TANF and SNAP Participation Fluctuations during the Great Recession and Implications for Family and Consumer Sciences Cooperative Extension Services
TANF and SNAP Participation Fluctuations during the Great Recession and Implications for Family and Consumer Sciences Cooperative Extension Services
Julie N. Zimmerman
University of Kentucky
Much of FCS Cooperative Extension’s work is focused on low-income families. Understanding the differential impact of the recent recession on the use of public assistance can provide important insights useful for directing FCS Extension programming efforts. This analysis uses a stepwise spatial comparison to examine changes in unemployment, SNAP cases, and TANF cases during the Great Recession by economic area in an example state, Kentucky. The results find that SNAP increases were similar throughout the state but TANF fluctuations varied. These results suggest that FCS Cooperative Extension may need to reevaluate their program implementation strategy and target economic areas where an increased need is likely. Additionally, resource management programs may need to target areas where SNAP participation increased but TANF participation did not.
policy, program, unemployment, stakeholder, Cooperative Extension
Much of the work of Family and Consumer Science (FCS) Cooperative Extension Services is aimed at low-income families, with many educational programs targeting resource management and healthy nutrition in this at-risk group. Many state FCS Cooperative Extension Services even receive millions of dollars in additional grant funds to implement Supplemental Nutrition Assistance Program Education (SNAP-Ed) in local communities and schools (U.S. Department of Agriculture [USDA], National Institute of Food and Agriculture 2009). The recent economic downturn, known as the Great Recession, has led to an increase in the number of families considered low-income and thus an increase in the number of families who would benefit from FCS Cooperative Extension Service low-income programs (Pavetti, Trisi, and Schott 2011). During the economic downturn many families found a need to rely on public assistance programs such as the Supplemental Nutrition Assistance Program (SNAP; formerly known as the Food Stamp Program) and the Temporary Assistance for Needy Families program (TANF; commonly known as welfare). However, a recent study by Pavetti et al. (2011) found that while need for these programs increased nationally, as identified by rising unemployment and poverty rates, increases in SNAP participation and TANF participation varied across states. Several states with increased unemployment and poverty were actually found to have decreases in their TANF participation.
The reasoning for the varying fluctuations in SNAP and TANF growth is potentially rooted in their structure. The 1996 passage of the Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA) ended the prior Aid to Families with Dependent Children entitlement program and replaced it with TANF (Swaminathan and Findeis 2004; Zuckerman 2000). Under PRWORA, federal funds would be disseminated to states to pay for their own TANF program, and as long as states could meet federal requirements, they would continue to receive federal funding (Lurie 1997; Schott 2011; Weaver 2002). One of the requirements is that federal funds cannot be used to extend benefits to TANF recipients beyond 60 months (Center on Budget and Policy Priorities [CBPP] 2012). Individual states, however, may choose to extend TANF benefits beyond the federal lifetime limit by using their own state funds and several have chosen to do so (CBPP 2012; Kassabian et al. 2011; Lurie 1997; Schott 2011; Weaver 2002). The ability of TANF to meet increasing need during an economic downturn has been questioned since the passing of PRWORA (Besharov and Germanis 2007; Gittleman 2001; Irving 2008; Pavetti 1999; Trisi and Pavetti 2012; Ziliak et al. 2000; Zuckerman 2000). The primary concern of TANF was that the work requirement expectation could only be met in an economic area where obtaining a job was feasible. Certain areas where economic prospects were low would thus have populations who would potentially use their TANF benefits early on in the program’s implementation and be unable to receive assistance later during an economic crisis. Unlike TANF, SNAP has remained a federal entitlement program (Andrews and Smallwood 2012; Pavetti et al. 2011; Schott 2011; USDA, Economic Research Service 2012b). In contrast to the varying state programs and funding structures of TANF, SNAP has more uniform program guidelines and more uniform program structures across states. SNAP is able to expand to meet increased need in all states.
The purpose of this study was to examine changes in SNAP and TANF cases relative to changes in unemployment during the Great Recession and discuss the implications for FCS Cooperative Extension Services programs targeting low-income families. This study seeks to identify areas where both SNAP and TANF participation grew, but also areas where SNAP and TANF growth varied, potentially signaling an area whose low-income families are receiving food assistance but not cash assistance. Areas in which families lack cash assistance may require additional support from FCS Cooperative Extension Services resource management programs. It is hypothesized that given the timeline requirements of TANF, areas with historically high issues with unemployment and poverty will not have increases in TANF similar to increases in SNAP. In addition, it is hypothesized that areas where employment issues and poverty have not have historically been a concern, TANF and SNAP growth will be similar.
A single state with varying economic areas was selected for the analysis. A single state was chosen because it allows for TANF requirements to be held constant, and thus variations in state programs to not interfere with the results. Kentucky was selected as the state to analyze, with its counties serving as the unit of analysis. Nationally, TANF increased 13.4 percent during the Great Recession, however, Kentucky was selected to analyze because it had a modest statewide TANF caseload increase (3.1 percent) during the time period compared to other states such as Colorado (48.3 percent) and Rhode Island (-28.8 percent, Pavetti et al. 2011). Similar to other states, the sample state contains distinct regions with certain economic areas similar to those of many other states. Moreover, the regions within the sample state reflect other parts of the nation, whether or not they lie within a single state. In Kentucky, the TANF program is known as the Kentucky Transitional Assistance Program and has like all TANF programs, has a specific set of guidelines (Kassabian et al. 2011). Applicants have an asset limit of $2,000 and all of a household’s vehicles are exempt from being counted as an asset. Adult recipients must participate for a minimum of 30 hours in a work-related activity, and benefits for recipients are set at the federally mandated 60 months. Kentucky is composed of 120 counties and three distinct regions (Dyk and Zimmerman 2000; Zimmerman 2000; Zimmerman and Renfro-Sargent 2000).
The 120 counties in Kentucky were coded using the 2003 Urban Influence Codes created by the USDA’s Economic Research Service (2012a). These codes distinguish between twelve different metropolitan/nonmetropolitan designations based on population size and county adjacency (USDA, Economic Research Service 2012b). Metropolitan areas have larger populations than nonmetropolitan areas. Within nonmetropolitan areas, nonmetropolitan micropolitan areas have higher populations than nonmetropolitan noncore areas. A description of the Urban Influence Codes and counties can be found in Table 1.
|Table 1. Number of Counties in Kentucky by Urban Influence Codes|
|Area||2003 Urban Influence Codes||Number of Counties|
|All Counties in Kentucky||120|
|Nonmetropolitan||3, 4, 5, 6, 7, 8, 9, 10, 11, 12||85|
|Nonmetropolitan Micropolitan||3, 5, 8||59|
|Nonmetropolitan Noncore||4, 6, 7, 8, 9, 10, 11, 12||26|
[Alt tag content for Table 1: This chart breaks down the number of counties by 2003 Urban Influence Code.]
Each county was also assigned a state regional code. While the regional codes were originally created to reflect a range of distinct characteristics in the state (Dyk and Zimmerman 2000; Zimmerman 2000; Zimmerman and Renfro-Sargent 2000), in this analysis they are used to reflect the employment and economic structure of the area. The three regions are Eastern, Central, and Western. Eastern is characterized by high rates of poverty and persistent poverty with much of the economy dependent upon mining, Central contains the state’s largest cities with an economy rooted more rooted in commercial business, and Western includes areas with more larger-scale agriculture. A description of the regional breakdown of counties can be found in Table 2.
|Region and Area||Number of Counties|
|Eastern All Counties||49|
|Central All Counties||36|
|Western All Counties||35|
[Alt tag content for Table 2: This chart breaks down number of counties by state region and 2003 Urban Influence Code.]
TANF and SNAP participation was measured using each program’s caseload numbers from December 2007 and December 2009. The data are taken from the Kentucky Cabinet for Health and Family Services, Department of Community Based Services data base. December 2007 and December were chosen for the analyses, as these dates reflect the time period encompassing the beginning and end of the Great Recession, along with being the time period analyzed in the Pavetti et al. (2011) study on the Great Recession. Change in unemployment was selected to reflect the increased need for SNAP and TANF participation. Data used for the number of unemployed are from the U.S. Bureau of Labor Statistics, Local Area Unemployment Statistics (2011a; 2011b).
Using the same methods previously developed to examine welfare reform in the sample state (i.e., Dyk and Zimmerman 2000; Zimmerman 2000; Zimmerman and Renfro-Sargent 2000), a stepwise spatial comparison was used at both the state and the regional levels. An illustration of the state analysis can be found in Appendix A and the regional analysis can be found in Appendix B. A stepwise spatial comparison moves through progressively more detailed ways of distinguishing counties. The first step in the analysis compared metropolitan/nonmetropolitan areas. The next step in the analysis compared metropolitan areas to areas within nonmetropolitan areas. This was done by distinguishing the nonmetropolitan area into micropolitan areas and noncore areas. Data were totaled for all of the counties in each of the coding groups and the total rates of change were calculated. The total rate of change for TANF and SNAP to unemployment from 2007 to 2009 was then compared for each part in the stepwise model.
Results of the state analysis demonstrate that unemployment increased throughout the state, however, public assistance programs fluctuated at different rates. Unemployment growth, which was lowest in nonmetropolitan areas, doubled in metropolitan areas. SNAP cases increased at nearly double the rate in metropolitan areas (31.4 percent) as they did in nonmetropolitan noncore areas (15.8 percent). TANF cases varied with the highest increase occurring in metropolitan areas and nonmetropolitan noncore areas actually demonstrating a decrease (-7.4 percent). Across regions, Central had the largest growth in all three variables and Eastern demonstrated the smallest fluctuations in variables, with TANF cases actually declining in these areas (6.1 percent). A description of the first step in the state analysis results can be found in Table 3, and a description the regional comparison can be found in Table 4.
|Table 3. Metropolitan/Nonmetropolitan Counties in Kentucky|
|Area||Variable||2007||2009||Percent Change 2007-2009|
|Nonmetropolitan Micropolitan||Number Unemployed||21,775||40,923||87.9%|
[Alt tag content for Table 3: This chart describes the change in state unemployment, SNAP caseloads, and TANF caseloads.]
|Table 4. Regions in Kentucky|
[Alt tag content for Table 4: This chart describes the change in state region unemployment, SNAP caseloads, and TANF caseloads.]
Results of the second step in the regional analysis demonstrate a great amount of variation in variable fluctuations between regions and within regions. Central had general increases in all variables, while Western and Eastern varied. Unemployment rose in all places, though the lowest growth occurred in the Eastern Nonmetropolitan Noncore area (71.8 percent) and the highest growth occurred in the Central Nonmetropolitan Noncore area (130.7 percent). Select areas within the Western and Central areas of the state both had decreases in TANF cases, with the most dramatic decrease occurring in the Eastern Nonmetropolitan Noncore area (-9.7 percent). SNAP caseload increase varied, with the highest increases occurring in Central areas, followed by Western areas, and finally, Eastern areas. Eastern areas had about half of the growth in SNAP cases that Central areas did. TANF cases varied greatly. All areas in Central Kentucky had growth in TANF cases, with the highest growth occurring in the Central Nonmetropolitan Micropolitan area (14.9 percent). Western areas of the state varied in their fluctuations, with some areas demonstrating TANF caseload increases and others demonstrating TANF caseload decreases. Eastern Kentucky also had TANF caseload fluctuations that varied, though this region had the largest decrease in TANF caseload participation in the Nonmetropolitan Noncore area (-9.7 percent). A full description of these results can be found in Table 5.
|Table 5. Kentucky Metropolitan, Nonmetropolitan Micropolitan, and Nonmetropolitan Noncore Counties|
|Central Metropolitan||Number Unemployed||50,238||101,427||101.9%|
|Central Nonmetropolitan||Number Unemployed||6,418||13,770||114.6%|
|Central Nonmetropolitan Micropolitan||Number Unemployed||3,696||7,489||102.6%|
|Central Nonmetropolitan Noncore||Number Unemployed||2,722||6,281||130.7%|
|Western Metropolitan||Number Unemployed||9,631||20,498||112.8%|
|Western Nonmetropolitan||Number Unemployed||15,488||29,398||89.8%|
|Western Nonmetropolitan Micropolitan||Number Unemployed||8,141||14,339||76.1%|
|Western Nonmetropolitan Noncore||Number Unemployed||7,347||15,059||105.0%|
|Eastern Metropolitan||Number Unemployed||3,056||5,663||85.3%|
|Eastern Nonmetropolitan||Number Unemployed||28,451||50,901||78.9%|
|Eastern Nonmetropolitan Micropolitan||Number Unemployed||9,938||19,095||92.1%|
|Eastern Nonmetropolitan Noncore||Number Unemployed||18,513||31,806||71.8%|
[Alt tag content for Table 5: This chart describes the change within each region’s unemployment, SNAP caseloads, and TANF caseloads.]
Results of this study show that SNAP and TANF fluctuations varied by economic area in the sample state. These findings show that the first hypothesis was correct, as SNAP increased across many economic area types, but TANF increased primarily in the two economic areas where unemployment and poverty issues were not high before the Great Recession. In addition, the second hypothesis was correct as SNAP increases and TANF increases were similar in areas where unemployment and poverty issues were not high before the Great Recession. These results suggest that FCS Cooperative Extension Services may need to, if they have not done so already, begin targeting areas with resource management and nutrition programs which have traditionally not been the focus of these types of programs. In particular, SNAP-Ed program efforts may need to be redirected to metropolitan areas where the highest growth in SNAP participation was found. Altering SNAP-Ed efforts to reflect areas with increased need could potentially reflect data driven decisions by FCS Cooperative Extension Services and further demonstrate the relevancy of the agency. Redirecting FCS Cooperative Extension Service efforts to these areas where new need was identified may be crucial as families in these areas may not have the same level of knowledge about resource management or healthy eating as traditionally low-income economic areas who have been receiving these types of programs for many years.
These findings suggest that there may be an opportunity for FCS Cooperative Extension to specifically design resource management programs to target economic areas where unemployment increased but TANF participation did not, as families in these areas may be experiencing need but not receiving cash assistance. If not already being completed, these findings may present an opportunity for FCS Cooperative Extension Services to work with community leaders, such as local politicians and stakeholders, to build local safety nets that aid families when TANF participation or SNAP participation does not increase despite increased resource need. A partnership between FCS Cooperative Extension Services and community leaders could include direct resource assistance along with educational program assistance. Further research on the topic may examine FCS Cooperative Extension Services low-income program efforts responsiveness to changing needs from the Great Recession.
Andrews, M., and D. Smallwood. 2012. “What’s behind the rise in SNAP participation?” Amber Waves 10: 1-6.
Besharov, D. J., and P. Germanis. 2007. “Welfare reform and the caseload decline.” Gender Issues 24: 32-58. Center on Budget and Policy Priorities. 2012. Policy basics: Introduction to SNAP. http://www.cbpp.org/cms/index.cfm?fa=view&id=2226
Dyk, P. H. and J. N. Zimmerman. 2000. The impacts and outcomes of welfare reform across rural and urban places in Kentucky. Policy Outcome Grant. Report to the U.S. Department of Health and Human Services, Assistant Secretary for Planning and Evaluation.
Gittleman, M. 2001. “Declining caseloads: What do the dynamics of welfare participation reveal?” Industrial Relations 40: 537-70.
Irving, S. K. 2008. “State welfare rules, TANF exits, and geographic context: Does place matter?” Rural Sociology 73: 605-30.
Kassabian, D., T. Vericker, D. Searle, and M. Murphy. 2011. Welfare rules databook: State TANF policies as of July 2010. http://www.urban.org/UploadedPDF/412381-Welfare-Rules-Databook.pdf
Kentucky Cabinet for Health and Family Services, Department of Community Based Services. 2009. The data book. http://chfs.ky.gov/dcbs/data_book.htm
Kentucky Cabinet for Health and Family Services, Department of Community Based Services. 2011 The data book. http://chfs.ky.gov/dcbs/data_book.htm
Lurie, I. 1997. “Temporary Assistance for Needy Families: A green light for states.” Publius27: 73-87.
Pavetti, L. A. 1999. “What will the states do when jobs are not plentiful? Policy and implementation challenges?” In Economic Conditions and Welfare Reform, Sheldon H. Danziger, ed. Kalamazoo, MI: W.E. Upjohn Institute for Employment Research
Pavetti, L., D. Trisi, and L. Schott. 2011. TANF responded unevenly to increase in need during downturn. http://www.cbpp.org/cms/index.cfm?fa=view&id=3379
Schott, L. 2011. An introduction to TANF. http://www.cbpp.org/cms/index.cfm?fa=view&id=936
Swaminathan, H., and J. L. Findeis. 2004. Policy intervention and poverty in rural America. American Journal of Agricultural Economics 86: 1289-96.
Trisi, D., and L. Pavetti. 2012. TANF weakening as a safety net for poor families. http://www.cbpp.org/cms/index.cfm?fa=view&id=3700
United States Bureau of Labor Statistics, Local Area Unemployment Statistics. 2011a. Labor force data by county, 2007 annual averages. http://www.bls.gov/lau/
United States Bureau of Labor Statistics, Local Area Unemployment Statistics. 2011b. Labor force data by county, 2009 annual averages. http://www.bls.gov/lau/
United States Census Bureau, Small Area Income and Poverty Estimates. 2011a. Estimates for Kentucky counties, 2007: All ages in poverty, 2007. http://www.census.gov/did/www/saipe/index.html
United States Census Bureau, Small Area Income and Poverty Estimates. 2011b. Estimates for Kentucky counties, 2009: All ages in poverty, 2009. http://www.census.gov/did/www/saipe/index.html
United States Department of Agriculture, Economic Research Service. 2012a. 2003 urban influence codes for Kentucky. http://www.ers.usda.gov/Data/UrbanInfluenceCodes/2003/LookUpUIC.asp?C=RandST=KY
United States Department of Agriculture, Economic Research Service. 2012b, May 5. Measuring rurality: New definitions in 2003. http://www.ers.usda.gov/Briefing/Rurality/NewDefinitions/
United States Department of Agriculture, Economic Research Service. 2012c. The food assistance landscape: FY 2011 annual report (Economic Information Bulletin No. 93).http://www.ers.usda.gov/media/376910/eib93_1_.pdf
United States Department of Agriculture, National Institute of Food and Agriculture. 2009. Nutrition: About SNAP-Ed. http://www.csrees.usda.gov/nea/food/fsne/about.html
Weaver, R. K. 2002. The structure of the TANF block grant (Policy Brief No. 22). http://www.brookings.edu/research/papers/2002/04/welfare-weaver
Ziliak, J. P., D. N. Figlio, E. E. Davis, and L. S. Connolly. 2000. “Accounting for the decline in AFDC caseloads: Welfare reform or the economy?” The Journal of Human Resources 35: 570-86.
Zimmerman, J. N. 2000. Counting cases across Kentucky: Declines in cash assistance 1993-1997 (Rural Issues Brief No. 2). Department of Community and Leadership Development, University of Kentucky.
Zimmerman, J. N., and M. Renfro-Sargent. 2000. After welfare reform: K-TAP cases across Kentucky (Rural Issues Brief No. 3). Department of Community and Leadership Development, University of Kentucky.
Zuckerman, D. M. 2000. “Welfare reform in America: A clash of politics and research.” Journal of Social Issues 56: 587-600.
Stepwise Spatial Comparison Model
[Alt tag content for Appendix A: This model describes the stepwise spatial comparison for the state. The model begins by examining the state. In the first step, the model examines metropolitan counties and nonmetropolitan counties in the state. In the second step, the model examines metropolitan counties and divides nonmetropolitan counties into nonmetropolitan micropolitan counties and nonmetropolitan noncore counties.]
Regional Stepwise Spatial Comparison Model
[Alt tag content for Appendix B: This model describes the stepwise spatial comparison for the state and regions. The model begins by examining the state. In the first step, the model examines the Eastern, Western, and Central regions of the state. In the second step, the model examines metropolitan counties and nonmetropolitan counties by region. In the third step, the model examines metropolitan counties and divides nonmetropolitan counties into nonmetropolitan micropolitan counties and nonmetropolitan noncore counties, again, by region.]
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