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Macroeconomic shocks in China: Do the distributional effects depend on the regional source?

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Abstract

There is a growing consensus that, as part of China’s “New Normal”, economic growth will slow permanently. Given China’s geographic diversity, the slowdown will likely have differential effects on China’s provinces which will interact with existing disparities. We investigate whether confining the initial effect of an aggregate shock to one of three regions (coast, centre and west) affects the inter-provincial distribution of its effects over time. We use two alternative models: a restricted VAR model of 28 provinces and three regions and a sequence of 28 four-equation VAR models. We find that the two methods give remarkably similar results—a shock to a particular region’s output has its main effects on the provinces in that region, although this differs over time and across regions. A shock which originates in the coastal region affects mainly the coastal provinces in both the short and long runs so that a growth reduction is likely to ameliorate existing inter-provincial disparities. However, there is more diffusion of the effects of a central shock, particularly in the long run. A shock to the western region also generates spillover effects in the long run although these are to the coastal provinces so that a growth slowdown will tend to reduce existing disparities.

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Notes

  1. This comparison excludes the “city-provinces” of Shanghai, Beijing and Tianjin for which the comparable ratios are even higher at 4.2, 4.3 and 4.2, respectively, in 2016. See Groenewold et al. (2008), Chapter 3, Xu et al. (2013) and Chen (2013) for more information on Chinese regional policy since the founding of the People’s Republic of China. Knight (2016) provides an interesting general discussion of the possible conflict between economic growth and other objectives, including equality. Nam (2017) reports a recent comparison of China’s regional (population) disparities to those of 100 other countries and concludes that China’s regional inequality is significantly greater than average.

  2. See Zhang (2014) for evidence that globalisation has benefitted the coast more than the rest of the country so that we might expect the opposite when growth slows.

  3. Originally, the Kuznets curve related the degree of household income inequality and growth, but the term has also been used to describe the relationship between regional inequality and growth (see, for example, Li and Haynes (2011), for China and Monastiriotis (2014), for the EU) and, more recently, an environmental Kuznets curve has been used to characterise the pollution–growth nexus (see, e.g., He and Yao (2017), for a recent application to China).

  4. Given data availability, the provincial level is a common level of spatial disaggregation in work on China—see, for example, Herrerías (2012) and Wang and Rickman (2018)—although some work also uses more disaggregated county-level data; see Cheong and Wu (2013) and Li and Fang (2014).

  5. We note, though, that our sample period ends 2 years later than the one in Chen and Groenewold (2018). Our experimentation with a shorter period ending in 2012 shows that the results are insensitive to the inclusion of these extra 2 years.

  6. Strictly speaking Beijing, Tianjin and Shanghai are municipalities which are under direct central government control rather than under provincial government control and in this sense have a greater similarity to provinces than to cities or municipalities. For this reason, they are commonly treated as provinces in empirical work. Hainan was separated from Guangdong province in 1988, and Chongqing was separated from Sichuan province in 1997. As a result, Wu (2004) combined Chongqing with Sichuan and Hainan with Guangdong, and we follow his scheme. Tibet is excluded due to missing data in some of years in our sample.

  7. We realise that our assumption is not likely to be completely accurate, but that we would argue that the regions are large enough that it is likely that the one-year effects of region-specific shocks will be confined substantially to the region.

  8. Neither model generates national IRFs and bounds. We have computed these as weighted averages of the three regional IRFs and bounds with weights equal to shares of national output averaged over 1982–2014. For the SoM approach, the regional IRFs and bounds are the averages across all 28 model iterations.

  9. In the IRFs in Appendix, the horizons are numbered such that the shock occurs at t = 1; then, t = 2 is one period after the shock and t = 10 is nine periods after the shock. Nothing much changes to the overall characterisation of the results if we make the long run shorter than 9 periods, say, 5 periods after the shock.

  10. In particular, we say that the provincial and national IRFs are coincident at a particular horizon if the provincial IRF lies within 5% of the distance between the national confidence bounds on either side of the national IRF at that horizon.

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Acknowledgements

An earlier version of this paper was presented at the Workshop on Regional, Urban and Spatial Economics at South-West University of Finance and Economics in Chengdu and at the Meeting of the European Regional Science Association in Vienna. We are grateful for useful comments received there. We have also benefitted from helpful comments by the editor and two anonymous referees. The research reported in this paper was partially funded by a National Natural Science Foundation of China Grant (No. 71773036).

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Authors and Affiliations

  1. School of Economics, Jinan University, Guangzhou, China

    Anping Chen

  2. Economics Programme, UWA Business School, M251, University of Western Australia, Perth, Australia

    Nicolaas Groenewold

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  1. Anping Chen
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  2. Nicolaas Groenewold
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Correspondence to Anping Chen.

Appendix

Appendix

See Figs. 4, 5, 6, 7, 8 and 9.

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Chen, A., Groenewold, N. Macroeconomic shocks in China: Do the distributional effects depend on the regional source?. Ann Reg Sci 62, 69–97 (2019). https://doi.org/10.1007/s00168-018-0885-2

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