PROGRESS IN GEOGRAPHY ›› 2019, Vol. 38 ›› Issue (10): 1514-1522.doi: 10.18306/dlkxjz.2019.10.007
• Special Issue | Empirical Study • Previous Articles Next Articles
LI Shiyang1,3,ZHU Huasheng1,2,*(),DING Yue1,4
Received:
2019-05-27
Revised:
2019-08-07
Online:
2019-10-28
Published:
2019-11-01
Contact:
ZHU Huasheng
E-mail:zhuhs@bnu.edu.cn
Supported by:
LI Shiyang,ZHU Huasheng,DING Yue. The impact of international trade on regional skill upgrading: Analysis of prefecture-level cities in the mainland of China[J].PROGRESS IN GEOGRAPHY, 2019, 38(10): 1514-1522.
Tab.1
Control variables, indicators and description in the literature"
变量名称 | 具体指标 | 指标说明 |
---|---|---|
地方教育投入 | 人均教育支出(edu)( | 在中国情境下,人力资本的提升需更多依靠地方政府财政投入;教育支出水平反映了地区政府对教育发展的支撑能力,将影响人力资本积累的效果 |
人力资本 | 每万人在校大学生数(hr)( | 人力资本存量是地区劳动力技能发生升级的基础,选取每万人在校大学生数作为衡量人力资本的指标 |
技术进步 | 科技经费占财政支出比例(tech)( | 技术进步将会改变雇主对劳动力的需求定位,增加对技能型劳动力的需求。可以从投入和产出2个角度来衡量 |
每万人专利申请数(patent)( | ||
经济规模 | 人均地区生产总值(GDP)( | 经济规模的扩大能同时增加高技能劳动和低技能劳动的就业,但对于增加高技能劳动就业的边际效应更大 |
企业属性 | 企业平均产值(scale)( | 大企业比小企业更能获得规模经济的好处,更愿意也更能够为员工提供培训投入,有助于其实现技能升级 |
外资企业占比(FE)( | 外资企业具有引致人才的优势,同时能够通过高效率的人力资源开发能力,为劳动者提供新知识和新技能 |
Tab.2
Panel regression results of the whole sample"
变量 | 模型1 | 模型2 |
---|---|---|
ln trade | -0.035*** | — |
ln exp | — | -0.048*** |
ln imp | — | 0.002 |
ln edu | 0.180*** | 0.194*** |
ln hr | 0.209*** | 0.210*** |
ln tech | -0.039* | -0.041* |
ln patent | 0.015 | 0.018 |
ln GDP | 0.306*** | 0.316*** |
ln scale | -0.006 | -0.025 |
ln FE | 0.101*** | 0.098*** |
loc | 0.158*** | 0.151*** |
size | 0.052* | 0.044 |
R2 | 0.751 | 0.754 |
Wald检验 | 1348.33*** | 1362.43*** |
N | 458 | 456 |
Tab.3
Panel regression results of the sub sample sets"
变量 | 模型1 | 模型2 | |||||||
---|---|---|---|---|---|---|---|---|---|
沿海城市 | 内陆城市 | 大城市 | 小城市 | 沿海城市 | 内陆城市 | 大城市 | 小城市 | ||
ln trade | 0.046* | -0.047*** | 0.007 | -0.061*** | — | — | — | — | |
ln exp | — | — | — | — | -0.030 | -0.040** | -0.059** | -0.056*** | |
ln imp | — | — | — | — | 0.053* | -0.011 | 0.049** | -0.010 | |
ln edu | 0.219*** | 0.126*** | 0.298*** | 0.158*** | 0.224*** | 0.142*** | 0.321*** | 0.179*** | |
ln hr | 0.273*** | 0.154*** | 0.323*** | 0.168*** | 0.264*** | 0.161*** | 0.320*** | 0.169*** | |
ln tech | 0.018 | -0.062** | -0.002 | -0.054** | 0.015 | -0.067** | -0.005 | -0.060** | |
ln patent | -0.054* | 0.041 | 0.040** | -0.010 | -0.038 | 0.042*** | 0.052*** | -0.009 | |
ln GDP | 0.136 | 0.493*** | -0.094 | 0.382*** | 0.168* | 0.491*** | -0.090 | 0.389*** | |
ln scale | 0.054 | -0.122*** | 0.027 | 0.014 | 0.025 | -0.137*** | -0.001 | -0.013 | |
ln FE | -0.042 | 0.147*** | 0.090*** | 0.046 | -0.048 | 0.142*** | 0.085*** | 0.039 | |
R2 | 0.814 | 0.725 | 0.891 | 0.663 | 0.814 | 0.728 | 0.895 | 0.669 | |
Wald检验 | 863.81*** | 636.70*** | 1374.90*** | 544.77*** | 863.68*** | 639.27*** | 1420.91*** | 543.10*** | |
N | 207 | 251 | 177 | 281 | 207 | 249 | 177 | 279 |
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