Research has shown that organizational resources within a community affect the health and well being of local residents. Our research questions are: what effects did the "Great Recession" have on organizational closures in the Phoenix SMA, why did some neighborhoods experience greater losses than others, and did these closures impact residents' health and well being? We argue that changes in population size, housing values, and municipal urban policies e.g., zoning laws, incentives, tax policies, and strategic (dis)investments, affected organizational densities which, in turn, affected residents? health and well being.
We will geo-code establishments that provided activities for children in 2010 and match them with geo-coded data on similar establishments which we gathered data on in 2003 and 2008. Indicators of community health and well being at the zip code and tract level will then be regressed on organizational densities, as well as changes in population size, home prices, and municipalities? land use policies.
The work has broader significance, because not all residents have equal access to high quality programs, activities, and opportunities. With the results from this research policy makers can better motivate providers to locate/stay in under-served areas and to ensure organizationally rich neighborhoods for its citizens.
12.00 Normal 0 false false false EN-US JA X-NONE Our primary research questions were: 1) in which communities in the Phoenix urbanized area are nonprofits, government agencies, and businesses located, 2) what happened to these establishments during the "Great Recession," and 3) what happened to the people in these communities as a consequence. To answer these questions we mapped all the establishments that provided activities and services to children (between 5 and 12 years of age) across the 21 municipalities, tribal lands, and unincorporated areas that make up the Phoenix urbanized area. This includes everything from parks, sports clubs, schools, congregations, amusement parks, and doctors’ offices to retail, museums, grocery stores, restaurants, etc. The data are from Dun & Bradstreet, the Urban Institute, phone books, and websites. Previously funded NSF support enabled us to do this for 2003 and 2008. The study period covered by this grant enabled us to map establishments for 2007 and 2009. Table 1 shows the numbers of businesses, nonprofits, congregations, and government agencies that we mapped for 2003, 2004, 2007, and 2009. Because these establishments are geo-coded (x and y coordinates are assigned to addresses for mapping), they can be analyzed either as points on the map or they can be aggregated into different areal units, e.g., tracts, school districts, tax districts, economic development zones, planning districts, zip codes, police districts, etc. This enables researchers to merge our data with their own, e.g., school districts can merge our data with theirs on absenteeism and school performance. Furthermore, our data can be integrated with all the information which is available at the tract, block group, and block level which the U.S. Census has collected. Thus various agencies, foundations, and businesses that have an interest in integrating our data with their data sets will have the opportunity to do so once the data are made public. To illustrate how we addressed our research questions, we present findings from a paper we gave at the American Sociological Association in August, 2013. This paper examined the spatial distribution of various kinds of for-profit retailers across the cities and suburbs that make up the Phoenix-Mesa-Scottsdale urbanized area. We examined the years 2003, 2007, 2008, and 2009, and had data on three types of large general retailers (big box stores [Walmart, K-mart, & Target], supermarkets, and wholesalers [Costco and Sam's Club]) and seven specialty retailers (eating places, small food stores, entertainment businesses, small general merchandisers, clothing stores, personal care businesses, and specialty goods stores). We wanted to know the effects of the recession on urban retailers and if the recession affected large general retailers different than specialty stores. Figures 1 and 2 describe growth & decline in different types of super stores and specialty stores. Supermarkets, wholesalers, big box stores as well as eating places, small food stores, entertainment businesses, small general merchandisers, clothing stores, personal care businesses, and specialty goods stores grew in numbers between 2003 and 2007 paralleling population growth in the metropolitan area. However, after 2007 (i.e., during the Great Recession) the number of specialty retailers all declined as well as the number of supermarkets. Wholesalers and large general retailers continued to grow in number between 2007 and 2008 and then flat lined between 2008 and 2009 as the recession worsened. We also found that between 2003 and 2007 five of the seven types of specialty retailers had steeper positive growth rates in census tracts where big box stores were located, and between 2007 and 2009 (the recessionary years) this pattern continued in all non-poverty areas. Even in poverty tracts, the pattern persisted except for eating places and small general merchandisers who were unaffected by a big box presence. This raised questions about the value of big box stores in communities and their impact on the stores that are located near them. Rather than ‘killing them off’, they seemed to support the increase in specialty stores both in good times and in bad. Next we will examine the consequences of decline for the people in the communities affected in a paper that will be presented at the World Congress of Sociology in summer, 2014. In conclusion, our ultimate goal is to improve the quality of life of all urban residents. Extensive research has shown considerable variation in the health, education, employment, and general welfare of urban and suburban residents. It also has shown that access to different essentials such as food and health care and amenities such as recreational facilities is unequal and varies by income, race, and ethnicity. We believe that much of this can be explained by understanding how and why public, nonprofit, and business establishments locate where they do. Hopefully, our research can show policy makers where the problems are and suggest policies they can implement to make life better for all community residents.
