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Studies of inequality in China are hampered by incomplete evidence on price dispersion across space, making it hard to distinguish real and nominal inequality. The Balassa-Samuelson effect leads one to expect higher prices for non-traded goods and services in areas with higher nominal incomes. But China’s CPI does not allow comparisons of the cost of living between locations, so without reliable measures of spatial price differences, it is unclear how much of the reported inequality (and its increase) in China is simply due to regional price variation.
A few prior studies have calculated spatial deflators for China but these rely mainly on prices for tradeable goods (Brandt and Holz 2006), or use short-cut methods based on food budget shares (Gong and Meng 2008, Almås and Johnsen 2012). But the recent evidence shows that traded goods prices converge rapidly in China (Lan and Sylwester 2010), so the main source of price dispersion across space should come from non-traded items. The most important non-traded item in consumption is housing. Deng, Gyourko and Wu (2012) show that variation over space in new dwelling prices in China is driven by the land market rather than by construction costs.How do we measure it?
In a recent paper (Gibson and Li 2013) we estimate a lower bound on the extent to which inequality in China may be overstated by ignoring spatial differences in the cost of living. Specifically, we use data on apartment prices in urban China to develop spatially-disaggregated indices of dwelling prices, which are then used as spatial deflators for GDP per capita of provinces and cities. We assume that cost of living variation over space reflects variation in housing costs only, so the true impact of deflation on inequality is at least as large as we show, since some other prices also will vary over space.
The focus is on urban housing because data on rural dwelling costs do not capture land prices, and are mainly construction costs that reflect prices for tradeable building materials. The absence of land prices is because the right to use rural residential land is available to all village collective members, who then are responsible for self-funding, self-building, and self-renovating their dwellings. In contrast, urban housing has been market-oriented since reforms in 1998. Moreover, census data reveal that a majority of urban residents purchase their dwellings, rather than either renting or living in a self-built dwelling.
The nature of real-estate development in urban China makes systematic between-city variation in new dwelling quality unlikely, which a hedonic analysis of new apartment prices confirms. Thus, published average prices of newly constructed urban dwellings are potentially informative about spatial cost of living differences. These data are available for provinces and for urban districts, which are the best approximation to a city proper. The provincial-level data show the highest prices are in a belt of provinces along the coast between Jiangsu and Hainan, and further north in Beijing and Tianjin. The average price of new apartments in Beijing in 2009 was CNY17,000 (US$2800) per square meter – twice as high as prices in urban Guangdong – and more than four times as high as average prices for new apartments in urban areas of the interior provinces.
There is a considerable heterogeneity within provinces, as shown in the map below for 286 cities (specifically, urban districts within prefecture-level cities; these are more consistently urban than the remaining area, which includes rural counties). Note that the map truncates Xinjiang, Tibet, and Qinghai – which contain just three urban districts – to focus on the main urbanized regions. It is apparent that some cities in interior provinces, such as Chengdu, Harbin, Ji’nan, Taiyuan, and Wuhan have much higher dwelling prices than revealed by the provincial average. Pu’er in Yunnan even falls into the highest price category shared by cities such as Guangzhou, Hangzhou, Shenzhen, Shanghai, and Beijing. Conversely, there are cities in coastal provinces with much lower prices than some cities in the interior. Consequently, housing-related variation in the cost of living will be more accurately portrayed at sub-provincial levels.
Figure 1. House prices
We combine these prices per square meter of new dwellings for each city or province with data from the national and regional accounts on “annual investment in urban residential assets”. Rapidly rising affluence in urban China means that apartments built even just 15 years ago are being demolished and replaced with larger ones that have better facilities. Consequently, the urban housing stock in China depreciates rapidly, with new construction of approximately eight million standard private apartments each year equivalent to about one-sixth of the existing stock of purchased urban dwellings. Micro data on the average share of housing costs in household budgets are not available down to the city level. Instead, to proxy for the importance of housing in local income, we use the ratio of annual residential investment to final consumption expenditure for each city or each province, when estimating a Törnqvist housing price index.
The Törnqvist index shows that, on average, nominal incomes outside Beijing should be inflated by 33% to put them on a comparable cost-of-living basis, even allowing just housing costs to differ between Beijing and other cities or provinces. The spatial price adjustment factor ranges from 1.03 for Shanghai—where housing prices are almost as high as in Beijing—to 1.42 for Chongqing, and 1.43 for Liaoning. Since nominal incomes are much lower in Chongqing and Liaoning, at least some reported inequality for China is just due to price variation over space.
If no account is taken of this spatial variation in the cost of living, the level of inequality is overstated by up to 35% (using the Theil index and considering inter-provincial differences). Since provincial averages hide a lot of heterogeneity, there is more inequality at city level, and the percentage overstatement from not spatially deflating is slightly less – at 30% (see Figure 2).
Figure 2. Inter-area inequality in real and nominal GDP per capita, 2010
The overstated inequality if using nominal income data is not as great if inequality is measured with the Gini index (in Figure 2, both the Theil and the Gini have been scaled to equal 1 for real inequality at provincial level). Nevertheless, not deflating for spatial cost of living differences still causes an upward bias in the Gini coefficient of 15-16%. Taking an average of the results for the two inequality measures, in round figures approximately one-quarter of apparent spatial inequality in China disappears once account is taken of cost of living differences, where these are just coming from house prices.Conclusion
This bias in inequality measures that are calculated from nominal data is likely to be rising over time. Under central planning and the hukou registration system, urban housing markets were absent at the beginning of the reform era, and spatial differentiation was more limited than now. Consequently, the spatial cost of living differentials, now being caused by the urban housing market, (reflecting the fixity of land) has grown from a low base. Thus, some of the apparent rise in inequality in China that is found by many studies, is probably just a growth in spatial price differences, rather than rising inequality in real incomes.References
Almås, Inqvild, and Ashild Johnsen (2012), "The Cost-of-living and its Implications for Inequality and Poverty Measures for China", Norwegian School of Economics, Bergen, Norway (unpublished).
Brandt, Loren, and Carsten Holz (2006), "Spatial Price Differences in China: Estimates and Implications", Economic Development and Cultural Change 55(1):43–86.
Deng Yongheng, Gyourko Joseph, and Wu Jing. 2012. Land and House Price Measurement in China. NBER Working Paper No. 18403. Cambridge, Mass.: National Bureau of Economic Research.
Gong, Honge, and Xin Meng (2008), "Regional Price Differences in Urban China 1986–2001: Estimation and Implication", IZA Discussion Paper No. 3621. Bonn: Institute for the Study of Labor.
Lan, Yuexing, and Kevin Sylwester (2010), "Does the Law of One Price Hold in China? Testing Price Convergence Using Disaggregated Data", China Economic Review 21(2):224–36.
Li, Chao, and John Gibson (2013), “Spatial price differences and inequality in the People’s Republic of China: housing market evidence”, Working Paper 06/13, Department of Economics, University of Waikato.