Evolution of the Quality of Life in the U.S.

• Theory of the Capitalization of Amenities
• Differences in Housing Costs and Wages
• Estimated Capitalization of Amenities
• Results on Evolution of Quality of Life
• Reference

Quality of life increasingly is identified as important to the economic well-being of a state or area. Quality of life is a catchall concept, covering a myriad of local amenities, such as air quality, traffic congestion, crime, tax burdens, public school quality, and the quality of other government services. These individual amenities have varying degrees of importance, and public policies aimed at improving or maintaining overall quality of life can benefit from information on the relative valuations.

Deriving such information is possible because the values of individual amenities are reflected in each locality’s housing costs and other market determined prices. This Economic Letter reports on recent research by Gabriel, Mattey, and Wascher (1996) (hereafter GMW) which uses data on amenities, housing costs, and wages to describe how the relative quality of life has changed among states in the U.S. and to identify which amenities appear to have contributed the most to the changes in the quality of life. They found that in adapting to the stresses of rapid population growth, some states did better than others in maintaining their quality of life. In the states where the quality of life has remained relatively favorable, some of the most important contributing factors were reduced state and local government income tax burdens, improved air quality, increased highway spending, and reduced commute times. In states where the quality of life deteriorated, some of the most important contributing factors were reduced spending on highways (relative to other government budget items) and increased traffic congestion and air pollution.

Theory of the Capitalization of Amenities

The point of departure for quality of life studies is the observation that, other things equal, people are willing to pay a premium to live in places with amenities, such as beautiful weather, that are otherwise provided free of charge. These amenity values tend to be capitalized in the cost of land and housing, which cannot be moved from place to place. Similarly, disamenities–such as pollution, traffic congestion, and crime–undermine housing values. Economic theory suggests that wages also reflect local amenities and disamenities: People are willing to take less pay when such discounts are compensated by amenities. Using simple models that embody this intuition, various researchers have studied regional patterns in housing costs and wages, quantifying how much households value particular amenities, and accumulating the results in indices of the quality of life.

Differences in Housing Costs and Wages

On the face of it, substantial differences in quality of life may exist, judging from the striking differences in housing costs across U.S. states. Calculations using data from the 1990 Census of Population and Housing show that, among the nine states in the Twelfth Federal Reserve District, average housing expenditures ranged from a low of about $4,500 per year in Idaho to a high of about $15,500 per year in Hawaii (Figure 1). Average housing expenditures also were relatively high in California, at about $13,500 per year, whereas housing costs in Alaska, Arizona, Nevada, Oregon, Utah, and Washington were close to the roughly $6,000 per year average for other U.S. states.

The variation in average wages across states also is large. The Census data show that, among western states, average wages per household ranged from a low of about $26,000 per year in Idaho to a high of about $40,500 per year in California. Average wages in Hawaii and Alaska also were close to $40,000 per year, whereas wages in Arizona, Nevada, Oregon, Utah, and Washington were close to the $32,000 per year average for other U.S. states.

The economics literature on regional differences in wages and housing expenditures cautions that in comparing these “prices” across areas, the items being compared are not identical. For example, the workers in high-wage states could be better paid because they are better educated, more skilled, and otherwise have characteristics which make them more productive than workers in states with lower wages. To account for the effects of differences in housing stock quality and worker quality on observed housing expenditures and wages, GMW (1996) construct quality-adjusted housing expenditure and wage series. Statistical analysis reveals, for example, that, other things equal, each additional room in a house tends to add about 16 percent to the rental value of the structure, and workers with a Masters level degree earn about 23 percent more than those with a high school education. Using an extensive set of housing stock and worker characteristics, the implicit contributions of characteristics to housing rental values and wages were calculated. Then, quality-adjusted housing expenditures and wages were computed for each U.S. state, using average U.S. characteristics of housing units and workers as a benchmark. GMW found that quality adjustment attenuates some of the sharpest differences across states in housing expenditures and wages, but the overall distribution is similar to that prior to quality adjustment. For example, of the $40,500 in average California wages per year, only about $2,000 per year is due to the higher labor force quality in California than in other U.S. states. Adjustments for differences in housing stock quality across states also were moderate.

Estimated Capitalization of Amenities

GMW then quantified the extent to which individual amenities in the states were capitalized in quality-adjusted wages, housing expenditures, and other components of the cost of living, using data for each state from 1981 to 1990. Consistent with the findings of earlier researchers who had focused only on compensating differentials at a point in time, they found that there is significant capitalization of climatic and recreational attributes of states. For example, households generally prefer to avoid temperature extremes, which were measured by amounts of heating and cooling effort required (in degree days). Households living in North Dakota, the state with the need for the most heating effort, are estimated to require a $15,642 premium in quality of life from this source relative to Hawaii, where no heating effort is needed (Figure 2). The model also suggests that extremely hot weather–as measured by the amount of cooling effort in degree days–is a significant disamenity, with the required quality of life premium (increase in wages or decrease in annual housing expenditures) ranging from about $7,000 to close to zero. High levels of precipitation, humidity, and wind speed also are disamenities, but their effects are not as economically significant as temperature extremes. With regard to recreational opportunities, the model shows that access to coastlines, inland water areas, national parks, and other federal lands each creates a small premium in housing values or a discount in wages.

The model sensibly shows that households dislike congested areas which increase their commuting times to work, and the range of contributions for this variable is large, with a quality of life premium from about $22,000 to $10,000 per year. Air quality also is important; in the most polluted states, carbon monoxide and ozone pollution hold down quality of life about $4,500 and $7,500 in annual wages, respectively. Conditional on a given level of the tax burden, the estimates suggest that households prefer relatively high levels of state and local government spending on highways and public welfare. Public school quality also is important; other things equal, the model suggests that lowering student-teacher ratios can raise housing values or make households more willing to accept lower wages.

Results on Evolution of Quality of Life

Given the set of attributes for each state and the estimated “implicit prices” at which they are capitalized into housing costs and wages, GMW aggregated these into indices of the quality of life and followed states’ rankings over the 1981 to 1990 period. Among the states that exhibited a significant evolution in relative quality of life, Alaska and Arizona stand out with large improvements in the rankings, while New Hampshire and a few western states–Idaho, Nevada, and New Mexico–are estimated to have deteriorated noticeably in the quality of life rankings.

By studying the evolution of particular characteristics, evaluated at their implicit prices, GMW were able to discover some interesting patterns in how changes in amenities have contributed to large improvement or deterioration in the quality of life rankings for individual states. Although many of the states with deteriorating quality of life rankings faced the pressures of rapid population growth during the 1980s (such as Nevada, New Hampshire, Georgia, Utah, Washington, New Mexico, and Hawaii), other states with improvements in estimated quality of life (Alaska, Arizona, Florida, and Colorado) also were among the states with the largest rates of population increase. The key to maintaining or increasing quality of life appears to have been in how governments responded to the population growth.

For example, the states that experienced deteriorating quality of life rankings tended to cut back on the share of state and local government expenditures devoted to highways and transportation infrastructure, and traffic congestion and average commuting times increased. Furthermore, when this population growth occurred in an area with initially relatively good air quality, air pollution control efforts–such as mandates to use cleaner-burning fuels–were not tightened, and carbon monoxide pollution in particular increased, relative to other states. In contrast, some other fast-growing states with more stringent air quality management regimes–particularly Arizona, California, and Colorado–experienced improved air quality.

Although these patterns are most evident in some individual states, summary statistics on the contribution of amenities to the evolution of quality of life rankings also reflect the patterns. For example, the statistics shown in Figure 3 pertain to the group of ten states that experienced the largest deterioration in quality of life rankings; on average, these states moved ten places toward the bottom of the quality of life rankings. The attribute with the largest average contribution to the deterioration was state and local government expenditures on highways, which accounted for a decline of 2.7 places in the rankings, on average. Increased commuting times, higher carbon monoxide levels, and a lower share of government spending on public welfare also were large sources of deterioration in quality of life for these states.

On the whole, these results suggest that most of the regional differences in housing costs and wages will persist, because they largely reflect relatively fixed climatic and recreational attributes of the states. However, housing values and wages can adjust to changes in the quality of life from the undesired effects of rapid population growth, and statistical analysis suggests that it is particularly important to avoid increased traffic congestion and air pollution.

Joe P. Mattey
Senior Economist

Reference

Gabriel, Stuart A., Joe P. Mattey, and William L. Wascher. 1996. “Compensating Differentials and Evolution of the Quality-of-Life among U.S. States.” Federal Reserve Bank of San Francisco Working Paper 96-07 (June). Online: at www.sf.frb.org.