Local Government Debt, the Spatial Spillover Effect, and Regional Economic Growth
WANG Bo, ZHAO Senyang, LUO Ronghua, PENG Long
School of Finance, Southwestern University of Finance and Economics; Shanghai Institute of International Finance and Economics/School of Finance, Shanghai University of Finance and Economics
Summary:
China's “Report on the Work of the Government” issued in 2020 emphasized the crucial role of local government debt in stabilizing investment and promoting economic growth. Under the premise of controllable risks, local government debt acts as the primary source of funds for the government to provide public goods and a tool for counter-cyclical adjustment, thus promoting economic and social development. China's 14th Five-Year Plan also elevates regional coordinated development to the level of a significant national strategy, proposing the building of urban agglomerations and metropolitan areas with “infrastructure interconnection” and “gridded transportation.” The regional distribution of the debt limit focuses on the three major urban agglomerations, and provincial-level local governments are also more inclined to allocate the debt limit toward urban agglomerations. The effect of local government debt on regional economic growth is therefore worthy of further discussion. We explore the effect and mechanism of local government debt on regional economic growth in the context of coordinated regional development and the rapid growth of urban agglomerations. We obtain balanced panel data for 271 prefecture-level cities in China from 2008 to 2018 and apply the Dynamic Spatial Durbin Model. The main conclusions are as follows. First, local government debt can promote economic growth in neighboring regions, and its spatial spillover effect does not change with the model settings, proxy variables, or estimation methods. In terms of the spatial spillover effects, geographically adjacent regions can enjoy the positive externalities brought by public goods such as transportation infrastructure, thus making a geographical connection. Second, the spatial spillover effect of local government debt on the economic growth of neighboring regions has a time dimension: the service life of transportation infrastructure is long, and thus, there is an accumulation over time, i.e., the effect over the long-term is more significant than that in the short-term. Third, interprovincial market segmentation and administrative barriers cause the spatial spillover effect of local government debt to be stronger within than outside the province. We identify the geographical limit of the spatial spillover effect to be around 400 km, and it is mainly evident in the developed eastern regions. Finally, local governments mainly incur debts so that they can invest in the construction of transportation infrastructure. Similar transport development in neighboring regions will enhance transport grids and interconnectivity, thus significantly contributing to the economic growth of the neighboring regions. We therefore identify improvements in transportation infrastructure as the mechanism behind the positive effect of local government debt on the economies of neighboring regions. Our conclusions offer the following policy implications. First, local government debt issuers should consider both direct and spatial spillover effects. Local government debt has a positive spatial diffusion effect through the development of the transportation network, as this encourages the economic growth of neighboring regions. The effect of local government debt can thus spill over to neighboring regions, and to maximize this effect, urban agglomerations and metropolitan areas should ensure that debt use is more efficient and strive to break through any barriers to enhance regional interconnectivity. This is conducive to optimizing the spatial structure of the population and economy and can ensure that investment is effective and satisfies consumer demand, thereby promoting regional economic growth. Second, In the key regions mentioned in the national major strategic deployment and the “14th Five-Year Plan”, moderately advanced infrastructure investment in regions with high investment efficiency and a high debt ceiling will help to play the role of debt funds in expanding effective investment and stabilizing growth. At the same time, moderately advanced infrastructure investment contributes to accumulating the spatial spillover effect of local government debt. Third, the crowding-out effect of local government debt should be prevented, and the level of debt should be monitored. As the scale of local government debt increases, its role in promoting local economic growth gradually weakens, and a crowing-out effect can occur. The government's premise is that the risks are controllable, and thus, China can continue to promote regional economic growth through a moderate amount of debt. For urban agglomerations experiencing robust economic development, an appropriate increase in local government debt can encourage further economic growth. In addition, the input-output efficiency of local government debt funds should be fully considered, the formulation of local debt-related policies should be tailored to local conditions and strike a balance between stabilizing growth (considering both local and regional economic growth) and preventing risks.
王博, 赵森杨, 罗荣华, 彭龙. 地方政府债务、空间溢出效应与区域经济增长[J]. 金融研究, 2022, 506(8): 18-37.
WANG Bo, ZHAO Senyang, LUO Ronghua, PENG Long. Local Government Debt, the Spatial Spillover Effect, and Regional Economic Growth. Journal of Financial Research, 2022, 506(8): 18-37.
Bavaud, F., 1998, “Models for Spatial Weights: A Systematic Look”, Geographical Analysis, 30(2): 153-171.
[21]
Checherita, C., and P. Rother., 2012, “The Impact of High Government Debt on Economic Growth and Its Channels: An Empirical Investigation for the Euro Area”, European Economic Review, 56(7): 1392~1405.
[22]
Chen, Z., Z.G. He, and C. Liu., 2020, “The Financing of Local Government in China: Stimulus Loan Wanes and Shadow Banking Waxes”, Journal of Financial Economics, 137(1): 42~71.
[23]
Corinne, A., and J.P. Lesage., 2011, “Quantifying Knowledge Spillovers Using Spatial Econometric Models”, Journal of Regional Science, 51(3): 471~496.
[24]
Donaldson, D., 2018, “Railroads of the Raj: Estimating the Impact of Transportation Infrastructure”, The American Economic Review, (108): 899~934.
[25]
Elhorst, J. P., 2012, “Dynamic Spatial Panels: Models, Methods, and Inferences”, Journal of Geographical Systems, 14(1), 5-28.
[26]
Elhorst, J. P., E. Zandberg, and J. De Haan., 2013, “The Impact of Interaction Effects among Neighboring Countries on Financial Liberalization and Reform: A Dynamic Spatial Panel Data Approach”, Spatial Economic Analysis, 8(3):293-313.
[27]
Elhorst, J. P., 2014, “Spatial Econometrics: From Cross-Sectional Data to Spatial Panels”, Published by Springer.
[28]
Han, F., R. Xie, and M. Lai., 2018, “Traffic Density, Congestion Externalities and Urbanization in China”, Spatial Economic Analysis, 13(4): 400~421.
[29]
Kim, Y. R., A.M. Williams, S. Park, and J.L. Chen., 2020, “Spatial Spillovers of Agglomeration Economies and Productivity in the Tourism Industry: The Case of the UK”, Tourism Management, (82): 104~201.
[30]
LeSage, J. P., and R.K. Pace., 2009, “Introduction to Spatial Econometrics”, Published by Chapman & Hall/CRC.
[31]
Pereira, A., and J.M. Andraz, 2004, “Public Highway Spending and State Spillovers in the USA”, Applied Economics Letters, 11(12): 785~788
[32]
Vaga, S.H., and J.P. Elhorst., 2017, “Regional Labour Force Participation across the European Union:A Time-space Recursive Modelling Approach with Endogenous Regressors”, Spatial Economic Analysis, (12): 138~160.
[33]
Yu, J.H., L.A. Zhou, G. Zhu., 2016, “Strategic Interaction in Political Competition: Evidence from Spatial Effects across Chinese Cities”, Regional Science and Urban Economics, (57): 23~37.