The purpose of this paper is to analyze the impact of high-tech talents flow on labor income share and explore the influencing mechanism. It can be proved that high-tech talents flow affects labor income share by production function, with technological progress as a mediator variable. The labor income share is the dependent variable, and the gravity of high-tech talents as the independent variable is the index to measure the high-tech talents flow, constructing the panel data model with the Malmquist index of technological progress as a mediator variable. Furthermore, the Malmquist index of technological progress is decomposed into catching-up of technological progress index and leapfrogging of technological progress index, which, respectively, replaces the Malmquist index of technological progress as a mediator variable in the panel data model. Regression analysis shows that technological progress is a mediator variable for high-tech talents flow to reduce labor income share, and the impact mainly comes from leapfrogging of technological progress. However, although the mediating effect of catching-up technological progress index is not significant at the significance level of 10%, it is a mediator variable for high-tech labor mobility to increase income share at the significance level of 20%. Finally, this paper predicts the change in labor income share from 2018 to 2027 by the fractional Hausdorff grey model, and the results show that it is an increasing trend. However, the Gini coefficient whose change trend is opposite to the labor income share remains high in the past two years, indicating that there are other factors affecting the income gap, such as the urbanization rate and the transportation convenience. The innovation of this paper is mainly to reveal that the leapfrogging of technological progress is the major cause of the high-tech talents flow rising income inequality gap, while the catching-up of technological progress is the source of the former narrowing the latter. The fractional Hausdorff grey model predicts that the key determinants of income inequality gap are more than labor income share.

中文翻译：

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高技术人才流动对劳动收入份额的影响预测：基于DEA和分数Hausdorff灰色模型

本文的目的是分析高科技人才流动对劳动收入分享的影响，并探讨其影响机制。可以证明，高科技人才的流动会通过生产功能影响劳动收入份额，而技术进步是中介变量。劳动收入份额是因变量，高科技人才的引力作为自变量是衡量高科技人才流动的指标，构建了以技术进步的马尔姆奎斯特指数为中介变量的面板数据模型。此外，技术进步的马尔姆奎斯特指数被分解为技术进步指数的赶超和技术进步指数的跨越，这分别代替了技术进步的马尔姆奎斯特指数作为面板数据模型中的中介变量。回归分析表明，技术进步是高科技人才减少劳动力收入份额的中介变量，其影响主要来自技术进步的跨越。但是，尽管赶超技术进步指数的中介作用在显着性水平为10％时并不显着，但在显着水平为20％的情况下，增加高科技工人的流动性是中介变量。最后，本文通过分数Hausdorff灰色模型预测了2018年至2027年劳动收入份额的变化，结果表明这一趋势正在增加。但是，在过去两年中，其变化趋势与劳动收入份额相反的基尼系数仍然很高，这表明还有其他因素会影响收入差距，例如城市化率和交通便利性。本文的创新之处主要在于揭示技术进步的跨越是导致高技术人才流动收入差距扩大的主要原因，而技术进步的追赶是前者缩小后者的根源。分数Hausdorff灰色模型预测，收入不平等差距的关键决定因素大于劳动收入份额。