Poverty, once heavily concentrated in urban neighborhoods, has dispersed into suburbs through the effects of employment losses and home foreclosures and through the movement of lower-income populations (Kneebone and Garr, 2010). Scholars have created classification schemes to capture the suburban variation that has become increasingly evident with these metropolitan shifts (Orfield, 2002; Mikelbank, 2004).  I build on previous research, developing and comparing typologies using two different datasets to pose the following questions: What measures are most appropriate to categorize and analyze suburbs by income levels? What are the impacts of regional location on suburban incomes?

For purposes of comparability and consistency, spatial poverty researchers have tended to use the proportion of people in a particular geography living below the Federal poverty threshold as their primary measure. In suburbs where poverty is less concentrated, some researchers have classified suburbs as poor if they have at least 20% of individuals living below the poverty line (Holliday and Dwyer 2009).  This approach, however, does not take into account cost of living differences across metropolitan areas.  A relative income measure that compares median income of the suburban census place to metropolitan area median income controls for regional variation.

The selected income measure and regional suburban differences are interrelated. The impacts of lowered incomes manifest themselves differently across regions and metropolitan areas depending on regulatory frameworks, the built environment, and pre-existing economic conditions. Decisions to fund specific poverty-reduction programs must take into account not only the fact that poverty exists in a certain area, but also its distinct spatially-related effects.

To examine these questions, I create two subsets of geographical Census data. The first dataset is composed of non-principal-city metropolitan census places with 20 percent of households below the poverty line.  In the second, the suburban census places selected have median incomes at 80 percent or less than the metropolitan area median.  Following a methodology similar to Hanlon (2009), I use principal components analysis (PCA) combined with cluster analysis and a set of employment, housing, and demographic variables to create two typologies of low-income and poor suburbs. I compare the characteristics of the two sets of clusters to assess overlaps and dissimilarities. Mapping the resulting clusters and conducting discriminant analysis within each cluster allows me to look more closely at the relationship between spatial location and the categorization of low-income or poor suburbs. The findings from comparing the typologies will inform policy-makers as they assess suburban income levels and formulate policy and programmatic responses.

References:

Kneebone, E., & Garr, E. (2010). "The Suburbanization of Poverty: Trends in Metropolitan America, 2000-2008." Washington DC: The Brookings Institution

Hanlon, B. (2009). "A Typology of Inner-Ring Suburbs: Class, Race, and Ethnicity in U. S. Suburbia." City & Community, 8(3), 221–246.

Holliday, A. and R. Dwyer. (2009), “Suburban Neighborhood Poverty in U.S. Metropolitan Areas in 2000,” City and Community, 8(2): 155-176.

Mikelbank, B. (2004). “A Typology of U.S. Suburban Places.” Housing Policy Debate, 15(4): 935-964.

Orfield, M. (2002) American Metropolitics: The New Suburban Reality. Washington, DC: Brookings Institution Press.