Evaluating the Performance of Inclusive Growth Based on the BP Neural Network and Machine Learning Approach

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.

Automated summary

This paper uses panel data from 281 cities in China from 2005 to 2020 to capture the factors driving urban inclusive growth (IG). The BP neural network algorithm combined with the DEA model is employed to measure the urban inclusive growth efficiency (IGE). Additionally, a series of machine learning algorithms are introduced to explore the drivers of urban IGE. The study finds that entrepreneurship and innovation contribute the most to IGE, and the contribution of influencing factors varies among different regions.