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Exploring the Structural Determinants of Food Stamp Program Participation in the South: Does Place Matter? Principle Investigator: Tim Slack, Ph.D., Department of Sociology, Louisiana State University, 126 Stubbs Hall, Baton Rouge, LA 70803. Phone: (225) 578-1116. Fax: (225) 578-5102. Email: slack@lsu.edu. Graduate Research Assistant: Candice A. Myers, M.A., Department of Sociology, Louisiana State University.
2008 SRDC RIDGE Program Research Themes Addressed: 1. High Poverty Areas 2. Impact of Immigrants on Nutrition Programs 3. Use of Food/Nutrition Programs 4. Policy Choices
Acknowledgements: This project was supported by the Research Innovation and Development Grants in Economics (RIDGE) Program of the Economic Research Service (ERS), U.S. Department of Agriculture (USDA), in partnership with the Southern Rural Development Center (SRDC), Mississippi State University. Previous iterations of the analysis provided in this report were presented at the 2009 mid-year (Atlanta, GA) and annual meetings (Washington, DC) of the RIDGE Program as well as the 2009 annual meetings of the Mid-South Sociological Society (Lafayette, LA), Rural Sociological Society (Madison, WI), and Southern Sociological Society (New Orleans, LA). Special thanks to Huizhen Niu of the Agricultural Economics Geographic Information Systems (AEGIS) Lab in the Department of Agricultural Economics and Agribusiness, LSU AgCenter, for her assistance in developing the maps presented in this report and used to diagnose spatial effects.
Project Summary
This report extends prior research on food assistance in the U.S. South by exploring the
relationship between county-level Food Stamp Program (FSP) participation and the local social
structure. Our research design is comparative in focus. We examine how the structural
determinants of FSP participation in the South differ in comparison to the non-South. We also
assess whether such factors differ in high poverty areas of the South—namely, the Black Belt,
Central Appalachia, Mid-South Delta, and Texas Borderland—in comparison to other regions of
the country. In short, we find that geographic space and place matter. Our results show that the
South—and high poverty areas in the South, in particular—are home to high regional
concentrations of FSP participation. Further, our results show that the structural determinants of
FSP participation differ between the South and non-South, and between high poverty areas and
the rest of the country, in significant ways. This research contributes to the scientific literature
on food assistance by: 1) bringing attention to the importance of local space and place by
building on the limited number of FSP studies that have focused on a scale between that of
individuals/households and states; and 2) utilizing unique data from the USDA Food and
Nutrition Service (FNS) that have yet to be assessed in the research literature. This study
addresses four of the seven 2008 SRDC RIDGE Program research themes, including: High
Poverty Areas; Impact of Immigrants on Nutrition Programs; Use of Food/Nutrition Programs;
and Policy Choices. All of these themes are of importance to public policy aimed at combating
food insecurity and better serving vulnerable populations in the South. It is our hope that the
information presented in this report will prove useful to policy makers and practitioners by
helping to identify communities that are likely to have special food assistance needs and by
helping to anticipate how local social change may influence such needs in the future.
Introduction and Problem Statement
This report extends prior research on food assistance in the U.S. South by exploring the
relationship between county-level Food Stamp Program (FSP) participation and the local social
structure. To do so, we take an explicitly comparative focus. We examine how the structural
determinants of FSP participation in the South differ in comparison to the non-South. We also
assess whether such factors differ in high poverty areas of the South—namely, the Black Belt,
Central Appalachia, Mid-South Delta, and Texas Borderland—in comparison to other regions of
the country. Our goal is to contribute to the scientific literature on food assistance by: 1)
bringing attention to the importance of local space and place by building on the limited number
of FSP studies that have focused on a scale between that of individuals/households and states;
and 2) utilizing unique data from the USDA Food and Nutrition Service (FNS) that have yet to
be assessed in the research literature. Our approach addresses four of the seven 2008 SRDC
RIDGE Program research themes, including: High Poverty Areas; Impact of Immigrants on
Nutrition Programs; Use of Food/Nutrition Programs; and Policy Choices. All of these themes
are of importance to public policy aimed at combating food insecurity and better serving
vulnerable populations in the South. It is our hope that the information presented in this report
will prove useful to policy makers and practitioners by helping to identify communities that are
likely to have special food assistance needs and by helping to anticipate how local social change
may influence such needs in the future.
Geographic space and place are increasingly being recognized as pivotal axes of social
inequality (Gans 2002; Gieryn 2000; Lobao 2004; Lobao and Saenz 2002; Lobao, Hooks, and
Tickamyer 2007; Massey 1996; Tickamyer 2000). This is a promising development because of
the potential it allows for drawing out “both the importance of where actors are located in 1
geographic space and how geographic entities themselves are molded by and mold stratification”
(Lobao et al. 2007: 3). Scholars concerned with spatial inequality have called for special
attention to issues of comparative advantage (and disadvantage) across space as well as to the
consideration of the subnational scale (Lobao 2004; Lobao and Hooks 2007; Tickamyer 2000). This
study speaks directly to these ideas.
Research has clearly demonstrated enduring relationships between geographic location
and economic hardship (Lichter and Johnson 2007; Lyson and Falk 1993; Massey and Denton
1993; Rural Sociological Society Task Force on Persistent Rural Poverty 1993; Slack et al. 2009;
Tickamyer and Duncan 1990; Wilson 1980). A prime example of this is the high and persistent
poverty that characterizes the South in comparison to other regions of the U.S., and areas such as
the Black Belt, Central Appalachia, Mid-South Delta, and Texas Borderland, in particular.
Related to the acute economic distress faced by many communities across the South is the fact that
the use of the FSP is much higher in the region compared to other areas of the country (Goetz,
Rupasingha, and Zimmerman 2004).
The FSP—more recently referred to as the Supplemental Nutrition Assistance Program or
SNAP—is the nation’s largest food assistance program and a critical component of the social safety
net for its least advantaged citizens (Nord 2001). The reach of the program is substantial, helping to
put “healthy food on the table for over 35 million people each month” (Food and
Nutrition Service 2009), a figure that equates to more than one-in-10 Americans. As such, the FSP
plays a critical role in addressing food assistance and nutrition issues faced by vulnerable populations, a
fact that is especially true in the South.
To date, the bulk of research on FSP dynamics has either focused on individual and household-level data drawn from large national panel surveys (Bhattarai, Duffy, and Raymond
2
2005; Frongillo, Jyoti, and Jones 2006; Gibson 2004, 2006; Gunderson and Oliveira 2001; Nord
2001; Van Hook and Balistreri 2006) or on state-level data (Figlio, Gunderson, and Ziliak 2000;
Tapogna et al. 2004; Wallace and Blank 1999; Wilde et al. 2000). Individual and household-
level studies provide important information on how the characteristics of individuals and
households influence FSP participation, but do not allow for a determination of the role of social
structure in influencing program outcomes. State-level studies, on the other hand, allow for the
identification of structural mechanisms that operate across geographic space, but potentially
mask important intra-state variation in FSP dynamics. A few recent studies have brought
attention to the importance of using county-level data to assess structural influences on the FSP
(Goetz et al. 2004; Hoyt and Scott 2004; Wilde and Dicken 2003). These studies have
highlighted significant spatial variation in FSP-related outcomes at the county level as well as
important aggregate mechanisms that shape these outcomes. For example, Goetz et al. (2004)
use FSP expenditure data to show that counties characterized by better labor market conditions
and higher per capita reductions in AFDC/TANF expenditures experienced the greatest declines
in FSP expenditures between 1995 and 1999. In a related vein, drawing on data for a sample of
U.S. states, Hoyt and Scott (2004) show strong relationships between county-level participation in
cash assistance programs (i.e., AFDC/TANF and SSI) and FSP participation. Last, Wilde and
Dicken (2003) use county-level administrative data from California to explore FSP participation by
race/ethnicity, showing Hispanics, in particular, to be a growing share of the FSP caseload in that
state. We use these studies as a point of departure in the analysis that follows. 3
Research Objectives
Our overarching aim is to assess how FSP participation varies across space and establish how
other place-based (i.e., county-level) characteristics serve to influence existing differences.
More specifically, we are interested in establishing how FSP participation in the South and high
poverty areas is related to five broad sets of county-level characteristics: labor market conditions,
population structure, human capital, residential context and inequality, and expenditures on cash
assistance programs (we elaborate on the independent variables that make-up each of these
dimensions in the following section). Accordingly, we examine three specific research
questions:
Q1: Does the prevalence of FSP participation differ between the South and non-South?
Q2: Does the prevalence of FSP participation differ between high poverty areas and other
regions of the country?
Q3: Do the aggregate mechanisms related to FSP participation differ between the regions of
interest?
Research Methods
Data and Measures
To address these questions we analyze data drawn from the Food and Nutrition Service (FNS)
and the Economic Research Service (ERS) of the USDA, and the U.S. Census Bureau. Our units of
analysis are counties. We elaborate on the definition of our measures below. 4
Dependent Variable
Our dependent variable—FSP participation—is based on data obtained from the FNS circa 2005.
The FNS collects FSP participation data twice a year for the report months of January and July.
We define FSP participation as the average percentage of a county’s total population receiving
FSP benefits in these two time periods across the years 2004, 2005, and 2006. While this
approach necessarily masks intra-county variation in FSP participation over the period, it has the
benefit of providing a very stable assessment of the typical level of participation in the program
at mid-decade.
The FNS collects and organizes its data by project area. A project area refers to the
political entity designated by the state as the administrative unit for program operations. In most states
project areas are counties, but in some states project areas range from entities such as cities to the
state as a whole. We exclude states (N=15) from our analysis that do not organize their FSP program
operations at the county level. These states are primarily located in the Northeast and Northwest.1 In
total, our sample is comprised of 2561 counties in 34 states. Figure 1
provides an illustration of our data coverage.
<Figure 1 about here> Regional Definitions
We begin by defining the South according to the geography delineated by the Census Bureau2
and then define the non-South as the remainder of the contiguous U.S. Given available data, the 1 Specifically, these states include Connecticut, Idaho, Maine, Massachusetts, Montana, Nebraska, New Hampshire, New York, Oregon, Rhode Island, Utah, Vermont, Washington, West Virginia, and Wyoming.
2 The U.S. Census Bureau defines the South as including the following states: Alabama, Arkansas, Delaware, Washington D.C., Georgia, Kentucky, Louisiana, Maryland, Mississippi, North Caroline, Oklahoma, South Carolina, Tennessee, Texas, Virginia, and West Virginia.
5
South includes at total of 1,330 counties in 16 states (West Virginia is the only state in the South
not included in the analysis) and the non-South includes a total of 1,231 counties in 18 states.3
We define the Texas Borderland as all Texas counties whose largest city is within 100 miles of
the U.S.-Mexico border (Saenz 1997; Slack et al. 2009). The Borderland stretches along the Rio
Grande River and includes a total of 41 counties. We define Central Appalachia according to the
geography delineated by the Appalachian Regional Commission (ARC).4 This region includes a
total of 78 counties in eastern Kentucky, northern Tennessee, and southwest Virginia.5 We
define the Delta by starting with the geography delineated by the Delta Regional Authority, but
further restrict our analysis to the core Delta counties in Arkansas, Louisiana, and Mississippi
(Slack et al. 2009).6 This region straddles the southern portion of the Mississippi River and
includes a total of 133 counties. Last, following Wimberley and Morris (1997), we define the
Black Belt as the crescent of counties spanning through the South from North Carolina down
through Texas, minus those counties included in our Delta definition. This region includes a total
of 494 counties in Alabama, Arkansas, Florida, Georgia, Louisiana, Mississippi, North Carolina,
South Carolina, Tennessee, Texas, and Virginia. 3 Specifically, the non-South includes counties in the following states: Arizona, California, Colorado, Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nevada, New Jersey, New Mexico, North Dakota, Ohio, Pennsylvania, South Dakota, and Wisconsin.
4 The complete Appalachian geography defined by the ARC includes a total of 420 counties in 13 states. We chose to focus on the central region because it is where economic hardship is most concentrated. 5 The ARC definition of Central Appalachia also includes 9 counties in West Virginia which are not represented in our analysis. 6 The complete Delta geography defined by the Delta Regional Authority includes a total of 240 counties in 8 states. We chose to focus on the core of this region because it better conforms to social, rather than political, definitions of the “Delta” and it is also where poverty is most concentrated
6
Additional Independent Variables
We assess the influence of a total of 20 additional independent variables tapping five dimensions
of place-based characteristics in relation to FSP participation. The five dimensions of
independent variables include county-level: labor market conditions, population structure, human
capital, residential context and inequality, and expenditures on cash assistance programs. All
independent variables are drawn from the U.S. Census Bureau or ERS circa 2000.7 We outline
and motivate our choice of independent variables below. Given the dearth of literature on the
structural determinants of FSP dynamics, we rely heavily on the poverty literature as a guide to
our variable choices with the expectation that aggregate factors associated with higher poverty
rates will also be associated with higher FSP participation and vice versa.
Labor Market Conditions. We draw on a range of variables that tap local labor market
conditions. Research has shown that labor market hardship is strongly related to poverty. We
therefore include the county-level unemployment rate with the expectation that higher
unemployment will be related to higher FSP participation. Consonant with McLaughlin,
Gardner, and Lichter (1999), we include a measure of secondary/peripheral sector employment
that captures the percentage of the civilian labor force employed in retail trade and particular
segments of the service sector (i.e., arts, entertainment, and recreation services; accommodation
and food services; and other services [primarily personal services and business and repair
services]), as well as agriculture, forestry, fishing, and hunting.8 We also include median
household income, an important measure of local economic development and well-being (Lobao
and Hooks 2003). In sum, we examine three variables that tap local labor market conditions: the 7 We rely on 2000 as the reference year for our independent variables because of the wealth of information made available by the decennial census.
8 Notably, this definition excludes higher skilled service work, including employment in educational services, and health care or social support services.
7
percentage unemployed, the percentage employed in secondary/peripheral sector jobs, and
median household income.
Population Structure. Several variables related to the population structure of places have
been demonstrated to be important predictors of poverty, and thus are expected to be associated
with FSP participation. For example, larger percentages of female-headed households are often
associated with lower income-levels and higher poverty (Lichter and McLaughlin 1995; Lobao
and Hooks 2003). Migration is also important, in that positive net migration can serve as a proxy
for a strong economy because migrants are drawn to areas with stronger job opportunities (Frey
and Liaw 2005), while areas characterized by population stagnation or loss tend to be associated
with higher poverty rates (Rupasingha and Goetz 2007). Age structure has also been shown to
be an important correlate of poverty, with places characterized by larger proportions of non-
working age populations tending to have higher poverty (Lobao and Hooks 2003). Immigration
is another factor that influences poverty. While research has shown poverty among immigrants
tends to exceed that of their native-born counterparts at the individual level (Crowley, Lichter,
and Qian 2006), at the aggregate level the percentage of the population that is foreign-born has
been shown to be associated with lower levels of poverty (Rupasingha and Goetz 2007), likely
reflecting the “pull” of immigrants to more attractive labor markets. Last, the literature reveals
that places characterized by higher minority concentrations tend to be characterized by higher
poverty rates (Friedman and Lichter 1998; Rupasingha and Goetz 2007; Saenz 1997; Voss et al.
2006). For these reasons, we examine six variables related to county-level population structure:
the percentage of households headed by single females with children, net migration, the
percentage of the population that is under 15 years and over 65 years of age (non-working age), the
percentage foreign-born, the percentage black, and the percentage Latino. 8
Human Capital. A large body of literature has demonstrated significant linkages between
aggregate-level human capital and poverty. Places with lower aggregate educational levels tend
to be home to higher poverty rates (Friedman and Lichter 1998; Rupasingha and Goetz 2007;
Voss et al. 2006). English-language fluency has also been identified as an important determinant
of poverty among immigrant populations (Crowley et al. 2006; Davila and Mora 2000; Davila,
Bohara, and Saenz 1993). We therefore examine two variables related to county-level human
capital in relation to FSP participation: the percentage of the population 25 years and older with
less than a high school degree (or equivalent) and the percentage of the population that does not
speak English well or at all.
Residential Context and Inequality. An extensive literature has examined the higher
incidence of poverty in rural as compared to urban areas (see Jensen, McLaughlin, and Slack
2003), though research has also documented lower Public Assistance receipt among the poor in
rural areas (Jensen and Eggebeen 1994). We therefore consider whether places are metropolitan,
micropolitan, or noncore in character (Economic Research Service 2007). Dummy variables are
employed for micropolitan and noncore areas with metropolitan serving as the reference
category. There is also an established literature that points to important relationships between
residential segregation and spatially concentrated hardship (Lichter et al. 2008; Massey and
Denton 1993). We therefore examine four different measures of county-level inequality: indexes
of dissimilarity for the poor versus non-poor, black versus white, and Latino versus white
populations, as well as the Gini coefficient of household income inequality. 9 We also consider
whether an area suffers from housing stress (yes/no) as defined by the ERS.10
9 The index of dissimilarity measures the evenness with which two groups are distributed across the component
geographic areas (i.e., census tracts) that make up a larger area (i.e., counties). In cases where two groups are perfectly segregated, the index of dissimilarity would take the maximum value of 100. In cases where two groups are perfectly integrated, the index of dissimilarity would take the minimum value of zero (Racial Residential Segregation Measurement Project 2008). The Gini coefficient of household income inequality is a ratio measure of income dispersion. In cases
9
Expenditures on Cash Assistance. Of the few county-level studies of FSP dynamics, both
Goetz et al. (2004) and Hoyt and Scott (2004) have shown significant positive relationships
between expenditures on means-tested cash assistance programs and participation in the FSP.
Therefore, we examine county per capita expenditures on TANF and SSI combined.
In sum, we plan to assess the influence of five different realms of variables on county-
level FSP participation: labor market conditions, population structure, human capital, residential
context and inequality, and expenditures on cash assistance. Table 1 provides descriptive
statistics for all of the variables employed in the analysis.
<Table 1 about here> Analytic Strategy
The analysis that follows is carried out using both descriptive statistics and Ordinary Least
Squares (OLS) regression. Our modeling strategy uses a lagged panel design, in which
independent variables are measured at an earlier point in time compared to the dependent
variable. As outlined above, our independent variables represent county-level measures circa
2000, while our dependent variable measures FSP participation circa 2005. An advantage of
lagged panel designs compared to simultaneous cross-sectional analysis (i.e., when all variables are
measured at the same point in time) is that the approach addresses problems associated with
endogeneity and simultaneity bias. where household incomes are perfectly unequal (i.e., one household accounts for all of the household income in a county, while all others have no income), the Gini coefficient would take the maximum value of 1. In cases where household incomes are perfectly equal (i.e., all households have exactly the same income), the Gini coefficient would take the minimum value of zero (Jones and Weinberg 2000).
10 A county is defined as experiencing housing stress if 30% or more of the households within a county had one or more of the following housing conditions in 2000: 1) lacked complete plumbing; 2) lacked complete kitchen; 3) paid 30% of more of income for owner costs or rent; or 4) had more than one person per room.
10
Before finalizing our models, we examined a series of regression diagnostics to assess
statistical threats to the accuracy of our models. Based upon these diagnostics, we use a natural
logarithm transformation to normalize the substantial skew in the distribution of county per
capita expenditures on TANF and SSI. To address multicollinearity we use factor analysis to
combine percent foreign-born, percent Latino, and percent with limited English proficiency into
a single factor score.11 We also tested for spatial effects in our data, an issue that has proven an
important consideration in aggregate analyses of poverty (Rupasingha and Goetz 2007; Voss et
al. 2006) and FSP expenditures (Goetz et al. 2004). These diagnostics revealed significant
regional clusters of counties with high and low FSP participation, necessitating the consideration
of spatial effects in our models (this is discussed further in the results section). Last, because
states vary in their approaches to FSP administration specifically and addressing poverty and
inequality more generally (Lobao and Hooks 2003), we control for state fixed effects.
Specifically, our models include 33 dummy variables (California serves as the reference group)
to control for state-specific county-invariant factors.
Results
Prevalence of FSP Participation
The geographic distribution of FSP participation is illustrated in Figure 2. Notable here are those
counties one standard deviation above the mean—“high” FSP participation—and those counties
one standard deviation below the mean—“low” FSP participation. Figure 2 clearly illustrates a
higher prevalence of FSP participation in the South and a lower prevalence of FSP participation
in the non-South. 11 We used principal component factor analysis with varimax rotation to combine these three variables into a single factor. All variables loaded on the same component and explain 86.9 percent of the variance in these variables among counties.
11
<Figure 2 about here>
Table 2 provides a description of FSP participation by region in tabular form. The data
show that county-level FSP participation rates are 5.0 percent higher in the South (13.2%)
compared to the non-South (8.2%) on average. The data also show that county-level FSP
participation rates in high poverty areas are well above the national average (10.4%), reaching
13.4 percent in the Black Belt, 18.5 percent in the Delta, 19.0 percent in the Borderland, and 21.9
percent in Appalachia, respectively.
Models of FSP Participation Spatial Effects As shown in Figure 2, county-level FSP participation is not randomly distributed across
geographic space. Rather there are clear regional concentrations of counties with both high and
low levels of participation. This clustering effect is quantified by the Moran’s I statistic, which
measures the degree to which the characteristics of spatial units are correlated with those of their
neighbors. In the absence of any spatial relationship the Moran’s I statistic has a mean of zero,
while a perfect positive correlation has a mean of 1, and a perfect negative correlation has a
mean of -1. As shown in Figure 3, the Moran’s I value for county-level FSP participation is
0.61, indicating significant positive spatial clustering—significant clustering of like values—
among counties.
Also shown in Figure 3 is the Moran scatterplot, which plots county-level FSP
participation (x-axis) against its spatial lag (y-axis) for each county. The data are standardized so
that units on the graph represent standard deviations from the mean. The spatial lag is calculated
as the standardized level of FSP participation averaged across a given county’s contiguous 12
neighbors. The quadrants of the scatterplot correspond to four types of spatial association: 1) the
upper right quadrant shows those counties with above average values on the dependent variable
that are neighbored by counties that also have above average values (high-high); 2) the lower left
quadrant shows those counties with below average values on the dependent variable that are
neighbored by counties that also have below average values (low-low); 3) the upper left quadrant
shows those counties with below average values on the dependent variable that are neighbored
by counties with above average values (low-high); and 4) the lower right quadrant shows those
counties with above average values on the dependent variable that are neighbored by counties
with below average values (high-low). The Moran’s I statistic discussed above (0.61) is the
slope of the line fitted to the data distributed across these quadrants. Substantively, what Figure
3 shows is that counties with high FSP participation tend to be surrounded by neighboring
counties that also have high FSP participation, while counties characterized by low FSP
participation levels tend to neighbored by counties that also exhibit low FSP participation.
<Figure 3 about here>
Figure 4 shows a Local Indicators of Spatial Association (LISA) map, which provides a
geographic illustration of the data presented in the Moran’s I scatterplot. The LISA map shades
significant clusters of counties falling into one of the four value dimensions displayed in the
scatterplot (high-high; low-low; low-high; and high-low), while counties that are not in
significant spatial clusters are not shaded. Notable here is that the regional clusters of high FSP
participation are all in the South and in high poverty areas in particular.
<Figure 4 about here>
13
Regression Analysis
In order to assess the relationship between FSP participation and regional location in the South, we
estimate region-specific models that regress the five dimensions of county characteristics— labor
market conditions, population structure, human capital, residential context and inequality, and
expenditures on cash assistance programs—on county-level FSP participation. Table 3
shows the results of these models.
<Table 3 about here>
In both the South and non-South, percent unemployed, percent households headed by
single mothers, percent non-working age, percent less than high school, and per capita
expenditures on cash assistance show significant positive relationships with FSP participation,
while the factor score tapping the presence of Latino immigrant populations shows a significant
negative relationship with FSP participation. It is also notable that the spatial lag term shows a
significant positive effect in both the South and non-South, reflecting the importance of
accounting for regional concentrations of high and low FSP participation.
Tests for statistical significance between the corresponding coefficients for the South and
non-South models (see Clogg, Petkova, and Haritou 1995; Paternoster et al. 1998) indicate a
number of significant regional differences in FSP participation. In the South, percent
unemployed, percent non-working age, percent less than high school, and black/white
segregation all exert significantly greater upward pressure on FSP participation than is true in the
non-South, while in small towns (micropolitan areas) in the South FSP participation is
significantly lower than in the non-South.
In Table 4 we turn our attention to FSP participation in high poverty areas. Model 1 shows coefficients for models assessing the association between regional dummy variables and
14
county-level FSP participation. Each of the four coefficients for the high poverty areas are
positive and significant compared to the rest of the U.S., reflecting higher than average FSP
participation in these regions. Again, the spatial lag term is positive and significant.
<Table 4 about here>
In Model 2 we assess whether these regional differences in FSP participation hold net of
other important predictors. The results reveal that Appalachia and the Borderland continue to
show significantly higher FSP participation even with the full range of predictors in the model.
In contrast, net of other factors the Delta shows no significant difference from other regions of
the country, while the Black Belt actually shows significantly lower FSP participation once all of
the predictors are considered. Again, the spatial lag remains positive and significant in the
expanded model.
Finally, we focus on understanding differences in the effects of the predictors of FSP
participation in each of the four high poverty areas. Table 5 shows coefficients from a single
regression model that examines interaction terms between each high poverty area dummy
variable and the full range of predictors. It is important to note that while the effects for each
high poverty area are displayed in separate columns in Table 5 to facilitate the presentation, the
coefficients are the product of a single model that includes all main effects, interactions, and
controls. The results show notable differences in the structural determinants of FSP participation
in each high poverty area. In the Borderland, percent female-headed households and percent
non-working age show particularly strong positive relationships with FSP participation, while
income inequality shows a particularly strong negative relationship. In Appalachia, percent
female-headed households also shows a particularly strong positive effect, while percent black is
associated with a particularly strong negative effect on FSP participation. In the Delta, the 15
results show percent non-working age holds a particularly strong positive relationship with FSP
participation, while median household income and percent less than high school show
particularly strong negative associations. Last, in the Black Belt percent non-working age and
black/white segregation show particularly strong positive relationships with FSP participation,
while percent female-headed households and poor/non-poor segregation are associated with
particularly strong negative effects. Again, even considering the effects of all of the variables
included in this model, the spatial lag continues to show a significant positive effect.
<Insert Table 5 about here>
Discussion and Implications Researchers are increasingly recognizing geographic space as a key dimension of social
inequality (Gans 2002; Gieryn 2000; Lobao 2004; Lobao and Saenz 2002; Lobao, Hooks, and
Tickamyer 2007; Massey 1996; Tickamyer 2000). In particular, scholars concerned with spatial
inequality have called for special attention to issues of comparative advantage (and
disadvantage) across space as well as to the consideration of the subnational scale (Lobao 2004;
Lobao and Hooks 2007; Tickamyer 2000). In addition, research has clearly demonstrated
enduring relationships between geographic location and economic hardship (Lichter and Johnson
2007; Lyson and Falk 1993; Massey and Denton 1993; Rural Sociological Society Task Force on
Persistent Rural Poverty 1993; Slack et al. 2009; Tickamyer and Duncan 1990; Wilson 1980). A
prime example of this is the high and persistent poverty that characterizes the South in
comparison to other regions of the United States, and areas such as the Appalachia, the Black
Belt, the Borderland, and the Delta, in particular. 16
In this study we speak to these issues by examining the spatial contours and structural
determinants of FSP participation at the county level. In short, we show that geographic space
and place matter. The U.S. South and high poverty in areas in the South, in particular, are home
to especially high regional concentrations of FSP participation. We show that even when
considering a full range of additional predictors suggested by the literature, the regional
clustering of high and low FSP participation rates continues to be a significant consideration.
Further, our results show that the structural determinants of FSP participation differ between the
South and non-South, and between high poverty areas and the rest of the country, in significant
ways.
This research contributes to the scientific literature on food assistance by: 1) bringing
attention to the importance of local space and place by building on the limited number of FSP
studies that have focused on a scale between that of individuals/households and states; and 2)
utilizing unique data from the USDA Food and Nutrition Service (FNS) that have yet to be
assessed in the research literature.
The consideration of geographic space and place also has important implications for
public policy. Indeed, this is underscored by the continued support of the Southern Rural
Development Center (SRDC), in partnership with the Economic Research Service (ERS) of the
USDA, for food and nutrition-related research focused on the U.S. South. The underlying
assumption of this programmatic focus is that the South faces unique challenges in terms of food
assistance and hunger and that research is needed to advance evidence-based public policy to
address these issues. Accordingly, this study addresses four of the seven 2008 RIDGE Program
research themes/topics, namely: High Poverty Areas; Impact of Immigrants on Nutrition
Programs; Use of Food/Nutrition Programs; and Policy Choices. All of these themes are of 17
importance to policy makers who seek to better understand the determinants of FSP use and how
to better serve vulnerable populations in the South, and beyond. It is our hope that the
information presented in this report will prove useful to policy makers and practitioners by
helping to identify communities that are likely to have special food assistance needs and by
helping to anticipate how local social change may influence such needs in the future. 18
References
Bhattarai, G.R., P.A. Duffy, and J. Raymond. 2005. “Use of Food Pantries and Food Stamps in Low-Income Households in the United States.” Journal of Consumer Affairs 39: 276- 298.
Clogg, C.C., E. Petkova, and A. Haritou. 1995. “Statistical Methods for Comparing Regression Coefficients between Models.” American Journal of Sociology 100:1261-1293. Crowley, M., D.T. Lichter, and Z. Qian. 2006. “Beyond Gateway Cities: Economic
Restructuring and Changing Poverty Among Mexican Immigrants.” Family Relations 55: 345-360.
Davila, A., and M.T. Mora. 2000. “English Skills, Earnings, and the Occupational Sorting of
Mexican Americans along the U.S.-Mexico Border.” International Migration Review 34: 133-157.
Davila, A., A.K. Bohara, and R. Saenz. 1993. “Accent Penalties and the Earnings of Mexican Americans.” Social Science Quarterly 74: 902-916. Economic Research Services. 2007. “Measuring Rurality: What is Rural?” United States
Department of Agriculture. Retrieved on June 8, 2008, from http://www.ers.usda.gov/ Briefing/Rurality/WhatisRural/.
Figlio, D.N., C. Gundersen, and J.P. Ziliak. 2000. “The Effects of the Macroeconomy and Welfare Reform on Food Stamp Caseloads.” American Journal of Agricultural Economics 82: 635-641. Food and Nutrition Service. 2009. “Food Stamp Program.” United States Department of Agriculture. Retrieved on June 8, 2008, from http://www.fns.usda.gov/FSP/.
Frey, W.H., and K..L Liaw. 2005. “Migration within the United States: Role of Race-Ethnicity.” Brookings-Wharton Papers on Urban Affairs 6: 207-262. Friedman, S., and D.T. Lichter. 1998. “Spatial Inequality and Poverty Among American Children.” Population Research and Policy Review 17: 91-109. Frongillo, E.A., D.F. Jyoti, and S.J. Jones. 2006. “Food Stamp Program Participation Is Associated with Better Academic Learning among School Children.” Journal of Nutrition 136: 1077-1080.
Gans, H.J. 2002. “The Sociology of Space: A Use-Centered View.” City and Community 1: 329- 339. Gibson, D. 2004. “Long-Term Food Stamp Program Participation is Differentially Related to Overweight in Young Girls and Boys.” Journal of Nutrition 134: 372-379.
19
Gibson, D. 2006. “Long-Term Food Stamp Program Participation Is Positively Related to Simultaneous Overweight in Young Daughters and Obesity in Mothers.” Journal of Nutrition 136: 1081-1085. Gieryn, T.F. 2000. “A Space for Place in Sociology.” Annual Review of Sociology 26: 463-496.
Goetz, S.J., A. Rupasingha, J.N. Zimmerman. 2004. “Spatial Food Stamp Participation Dynamics in U.S. Counties.” The Review of Regional Studies 34: 172-190.
Gundersen, C., and V. Oliveira. 2001. “The Food Stamp Program and Food Insufficiency." American Journal of Agricultural Economics 83: 875-887.
Hoyt, W.H., and F.A. Scott. 2004. “Geographic Variation in Food Stamp and Other Assistance Program Participation Rates: Identifying Poverty Pockets in the South.” Final report submitted to the Southern Regional Development Center, Mississippi State University, Mississippi State, MS.
Jensen, L., and D.J. Eggebeen. 1994. Nonmetropolitan poor children and reliance on public assistance. Rural Sociology 59: 45-65. Jensen, L., D.K. McLaughlin, and T. Slack. 2003. “Rural Poverty: The Persisting Challenge.” Pp. 118-131 in D.L. Brown and L.E. Swanson (eds.), Challenges for Rural America in the Twenty-First Century. University Park, PA: The Pennsylvania State University Press. Jones, A.F., and D.F. Weinberg. 2000. “The Changing Shape of the Nation’s Income Distribution.” Current Population Reports. U.S. Census Bureau.
Lichter, D.T., and K.M. Johnson. 2007. “The Changing Spatial Concentration of America’s Rural Poor Population.” Rural Sociology 72: 331-358.
Lichter, D.T., and D.K. McLaughlin. 1995. “Changing Economic Opportunities, Family Structure, and Poverty in Rural Areas.” Rural Sociology 60:688-706. Lichter, D.T., D. Parisi, M.C. Taquino, and B. Beaulieu. 2008. “Race and the Micro-Scale
Concentration of Poverty.” Cambridge Journal of Regions, Economy and Society 1: 51- 67.
Lobao, L.M. 2004. “Continuity and Change in Place Stratification: Spatial Inequality and Middle-Range Territorial Units.” Rural Sociology 69: 1-30.
Lobao, L.M., and G. Hooks. 2003. “Public Employment, Welfare Transfers, and Economic Well-Being across Local Populations: Does a Lean and Mean Government Benefit the Masses?” Social Forces 82:519-556.
Lobao, L.M., and R. Saenz. 2002. “Spatial Inequality and Diversity as an Emerging Research Area.” Rural Sociology 67: 497-511.
20
Lobao, L.M. and G. Hooks. 2007. “Advancing the Sociology of Spatial Inequality: Space, Places, and the Subnational Scale.” Pp. 29-61 in The Sociology of Spatial Inequality, edited by L.M. Lobao, G. Hooks, and A.R. Tickamyer. Albany, New York: State University of New York Press. Lobao, L.M., G. Hooks, and A.R. Tickamyer. 2007. The Sociology of Spatial Inequality. Albany, NY: State University of New York Press. Lyson, T.A., and W.W. Falk. 1993. Forgotten Places: Uneven Development in Rural America. Lawrence, KS: University Press of Kansas.
Massey, D.S. 1996. “The Age of Extremes: Concentrated Affluence and Poverty in the Twenty- First Century.” Demography 33: 395-412.
Massey, D.S, and N.A. Denton. 1993. American Apartheid: Segregation and the Making of the Underclass. Cambridge, MA: Harvard University Press.
McLaughlin, D.K., E. Gardner, and D.T. Lichter. 1999. “Economic Restructuring and the Changing Prevalence of Female-Headed Families in America.” Rural Sociology 64: 394- 416.
Nord, M. 2001. “Food Stamp Participation and Food Stamp Security.” Food Review 24: 13-19. Paternoster, R., R. Brame, P. Mazerolle, A. Piquero. 1998. “Using the Correct Statistical Test for the Equality of Regression Coefficients.” Criminology 36:859-866.
Racial Residential Segregation Project. 2008. “Residential Segregation: What It Is and How We Measure It.” Retrieved on June 8, 2008, from http://enceladus.isr.umich.edu/ race/seg.html.
Rupasingha, A., and S.J. Goetz. 2007. “Social and Political Forces as Determinants of Poverty: A Spatial Analysis.” The Journal of Socio-Economics 36: 650-671.
Rural Sociological Society Task Force on Persistent Rural Poverty. 1993. Persistent Poverty in Rural America. Boulder, CO: Westview Press.
Saenz, R. 1997. “Ethnic Concentration and Chicano Poverty: A Comparative Approach.” Social Science Research 26:205-28. Slack, T., J. Singelmann, K. Fontenot, D.L. Poston, Jr., R. Saenz, and C. Siordia. 2009. “Poverty in the Texas Borderland and Lower Mississippi Delta: A Comparative Analysis of Differences by Family Type.” Demographic Research 20: 353-376. Tapogna, J., A. Suter, M. Nord, M. Leachman. 2004. “Explaining Variations in State Hunger Rates.” Family Economics & Nutrition Review 16: 12-21.
21
Tickamyer, A.R. 2000. “Space Matters! Spatial Inequality in Future Sociology.” Contemporary Sociology 29: 805-813. Tickamyer, A.R., and C.M. Duncan. 1990. “Poverty and Opportunity Structure in Rural America.” Annual Review of Sociology 16: 67-86.
Van Hook, J., and K.S. Balistreri. 2006. “Ineligible Parents, Eligible Children: Food Stamps Receipt, Allotments, and Food Insecurity among Children of Immigrants.” Social Science Research 35: 228-251.
Voss, P.R., D.D. Long, R.B. Hammer, and S. Friedman. 2006. “County Child Poverty Rates in the U.S.: A Spatial Regression Approach.” Population Research and Development Review 25: 369-391. Wallace, G., and R.M. Blank. 1999. “What Goes Up Must Come Down? Explaining Recent
Changes in Public Assistance Case Loads.” Pp. 49-89 in S.H. Danziger (ed.), Economic Conditions and Welfare Reform. Kalamazoo, MI: W.E. Upjohn Institute for Employment Research.
Wilde, P., and C. Dicken. 2003. “Using County-Level Data to Study the Race and Ethnicity of Food Stamp Program Participants in California.” Paper prepared for the American Agricultural Economics Association Meeting, Montreal, Canada. Wilde, P., P. Cook, C. Gunderson, M. Nord, and L. Tiehen. 2000. “The Decline in Food Stamp Program Participation in the 1990’s.” Food Assistance and Nutrition Report No. 7. Washington, D.C.: USDA.
Wilson, W.J. 1980. The Declining Significance of Race: Blacks and Changing American Institutions, 2nd Edition. Chicago: University of Chicago Press.
Wimberley, R.C., and L.V. Morris. 1997. The Southern Black Belt: A National Perspective. The University of Kentucky: TVA Rural Studies.
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Table 1. Descriptive statistics Variables Mean Minimum Maximum Dependent variable FSP participation 10.80 0.00 50.70 Independent variables Labor market conditions
Percent unemployed 3.35 0.00 17.30 Percent secondary/peripheral jobs 31.01 15.05 67.11 Median household income 35,086 15,805 82,929
Population structure Percent female-headed households 6.24 0.00 24.38 Net migration 5.70 -41.10 133.80 Percent black 10.02 0.00 86.07 Percent non-working age 35.63 17.91 49.29 Percent foreign-born¹ 3.30 0.00 50.90 Percent Latino¹ 6.52 0.00 98.10
Human capital Percent less than h.s. 23.73 3.04 65.29 Percent not English proficient¹ 1.64 0.00 28.60
Residential context and inequality Metropolitan 0.35 0.00 1.00 Micropolitan 0.22 0.00 1.00 Non-core 0.43 0.00 1.00 Gini 42.53 32.80 56.30 Housing stress 0.15 0.00 1.00 Index of dissimilarity: poor vs. non-poor 24.29 0.00 54.90 Index of dissimilarity: black vs. white 49.55 0.00 96.40 Index of dissimilarity: Latino vs. white 32.22 0.00 73.70
Expenditures on cash assistance Per capita expenditures on TANF and SSI² 174.97 0.00 3,920.32
N=2,561 ¹ Original distributions presented, but variables are included in a factor analysis score for analysis. ² Original distribution presented, but variable was transformed to its natural log for analysis.
23
Table 2. County-level FSP participation by region, 2004-2006 Region N Mean Minimum Maximum Nation 2,561 10.8 0.0 50.7 South 1,330 13.2 0.0 45.9 Non-South 1,231 8.2 0.3 50.7 Appalachia 78 21.9 10.6 45.9 Black Belt 494 13.4 2.0 39.3 Borderland 41 19.0 2.3 35.9 Delta 133 18.5 4.6 40.1
24
Table 3. Unstandardized OLS regression coefficients from region-specific models of county-level FSP participation, 2004-2006 Independent variables South Non-South Labor market conditions
Percent unemployed 0.724***† 0.298** Percent secondary/peripheral jobs -0.030 0.009 Median household income -0.002 -0.003
Population structure Percent female-headed households 0.789*** 0.991*** Net migration 0.007 0.017 Percent black -0.024* 0.023 Percent non-working age 0.270***† 0.165*** Latino/immigrant/English -0.330** -0.357*
Human capital Percent less than h.s. 0.229***† 0.080***
Residential context and inequality Metro (ref.) Micro -0.514*† 0.246 Non-core -0.189 0.204 Gini 0.076 0.169*** Housing stress (1=yes) 1.311*** 0.781 Poor/non-poor D -0.009 -0.023 Black/white D 0.024***† 0.004 Latino/white D -0.004 0.002
Expenditures on cash assistance Ln per capita expenditures on TANF/SSI 1.719*** 1.336***
Spatial lag 0.293*** 0.433*** Intercept -25.508*** -25.592*** R-squared 0.837 0.743 N 1,330 1,231 Notes: Models include controls for state fixed effects. Median household income coefficient multiplied by 100. * p<.05; ** p<.01; *** p<.001; † Different from non-South model at p < .05.
25
Table 4. Unstandardized OLS regression coefficients of county-level FSP participation in high poverty regions, 2004-2006 Independent variables Model 1 Model 2 Borderland 2.655*** 1.155* Appalachia 2.282*** 2.194*** Delta 2.309*** -0.543 Black Belt 1.701*** -0.962*** Labor market conditions
Percent unemployed 0.508*** Percent secondary/peripheral jobs -0.021 Median household income -0.002
Population structure Percent female-headed households 0.843*** Net migration 0.010 Percent black -0.003 Percent non-working age 0.198*** Latino/immigrant/English -0.343**
Human capital Percent less than h.s. 0.162***
Residential context and inequality Metro (ref.) Micro -0.100 Non-core -0.013 Gini 0.131*** Housing stress (1=yes) 1.109*** Poor/non-poor D -0.017 Black/white D 0.010* Latino/white D -0.006
Expenditures on cash assistance Ln per capita expenditures on TANF/SSI 1.648***
Spatial lag 0.850*** 0.310*** Intercept 0.723 -27.356*** R-squared 0.602 0.823 N 2,561 2,561 Notes: Models include controls for state fixed effects. Median household income coefficient multiplied by 100. * p<.05; ** p<.01; *** p<.001.
26
Table 5. Unstandardized OLS regression regional interaction coefficients from models of county-level FSP participation in high poverty regions, 2004-2006 Independent variables Borderland Appalachia Delta Black Belt Labor market conditions
Percent unemployed 0.175 0.229 0.063 0.048 Percent secondary/peripheral jobs -0.130 -0.294 0.068 0.005 Median household income -0.028 -0.045 -0.025* -0.005
Population structure Percent female-headed households 1.134* 0.702* -0.077 -0.350** Net migration 0.026 0.080 -0.016 -0.024 Percent black -0.419 -0.686* -0.004 0.039 Percent non-working age 0.670** -0.160 0.296* 0.112* Latino/immigrant/English -0.525 -2.866 -0.810 0.139
Human capital Percent less than h.s. 0.106 0.028 -0.122* -0.016
Residential context and inequality Metro (ref.) Micro 1.151 -2.008 -1.365 -0.499 Non-core 0.602 -0.847 -0.848 -0.781 Gini -0.966*** -0.097 -0.188 -0.039 Housing stress (1=yes) -0.162 0.162 0.362 0.015 Poor/non-poor D -0.002 0.171 0.029 -0.055* Black/white D -0.064 0.016 -0.002 0.033* Latino/white D -0.018 0.048 0.011 -0.004
Expenditures on cash assistance Ln per capita expenditures on TANF/SSI -0.564 1.542 0.636 -0.213
Spatial lag 0.276*** Intercept -26.026 R-squared 0.840 N 2,561 Notes: Cell entries are coefficients produced by a single regression model including all main effects, all regional interactions, the spatial lag, and state fixed effects. Median household income coefficient multiplied by 100. * p<.05; ** p<.01; *** p<.001.
27
No county-level data
County-level data
Figure 1. County-level FSP data coverage 28
N
W E
No data S
Low participation (< 1 S.D.)
Average participation
High participation (> 1 S.D.)
Figure 2. County-level FSP participation, 2004-2006
29
Moran’s I = 0.6093 Figure 3. Moran scatterplot of county-level FSP participation, 2004-2006
30
Figure 4. LISA cluster map of county-level FSP participation, 2004-2006
31
About the Authors
Tim Slack, Principal Investigator: Tim Slack holds a Ph.D. in rural sociology from Penn State
University and is currently an Assistant Professor of Sociology at Louisiana State University.
Dr. Slack’s research interests are in the areas of poverty and inequality, rural sociology, social
demography, and work and labor markets. For further information visit www.soc.lsu.edu/slack.
Candice A. Myers, Graduate Research Assistant: Candice A. Myers holds a M.A. in
sociology from Louisiana State University. She is currently pursuing her Ph.D. in sociology at LSU
and plans to examine poverty and hardship-related issues for her dissertation research. For further
information visit www.soc.lsu.edu/myers. 32
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