Restructuring for Credit: The Political Economy of Debt Crisis in China
This thesis investigates the persistent expansion of local government debt in China, focusing on the adaptive strategies of Local Government Financing Vehicles (LGFVs) following the central government's 2014 regulatory tightening. We argue that despite regulations designed to impose fiscal discipline, LGFVs sustained and even accelerated debt accumulation by strategically restructuring their asset portfolios. This strategy involved local governments injecting state-owned enterprises into LGFVs. This enhanced their balance sheets and signaled creditworthiness to the bond market and regulator, effectively circumventing the intended regulations. A case study shows the micro-mechanisms of this strategy, explaining how asset injections improved financial metrics and secured bond market access. These qualitative findings are then tested using a triple-difference estimator on a panel dataset of 2,783 LGFVs from 2010 to 2019. The results show that following the 2014 regulations, restructured LGFVs in cities highly dependent on land finance increased their bond issuance by approximately 44.6% relative to LGFVs in less land finance dependent cities without asset injection. The findings imply that central regulatory efforts did not lead to deleveraging but instead triggered a new cycle of local adaptive responses. This dynamic is rooted in the enduring principal-agent dilemma between central and local governments, a consequence of the structural fiscal gaps created by the 1994 Tax-Sharing Reform. The thesis thus contributes to the literature on China's political economy, party-state capitalism, and the government debt bias by documenting a specific mechanism of regulatory adaptation and its significant impact on subnational debt dynamics.
Publication Details
- Journal
- Stanford Digital Repository
- Published
- 2025-06-06
- DOI
- https://doi.org/10.25740/js084fx2027
- Primary Topic
- China's Socioeconomic Reforms and Governance
- Type
- article
- Field-Weighted Citation Impact
- 0.00