School of Management, China University of Mining and Technology-Beijing, Beijing 100086, China.
School of Economics, Beijing Technology and Business University, Beijing 100048, China.
Comput Intell Neurosci. 2022 Jun 30;2022:9491748. doi: 10.1155/2022/9491748. eCollection 2022.
In this paper, we use the panel data of 281 cities in China from 2005 to 2020 for capturing the factors driving urban inclusive growth (IG). In doing this, we employ the BP neural network algorithm combined with the DEA model to measure the urban inclusive growth efficiency (IGE). Furthermore, a nest of machine learning (ML) algorithms are introduced to explore the drivers of urban IGE, which overcomes the defects of endogeneity and multicollinearity of traditional econometric methods. We find for the overall sample that entrepreneurship and innovation contribute the most to IGE, accounting for about 35%, respectively, and they are the most critical drivers, while the heterogeneity test results reveal that the contribution of influencing factors has changed for different regions such as the eastern region, the central region, and the western region. Based on the experimental results of the ML model, we provide some policy suggestions for China and similar developing countries and emerging economies to promote IG.
在本文中，我们使用了 2005 年至 2020 年中国 281 个城市的面板数据，以捕捉驱动城市包容性增长（IG）的因素。为此，我们采用了结合 DEA 模型的 BP 神经网络算法来衡量城市包容性增长效率（IGE）。此外，我们引入了一组机器学习（ML）算法来探索城市 IGE 的驱动因素，这克服了传统计量经济学方法的内生性和多重共线性的缺陷。我们发现，对于整体样本，创业和创新对 IGE 的贡献最大，分别约为 35%，它们是最关键的驱动因素，而异质性测试结果表明，影响因素的贡献对于东部地区、中部地区和西部地区等不同地区已经发生了变化。基于 ML 模型的实验结果，我们为中国和类似的发展中国家和新兴经济体提供了一些促进 IG 的政策建议。
