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

Abstract

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

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.

Figures/Tables 8

Fig.1

Fig.2

Fig.3

Fig.4

Tab.1

Tab.2

Tab.3

Regression results

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

Tab.3

Fig.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

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

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