Explaining Variations in State Hunger Rates

John Tapogna, MPP A large and rapidly expanding body of research has examined causes of household-level ECONorthwest food insecurity and hunger. A definitive explanation has not emerged that links State prevalence rates of hunger to State-level characteristics such as poverty, employment, Allison Suter, MPP and per capita income. In this article, we examined the effect of State-level economic ECONorthwest and demographic characteristics on State prevalence rates of food insecurity and hunger. Using food-security data from the U.S. Department of Agriculture and Census data on all Mark Nord, PhD 50 States and the District of Columbia, we first estimated, by using ordinary least squares Economic Research Service regression, the associations of food insecurity and hunger with a small number of carefully U.S. Department of Agriculture chosen State-level factors. Based on these associations, we then examined the extent to which these factors explained the high rate of hunger in Oregon and, as a contrast, the Michael Leachman, PhD lower-than-expected rate of hunger in West Virginia. Findings of our study suggest that Oregon Center for Public Policy to reduce hunger rates, policymakers should consider ways to mitigate income shocks associated with high mobility and unemployment and reduce the share of income spent on rent by low-income families.

he U.S. Department of School Lunch Programs (Food Agriculture (USDA) monitors Research and Action Center, 2003b). T annually the food security of America’s Second Harvest, the U.S. households. This monitoring Nation’s largest hunger-relief organi- includes calculating the share of zation, has also relied on the USDA’s households that are food insecure— hunger estimates in supporting efforts meaning that they had difficulty at to alleviate hunger (America’s Second times during the year having enough Harvest, 2002). to eat—and the share of households in which people were hungry at times State government agencies and the during the year because of their food media have used the USDA’s State- insecurity. The USDA reports these level statistics to draw attention to statistics for the Nation and for each the problem of hunger. In Idaho and State (Nord, Jemison, & Bickel, 1999; Tennessee, newspaper editorial boards Nord, Andrews, & Carlson, 2002). have taken the opportunity to use hunger estimates to suggest policy The USDA’s Food and Nutrition (Idaho Statesman, 2002; Cooper, Service (FNS) uses these statistics to 2002). The State-level estimates have assess the level of need for its food received considerable attention in the assistance programs and to measure Pacific Northwest, particularly in their performance. Advocates for Oregon, where posted rates have been programs that serve low-income at or near the top of the USDA’s hunger families have used these statistics to rankings (Graves, 2002; Harrison, call for a variety of policy initiatives. 2002; Cook, 2002). In spring 2003, The Food Research and Action Center Oregon Governor Ted Kulongoski (FRAC), a prominent national organi- convened a hunger summit and zation seeking to end hunger, recently discussed possible solutions with urged Congress to authorize additional human service providers, business funding for the Summer Nutrition and executives, and academic experts and

12 Family Economics and Nutrition Review has since made the eradication of difficulty obtaining enough food. These hunger a top priority of his adminis- Background questions (i.e., the U.S. Food Security tration. Subsequently, the Governor Survey Module) are included in an announced a strategic plan— In 1990, Congress enacted the National annual nationally representative survey principally focused on job creation— Nutrition Monitoring and Related as a supplement to the monthly Current to reduce the State’s hunger rate. Research Act (U.S. Department of Population Survey (CPS) of the U.S. However, with no precise information Agriculture [USDA], 2002a). Under Census Bureau. Based on the number about how job growth or unemploy- the national plan mandated by this Act, of food-insecure conditions they report, ment relates to hunger, the Governor the USDA and the U.S. Department surveyed households are identified as was unable to predict the degree to of Health and Human Services (HHS) food secure, food insecure without which his approach would affect formed the Food Security Measure- hunger, or food insecure with hunger. the State’s hunger rate, if at all ment Project. Several Federal agencies, (Kulongoski, 2003). as well as academic and private A large and rapidly expanding body researchers, worked as a team to of research has examined causes of The high hunger rates of Oregon and develop standardized measures of food insecurity and food insufficiency its Northwest neighbors (Washington household food security that could (a related measure based on a single and Idaho) have surprised policy- be used nationally as well as in State question used in earlier surveys). makers and the Federal officials who and local surveys. To date, however, almost all of this oversee USDA’s Current Population research has examined these asso- Survey Food Security Supplement The team working on the Food ciations at the household level. The (CPS-FSS) (Nord et al., 1999). A Security Measurement Project used, annual reports of food security by the definitive explanation linking State as its starting point, the definitions USDA reveal that households headed prevalence rates of hunger to State- of food security, food insecurity, and by single parents, especially women, level characteristics such as poverty, hunger established by the American and Black and Hispanic households employment, and per capita income has Institute of Nutrition (Anderson, 1990). were more likely than others to be not emerged. Because the underlying Whereas food security means assured food insecure (Nord et al., 2002). reasons have—to this point—gone access by all people at all times to Poor households have rates of food unexplained, policy responses have enough food for active, healthy lives, insecurity far above the national been hampered and some observers food insecurity means limited or average, and food insecurity is more have challenged methods used in uncertain availability of nutritionally prevalent in the South and West than the survey and deemed the USDA’s adequate and safe foods or limited or in the Northeast and Midwest (Nord findings inaccurate or misleading uncertain ability to acquire acceptable et al., 2002). (Charles, 2003). foods in socially acceptable ways 1 (Anderson, 1990). Hunger refers to Using data from the Survey of Income In this article, we examined the effects the uneasy or painful sensation caused and Program Participation (SIPP by of State-level economic and demo- by lack of food. As measured and the Census Bureau), Gundersen and graphic characteristics on State prev- described by the project, hunger refers Gruber (2001) used a variety of alence rates of food insecurity and specifically to hunger that results indicators to compare food-insufficient hunger. Using food-security data and from food insecurity (USDA, 2003b). households with food-sufficient ones. Census data of all 50 States and the They found that “income shocks” District of Columbia, we first estimated Based on these definitions and earlier were a major factor leading to food the associations of food insecurity and research, the members of the project insufficiency (especially for house- hunger with a small number of care- developed a series of questions about holds that lacked savings) and that fully chosen State-level factors. behaviors and experiences known to rates of food insufficiency were lower Based on these associations, we then characterize households that are having among homeowners, households examined the extent to which these headed by senior citizens, and married factors explained the high rate of couples without children than among 1 hunger in Oregon and, as a contrast, Current methods of measuring food insecurity other households. The authors also may not fully take into account whether food the lower-than-expected rate of hunger was acquired in socially acceptable ways. In speculated that moves by a household in West Virginia. particular, reliance on Federal and community might reduce the amount of resources food assistance programs by a household is not available to buy food, but they found directly considered in assessing the food- no statistically significant differences security status of the household.

2004 Vol. 16 No. 2 13 between food-insufficient and food- household-level factors account for 1999, September 2000, and December sufficient households in this regard. the differences in prevalence rates 2001. The CPS-FSS is a nationally Gunderson and Gruber (2001) of food insecurity and hunger across representative survey of about 50,000 concluded that, compared with their States. In an analysis of rates of households that is conducted annually counterparts, food-insufficient State hunger estimated by a FRAC- by the U.S. Census Bureau for the households faced more unemployment, sponsored survey, Ryu and Slottje USDA. Representative of both the losses to the receipt of food stamps, (1999) concluded that high school U.S. civilian noninstitutionalized and other income shocks and were graduates were less likely to be hungry population and each State, the CPS- less able to withstand these shocks by than were those who did not receive a FSS is conducted as a supplement to using savings. Thus, these researchers high school diploma. Nord et al. (1999) the monthly CPS, a labor force survey suggested that food insufficiency reviewed USDA-measured rates and conducted by the Census Bureau for should be addressed with policies that demonstrated a strong association the Bureau of Labor Statistics. House- mitigate income shocks commonly between State poverty and prevalence holds are classified as food secure, experienced by low-income families. rates of food insecurity. However, the food insecure without hunger, or food authors also acknowledged that the insecure with hunger,2 a classification Other studies have also examined association was not perfect and pointed that is based on the number of food- causes of household-level hunger. in particular to Washington and Oregon insecure conditions they report in Similar findings have emerged. Rose, as exceptions to the general pattern. response to the 18 questions in the Gundersen, & Oliveira (1998) found They concluded: “. . . reasons for food-security module. that high school graduates, home- these unexpected high rates of food owners, and seniors were less likely insecurity in the Pacific Northwest For most monitoring and analytic than others to be food insufficient. are not known, and further research purposes, the CPS sample size in most Their findings showed that Whites, is needed on this subject” (p. 8). States is too small to produce annual compared with other racial groups, had food insecurity or hunger rates with the lowest rates of food insufficiency. sufficient reliability. Consequently, the Not surprisingly, Rose and colleagues Data and Empirical Model USDA routinely reports State-level also concluded that the less money a food insecurity and hunger rates as household had, the more likely it was We were interested in explaining 3-year averages. We used the 3-year to be food insufficient. State-level variations in two related averages for 1999 to 2001 (Nord et al., prevalence rates: food insecurity and 2002) as our main analytic variables. In a more recent study, Nord (2003) food insecurity with hunger, the more found hunger to be associated strongly severe condition. State-level preva- Our method to assess the associations with low income, as expected, and also lence rates of food insecurity and of State-level food insecurity and found that, even with analytic controls hunger for our analysis were taken hunger rates with State economic and for income, hunger was associated from work by Nord et al. (2002)—the demographic characteristics was a strongly with unemployment, part-time most recent statistics on food security straightforward application of ordinary employment for economic reasons that are published by the USDA. These least squares (OLS) regression (i.e., because more work could not statistics are particularly well suited analysis. We hypothesized that a be found), not working because of a for analysis of the associations of number of State-level characteristics disability, recent household moves, State-level characteristics with State independently affect State-level food- and low education. Hunger rates were hunger rates, because they span 1999 insecurity and hunger rates. The found to be lower for homeowners to 2001—a period that overlaps the relationship between the State hunger and for households with the elderly— collection of data through the 2000 rate Y and the explanatory variables X especially households with retired Decennial Census and the Census is generally assumed to take this form: elderly—compared with their Supplemental Survey. State-level respective counterparts. Y β + β X + β X + .... + β X + ε . statistics based on these Census data i = 0 1 1i 2 2i n ni i are highly precise. All of these analyses were based on household-level associations. To date, The USDA’s statistics on food in- 2A complete description of the CPS little research attention has been given security and hunger are based on data sample design is available at http:// to State-level food insecurity and collected in the CPS-FSS of April www.bls.census.gov/cps/tp/tp63.htm. hunger and the extent to which these

14 Family Economics and Nutrition Review OLS provides estimates of the values Table 1. Descriptive statistics for the 50 States of the β terms, which quantify the relationship between each of the Standard explanatory variables and hunger Variables1 Mean deviation or food insecurity. We analyzed the associations between food insecurity Percentage and explanatory variables in a separate Percent 2 points model. Share of population experiencing food insecurity We selected the explanatory variables with hunger 3.1 0.9 (X , X , etc.) based on our review 1i 2i Share of population experiencing food insecurity 10.2 2.2 of the literature and discussions with experts on food insecurity and hunger. Share of population in a different house 16.4 2.7 The limited degrees of freedom in this cross-sectional analysis called for a Peak unemployment rates during 1999-2001 5.0 1.1 parsimonious model. The literature and program experts identified associations Share of population living in poverty 12.1 3.3 between five individual characteristics (change of residence, unemployment Share of renters paying more than 50 percent of income on gross rent 16.4 1.8 status, poverty status, age, and race) and food insecurity and hunger. We Share of population non-Hispanic White 74.9 16.1 additionally included a measure of housing cost because a number of Share of population under age 18 25.5 1.9 observers had identified a correlation between high housing costs and food 1Percentages for all variables are for 2000 unless noted otherwise. 2 insecurity. Housing is a major item These figures report the simple average of 50 individual State observations with each State’s observation given equal weight. That is, California’s observation is given the same weight as North Dakota’s. in the budget of most low-income Consequently, the figure does not represent a U.S. average, which would vary the States’ weighting by households and, if too high, can their size. “crowd out” resources available for food (Gundersen & Gruber, 2001; Rose et al., 1998; Food Research and Action Center, 2003a). Households can move for a number • Average of 1999, 2000, and 2001 of reasons—some positive (e.g., peak unemployment rates. We house upgrade or relocation to a constructed this variable as the Hypothesized Relationships new job) and some problematic average of the peak State un- (e.g., evictions or household employment rates in each of three In this section, we discuss the dissolutions such as divorces or years: 1999, 2000, and 2001. The hypothesized relationship between separations). Household-level 3 years coincide with the period change of residence, unemployment research has suggested that, of measurement for the dependent status, poverty status, age, and race overall, households that have variables. We selected the peak and rates of food insecurity and hunger. moved recently, compared with rate in each year, rather than the We describe these variables as well households that have not moved average, to capture the worst as report the means and standard recently, were more likely to be economic conditions reported deviations (table 1). food insecure. We hypothesized in the States. Peak unemployment that this measure is a proxy for rate is likely to be a better measure • Percentage of households in income shocks, which Gundersen of the share of the labor force that 2000 that moved within the last and Gruber (2001) demonstrated experienced job loss and a related year. The Census Supplemental had a positive relationship with income shock at some time during Survey reports the share of hunger. The variable’s mean across the year. This measure is, therefore, households in a State that indicate States was 16.4 percent, and the temporally consistent with the whether they changed dwellings standard deviation was 2.7 measures of food insecurity and between 1999 and 2000. percentage points. hunger, which reflect the most

2004 Vol. 16 No. 2 15 problematic food-access conditions used the share of renter-households coefficients and the values of each of the year. (Households were in the State that spent more than State’s independent variables, we classified as food insecure or 50 percent of their incomes on calculated the rates of hunger predicted food insecure with hunger if they grossrent as an explanatory by the regression model for each State. experienced these conditions at any variable.3 We anticipated that We also calculated the contribution of time during the year.) Based on the within the group of renting house- each factor to Oregon’s higher-than- work of Gundersen and Gruber holds, those with high rents relative average hunger rates. As a counter- (2001) and others (Rose et al., to their incomes would be particu- example, we examined the contribution 1998), we hypothesized that high larly prone to hunger. We used the of each factor to the hunger rate in peak unemployment would be variable from the 2000 Decennial West Virginia, which was near the associated with high food insecurity Census. The mean for the variable national average despite a relatively and hunger rates. We used the was 16.4 percent; its standard high State poverty rate. applicable variable from the Local deviation was 1.8 percentage Area Unemployment Statistics points. series of the Bureau of Labor Results Statistics. Its mean was 5.0 percent; • Population share of non- the standard deviation, 1.1 percent- Hispanic Whites. Previous Because of the limited number of age points. research has offered mixed observations (51) and the estimation findings about the effect of race error associated with prevalence • State poverty rate. Other studies and ethnicity on hunger or food rates of State-level hunger, the model have indicated that a household’s insufficiency (Gundersen & Gruber, predicted State hunger rates quite well. income level is a determinant of 2001; Rose et al., 1998; Gundersen Overall, the six independent variables food insufficiency (Gundersen & & Oliveira, 2001; Nord, 2003). We explained 64 percent (unadjusted R2) Gruber, 2001; Rose et al., 1998; included the variable that measured of the variation in State hunger rates— Gundersen & Oliveira, 2001; Nord, the share of a State’s population that a high rate for this type of model— 2003). Moreover, the most recent was non-Hispanic White, but we and 74 percent (unadjusted R2) of USDA report showed that 12.9 had no a priori assumption about its the variation of State rates of food percent of households with incomes effect on hunger rates. This variable insecurity (table 2). Moreover, the below the Federal poverty level averaged 74.9 percent; its standard measured relationships between most experienced hunger, compared deviation was 16.1 percentage of the independent variables and State with a national average of only points. rates of hunger and food insecurity 3.3 percent (Nord et al., 2002). were statistically significant and Therefore, we anticipated that • Population share under age 18. sufficiently strong to be of substantive States with higher poverty rates Researchers have indicated that importance. Also, both in-sample and would also register higher hunger larger households, and particularly out-of-sample predictions ranked rates. State poverty rates, measured large households with children, Oregon with the second highest for calendar year 1999 through the have higher hunger rates (Rose hunger rate. 2000 Decennial Census, averaged et al., 1998). We anticipated that 12.1 percent; the standard devia- as a State’s share of the population Examination of the estimated relation- tion, 3.3 percentage points. under age 18 rose, so would its ships between each of the independent hunger rate. The mean for this variables and State hunger and in- • Share of renters spending more variable was 25.5 percent; its security rates revealed that the than 50 percent of income on standard deviation was 1.9 “different house,” or mobility variable, gross rent. Just as limited income percentage points. had the most robust and consistent can put a household at risk for relationship with State hunger and hunger, high expenses can do the Finally, we explored the extent to food insecurity rates. The hunger same. Past studies have reported which the regression model could model suggests that each percentage- that renters were more likely than account for the high rate of hunger point increase in the share of a State’s homeowners to be food insecure in Oregon. Based on the regression households that reported changing (Gundersen & Gruber, 2001; Rose dwellings between 1999 and 2000 et al., 1998; Gundersen & Oliveira, was associated with a 0.13-percentage- 3Gross rent consists of direct rental costs plus 2001; Nord, 2003). Therefore, we essential utilities. point increase in the State’s hunger

16 Family Economics and Nutrition Review Table 2. Estimated relationships between selected State characteristics and rates of hunger and food insecurity

Food insecurity Food insecurity with hunger (with or without hunger) Regression Standard Regression Standard coefficient error coefficient error

Share of population in a different house 0.132 (0.034)* 0.280 (0.073)*

Peak unemployment rates during 1999-2001 0.314 (0.100)* 0.187 (0.215) A 1-percentage-point increase in peak unemployment rates was Share of population living in poverty 0.034 (0.031) 0.360 (0.067)* associated with an increase of 0.31 percentage points in a Share of renters paying more than State’s hunger rate. 50 percent of income on gross rent 0.130 (0.055)* 0.276 (0.118)*

Share of population non-Hispanic White 0.011 (0.006) 0.014 (0.013)

Share of population under age 18 0.112 (0.047)* 0.434 (0.101)*

Constant -0.069 (0.018)* -0.164 (0.040)*

R2 0.638 0.736 Adjusted R2 0.588 0.700

Note: The data are based on ordinary least squares analysis. *p < .05. rate. The magnitude of the coefficient likelihood of food insufficiency was roughly twice as large in the (Gundersen & Gruber, 2001; Nord, estimate of food insecurity (but the 2003). We also found unemployment to level of food insecurity was also much put upward pressure on food insecurity higher, so the proportional association rates; this association, however, was was similar or somewhat smaller). weaker than the one for hunger and We interpret the coefficient of the was not statistically significant. “different house” variable as primarily measuring the associations of food As expected, high poverty rates also insecurity and hunger with economic put upward pressure on hunger and shocks and family disruptions. food insecurity rates. This association for hunger, however, was not statis- Effects of peak unemployment rates tically significant. The relatively high also were quite strong. A 1-percentage- correlation between State-level poverty point increase in peak unemployment and unemployment measures accounted rates was associated with an increase for the weakness of the estimated of 0.31 percentage points in a State’s relationship between poverty and hunger rate. This relationship is hunger on the one hand and between consistent with earlier research peak unemployment and food in- findings that job loss and income security on the other. Because States shocks are associated with a higher with high poverty rates tended also to

2004 Vol. 16 No. 2 17 have high peak unemployment rates, gross rent was related to a 0.13- spurious, resulting from other charac- the models had difficulty disentangling percentage-point increase in the State’s teristics of States with large elderly the independent effects of poverty and hunger rate. For example, the 8.9- population shares. unemployment. In the case of the percentage-point difference between hunger model, the stronger association New York (the Nation’s highest) and We also examined the extent to which with the unemployment variable left South Dakota (the Nation’s lowest) the regression models accounted for little residual association with the and the housing-burden measure is hunger rates in Oregon and West poverty rate. However, when we expected to result in a 1.1-percentage- Virginia (table 3). Oregon registered removed the unemployment variable point difference in hunger rates one of the highest hunger rates (5.8 from the model (analysis not shown), between the two States (data not percent) in the Nation; yet, it had a the poverty variable became statis- shown). poverty rate slightly below the national tically significant. In the case of the average (11.6 vs. 12.1). West Virginia, food-insecurity model, poverty had We had no expectations about the on the other hand, had a hunger rate the strong relationship with food effects of the non-Hispanic White near the national average (3.3 percent); insecurity; removing it from the model variable on rates of hunger and food yet, it had the fifth highest poverty rate resulted in a statistically significant insecurity. The variable showed a of all States (17.9 percent). We association with unemployment. positive but weak and statistically estimated—based on the model’s insignificant relationship with the regression coefficients and the States’ The additional analyses with poverty dependent variables. The lack of a values on each independent variable— rates and peak unemployment rates, conclusive relationship is consistent how Oregon’s and West Virginia’s omitted in turn, also confirmed that the with previous, generally mixed, hunger rates would change if the peak unemployment variable was more findings reported by researchers State’s levels were equal to the mean strongly associated with hunger rates (Rose et al., 1998). for all 50 States.5 than with food insecurity rates while the poverty variable was more strongly As the share of a State’s population For example, Oregon’s share of renters associated with food-insecurity rates under age 18 increased, so did both paying more than 50 percent of their (data not shown). These findings hunger and food insecurity. A 1- income in rent is 2.9 percentage points suggest that economic shocks at the percentage-point increase in the State’s higher than the U.S. average (19.3 household level, for which peak population share under age 18 was vs. 16.4 percent, table 3 and table 1, unemployment is a proxy at the State significantly associated with a 0.11- respectively). If Oregon’s rate fell to level, are associated with the more percentage-point increase in hunger the 50-State mean, we estimated that severe hunger condition. In States and a 0.43-percentage-point increase the State’s hunger rate would fall with high poverty rates, by contrast, in food insecurity. We were concerned by 0.4 percentage points (table 3). low-income households and their that this variable could be confounding Oregon’s high levels of peak unem- communities are more likely to have the effects of a larger share of children ployment rate and residential mobility, adjusted to sustained low levels of with a smaller share of elderly in the as measured by the share of the popu- income. Persistently poor households State. However, including the elderly lation in a different house, explained are likely to have developed ways to population share in the model (analysis even more of the gap between avoid hunger by relying on family, not shown) resulted in no substantial Oregon’s hunger rate and those of friends, and local institutions and by change in the coefficient on the share other States. For each of these two altering their consumption patterns. of the State’s population under age 18.4 variables, if Oregon’s rate fell to the Community institutions in States with The measured associations of hunger 50-State mean, the model predicted consistently high poverty rates will and food insecurity with the elderly that the State’s hunger rate would have had time to adjust and better population share remained, even when decline by 0.6 percentage points. reach families in need. all households with elderly were excluded from the sample used in the In West Virginia, high peak unem- High housing costs were strongly analysis for calculating rates of food ployment pushed the hunger rate up. associated with hunger and food- insecurity and hunger. We thus Bringing peak unemployment down to insecurity rates. Our model estimated concluded that the association was that a 1.0-percentage-point increase in the share of a State’s renters who paid 5These values are not national averages because 4To obtain the detailed data for each State, more than 50 percent of income for they are unweighted; they are means for the 50 please contact the first author. States.

18 Family Economics and Nutrition Review Table 3. Estimated effect of key characteristics on hunger rates in Oregon policymakers unsure about how to address the problem of hunger and led and West Virginia critics to question the validity of the USDA survey and its measurement Oregon West Virginia techniques. The ability to associate Estimated Estimated Rate effect1 Rate effect1 State hunger rates to key social and economic conditions at the State level, Percent Percentage Percent Percentage as demonstrated in this study, sheds point point light on State rankings and, by doing so, both lends credibility to the State Share of population hunger statistics and provides policy- in a different house 21.1 -0.6 12.9 0.5 makers with some guidance about policy responses. Nevertheless, this Peak unemployment rates relatively simple cross-sectional during 1999-2001 7.0 -0.6 6.9 -0.6 analysis points only to associations Share of population living in poverty 11.6 0.0 17.9 -0.2 between hunger and food insecurity and the hypothesized explanatory Share of renters paying more than variables. Our work falls short of 50 percent of income on gross rent 19.3 -0.4 17.7 -0.2 establishing definitive causal relationships. Share of population non-Hispanic White 83.5 -0.1 94.5 -0.2 The findings suggest that highly Share of population under age 18 24.7 0.1 22.2 0.4 transient populations put upward Total -1.6 -0.3 pressure on the hunger rates in their State hunger rate 5.8 3.3 States. High mobility serves as a proxy for a variety of lifetime disruptions— 1The effect refers to the estimated change in hunger rate if the rate equaled the mean hunger rate of the 50 divorce, separation, eviction, and other States. For example, Oregon’s share of the population in a different house in 2000 was 18 percentage points shocks to family income—that put higher than the 50-State mean (21.1 vs 3.1). If Oregon’s mean were the same as that of the 50 States, Oregon’s hunger rate would fall by 6 percentage points. people and families at risk of hunger and food insecurity. This risk may be exacerbated by the diminished social the 50-State mean (5 percent) would smaller-than-average share of children cohesion that characterizes highly lower the hunger rate by 0.6 percentage in the population. Taken together, these mobile populations. points. West Virginia’s high poverty factors resulted in a hunger rate in West rate (17.9 percent) was estimated to Virginia that was similar to the mean Paradoxically, good regional economic push up the hunger rate only 0.2 for the 50 States. conditions often lead to high levels percentage points. As we observed, of mobility. States with booming with peak unemployment in the model, economies attract an influx of job the effect of the poverty rate was small. Policy Implications and seekers. States with a high percentage Furthermore, West Virginia’s share Conclusions of seasonal jobs may experience sub- (17.7 percent) of renters paying more stantial internal migration during the than 50 percent of their income for Prior research provided considerable year. States with strong economies may gross rent was nearer the 50-State insight about factors affecting experience rapid growth in housing mean (16.4 percent) than was Oregon’s household-level hunger, food in- prices, resulting in both high housing (19 percent), putting a smaller upward security, and food insufficiency but costs for residents and relatively large pressure on the hunger rate. The most little information about the extent to portions of the population shifting into important difference between the two which these factors explained differ- new or less expensive areas. People States, however, was that the factors ences in State prevalence rates. living through these types of economic pushing the hunger rate up were largely conditions may be at a higher risk of offset by West Virginia’s much lower The lack of an intuitively satisfying hunger; because, they are more likely rate of residential mobility, well below explanation for high estimated hunger than others to be living in new the U.S. mean, and the considerably rates in the Pacific Northwest left neighborhoods, distant from family

2004 Vol. 16 No. 2 19 and friends and disconnected from the trains workers. Plans on both fronts local infrastructure of social support. are necessary to help State economies Religious institutions and government and their hungry citizens. Economic programs may not effectively reach development efforts that lower poverty people who have lived in the area for rates, reduce seasonal fluctuations in only short periods. unemployment rates, and provide jobs in rural areas experiencing high In trying to lower hunger rates in unemployment may be particularly highly mobile States in the West and effective in fighting hunger. South, policymakers may want to focus their efforts on vulnerable, mobile Another policy direction to emerge populations—newcomers, seasonal relates to increasing the supply of workers, and displaced renters, for affordable housing. Findings of this example. In doing so, policymakers study indicate a substantial reduction in in these States can assume a role in the hunger rate for moderate decreases overcoming, or partially offsetting, in the share of renters who pay more the lack of social cohesion in their than 50 percent of their income on communities. If some Western and gross rent. States with the largest share Southern States lack natural support of such renters, such as Oregon, have networks (e.g., family and long-time room to improve and the potential to neighbors) found in the Northeast or address concerns of both housing and Midwest, citizens and policymakers hunger advocates. Competing pro- can attempt to substitute for the lack posals have been offered to increase of cohesion through nonprofit or the supply of affordable housing: public efforts. construction of more affordable housing projects and vouchers for For example, a highly developed existing units, on the one hand, and network of food banks may prove relaxation of land-use controls to more important in Oregon than in lower the price of land, on the other States in other regions with more hand. If further research demonstrates stable populations. Also, a state-of- that these approaches do, in fact, the-art information and referral system, increase the supply of low- and as envisioned by United Way’s 211 moderate-cost housing, then both coalition, can provide much-needed may reduce the prevalence of hunger, direction to those who relocate and whatever the other strengths and need to know what resources are weaknesses of these approaches available to them. Policymakers can might be. also reform the State unemployment insurance programs to better reach In each State that has a high prevalence seasonal workers, focus food stamp of hunger, a different combination of outreach efforts on newcomers, and factors may be responsible. The results devise effective support programs of this study can help policymakers and for displaced renters. the concerned public in each of these States understand more fully the factors The association between unemploy- that particularly affect their State. We ment and hunger suggests that an hope that this improved understanding economic development policy could will lead to increasingly effective serve a dual purpose as an anti-hunger policies, programs, and community strategy. Many governors have indi- institutions to reduce hunger and food cated that they want an integrated insecurity. approach to economic development— one that stimulates job growth and

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