Determinants and Pathways for Inclusive Growth in China: Investigation Based on Artificial Intelligence (AI) Algorithm

Shuangshuang Fan (), Yichao Li, William Mbanyele () and Xiufeng Lai ()
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Shuangshuang Fan: Wenzhou University of Technology
Yichao Li: Hainan University
William Mbanyele: Shandong University
Xiufeng Lai: China University of Mining and Technology-Beijing

Computational Economics, 2025, vol. 65, issue 3, No 4, 1264 pages

Abstract: Abstract Can artificial intelligence (AI) algorithms help policymakers in their decisions on inclusive growth? In this study, we introduce artificial intelligence algorithms to calibrate China's inclusive growth determinants. We uncover various factors that significantly influence inclusive growth using machine learning forecasts. Furthermore, our results using best practice methods outperform findings from traditional regression-based strategies on other dimensions, which miss non-linear interactions in their estimations. However, we observe that when the actual value of the inclusive growth index is too large, the accuracy of the machine learning model is diminished. Meanwhile, the results of heterogeneity analysis reveal that the determinants of inclusive growth in cities with different region and different marketization level are distinct. In addition, we adopt the scenario simulation and prediction approach to reveal the best policy measure to promote inclusive growth in China. Our findings indicate that machine learning holds promise for understanding how inclusive growth can be achieved and can assist real-world economies in enhancing inclusive growth.

Keywords: Inclusive growth; Machine learning; Artificial intelligence algorithms; Influencing factors; China (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10614-024-10591-8

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