地理科学 ›› 2019, Vol. 39 ›› Issue (8): 1199-1207.doi: 10.13249/j.cnki.sgs.2019.08.001
• • 下一篇
收稿日期:
2018-07-18
修回日期:
2018-09-05
出版日期:
2019-08-10
发布日期:
2019-10-10
通讯作者:
刘晔
E-mail:liuye25@mail.sysu.edu.cn
作者简介:
王若宇(1994-),男,广东广州人,博士研究生,主要从事城市地理研究。E-mail: wangry6@mail2.sysu.edu.cn
基金资助:
Wang Ruoyu,Huang Xu,Xue Desheng,Liu Ye()
Received:
2018-07-18
Revised:
2018-09-05
Online:
2019-08-10
Published:
2019-10-10
Contact:
Liu Ye
E-mail:liuye25@mail.sysu.edu.cn
Supported by:
摘要:
基于2005~2015年全国统计年鉴数据,采用统计地图法和空间自相关分析,分析中国高校科研人才的时空分布特征(不包括港澳台数据),并采用面板负二项回归模型,识别高校科研人才空间分布的决定因素。研究结果表明:①中国的高校科研人才分布表现出较强的空间不均衡性,总体趋势为东南密集,西北稀疏,科研人才高度集中在北京、天津、上海;②高校科研人才分布具有一定的空间集聚特征,其热点地区为北京、天津、上海和江苏等沿海省市,冷点地区集中分布在广大的西部地区和黑龙江省。2005~2015年,高级职称人才的空间集聚程度有所减弱,而中初级职称科研人才的空间集聚程度有所加强;③人才政策、高等教育规模和层次、公共服务水平和信息化水平是所有高校科研人才空间分布的主要影响因素。从职称差异的角度来看,薪酬和失业率等经济因素仅对初级职称科研人才的分布有影响,医疗、文化公共服务水平和人才政策仅对中高级职称科研人才的分布有影响,高等教育的规模与层次对各种职称人才的分布均有影响;从地区差异的角度来看,西部地区高校科研人才受到经济因素影响更为明显,而中东部高校科研人才受到公共服务水平影响的更为明显。
中图分类号:
王若宇,黄旭,薛德升,刘晔. 2005~2015年中国高校科研人才的时空变化及影响因素分析[J]. 地理科学, 2019, 39(8): 1199-1207.
Wang Ruoyu,Huang Xu,Xue Desheng,Liu Ye. Spatio-temporal Change of Facaulty Members of Higher Education Institute and Its Influential Factors in China in 2005-2015[J]. SCIENTIA GEOGRAPHICA SINICA, 2019, 39(8): 1199-1207.
表1
计量模型估计结果"
全部样本 | 分职称样本 | 分区域样本 | |||||||
---|---|---|---|---|---|---|---|---|---|
Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | |||
(所有科研人员) | (高级职称) | (中级职称) | (初级职称) | (东部) | (中部) | (西部) | |||
logW | -0.117(0.085) | -0.341(0.289) | -0.133(0.086) | 0.141**(0.042) | -0.430(0.175) | -0.056(0.053) | 0.032**(0.016) | ||
cost | 0.000(0.001) | 0.000(0.001) | 0.000(0.003) | 0.000(0.004) | -0.004(0.003) | 0.000(0.000) | 0.000(0.000) | ||
unemp | -0.009(0.008) | -0.049(0.012) | -0.012(0.013) | -0.020**(0.010) | 0.007(0.009) | -0.001(0.013) | -0.014(0.020) | ||
logHP | 0.013(0.026) | 0.027(0.037) | 0.076(0.067) | 0.005(0.073) | 0.079(0.054) | 0.001(0.036) | -0.027(0.051) | ||
logPI | 0.015**(0.007) | 0.043**(0.020) | 0.081**(0.035) | 0.010(0.061) | 0.020**(0.010) | 0.061**(0.031) | 0.027**(0.010) | ||
logUS | 0.486***(0.030) | 0.335***(0.044) | 0.471***(0.049) | 1.302***(0.083) | 0.480***(0.050) | 0.491***(0.050) | 0.664***(0.065) | ||
college | 0.046**(0.018) | 0.051**(0.023) | 0.092***(0.018) | 0.012(0.026) | 0.106**(0.026) | 0.093**(0.048) | 0.124(0.096) | ||
logD | 0.081**(0.033) | 0.154**(0.047) | 0.106**(0.045) | 0.265(0.237) | 0.004(0.041) | 0.381***(0.049) | 0.051(0.078) | ||
st | -0.001(0.002) | 0.002(0.004) | -0.006(0.004) | 0.022(0.013) | -0.005(0.004) | 0.001(0.003) | 0.007(0.006) | ||
logAG | 0.022***(0.008) | 0.029**(0.012) | -0.015(0.010) | 0.005(0.022) | 0.013**(0.006) | 0.002(0.010) | 0.034(0.028) | ||
logIM | 0.068***(0.015) | 0.002(0.021) | 0.097(0.066) | 0.130***(0.042) | 0.061(0.050) | 0.056**(0.028) | 0.021(0.032) | ||
logSE | -0.020(0.027) | -0.053(0.047) | -0.149(0.127) | -0.002**(0.001) | -0.034(0.025) | -0.084(0.060) | -0.034(0.026) | ||
常数 | 5.951***(0.383) | 6.994***(0.539) | 4.154***(0.687) | -1.589(1.066) | 7.737***(0.915) | 4.801***(0.702) | 3.973(1.031) | ||
时点固定效应 | Yes | Yes | Yes | Yes | Yes | Yes | Yes | ||
Log likelihood | -3169.855 | -2795.028 | -2828.415 | -2821.975 | -1141.646 | -788.258 | -1144.396 | ||
AIC | 6389.711 | 5640.056 | 5706.830 | 5693.949 | 2333.291 | 1626.515 | 2338.793 | ||
样本数 | 341 | 341 | 341 | 341 | 121 | 88 | 132 |
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