Research on Floating Population and Enterprise Innovation Based on LightGBM
DOI:
https://doi.org/10.54691/bcpbm.v31i.2672Keywords:
machine learning; lightgbm; floating population; enterprise innovation.Abstract
The research on the relationship between the floating population and social-economic development has always been a key research issue in population economics. This study first introduced the LightGBM to examine multi-dimensional floating population characteristics can predict enterprise innovation. Taking the dynamic survey data of China's floating population from 2011 to 2018 and listed enterprises as samples, the study verifies whether the characteristics of a floating population can predict enterprise innovation. Further, it analyzes the characteristics of the floating population and its prediction model, which has an excellent ability to predict enterprise innovation. The researchers have found that: 1) On the whole, the prediction ability to float population characteristics to enterprise innovation is limited. 2) Among many characteristics, the prediction ability of population mobility dimensional characteristics is higher. 3) There is a nonlinear relationship between characteristics and enterprise innovation, and there is a population crowding effect, which is consistent with the previous theory. 4) It is tested that the population crowding effect is through mechanisms such as human capital. This study uses the machine learning method to explore the characteristics of floating population more comprehensively and inspires the formulation and implementation of the government's industrial and talent policies and the development decisions of enterprises.
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