Measuring quality of life requires decisions about factors that contribute to wellbeing, their relative importance and their geographic scale. Credit: Sebast1an|Dreamstime.com.
Personal definitions of “quality of life” vary widely. For some, it’s an easy walk to a coffee shop to meet up with friends. Others appreciate a nearby lake for solo fishing. But quality of life is not only shaped by such hyperlocal factors. Consider rural hospitals that people rely on for maternity, emergency and general healthcare. When closures increase average driving distances to medical care by more than 20 miles, everyone in the region feels the impact.
That’s one reason why economists like Yang Cheng are interested in comparing local and regional measures of quality of life across U.S. counties. Another reason: In the world of community economic development, the idea of “jobs following people” is replacing the traditional notion of “people following jobs.” Thus, many towns are embracing placemaking policies to foster economic growth by improving their residents’ wellbeing and sense of belonging.
“Deciding if a community’s livability is a local or regional attribute helps determine the nature of these placemaking policies,” says Cheng, an AAE postdoctoral researcher. “It means deciding whether a new policy should focus on a single town or a larger region.”
Cheng and AAE professors Steve Deller and Tessa Conroy addressed two challenges for measuring quality of life in a recently published study. The first is the geographic scale: Is a resident’s experience shaped only by their home county or by the larger region in which they live, work and recreate? The second is the definition of wellbeing itself: Are all dimensions equally important or should some carry more weight than others?
For the study, the team combined data from three different sources for 3,100 continental U.S. counties. This is a heterogeneous space where rural areas cover 97% of land while housing 20% of people. Following the core principles of the Rural Livability project led by Conroy, the researchers chose measures of wellbeing that reflect lived experiences in both rural and urban counties. They avoided urban-centric metrics of economic growth and concepts that are more relevant to urban areas, such as walkability scores.
The team used six county-level measures that capture both rural and urban prosperity: poverty rate, unemployment rate, housing-related financial stress (proportion of households spending >35% of income on housing), level of education (proportion of residents without a high-school diploma), physical health (average number of self-reported “feeling unwell” days during the past month) and pollution (three-year average number of days when fine-particle air pollution (PM2.5) exceeded federal standards).
Next, the team used different statistical methods to calculate local and regional quality-of-life indices as weighted averages of those six factors. Their goal was to examine how the weighting scheme and geographic scale of the resulting indices shaped the overall picture of place-based livability.
Three local indices applied either equal or statistically derived weights to calculate weighted averages. Equal weights for all six dimensions produced the simple arithmetic mean, used in many quality-of-life rankings in popular magazines. Unequal weights allowed some factors, such as health, to be more important than others. The three local indices ignored the fact that nearby counties may also contribute to someone’s quality of life. In contrast, three regional indices either used physical distances between counties to model spatial dependencies or allowed data-driven weights to vary across regions.
The researchers visually compared the six rankings derived from ordering each index from highest to lowest. At the national level, the simple arithmetic mean aligned closely with the five other indices based on more complex statistical methods. This, however, obscured important differences for some rural counties and those that differed from nearby counties.
“Remote rural places were more sensitive than urban places to the choice of method, and local versus regional indices produced very different rankings when a rural area was surrounded by counties with very different socioeconomic characteristics,” says Cheng. “This means the lens for evaluating quality of life can change the narrative for rural places, especially for the most remote rural communities.”
A good example in Wisconsin is Pierce County, ranked below 800 by a local but above 160 by a regional index. The reason was its location in the Twin Cities metropolitan commuting zone, adjacent to affluent suburban counties in Minnesota and Wisconsin that have less poverty, lower unemployment and a lower cost-of-housing burden than Pierce County itself. Thus, a regional measure that accounts for interactions across nearby counties made Pierce County look considerably better than a purely local lens.
Common amenities with regional spillovers include rural hospitals—with more than 200 reported closures since 2005—and community colleges. Closures not only reduce access to healthcare and higher education, straining medical offices and public colleges in nearby counties. Since hospitals and colleges also tend to be large employers, their closures often increase regional poverty. Amenities with positive spillover effects include new recreational trails that attract both locals and people from surrounding areas.
One of the study’s take-home messages: Communities interested in collaborative placemaking initiatives should focus on regional indices. The Rural Livability project plans to inform those efforts by studying which place-based features matter most to the people these areas may wish to attract.
Cheng also notes that physical proximity—the basis for calculating regional indices—ignores the fact that East Coast areas with high population densities may have different spillover effects than sparsely populated parts of northern Wisconsin.
“Instead of using physical proximity, we could assess human interactions across county boundaries through trade or other partnerships,” says Cheng. “Measuring regional quality of life by estimating spatial dependencies from such existing interactions could be a very interesting follow-up study.”