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The Impact of Housing Costs on Regional Income Inequality


Tuesday, August 28, 2018

housing income inequality
Thinkstock/EmJcox

Regional income inequality appears to be more severe in China than in the U.S. But the degree of disparity shrinks in China and expands in the U.S. when housing costs are taken into account, according to a recent article in the Regional Economist.

Assistant Vice President and Economist Yi Wen and Research Associate Brian Reinbold explored the impact of housing costs on standards of living.

“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,” they wrote. “For example, housing prices can vary tremendously across a nation; they also vary based on urban, suburban and rural geography.”

Income Inequality

The authors first looked at 2015 per capita disposable income in China’s provinces and the municipalities of Beijing and Shanghai. They found that the highest regional income (which was in Beijing and Shanghai) was four times greater than that of the poorest provinces (Gansu and Xizang).

They then used the housing data to calculate a Gini coefficient, which is a standard way to measure inequality.

The resulting Gini coefficient for Chinese regional income inequality was 0.19. The coefficient takes a value between 0 and 1, with 0 representing perfect equality and 1 indicating maximum inequality. Since the authors are comparing average income between regions, the Gini coefficient will be less than if they were comparing individuals within the entire 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, however, 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.

For the U.S., they looked at similar income for U.S. states and the District of Columbia, finding that per capita income in the wealthiest area (D.C.) was about twice as large as per capita income in the poorest state (Mississippi).

The resulting Gini coefficient for U.S. regional income inequality was 0.08, less than half the level of China’s.

By looking only at income, the authors noted that “regional income inequality is much greater in China than in the U.S.”

The Housing Impact

The authors then looked at the regional differences in housing costs. Why look at housing?

  • Housing typically represents a large portion of consumer spending.
  • Unlike other types of consumer spending, such as groceries, housing isn’t tradeable or mobile. For example, groceries are tradeable and mobile across a country through a nationwide grocery market, which means these costs do not dramatically change across regions.

“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,” Wen and Reinbold wrote.

Examining 2015 housing prices The authors used housing prices of over 100 Chinese cities to construct regional housing price levels. For the U.S., they used Zillow’s median house listing price by state for a mix of new and existing homes. in both countries, they found:

  • Housing prices in Shanghai, China’s most expensive city, were nearly nine times greater than Shaanxi, China’s least expensive province.
  • The price of housing in D.C., the most expensive U.S. region, was more than five times the price in Indiana, the least expensive state.

The data showed that although regional housing prices are unequal in both China and the U.S., the disparity is greater in China.

“Interestingly, regions with high per capita disposable income also tend to have high housing prices,” they wrote. “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."

Taking this housing cost data, the authors adjusted the per capita income figures and recalculated the Gini coefficients, seen in the table below.

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 capital 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.
Federal Reserve Bank of St. Louis

China’s Gini coefficient of adjusted income is 0.16, which is a smaller figure than the coefficient based only on disposable income. This suggests improved distribution and equality when taking housing costs into considering, the authors noted.

In the case of the U.S., regional inequality increased when housing costs are taken into account. “The (U.S.) Gini is 0.12, which is over a 40 percent increase compared to the Gini in disposable income,” Wen and Reinbold wrote.

In terms of adjusted income, Indiana is the “richest” state in terms of living standard, while Hawaii is the “poorest,” they noted.

Conclusion

Wen and Reinbold said that adjusting income by the cost of living can provide useful insights about living standards. However, such costs aren’t equal across regions, especially with respect to housing.

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

Notes and References

1 Since the authors are comparing average income between regions, the Gini coefficient will be less than if they were comparing individuals within the entire 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, however, 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.

2 The authors used housing prices of over 100 Chinese cities to construct regional housing price levels. For the U.S., they used Zillow’s median house listing price by state for a mix of new and existing homes.

Additional Resources

Posted In HousingFinancial  |  Tagged yi wenbrian reinboldincome inequalityhousingregional economics
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