The Surprising Relationship Between Inequality and Real Estate

The city of Norilsk, in northern Siberia, sits on one of the earth’s largest reserves of nickel and platinum. Because nickel is a necessary component of steel, Soviet planners made the development of Norilsk a priority during the 1930s. Stalin sent a delegation of experts to explore the region, and the experts reported that it would be difficult to attract workers to the city even by offering premium wages. Conditions there were simply too hellish: extremely cold temperatures, sometimes dropping to 45 degrees below zero, five months of winter darkness, and a depressing landscape with virtually no vegetation made it one of the most hostile environments on the planet. Although the precious metal reserves in the area could, in theory, support many jobs, planners could not come up with wages high enough to compensate workers for the horrible living conditions. For someone like Stalin, of course, these were trivial details. The feared state police, the NKVD, took over responsibility for the development of the city and turned it into a gulag—a Soviet labor camp. About 100,000 political prisoners died building the city and laboring in its mines. For decades the melting of the snowpack during the summer months revealed the bones of workers who had perished.

 In the Soviet Union, as in other communist regimes in Eastern Europe and China, the state had the power to forcibly move labor where it was needed. This gave rise to “artificial” cities such as Norilsk—cities that would not have existed in a free society. In the United States, workers are free to choose where they want to live. As we saw, Americans take advantage of this and move around more than citizens of most other countries. But there is a catch to this freedom. Living in places that are perceived as more desirable, either because they offer a higher quality of life or because they offer better jobs, tends to cost more. This is not surprising. Unlike the Soviet economy, which assigned resources based on a five-year plan, a market economy uses prices to allocate resources, and in this case the scarce resource is land in attractive cities. If a city has great weather, Americans tend to move there in large numbers, and in doing so they bid up the price of real estate. Good weather may not have a sticker price, but we implicitly pay for it, just as we pay for a nicer car or a larger TV. The same is true for good public schools, low crime rates, and excellent local restaurants. Every attractive feature of a city ends up being capitalized, at least in part, into higher property values.

 This simple observation has an unexpected implication: those who actually end up benefiting from these features are not necessarily those who are directly affected by them. Pollution levels in Southern California have decreased dramatically over the past twenty years, especially in Los Angeles, thanks to cleaner-burning gasoline and more aggressive regulation. Some neighborhoods have experienced more improvements than others, with ozone reductions ranging from 3 to 33 percent, depending on the area. You might think that residents of the neighborhoods that have experienced the largest pollution declines are the net winners, but that depends on whether they own or rent their homes. One study found that the larger the decline in pollution levels, the larger the increase in the desirability of the neighborhood and therefore the higher the price of real estate. In one low-income neighborhood, for example, ozone concentrations declined by 24 percent but housing costs increased by 10.8 percent. The price increase benefited property owners, who became both healthier and richer, but left renters healthier but poorer. Effectively, the price change acted as an unintended redistribution mechanism that shifted some of the benefits of air-quality improvements away from one group and toward another.

The same principle applies when a city labor market improves and local jobs are created. In the United States, we can see a clear correlation between local labor market conditions and the cost of living. Table 3 shows the metropolitan areas with the highest and lowest costs of living today. To create the table, I used data on about one million households, including both renters and homeowners, and data from the Bureau of Labor Statistics on the price of consumer goods. To measure cost of living, one needs to add up the local price of all the things consumed by residents. How does the average American household spend its money? Most people give the wrong answer. People tend to grossly overestimate the amount of money they spend on food, gas, and groceries, probably because they purchase these items regularly. In reality, the average American spends only 14 percent of her income on food and beverages and 17 percent on transportation. This is not very much. The other categories account for even less of the family budget: apparel (3 percent), medical care (6 percent), recreation (5 percent), education and communication (6 percent). (The way Americans divide their family budget is fairly similar to the way families in other countries do, with the main exception being Italian families, whose share of clothing expenditures is double that of Americans.) By far the largest item in the budget is housing, which accounts for 40 percent of spending. This means that most of the differences in cost of living among metropolitan areas reflect differences in the cost of housing, which in turn mostly reflect differences in the cost of land. Other differences arise from the price of local services—things like haircuts and restaurant meals—but these count considerably less, because their share of the budget is smaller. Moreover, they too mostly reflect the cost of land. For example, a haircut is more expensive in New York than in Dallas because it costs more to rent a store and because the salary of the hairstylist is higher to compensate for the higher cost of living. The same is true for restaurant meals, therapy sessions, legal services, and nanny services.

 TABLE 3: METROPOLITAN AREAS WITH HIGH AND LOW COSTS OF LIVING

HIGHEST COST OF LIVING	LOWEST COST OF LIVING
1. San Jose, CA	271. Youngstown-Warren, OH/PA
2. Stamford, CT	272. Lima, OH
3. San Francisco–Oakland– Vallejo, CA	273. Terre Haute, IN
4. Santa Cruz, CA	274. Sharon, PA
5. Santa Barbara–Santa Maria–Lompoc, CA	275. St. Joseph, MO
6. Ventura–Oxnard–Simi Valley, CA	276. Lynchburg, VA
7. Boston, MA	277. Williamsport, PA
8. Honolulu, HI	278. Joplin, MO
9. Santa Rosa–Petaluma, CA	279. Brownsville–Harlingen–San Benito, TX
10. Salinas–Seaside–Monterey, CA	280. Duluth-Superior, MN/WI
11. New York–Northeastern NJ	281. Johnson City–Kingsport–Bristol, TN/VA
12. Washington, DC/MD/VA	282. Altoona, PA
13. Los Angeles–Long Beach, CA	283. Alexandria, LA
14. San Diego, CA	284. McAllen-Edinburg-Pharr-Mission, TX
15. Seattle-Everett, WA	285. Danville, VA
16. Trenton, NJ	286. Gadsden, AL
17. Bridgeport, CT	287. Anniston, AL
18. Fort Lauderdale–Hollywood–Pompano Beach, FL	288. Johnstown, PA
19. Austin, TX
20. Anchorage, AK

 The table confirms that areas at the top of the list tend to be the ones with the strongest labor markets—the ones where wages and productivity are highest. San Jose is first, followed by Stamford and San Francisco. Many American innovation hubs are in the top group—Boston, Washington, D.C., San Diego, Seattle, and Austin. Anchorage is an exception, because many of its necessities have to be imported. Because the data reflect the entire metropolitan area, New York is only number eleven; taken alone, the city of New York would be at the top of the list. By contrast, the areas with the most affordable cost of living tend to have the weakest labor markets. At the very bottom of the list we find Johnstown, Pennsylvania, a declining manufacturing town, where the cost of living is four times lower than that of San Jose. Other metro areas near the bottom include Anniston, Alabama; Gadsden, Alabama; and Danville, Virginia. The relation between the strength of a labor market and the cost of housing is not deterministic, but it depends on several factors, including quality of life (better quality of life means higher housing costs, all other things being equal) and how easy it is to build new houses to accommodate increases in demand (easier housing development means lower costs).

These facts have a bearing on how we interpret measures of inequality among workers and between cities. Let’s start with the latter. When the labor market in a city strengthens, both workers’ earnings and the cost of housing tend to increase. These increases have two separate effects on residents. First, the increase in housing costs offsets some of the increase in salaries. In cities like Johnstown, people have low nominal salaries, but since mortgages are lower than elsewhere, an average salary has more purchasing power. By contrast, people in New York, Washington, and Boston have higher nominal salaries, but their effective salaries are not as high, because much of their wage tends to go toward paying the mortgage. This helps explain why not everyone has left Johnstown to move to Boston or New York. In practice, differences in average earnings among U.S. cities adjusted for cost of living are about 25 percent smaller than unadjusted differences.10

 However, this is not the end of the story. Just as with improvements in air quality, the effect of a strong labor market on a family ultimately depends on whether that family belongs to the 70 percent of Americans who own their homes or the 30 percent who rent. Homeowners in strengthening labor markets gain twice, both because of higher wages and because of higher property values. For them, the effect on well-being is larger than the increase in purchasing power because of the capital gains on their property. This highlights an unexpected conclusion: a significant part of the wealth created by America’s dynamic innovation sector accrues not just through the labor market but through the housing market. These capital gains are an important channel through which residents of innovation hubs benefit from the strength of their local economy. For renters, however, the effect of higher earnings is tempered by the increase in their monthly housing costs. Therefore, the ultimate effect on their well-being depends on which of these two forces prevail. The larger the increase in wages and the smaller the increase in rents, the better for them. As in the case of air quality, the change in real estate prices effectively redistributes the wealth created by job growth from one group to another. As we will soon discover, local governments have the power to manage the increases in local cost of living and can therefore determine whether homeowners or renters are the ones to gain the most from a strengthening labor market.

 This relationship between local labor markets and cost of living also affects the way we think about inequality between workers. Most of the public debate on inequality focuses on the striking differences in salaries and incomes, but what really matters is how much people can buy with their earnings. When economists started measuring inequality this way, they found that the difference in consumption between rich and poor—from groceries to clothes, electronics to health care—is not as large as the difference in salary. How can the consumption gap between the rich and the poor be smaller than the income gap?

An important explanation for this apparent contradiction has to do with where people live. In recent research, I found that since 1980, the amount that the typical college graduate spends on housing has grown much faster than the amount the typical high school graduate spends. This trend does not just reflect better or larger houses owned by college graduates. It mostly reflects differences in where groups with different skills tend to congregate. As we have seen, over the past three decades, jobs for college graduates have increasingly concentrated in expensive metropolitan areas—brain hubs like San Jose, San Francisco, Boston, New York, and Washington, D.C.—while jobs for high school graduates have increasingly concentrated in heartland cities with a low cost of living. While in 1980 the difference in housing costs between the two groups was small, it has grown by more than three times. This is important, because it implies that college graduates end up spending more for housing and therefore have less money for other goods and services. It is as if college graduates have experienced a higher inflation rate than high school graduates. Therefore the difference in living standards between highly educated Americans and less educated Americans, while large, is actually somewhat smaller than you might think.