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The Great Recession and the Changing Geography of Food Stamp Receipt

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An Erratum to this article was published on 09 January 2014

Abstract

The Great Recession has been distinctive in driving up unprecedented levels of participation in the Supplemental Nutrition Assistance Program (SNAP). This study extends the literature on the geography of SNAP receipt by (1) examining change in SNAP receipt across US counties during the Great Recession and (2) identifying how changes in other local characteristics were associated with this outcome. Our analysis draws on data from the US Department of Agriculture and other secondary sources. We use descriptive statistics, mapping, and weighted least squares spatial regression models to examine county-level variation (N = 2,485) in the percentage-point change in SNAP receipt between 2007 and 2009. Our findings reveal substantial local-level variation in the change in SNAP stamp use during the downturn. We find that counties with the greatest levels of change in SNAP participation tend to be regionally clustered. Our regression analysis shows that areas where the signature characteristics of the Great Recession were most pronounced (i.e., home foreclosures and unemployment) were precisely the places where SNAP use jumped most, not places with historically high levels of SNAP participation. Overall, this study demonstrates that change in SNAP receipt was geographically uneven during the Great Recession, and that local and regional configurations matter in shaping this variation. These results hold a range of implications for public policy, including opportunities for regionally targeted outreach and investment in SNAP and the use of the program as a responsive form of local stimulus during periods of economic crisis.

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Notes

  1. A product of the 1960s War on Poverty, the program was signed into law as the Food Stamp Act of 1964. The name of the program officially changed from the Food Stamp Program to the Supplemental Nutrition Assistance Program (SNAP) in 2008.

  2. It should be noted that we are measuring changes in take-up rather than eligibility. In Fiscal Year 2010 about three-quarters of those eligible for SNAP participated in the program (Rosenbaum 2013).

  3. Specifically, the states excluded from our analysis due to a lack of available data include Connecticut, Idaho, Maine, Massachusetts, Missouri, Montana, Nebraska, New Hampshire, New York, Oregon, Rhode Island, Utah, Vermont, Washington, West Virginia, and Wyoming. In an effort to assess the potential selection bias associated with the exclusion of these states, we conducted a t test to compare the mean change in SNAP receipt at the state level between states that were included in our analysis and states that were excluded. The results showed no significant difference in the mean change in SNAP use between these two groups of states (p = .80). In the absence of a clear cut method for imputing such a large amount of data, we elected to rely on “available case analysis,” which is advocated as the best approach for dealing with missing data in regression analysis, especially when statistical power is not an issue (Howell 2008).

  4. Defined by the Office of Management and Budget, a metropolitan (metro) area contains a core urban area of 50,000 or more population, and a micropolitan (micro) area contains an urban core of at least 10,000 (but less than 50,000) population. Each metro or micro area consists of one or more counties and includes the counties containing the core urban area as well as any adjacent counties that have a high degree of social and economic integration with the urban core (as measured by commuting to work). Non-core areas are counties that fit neither the metro nor micro definition. The index of dissimilarity measures the evenness with which two groups are distributed across component geographic areas (here census tracts) that make up a larger area (here counties). In cases where two groups are perfectly segregated, the index of dissimilarity takes the maximum value of 100. In cases where two groups are perfectly integrated, the index of dissimilarity takes the minimum value of zero (Racial Residential Segregation Measurement Project 2008).

  5. The spatial lag was produced by a “rook” weights matrix, which defines a location’s neighbors as those areas with shared borders. We use this method because it provides better model fit than the “queen” weights matrix, which is the other commonly used contiguity-based spatial weights measure.

  6. The adjusted R2 for each separate construct controlling for state-fixed effects is poverty experience .37; labor market conditions .43; population structure .36; human capital .36; and residential context .39.

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Acknowledgments

This research was supported in part by the Research Innovation and Development Grants in Economics (RIDGE) Program administered by the Southern Rural Development Center (SRDC) and supported by the US Department of Agriculture (USDA), Economic Research Service (ERS).

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Slack, T., Myers, C.A. The Great Recession and the Changing Geography of Food Stamp Receipt. Popul Res Policy Rev 33, 63–79 (2014). https://doi.org/10.1007/s11113-013-9310-9

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