.112)	(4.074)
sta	-0.309^**	-0.306^**	-0.276^**	-0.256^***	-0.222^***	-0.221^***	-0.122^**	-0.103^*	-0.132^**
sta	(-2.291)	(-2.251)	(-2.003)	(-3.385)	(-2.915)	(-2.854)	(-2.165)	(-1.827)	(-2.314)
doc	0.340^***	0.339^***	0.337^***	0.211^***	0.207^***	0.221^***	0.199^***	0.192^***	0.197^***
doc	(4.084)	(4.090)	(4.035)	(4.526)	(4.482)	(4.728)	(5.730)	(5.572)	(5.696)
roa	0.194^***	0.194^***	0.198^***	0.175^***	0.172^***	0.176^***	0.156^***	0.153^***	0.157^***
roa	(4.657)	(4.670)	(4.776)	(7.458)	(7.388)	(7.630)	(8.964)	(8.883)	(9.217)
gre	-0.064	-0.064	-0.07	0.009	0.007	0.005	-0.029	-0.031	-0.035
gre	(-1.025)	(-1.035)	(-1.132)	(0.259)	(0.197)	(0.156)	(-1.112)	(-1.201)	(-1.383)
pm	-0.006	-0.006	-0.024	-0.116^***	-0.103^**	-0.127^***	-0.075^**	-0.062^**	-0.077^**
pm	(-0.079)	(-0.082)	(-0.300)	(-2.807)	(-2.480)	(-2.757)	(-2.426)	(-1.997)	(-2.224)
dem	0.022	0.023	0.021	-0.005	0.004	-0.004	0.002	0.008	0.0001
dem	(1.013)	(1.001)	(0.900)	(-0.449)	(0.280)	(-0.279)	(0.276)	(0.862)	(-0.005)
edu	0.525^***	0.525^***	0.516^***	0.223^***	0.223^***	0.206^***	0.122^***	0.123^***	0.112^***
edu	-16.195	(16.189)	(15.955)	(12.274)	(12.317)	(11.387)	(9.058)	(9.153)	(8.449)
sef	-0.407^***	-0.407^***	-0.380^***	-0.193^***	-0.190^***	-0.183^***	-0.124^***	-0.127^***	-0.148^***
sef	(-3.973)	(-3.992)	(-3.610)	(-3.351)	(-3.332)	(-3.085)	(-2.897)	(-2.986)	(-3.375)
den	0.037	0.038	0.052	-0.088^***	-0.078^***	-0.074^***	-0.097^***	-0.089^***	-0.093^***
den	(0.867)	(0.885)	(1.181)	(-3.658)	(-3.217)	(-2.962)	(-5.412)	(-4.968)	(-5.033)
ρ		0.022			0.315^**			0.370^***
ρ		(0.112)			(2.195)			(2.780)
γ			0.584^***			0.727^***			0.830^***
γ			(3.881)			(6.972)			(12.399)
R²	0.732	0.736	0.735	0.738	0.740	0.741	0.688	0.687	0.691
Log-likelihood	-658.406	-658.404	-655.316	-243.436	-241.965	-237.592	-30.916	-29.519	-18.921
Durbin-Watson	1.651			1.598			1.471
LM(lag)	0.011			3.528^*			2.766^*
R-LM(lag)	5.505^**			0.118			5.760^**
LM(error)	6.407^**			12.380^***			33.041^***
R-LM(error)	11.901^***			8.970^***			36.035^***
N	717			717			717

表3

图5

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类型	变量	描述
经济因素	tez	各城市第三产业增加值占GDP的比重(%)
经济因素	wag	各城市城镇在岗职工平均工资(元)
社会舒适性因素	sta	各城市中学生师比(人/人)
	doc	各城市每万人执业医生数(人/万人)
	roa	各城市市辖区人均道路面积(m²/人)
自然环境舒适性因素	gre	各城市市辖区建成区绿化覆盖率(%)
	pm	各城市年均PM_2.5浓度(μg/m³)
	dem	各城市海拔高度(m)
控制变量	edu	各城市万人高等院校在校生数(人/万人)
	sef	各城市财政支出中的科技支出和教育支出占比(%)
	den	各城市人口密度(人/km²)

指标	2005年		2010年		2015年
指标	Moran's I	Z值	Moran's I	Z值	Moran's I	Z值
研究生学历劳动力占比	-0.009	-0.243	0.002	0.243	0.006	0.406
本科学历劳动力占比	0.170^***	6.960	0.140^***	5.808	0.138^***	5.556
大专学历劳动力占比	0.312^***	12.322	0.226^***	8.888	0.143^***	5.622

中国人才分布的学历梯度分异性：时空格局及影响机理

Heterogeneity of educational attainment of talents in China: Spatial and temporal patterns and driving factors

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