Inter-city competition and local government debts in China: a spatial econometric analysis

ABSTRACT

Debts incurred by Chinese cities have skyrocketed. Policymakers and scholars are concerned with potential default risks and political, economic, and social impacts of a possible debt default. It has also drawn attention to drivers of the rapidly increasing municipal debts. This article examines the extent to which competition among Chinese cities affected the debts they accumulated. Drawing from the literature of local government strategic interaction and fiscal competition, we hypothesised that spillover effects might exist among Chinese cities’ decisions and behaviours to issue how much bonds. With access to a panel dataset of 285 cities over 2008–2016, we applied the spatial panel regression analysis to capture and gauge the spillover effects on debt accumulation of Chinese cities. Findings confirm the spillover effects among Chinese cities and support the role that inter-city competition has played in the rapid accumulation of municipal debts.

KEYWORDS:

• Inter-city competition
• spillover effects
• local government debts
• local government financing vehicles
• spatial econometric analysis

Acknowledgements

The authors appreciate and recognise financial support of the Lincoln Institute of Land Policy.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1. According to the Opinions of the State Council on Strengthening the Management of Local Government Debt (Guo Fa [2014] No. 43), from October to December 2014, the Ministry of Finance required local finance departments to better screen and account government debts. In order to incorporate existing debts into the recognised government debts as much as possible, and use budget revenues to repay debts with lower costs, local governments encouraged LGFVs to accelerate the financing business within the last three months in 2014 (Mao & Xu, 2019). This resulted in a sharp increase in 2014.

2. The terminology of SAR, SEM, and SDM is from LeSage and Pace (2009).

3. Please refer to Elhorst (2010) for a detailed discussion of test procedures for spatial panel models.

4. LM test no spatial lag= 0.06, P-value 0.81; LM test no spatial error=0.57, P-value 0.45. Both null hypotheses that there is no spatial lag and no spatial error are rejected; the robust LM tests do not yield a clear indication as well. Robust LM test no spatial lag=5.04, P-value 0.03; Robust LM test no spatial error=5.35, P-value 0.02.

5. Please refer to Elhorst (2010, p. 10) for a detailed discussion and formulas.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.

Notes on contributors

Yanbing Han

Yanbing Han, Ph.D., is a Research Assistant Professor in the School of Government at Nanjing University. He received the PhD degree in Public Affairs from Florida International University. His research focuses on public budget and finance, infrastructure public-private partnership, and e-government.

Min Xiong

Min Xiong, Ph.D., is a Post-doctoral Fellow in the School of Public Affairs, Zhejiang University. Her research interests centre on public-private partnerships, collaborative governance, and network governance.

Shaoming Cheng

Shaoming Cheng is an Associate Professor in the Department of Public Policy and Administration at Florida International University. His research interests centre on entrepreneurship and small business development policy as well as regional economic health, performance, and development.

Hai (David) Guo

Hai (David) Guo is an Associate professor at Department of Public Policy and Administration, Steven J. Green School of International and Public Affairs, Florida International University. His research focuses on state and local public finance, budgeting, and financial management.