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Housing Costs and Regional Income Inequality in China and the U.S.

KEY TAKEAWAYS

  • Living standards within a country can vary greatly due to differences in regional housing costs.
  • Adjusting income by regional housing prices provides a better picture of income inequality.
  • When accounting for housing prices, the degree of inequality among China’s provinces improves. For U.S. states, this measure of inequality worsens.

By

Brian Reinbold , Yi Wen

Measuring income inequality has long been a key focus in welfare economics. Economists have documented that income inequality has increased in virtually all advanced economies, but it has remained mixed for developing and emerging economies.See Dabla-Norris et al.

However, solely looking at income tells only part of the story about the difference in people’s living standards because income does not reveal information about the cost of living. For example, housing prices can vary tremendously across a nation; they also vary based on urban, suburban and rural geography.

Since housing typically takes a large share of an individual’s income and living space is one of the most important and scarce commodities, housing prices can greatly affect living standards. In other words, the purchasing power of a dollar is not the same across regions due to variations in the cost of living, especially housing. Therefore, factoring in cost of living can yield fruitful insights about true inequality.

In this article, we look at regional per capita disposable income data for China, a developing nation, and the U.S., an advanced economy, to see how income inequality compares between two large countries with a substantial income gap.

Disposable income is income less taxes, which is the income an individual has available for consumption. Looking at regional data will also allow us to see how income varies across a geographic region. Note that we are using regional per capita disposable income within a country, which allows us to characterize the degree of inequality for a region’s average household in relation to different regions. Thus, we are unable to say anything about inequality across individuals.

We will then adjust regional average income by housing prices in a region to see how that affects inequality in living standards.

We calculate the Gini coefficient for each country using each region’s per capita disposable income to measure income inequality. The Gini coefficient takes values between 0 and 1. A value of 1 indicates maximum inequality, while a value of 0 means perfect equality. Since we are using average income in a region, the Gini coefficient will be less than if we were using each individual’s own income in each country.For example, an individual whose income is in the 90th percentile will have an income many times greater than that of someone in the bottom 10th percentile. This huge difference in income between the top and bottom raises the Gini coefficient. When looking at regional income data, the relative difference between wealthiest regions and the poorest will not be nearly as large. Therefore, the Gini coefficient will be smaller in this case.

Per Capita Disposable Income

First, we look at per capita disposable income by province and the municipalities of Beijing and Shanghai for China (excluding Hong Kong and Taiwan). Income tends to be concentrated along the rapidly developing eastern coast, especially in the municipalities of Beijing and Shanghai. Both municipalities have the highest average per capita disposable income, at just under 50,000 yuan. (The exchange rate is about six yuan per dollar.)As of March 29, 2018. The innermost provinces in western China, Gansu and Xizang, have the lowest per capita disposable income, at around 12,000 yuan. The highest regional income per capita is four times greater than the poorest. The cross-regional average of per capita disposable income is about 22,000 yuan, and the cross-regional median of per capita disposable income is about 18,600 yuan. The Gini of cross-regional income per capita is 0.19.

Now looking at U.S. states and the District of Columbia (D.C.), we see a similar but less pronounced skew in income distribution. The richest region is D.C., with per capita disposable income of around $63,000. The poorest state is Mississippi, with per capita income of around $32,000. In the U.S., the highest regional income per capita is two times greater than the poorest. This is less than half of the difference in China. The cross-regional average of per capita disposable income is $42,027, the cross-regional median of per capita disposable income is $40,829, and the cross-regional Gini of per capita disposable income is 0.08.

We see that regional income inequality is much greater in China than in the U.S. The Gini coefficient in China is more than twice as large as that in the U.S.

China’s rapid development has contributed to inequality so far, but the historical experience of the U.S. suggests that China’s regional inequality may start to shrink as China further develops.

Adjusting Regional Income by Regional Housing Prices

Since housing usually represents a large portion of consumer expenditures, housing prices can greatly affect a household’s living standards. Although it may not account for most of consumption, it is the most important component of spending.

For example, given a certain income, a person could afford either a small apartment in New York City or a large, single-family house in St. Louis.

Because most daily consumption goods are tradable and mobile across a country through a nationwide grocery market, the cost of living in terms of daily consumption goods does not change dramatically across regions. However, housing is a specially localized good, and it is not tradable or mobile. Therefore, the main source of the cross-regional difference in living standards comes from the difference in housing prices rather than grocery prices, even if grocery consumption accounts for a larger proportion of consumer spending.

In addition, housing prices have increased both in the U.S. (except during the recent financial crisis) and in China, so people’s living standard—the purchasing power of their income—must have changed in recent years. To measure this effect, we use regional housing prices to adjust per capita disposable income. Notice that cross-country comparison is meaningful in our context only if we use nominal housing prices instead of a housing price index.

In 2015, housing on average accounted for about 22 percent of consumption expenditure in China,This is based on authors’ calculation using consumption expenditure data from the National Bureau of Statistics of China. and it represented about 33 percent of household expenditure in the U.S. in 2016.This is based on authors’ calculations using consumption expenditure data from the Bureau of Labor Statistics. See BLS. We used housing prices of over 100 Chinese cities to construct regional housing price levels. The average price of new housing across China’s provinces is 855 yuan per square foot, and the median price is 649 yuan. Home prices in the most expensive city, Shanghai, are nearly nine times greater than in the least expensive province, Shaanxi.

For the U.S., we look at Zillow’s median house listing price by state for a mix of new and existing homes by state. The cross-regional average is $143 per square foot, and the median price is $124. The price of housing in D.C., the most expensive region, is over five times higher than home prices in Indiana, the least expensive state.

Housing prices are unequal across regions in both China and the U.S., but the disparity is greater in China. Interestingly, regions with high per capita disposable income also tend to have high housing prices. This suggests that, everything else being equal, a high-income region does not necessarily have a high living standard when the cost of housing is taken into account.

To create a measure of living standard, we adjusted disposable income by housing prices for both China and the U.S. Namely, we divided the regional nominal per capita disposable income by its respective regional housing price in each region.

Across China’s provinces, the standard of living varies much less than income alone. The province with the highest living standard is Shaanxi, and the province with the lowest living standard is Hainan.

Indeed, the Gini for living standard is 0.16. This is less than the previous measure based solely on disposable income in China, suggesting improved distribution and equality. Municipalities with seemingly high income (Shanghai and Beijing) now have low standards of living in terms of housing affordability.

Gini Coefficients: China and the U.S.

2015 Regional Per Capita Disposable Income 2015 Regional Housing Prices 2015 Regional Standards of Living*
Gini in China 0.19 0.30 0.16
Gini in U.S. 0.08 0.21 0.12

*A region’s per capita disposable income adjusted by its average housing price

SOURCES: National Bureau of Statistics of China, U.S. Bureau of Economic Analysis, China Index Academy/Soufun, Zillow, Haver Analytics and authors’ calculations.

In the U.S., however, inequality in living standard actually increases across states. The Gini is 0.12, which is over a 40 percent increase compared to the Gini in disposable income. Indiana is now the “richest” state in terms of living standard, and Hawaii is now the “poorest” state in terms of living standard, or housing affordability. The living standard is about 4.5 times greater in Indiana than in Hawaii. Much of this inequality is driven by states where high home prices greatly reduce living standards relative to the median of states.

One caveat is that not everyone is a homeowner. So alternatively we could use the absolute rental cost in each region to adjust regional average disposable income to capture the renter population’s inequality. But to the extent that rental cost is proportional to housing prices, our measure of living standard may not change dramatically if disposable income is adjusted by rental cost instead.

Conclusion

Adjusting income by cost of living can provide useful insights about living standards because, ultimately, household income means only as much as the purchasing power of that income. Yet the cost of living is not equal across regions, especially with respect to nontradable and nonmobile consumption goods such as housing. People in high-income regions may have to pay a disproportionately higher cost to enjoy the same consumption bundle.

We see that regional inequality is substantially less severe in the U.S. than in China when considering only disposable income. However, this gap in inequality between the two countries shrinks significantly once regional variations in the cost of housing are taken into account.

In terms of purchasing power of income on nontradable goods like housing, the cross-regional inequality in China is not much more extreme than that in the U.S., although housing is much more affordable in the U.S. than in China—thanks to much higher per capita income and significantly more arable land in the U.S.

Still, this gap in per capita income remains enormously large. It will take China 60 years—about two generations—to erase the difference, assuming the country can maintain a growth rate that is 4 percentage points higher than the U.S. rate during that time.

Endnotes

  1. See Dabla-Norris et al.
  2. For example, an individual whose income is in the 90th percentile will have an income many times greater than that of someone in the bottom 10th percentile. This huge difference in income between the top and bottom raises the Gini coefficient. When looking at regional income data, the relative difference between wealthiest regions and the poorest will not be nearly as large. Therefore, the Gini coefficient will be smaller in this case.
  3. As of March 29, 2018.
  4. This is based on authors’ calculations using consumption expenditure data from the National Bureau of Statistics of China.
  5. This is based on authors’ calculations using consumption expenditure data from the Bureau of Labor Statistics. See BLS.

References

Bureau of Labor Statistics. Consumer Expenditures—2016. News release, Aug. 29, 2017. See www.bls.gov/news.release/cesan.nr0.htm.

Dabla-Norris, Era; Kochhar, Kalpana; Suphaphiphat, Nujin; Ricka, Frantisek; and Tsounta, Evridiki. Causes and Consequences of Income Inequality: A Global Perspective. IMF Staff Discussion Note, International Monetary Fund, June 2015.

ABOUT THE AUTHORS
Brian Reinbold 

Brian Reinbold is a research associate at the Federal Reserve Bank of St. Louis.

Yi Wen 

Yi Wen is an economist and assistant vice president at the Federal Reserve Bank of St. Louis. His research interests include macroeconomics and the Chinese economy. He joined the St. Louis Fed in 2005. Read more about the author and his research.

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