人才聚集对社会平均生产率的影响研究

在借鉴并改进经济适度人口理论的基础上，提出了衡量人才聚集程度的指标——“空间人才密度”，构建了人才聚集与社会平均生产率及区域经济之间的适度关系理论模型，并应用该模型对京津冀与长三角进行对比分析，提出了针对性政策建议。研究结果表明，“空间人才密度”指标能够有效衡量人才在空间上的聚集程度，空间人才密度与人均GDP间呈倒U型关系，且随时间持续动态演化。总体上，我国人才聚集态势良好，经济发达地区已达到或接近经济适度值。但从京津冀现状来看，三省市均处于人才相对稀缺状态，制约区域经济发展的主因是人才数量和人才使用效率偏低，而非区域内的人才配置。

Impact of talent agglomeration on social average productivity

As the most active economic factor, talent is increasingly concentrated in specific regions and industries, thus forming the macro-level phenomenon of talent agglomeration. Talent agglomeration is typically considered as a positive signal of focal region’s economic development, largely because of its economical effect of knowledge spillover and cooperation among talent. Whereas there also arises an emerging concern about the excessive talent agglomeration, which leads to the diseconomies of scale, and finally reduces talent utilization efficiency in a holistic perspective. Given that the economical effect and uneconomical effect are often mixed with each other, extant research mostly theoretically analyze the uneconomical effect of talent agglomeration. What’s more, extant research mostly focuses on a focal region’s talent agglomeration, ignoring the limitation of talent in a holistic system and the negative externalities of focal region’s talent agglomeration. Hence, it is imperative to substantively understand the relationship between talent agglomeration and social average productivity, then conduct talent allocation in a holistic perspective.Sauvy’s theory of economic optimum population contends that there is an inverted U-shaped relationship between population size and social average productivity, then conducts a mathematic derivation about the trend of total production and marginal production. According to Sauvy’s theory, the population size where the social average productivity achieves its maximum is termed as economic optimum population, which will increase with the technical advancement. Drawn on Sauvy’s theory of economic optimum population, this study proposed the index of “spatial talent density” to measure talent agglomeration, and theoretically modelled the moderate relationship among talent agglomeration, social average productivity and regional economy. Overcoming the Sauvy’s defect of “ignoring the regional area size and the population heterogeneity”, spatial talent density can effectively measure the extent of talent agglomeration in space. According to Sauvy’s theory, talent agglomeration firstly influences the social average productivity, then exerts indirect influence on regional economy. In the early stage of talent agglomeration, the economical effect surpasses the uneconomical effect. However, in the middle and late stage of talent agglomeration, the uneconomical effect gradually surpasses the economical effect, so there is an inverted U-shaped relationship between talent agglomeration and social average productivity. Based on the inverted U-shaped relationship between talent agglomeration and social average productivity, this study conducted mathematic derivation and elicited the trend of regional economic performance and contribution margin of talent. This study reached a conclusion that the precondition of maximizing interregional economic performance is that the contribution margin of talent among all regions are equal.Leveraging the panel dataset of 31 regions in the Chinese mainland from 2000 to 2014, this paper empirically examined the relationship between talent agglomeration and social average productivity, which was the foundation of the entire theoretical model. The results show that the relationship between spatial talent density and social average productivity is inverted U type which evolves over time. There is an economic optimum value for talent agglomeration where the social average productivity achieves its maximum. In general, the talent agglomeration in China has been developing well and talent agglomeration in developed areas has already reached or been approaching to the optimum population. The economic optimum value of talent agglomeration is continually increasing by years, which means that the regional carrying capacity of talent is increasing steadily.Based on the empirical results, this study carried out a comparative study between Beijing-Tianjin-Hebei Region and Yangtze River Delta. Taken Beijing, Tianjin and Hebei as three separate part, the main weakness of Beijing-Tianjin-Hebei Region is Hebei’s shortage of talent. The spatial talent density of Hebei is extremely low, which can’t satisfy the tremendous talent demand for Beijing and Tianjin to generate economical effect, and finally lead all three regions are relatively lack of talent. Compared to Yangtze River Delta, Beijing-Tianjin-Hebei Region’s spatial talent density and talent utilization efficiency are significantly lower, whereas its talent allocation efficiency is significantly higher. Thus, simply reallocating the talent among the three regions is unlikely to enhance the economic performance of Beijing-Tianjin-Hebei Region, which is contrary to our intuition that “the talent of Beijing and Tianjin is oversaturated and talent’s migration from Beijing and Tianjin to Hebei will significantly enhance the economic performance of Beijing-Tianjin-Hebei Region”. Thus, the main restraints of Beijing-Tianjin-Hebei Region are its talent population and talent utilization efficiency, rather than the interregional reallocation of talent.Finally, this study puts forward pertinent policy recommendations for Beijing-Tianjin-Hebei Region. First of all, Beijing-Tianjin-Hebei Region can relieve its shortage of talent via both internal talent nurturing and external talent agglomerating. The key of internal talent nurturing is exploit the educational potentiality of Hebei by introducing the advanced educational resources from Beijing and Tianjin, whereas the key of external talent agglomerating is to take advantage of the Beijing and Tianjin’s attractiveness for talent. On the other hand, Beijing-Tianjin-Hebei Region can also promote the talent utilization efficiency by creating facilitating environment and innovating talent sharing mechanism.