Abstract
The rapid pace of change in China’s housing market has called particular attention. This paper studies how the housing boom in China was transmitted via spillovers from one local market to another between 2003 and 2012, an episode during which housing prices increased rapidly. We identify the beginning of housing booms of a focal market as the quarters in which each city experienced a positive and statistically significant structural break in its house price growth series, and find that housing booms start first in the Beijing–Tianjin–Hebei region and the Yangtze River Delta region. Further, regression results show that there exist spatial spillover effects both on the extensive margin and intensive margin from the neighboring markets in China, and that the focal city housing market significantly responds to the housing booms in the two economic regions. Controlling market fundamentals does not vary the spillover patterns identified. We conclude that there is a distinctive dynamic spillover process in China that the metropolitan economic regions experience booms first, and then, the booms are transmitted to the geographically adjacent cities.
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Notes
The growth poles in China are based on the political argument that resources are concentrated in big cities leading to high economic growth rate, which are generally referred to as “urban-biased” policy (Yang 1999).
The main specification looks like a difference-in-difference specification with staggered treatment timing. Several recent papers highlight that this specification is potentially problematic (see, for example, Goodman-Bacon (2018), Callaway and Sant’Anna (2019)). But our research design is not to set up a specification to evaluate the causal effects of an event as a legit difference-in-difference specification does, but rather to find out whether the neighboring housing markets entering booms corresponds to a higher chance for the focal housing market to enter a boom or a higher price level of the focal housing market, and we further explore the dynamics of these responses as in DeFusco et al. (2018). From this perspective, our specification is different from the difference-in-difference specification with staggered treatment timing.
Note that \(\theta _{N,-1}\) is negative and statistically significant, which implies that the probability that focal city i enters a housing boom at quarter t significantly decreases if quarter t is one year before its neighboring cities enter housing booms. Hence, “no pre-trend” indicates that we do not detect a positive spillover effect from the neighboring cities one or two years before the neighboring cities enter housing booms.
The negative value of \(\theta _{N,4}\) in Table 3 indicates that the focal city’s housing price level significantly decreases 3 years after the neighboring markets’ booms. In other words, the neighboring markets’ spillover effect lasts about two years.
Tables 2 and 3 show the estimation results of \(\theta _{N,\rho }\), where N is the city set that incorporates all the 5 closet cities to focal city i. Note that Fig. 4 presents similar but different estimation results from those in Tables 2 and 3, showing how \(\theta _{n,\rho }\) evolves over time, where \(n = 1, 2, 3, 4, 5\) represents each of the 5 closest cities to the focal city i. Here, Neighbor 1 in Fig. 4 corresponds to the closest city to the focal city i, Neighbor 2 in Fig. 4 corresponds to the second closest city to the focal city i, and so on.
The negative and significant estimate observed in Tables 5 is similar to the explanation of the negative and significant estimate in Table 3. They indicate that the focal city’s housing price level significantly decreases 3 years after the Beijing–Tianjin–Hebei region’s booms. The same applies for the negative and significant estimate for the Yangtze River Delta region on the intensive margin. In other words, the economic regions’ spillovers last about two years.
Detailed estimation results are shown in “Appendix.”
In China, land finance is a common operation for local city governments to generate fiscal incomes, leading to disturbance in the local housing markets (Pan et al. 2015). Meanwhile, local leaders’ promotion mechanisms in China may lead to tournament competition among local governments (Yu et al. 2016), which acts as the yardstick competition mechanism for spillovers (Caldeira 2012). That is to say, when the focal city government observes the neighboring cities’ fiscal expansions, they tend to expand their own fiscal incomes/expenditure. Hence, the fiscal variables could be used as mechanism variables for the housing booms to spread across neighboring cities. Specifically, we use fiscal expenditure (net fiscal expenditure growth rate), income (net fiscal income growth rate) and infrastructure investment (net total public library collection growth rate). Please note that we use the infrastructure investment variable because the public infrastructure is largely funded by the local city governments and is therefore related to fiscal incomes/expenditure and the land finance situations.
Note that when testing the high-speed railway construction channel, we use the sample after 2008. This is because China started building high-speed rail on a large scale after 2008.
It may be better to use lagged values of the mechanism variables to capture the causality relationships. Because the results have shown the lagging effects of the fiscal expansions, we do not further use the lagged values of fiscal/infrastructure variables.
See http://english.www.gov.cn/premier/news/2017/03/16/ for details.
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Acknowledgements
We are very grateful to the editor-in-chief, Joakim Westerlund, and two anonymous referees for their constructive comments and suggestions that have greatly improved the paper. Xintong Yang acknowledges the financial support from the National Natural Science Foundation of China (Grant No. 72003136). All errors are our own.
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Yang, X., Zhang, Y. & Li, Q. The role of price spillovers: what is different in China. Empir Econ 60, 459–485 (2021). https://doi.org/10.1007/s00181-020-01989-y
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DOI: https://doi.org/10.1007/s00181-020-01989-y