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How do Chinese urban investment bonds affect its economic resilience? Evidence from double machine learning

Yan Fang, Yinglin Liu, Yi Yang, Brian Lucey, Mohammad Abedin Orcid Logo

Research in International Business and Finance, Volume: 74, Start page: 102728

Swansea University Author: Mohammad Abedin Orcid Logo

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DOI (Published version): 10.1016/j.ribaf.2024.102728

Abstract

This paper employs the double machine learning model to investigate the impact of urban investment bonds on economic resilience. To deal with a broad set of macroeconomic and industry variables, LASSO is used for model estimation. The sample consists of 239 Chinese cities that issued debt and loan i...

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Published in: Research in International Business and Finance
ISSN: 0275-5319
Published: Elsevier BV 2025
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URI: https://cronfa.swan.ac.uk/Record/cronfa68661
Abstract: This paper employs the double machine learning model to investigate the impact of urban investment bonds on economic resilience. To deal with a broad set of macroeconomic and industry variables, LASSO is used for model estimation. The sample consists of 239 Chinese cities that issued debt and loan instruments between 2016 and 2021. The results show that 1) urban investment bonds have a positive, inverted U-shaped effect on economic resilience; 2) the ability to recover from an economic shock plays an important role in constructing the Chinese economic resilience index. The heterogeneity analysis reveals that the impact of urban investment bonds on economic resilience varies according to cities’ locations, industrial structure, and financial structure. Furthermore, the mechanism analysis demonstrates that urban investment bonds enhance economic resilience by promoting infrastructure development. These findings provide helpful guidance for China and other developing countries to ensure financing security and maintain robust economic growth.
Keywords: Economic Resilience, Urban Investment Debts, Double Machine Learning, LASSO Technique, Heterogeneity Analysis
College: Faculty of Humanities and Social Sciences
Funders: This paper was supported by the Major Program of the National Social Science Foundation of China (Grant No. 21BTJ047)
Start Page: 102728

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