知识密集型服务业集聚就业效应测度模型及其应用

发布时间：2018-05-26 15:04

本文选题：知识密集型服务业集聚 + 就业效应　；参考：《湖南大学》2013年硕士论文

【摘要】：随着金融业以及科学研究与技术服务业等知识密集型服务业集聚程度的日益加深，知识密集型服务业集聚效应已成为产业集聚效应研究的一个重要方面。目前，我国的知识密集型服务业还处于发展的初级阶段，需要大量高素质的知识工作者。尽管中国劳动力资源充足，但知识工作者的供给却存在大量缺口。与此同时，，知识密集型服务业对就业的拉动作用并不明显。因此，深入分析知识密集型服务业集聚及其他相关因素对就业水平的具体作用机制，制定合理科学的就业提升策略，具有深远的现实指导意义。本文对知识密集型服务业集聚的就业效应作用机制进行了详细的阐述，并对测度知识密集型服务业集聚就业效应的理论模型进行了推导。基于理论分析，纳入综合了地理因素和经济因素的空间权值矩阵的影响，利用空间面板数据模型实证分析了知识密集型服务业集聚、经济基础、工资水平和城市化水平对我国就业水平的具体作用机制。实证研究结果表明：（1）我国整体就业水平、知识密集型服务业集聚水平、工资水平和城市化水平较低，整体经济基础较薄弱，且省域间差异较大；（2）各省域就业水平、知识密集型服务业集聚、经济基础、工资水平和城市化水平的空间相关性比较显著；（3）知识密集型服务业集聚在空间维度能显著促进全行业就业水平的提高，在时间维度对就业水平的推动作用较小；（4）经济基础能显著促进知识密集型服务行业就业水平的提高，但对其他行业就业水平的促进作用较小；（5）工资水平对就业水平有轻微的抑制作用，但其就业弹性系数的绝对值较小；（6）城市化水平能显著促进知识密集型服务业就业水平的提高，但对全行业就业水平的促进力度不足。因此，政府应加大力度调整产业布局，积极引导知识密集型服务业有效集聚，同时协调统筹经济增长与提升就业水平，并大力提高城市化水平；企业应制定合理的薪资管理制度，同时完善企业员工培训制度。据此，有效提高各省域以及全国范围内的就业水平。
[Abstract]:With the increasing agglomeration of knowledge intensive services such as financial industry, scientific research and technology service industry, the agglomeration effect of knowledge-intensive service industry has become an important aspect of industrial agglomeration research. At present, our country's knowledge-intensive service industry is still in the primary stage of development, and needs a large number of high-quality knowledge workers. Despite China's abundant labor resources, there is a large gap in the supply of knowledge workers. At the same time, the knowledge-intensive service industry does not play a significant role in stimulating employment. Therefore, it is of profound practical significance to analyze the specific mechanism of the agglomeration of knowledge-intensive service industry and other relevant factors on the employment level and to formulate a reasonable and scientific employment promotion strategy. In this paper, the mechanism of employment effect of knowledge-intensive service agglomeration is expounded in detail, and the theoretical model of measuring the employment effect of knowledge-intensive service agglomeration is deduced. Based on theoretical analysis, this paper takes into account the influence of spatial weight matrix of geographical and economic factors, and empirically analyzes the agglomeration and economic base of knowledge-intensive service industry by using spatial panel data model. The specific mechanism of wage level and urbanization level to employment level of our country. The empirical results show that the overall employment level of China, the concentration level of knowledge-intensive service industry, the low level of wages and urbanization, the weak overall economic base, and the great difference between provinces are the employment level of each province. The spatial correlation between knowledge intensive service industry agglomeration, economic base, wage level and urbanization level is significant (3) the agglomeration of knowledge intensive service industry in spatial dimension can significantly promote the improvement of the employment level of the whole industry. The economic basis can significantly promote the employment level of knowledge-intensive service industry. However, the promotion of employment level in other industries is less significant. (5) the wage level has a slight inhibitory effect on the employment level, but the absolute value of the employment elasticity coefficient is smaller. (6) the urbanization level can significantly promote the increase of employment level in the knowledge-intensive service industry. But the promotion of the whole industry employment level is insufficient. Therefore, the government should make greater efforts to adjust the industrial layout, actively guide the effective agglomeration of knowledge-intensive services, coordinate economic growth and enhance employment level, and vigorously improve the level of urbanization; Enterprises should establish reasonable salary management system and perfect employee training system. Accordingly, raise the employment level of each province and countrywide effectively.
【学位授予单位】：湖南大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：F224;F719;F249.2

【参考文献】