This article was downloaded by: [UNIVERSITY OF ADELAIDE LIBRARIES]On: 17 November 2014, At: 16:47Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

Journal of PovertyPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/wpov20

Predictors of U.S. Food InsecurityAcross Nonmetropolitan, Suburban,and Principal City Residence During theGreat RecessionAlisha Coleman-Jensen aa USDA Economic Research Service , Washington , DC , USAPublished online: 25 Oct 2012.

To cite this article: Alisha Coleman-Jensen (2012) Predictors of U.S. Food Insecurity AcrossNonmetropolitan, Suburban, and Principal City Residence During the Great Recession, Journal ofPoverty, 16:4, 392-411, DOI: 10.1080/10875549.2012.720657

To link to this article: http://dx.doi.org/10.1080/10875549.2012.720657

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the“Content”) contained in the publications on our platform. However, Taylor & Francis,our agents, and our licensors make no representations or warranties whatsoever as tothe accuracy, completeness, or suitability for any purpose of the Content. Any opinionsand views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Contentshould not be relied upon and should be independently verified with primary sourcesof information. Taylor and Francis shall not be liable for any losses, actions, claims,proceedings, demands, costs, expenses, damages, and other liabilities whatsoever orhowsoever caused arising directly or indirectly in connection with, in relation to or arisingout of the use of the Content.

This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

Journal of Poverty, 16:392–411, 2012ISSN: 1087-5549 print/1540-7608 onlineDOI: 10.1080/10875549.2012.720657

Predictors of U.S. Food Insecurity AcrossNonmetropolitan, Suburban, and Principal City

Residence During the Great Recession

ALISHA COLEMAN-JENSENUSDA Economic Research Service, Washington, DC, USA

In 2009, 14.7% of U.S. households were food insecure, lacking con-sistent access to adequate food. Food insecurity increased in allresidence areas with the “Great Recession” beginning in 2007, withthe greatest increase in suburbs. The Current Population SurveyFood Security Supplement was used to examine the likelihood offood insecurity across residence areas. Net of income and otherhousehold characteristics, suburban households were more likelyto be food insecure than nonmetropolitan households and as likelyto be food insecure as principal city households. The results suggestthat during the recession economic hardship spread increasinglyto suburbs.

KEYWORDS food insecurity, food security, food assistance, resi-dence, suburban, urban, metropolitan, nonmetropolitan, rural

INTRODUCTION

Historically poverty has been, and is currently, more prevalent in principalcities and nonmetropolitan areas than in suburban areas of the United States(DeNavas-Walt, Proctor, & Smith, 2010). The prevalence of food insecurity,lacking consistent access to adequate food for an active healthy life for all

This article not subject to U.S. copyright law.The views expressed here are those of the author and cannot be attributed to the

Economic Research Service or the U.S. Department of Agriculture. A previous version of thismanuscript was presented at the Annual Meetings of the Rural Sociological Society, August2010, Atlanta, GA.

Address correspondence to Alisha Coleman-Jensen, USDA Economic Research Service,Food Economics Division, Food Assistance Branch, 355 E St. SW, Washington, DC 20024-3221,USA. E-mail: acjensen@ers.usda.gov

392

Dow

nloa

ded

by [

UN

IVE

RSI

TY

OF

AD

EL

AID

E L

IBR

AR

IES]

at 1

6:47

17

Nov

embe

r 20

14

U.S. Food Insecurity Across Residence 393

household members, follows this same pattern. In 2009, 14.7% of all house-holds were food insecure. The prevalence varied somewhat by residence as17.2% of principal city households, 14.2% of nonmetropolitan, and 13.2% ofsuburban households were food insecure (Nord, Coleman-Jensen, Andrews,& Carlson, 2010). With the onset of the Great Recession, food insecurityincreased substantially for all residence areas from 2007 (13.5% for principalcity, 11.7% for nonmetropolitan, and 9% for suburban households) to 2008(17.7% for principal city, 14.2% for nonmetropolitan, 12.7% for suburban)(Nord, Andrews, & Carlson, 2008, 2009). The percentage increase in foodinsecurity was the greatest in suburban areas.

This article considers trends in food insecurity across residence, focus-ing on the most recent recession. Although the prevalence of food insecurityremains lower in suburban areas than in principal cities or nonmetropolitanareas, the effects of the recession, in terms of increases in the prevalenceof food insecurity, appear to have been greatest in suburbs. Increasing foodinsecurity in suburbs is consistent with an increasing use of emergency foodassistance in suburban areas during the recession (Andrews, 2010) as wellas an increasing prevalence of suburban poverty (Kneebone & Garr, 2010b).In this article, logistic regression models were used to examine the associ-ation between food security and residence during the Great Recession. Theshare of households participating in community food assistance programs byfood security status and residence and a logistic regression model predictingfood pantry use were estimated.

BACKGROUND AND LITERATURE REVIEW

Residence and Food Insecurity

The recession beginning in December 2007 appears to have differed in sever-ity across residence.1 Unemployment was higher in metro counties thannonmetro counties, and employment decline was greater in metro areas(Parker, Kusmin, & Marre, 2010). The increase in the percentage of peo-ple in poverty from 2007 to 2009 was greater in metro than in nonmetroareas (DeNavas-Walt, Proctor, & Smith, 2009; DeNavas-Walt et al., 2010).Meanwhile, the economic downturn had similar effects across suburban andprincipal city metro areas. Within metro areas, the increase in poverty wascomparable between principal city and suburban areas (Kneebone, 2010).On average, among the 100 largest metro areas, unemployment rates weresimilar between principal cities and suburbs. Within metropolitan areas,suburbs and principal cities had similar increases in unemployment rates(Kneebone & Garr, 2010a).

Increases in poverty and unemployment associated with the economicdownturn may have been more severe in metro areas, but other characteris-tics of places also relate to household food insecurity. One such characteristic

Dow

nloa

ded

by [

UN

IVE

RSI

TY

OF

AD

EL

AID

E L

IBR

AR

IES]

at 1

6:47

17

Nov

embe

r 20

14

394 A. Coleman-Jensen

of places that may contribute to or exacerbate the effects of food insecurityare food deserts. Rural communities may lack large and less expensive foodretailers. Rural households may have to pay higher prices in local stores orincur greater travel costs for lower prices further away (Blanchard & Lyson,2002). The further a household must travel to a grocery store, the highertheir likelihood of food insecurity. This effect is exacerbated for householdswithout a car (Stracuzzi & Ward, 2010). Rural food deserts are often char-acterized by an inadequate transportation infrastructure (U.S. Department ofAgriculture [USDA], 2009).

Principal city residents, particularly those that live in low-income andminority areas, may also be disadvantaged by food deserts and have dif-ficulty accessing healthful food (Larson, Story, & Nelson, 2009; USDA,2009). Depending on the quality of public transportation, principal cityresidents, similarly to rural households, may have difficulty getting to thestore. Typically, food deserts are more of a principal city or nonmetro phe-nomenon than a suburban one, perhaps contributing to the lower prevalenceof suburban food insecurity.

Local efforts to reduce food insecurity are also important, but capacitymay differ within and across residence. When local food assistance groupsprovide programs such as food pantries and soup kitchens, residents are lesslikely to experience food insecurity (Morton, Bitto, Oakland, & Sand, 2005).However, emergency food assistance programs may have different charac-teristics across place, and not all places have access to such resources. Urbanemergency food systems tend to be more formalized with more funding andstronger links to federal food assistance programs than rural emergency foodprograms resulting in urban clients’ greater likelihood of receiving refer-rals to other resources. Furthermore, rural food pantries serve larger areasthat may require rural residents to travel greater distances to access services(Molnar, Duffy, Claxton, & Bailey, 2001). Similar to nonmetro areas, suburbansocial service agencies often have larger service areas than agencies in cities(Allard & Roth, 2010). Compared to principal cities, suburban areas are morelikely to lack human service agencies including emergency food providers toassist low-income families (Murphy & Wallace, 2010). This is also true whencomparing high-poverty suburban and principal city neighborhoods.

Food pantries have become increasingly important during the mostrecent recession, and their availability across residence may be changing.From 2007 to 2009, the number of households using food pantries increasedby 44%. For the first time since 2001, in 2009 the number of food pantry usersin suburban areas was higher than the number in principal cities (Andrews,2010). In a Feeding America study of emergency food providers, the percent-age of all food pantry clients living outside principal city areas, in suburbanor rural areas, increased from 48% in 2005 to 53% in 2009 (Mabli, Cohen,Potter, & Zhao, 2010). During this time there appears to have been a shiftin food pantry clientele that demonstrates increased need for and use of

Dow

nloa

ded

by [

UN

IVE

RSI

TY

OF

AD

EL

AID

E L

IBR

AR

IES]

at 1

6:47

17

Nov

embe

r 20

14

U.S. Food Insecurity Across Residence 395

pantries in suburban areas. All types of suburban social services, not justemergency food providers, have reported increased need for services includ-ing seeing clients during the recession that have no previous experienceswith the social safety net (Allard & Roth, 2010).

Participation rates in emergency food programs and federal foodassistance vary across residence. Previous research has found that rural low-income households are less likely than urban low-income households toaccess food pantries and meal programs (Morton, Bitto, Oakland, & Sand,2008). However, another study found that low-income nonmetro house-holds are more likely than low-income metro households to access foodpantries and participate in SNAP2 (Bhattarai, Duffy, & Raymond, 2005). FoodStamp/SNAP participation rates from the late 1990s and 2008 indicate higherparticipation among eligible rural households than urban households (Leftin,2010; McConnell & Ohls, 2002).

Household Characteristics and Food Insecurity

Several household characteristics are strongly related to food insecurity,including income, adult educational attainment, employment, and familystructure. By definition, food insecurity is related to a lack of resources.In 2008, among households with incomes below the poverty threshold,42.2% were food insecure, whereas 7.7% of households with income above185% of poverty were food insecure (Nord et al., 2009). Food insecurity ismore prevalent among households whose adult members have low edu-cational attainment than among households with higher education (Nord,2009). Households with a head working part-time, multiple jobs, or variedhour employment are more likely to be food insecure than households witha head employed in full-time work, net of income and other characteris-tics (Coleman-Jensen, 2011). Households with children headed by a singlemother are more likely to be food insecure than those headed by a marriedcouple (Nord et al., 2009).

Hypotheses

This study examines the likelihood of households experiencing food inse-curity during the Great Recession focusing on the relative well-being ofhouseholds across residence. It is difficult to advance hypotheses regardinghow residence areas have fared in terms of food insecurity because there areseveral factors that vary across residence and have differing effects on thedirection of food insecurity. For example, nonmetro counties and principalcities may be the most affected by food deserts, suburbs and principal citiesmay have been the most affected by rising unemployment, whereas accessto formal emergency food providers may differ across residence. Residence-specific logistic regression models were estimated to explore the possibility

Dow

nloa

ded

by [

UN

IVE

RSI

TY

OF

AD

EL

AID

E L

IBR

AR

IES]

at 1

6:47

17

Nov

embe

r 20

14

396 A. Coleman-Jensen

that the relationships between predictors and food insecurity differ acrossresidence areas. Participation in emergency food assistance programs acrossresidence was examined to determine whether differential participation mayexplain differences in food insecurity rates across residence.

METHOD

Data and Measures

Data for the analysis come from the Current Population Survey Food SecuritySupplement (CPS-FSS). An overview of trends in food insecurity used datafrom 2004 through 2009. For the primary analysis only data from the2009 CPS-FSS was used. About 46,000 households completed the CPS-FSS in2009. All analyses and figures shown were estimated using the food securitysupplement weights that produce nationally representative estimates.

Household food security status is based on responses to an 18-item questionnaire. These questions refer to conditions and behaviors thatdescribe households having difficulty putting enough food on the table.Households with children answer all 18 items. Households without chil-dren answer only the first 10 items referring to food insecure conditionsamong adults. Households responding affirmatively to three or more ques-tions are categorized as food insecure. Households with children respondingaffirmatively to eight or more questions are classified as having very lowfood security—severe food insecurity characterized by reduced intake anddisrupted eating patterns. Households without children responding affirma-tively to six or more items are classified as having very low food security(Bickel, Nord, Price, Hamilton, & Cook, 2000; Nord et al., 2010).

The household food security scale was used to classify households byfood security status for descriptive statistics showing the prevalence of foodinsecurity across household characteristics (Figure 1 and Table 1). The house-hold food security scale was also used to show the percentage of householdsusing emergency food assistance programs by food security status (Table 4).However, the adult food security scale was used in the multivariate analysis(Table 2 and Table 3). The adult scale comprises the 10 questions that referonly to food insecurity among adults. The adult scale is preferable to thehousehold scale for modeling purposes because the adult scale provides amore comparable measure of food security status for households with andwithout children.

Residence is defined according to the June 2003 Office of Managementand Budget classifications (OMB, 2003). Three classifications are usedaccording to respondent’s county of residence: principal cities, suburbs, andnonmetropolitan counties. A Metropolitan Statistical Area (MSA) includes atleast one urbanized area with a population of 50,000 or more. One or moresignificant places within an MSA can be defined as a principal city according

Dow

nloa

ded

by [

UN

IVE

RSI

TY

OF

AD

EL

AID

E L

IBR

AR

IES]

at 1

6:47

17

Nov

embe

r 20

14

U.S. Food Insecurity Across Residence 397

to population size and employment characteristics. Balance or suburbanrefers to areas within MSAs that are not principal cities but are economicallyintegrated with the principal city. Because residence classification is assignedby county, some “balance” metro areas are in less densely populated areasmore accurately described as exurban. Some areas defined as metropolitanare not further classified as principal city or suburban. Households in theseareas are not included in analyses that separate households into principalcity, suburban, and nonmetropolitan counties. Nonmetropolitan is a residualcategory that refers to all counties that are not defined as metropolitan.

Other variables used to describe household characteristics are house-hold composition, race and ethnicity of the household reference person, thehousehold income relative to the poverty threshold measured as a ratio ofincome to poverty, Census region, employment, and educational attainmentof the highest educated adult. Household composition is defined accordingto presence of children and marital status. Other households with childrenare those with children in complex living arrangements, such as children ofother relatives. Race and ethnicity is defined as White, non-Hispanic; Black,non-Hispanic; Hispanic; or Other. Hispanics may be of any race. Employmentis defined by the employment status of all household adults. Households aredistributed among the following mutually exclusive categories: one or moreemployed full-time; retired; part-time, non-economic reasons; part-time, eco-nomic reasons; unemployed looking for work, none employed; disabled,none in the labor force; none in the labor force, none disabled. If one ormore adults is working full-time the household is categorized as one ormore employed full-time. If no members are employed full-time, but some-one in the household is retired, the household is categorized as retired.Next, if no members are employed full-time or retired, and one or moremembers works part-time households are further classified by whether theyare working part-time for noneconomic or economic reasons. If there are noemployed or retired household members, households are defined as unem-ployed if someone is in the labor force and looking for work. If no membersare in the labor force or retired, households are differentiated by whethera household member indicates he or she is out of the labor force due todisability.

Plan of Analysis

The analysis and results are displayed in four tables. The first section of theanalysis is descriptive and shows the prevalence of food insecurity and verylow food security by residence over time (Figure 1) and across householdcharacteristics (Table 1). Logistic regression models predicting the likelihoodof food insecurity are estimated for the sample as a whole (Table 2) and res-idence specific logistic regression models are shown (Table 3). To augment

Dow

nloa

ded

by [

UN

IVE

RSI

TY

OF

AD

EL

AID

E L

IBR

AR

IES]

at 1

6:47

17

Nov

embe

r 20

14

398 A. Coleman-Jensen

the results described in Tables 2 and 3, participation in emergency foodassistance programs by food security status and residence was examined.

RESULTS

Prevalence of Household Food Insecurity

The prevalence of food insecurity and very low food security by residenceand year from 2004 to 2009 is shown in Figure 1. For each year, food inse-curity was most prevalent in principal cities, followed by nonmetropolitanareas and was least prevalent in suburbs. The increase in food insecurityand very low food security from 2007 to 2008 was evident across residences.Although food insecurity was less prevalent in suburbs than other residenceareas, the effects of the recession appeared greatest in suburbs. From 2007 to2008, food insecurity increased 41% in the suburbs, 31% in principal cities,and 21% in nonmetro areas. From 2008 to 2009, the prevalence of foodinsecurity did not change significantly in any residence area.

The prevalence of food insecurity by residence across household char-acteristics is shown in Table 1. In the table, different superscripted lettersdenote significant differences in the estimated percentages of food insecurehouseholds between residence areas (t > 1.96).3 The tests of statistical sig-nificance noted in the table are two-way tests between principal cities andsuburbs, suburbs and nonmetro counties, and principal cities and nonmetrocounties.

FIGURE 1 Prevalence of food insecurity and very low food security by residence and year.Source: Data from CPS Food Security Supplement.

Dow

nloa

ded

by [

UN

IVE

RSI

TY

OF

AD

EL

AID

E L

IBR

AR

IES]

at 1

6:47

17

Nov

embe

r 20

14

U.S. Food Insecurity Across Residence 399

TABLE 1 Prevalence of Food Insecurity by Residencea and Household Characteristics, 2009

Percent food insecure

Allhouseholds

Principalcity Suburban Nonmetropolitan

All households 14.7 17.2c,e 13.2c 14.2e

Household compositionWith children < age 18 21.3 26.5c,e 18.5c 20.5e

With children < age 6 22.9 27.8c,e 19.9c 21.9e

Married-couple families 14.7 19.5c,e 12.2c,d 15.2d,e

Male head 27.8 31.1 25.7 20.7Female head 36.6 37.5 37.8 34.7Other household with child 32.6 39.6 33.2 20.7

With no children < age 18 11.4 13.0c,e 10.2c 11.4More than one adult 9.2 10.9c,e 8.1c 9.0Men living alone 14.5 16.4c,e 12.9c 15.0Women living alone 14.7 14.5 14.6 15.0

With elderly 7.5 9.8c 6.2c 7.8Elderly living alone 5.8 7.5c 5.0c 6.0

Race/ethnicity of household reference personWhite non-Hispanic 11.0 10.6e 10.3d 12.3d,e

Black non-Hispanic 24.9 26.5c 22.0c 24.7Hispanic 26.9 28.6 26.1 23.1Other 13.2 11.6e 12.2d 21.5d,e

Household income-to-poverty ratio0.00 up to 1.00 43.0 43.8 45.1d 38.9d

Greater than 1.00, up to 1.50 31.0 32.2 33.9d 28.1d

Greater than 1.50, up to 2.00 23.5 27.1e 24.3d 18.3d,e

Greater than 2.00, up to 3.00 14.6 16.7e 14.8d 11.4d,e

Greater than 3.00, up to 4.00 8.8 12.1c,e 8.5c 6.1e

Greater than 4.00 2.8 2.9 3.0d 2.0d

Income missing 9.0 11.6c,e 8.3c 6.6e

Census geographic regionNortheast 12.2 14.5c 10.6c 12.6Midwest 13.9 20.3c,e 11.5c 12.9e

South 15.9 18.0c 14.1c 16.2West 15.5 16.3e 16.1d 12.6d,e

Employment status of adultsOne or more employed full-time 11.8 13.6c,e 10.9c 11.3e

Retired 6.5 9.1c,e 5.1c 6.7e

Part-time, noneconomic reasons 19.3 21.7 18.7 20.8Part-time, economic reasons 41.9 41.9 42.3 37.9Unemployed looking for work 46.0 45.1 48.0 40.4Disabled, none in the labor force 41.3 40.7 43.3 38.6None in the labor force, none

disabled29.7 27.6 32.9 27.0

Educational attainment of adultsLess than high school 28.5 33.2c,e 26.8c 24.4e

Completed high school or GeneralEquivalency Diploma

19.9 24.7c,e 18.3c 16.3e

(Continued)

Dow

nloa

ded

by [

UN

IVE

RSI

TY

OF

AD

EL

AID

E L

IBR

AR

IES]

at 1

6:47

17

Nov

embe

r 20

14

400 A. Coleman-Jensen

TABLE 1 (Continued)

Percent food insecure

Allhouseholds

Principalcity Suburban Nonmetropolitan

Some college (including 2-yeardegree)

17.7 21.2c,e 17.4c 15.1e

Four-year degree or higher 6.0 7.2c 5.5c 5.8Number of households (1,000)b 118,174 32,690 48,698 19,838

Source: Data from CPS-FSS 2009.aMetropolitan area residence is based on the 2003 Office of Management and Budget delineation.Residence inside or outside of principal cities is not identified for about 17% of households inmetropolitan statistical areas.bData are weighted to represent the U.S. population. Number of households in the sample equals 45,363.The number of households in the sample by residence are as follows: principal city n = 10,682; suburbann = 16,056; nonmetropolitan n = 10,229.cThe difference in percentage between principal city and suburban households is statistically significant,p < 0.05.dThe difference in percentage between suburban and nonmetropolitan households is statisticallysignificant, p < 0.05.eThe difference in percentage between principal city and nonmetropolitan households is statisticallysignificant, p < 0.05.

The significant differences in percentages of households that were foodinsecure generally follow the expected pattern of a higher prevalence offood insecurity in principal cities, then nonmetro areas, and the lowestprevalence in the suburbs. A few statistically significant differences thatdeviate from this pattern should be noted. One difference of interest wasthe higher prevalence of food insecurity among non-Hispanic Whites and“Other” race and ethnic groups in nonmetro areas compared to principalcities and suburbs. In nonmetro counties, 21.5% of other race and ethnicgroups were food insecure, much higher than the percentage in principalcities (11.6%) and suburbs (12.2%). This difference was related to the higherconcentration of Native Americans in the “Other” race and ethnic groupin nonmetro areas, consistent with higher poverty rates for this group (innonmetro counties, 74.9% of “Other” household heads identified themselvesas American Indian or Alaskan Native, compared to 18.6% in suburbs and11.5% in principal cities). Another difference of note was the higher preva-lence of food insecurity in suburban areas for households with incomes lessthan 300% of the poverty threshold compared to nonmetro households withincomes less than 300% of poverty. Regionally, food insecurity was signif-icantly more prevalent in principal cities than in suburbs in the Northeast,Midwest, and South. The prevalence was significantly higher in the Midwestand West in principal cities than in nonmetro areas. Food insecurity wassignificantly more prevalent in the suburban West than in the nonmetroWest.

Dow

nloa

ded

by [

UN

IVE

RSI

TY

OF

AD

EL

AID

E L

IBR

AR

IES]

at 1

6:47

17

Nov

embe

r 20

14

U.S. Food Insecurity Across Residence 401

Logistic Regression Models Predicting Adult Food Insecurity

A logistic regression model predicting adult food insecurity using the2009 CPS-FSS is shown in Table 2. When income and other demographiccharacteristics were accounted for, suburban and principal city householdswere more likely than nonmetropolitan households to be food insecure.Suburban households were 30% more likely to be insecure than nonmetrohouseholds, and principal city households were 28% more likely to be inse-cure. This suggests that the higher prevalence of food insecurity in nonmetroareas compared to suburbs shown in the bivariate statistics was related topopulation characteristics. Poverty is generally more prevalent in rural thanin suburban areas, and educational attainment and earnings are lower innonmetro than metro counties. After these population differences were con-trolled across residence, rural residence did not carry an additional risk forbecoming food insecure. Rather, rural residence appeared to be protective offood insecurity. The pseudo R2 (R2

L) for this model is 19.5 (Menard, 2001).In estimations not shown, suburban was entered as the reference cate-

gory for residence with all other variables in the model the same as thoseshown in Table 2. The coefficients for principal city and not identified areaswere not significantly different from suburbs. Nonmetropolitan householdswere significantly less likely to be insecure (parameter estimate of −0.26, p< .001) than suburban households. It is worth noting that when house-hold characteristics were controlled, principal city households were notmore likely to be food insecure than suburban households (principal cityparameter estimate of –0.02, p = .61), even though in bivariate statistics theprevalence of food insecurity was higher in principal cities than suburbs.Household characteristics were significantly related to food insecurity, con-sistent with prior research reported above (see Table 2). Households in theSouth were more likely to be food insecure than households in the Northeast.

Residence-specific logistic regression models are displayed in Table 3.These models predict the likelihood of adult food insecurity separately forprincipal city, suburban, and nonmetro households. These models are easierto interpret than a single model with interactions between residence and allother characteristics. Interactions are tested for significance by comparing theconfidence intervals of the coefficients across models.

The discussion focuses on the main significant differences among thethree models. The relationship between food security and household com-position differed across residence. Net of other factors, being a single mothermattered more in suburbs than it did in other residence areas. In suburbs,women living alone were also more likely to be food insecure than marriedcouples, but there was no significant difference in the likelihood of food inse-curity between women living alone and married couples in principal citiesor nonmetro areas. Supports for households headed by a woman alone withor without children may be inadequate in suburban areas. Alternatively, it

Dow

nloa

ded

by [

UN

IVE

RSI

TY

OF

AD

EL

AID

E L

IBR

AR

IES]

at 1

6:47

17

Nov

embe

r 20

14

402 A. Coleman-Jensen

TABLE 2 Logistic Regression Model Predicting Adult Food Insecurity, 2009

Parameterestimate

Standarderror

pvalue

Oddsratio

InterceptResidence (Reference: Nonmetropolitan)

−3.52 0.09 <.0001

Unidentified 0.18 0.05 <.001 1.20Suburban 0.26 0.05 <.0001 1.30Principal city 0.25 0.05 <.0001 1.28

With children < age 6 −0.11 0.05 0.02 0.89Household composition (Reference: Married couple with children)

Male head with children 0.26 0.08 <.01 1.30Female head with children 0.39 0.05 <.0001 1.47Other household with child 0.41 0.16 0.01 1.51More than one adult −0.04 0.05 0.39 0.96Men living alone 0.01 0.06 0.92 1.01Women living alone 0.19 0.06 <.01 1.21

With elderly −0.53 0.06 <.0001 0.59Race/ethnicity of household reference person (Reference: White, non-Hispanic)

Black, non-Hispanic 0.30 0.04 <.0001 1.34Hispanic 0.28 0.05 <.0001 1.32Other 0.03 0.07 0.62 1.04

Household income-to-poverty ratio (Reference: Greater than 4.00)0.00, up to 1.00 2.46 0.07 <.0001 11.74Greater than 1.00, up to 1.50 2.26 0.07 <.0001 9.60Greater than 1.50, up to 2.00 1.95 0.07 <.0001 7.00Greater than 2.00, up to 3.00 1.48 0.07 <.0001 4.38Greater than 3.00, up to 4.00 1.00 0.08 <.0001 2.71Income missing 1.00 0.07 <.0001 2.72

Census geographic region (Reference: Northeast)Midwest 0.08 0.05 0.12 1.08South 0.11 0.05 0.02 1.12West 0.18 0.05 <.001 1.20

Employment status of adults (Reference: One or more employed full-time)Retired −0.54 0.07 <.0001 0.58Part-time, non-economic reasons −0.03 0.07 0.66 0.97Part-time, economic reasons 0.78 0.07 <.0001 2.19Unemployed looking for work 1.00 0.06 <.0001 2.73Disabled, none in the labor force 0.72 0.06 <.0001 2.05None in the labor force, none

disabled−0.01 0.08 0.94 0.99

Educational attainment of adults (Reference: Completed high school or GeneralEquivalency Diploma)Less than high school 0.07 0.05 0.19 1.07Some college (including 2-year

degree)0.02 0.04 0.59 1.02

Four-year degree or higher −0.58 0.05 <.0001 0.56Number of households in samplea 45,363−2 log-likelihood 29171.42AIC 29237.42Likelihood ratio 7064.48df 32R2

L 19.50

Note. AIC = Akaike’s Information Criterion. Source: Data from CPS-FSS 2009.aAnalysis is weighted to represent the U.S. population using a normalized weight.

Dow

nloa

ded

by [

UN

IVE

RSI

TY

OF

AD

EL

AID

E L

IBR

AR

IES]

at 1

6:47

17

Nov

embe

r 20

14

TAB

LE3

Res

iden

ceSp

ecifi

cLo

gist

icReg

ress

ion

Model

sPre

dic

ting

Adult

Food

Inse

curity

,20

09

Princi

pal

city

model

Suburb

anm

odel

Nonm

etro

polit

anm

odel

Par

amet

eres

timat

eSt

andar

der

ror

pva

lue

Odds

ratio

Par

amet

eres

timat

eSt

andar

der

ror

pva

lue

Odds

ratio

Par

amet

eres

timat

eSt

andar

der

ror

pva

lue

Odds

ratio

Inte

rcep

t−3

.50

0.17

<.0

001

−3.2

20.

13<

.000

1−3

.56

0.21

<.0

001

With

child

ren

<ag

e6

−0.0

4d0.

090.

640.

96−0

.19

0.08

0.02

0.83

−0.3

5d0.

10<

.001

0.71

House

hold

com

posi

tion

(Ref

eren

ce:M

arried

couple

with

child

ren)

Mal

ehea

dw

ithch

ildre

n0.

240.

160.

121.

270.

30c

0.15

<.0

51.

35−0

.21c

0.20

0.30

0.81

Fem

ale

hea

dw

ithch

ildre

n0.

21b

0.10

0.04

1.24

0.67

b,c

0.09

<.0

001

1.95

0.36

c0.

11<

.01

1.43

Oth

erhouse

hold

with

child

1.08

d0.

35<

.01

2.94

0.38

0.31

0.23

1.46

−0.2

6d0.

350.

460.

77M

ore

than

one

adult

−0.1

40.

100.

170.

870.

050.

090.

551.

05−0

.08

0.10

0.47

0.93

Men

livin

gal

one

0.02

0.12

0.83

1.03

0.08

0.11

0.45

1.09

−0.1

70.

130.

190.

85W

om

enliv

ing

alone

−0.0

2b0.

120.

890.

980.

49b,c

0.11

<.0

001

1.63

0.02

c0.

130.

871.

02W

ithel

der

ly−0

.52

0.11

<.0

001

0.59

−0.6

30.

10<

.000

10.

54−0

.48

0.11

<.0

001

0.62

Rac

e/et

hnic

ityofhouse

hold

refe

rence

per

son

(Ref

eren

ce:W

hite

,non-H

ispan

ic)

Bla

ck,non-H

ispan

ic0.

42d

0.08

<.0

001

1.53

0.27

0.08

<.0

11.

310.

08d

0.11

0.43

1.09

His

pan

ic0.

42d

0.08

<.0

001

1.52

0.32

0.08

<.0

001

1.38

0.10

d0.

130.

421.

11O

ther

0.03

0.13

0.83

1.03

0.05

0.12

0.70

1.05

0.18

0.15

0.22

1.20

House

hold

inco

me-

to-p

ove

rty

ratio

(Ref

eren

ce:G

reat

erth

an4.

00)

0.00

,up

to1.

002.

35d

0.14

<.0

001

10.4

92.

45c

0.11

<.0

001

11.5

52.

93c,

d0.

19<

.000

118

.65

Gre

ater

than

1.00

,up

to1.

502.

13d

0.14

<.0

001

8.41

2.28

c0.

11<

.000

19.

792.

75c,

d0.

19<

.000

115

.60

Gre

ater

than

1.50

,up

to2.

002.

030.

15<

.000

17.

621.

850.

12<

.000

16.

362.

160.

19<

.000

18.

64G

reat

erth

an2.

00,up

to3.

001.

510.

14<

.000

14.

531.

380.

11<

.000

13.

961.

790.

19<

.000

16.

01G

reat

erth

an3.

00,up

to4.

001.

28b

0.16

<.0

001

3.60

0.87

b0.

12<

.000

12.

401.

070.

21<

.000

12.

91In

com

em

issi

ng

1.14

0.14

<.0

001

3.13

0.83

0.11

<.0

001

2.30

1.11

0.19

<.0

001

3.02

Cen

sus

geogr

aphic

regi

on

(Ref

eren

ce:N

orthea

st)

Mid

wes

t0.

48b,d

0.10

<.0

001

1.61

−0.0

7b0.

090.

420.

93−0

.19d

0.11

0.09

0.83

South

0.41

b,d

0.09

<.0

001

1.50

0.01

b0.

080.

921.

01−0

.15d

0.11

0.16

0.86

Wes

t0.

32d

0.09

<.0

011.

370.

21c

0.09

0.02

1.23

−0.1

1c,d

0.13

0.39

0.89

(Con

tin

ued

)

403

Dow

nloa

ded

by [

UN

IVE

RSI

TY

OF

AD

EL

AID

E L

IBR

AR

IES]

at 1

6:47

17

Nov

embe

r 20

14

TAB

LE3

(Contin

ued

)

Princi

pal

city

model

Suburb

anm

odel

Nonm

etro

polit

anm

odel

Par

amet

eres

timat

eSt

andar

der

ror

pva

lue

Odds

ratio

Par

amet

eres

timat

eSt

andar

der

ror

pva

lue

Odds

ratio

Par

amet

eres

timat

eSt

andar

der

ror

pva

lue

Odds

ratio

Em

plo

ymen

tst

atus

ofad

ults

(Ref

eren

ce:O

ne

or

more

emplo

yed

full-

time)

Ret

ired

−0.1

6b,d

0.13

0.23

0.86

−0.7

8b0.

12<

.000

10.

46−0

.55d

0.13

<.0

001

0.58

Par

t-tim

e,non-e

conom

icre

asons

0.02

0.12

0.86

1.02

0.00

0.12

1.00

1.00

0.16

0.13

0.22

1.17

Par

t-tim

e,ec

onom

icre

asons

0.83

0.13

<.0

001

2.29

0.71

0.13

<.0

001

2.04

0.53

0.18

<.0

11.

70U

nem

plo

yed

looki

ng

for

work

0.86

b0.

11<

.000

12.

371.

19b

0.10

<.0

001

3.27

0.91

0.13

<.0

001

2.48

Dis

able

d,none

inth

ela

bor

forc

e0.

680.

12<

.000

11.

980.

800.

12<

.000

12.

220.

660.

12<

.000

11.

93

None

inth

ela

bor

forc

e,none

dis

able

d−0

.16

0.14

0.26

0.85

0.11

0.16

0.48

1.12

0.10

0.19

0.60

1.11

Educa

tional

Attai

nm

entofA

dults

(Ref

eren

ce:Com

ple

ted

hig

hsc

hoolor

Gen

eral

Equiv

alen

cyD

iplo

ma)

Less

than

hig

hsc

hool

−0.0

4d0.

090.

690.

960.

050.

100.

591.

060.

31d

0.10

<.0

11.

36So

me

colle

ge(incl

udin

g2-

year

deg

ree)

−0.0

20.

070.

800.

980.

040.

070.

571.

040.

160.

080.

041.

17

Four-

year

deg

ree

or

hig

her

−0.5

7d0.

09<

.000

10.

57−0

.62c

0.08

<.0

001

0.54

−0.2

8c,d

0.11

0.01

0.76

Num

ber

ofhouse

hold

sin

sam

ple

a10

,682

16,0

5610

,229

−2lo

g-lik

elih

ood

7682

.07

9317

.52

6528

.27

AIC

7742

.07

9377

.52

6588

.27

Like

lihood

ratio

1702

.89

2649

.62

1479

.65

df

2929

29R

2L

18.1

422

.14

18.4

8

Not

e.A

IC=

Aka

ike’

sIn

form

atio

nCrite

rion.

Sou

rce:

Dat

afr

om

CPS-

FSS

2009

.aA

nal

yses

are

wei

ghte

dto

repre

sentth

eU

.S.popula

tion

usi

ng

anorm

aliz

edw

eigh

t.bThe

diffe

rence

inth

eco

effici

ents

bet

wee

nprinci

pal

city

and

suburb

anhouse

hold

sis

stat

istic

ally

sign

ifica

nt,

p<

0.05

.cThe

diffe

rence

inth

eco

effici

ents

bet

wee

nsu

burb

anan

dnonm

etro

polit

anhouse

hold

sis

stat

istic

ally

sign

ifica

nt,

p<

0.05

.dThe

diffe

rence

inth

eco

effici

ents

bet

wee

nprinci

pal

city

and

nonm

etro

polit

anhouse

hold

sis

stat

istic

ally

sign

ifica

nt,

p<

0.05

.

404

Dow

nloa

ded

by [

UN

IVE

RSI

TY

OF

AD

EL

AID

E L

IBR

AR

IES]

at 1

6:47

17

Nov

embe

r 20

14

U.S. Food Insecurity Across Residence 405

may be that married-couple households in nonmetro and principal city areaswere not as protected from food insecurity as married-couple households insuburban areas as the difference in the likelihood of food insecurity betweenmarried-couple and female-headed households was smaller in nonmetroareas and principal cities.

The coefficients for households with incomes below 1.5 times thepoverty threshold were significantly higher in nonmetro than in subur-ban or principal city areas, but the overall relationship between foodinsecurity and income was the same across residence. There were signif-icant differences between all three models in the coefficients for region.Principal city households in the Northeast were less likely to be foodinsecure than principal city households in other regions. Northeasternprincipal cities seem to have some protective effect from food insecu-rity not apparent in cities in other regions or in suburban or nonmetroareas.

With regard to employment, retired households in suburbs and non-metro areas were less likely to be food insecure than full-time workinghouseholds. In the suburbs, unemployed households had particularly highodds of food insecurity relative to full-time working households.

Participation in Emergency Food Assistance Programs by HouseholdFood Security and Residence

The percentage of households that used food pantries or soup kitchens isshown in Table 4. The table also shows the percentage of households thatindicated there was a food pantry in their community they could use if theyneeded. Use of and access to community food assistance programs mayvary across residence areas and relate to food insecurity. Food pantry usewas higher among nonmetro households than households in other areas.Of households with very low food security, 34.5% used food pantries innonmetro areas, whereas 30% use pantries in principal cities and 28.9% usedpantries in suburbs. Among nonusers of food pantries, a higher proportionof nonmetro residents than principal city or suburban residents indicated thatthere was a pantry in their community. A significantly higher proportion ofsuburban nonusers indicated there was a food pantry in their communitythan principal city nonusers. About 76% to 83% of nonmetro householdswho did not use a food pantry, depending on food security status, indicatedthat there was a food pantry in their community. This compares to 65% to68% of suburban households knowing about a local food pantry and 58% to63% of principal city households knowing about a food pantry. It is impor-tant to note that these percentages reflect not only whether pantries werepresent but also whether households knew about them. Pantries may bemore prevalent in nonmetro communities, but rural households in smallercommunities may also be more likely to know about local resources than

Dow

nloa

ded

by [

UN

IVE

RSI

TY

OF

AD

EL

AID

E L

IBR

AR

IES]

at 1

6:47

17

Nov

embe

r 20

14

TAB

LE4

Use

ofFo

od

Pan

trie

san

dEm

erge

ncy

Kitc

hen

sby

House

hold

Food

Secu

rity

Stat

us

and

Res

iden

ce,20

09

Princi

pal

city

Suburb

anN

onm

etro

polit

an

Shar

eof

food-s

ecure

house

hold

s

Shar

eof

food-

inse

cure

house

hold

s

Shar

eof

house

hold

sw

ithve

rylo

wfo

od

secu

rity

Shar

eof

food-s

ecure

house

hold

s

Shar

eof

food-

inse

cure

house

hold

s

Shar

eof

house

hold

sw

ithve

rylo

wfo

od

secu

rity

Shar

eof

food-s

ecure

house

hold

s

Shar

eof

food-

inse

cure

house

hold

s

Shar

eof

house

hold

sw

ithve

rylo

wfo

od

secu

rity

Per

centa

gea

that

use

dpan

trie

sor

kitc

hen

sduring

the

pre

vious

year

Food

pan

trie

s1.

6d21

.8d

30.0

d1.

2c21

.7c

28.9

c2.

3c,d

28.1

c,d

34.5

c,d

Food

pan

try

inco

mm

unity

(am

ong

those

not

usi

ng

pan

try)

58.0

b,d

61.4

b,d

63.2

b,d

65.2

b,c

66.4

b,c

67.8

b,c

75.9

c,d

76.3

c,d

82.8

c,d

Em

erge

ncy

kitc

hen

s0.

2b4.

0b,d

8.0b

,d0.

1b2.

0b,c

4.2b

,c0.

11.

4c,d

1.2c,

d

Sou

rce:

Dat

afr

om

CPS-

FSS

2009

.aPer

centa

ges

are

wei

ghte

dto

repre

sentth

eU

.S.popula

tion.

bThe

diffe

rence

inper

centa

gebet

wee

nprinci

pal

city

and

suburb

anhouse

hold

sis

stat

istic

ally

sign

ifica

nt,

p<

0.05

.cThe

diffe

rence

inper

centa

gebet

wee

nsu

burb

anan

dnonm

etro

polit

anhouse

hold

sis

stat

istic

ally

sign

ifica

nt,

p<

0.05

.dThe

diffe

rence

inper

centa

gebet

wee

nprinci

pal

city

and

nonm

etro

polit

anhouse

hold

sis

stat

istic

ally

sign

ifica

nt,

p<

0.05

.

406

Dow

nloa

ded

by [

UN

IVE

RSI

TY

OF

AD

EL

AID

E L

IBR

AR

IES]

at 1

6:47

17

Nov

embe

r 20

14

U.S. Food Insecurity Across Residence 407

do more urban households. The structure of pantries may differ across resi-dence in ways that make them more or less salient in the community. A largershare of principal city households used emergency kitchens than suburbanor nonmetro households. Use of emergency kitchens was much less com-mon than use of food pantries across all residence areas and food securitycategories.

The use and availability of food pantries in nonmetro areas may bea factor related to the smaller increase in the prevalence of food inse-curity compared to other residence areas. A logistic regression modelpredicting food pantry participation was estimated (analyses not shown).When household characteristics were controlled, there was no differencein the likelihood of food pantry use between nonmetro and suburbanhouseholds. However, principal city households were less likely to usea pantry than were nonmetro or suburban households. Principal cityhouseholds may be less likely to use pantries in part because they havegreater access to emergency kitchens. The lack of a significant differencebetween suburban and nonmetro households in the likelihood of foodpantry use suggests that lack of food pantries does not explain subur-ban households’ greater likelihood of food insecurity relative to nonmetrohouseholds.

CONCLUSIONS AND DISCUSSION

The prevalence of food insecurity increased across all U.S. residence areasfrom 2007 to 2008 and stayed at those increased levels in 2009. Althoughfor each of these years food insecurity rates were lowest in the suburbs,moderate in nonmetro counties and highest in principal cities, the per-centage increase in food insecurity was the highest in suburban areas.During the Great Recession, food insecurity rates in nonmetro and subur-ban areas appeared to be converging somewhat as the prevalence of foodinsecurity was not significantly different between suburbs and nonmetroareas in 2009. These statistics are consistent with increases in suburbanpoverty over the past decade. That the increase in food insecurity innonmetro counties was not as steep as the increase in suburban andprincipal city counties is consistent with other research finding that employ-ment declines were less in nonmetro than metro areas during the recentrecession.

In multivariate models with controls for household characteristics,households in nonmetro areas were significantly less likely to be food inse-cure than principal city and suburban households. Poverty and associatedconcerns such as food insecurity are often considered an inner city orrural problem. However, there is increasing evidence that suburban areasare also being affected by economic hardships. The recession beginning

Dow

nloa

ded

by [

UN

IVE

RSI

TY

OF

AD

EL

AID

E L

IBR

AR

IES]

at 1

6:47

17

Nov

embe

r 20

14

408 A. Coleman-Jensen

in 2007 resulted in job losses and unemployment levels that were simi-lar between principal cities and suburbs (Kneebone & Garr, 2010a). Thisstudy shows that controlling for income and other household characteris-tics households in suburban areas are as likely as principal city householdsto be food insecure and more likely than nonmetro households to be foodinsecure.

Suburban female-headed households appear particularly vulnerable tofood insecurity relative to married-couple households. Although female-headed families with children are more likely to be food insecurethan married-couple families across residence areas, the coefficient wassignificantly larger in suburban than principal city or nonmetro areas.Suburban neighborhoods may be lacking informal social supports as wellas not-for-profit agencies that support female-headed families. In addition tofemale-headed families with children, single women living alone in suburbanareas are also significantly more likely than married couples with childrento be food insecure. Female-headed households with or without childrenin suburban areas may lack needed support from human service agencies.Additionally, unemployed households in the suburbs face particularly highodds of food insecurity, something that may also be related to a lack ofassistance and support services in suburban areas.

Suburban households had a lower prevalence of food pantry participa-tion than nonmetro households. However, in multivariate models controllingfor household characteristics, suburban households were just as likely asnonmetro households were to use food pantries. They were more likely touse pantries than were principal city households. However, access to emer-gency food providers may be very uneven within residence areas. Somelow-income suburban households living in more affluent areas may havelittle or no access to such services.

One important caveat that can hardly be overstated is that there is likelysignificant variation within residence areas in the prevalence of food inse-curity and the use and availability of food assistance programs. Althoughoverall, households in nonmetro areas were less likely than households inmetro areas to be food insecure, nonmetro persistent poverty counties andthose hardest hit by the recession are likely among those with the highestfood insecurity. Unfortunately, variation within counties and across nonmetrocounties cannot be identified with CPS data.

NOTES

1. Throughout the manuscript, nonmetropolitan (nonmetro) and rural are used interchangeably.The terms urban and metropolitan (metro) are used to refer to aggregated principal city and suburbanareas.

2. The name of the Food Stamp Program was changed to SNAP (Supplemental Nutrition AssistanceProgram) in 2008.

Dow

nloa

ded

by [

UN

IVE

RSI

TY

OF

AD

EL

AID

E L

IBR

AR

IES]

at 1

6:47

17

Nov

embe

r 20

14

U.S. Food Insecurity Across Residence 409

3. Standard errors of estimates are calculated based on a design factor of 1.6 to account forthe complex sampling design of the CPS. Standard errors for estimates of nonmetro households havean additional design factor of 1.5 based on sampling for these areas. Therefore, the standard errorof estimated proportions shown is calculated as the square root of (P × Q × 1.6 / N), where P isthe estimated proportion, Q is 1 − P, and N is the unweighted number of households. For estimatedproportions of food insecure nonmetro households, the standard error is calculated as the square root of(P × Q × 1.6 × 1.5 / N) (See footnote 1 in Nord, Coleman-Jensen, Andrews, & Carlson, 2010).

REFERENCES

Allard, S. W., & Roth, B. (2010). Strained suburbs: The social service chal-lenges of rising suburban poverty. Metropolitan Policy Program. Washington,DC: Brookings. Retrieved from http://www.brookings.edu/reports/2010/1007_suburban_poverty_allard_roth.aspx.

Andrews, M. (2010). More Americans relied on food assistance during recession.Amber Waves, 8(4), 4.

Bhattarai, G. R., Duffy, P. A., & Raymond, J. (2005). Use of food pantries and foodstamps in low-income households in the United States. Journal of ConsumerAffairs, 39, 276–298.

Bickel, G., Nord, M., Price, C., Hamilton, W. L., & Cook, J. T. (2000). Guide to mea-suring household food security, revised 2000. Washington, DC: U.S. Departmentof Agriculture, Food and Nutrition Service. Retrieved from www.fns.usda.gov/fsec/files/fsguide.pdf.

Blanchard, T., & Lyson, T. (2002, November). Access to low cost groceries innonmetropolitan counties: Large retailers and the creation of food deserts. Paperpresented at the Rural Diversity Conference, Washington, DC. Retrieved fromhttp://srdc.msstate.edu/measuring/ruraldiversity.htm.

Coleman-Jensen, A. J. (2011). Working for peanuts: Nonstandard work and foodinsecurity across household structure. Journal of Family and Economic Issues,32, 84–97.

DeNavas-Walt, C., Proctor, B. D., & Smith, J. C. (2009). Income, poverty, andhealth insurance coverage in the United States: 2008. Current Population Reports(P60-236). Washington, DC: U.S. Census Bureau. Retrieved from http://www.census.gov/prod/2009pubs/p60-236.pdf.

DeNavas-Walt, C., Proctor, B. D., & Smith, J. C. (2010). Income, poverty, andhealth insurance coverage in the United States: 2009. Current Population Reports(P60-238). Washington, DC: U.S. Census Bureau. Retrieved from http://www.census.gov/prod/2010pubs/p60-238.pdf.

Kneebone, E. (2010). The great recession and poverty in metropolitan America.Metropolitan Policy Program. Washington, DC: Brookings. Retrievedfrom http://www.brookings.edu/papers/2010/1007_suburban_poverty_acs_kneebone.aspx.

Kneebone, E., & Garr, E. (2010a). The landscape of recession: Unemployment andsafety net services across urban and suburban America. Metropolitan PolicyProgram. Washington, DC: Brookings. Retrieved from http://www.brookings.edu/~/media/Files/rc/papers/2009/0722_recession_kneebone/0722_recession_report.pdf.

Dow

nloa

ded

by [

UN

IVE

RSI

TY

OF

AD

EL

AID

E L

IBR

AR

IES]

at 1

6:47

17

Nov

embe

r 20

14

410 A. Coleman-Jensen

Kneebone, E., & Garr, E. (2010b). The suburbanization of poverty: Trends inmetropolitan America, 2000 to 2008. Metropolitan Policy Program. Washington,DC: Brookings. Retrieved from http://www.brookings.edu/~/media/Files/rc/papers/2010/0120_poverty_kneebone/0120_poverty_paper.pdf.

Larson, N. I., Story, M. T., & Nelson, M. C. (2009). Neighborhood environments:Disparities in access to healthy foods in the U.S. American Journal of PreventiveMedicine, 36(1), 74–81.

Leftin, J. (2010). Trends in supplemental nutrition assistance program participationrates: 2001 to 2008. Current perspectives on SNAP participation. Washington,DC: U.S. Department of Agriculture, Food and Nutrition Service, Office ofResearch and Analysis. Retrieved from http://www.fns.usda.gov/ORA/menu/Published/SNAP/FILES/Participation/Trends2001-2008.pdf.

Mabli, J., Cohen, R., Potter, F., & Zhao, Z. (2010). Hunger in America 2010:National report prepared for Feeding America. Chicago, IL: Feeding America.Retrieved from http://feedingamerica.org/faces-of-hunger/hunger-in-america-2010/hunger-report-2010.aspx.

McConnell, S., & Ohls, J. (2002). Food stamps in rural America: Special issuesand common themes. In B. A. Weber, G. J. Duncan, & L. A. Whitener (Eds.),Rural dimensions of welfare reform (pp. 413–432). Kalamazoo, MI: W.E. UpjohnInstitute for Employment Research.

Menard, S. (2001). Applied logistic regression analysis. Thousand Oaks, CA: Sage.Molnar, J. J., Duffy, P. A., Claxton, L., & Bailey, C. (2001). Private food assistance in a

small metropolitan area: Urban resources and rural needs. Journal of Sociologyand Social Welfare, 28(3), 187–209.

Morton, L. W., Bitto, E., Oakland, M., & Sand, M. (2005). Solving the problemsof Iowa food deserts: Food insecurity and civic structure. Rural Sociology, 70,94–112.

Morton, L. W., Bitto, E. A., Oakland, M. J., & Sand, M. (2008). Accessing foodresources: Rural and urban patterns of giving and getting food. Agricultureand Human Values, 25, 107–119.

Murphy, A. K., & Wallace, D. (2010). Opportunities for making ends meet andupward mobility: Differences in organizational deprivation across urban andsuburban poor neighborhoods. Social Science Quarterly, 91(5), 1164–1186.

Nord, M. (2009). Food insecurity in households with children: Prevalence, severity,and household characteristics (EIB-56). Washington, DC: U.S. Department ofAgriculture, Economic Research Service. Retrieved from http://www.ers.usda.gov/publications/eib56/.

Nord, M., Andrews, M., & Carlson, S. (2008). Household food security inthe United States, 2007 (ERR-66). Washington, DC: U.S. Department ofAgriculture, Economic Research Service. Retrieved from http://www.ers.usda.gov/publications/err66/.

Nord, M., Andrews, M., & Carlson, S. (2009). Household food security in theUnited States, 2008 (ERR-83). Washington, DC: U.S. Department of Agriculture,Economic Research Service. Retrieved from http://ers.usda.gov/Publications/Err83/.

Nord, M., Coleman-Jensen, A., Andrews, M., & Carlson, S. (2010). Household foodsecurity in the United States, 2009 (ERR-108). Washington, DC: U.S. Department

Dow

nloa

ded

by [

UN

IVE

RSI

TY

OF

AD

EL

AID

E L

IBR

AR

IES]

at 1

6:47

17

Nov

embe

r 20

14

U.S. Food Insecurity Across Residence 411

of Agriculture, Economic Research Service. Retrieved from http://www.ers.usda.gov/Publications/ERR108/ERR108.pdf.

Office of Management and Budget. (2003, June 6). Revised definitions of metropoli-tan statistical areas, new definitions of micropolitan statistical areas andcombined statistical areas, and guidance on uses of the statistical defi-nitions of these areas. OMB Bulletin, 03–04. Retrieved from http://www.whitehouse.gov/omb/bulletins_b03-04/

Parker, T. S., Kusmin, L. D., & Marre, A. W. (2010). Economic recovery: Lessonslearned from previous recessions. Amber Waves, 8(1), 42–47.

Stracuzzi, N., & Ward, S. (2010). What’s for dinner? Finding and affordinghealthy foods in New Hampshire communities (New England Issue BriefNo. 21). Durham, NH: University of New Hampshire, Carsey Institute.Retrieved from http://www.carseyinstitute.unh.edu/publications/IB_Stracuzzi-Ward_Healthy_Food.pdf.

U.S. Department of Agriculture. (2009). Access to affordable and nutritiousfood: Measuring and understanding food deserts and their consequences(Administrative Publication No. AP-036). Washington, DC: U.S. Department ofAgriculture, Economic Research Service. Retrieved from http://www.ers.usda.gov/Publications/AP/AP036/AP036.pdf.

Dow

nloa

ded

by [

UN

IVE

RSI

TY

OF

AD

EL

AID

E L

IBR

AR

IES]

at 1

6:47

17

Nov

embe

r 20

14