United States 

Department 

of Agriculture

Economic 

Research 

Service

June 2009

Access to Affordable and 

Nutritious Food: Measuring and 

Understanding Food Deserts and 

Their Consequences 

Report to Congress
This report was prepared by the Economic Research Service (ERS), the Food 

and Nutrition Service (FNS), and the Cooperative State Research, Education, 

and Extension Service (CSREES) of the U.S. Department of Agriculture under 

the direction of Michele Ver Ploeg of ERS. Contributors include Vince Breneman, 

Tracey Farrigan, Karen Hamrick, David Hopkins, Phil Kaufman, Biing-Hwan Lin, 

Mark Nord, Travis Smith, and Ryan Williams of ERS; Kelly Kinnison, Carol Olander, 

and Anita Singh of FNS; Elizabeth Tuckermanty of CSREES; Rachel Krantz-Kent 

and Curtis Polen of the Bureau of Labor Statistics; and Howard McGowan and 

Stella Kim of the U.S. Census Bureau.
United States

Department

of Agriculture

Report to Congress

Access to Affordable and 

Nutritious Food

Measuring and Understanding Food 

Deserts and Their Consequences

June 2009

Abstract

The Food, Conservation, and Energy Act of 2008 directed the U.S. Department of 

Agriculture to conduct a 1-year study to assess the extent of areas with limited access 

to affordable and nutritious food, identify characteristics and causes of such areas, 

consider how limited access affects local populations, and outline recommendations 

to address the problem. This report presents the fi ndings of the study, which include 

results from two conferences of national and international authorities on food deserts and 

a set of commissioned research studies done in cooperation with the National Poverty 

Center at the University of Michigan. It also includes reviews of existing literature, a 

national-level assessment of access to supermarkets and large grocery stores, analysis 

of the economic and public health effects of limited access, and a discussion of existing 

policy interventions. The study uses a variety of analytical methods and data to assess 

the extent of limited access to affordable and nutritious food and characteristics of areas 

with limited access. Overall, fi ndings show that a small percentage of consumers are 

constrained in their ability to access affordable nutritious food because they live far 

from a supermarket or large grocery store and do not have easy access to transportation. 

Urban core areas with limited food access are characterized by higher levels of racial 

segregation and greater income inequality. In small-town and rural areas with limited 

food access, the lack of transportation infrastructure is the most defi ning characteristic. 

Existing data and research are insuffi cient to conclusively determine whether areas with 

limited access have inadequate access.

Acknowledgments

This report was prepared by the Economic Research Service (ERS), the Food and 

Nutrition Service (FNS), and the Cooperative State Research, Education, and Extension 

Service (CSREES) of the U.S. Department of Agriculture under the direction of Michele 

Ver Ploeg of ERS. Contributors include Vince Breneman, Tracey Farrigan, Karen 

Hamrick, David Hopkins, Phil Kaufman, Biing-Hwan Lin, Mark Nord, Travis Smith, 

and Ryan Williams of ERS; Kelly Kinnison, Carol Olander, and Anita Singh of FNS; 

Elizabeth Tuckermanty of CSREES; Rachel Krantz-Kent and Curtis Polen of the Bureau 

of Labor Statistics; and Howard McGowan and Stella Kim of the U.S. Census Bureau.
ii

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Contents

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Chapter 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Chapter 2 The Extent of Limited Food Access 

 in the United States. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Chapter 3 The Interaction of Neighborhood 

 and Household Characteristics in Explaining 

 Areas With Limited Access . . . . . . . . . . . . . . . . . . . . . . . . . 39

Chapter 4 Food Access and Its Relationship to Diet 

 and Health Outcomes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51

Chapter 5 Food Access and Its Relationship to Food Choice . . . . . . . . 61

Chapter 6 The Economics of Supermarket and 

 Grocery Store Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

Chapter 7 Community Food Projects . . . . . . . . . . . . . . . . . . . . . . . . . . 91

Chapter 8 Policy Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

Chapter 9 Future Research Needs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

Appendix A Access to Affordable and Nutritious Food: 

 Understanding Food Deserts Workshop Agenda . . . . . . . . 123

Appendix B Measures of Access Used in Food Desert 

 and Related Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

Appendix C Methods and Supporting Tables and Figures 

 for Measuring the Extent of Limited Access in

 the United States. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

Appendix D Examples of Community Food Projects 

 by Type of Project . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141
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Summary

Increases in obesity and diet-related diseases are major public health 

problems. These problems may be worse in some U.S. communities because 

access to affordable and nutritious foods is diffi cult. Previous studies suggest 

that some areas and households have easier access to fast food restaurants 

and convenience stores but limited access to supermarkets. Limited access 

to nutritious food and relatively easier access to less nutritious food may 

be linked to poor diets and, ultimately, to obesity and diet-related diseases. 

Congress, in the Food, Conservation, and Energy Act of 2008, directed the 

U.S. Department of Agriculture (USDA) to conduct a 1-year study to assess 

the extent of the problem of limited access, identify characteristics and 

causes, consider the effects of limited access on local populations, and outline 

recommendations to address the problem.

This report presents the fi ndings of the study, which include results from two 

conferences of national and international authorities on food deserts and a 

set of commissioned research studies done in cooperation with the National 

Poverty Center at the University of Michigan. It also includes reviews of 

existing literature, a national-level assessment of access to supermarkets and 

large grocery stores, analysis of the economic and public health effects of 

limited access, and a discussion of existing policy interventions. A variety of 

analytical methods and data are used to assess the extent of limited access to 

affordable and nutritious food and characteristics of areas with limited access. 

Findings

Access to a supermarket or large grocery store is a problem for a small 

percentage of households. Results indicate that some consumers are 

constrained in their ability to access affordable nutritious food because they 

live far from a supermarket or large grocery store and do not have easy access 

to transportation. Three pieces of evidence corroborate this conclusion: 

• Of all households in the United States, 2.3 million, or 2.2 percent, live 

more than a mile from a supermarket and do not have access to a vehicle. 

An additional 3.4 million households, or 3.2 percent of all households, 

live between one-half to 1 mile and do not have access to a vehicle.

• Area-based measures of access show that 23.5 million people live in 

low-income areas (areas where more than 40 percent of the population 

has income at or below 200 percent of Federal poverty thresholds) 

that are more than 1 mile from a supermarket or large grocery store. 

However, not all of these 23.5 million people have low income. If 

estimates are restricted to consider only low-income people in 

low-income areas, then 11.5 million people, or 4.1 percent of the total 

U.S. population, live in low-income areas more than 1 mile from a 

supermarket. 

• Data on time use and travel mode show that people living in low-income 

areas with limited access spend signifi cantly more time (19.5 minutes) 

traveling to a grocery store than the national average (15 minutes). 

However, 93 percent of those who live in low-income areas with 
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United States Department of Agriculture

limited access traveled to the grocery store in a vehicle they or another 

household member drove. 

These distance and time-based measures are national estimates that 

do not consider differences between rural and urban areas in terms of 

distance, travel patterns, and retail market coverage. 

Urban core areas with limited food access are characterized by higher levels 

of racial segregation and greater income inequality. In small-town and rural 

areas with limited food access, the lack of transportation infrastructure is the 

most defi ning characteristic. 

These area- or distance-based results are in line with a nationally 

representative survey of U.S. households conducted in 2001. Responses 

to direct questions about food access show that nearly 6 percent of all U.S. 

households did not always have the food they wanted or needed because 

of access-related problems. More than half of these households also 

lacked enough money for food. It is unclear whether food access or income 

constraints were relatively greater barriers for these households. 

Supermarkets and large grocery stores have lower prices than smaller 

stores. A key concern for people who live in areas with limited access is 

that they rely on small grocery or convenience stores that may not carry 

all the foods needed for a healthy diet and that may offer these foods and 

other food at higher prices. This report examines whether prices of similar 

foods vary across retail outlet types and whether the prices actually paid 

by consumers vary across income levels. These analyses use proprietary 

household-level data that contain information on food items purchased by 

approximately 40,000 demographically representative households across the 

United States. Results from these analyses show that when consumers shop 

at convenience stores, prices paid for similar goods are, on average, higher 

than at supermarkets. 

Low-income households shop where food prices are lower, when they 

can. Findings also show that food purchases at convenience stores make up 

a small portion of total food expenditures (2 to 3 percent) for low-income 

consumers. Low- and middle-income households are more likely to purchase 

food at supercenters, where prices are lower. Administrative data on SNAP 

benefi t redemptions from 2008 show that 86 percent of SNAP benefi ts were 

redeemed at supermarkets or large grocery stores. Research that considers 

the prices paid for the same food across household income levels indicates 

that while some of the very poorest households—those earning less than 

$8,000 per year—may pay between 0.5 percent and 1.3 percent more for 

their groceries than households earning slightly more, households earning 

between $8,000 and $30,000 tend to pay the lowest prices for groceries, 

whereas higher income households pay signifi cantly higher prices. 

The study also examined food shopping behavior and the types of food 

purchased for SNAP participants and other low-income households. Data 

from the 1996/1997 NFSPS show that SNAP participants were, on average, 

1.8 miles from the nearest supermarket. However, the average number of 

miles both SNAP participants and eligible nonparticipants traveled to the 

store most often used was 4.9 miles. These same data show that SNAP 
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Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

United States Department of Agriculture

participants who did not shop at supermarkets purchased less noncanned 

fruit, noncanned vegetables, and milk than SNAP participants who shopped 

frequently at a supermarket. 

Easy access to all food, rather than lack of access to specifi c healthy foods, 

may be a more important factor in explaining increases in obesity. Many 

studies fi nd a correlation between limited food access and lower intake of 

nutritious foods. Data and methods used in these studies, however, are not 

suffi ciently robust to establish a causal link between access and nutritional 

outcomes. That is, other explanations cannot be eliminated as the cause of 

lower intake. A few studies have examined food intake before and after 

healthy food options become available (either within existing stores or 

because new stores opened). The fi ndings are mixed—some show a small 

but positive increase in consumption of fruits and vegetables, while others 

show no effect. 

The causal pathways linking limited access to nutritious food to measures 

of overweight like Body Mass Index (BMI) and obesity are not well 

understood. Several studies fi nd that proximity of fast food restaurants 

and supermarkets are correlated with BMI and obesity. But increased 

consumption of such healthy foods as fruits and vegetables, low-fat milk, 

or whole grains does not necessarily lead to lower BMI. Consumers 

may not substitute away from less healthy foods when they increase their 

consumption of healthy foods. Easy access to all food, rather than lack 

of access to specifi c healthy foods, may be a more important factor in 

explaining increases in BMI and obesity.

Understanding the market conditions that contribute to differences in 

access to food is critical to the design of policy interventions that may 

be effective in reducing access limitations. Access to affordable and 

nutritious food depends on supply (availability) and consumer demand. 

Consumer behavior, preferences, and other factors related to the demand 

for some foods may account for differences in the types of foods offered 

across different areas. Food retailer behavior and supply-side issues such 

as higher costs to developing stores in underserved areas may also explain 

variation across areas in which foods are offered and what stores offer them. 

If high development costs serve as a barrier to entry for supermarkets in 

some areas with low access, then subsidy programs or restructured zoning 

policies may be effective solutions. If consumer demand factors, such as 

inadequate knowledge of the nutritional benefi ts of specifi c foods, contribute 

to differences in access by reducing demand, then a public health campaign 

may be a preferred strategy. Several local and State-level efforts are 

underway that could provide the basis for a better understanding of the types 

of interventions that may work best. 

Food has been used as a tool for community development. Projects such 

as farmers’ markets, community gardens, promotion of culturally specifi c 

foods for ethnic minorities and Native Americans, local food production 

and promotion, youth agricultural and culinary training programs, and many 

other types of programs have all been implemented in a variety of settings, 

both urban and rural. USDA’s Community Food Projects Competitive Grant 

program has much experience in funding and nurturing such programs. 
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The current state of research is insuffi cient to conclusively determine 

whether some areas with limited access have inadequate access. Future 

research should consider improved methods to measure access levels, 

availability, and prices of foods faced by individuals and areas. More 

research is needed to understand how access, availability and price affect 

the shopping and consumption behaviors of consumers. Data linking 

information on the types of foods consumers purchase and eat with measures 

of consumers’ levels of access and the prices they face could help explain the 

economic consequences of food access. Studies that use improved methods 

and data to determine how food access affects diet, obesity, and other health 

outcomes are also needed to help explain the health consequences of food 

access. 

Methods

To conduct the analysis of the extent of food deserts, a comprehensive 

database was developed that identifi ed the location of supermarkets and 

large grocery stores within the continental United States. Food access was 

estimated as the distance to the nearest supermarket or large grocery store. 

The analysis was refi ned by examining households without vehicles and 

specifi c socio-demographic subpopulations drawn from the 2000 Census. 

Multivariate statistical analysis was applied to identify the key determinants 

of areas with low access to supermarkets and large grocery stores. 

Research also examined national-level data on questions of household 

food adequacy and access from the 2001 Current Population Survey. This 

information was complemented with national-level data on time spent 

traveling to grocery stores from the 2003-07 American Time Use Survey. To 

consider the economic consequences of limited access, ERS also analyzed 

demand for certain nutritious foods for a sample of participants in the 

Supplemental Nutrition Assistance Program (SNAP, formerly the Food 

Stamp Program), using data from the National Food Stamp Program Survey 

(NFSPS) of 1996/1997. Variation in prices for similar foods purchased at 

different store types, as indicated by hedonic models and data from the 2006 

Nielsen Homescan panel, was also estimated. 

ERS collaborated with other agencies and institutions to complete this 

study. USDA’s Food and Nutrition Service (FNS) compiled information 

on an extensive body of work examining food access for SNAP and other 

low-income households. USDA’s Cooperative State Research, Education, 

and Extension Service (CSREES) provided information on the Community 

Foods Projects and lessons learned in the administration of the projects. 

The national-level food desert analysis was complemented by a review of 

existing literature and the commissioning of additional studies by experts 

in the fi eld. A workshop held in October 2008 convened leading experts 

in the study of retail food and grocery store access, key stakeholders from 

community development organizations, grocery retailer organizations, other 

government agencies, congressional members and staff, and related public 

interest groups. The workshop included presentations and panel discussions 

of such topics as defi ning and describing dimensions of food deserts, 

implications of low access for food and nutrition assistance programs, 
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Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

United States Department of Agriculture

consequences of food deserts, and programs and policies to mitigate the 

adverse effects of food deserts. 

USDA, in cooperation with the National Poverty Center at the University of 

Michigan, commissioned several studies by experts in food access to better 

understand concepts of low access to affordable and nutritious foods and 

the degree to which access varies across different types of areas. The intent 

of these papers was to describe characteristics of the food environment and 

the demographic, economic, and health conditions that typify areas with 

low food access and to compliment the national-level fi ndings with more 

detailed and local-level information. Results from studies were presented in a 

conference. 

USDA, in cooperation with the Institute of Medicine of the National 

Academies, conducted a 2-day workshop in January 2009 on the public 

health implications of food deserts. Workshop presentations covered 

methods for assessing and research fi ndings on the impacts of food deserts on 

such outcomes as diet (including examination of specifi c foods, such as fruit 

and vegetable consumption and intake of high-energy, low-nutrient foods), 

prevalence of obesity and overweight; and diseases associated with poor 

diets. In addition, presentations covered promising strategies for mitigating 

the impacts of food deserts that have been suggested, implemented, or are in 

the planning stages. The workshop provided the basis for the review of the 

public health literature.

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CHAPTER 1

Introduction 

Increases in rates of obesity and related chronic diseases that may be linked 

with poor diets, such as diabetes and heart disease, are major public health 

concerns. Some advocates, community leaders, and researchers are worried 

that these problems, and poor diets in general, may be more severe in certain 

poor and rural American communities because these areas have limited 

access to affordable and nutritious foods. A primary concern is that some 

poor or rural areas do not have access to supermarkets, grocery stores, or 

other food retailers that offer the large variety of foods needed for a healthy 

diet (for example, fresh fruits and vegetables, whole grains, fresh dairy and 

meat products). Instead, individuals in these areas may be more reliant on 

food retailers or fast food restaurants that only offer more limited varieties 

of foods. It is hypothesized that the relative lack of access to full-service 

grocery stores and the easier access to fast and convenience foods may be 

linked to poor diets and, ultimately, to obesity and other diet-related diseases. 

It was this concern that led Congress, in the Food, Conservation, and Energy 

Act of 2008, (hereafter referred to as the 2008 Farm Bill) to direct the U.S. 

Department of Agriculture (USDA) to conduct a 1-year study of areas 

with limited access to affordable and nutritious food. The 2008 Farm Bill 

directed USDA to assess the extent of the problem of limited access, identify 

characteristics and causes of limited access and the effects limited access 

has on local populations, and outline recommendations for addressing the 

causes and effects of limited access. The USDA study was conducted by 

a team of researchers, policy analysts, and program leaders from USDA’s 

Food and Nutrition Service (FNS), Cooperative State Research, Education, 

and Extension Service (CSREES), and the Economic Research Service 

(ERS), which served as the lead agency. This report provides the analysis 

and fi ndings of the USDA study. A number of information-gathering and 

data-analysis activities were conducted as part of the study. Each of these 

activities and their purposes is described later in this introduction.1 

Defi nitions, Concepts, and Background Literature

TThe language in the 2008 Farm Bill defi ned a food desert as an “area in 

the United States with limited access to affordable and nutritious food, 

particularly such an area composed of predominantly lower income 

neighborhoods and communities” (Title VI, Sec. 7527). In order to consider 

the extent of such areas, the following questions fi rst need to be answered:

• What is affordable food and nutritious food?

• What does it mean to have (or not have) access to such food?

• Do individuals or do areas lack access?

The concern over food deserts is that some consumers have diffi culty 

accessing food retailers that offer affordable and nutritious food.2 The 

ease or diffi culty in getting to a food retailer depends on the location of the 

store in relationship to the consumer and the consumer’s travel patterns, the 

 1One additional activity that is not 

further described in this report is the 

Workshop on Access to Affordable and 

Nutritious Foods: Understanding Food 

Deserts held on October 9, 2008, in 

Washington, DC. An agenda for this 

workshop is included in appendix A.

 2We note that this study focuses 

on the ease at which households and 

individuals can get to stores that sell the 

foods they want at affordable prices. 

We do not focus on the related concept 

of food security, which measures 

whether households or individuals have 

access to enough food for an active, 

healthy life. The concepts are clearly 

related, but, in general, food security 

measures focus less on physical access 

and more on whether a household 

can afford food. For example, some 

individuals or households may have 

low food security but may live only 

one block from a large supermarket, so 

that physical access to food is less of 

a problem than whether the family can 

afford to buy the food.
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consumer’s individual characteristics (e.g., income, car ownership, disability 

status), and neighborhood characteristics (e.g., the availability of public 

transportation, availability of sidewalks, and crime patterns in the area). 

Before considering how many people and places may be affected by limited 

access to affordable and nutritious food, one must fi rst determine what is 

meant by “nutritious food” and “affordable,” and how access to affordable 

and nutritious foods can be measured. 

It may be easy to identify some foods as highly nutritious and others as 

much less nutritious, but the nutrition levels of most foods fall somewhere 

in between. Those foods that may be easily identifi ed as highly nutritious 

are available in different forms (fresh, frozen, canned, in prepared sauces 

or dishes). They can also be purchased at many food outlets, including 

those that many consider lacking in nutritious foods, such as fast food 

restaurants. It is likely that even the smallest food retailers stock foods that 

have nutritional merits; however, it is also likely that some retailers may 

offer very few of these options. No one food can fulfi ll the recommendations 

for a healthy diet. So measuring what “nutritious” food is and where it can 

be found must necessarily encompass a broad array of foods and sources of 

foods. 

Affordability of food refers to the price of a particular food and the relative 

price of alternative or substitute foods. Affordability of food is also impacted 

by the budget constraints faced by consumers, who must consider not only 

the prices of different foods to meet their food needs, but also the prices 

of other necessities (e.g., housing, clothing, and transportation). USDA 

provides guidance on national standards for nutritious diets at various 

costs levels—the Thrifty, Low-cost, Moderate-cost and Liberal Food Plans 

(Carlson et al., 2007a; Carlson et al., 2007b). Within each plan is a market 

basket of foods in quantities that refl ect current dietary recommendations, 

food composition data, food prices, and actual consumption patterns. 

According to the Low-cost Food Plan, a family of four with two adults 

(age 19 to 50) and two children (ages 6 to 8 and 9 to 11) could consume a 

nutritious diet for $175.60 per week (USDA, 2009).

In addition to considering food prices, consumers also consider travel 

and time costs in deciding where to shop and what to buy. There are also 

monetary and time costs in preparing and serving food, as well as cleaning 

up. The time costs of these activities may affect consumer decisions about 

whether to shop for and prepare a home-cooked meal, buy products that 

require less preparation time, or eat a meal prepared by a restaurant. 

Measuring access to affordable and nutritious food is an enormous data 

collection task that requires information on all the food retailers in a 

neighborhood or within the reach of the consumer, the types and prices of 

food sold in these stores, and a measure of the quality of the food. Many 

studies approximate the availability of these foods and a wide range of other 

foods by using the existence of supermarkets and grocery stores, arguing that 

these stores are known to carry a variety of foods and have many options 

for “nutritious foods,” such as fresh, frozen, and canned, and carry them at 

the lowest prices. But focusing only on supermarkets and larger grocery 

stores is likely to underestimate the availability of healthy foods since some 

of these foods are also available at small grocery stores, convenience stores, 
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pharmacies, dollar stores, farmers’ markets, and restaurants. There is also 

some evidence of substitutability in stores—that is, areas without large 

chain supermarkets are often served with independent, and often smaller, 

grocery stores (Neckerman et al., 2009; Powell, 2009). These smaller stores 

may have adequate and affordable food choices, so that in ignoring them, 

researchers may underestimate the food that is available in those areas. 

The limitations of considering only supermarkets and large grocery stores 

in measuring the availability of food are well recognized in the literature on 

food access. This has led to more localized studies that collect an extensive 

amount of data on the food environment. Some studies collect additional 

information about the locations of food retailers other than supermarkets, 

such as farmers’ markets, meat markets, bakeries, or veggie carts (see, 

for example, Neckerman et al., 2009). Other studies actually measure a 

store’s contents to see if “healthy foods” are sold, how much shelf space 

is dedicated to them, and in which forms they are sold (e.g., fresh, frozen, 

or canned; low-fat or regular) (see, for example, Rose et al., 2009; Sharkey 

and Horel, 2009). Standardized tools for conducting such studies have also 

been developed and tested, such as the Nutrition Environment Measures 

Survey (NEMS) (http://www.sph.emory.edu/NEMS/.) Such extensive data 

collections have been conducted on more localized levels, for example, in 

New Orleans, New York City, and six rural counties in Texas. But because 

these efforts require such intensive data collection and resources, they are not 

easily conducted on a national level. 

Studies of food access have also measured the availability and prices of 

foods in USDA’s Thrifty Food Plan (TFP) in stores as a standardized way 

to compare availability and affordability of foods in geographic areas (for 

example, Block and Kouba, 2005; Hendrickson et al., 2006; Mantovani et al., 

1997; Rose et al., 2009). Such uses of the TFP provide an absolute measure 

of availability and price (as opposed to a relative measure) and allow 

aggregation across store types (Bitler and Haider, 2009). 

Once the availability and price of food has been measured, studies of food 

access typically then measure how easy it is for consumers to access the 

food. The ease or diffi culty of food access has been measured many ways.4 

One common method is to measure distance from consumers’ residences 

to the nearest food retailer that offers healthy and affordable foods (often to 

supermarkets or large grocery stores).5 Distances in sparsely populated areas 

are often not directly comparable to distance in densely populated areas. As 

a result, many studies consider access in rural areas separately from access 

in suburban and urban areas. “Walkable” distance measures have often been 

used to characterize access in urban areas. The defi nition of such a distance 

is often 1 kilometer or about a half mile (app. table B.1). Similar concepts 

for less densely populated suburban and rural areas have not been applied, 

but often a distance is designated to distinguish access limitations. For 

example, areas more than 10 miles from a supermarket have been called food 

deserts (Blanchard and Lyson, 2006; Morton and Blanchard, 2007). These 

designations of what may be considered an “acceptable” distance to a food 

source in less densely populated areas are somewhat arbitrary, especially 

considering that without a car, any distance of more than a mile or so could 

be considered unacceptably far. 

 4Appendix table B.1 summarizes the 

measures of access to health foods used 

by over 30 studies. Figure B.1 shows 

the locations of these studies. 

 5Distance is usually measured from 

the centroid of an area (e.g., ZIP Code, 

census tract, or block) to the nearest 

supermarket. 
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Distance is almost always measured as distance from a residential area 

to a store, assuming home to store travel is the way most people access 

supermarkets. But people do not just travel from home to store. They travel 

to work, school, church, and beyond and often purchase food on the way. 

Using an access measure that only considers distance from home is likely to 

underestimate the options available for food shopping. 

Measures of distance to the nearest food retailer do not consider whether 

the consumer has other choices that can offer better products or lower 

prices. Thus, many studies have tried to capture the amount of choice 

consumers have in their measures of food access. Apparicio et al. (2007) 

and Sparks et al. (2009) calculate the distance to three different stores, or 

the distance to three different chain supermarkets to add a dimension of the 

level of competition in an area. Density measures that count the number of 

stores in a certain geographical area are also often used to describe the food 

environment. For example, measures such as the number of supermarkets, 

fast food restaurants, or convenience stores per resident within a census tract 

or the ratio of fast food restaurants to supermarkets per capita have been used 

to describe food environments within a geographic area (see, for example, 

Gallagher, 2007 and 2006). Density measures add richness to a measure of 

the food environment by looking beyond distance. Further, relatively higher 

densities of a store type could be a signal of the level of competition among 

that type of store and may signal lower prices. 

A problem with both distance and density measures, however, is that they 

only measure “potential access,” and not “realized access.”6 Potential access 

shows where consumers could possibly shop, while realized access shows 

where consumers actually shop. A consumer that does not care to eat at 

fast food restaurants or convenience stores may have high access to these 

stores but may pass by them on the way to a supermarket that is farther 

away. And even if the concentration of convenience stores is higher in some 

neighborhoods, most of the food shopping could be conducted at larger 

supermarkets. For example, Broda et al. (forthcoming) fi nd that compared 

with higher income families, low-income families spend slightly more of 

their food budget at convenience stores, which offer prices that are, on 

average, greater than those in traditional grocery stores. However, the study 

also found that compared with higher income families, low-income families 

spend a greater share of total expenditures at supercenters, where lower 

prices almost completely offset the higher prices at convenience stores. To 

further illustrate this point, data show that, on average, SNAP participants 

lived 1.8 miles from the nearest supermarket but traveled 4.9 miles to the 

foodstore they most often used (Cole, 1997). (More details on both of these 

fi ndings are provided in chapter 5).

Area-based versus individual based concepts of access

Studies that use area-based measures of access, either distance or density, 

usually focus only on areas with high concentrations of vulnerable 

populations.7 Examining only areas in which a relatively high proportion of 

poor people live, for example, will miss many poor people who live in less 

poor areas, but who may also have limited access.8 Further, not everyone 

who lives in low-income areas is poor. There will certainly be people who 

live in a low-income area with limited access but who themselves have 

 6This helpful distinction is made by 

Sharkey and Horel, 2009. 

 7Most studies consider only areas 

with high concentrations of poor people. Some also consider areas with low 

vehicle ownership rates, high concentrations of elderly, and the availability 

of public transportation. See Neckerman et al., 2009, for more details. 

8A separate concern is for those who 

are too poor to buy food regardless of 

how accessible it is. USDA’s Household Food Security in the United States 

series reports the percent of Americans 

who do not have access to enough food 

for an active, healthy life for all household members. In 2007, 11.1 percent 

of households were food insecure at 

some time during the year and about 

4.1 percent of all households had very 

low food security some time during the 

year (Nord et al., 2008).
5

Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

United States Department of Agriculture

adequate resources to travel to a supermarket regularly. Ownership of, 

or easy access to, a motorized vehicle may be the best marker of access 

regardless of whether someone lives in a poor area or not. The majority of 

U.S. households own cars (89.7 percent).9 Vehicle ownership rates among 

those living in rural areas (94.6 percent) are higher than among those living 

in urban areas (87.8 percent). Those with low incomes are less likely to own 

a vehicle, but time use and travel mode data reported in Chapter 2 show that 

most people, even low-income people, take their own vehicles or drive with 

someone else to do their grocery shopping.

The distinction between individual-level access and area-based access has 

signifi cant implications for measuring the size of the problem of limited 

access—that is, the number of people with limited access. Chapter 2 

illustrates this. The distinction also has implications for the design of 

policies that may be most cost effective in reducing the problem. For 

example, if those people who have low incomes and limited access are 

scattered throughout areas with lower concentrations of poor people, then 

opening up a new supermarket may be less effective than policies that make 

individual or group transportation to stores less expensive (for example, bus/

transit subsidies, store shuttle services, or improved bus routes). However, 

if people with low income and low access are concentrated in certain areas, 

then fi nding a way to open a new store or improve the variety of foods 

carried in existing stores in that area may be more effective.

The bulk of studies of food access fi nd relative differences across areas 

in access to some types of food retailers and foods.10 Researchers have 

documented the inequality of access to supermarkets in urban inner city 

areas (Donohue, 1997), while others have focused on differences in access 

to supermarkets in poor versus nonpoor areas (O’Conner and Abell, 1992; 

Cotterill and Franklin, 1995; Pike, 2000). Moore and Diez Roux (2006) 

investigated racial disparities in the number and variety of grocery stores 

in neighborhoods. Zenk et al. (2005) compared distances to the nearest 

supermarket among poor White and poor non-White households. Extending 

that approach, Gallagher (2007 and 2006) compared differences in the 

ratio of supermarkets to other foodstores in a neighborhood in Detroit and 

Chicago. More recently, Neckerman et al. (2009) examined the retail food 

environment in New York City. They considered the characteristics of 

households, such as race, income, and forms of available transportation, 

including vehicle ownership or access to mass transit, as factors affecting a 

household’s foodstore access. Findings show lower access to supermarkets 

and other healthy food stores for neighborhoods composed primarily of 

African-Americans, where populations were heavily reliant on mass transit 

for transportation. 

While there may be relative disparities in access to specifi c types of food 

retailers, there is not general agreement on whether areas with relatively 

less access have inadequate access to food. That is—there is not a widely 

agreed standard above which an area has “adequate” access to affordable and 

nutritious food and below which, an area has “inadequate” access to food. 

To draw such a distinction would require more systematic consideration of 

what inadequate access to affordable and nutritious food means, which would 

require collection of very detailed data on food availability and price. That 

does not mean that there are not areas with inadequate access. The research 

 9See chapter 3 for details on household access to vehicles.

10 Larson et al. (2009) (Not in references) reviewed 54 studies of disparities in access to healthy food. 
6

Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

United States Department of Agriculture

highlighted above certainly shows some areas may have inadequate access. 

Rather, the point here is that the data and methods that have been used to 

document relative differences cannot be implemented easily on a national 

level to make a national-level distinction. 

Absolute standards of similar concepts, such as poverty or food insecurity, 

have been made and are commonly used in describing conditions of the 

U.S. economy and the well-being of U.S. households and in making public 

policy. While these absolute distinctions are certainly not universally agreed 

upon, there is a much deeper and richer literature from which a concept like 

poverty can be defi ned and measured than there is for food deserts.

Report Outline

A variety of data and methods was used to assess the extent of limited access 

to affordable and nutritious food, including both individual measures of 

access and area-based measures of access. Responses to a national-level 

household survey of food adequacy and access were analyzed, as were 

data estimating differences among households in the time spent traveling 

to grocery stores and the travel mode used. To determine the extent of 

areas with limited access, a comprehensive database was developed to 

identify the location of supermarkets and large grocery stores within the 

continental United States. Food access was estimated as the distance to 

the nearest supermarket or large grocery store, which is used as a proxy for 

the availability of affordable and nutritious food. The analysis specifi cally 

considered distance to the nearest supermarket for low-income populations 

and for households without access to a vehicle. Differences in rural, urban, 

and areas in between were considered. These results are presented in 

Chapter 2. Chapter 3 uses the same data on the location of supermarkets to 

analyze the household and neighborhood characteristics that distinguish areas 

with low access from areas with better access. The novel contribution of 

these analyses is that they are national in scope and combine two databases 

on supermarket and grocery store location. 

Supermarkets are not the only sources of healthy and affordable foods. 

Many smaller scale sources may be used by those who are underserved by 

supermarkets. However, a complete assessment of the food environment of 

every area in the United States is an enormous task that is beyond the scope 

of this study. Instead, USDA cooperated with the National Poverty Center 

(NPC) at the University of Michigan to commission six studies of the food 

environment at more localized levels. These studies provide more detail on 

the food environment in New York City; Indianapolis, Indiana; New Orleans, 

Louisiana; Salt Lake County, Utah; the Brazos Valley in rural Texas; and 

Portland, Oregon. Methods and fi ndings from these studies, along with the 

national level analyses, are discussed in Chapters 2 and 3.11 

People who live in areas with limited access may be more prone to poor diets 

and have poor health outcomes, such as obesity or diabetes, because they 

lack access to healthy foods and may have too easy access to less healthy 

foods. Chapter 4 considers the extent of knowledge on the relationship 

between limited access and diet and health outcomes. This chapter draws 

heavily upon a workshop summary of the Institute of Medicine (IOM) 

and the National Academies. This workshop, sponsored by ERS, was 

11Drafts of these papers and an 

agenda for a conference that featured 

the papers are available on the NPC 

website: www.npc.umich.edu/news/

events/food-access/index.php. Final 

versions of these papers and a summary 

of the papers will accompany the fi nal 

version of this report.
7

Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

United States Department of Agriculture

conducted on January 26-27, 2009. It included sessions on measuring 

access; methodological challenges in assessing causal relationships between 

food access and diet and health outcomes; reviews of existing knowledge 

about the links between access and diet and health outcomes; and promising 

strategies for mitigating the impacts of food deserts that have been suggested, 

implemented, or are in the planning stages. A workshop summary will be 

published in June 2009. 

Populations that live in areas with limited access to affordable and nutritious 

food may adjust their food shopping behaviors and diets based on the food 

environment in their area. These adjustments could be due to the lack of 

availability of some foods or to the relative prices of different foods offered 

from different food retailers. Chapter 5 considers how food access relates 

to food choice—that is, whether consumers in areas with limited access 

face higher prices for similar goods and whether they have different food 

purchasing behaviors. Comparisons of the prices that consumers paid for 

similar foods (milk, ready-to-eat cereal, and bread) purchased at different 

retail outlets (supermarkets and grocery stores vs. convenience stores) 

are made using hedonic price models. Differences in the prices offered 

at different retail outlets could lead consumers to adjust where they shop 

and what they purchase. The chapter also considers shopping behavior for 

populations with limited access, which can further the understanding of the 

adjustments that consumers make to different prices and retail availability. 

A summary of a body of work conducted by FNS on the shopping patterns 

of participants of the Supplemental Nutrition Assistance Program (SNAP—

formerly called the Food Stamp Program) is provided in the chapter. ERS 

also analyzed how SNAP participants’ expenditures on foods in several food 

groups (e.g., canned and noncanned fruits and vegetables) varied by selfreported measures of access to supermarkets. Spending on these food groups 

by people with relatively easy access to supermarkets is compared with 

spending by those with less access to supermarkets. Finally, fi ndings from 

a study conducted by ERS and external researchers on whether poor people 

pay more for similar foods relative to higher income people are integrated. 

Economic and market conditions may contribute to the existence of food 

deserts. The costs facing food retail businesses and the choices available 

to consumers could both account for differences among stores in where 

they choose to locate. Chapter 6 provides an economic framework for 

understanding supply and demand for food and factors that may account for 

difference in access to food retailers across different areas. USDA, through 

NPC, contracted with two economists, Marianne Bitler and Steven Haider, to 

provide an economic framework for understanding food access issues. The 

chapter draws heavily upon this paper (Bitler and Haider, 2009). 

In addition to administering SNAP and other nutritional assistance programs, 

USDA administers programs to improve food security in low-income 

communities. States and localities have also implemented programs to 

increase access to affordable and nutritious food for underserved populations. 

Private retailers have responded to the needs of low-income and bargain 

food shoppers. Chapter 7 describes USDA’s Community Foods Project 

Competitive Grants Program and lessons learned from this program. Chapter 

8 highlights several programs that have been implemented by States and 
8

Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

United States Department of Agriculture

localities, as well as describes several other policy options that may be 

considered to reduce the effects of limited access. 

Not all of the questions about the extent, causes, and consequences of food 

deserts will be answered in this report. The fi nal chapter, Chapter 9, outlines 

an agenda for further research on the causes and consequences of areas with 

limited access to affordable and nutritious food.

References

Apparicio, P., M.S. Cloutier, and R. Shearmur (2007). “The Case of 

Montréal’s Missing Food Deserts: Evaluation of Accessibility to Food 

Supermarkets,” International Journal of Health Geographics 6(4).

Bitler, Marianne, and Steven J. Haider (2009). “An Economic View of Food 

Deserts in the United States,” National Poverty Center Working Paper, www.

npc.umich.edu/news/events/food-access/index.php 

Blanchard, T.C., and T. Lyson (2006). Food Availability and Food Deserts 

in the Nonmetropolitan South, Assistance Needs of the South’s Vulnerable 

Populations, Number 12.

Block, D., and J. Kouba (2005). “A Comparison of the Availability and 

Affordability of a Market Basket in Two Communities in the Chicago Area,” 

Public Health Nutrition 9(7): 837-845.

Broda, C. E. Leibtag, and D.E. Weinstein (2009). “The role of prices in 

measuring the poor’s living standard,” Journal of Economic Perspectives, 23 

(2): Spring 2009: 000-000 (forthcoming). 

Carlson, A., M. Lino, W.Y. Juan, K. Hanson, and P.P. Basiotis (2007a). 

Thrifty Food Plan, 2006, CNPP-19, U.S. Department of Agriculture, Center 

for Nutrition Policy and Promotion. 

Carlson, A., M. Lino, and T. Fungwe (2007b). The Low-Cost, ModerateCost, and Liberal Food Plans, 2007, CNPP-20, U.S. Department of 

Agriculture, Center for Nutrition Policy and Promotion. 

Cole, N. (1997, February) Evaluation of the Expanded EBT Demonstration in 

Maryland: Patterns of Food Stamp and Cash Welfare Benefi t Redemption. 

Report submitted to U.S. Department of Agriculture, Food and Nutrition 

Service by Abt Associates, Inc. 

Cotterill, R., and A. Franklin (1995). “The Urban Grocery Store Gap,” Food 

Marketing Policy Center, University of Connecticut.

Donohue, R.M. (1997). Abandonment and Revitalization of Central City 

Retailing: The Case of Grocery Stores, The University of Michigan DAI-A 

58/10.

Gallagher, M. (2007). Examining the Impact of Food Deserts on Public 

Health in Detroit, Mari Gallagher Research & Consulting Group.
9

Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

United States Department of Agriculture

Gallagher, M. (2006). Examining the Impact of Food Deserts on Public 

Health in Chicago, Mari Gallagher Research & Consulting Group.

Hendrickson, D., C. Smith, and N. Eikenberry (2006). “Fruit and Vegetable 

Access in Four Low-income Food Deserts Communities in Minnesota,” 

Agriculture and Human Values, 23: 371-383.

Larson, N.I., M.T. Story, and M.C. Nelson (2009). “Neighborhood 

environments: Disparities in access to healthy foods in the U.S,” American 

Journal of Preventive Medicine, 36(1): 74-81.e10. 

Mantovani, R.E., L. Daft, T.F. Macaluso, J. Welsh, and K. Hoffman (1997). 

Authorized Food Retailer Characteristics Study (Technical Report IV): 

Authorized Food Retailers’ Characteristics and Access Study, Report 

submitted to U.S. Department of Agriculture, Food and Nutrition Service, by 

Macro International, Inc.

Moore, L., and A. Diez Roux (2006). “Associations of Neighborhood 

Characteristics With the Location and Type of Food Stores,” American 

Journal of Public Health, February, Vol. 96, No. 2, pp. 325-331.

Moore, L., and A. Diez Roux (2006). “Associations of Neighborhood 

Characteristics with the Location and Type of Food Stores,” American 

Journal of Public Health, February, Vol. 96, No. 2, pp. 325-331.

Morton, L.W., and T.C. Blanchard (2007). “Starved for Access: Life in 

Rural America’s Food Deserts,” Rural Realities 1(4): 1-10.

Neckerman, K.M., M. Bader, M. Purciel, and P. Yousefzadeh (2009). 

“Measuring Food Access in Urban Areas,” National Poverty Center Working 

Paper, www.npc.umich.edu/news/events/food-access/index.php 

Nord, M., M. Andrews, and S. Carlson (2008). Household Food Security in 

the United States, 2007, Economic Research Report 66, Economic Research 

Service, U.S. Department of Agriculture. 

O’Connor, J., and B. Abell. “Successful Supermarkets in Low Income Inner 

Cities,” www.nal.usda.gov/foodstamp/FOODSTAMPREPORTS/FSP-341.

PDF.

Pike, J. (2000). “Boston’s Poor Pay More For Food, Theft and Other 

Problems Increase Business Costs, Prices,” Massachusetts News, August 2.

Powell, L. (2009). “Food Deserts: National Overview by Demographics and 

Socio-economic Status,” Presentation for the Institute of Medicine Workshop 

on the Public Health Effects of Food Deserts, January 26. 

Rose, D., J.N. Bodor, C.M. Swalm, J.C. Rice, T.A. Farley, and P.L. 

Hutchinson (2009). “Deserts in New Orleans? Illustrations of Urban Food 

Access and Implications for Policy,” National Poverty Center Working 

Paper, http://www.npc.umich.edu/news/events/food-access/index.php
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Sharkey, J.R., and S. Horel (2009). “Characteristics of Potential Spatial 

Access to a Variety of Fruits and Vegetables in a Large Rural Area,” 

National Poverty Center Working Paper, www.npc.umich.edu/news/events/

food-access/index.php 

Sparks, A., N. Bania, and L. Leete (2009). “Finding Food Deserts: 

Methodology and Measurement of Food Access in Portland, Oregon,” 

National Poverty Center Working Paper, www.npc.umich.edu/news/events/

food-access/index.php 

U.S. Department of Agriculture (2009). Offi cial USDA Food Plans: Cost 

of Food at Home at Four Levels, U.S. Average, June, 2008, www.cnpp.usda.

gov/Publications/FoodPlans/2008/CostofFoodJun08.pdf 

Zenk S, N., Schulz A, J., Israel B, A., James S, A., et al. (2005). 

“Neighborhood Racial Composition, Neighborhood Poverty, and the Spatial 

Accessibility of Supermarkets in Metropolitan Detroit,” American Journal of 

Public Health, 95(4):660.
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Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

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CHAPTER 2

The Extent of Limited Food 

Access in the United States 

This chapter describes the extent of limitations in access to affordable and 

nutritious food in the United States in three separate sections. The fi rst 

section provides estimates of individual measures of access, based on survey 

data on the number of U.S. households that indicate food access limitations. 

The second section uses a geographical, area-based approach to measure 

access to supermarkets. A national level directory of supermarkets is 

developed and geocoded. Distance-based measures of supermarket access 

are produced for the entire continental U.S. population and by selected 

economic and demographic characteristics of the population. Median 

distances to supermarkets are computed and a three-category distinction 

of low, medium, and high access is used to describe supermarket access 

for the entire U.S. and separately for low-income neighborhoods and for 

people outside of those areas. Information on vehicle ownership, which is 

an important individual-level characteristic related to the ease at which a 

variety of nutritious foods can be accessed, is also provided for those that 

live far from supermarkets. The third section of this chapter uses data from 

the American Time Use Survey (ATUS) to estimate the amount of time 

households spend traveling to the grocery store. The mode of transportation 

used is also considered. 

Most of the previous studies on food access have focused on specifi c 

geographic areas such as cities or counties or even States. The analysis in 

this chapter is unique because it uses multiple methods to provide a broad 

overview of access to affordable and nutritious food on a national level. 

Individual-Level Measures of Food Access From 

National Household Surveys

The vast majority of the literature on food deserts and much of the rest of 

this report focus on area-based measures of food access—that is, measures 

of access to stores or food outlets for a geographically designated area, often 

areas with high concentrations of low-income individuals. These measures 

inherently assume that everyone within a geographic area has the same level 

of access as everyone else in that area. But some people who live in areas 

with concentrated poverty may not be poor, may own their own vehicle, and 

may be able to access affordable and nutritious food. Other poor individuals 

may live outside of areas with concentrated poverty but may have limited 

access to food. An alternative measure of food access is the one presented in 

this section—access for individuals directly, regardless of where they live. 

Since 1995, USDA has collected information annually on food spending, 

food access and adequacy, and sources of food and nutrition assistance for 

the U.S. population. The information is collected in an annual food security 

survey, conducted as a supplement to the nationally representative Current 

Population Survey (CPS). Data from the Food Security Supplement of the 

CPS (CPS-FSS) is used to provide estimates of the prevalence and severity 

of food insecurity in U.S. households (see, for example, Nord et al., 2008). 
12

Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

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In addition to asking food security questions, the CPS-FSS, until 2001, asked 

a general question about whether the household had enough of the kinds of 

foods it wanted and needed. Those households who responded that they 

did not have enough of the kinds of foods they wanted were asked followup 

questions about why they did not have enough food. Respondents could 

answer by choosing from among several options, including options directly 

related to store access (see box, “CPS-FSS Questions on Food Access”). 

Table 2.1 provides the population weighted responses to these questions 

and provides a direct measure of the percent of households that do not 

always have enough of the foods they want because of access limitations. 

Eighty-one percent of households always had the kinds of foods they wanted 

to eat. Sixteen percent always had enough food to eat but did not always 

Box CPS-FSS Questions on Food Access

SS1 Which of these statements best describes the food eaten in your household-

-enough of the kinds of food we want to eat, enough but not always the kinds 

of food we want to eat, sometimes not enough to eat, or often not enough to 

eat?

 1. Enough of the kinds of food we want to eat (SKIP TO SX1CK)

 2. Enough but not always the kinds of food we want to eat (SKIP TO SS1B)

 3. Sometimes not enough to eat (SKIP TO SS1C)

 4. Often not enough to eat (SKIP TO SS1C)

Those who gave response #2, “enough but not always the kinds of foods we want to 

eat” were asked SS1B:

SS1B Here are some reasons why people don’t always have the kinds of food they 

want. For each one, please tell me if that is a reason why YOU don’t always 

have the kinds of food you want to eat.

 READ LIST. MARK ALL THAT APPLY.

 YES NO

Not enough money for food [ ] [ ]

Kinds of food we want not available [ ] [ ]

Not enough time for shopping or cooking [ ] [ ]

Too hard to get to the store [ ] [ ]

On a special diet [ ] [ ]

Those who responded to question SS1 with response #3 or #4, “sometimes” or “often 

not enough to eat” were asked SS1C: 

SS1C Here are some reasons why people don’t always have enough to eat. For 

each one, please tell me if that is a reason why YOU might not always have 

enough to eat.

READ LIST. MARK ALL THAT APPLY.

 YES NO

Not enough money for food [ ] [ ]

Not enough time for shopping or cooking [ ] [ ]

Too hard to get to the store [ ] [ ]

On a diet [ ] [ ]

No working stove available [ ] [ ]]

Not able to cook or eat because

 of health problems [ ] [ ]

Notes: Question SS1 is still asked in the CPS-FSS. Up until 2001, questions SS1B 

and SS1C were also asked as follow up questions. For questions SS1B and SS1C, 

multiple responses were accepted.
13

Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

United States Department of Agriculture

have the kinds of foods they wanted to eat. Another 3 percent sometimes or 

often did not have enough food to eat. 

Respondents who reported they had enough to eat but did not always have 

the kinds of foods they wanted were asked why they did not have the kinds 

of foods they wanted. Among all households, 5.1 percent reported that they 

did not have the kinds of foods they wanted because it was either too hard to 

get to the store or the foods they wanted were not available. Respondents 

who reported that they sometimes or often did not have enough food to eat 

were also asked why. A total of 0.6 percent of all households said they did 

not always have enough to eat and that it was because it was too hard to get 

to the store. Thus, based on these questions, 5.7 percent of all households 

reported they did not always have the food they want or need because of 

access limitations. 

Not all of these access limitations refl ect a lack of a nearby store with 

adequate food. Some who report that it is too hard to get to the store may 

be disabled or elderly and frail. This group may very well have food access 

problems, but it is not necessarily because they do not have nutritious food 

options nearby. Further, of the 5.7 percent who cited access problems, more 

than half, or 3.0 percent, also cited that they did not have enough money 

for food. Another reason these responses may not indicate access problems 

related to nearby availability of food is that the condition “enough but not 

always the kinds of foods we want to eat” does not necessarily indicate 

whether the food available was nutritionally adequate or not. Despite 

these caveats, these estimates from the CPS-FSS could be considered an 

estimate of the number of households that face food access limitations.

Table 2.1

National estimates of the percent of households who do not 

have enough of the kinds of foods they want because of food 

access limitations

 Percent of 

 all households

Households that always had the kinds of foods 

 they wanted to eat 81.0

Households that had enough to eat but did not 

 always have the kinds of foods they wanted to eat 16.0

Households that sometimes or often did not 

 have enough to eat 3.1

Reported reasons for not always having the kinds of foods or enough food:

Households that always had enough to eat, but did not always have 

 the kinds of foods they wanted to eat because it was too hard to get

 to the store or the kinds of foods they wanted were not available 5.1

Households that sometimes or often did not have enough 

 to eat because it was too hard to get to the store 0.6

 Total with food access limitations 5.7

Notes: Some of those who reported it was “too hard to get to the store” may be elderly or 

disabled. Of the 5.7 percent who reported access limitations, more than half (3.0 percent of all 

households) also cited that they did not have enough money for food.

Source: USDA, ERS calculations based on 2001 CPS-FSS survey data.
14

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Area-Based Measures of Access to Affordable and 

Nutritious Food

Individual measures of access provide one estimate of the number of people 

affected by limited access. The primary intent of the congressional mandate 

was to focus on area-based measures of access. Area-based measures are 

important because characteristics of the areas where people live, work, or 

travel may affect access to healthy and affordable food, which may affect 

diet and health. 

This section examines the extent of areas in the U.S. that have low access to 

supermarkets, a reliable source of nutritious and affordable foods. A national 

supermarket directory is fi rst developed and geocoded. Data on population, 

income, and other household characteristics from the 2000 Census are 

aggregated to square kilometer grids that cover the continental United States. 

The distances are measured from the center of these 1-kilometer grids to the 

nearest supermarket for the entire U.S. population, for low-income areas and 

higher income areas, and by characteristics of individuals or households. 

Access to supermarkets is described using these distance measures fi rst 

for the entire U.S. population, then separately for Census Urbanized Area 

designations. The descriptions use simple population-weighted median 

distance to stores overall and across Urbanized Area and population 

characteristics. Each area is assigned to one of three categories of access 

based on whether the distance to the nearest supermarket is within a range 

of “walkable” distances. For rural areas, a “drivable” distance measure is 

considered.

Data and Methods 

Defi nition of food retail outlets that offer affordable 

and nutritious food

Food is sold in a wide range of retail outlets, including traditional 

foodstores (e.g., supermarkets, grocery stores and convenience stores), and 

nontraditional retail stores that carry food products with other merchandise. 

Among the various forms of food retailers, supermarkets, supercenters, and 

warehouse club stores combined account for the largest share of food sales, 

75.2 percent of the total in 2008 (Economic Research Service, 2009). These 

larger retail outlets typically offer all major food departments, including fresh 

produce, meat, poultry and seafood, as well as more economical package 

sizes and lower cost store brands and generic brands of packaged foods. 

Many studies have shown large retail outlets are more affordable relative 

to other retail food outlets (Andrews et al., 2001; Chung and Myers, 1999; 

Nayga and Weinberg, 1999; Kaufman et al., 1997). 

An ERS review of studies of food prices found that supermarket prices are 

10 percent lower, on average, than those of smaller foodstores, in part, due 

to lower per unit costs resulting in lower margins over cost of goods sold 

(Kaufman et al., 1997). Neckerman, et al. (2009) cite a number of audit 

surveys of food prices, fi nding that store type is highly associated with 

price and that supermarkets, larger chain stores, or discount stores such as 

supercenters, tend to offer lower prices. 
15

Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

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Montovani et al. (1997) examined characteristics and services of a nationally 

representative sample of 2,400 stores authorized to receive benefi ts from the 

Supplemental Nutrition Assistance Program (SNAP). Price, quality, and 

variety of store foods were assessed in terms of the market basket of goods 

that refl ect the Thrifty and Low Cost Food Plans.13 This analysis focused on 

product availability and cost in areas with different concentrations of poverty. 

In urban areas, market basket costs in supermarkets and large grocers were 

nearly equivalent across levels of poverty. Prices were less at “other” 

stores located in high-poverty areas versus those in lower poverty areas. 

Availability of market basket items did not vary by poverty level among 

supermarkets in urban areas. Variety did vary by poverty level for large 

grocers. Fresh produce and fresh seafood were less available in large grocers 

located in high-poverty areas. Fresh meat was more available, however, 

at large grocers in these locations. In rural areas, market basket costs were 

consistently similar in higher and lower poverty areas. With the exception 

of fresh seafood, a similar proportion of market basket items was available in 

supermarkets and large grocery stores, regardless of the area’s poverty level. 

Food quality was similar across different store types and poverty levels in 

rural areas. Results from this analysis confi rm that, on average, supermarkets 

and large grocery stores offer lower prices and more variety than other store 

types. Large grocers were more similar to supermarkets than other store 

types, especially in rural zip codes. 

The analysis uses supermarkets and large grocery stores (hereafter defi ned 

simply as “supermarkets”) as proxies for food retailers that offer a variety 

of nutritious, affordable retail foods. The industry-standardized defi nition 

requires that to be considered a supermarket, a retailer must have annual 

sales of at least $2 million and contain all the major food departments found 

in a traditional supermarket, including fresh meat and poultry, produce, dairy, 

dry and packaged foods, and frozen foods.14, 15 

Two separate national-level directories of foodstores from the year 2006 were 

used to develop a comprehensive list of supermarkets in the U.S. The fi rst 

directory is a list of authorized stores that accept SNAP benefi ts. More than 

166,000 outlets were authorized in 2006, but only approximately 34,000 met 

the supermarket defi nition criteria. In addition to the store name and address, 

SNAP data include a store type classifi cation, the most recent authorization 

year’s total sales and total food sales, and total SNAP redemptions. The 

SNAP data were augmented with additional supermarket data from Trade 

Dimensions TDLinx (a Nielsen company), a proprietary source of individual 

supermarket store listings also for the year 2006.16 This data set includes 

the name and address of supermarkets, the type of supermarket, annual sales 

volume range, and other supermarket characteristics. Details on how these 

data were merged and cleaned can be found in Appendix C. The combined 

list of supermarkets was converted into a GIS-useable format by geocoding 

the street addresses into store point locations. The fi nal combined data set 

included locations for 40,108 supermarkets and supercenters nationwide. 

By combining the two store listings and using outside sources for 

verifi cation, a more comprehensive national list of supermarkets and 

supercenters was obtained. By comparison, a study of Salt Lake County, 

Utah, revealed that there can be considerable disagreement across sources 

of data on the presence of foodstores when conducting access studies (Fan 

13Market basket quality was 

measured in terms of availability of 

acceptable items as guided by a USDA 

publication on buying quality food 

(1975).

14The $2 million annual sales 

requirement has been used by the 

retail food industry since at least 

1980. If adjusted for annual infl ation, 

the equivalent in 2008 dollars is 

approximately $4.5 million. By 

using the unadjusted annual sales, 

we potentially include medium-sized 

grocery stores in both the industry and 

SNAP store directories. 

15Supercenters are included in our 

defi nition of supermarkets. However, 

warehouse club stores, also known 

as wholesale club stores, were not 

included in this study for two reasons. 

First, warehouse/wholesale club stores 

are not considered to be supermarkets 

by the industry, and second, few of 

these stores have applied to accept 

SNAP benefi ts. 

16TDLinx data only include 

information on stores that meet the 

industry standard defi nition of a 

supermarket. 
16

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United States Department of Agriculture

et al., 2009). In a study of access to foodstores in Detroit, Gallagher (2007) 

also describes discrepancies in supermarket classifi cation when using SNAPauthorized store data. 

Using supermarkets and supercenters may underestimate the availability 

of affordable and nutritious food. Smaller grocery stores, neighborhood 

markets, or “dollar stores,” for example, sometimes include a range of 

healthful, affordable foods.17 Rose et al. (2009) and Sharkey and Horel 

(2009) both conducted in-store audits of food availability in these store 

types in New Orleans and in the Brazos Valley, Texas. The problem with 

including these stores in the analysis is that the range of foods sold in these 

stores was highly varied (Franco et al., forthcoming; Neckerman et al., 2009; 

Rose et al. 2009; and Sharkey and Horel, 2009). It would be impossible to 

do a complete national level audit of the contents of these stores. Reliable 

data on the relative pricing of foods sold in these stores is not available 

either, which means it cannot be asserted with confi dence that these stores 

are a source for a wide range of affordable and nutritious food. 

Food is also sold in restaurants, fast food outlets, and related foodservice 

establishments. In fact, nearly half of all food spending is on food away 

from home (Martinez and Kaufman, 2008). In considering the effect of 

food access on diet and health, access to restaurants and other foodservice 

establishments is important because food from these sources accounts for 

a signifi cant part of the total diet. However, this national-level analysis 

does not consider access to restaurants and other food service outlets. 

Compared with foodstores and other retail food outlets, the monetary costs 

of an equal quantity of food purchased in a restaurant are higher than the 

costs at supermarkets or grocery stores. The cost of food sold in restaurants 

represents about one-third of the price at which it is sold, so that two-thirds 

of the cost of restaurant food is the premium of having someone prepare 

and serve it to the customer. In a grocery store, the cost of food accounts 

for about three-fourths of the retail price, on average. Thus, while eating at 

a restaurant may diminish the time costs of buying and preparing food for 

a consumer, those costs are eventually paid for by the consumer. For these 

reasons, food eaten at restaurants is less affordable due to its higher per unit 

cost relative to foodstores and other retail food outlets. 

Defi ning and measuring the geographical unit of interest

This study uses the Socioeconomic Data and Applications Center 

(SEDAC) grids data, which is based on information from the 2000 Census 

of Population (SEDAC, 2006). These population data (including some 

socioeconomic and demographic data), which are released at the block 

group level, are fi rst allocated to blocks and then allocated aerially down to 

roughly 1-square-kilometer grids across the continental United States. These 

data provide two important benefi ts for the analysis. First, they give better 

estimates of where people and households are located than data on larger 

geographic areas, such as census tracts. Second, the process of allocating 

census data to 1-square-kilometer grid cells transforms the irregular shapes 

and sizes of census geographies into regularized grid cells, providing for 

faster spatial computation needed for national-level analysis.

 17Specialized foodstores, such as 

produce markets, meat and seafood 

markets, and retail bakeries, can serve 

as a source for affordable and nutritious 

food; however, they typically do not 

provide the full range of foods that 

supermarkets and supercenters do.
17

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United States Department of Agriculture

Measuring access from the geographical unit to the foodstore

This study uses distance to the nearest supermarket as a measure of access.18 

For each grid cell, the distance is calculated from its geographic center to 

the nearest supermarket. Median distances to the nearest supermarket are 

calculated for the Nation as a whole and across different subpopulations. 

Based on the grid measure of distance to the nearest supermarket, three 

categories of access (high, medium, and low) are created for two types of 

access—walking access and driving access. Walking access measures a 

range of distances for which it is feasible to walk to a supermarket, while 

drivable access measures a range of distances for which it is feasible to 

drive to a supermarket. A time-based distance measure equivalent for both 

walking and driving is developed. The walkability range is categorized 

as either 1) high, if a supermarket is within a half mile; 2) medium, if a 

supermarket is between 1⁄2 and 1 mile; and 3) low, if the nearest supermarket 

is more than a mile away. For rural areas, a drivability range is also 

measured. Drivability is categorized as either 1) high, if a supermarket is 

within 10 miles; 2) medium, if a supermarket is between 10 and 20 miles; 

and 3) low, if a supermarket is greater than 20 miles away. 

Defi ning vulnerable subpopulations of interest 

This national-level assessment of access to affordable and nutritious food 

fi rst characterizes access for the entire U.S. population. But the interest 

here is in subpopulations that may be particularly vulnerable to access 

barriers. The study considers supermarket access across the following four 

subpopulations: 

• Low-income individuals, where anyone living in a household with 

income less than or equal to 200 percent of the Federal poverty 

thresholds for family size is considered low-income.19 

• Household vehicle access, where households that do not have access to 

an automobile, van, or truck of 1-ton-load capacity or less are considered 

separately from those households who do have access. 

• Race and ethnicity, where non-Whites include those individuals who 

identifi ed their race as something other than “White” or their ethnicity as 

Hispanic (regardless of race).

• Elderly or nonelderly status, where individuals over age 65 are 

considered elderly. 

Specifying areas with high concentrations of low-income people 

This study is interested not only in vulnerable individuals and households 

but also in vulnerable areas—neighborhoods that are highly deprived and for 

which, food access could be limited. Areas (the 1-kilometer-square grids) are 

identifi ed as low-income areas if more than 40 percent of the people in the 

grid had income at or below 200 percent of the Federal poverty thresholds 

using kernel-density smoothing. Comparisons of distances to supermarkets 

across these low-income and higher income areas are made. 

18Chapter 4 also uses a variety measure of distance, which is the distance 

to three different supermarkets. 

19In 2008, the poverty threshold for 

a family of two adults and two children 

was $21,835, so 200 percent of this 

threshold would double the threshold to 

$43,670.
18

Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

United States Department of Agriculture

Finally, because it is diffi cult to use the same measures to characterize access 

in densely populated urban areas compared with less populated suburban and 

rural areas, a separate analysis is conducted using Census Urbanized Area 

defi nitions. The three defi nitions are as follows: Urban Areas, densely settled 

that contain 50,000 or more people, such as a core city and surrounding 

suburbs; Urban Clusters, densely settled local areas that have at least 2,500 

people but fewer than 50,000 people, such as smaller cities and towns; and 

Rural Areas, low-density areas with populations of less than 2,500, including 

all areas not classifi ed as either Urban Areas or Urban Clusters.

Results and Findings

This section presents a national overview of access to supermarkets. 

Separate analyses of access are also conducted for each of three Censusdesignated urbanicity types. Measures of access are presented for the 

overall population and then by each of the four vulnerable subpopulations. 

Differences in supermarket access are compared between low-income and 

higher income areas within each urbanicity type. 

National access overview

Table 2.2 shows supermarket access for selected individual characteristics 

of vulnerable populations. Median distance to the nearest supermarket is 

given.20 The number and percentage of individuals or households that have 

high, medium, and low access are also presented. The data in this table are 

for the Nation as a whole, not separately by areas or by urbanicity. 

Overall, median distance to the nearest supermarket is 0.85 miles. Median 

distance for low-income individuals is about 0.1 of a mile less than for those 

with higher income, and a greater share of low-income individuals (61.8 

percent) have high or medium access to supermarkets than those with higher 

income (56.1 percent).

Overall, ethnic and racial minorities have better access to supermarkets 

than Whites. Median distance to the nearest supermarket for non-White 

individuals is 0.63 miles, compared with 0.96 miles on average for Whites. 

Similarly, a smaller percentage of non-Whites (26.6 percent) have low access 

to supermarkets than do Whites (48.2 percent). These differences do not 

consider income, only race/ethnicity. 

There are not great differences in access to supermarkets by elderly status. 

In terms of distance to the supermarket, the elderly compare very much with 

the nonelderly, overall. Despite their similar distances to supermarkets, the 

elderly could face additional barriers to access due to disability or inability to 

drive to the supermarket. 

Vehicle access is perhaps the most important determinant of whether or 

not a family can access affordable and nutritious food. Table 2.3 focuses 

specifi cally on vehicle ownership for the entire Nation. It shows the total 

number of households in the U.S., the number without access to a vehicle and 

their distance to the nearest supermarket. The table reports these statistics 

for all households in the U.S. and for all households in low-income areas and 

then separately by the three urbanicity categories. The study focuses only on 

 20The median distance is the point at 

which over the range of distances, half 

of the population is closer to that point, 

while half is farther away. 
19

Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

United States Department of Agriculture

Table 2.2

Supermarket access by household income, race/ethnicity, age, and vehicle access (walking distance)

Distance to nearest supermarket miles

High access

(0.5 miles or less)

Medium access 

(Between 0.5-1 mile)

Low access

(More than 1 mile)

Number 

(millions) Percent 

Median1

(miles)

Number 

(millions) Percent

Number 

(millions) Percent

Number 

(millions) Percent

Income:2

 Low-income 79.3 28.8 0.76 22.6 28.5 26.4 33.3 30.2 38.1

 Higher-income 196.1 71.2 0.87 43.8 22.3 66.3 33.8 86.1 43.9

All income levels 275.5 100.0 0.84 66.5 24.1 92.7 33.7 116.3 42.2

Race/ethnicity:

 Non-White 85.7 30.7 0.63 31.4 36.6 31.5 36.8 22.8 26.6

 White 193.9 69.3 0.96 39.1 20.2 61.3 31.6 93.4 48.2

All races/ethnicities 279.6 100.0 0.86 70.5 25.2 92.8 33.2 116.3 41.6

Age:

 Age 65 or more 34.8 12.4 0.81 8.9 25.7 11.8 33.9 14.1 40.4

 Less than age 65 244.8 87.6 0.82 61.6 25.2 81.0 33.1 102.2 41.8

All ages 279.6 100.0 0.82 70.6 25.2 92.7 33.2 116.3 41.6

Vehicle access:

 Households without

 vehicle 10.8 10.3 0.55 5.0 46.2 3.4 31.7 2.4 22.1

 Households with

 vehicle 94.1 89.7 0.84 22.2 23.6 31.7 33.7 40.2 42.7

All households 104.9 100.0 0.81 27.2 25.9 35.1 33.5 42.5 40.6

1Medians are weighted by population of each square kilometer grid area. 

2Low-income households are those with income less than or equal to 200 percent of the Federal poverty threshold for family size.

Sources: USDA, ERS analysis based on data from Census of Population, 2000 and the ERS-compiled supermarket directory for the 

contiguous U.S. in 2006.

Table 2-3

Household vehicle access and supermarket access

Geographic area Total households1

Households without access to a vehicle

Between 1/2 to 1 mile 

from a supermarket

More than 1 mile 

from a supermarket

Number Percent Number Percent

Millions Millions Millions

Total U.S. 104.9 3.4 3.2 2.4 2.3

 Low-income areas 25.1 1.6 6.4 0.9 3.8

Urban areas 69.9 2.9 4.1 1.1 1.5

 Low-income areas 15.6 1.3 8.3 0.4 2.5

Urban clusters 9.7 0.4 4.1 0.2 2.5

 Low-income areas 3.6 0.2 5.6 0.1 3.3

Rural areas 25.3 0.2 0.8 1.1 4.4

 Low-income areas 5.9 0.1 1.7 0.4 7.4

1 This column shows the total number of households regardless of vehicle access.

Source: USDA, ERS analysis based on data from Census of Population, 2000 and the ERS-compiled supermarket directory for the 

contiguous U.S. in 2006.
20

Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

United States Department of Agriculture

households with medium or low access (those more than one-half mile from 

a supermarket) since those who have high access can walk to a supermarket 

that, at most, is one-half mile away. 

Only 2.4 million households, or 2.3 percent of all 104.9 million households 

in the U.S., live more than a mile from a supermarket and do not have access 

to a vehicle. An additional 3.4 million households, or 3.2 percent of all 

households, do not have access to a vehicle and are between one-half to 1 

mile from a supermarket. Thus, for the total U.S. population, between 2.3 

and 5.5 percent of all households may be outside of a walking distance to a 

supermarket and lack access to a vehicle. 

Not surprisingly, the percentage of households without access to vehicles 

is higher in low-income areas. Overall, 0.9 million households do not 

have access to a vehicle and live in low-income areas more than a mile 

from a supermarket. This represents 3.6 percent of all households in 

low-income areas. A much greater percentage of households without 

vehicles in low-income areas is between one-half to 1 mile from the nearest 

supermarket—1.6 million households, or 6.4 percent of all low-income 

households.

Table 2.3 also presents the number of households without access to vehicles 

and distance to supermarkets by urbanicity. These estimates show that 1.1 

million households, or 4.3 percent of all rural households, lacks access to a 

vehicle and lives more than 1 mile from a supermarket. It is not surprising 

that people in rural areas live farther from the nearest supermarkets. But it is 

perhaps unexpected that a greater percentage lack access to a vehicle. Urban 

areas have the smallest percentages of households without access to a vehicle 

that are more than a mile from a supermarket. For urban areas, 4.1 percent of 

households are between one-half to 1 mile from the nearest supermarket and 

do not have access to a vehicle. 

The analysis now turns specifi cally to supermarket access for areas with high 

concentrations of low-income people. Map 2.1 shows low-income areas in 

the U.S., which are 1-kilometer grid cells where more than 40 percent of the 

total population has income less than or equal to 200 percent of the Federal 

poverty level. The map shows the dispersion of low-income areas across the 

country, but the map also shows greater concentration of low-income areas in 

the South, Southwest, and Upper Plains States. Rural low-income areas are 

better refl ected on the map than urban low-income areas, which are diffi cult 

to see on the national level view the map provides. 

Table 2.4 focuses on these low-income areas. The right half of the table 

shows the number (and percent) of people in low-income areas by access 

level. It also shows the percent of the total U.S. population represented in 

these low-income areas. The left half of the table focuses only on those 

people with incomes below 200 percent of Federal poverty guidelines. It is 

worth noting here, and will be supported later in the chapter, that low-income 

people who live outside of low-income areas are, in general, farther from 

supermarkets than low-income people who live in low-income areas.
21

Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

United States Department of Agriculture

Table 2-4

Supermarket access for people in low-income and higher-income areas (walking distances)

Low-income areas1

Access level2

(walking)

All people in low-income areas All low-income people in low-income areas

Total number

(millions) 

Percent of people in 

low-income areas

Percent of total 

U.S. population 

(millions) Total number

Percent of lowincome peoplePercent of total 

U.S. population

High 22.9 32.1 8.2 12.1 33.5 4.3 

Medium 24.9 34.9 8.9 12.5 34.7 4.5 

Low 23.5 33.0 8.4 11.5 31.8 4.1 

Subtotal in lowincome areas 71.3 100.0 25.5 36.0 100.0 12.9 

Total U.S. population 279.6 79.3

1Low-income and non-low-income areas defi ned according to ERS criteria. See text for details.

2High access defi ned as less than or equal to 1/2 mile of a supermarket. Medium access defi ned as more than 1/2 mile but less than or equal to 

one mile from a supermarket. Low access defi ned as more than one mile from a supermarket.

Source: USDA, ERS analysis based on data from Census of Population, 2000 and the ERS-compiled supermarket directory for the contiguous 

U.S. in 2006.

Map 2.1

Low income areas of the contiguous 48 United States (1 km grids in which 40 percent 

of population have incomes below 200 percent of the Federal poverty level)

Low income areas
22

Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

United States Department of Agriculture

The fi rst point to note in table 2.4 is that only 33 percent of people in 

low-income areas live in areas with low access. In contrast, 32 percent of 

people in low-income areas live in high-access areas and 35 percent live 

in medium-access areas. Thus, one estimate of the number of people who 

live in low-income areas with low access to supermarkets is 23.5 million, 

which is 8.4 percent of the total U.S. population (out of a total of 279.6 

million people in 2000). If those with medium-access levels who live in 

low-income areas are included, then 48.4 million, or 17 percent of the total 

U.S. population, is more than half a mile from a supermarket. 

Not all people in low-income areas, however, have low income. In fact, 

only about half have income less than 200 percent of the Federal poverty 

thresholds (36 million out of a total of 71 million). It is likely that those who 

are not low income but live in low-income areas have adequate resources to 

access supermarkets even if they are more than a mile away. If the analysis 

excludes those who live in low-income areas, but who are not themselves, 

low income, then only 11.5 million, or 4.1 percent of the total U.S. 

population, has low access to supermarkets. Including those low-income 

people in low-income areas with medium access yields a total of 24 million 

people, or 8.6 percent of the total population. 

Supermarket access in urban areas

Thus far, the study has considered urban, rural, and all areas in between 

similarly. But distance does not mean the same thing in rural areas as 

in urban areas. The focus next turns to analysis of access by urbanicity. 

Each grid area is assigned to one of three Census Urbanized Areas: Urban 

Areas, Urban Clusters, and Rural Areas. Together, these three urban types 

characterize the range of urbanicity found in the U.S. population. A separate 

analysis of access is conducted for each urban type at the national level. 

Populations within an urban type are assumed to have similar levels of 

density, measured as population per unit of area, such as per square mile or 

square kilometer. Areas having similar population densities are more likely 

to have similar levels of the built environment—the infrastructure (e.g., 

networks of roads, transportation services, utilities, communication networks, 

and government services) and businesses, manufacturing plants, and retail 

stores. Analysis by urban type results in measurement of access within 

similar built environments, resulting in greater comparability across similar 

populations regardless of location. 

Table 2.5 shows measures of access to supermarkets in urban areas only. 

Median distances to supermarkets are calculated for each of the four 

vulnerable populations as are the percentage of the populations with high, 

medium and low access to supermarkets. To better understand how access 

to supermarkets differs for vulnerable and disadvantaged groups, the study 

compares access measures by each of the four economic and demographic 

characteristics: income, race/ethnicity, vehicle access, and elderly status. 

For each of the vulnerable populations, access for those who live in areas 

with high concentrations of low-income individuals is compared with that 

of those who live outside of areas with high concentrations of low-income 

individuals.
23

Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

United States Department of Agriculture

Table 2.5 fi rst presents data on supermarket access for urban areas. A greater 

share of low-income individuals lives outside of low-income areas (53.6 

percent) than in low-income areas (46.4 percent). Further, low-income 

individuals who live in higher income areas live farther from supermarkets 

than those who live in low-income urban areas. About 15 percent of those in 

low-income urban areas are more than a mile from a supermarket, compared 

with 29 percent for those in higher income areas. Median distances to 

supermarkets refl ect this as well. 

The next rows focus specifi cally on access for low-income individuals, which 

make up 27.2 percent of all urban dwellers. Results show that 43.3 percent 

of low-income individuals who live in low-income areas are within one-half 

mile of a supermarket and another 41.5 percent of are between half a mile to 

one mile from the nearest supermarket. The remaining 15.1 percent of the 

Table 2.5

Urban area access to supermarkets--overall and for income and demographic 

subpopulations (walking distance)

Distance to nearest supermarket

High access

(0.5 miles or less)

Medium access 

(Between 0.5-1 mile)

Low access

(More than 1 mile)

Population

Income 

level of 

area

Number 

(millions)

Total 

percent

Subpopulation 

percent 

Median 

(miles)

Number 

(millions) Percent

Number 

(millions) Percent

Number 

(millions) Percent

Total population 

of urban areas

Low 

income 45.3 100.0 24.4 0.57 19.2 42.5 19.0 42.1 7.0 15.4

Higher 

income 140.6 100.0 75.6 0.71 42.1 30.0 57.9 41.2 40.6 28.9

 Total 185.9 100.0 100.0 61.4 33.0 76.9 41.4 47.6 25.6

Subpopulations

Population with 

low income

Low 

income 23.5 51.9 46.4 0.56 10.2 43.3 9.8 41.5 3.6 15.1

Higher 

income 27.1 19.3 53.6 0.65 9.0 33.1 11.5 42.3 6.7 24.6

 Total 50.6 27.2 100.0 19.1 37.8 21.2 41.9 10.2 20.2

Households 

without access 

to a vehicle

Low 

income 3.4 22.0 40.5 0.50 1.7 50.2 1.3 38.4 0.4 11.3

Higher 

income 5.1 9.3 59.5 0.42 2.8 56.1 1.6 30.8 0.7 13.1

 Total 8.5 12.2 100.0 4.6 53.7 2.9 33.9 1.1 12.4

Non-White 

population

Low 

income 30.8 68.1 44.2 0.55 13.7 44.5 12.6 41.0 4.5 14.5

Higher 

income 38.9 27.7 55.8 0.60 15.3 39.4 15.6 40.1 8.0 20.5

 Total 69.8 37.5 100.0 29.1 41.6 28.2 40.5 12.5 17.9

Elderly 

population

Low 

income 4.5 10.0 20.3 0.58 1.8 40.8 2.0 43.7 0.7 15.6

Higher 

income 17.7 12.6 79.7 0.69 5.4 30.5 7.5 42.5 4.8 27.0

 Total 22.2 11.9 100.0 7.2 32.6 9.5 42.7 5.5 24.6

Source: Source: USDA, ERS analysis based on data from Census of Population, 2000 and the ERS-compiled supermarket directory for the 

contiguous U.S. in 2006.
24

Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

United States Department of Agriculture

low-income individuals who live in low-income areas resided more than a 

mile from the nearest supermarket.

Similar statistics were calculated for low-income urban individuals who 

live outside of low-income areas. Applying the access categories, 33.1 

percent had high levels of access, 42.3 percent had medium access, and 24.6 

percent had low access. This is greater than the 15.1 percent of low-income 

individuals living in low-income areas that had low access. 

Maps 2.2 and 2.3 illustrate these measures of access for two urban areas, the 

Washington, DC, and St. Louis, Missouri, metro areas. For Washington, DC, 

there were two low-income areas, outlined in black, that are largely contained 

within the city’s boundaries. The St. Louis area has a large low-income area 

that spans a good portion of the City of St. Louis in Missouri and across 

the Mississippi River into Illinois. In both maps, circles shaded light green 

indicate areas that are within a 1-mile radius of a supermarket. The color 

shadings of the areas indicate population density where the darker shading 

indicates grids with more people and the lighter shading indicates grids 

with fewer people. This study focuses particular attention on areas that are 

Map 2.2

Washington, DC Urban Area
25

Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

United States Department of Agriculture

outside of the one-mile radius of a supermarket and in the neighborhoods 

with low income. 

Map 2.2 shows that there are some low-income areas within DC and on the 

border with Prince Georges County, Maryland, which are outside of 1 mile 

from a supermarket. But for most of these areas, the population density 

is low to moderate. The situation looks more severe in the St. Louis area. 

While the most densely populated grids are within one mile of a supermarket, 

there are several low-income grids with moderate to high densities that are 

more than a mile from a supermarket, especially in the central and northern 

part of St. Louis, Missouri. The low-income area just east of the Mississippi 

River in Illinois has few supermarkets, but there are few grids outside of one 

mile that have moderate to high population densities. The Illinois side of the 

river has many grids with low population densities that are more than a mile 

from a supermarket.

Table 2.5 also shows supermarket access for households without access to 

vehicles, overall, and then separately by whether or not the households live in 

low-income or higher income areas. About 12.2 percent of urban households 

Map 2.3

St. Louis, Missouri Urban Area
26

Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

United States Department of Agriculture

do not have access to a vehicle; however, this number is almost twice as 

large—22 percent—for households in low-income areas. Those who live 

in higher income areas are much more likely to have access to a vehicle—

only 9.3 percent do not have access to a vehicle. Just over 50 percent of 

low-income-area households without access to a vehicle had a high level of 

access. Another 38.4 percent of this population had medium access. The 

remaining 11.3 percent of households living in low-income areas without 

access to vehicles were more than 1 mile from the nearest supermarket and 

had low access to supermarkets. Among households that lived outside of 

low-income areas and did not have access to a vehicle, a greater percentage 

had high access than similar households that lived in low-income areas, but 

fewer had medium access and more had low access. 

About 15 percent of non-White individuals living in low-income areas have 

low access to supermarkets. An additional 41 percent have medium access. 

This compares with 21 percent of non-White individuals living outside of 

low-income areas with low access and 40 percent with medium access. 

A greater share of the elderly population in low-income areas had high access 

(40.8 percent) than those living outside low-income areas (30.5 percent). 

While both groups have about the same percentage of the population of 

elderly with medium access, the greatest difference occurred among the 

low-access group. Almost 16 percent of the elderly who lived in low-income 

areas were outside of 1 mile from the nearest supermarket, but 27 percent 

of those living outside low-income areas were more than a mile from a 

supermarket.

Supermarket access in urban clusters

The population in urban clusters (areas with at least 2,500 people but fewer 

than 50,000 people) makes up 9.1 percent of the total U.S. population. Table 

2.6 presents accessibility measures for vulnerable populations in low-income 

and higher income areas within urban clusters. 

Individuals with income below 200 percent of Federal poverty thresholds 

represent 34.6 percent of the total urban cluster population. Of these 

individuals, 54.4 percent lived in low-income areas while 45.6 percent lived 

outside of low-income areas. Almost 29 percent of low-income individuals 

who live in low-income areas are within half a mile of a supermarket. 

Another 42.0 percent were between half a mile and 1 mile. The remaining 

29.3 percent of low-income individuals in low-income areas were outside of 

a mile from the nearest supermarket. Thus, about 30 percent of low-income 

individuals in low-income urban clusters have low access to supermarkets. 

The percentage of low-income individuals with low access to supermarkets is 

greater for those who live outside of low-income areas within urban clusters. 

Thirty-fi ve percent lived more than a mile from a supermarket and an 

additional 39.5 percent were between a half mile and a mile. Only 26 percent 

were within a half mile of a supermarket. 

Less than 10 percent of households in urban clusters lacked access to a 

vehicle. Among households without access to vehicles, those who lived in 
27

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United States Department of Agriculture

low-income areas had slightly better access to supermarkets than those who 

lived outside of low-income areas, but the distributions are very similar. 

Supermarket access in rural areas

Because the population in rural areas is dispersed, this analysis measures 

access to the nearest supermarket according to driving distances. Rural 

areas represent 36.9 percent of the total land area of the U.S., but only 24.4 

percent of the U.S. population. Just over 29 percent of the rural population 

is low-income, which is lower than the percent in urban clusters but greater 

than the percent in urban areas (table 2.7). 

Almost 39 percent of low-income individuals in rural areas lived in 

low-income areas, while the remaining 61 percent lived in higher income 

rural areas. Among low-income individuals, those living in higher income 

Table 2.6

Urban cluster access to supermarkets--overall and for income and demographic 

subpopulations (walking distance)

Distance to nearest supermarket

High access

(0.5 miles or less)

Medium access 

(Between 0.5-1 mile)

Low access

(More than 1 mile)

Population

Income 

level of 

area

Number 

(millions)

Total 

percent

Subpopulation 

percent 

Median 

(miles)

Number 

(millions) Percent

Number 

(millions) Percent

Number 

(millions) Percent

Total population 

of urban clusters

Low 

income 9.9 100.0 38.8 0.72 2.8 28.7 4.1 41.8 2.9 29.6

Higher 

income 15.6 100.0 61.2 0.82 3.6 23.4 5.9 38.0 6.0 38.6

 Total 25.5 100.0 100.0 6.5 25.4 10.1 39.5 8.9 35.1

Subpopulations

Population with 

low income

Low 

income 4.8 48.6 54.4 0.71 1.4 28.7 2.0 42.0 1.4 29.3

Higher 

income 4.0 25.8 45.6 0.77 1.0 25.5 1.6 39.5 1.4 35.0

 Total 8.8 34.6 100.0 2.4 27.3 3.6 40.9 2.8 31.9

Households 

without access 

to a vehicle

Low 

income 0.5 13.5 52.3 0.66 0.2 32.4 0.2 43.1 0.1 24.5

Higher 

income 0.4 7.3 47.6 0.69 0.1 30.5 0.2 41.5 0.1 28.0

 Total 0.9 9.6 99.9 0.3 31.5 0.4 42.3 0.2 26.1

Non-White 

population

Low 

income 4.0 40.8 66.7 0.75 1.1 26.4 1.7 41.5 1.3 32.1

Higher 

income 2.0 12.9 33.3 0.85 0.4 22.3 0.7 36.3 0.8 41.5

 Total 6.0 23.7 100.0 1.5 25.0 2.4 39.7 2.1 35.3

Elderly 

population

Low 

income 1.4 13.8 35.2 0.68 1.5 30.7 0.6 42.9 0.4 26.4

Higher 

income 2.5 16.1 64.8 0.78 1.5 25.0 1.0 39.5 0.9 35.6

 Total 3.9 15.2 100.0 3.0 27.0 1.6 40.7 1.3 32.3

Source: USDA, ERS analysis based on data from Census of Population, 2000 and the ERS-compiled supermarket directory 

for the contiguous U.S. in 2006.
28

Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

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areas (93 percent) had better access than those who lived in low-income 

areas (85 percent). This is in contrast to low-income populations in urban 

areas and urban clusters where low-income individuals who lived in 

low-income areas were closer to supermarkets than low-income individuals 

who lived in higher income areas. For rural populations, supermarket access 

typically involves driving to an urban area or urban cluster, where higher 

population densities are more likely to contain larger stores. Map 2.4 shows 

supermarket access of South Dakota, a largely rural state. This map uses 

light blue shading to indicate the location of low-income areas. Circles 

with the 10 and 20 mile radii around a supermarket indicate which areas are 

within each of these distances of a supermarket. Finally, the brown shading 

scheme (white to dark brown) indicates population density (low to high). 

The map shows that there are several small towns in low-income portions 

of the State that are outside of a driving range from a supermarket (more 

Table 2.7

Rural areas access to supermarkets--overall and for income and demographic 

subpopulations (driving distance)

Distance to nearest supermarket

High access

(10 miles or less)

Medium access 

(Between 10-20 miles)

Low access

(More than 20 miles)

Population

Income 

level of 

area

Number 

(millions)

Total 

percent

Subpopulation 

percent 

Median 

(miles)

Number 

(millions) Percent

Number 

(millions) Percent

Number 

(millions) Percent

Total population 

of rural areas

Low 

income 16.1 100.0 23.6 4.11 13.8 85.7 1.9 11.7 0.4 2.6

Higher 

income 52.1 100.0 76.4 3.42 49.1 94.1 2.8 5.3 0.3 0.6

 Total 68.2 100.0 100.0 62.8 92.1 4.6 6.8 0.7 1.0

Subpopulations

Population with 

low income

Low 

income 7.7 48.1 38.9 4.21 6.6 85.0 0.9 12.0 0.2 3.0

Higher 

income 12.2 23.3 61.1 3.70 11.3 92.6 0.8 6.6 0.1 0.8

 Total 19.9 29.2 100.0 17.8 89.7 1.7 8.7 0.3 1.6

Households 

without access 

to a vehicle

Low 

income 0.6 9.4 40.2 3.37 0.5 87.3 0.1 10.4 0.0 2.3

Higher 

income 0.8 4.2 59.8 3.08 0.8 93.7 0.0 5.7 0.0 0.7

 Total 1.4 5.4 100.0 1.2 91.1 0.1 7.6 0.0 1.3

Non-White 

population

Low 

income 4.8 29.9 48.4 3.65 4.1 84.5 0.6 12.0 0.2 3.5

Higher 

income 5.1 9.8 51.6 2.98 4.8 94.6 0.2 4.8 0.0 0.6

 Total 9.9 14.6 100.0 8.9 89.8 0.8 8.3 0.2 2.0

Elderly 

population

Low 

income 2.1 13.3 24.4 4.04 1.8 84.4 0.3 12.7 0.1 2.9

Higher 

income 6.6 12.7 75.6 3.32 6.1 92.6 0.4 6.6 0.1 0.8

 Total 8.7 12.8 100.0 7.9 90.6 0.7 8.1 0.1 1.3

Source: USDA, ERS analysis based on data from Census of Population, 2000 and the ERS-compiled supermarket directory 

for the contiguous U.S. in 2006.
29

Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

United States Department of Agriculture

than 20 miles). Individuals in these areas, and in the surrounding sparsely 

populated areas, are likely to have considerable time and out-of-pocket costs 

to accessing food sources. 

There are also several Indian reservations in South Dakota, as well as in 

other rural areas, primarily in the Midwest and West. These areas may be 

served by the Food Distribution Program on Indian Reservations (FDPIR), a 

commodity food assistance program for eligible Native Americans offered as 

an alternative to the SNAP.21 The locations of these distribution centers are 

not yet incorporated in the analysis but will be in the future. 

Obviously, those outside of walking distance to a store who do not have 

a vehicle in rural areas will face much higher transportation costs, both in 

terms of hiring transportation (taxi, shuttle, or public transportation if they 

exist at all) and in time costs in walking to the store or waiting for a friend 

or family member to take them to a store. These households make up only 

a small share (5.4 percent) of the rural population in total and 9.4 percent of 

low-income people, yet most of them live between 1 mile and 10 miles from 

a supermarket. Although this is a small portion of the overall population, the 

 21The FDPIR operates in 23 States 

and served 90,100 persons in FY2008, 

primarily individuals living on or near 

Indian reservations

Map 2.4

Supermarket access in South Dakota
30

Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

United States Department of Agriculture

population certainly has very limited access to supermarkets based on driving 

distances. 

The non-White population represents 14.6 percent of the total rural 

population, or 9.9 million persons. Of the non-White population, 51.6 

percent live in low-income areas. Almost 90 percent of the non-White 

population lives within 10 miles of a supermarket. Non-White individuals 

that live in low-income areas are more likely to reside more than 20 

miles from a supermarket than non-White individuals who did not live in 

low-income areas (3.5 percent, compared with less than 1 percent). 

Over 84 percent of elderly individuals in low-income rural areas were within 

10 miles of a supermarket, while another 12.7 percent were between 10 

and 20 miles. Only 2.9 percent were more than 20 miles from the nearest 

supermarket. 

Time Costs of Access to Food

In addition to the effects of price, income, and the availability of different 

foods, consumers’ food shopping and consumption behavior may also be 

affected by the time costs of food shopping and food preparation. Higher 

time costs of travel to grocery stores are likely associated with lower 

demand for those stores or for some foods. Likewise, greater time costs 

of preparing some foods may be associated with lower demand for those 

foods. ERS analysis of time diary data from the ATUS provides information 

about variations in time costs across areas with different levels of access to 

supermarkets and across different population groups. 

The ATUS collects information on how Americans spend their time. 

Sponsored by the Bureau of Labor Statistics (BLS, http://stats.bls.gov/

tus/home.htm) and conducted by the U.S. Census Bureau, the ATUS is a 

continuous, monthly survey that started in January 2003. The ATUS sample 

is nationally representative of civilian Americans age 15 and older, and the 

data include about 13,000 completed interviews annually. Estimates from 

the survey show the range of detailed activities performed daily, how much 

time is spent in each activity as well as where and with whom, and how time 

is allocated by demographic group, labor force status, and weekdays versus 

weekends.

Table 2.8 shows the average time spent in travel to grocery shopping on an 

average day by level of access to the nearest supermarket (as defi ned above). 

The table shows the average minutes spent traveling to grocery stores for 

shoppers who live in low-income areas with low, medium, and high access 

to supermarkets.22 These averages are compared with the national average. 

Overall, the national average time spent traveling, one-way, to the grocery 

store was almost 15 minutes, and about 14 percent of the population traveled 

to the grocery store on an average day. 

Time spent traveling to the grocery store was greater in low-income areas 

with low-access. The average time spent traveling to the grocery store for 

those who lived in these areas, 19.5 minutes, was signifi cantly greater than 

the average time spent traveling to the grocery store for those in low-income 

areas with high access (15.5 minutes) and for those in low-income areas 

 22Survey respondents did not report 

which type of “grocery” store they visited, only that they reported the activity 

of grocery shopping.
31

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Table 2.8

Average time spent in travel to grocery shopping on an average day by access to grocery stores

Average time is one-way, not total travel time (based on the shortest one-way time). Pooled 2003-2007 American Time Use Survey data

Average minutes per day 

of travel related to grocery 

shopping, for those who 

grocery shopped

Average % engaged 

in travel related to 

grocery shopping 

(on an average day)

Average

minutes, 

90% CI 

min

Average

minutes, 

90% CI 

max

Average 

percent, 

90 CI min

Average 

percent, 

90% CI 

max

Minutes Percent

Total population, age 15+, 2003-07 15.0 14.0 14.7 15.3 13.7 14.3

Low-income areas

 Low access 19.5 12.1 18.1 20.9 11.1 13.1

 Medium access 14.1 13.5 13.0 15.1 12.5 14.5

 High access 15.5 12.3 14.3 16.7 11.3 13.4

Not-low-income areas

 Low access 15.9 14.4 15.2 16.5 13.7 15.1

 Medium access 12.5 14.7 12.1 12.9 14.1 15.3

 High access 13.3 16.3 12.6 14.1 15.4 17.3

Metropolitan areas, 2005-07 14.2 13.9 13.8 14.6 13.4 14.3

Low-income areas

 Low access 20.4 12.9 17.5 23.3 10.7 15.0

 Medium access 14.4 11.7 13.1 15.6 10.2 13.1

 High access 15.5 11.5 13.7 17.3 10.0 13.1

Not-low-income areas

 Low access 15.5 14.3 14.8 16.3 13.4 15.2

 Medium access 12.1 14.0 11.5 12.6 13.2 14.7

 High access 12.9 15.9 11.9 13.7 14.6 17.2

Nonmetropolitan areas, 2005-07 16.9 12.2 15.8 18.0 11.5 13.0

Low-income areas

 Low access 18.8 11.0 16.9 20.6 9.6 12.4

 Medium access 11.2 13.6 8.4 14.0 10.7 16.6

 High access -- 11.8 -- -- 6.3 17.4

Not-low-income areas

 Low access -- 16.0 -- -- 8.3 23.6

 Medium access -- -- -- -- -- --

 High access -- -- -- -- -- --

Note: 2003 Met/nonmet classifi cation used for 2005-07 data.

Income, 2003-07

Household Income <= 200% 

 poverty threshold 15.8 13.6 15.3 16.4 13.0 14.2

Low-income areas

 Low access 19.3 13.7 17.3 21.3 12.2 15.1

 Medium access 14.2 13.4 13.1 15.3 12.0 14.7

 High access 16.4 12.5 14.5 18.2 11.0 14.0

Not-low-income areas

 Low access 16.3 14.7 15.0 17.6 13.2 16.3

 Medium access 13.6 13.3 12.6 14.7 12.0 14.6

 High access 2.3 16.7 11.1 13.6 14.6 18.8

Household Income > 200% poverty 

 threshold 14.2 14.2 13.9 14.6 13.8 14.7

Low-income areas

 Low access 20.5 11.3 18.3 22.6 9.8 12.8

 Medium access 12.1 14.1 10.9 13.4 12.3 15.8

 High access 13.5 12.6 11.9 15.2 10.9 14.4

Not-low-income areas

 Low access 15.6 14.3 14.8 16.4 13.4 15.2

 Medium access 11.8 15.0 11.4 12.3 14.2 15.9

 High access 13.4 16.3 12.3 14.4 15.1 17.6
32

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United States Department of Agriculture

with medium access (14.1 minutes). These differences are large and 

statistically signifi cant. In addition, those in low-access areas shopped less 

frequently—on average once every 8 days versus a national average of once 

every 7 days. 

The difference in average time spent traveling to the grocery store by access 

level may not be surprising given that this study’s defi nition of access is 

based on distance, and that, all else equal, it is expected that those who live 

more than 1 mile from a supermarket would spend more time traveling to 

the grocery store than those who live less than half a mile or less than a mile 

from the supermarket. To put these averages into context, table 2.8 also 

reports average time spent traveling to grocery stores by households in higher 

income areas separately by their access levels. As expected, those with low 

access spend the most time traveling to the grocery store (15.8 minutes) 

compared with those who are closer. But the average of those in higher 

income areas that are more than a mile from a store is still almost 4 minutes 

shorter than the 19.5 minute average of those in low-income areas who are 

more than a mile from a grocery store. 

Table 2.8 also considers average time spent traveling to get groceries for 

sample members living in metropolitan (metro) areas compared with those 

living in nonmetropolitan (nonmetro) areas.23 Not surprisingly, those who 

live in nonmetro areas spent more time, on average (16.9 minutes), traveling 

to the grocery store than those living in metro areas (14.2 minutes), and a 

smaller percent of nonmetro residents shopped on an average day than metro 

residents. Surprisingly, those in nonmetro areas with low access spend about 

the same amount of time traveling to supermarkets as those in metro areas 

with low access. 

The fi nal set of averages shown in table 2.8 compares average time spent 

traveling to grocery stores for those with household income below 200 

percent of Federal poverty guidelines and for those with income above 200 

percent of poverty. Also included are national estimates for the 13 percent of 

the sample with missing income information. Those with low income who 

23There are not enough households 

in urban clusters or rural areas in the 

sample to examine average minutes 

spent traveling to supermarkets across 

the same urbanicity categories used 

above, so here we focus on metro and 

nonmetro statistical areas.

Table 2.8

Average time spent in travel to grocery shopping on an average day by access to grocery stores (continued)

Average time is one-way, not total travel time (based on the shortest one-way time). Pooled 2003-2007 American Time Use Survey data

Average minutes per day 

of travel related to grocery 

shopping, for those who 

grocery shopped

Average % engaged 

in travel related to 

grocery shopping 

(on an average day)

Average

minutes, 

90% CI 

min

Average

minutes, 

90% CI 

max

Average 

percent, 

90 CI min

Average 

percent, 

90% CI 

max

Minutes Percent

Household Income missing 16.3 13.9 15.4 17.2 13.1 14.7

Low-income areas

 Low access 17.7 9.7 14.4 21.1 7.5 12.0

 Medium access 19.4 12.3 14.5 24.5 9.6 14.9

 High access 17.0 11.2 12.8 21.1 8.8 13.5

Not-low-income areas

 Low access 16.5 14.4 14.9 18.0 12.5 16.3

 Medium access 14.4 15.0 12.9 15.8 13.1 16.9

 High access 14.4 15.9 12.4 16.4 13.5 18.3

-- indicates that estimate is suppressed due to small cell size.

Source: 2003-2007 American Time Use Survey data, Current Population Survey sampling frame from Census Bureau. 

Store access levels are tract-level classifi cations based on categories of access used in Chapter 2. 

Data with missing tract-level classifi cation were included in Total population, age 15+ estimates.
33

Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

United States Department of Agriculture

live in low-income areas with low access spend about the same amount of 

time traveling to grocery stores (19.3 minutes) as those who do not have low 

income but who live in low-income areas with low access to grocery stores 

(20.5 minutes). In contrast, those with low income who live in low-income 

areas with medium or high access take more time to get to the grocery store 

(14.2 minutes and 16.4 minutes, respectively) than those who do not have 

low income but who live in low-income areas with medium (12.1 minutes) 

or high access (13.5 minutes). It is possible that these higher income 

individuals in low-income areas have access to their own vehicles for grocery 

shopping and choose to shop outside their neighborhoods. 

These data show some expected patterns, but it is diffi cult to interpret 

without further information. For example, it is not known whether shoppers 

in the time use sample go to the nearest grocery store to do their shopping or 

if they are selectively shopping further from their neighborhood because of 

price or availability factors. If the latter is true, these time use estimates do 

not refl ect true differences in access, just differences in choice. Of course, it 

is not clear whether these differences in choices are correlated with the arealevel measures of access or not—for example, if those in low-access areas 

pass several stores that may not have the foods they want to get to stores with 

better selection or price. 

Table 2.9 shows the mode of transportation used in getting to grocery 

stores.24 These results show that the majority of people who shopped for 

groceries drove to the store as either the driver of a vehicle or as a passenger 

with another household member. Those with the lowest levels of access 

were the most likely to drive to the grocery store (93.3 percent, compared 

with 87.1 percent for medium-access shoppers and 65.3 percent for highaccess shoppers). Those who lived closest to grocery stores were more likely 

to walk or bicycle to the store than those in low or medium access areas (23.1 

percent, compared with 2.3 and 5.4 percent for those with low and medium 

access). Very few shoppers used public transportation to get to a grocery 

store. Only 4 percent of shoppers in low-access areas got rides to the grocery 

store with nonhousehold members or in taxis, while 10 percent of shoppers in 

high-access areas got rides to grocery stores with nonhousehold members or 

in taxis. 

Grocery shoppers from low-access areas were more likely to have been 

accompanied by children on their trips to the grocery store than others—29.1 

percent versus a national average of 22.8 percent. Having children along 

on the trip is likely to make the trip more cumbersome, making travel and 

grocery shopping more diffi cult for these low-access shoppers.

The last rows in table 2.9 show whether grocery shoppers shop from home 

or from work, or their trip chaining patterns.25 For the majority of shoppers, 

the time distance from the grocery store to home is shorter than the time 

distance from the grocery store to work (about 92 percent). But for about 8 

percent, the time distance from work to the grocery store was shorter than 

the time distance from home to the grocery store (5.9 percent directly from 

work to the grocery store and 2.1 percent clustered with other stops from 

work to the grocery store). Interestingly, those in low-income areas with low 

access were the most likely to access grocery stores directly, bunched with 

other activities, or from work (7.7 percent directly from work and 3.6 percent 

24Mode of transportation estimates 

are for all grocery shoppers, not just 

low-income grocery shoppers. 

25Appendix C contains information on 

how trip chaining was classifi ed. 
34

Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

United States Department of Agriculture

bunched with other activities from work). Those from low-income areas that 

had medium or high levels of access were less likely to access grocery stores 

from work. These data indicate that some of those who live in low income 

areas with low access choose grocery stores closer to work than to home. 

Data presented here are for the entire U.S. population. One study specifi cally 

focused on a sample of low-income people. The National Food Stamp 

Program Survey of 1996/1997 (NFSPS) surveyed a sample of participants 

of the SNAP and eligible nonparticipants. Sample members were asked 

about the modes of transportation and out-of-pocket costs used to travel 

to stores where they shopped for food and about how much time it took to 

travel to foodstores (Ohls et al., 1999). Close to 76 percent of participants 

and 85 percent of eligible nonparticipants reported use of a car to shop. 

Food stamp participants either drove (45 percent) or got a ride with family 

or friends (31 percent). Among the 22 percent of participants who reported 

some transportation expenses, the average cost per shopping trip was $6.54. 

Average round trip travel time to the most frequently used store was 23-24 

minutes for participants and eligible nonparticipants. The survey compared 

these patterns across participants who lived in urban, mixed, and rural areas. 

Table 2.9

Characteristics of grocery shopping by level of access to supermarkets

Characteristics are of one-way shortest travel time to grocery store. 

Pooled 2003-2007 American Time Use Survey data

Low-income areas Not-low-income areas

Total

Low 

access

Medium 

access

High 

access

Low 

access

Medium 

access

High 

access

Percent

Mode of transportation

 1. Car, truck, motorcycle (driver or passenger w/hh member) 90.2 93.3 87.1 65.3 96.7 92.3 83.9

 2. Walking or bicycle 4.8 2.3 5.4 23.1 0.3 3.1 10.0

 3. Public transportation (bus, subway/train) 0.3 0.1 0.9 1.9 0.0 0.3 0.3

 4. Other (passenger w/nonhh member, boat/ferry, 

 taxi/limo, unspecifi ed) 4.8 4.3 6.6 9.7 2.9 4.4 5.8

Total 100.0 100.0 100.00 100.0 100.0 100.0 100.00

With whom

 Alone 48.8 40.0 39.0 40.8 49.9 52.7 53.6

 With household members 42.1 49.3 49.9 46.1 41.1 39.5 37.9

 With others, not household members 9.1 10.8 11.1 13.1 9.1 7.8 8.6

Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0

 With children (persons under 18 years old) 22.8 29.1 28.2 32.8 20.3 22.6 19.4

Note that “with whom” is for travel to grocery store, and not grocery shopping. The person or 

persons with the respondent may only be present for part of the travel. 

Trip chaining

 Home to store, direct / Store to home direct 63.6 54.8 64.1 61.0 64.5 68.4 66.6

 Home to store, clustered activities/ Store to home, clustered activities 28.4 33.9 31.1 34.9 26.5 25.8 26.3

 Work to store, direct / Store to work direct 5.9 7.7 3.5 3.3 6.6 3.9 5.9

 Work to store, clustered activities/ Store to work, clustered activities 2.1 3.6 1.4 0.8 2.5 1.9 1.3

Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Source: 2003-2007 American Time Use Survey data, Current Population Survey sampling frame from Census Bureau. Store 

access levels are tract-level classifi cations based on categories of access used in Chapter 2. Data with missing tract-level 

classifi cation were included in Total population estimates.
35

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United States Department of Agriculture

Average distance, time, and out-of-pocket cost to the most often used store 

were greater for those living in rural settings. Rural participants were less 

likely than their urban area counterparts to report out-of-pocket expenses 

associated with food shopping—perhaps because 94 percent either drove or 

got a ride with others. 

Time costs to travel to grocery stores are only part of the time costs involved 

in healthy eating—preparing nutritious food can be more costly in terms 

of time than prepared meals or restaurant meals. Previous ERS research 

examined time spent in food preparation for women across income levels 

and family composition. This study found that being a mother who worked 

full-time or a single mother were more important in explaining differences in 

time spent in food preparation than were either earnings or income (Mancino 

and Newman, 2007). Specifi cally, food preparation time falls as mothers 

spend more time working outside the home. Single women with children 

spend less time preparing food than married women.

Summary 

The number of people who have low access to healthy food depends upon 

which measure is used. Direct questions from a nationally representative 

sample of U.S. households in 2001 show that up to 5.7 percent of all U.S. 

households did not always have the food they wanted or needed because of 

access-related problems. Households that live far from a supermarket and 

that do not have vehicles likely have limited access to nutritious food. Of 

all households in the U.S., 2.3 million, or 2.2 percent, live more than a mile 

from a supermarket and do not have access to a vehicle. An addition 3.4 

million households, or 3.2 percent of all households, live between one-half to 

1 mile and do not have access to a vehicle. 

Area-based measures of access show that 23.5 million people live in 

low-income areas that are more than 1 mile from a supermarket, which 

represents 8.4 percent of the total U.S. population. However, not all of these 

23.5 million are themselves, low income. If only the low-income people 

in low-income areas are considered, then 11.5 million, or 4.1 percent of the 

total U.S. population, lives in low-income areas more than 1 mile from a 

supermarket. Both of these estimates are national totals that do not consider 

differences in distance, travel modes and travel patterns, and retail markets 

for urban versus rural areas. 

Within urban areas, 10.1 million low-income individuals (20.2 percent) were 

more than 1 mile from the nearest supermarket. Of the total, 3.6 million 

lived in low-income areas. Within urban clusters, 1.4 million persons, 

or 29.3 percent of the low-income population, were more than 1 mile 

from the nearest supermarket. Of the 16.1 million persons living in rural 

low-income areas, 85.7 percent (13.7 million persons) were within 10 miles 

of a supermarket. Another 11.7 percent (1.9 million persons) were between 

10 and 20 miles distant, and only 2.6 percent were more than 20 miles from 

a supermarket. These differences underscore the importance of owning a 

vehicle or having access to affordable transportation in rural areas. 

Data on time use and travel mode show that those who live in low-income 

areas that are more than a mile from a supermarket spend more time (19.5 
36

Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

United States Department of Agriculture

minutes) traveling to grocery stores than the national average (15 minutes). 

However, 93 percent of those who live in low-income areas more than a mile 

from a supermarket traveled to the grocery store in a vehicle they or another 

household member drove. 

While considerable efforts were made to develop these data, measures, and 

methods, different assumptions and measures are likely to produce differing 

outcomes and conclusions when applied to the same data. More detailed 

information which is not currently available would likely result in more 

precise fi ndings. It is hoped that the methods and fi ndings in this chapter 

will stimulate new research to provide additional insights about the nature 

and extent of low-income populations faced with low access to sources of 

nutritious and affordable foods.

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Donohue, R.M. (1997). “Abandonment and Revitalization of Central City 

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Economic Research Service.

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Moore, L., and A. Diez Roux (2006). “Associations of Neighborhood 

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2005; 95(4):660.
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Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

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CHAPTER 3

The Interaction of Neighborhood and 

Household Characteristics in Explaining 

Areas With Limited Access

The food deserts literature suggests that those who have better access 

to supermarkets tend to have healthier diets and lower levels of obesity 

and related diseases (Laraia et al., 2007; Larson et al., 2009). However, 

the extent to which limited access to supermarkets and other differential 

aspects of the food environment contribute to known economic and racial 

health disparities remains unclear. That lack of clarity stems in part from 

confl icting fi ndings with regard to access to supermarkets for low-income, 

minority, and racially mixed neighborhoods. Some researchers have 

found economically disadvantaged populations and neighborhoods with 

high concentrations of racial minorities to have better access than their 

counterparts (e.g., Moore and Diez Roux, 2006), while others have found the 

reverse (e.g., Burns and Inglis, 2007). There is substantial literature showing 

that low-income and minority populations are disproportionately at risk 

when it comes to major public health concerns (e.g., Diez Roux et al., 2001; 

Odoms-Young et al., 2009). But critical gaps remain in the understanding of 

the associations between the neighborhood food environment and indicators 

of health. Investigators have made considerable gains in the development of 

approaches to better characterize neighborhood food environments and thus 

identify the mechanisms that underlie those associations between the food 

environment and poor health outcomes (e.g., Moore et al., 2008; Sharkey, 

2009), but doing so continues to be a major challenge. 

This chapter uses physical distance to the nearest supermarket as a measure 

of access, but also extends the analysis to include distance to the third 

nearest supermarket, which serves as a proxy for variety (Apparicio et al., 

2007). Access in relation to the neighborhood and household socioeconomic 

environment is also explicitly examined. Examining areas of low-access 

to food from a socioeconomic perspective as such is both theoretically and 

empirically important. In theory, those with the lowest incomes, living in the 

most disadvantaged places, will have limited provisional access in general, 

whether in reference to food, health care, transportation, or other services 

and resources. Further, households that lack access to a vehicle (particularly 

in rural areas) or have very few fi nancial resources may have diffi culty 

purchasing affordable and nutritious food, which in turn may translate into 

poor health (Bostock, 2001). Lack of access to food and poor health could be 

amplifi ed if the same households live in a disadvantaged neighborhood, such 

as one where public transportation options are nonexistent and food retail 

choices are limited (Ford and Dzewaltowski, 2008). 

There are a number of phrases used in the health inequalities literature 

to capture the relationship between household and neighborhood risk 

factors (e.g., pathways of disadvantage). The one utilized in this study 

is “deprivation amplifi cation” (Macintyre et al., 2008). Deprivation 

amplifi cation is best explained as a process that could impact an individual’s 

health whereby, for example, risk factors for obesity, such as low-income, 

combined with limited knowledge about nutrition are intensifi ed by exposure 
40

Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

United States Department of Agriculture

to a food retail environment that offers too few choices for nutritious food 

and/or too many options for less nutritious alternatives. Thus, the food 

environments of low-income populations require special consideration due 

to the vulnerability of the individuals as well as that of the unique social and 

physical setting in which they live (Gittelsohn and Sharma, 2009). 

Empirical evidence suggests that deprivation amplifi cation may be more of 

a problem in the United States than in the United Kingdom and elsewhere 

where food desert research has been conducted. That is, while research 

on food deserts in the United States is in its infancy, research completed 

to date shows that in comparison to other country studies (e.g., Pearce et 

al., 2009) complex social and physical contexts are greater determinants of 

health outcomes for the low-income population of the United States than 

for the European counterparts. Cummins and Macintyre (2006) suggest 

that one explanation for this uniquely U.S. “contextual effect” may be 

that “residential segregation along socioeconomic and racial lines may be 

more pronounced in the USA and planning regulations less focused on 

compensating for such segregation than in the UK, continental Europe, or 

Australia.” A number of studies lend support to this notion (e.g., Franco 

et al., 2008; Galvez et al., 2008; Morland et al., 2002). Thus, while the 

main objective of this analysis is to determine the characteristics that best 

differentiate groups with dissimilar levels of supermarket access (i.e., low, 

medium, or high), the secondary objective is to examine segregation based 

on race and income inequality as a determinant of low supermarket access in 

comparison to other neighborhood and household contextual risk factors.

Research Method and Indicators

The research method applied to this study is referred to as multiple 

discriminant analysis, or MDA. This approach is useful to understand the 

differences between groups, to identify which variables best capture those 

differences, to describe the dimensionality of groups, or to test theories 

or taxonomies (Huberty and Lowman, 1997).26 In health geography and 

inequality research it is often used to discover the variables that contribute 

to group separation and to describe grouping variable effects. For example, 

Hemphill et al. (2008) used discriminant analysis to explore the relationship 

between the placement of fast food outlets and neighborhood-level 

socioeconomic variables, where neighborhoods were classifi ed as high-, 

medium-, or low-access based on the number of fast food opportunities 

available to them. The study found signifi cant differences between the three 

levels of fast food accessibility across the socioeconomic variables, with 

successively greater percentages of unemployment, low-income, and renters 

in neighborhoods with increasingly greater access to fast food restaurants. 

Several of these variables were also found to be predictive of greater access 

to fast food restaurants.

Similarly, discriminant analysis is used here to capture the characteristics 

that best differentiate urban and rural neighborhoods grouped into low-, 

medium-, and high-access categories given distance to fi rst and third nearest 

supermarkets (i.e., supermarket proximity and variety). Those relative 

accessibility measures were determined based on the criterion given in the 

previous chapter (i.e., with low-access defi ned by a distance greater than 

1 mile for walking and 20 miles for driving, medium-access defi ned by a 

26Discriminant analysis is conceptually and mathematically analogous 

to multiple regression analysis. Both 

techniques involve calculating from a 

set of continuous predictor variables 

to a criterion. The primary difference is that the dependent variables 

in discriminant analysis are linearly 

combined mathematically to maximally discriminate between the groups, 

thereby emphasizing group differences 

and deemphasizing group similarities. 

In other words, multiple discriminant 

analysis calculates a linear equation 

using standardized discriminant function coeffi cients, which are analogous 

to beta weights in regression. As such, 

similar to regression, these coeffi cients 

identify the relative importance of each 

continuous variable in predicting the 

criterion. However, unlike regression 

analysis, the dependent variables are 

linearly combined to create a synthetic 

or composite dependent variable that 

separates or maximally differentiates 

the groups.
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United States Department of Agriculture

distance between one-half and 1 mile for walking and between 10 and 20 

miles for driving, and high-access defi ned by a distance less than one-half 

mile for walking and 10 miles for driving). However, for this analysis, the 

geographic area of interest is neighborhoods, for which Census tracts are 

commonly used proxies. As such, the grid-defi ned distance measures used in 

Chapter 2 were aggregated to the Census tract for this study.27

As with the area-based analysis in Chapter 2, little variation was found 

among subpopulations with respect to distance measures and access levels. 

For example, based on distance alone, the low-income population was found 

to be no worse off than female-headed households with children, or the 

African-American population, the severely poor (50 percent of the poverty 

income threshold), or any other vulnerable population. Likewise, non-White 

and low-income populations were found to have better access than their 

counterparts, and lack of access to a vehicle within the household did not 

emerge as a defi ning characteristic of low access. With respect to the latter, 

walking distance to a variety of supermarkets proved to be the greatest access 

barrier for low-income households with or without a vehicle, in either urban 

or rural neighborhoods. Yet, the same was determined for all neighborhoods 

regardless of whether they were low-income or not, leading us to conclude 

that low access in general is not specifi cally a low-income area phenomenon. 

FFor this reason, this study does not limit the discriminant analysis to 

low-income areas; it includes all neighborhoods in the analytical models. 

In keeping with the geography applied to the area-based analysis, the study 

categorizes those neighborhoods as being located in metro core, micropolitan 

or small town core, and rural tracts, which are census tract-level equivalents 

of urbanized areas, urban clusters, and rural areas, respectively.28 Using that 

geography in association with the access groups (a dependent variable), 12 

independent discriminant analyses are conducted—one for each of the three 

geographic groups given walking and driving distances to fi rst and third 

nearest supermarkets (see table 3.1 for group frequencies). 

A number of factors were included in each analysis in addition to a select 

set of socio-economic indicators believed to be potential predictors of group 

membership given existing literature on vulnerable people and places.29 The 

factors consisted of indicators of neighborhood and household composition 

that have previously been associated with deprivation, such as householder 

age and race/ethnicity, income and education levels, household structure, 

area population density, and degree of rurality. The predictor variables 

consisted of indicators of neighborhood context that are hypothesized to be 

associated with deprivation amplifi cation, such as depth and persistence of 

poverty, housing values and vacancy rates, area unemployment and labor 

force participation rates, low-income concentration, and segregation based 

on race and income. Most of these variables are straightforward and require 

little explanation, but there are a few exceptions for which additional details 

are provided below. Summary statistics for each are given in table 3.2. 

• Dissimilarity index of segregation: This variable captures dissimilarity 

by race or the evenness with which one racial population group is located 

(or segregated) within an area with respect to another racial group. The 

dissimilarity statistic is interpreted as the proportion of one racial group 

that would need to relocate to another neighborhood (census tract) in 

27Specifi cally, tract-level distance 

measures were calculated by fi rst assigning grids to 2000 Census defi ned 

tracts. Then median distances for all 

grids within a tract were computed, 

including total tract population and 

population-specifi c distances to the 

nearest and third closest supermarket 

based on income, age, race/ethnicity, 

and various household characteristics 

(e.g., households without a vehicle 

and female-headed households with 

children).

 28These tract-level geographic 

designations are based on ERS 2000 

Rural-Urban Commuting Area (RUCA) 

Codes, which were developed using 

the same theoretical concepts used by 

the Offi ce of Management and Budget 

to defi ne county-level metropolitan 

and micropolitan areas. Metropolitan 

cores (code 1) are defi ned as census 

tract equivalents of urbanized areas. 

Micropolitan and small town cores 

(codes 4 and 7) are tract equivalents of 

urban clusters. And, rural tract (code 

10) designation is defi ned by a primary 

commuting fl ow that is local or to 

another rural tract. 

 29All factor and predictor variable 

data stem from RAND’s Center for 

Population Health and Health Disparities Data Core (http://www.rand.org/

health/centers/pophealth/data.html). 

The RAND data include a range of 

census-based variables tabulated to 

capture various characteristics known 

to be correlated with health disparities, 

such as standard measures of socioeconomic disadvantage (e.g., unemployment, poverty, and education), 

disability by type and population, cost 

of living, segregation, unevenness, 

and inequality by race and/or income, 

alternative measures of environmental 

pollution by level and type, and various forms of physical access based on 

extent of roadway connectivity and 

complexity. The full range of potential 

factor and predictor variables from the 

RAND data were initially considered 

for analysis and selection of variables 

was based on a standard variable reduction process (e.g., univariate analysis to 

determine signifi cance and elimination 

of redundant or overlapping variables).
42

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order to be evenly distributed across the county (or metro area) with 

respect to other racial groups. A dissimilarity statistic value of 0 refl ects 

absolute integration while a value of 1 refl ects absolute segregation.

• Gini index of segregation: This variable represents income inequality by 

race. The Gini statistic is understood much like the dissimilarity statistic. 

It ranges in value from 0 to 1 with 0 indicating complete equality and 1 

indicating complete inequality. In other words, higher values indicate 

that the study area is more unequal in terms of how income is distributed 

among racial groups while lower values mean that income is more 

equally distributed.

• Roadway connectivity (alpha): This measure is used to examine 

connectivity with respect to the availability of alternative travel routes 

within a tract. The alpha value represents the ratio of the actual number 

of complete loops to the maximum number of possible loops given the 

number of tract intersections. A higher alpha value indicates a greater 

degree of roadway complexity and connectivity, and thereby serves as an 

indicator of access to supermarkets given the extent of the area’s physical 

transportation infrastructure. 

• Index of disadvantage: This is a normalized socioeconomic status 

measure for all census tracts developed from six measures understood 

to represent disadvantage: percent of adults older than age 25 with less 

than a high school education; percent male unemployment; percent of 

households with income below the poverty line; percent of households 

Table 3.1

Access Category Variables and Corresponding Percent Frequency: Metro Core, 

Micropolitan / Small Town Core, and Rural Tracts

Access category variables Percent of metro core tracts

Percent of micropolitan 

or small-town core tracts Percent of rural tracts 

Percent

Walking--1 store (proximity)

 High access 31.5 12.4 1.3

 Medium access 42.6 46.9 8.6

 Low access 25.9 40.7 90.1

Driving--1 store (proximity)

 High access 100.0 99.8 75.8

 Medium access 0.0 0.1 17.8

 Low access 0.0 0.1 6.4

Walking--3 stores (variety)

 High access 0.0 0.0 0.0

 Medium access 12.8 3.3 0.3

 Low access 87.2 96.7 99.7

Driving--3 stores (variety)

 High access 100.0 84.5 32.5

 Medium access 0.0 11.0 45.5

 Low access 0.0 4.5 22.0

Source: USDA, ERS estimations based on 2006 ERS-compiled directory of supermarkets and RAND’s Center for Population Health 

and Health Disparities Data Care.
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receiving public assistance; percent of female-headed households with 

children; and median household income. The index is scaled such that 

the values fall between 0 and 100, with the lower number indicating a 

greater degree of tract disadvantage than a tract with a higher number.

• Linguistically isolated households: A linguistically isolated household is 

defi ned as one in which no member age 14 or older speaks only English 

or speaks a non-English language while also being able to speak English 

well. In other words, for a household to be declared linguistically 

isolated then at least one member age 14 or older must have some 

diffi culty with the English language. 

• Persistent poverty counties: This designation is based on the ERS 

2004 County Typology, which classifi es all U.S. counties according 

to six non-overlapping categories of economic dependence and seven 

overlapping categories of policy-relevant themes. Persistent poverty 

counties belong to the latter group and are defi ned as such if 20 percent 

or more of their populations were living in poverty over the last 30 years 

(measured by 1970, 1980, 1990, and 2000 decennial censuses). 

Table 3.2

Summary Descriptives; Factor and Predictor Variables: Metro Core, 

Micropolitan / Small Town Core, and Rural Tracts

Rural core Micro/Small-town core Metro core

Variable Mean

Std. 

deviation Mean

Std. 

deviation Mean

Std. 

deviation

Region (NE, MW, S, W) 2.59 0.91 2.6314 0.91390 2.5416 1.09064

Segregation by Race (dissimilarity) 0.24 0.15 0.29 0.12 0.44 0.14

Segregation by income (Gini) 0.29 0.17 0.37 0.15 0.57 0.16

Land area of tract in miles 452 2146 50 238 4 22

Roadway connectivity (alpha) 0.11 0.06 0.17 0.07 0.17 0.08

Median value of owner-occupied housing $72,056 $53,004 $85,896 $50,248 $153,295 $123,395

Percent rural population 95.6% 18.2% 18.3% 20.5% 3.3% 10.8%

Percent Black or African-American population 5.5% 13.8% 10.4% 18.5% 15.6% 25.3%

Percent Hispanic population 4.8% 11.4% 8.6% 16.2% 13.5% 20.1%

Percent linguistically isolated households 5.8% 10.5% 5.7% 7.3% 11.2% 10.4%

Percent of tract poverty population who are 

65+ years old 14.2% 8.4% 12.7% 8.1% 11.8% 12.2%

Percent of tract poverty population who are 

children 31.4% 10.3% 32.2% 10.5% 29.7% 14.6%

Percent vacant housing units 20.2% 15.3% 10.5% 8.4% 6.6% 6.6%

SES index of disadvantage 74.94 13.14 75.7635 6.24350 78.0322 9.80811

Percent persistent poverty county 14.8% 35.5% 9.9% 29.9% 1.7% 13.0%

Valid N (listwise) N = 4141 N = 6392 N = 40436

Source: USDA, ERS estimates based on 2006 ERS-compiled directory of supermarkets and RAND’s Center for Population 

Health and Health Disparities Data Care.
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Discussion of Results

MDA yields two types of output that are particularly useful for this 

investigation. The fi rst is the structure coeffi cients, which are useful for 

determining the characteristics that contribute the most to group separation 

(i.e., low-, medium-, and high-access groups). The second is the set of 

standardized discriminant coeffi cients (similar to beta coeffi cients in 

regression analysis), which are useful for identifying the characteristics that 

best predict group membership and can thereby be thought of as indicators of 

associated risk. That output for all 12 discriminant analyses, and, therefore, 

the three geographies (metro core, micropolitan/small-town core, and rural 

core), are summarized in two typology tables. 

Table 3.3 provides information on the characteristics of supermarket 

accessibility walking proximity and variety, while table 3.4 provides 

similar information for driving proximity and variety. Both tables 

give corresponding structure coeffi cients (access group separation) and 

standardized discriminant coeffi cients (access group predictors) with the top 

three most infl uential (on the basis of absolute value) highlighted. Those 

values can be interpreted following an example for rural core neighborhoods 

from table 3.3. 

Considering walking proximity, roadway connectivity has the strongest 

correlation with the grouping variable (.797), followed by percent of vacant 

housing units (-.356) and percent rural population (-.289). This means that 

the extent and complexity of the road system contributes most to groupings 

of low-, medium-, and high-access. Its relative contribution can be 

understood by taking the square of the coeffi cient (.635), which indicates that 

63.5 percent of the variance in the composite grouping variable is explained 

by the roadway connectivity variable. In other words, roadway connectivity 

is the characteristic that best discriminates among groups of neighborhoods 

categorized as having low-, medium-, or high-access to the nearest 

supermarket given walking distance. It also happens to be the strongest 

predictor of group membership (.913). 

The second and third most powerful predictors for rural core neighborhoods 

were found to be the index of disadvantage (-.311) and the percent of the 

tract poverty population who are 65 years of age or older (.252). This fi nding 

implies, for example, that limited access is associated with a combination of 

indicators of socioeconomic disadvantage. This reinforces the need to give 

greater consideration to the notion of deprivation amplifi cation in access 

and related health inequalities research. Further, the relevance of the elderly 

poverty population highlights that some people may face multiple barriers 

to access. Thus, continued efforts to improve measurement and advance 

multivariate techniques are needed. 

The discriminant analysis results presented in tables 3.3 and 3.4 offer 

evidence in support of the hypothesis that segregation by race and income are 

associated with limited access. Segregation by race and income inequality 

are the dominant predictors among all neighborhood and household context 

variables that predict low-, medium-, and high-access levels. This was 

particularly true for access based on driving distances in rural core and 

micro/small-town core neighborhoods. However, some variability in 
45

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Table 3.3

Group Separation and Predictor Variables for Low-, Medium-, and High-Access to Supermarkets: 

Walking Proximity and Variety for Metro Core, Micropolitan / Small Town Core, and Rural Neighborhoods

Walking proximity Walking variety

Access group separation

Rural 

core

Micro/Smalltown coreMetro 

core Rural core

Micro/Smalltown coreMetro 

core

Region (NE, MW, S, W) -0.125 0.114 0.115 0.218 0.062 -0.035

Segregation by race (dissimilarity) 0.072 -0.002 0.232 -0.296 -0.110 0.294

Segregation by income (Gini) 0.061 0.006 0.245 -0.323 -0.137 0.303

Land area of tract in miles -0.181 0.208 -0.266 0.091 0.207 -0.126

Roadway connectivity (alpha) 0.797 -0.740 0.582 0.019 -0.717 0.465

Median value of owner-occupied housing -0.227 0.189 0.035 0.204 -0.024 0.204

Percent rural population -0.289 0.901 -0.677 0.878 0.833 -0.253

Percent Black or African-American population -0.065 0.046 0.130 0.066 0.045 0.108

Percent Hispanic population 0.093 -0.079 0.379 0.068 -0.155 0.506

Percent linguistically isolated households -0.118 -0.020 0.489 0.089 -0.188 0.778

Percent of tract poverty population who are 

 65+ years old 0.201 -0.027 -0.062 0.258 0.091 -0.091

Percent of tract poverty population 

 who are children -0.129 0.039 -0.038 0.456 0.188 -0.026

Percent vacant housing units -0.356 0.184 -0.027 0.207 0.135 -0.046

SES index of disadvantage -0.234 0.159 -0.304 0.834 0.318 -0.327

Persistent poverty county -0.120 0.047 -0.070 0.071 0.022 -0.055

Walking proximity Walking variety

Access group predication

Rural 

core

Micro/Smalltown coreMetro 

core Rural core

Micro/Smalltown coreMetro 

core

Region (NE, MW, S, W) -0.026 0.046 0.211 0.340 0.030 -0.050

Segregation by face (dissimilarity) 0.197 -0.604 0.322 0.167 0.532 0.554

Segregation by income (Gini) -0.168 0.696 -0.228 -0.291 -0.600 -0.463

Land area of tract in miles -0.251 0.046 -0.070 -0.112 0.002 -0.024

Roadway connectivity (alpha) 0.913 -0.387 0.478 -0.098 -0.512 0.388

Median value of owner-occupied housing 0.105 -0.046 0.096 -0.104 -0.382 0.353

Percent rural population -0.061 0.728 -0.501 0.387 0.479 0.014

Percent Black or African-American population -0.075 0.088 0.067 0.080 0.171 0.175

Percent Hispanic population 0.050 0.051 0.117 0.025 0.120 0.258

Percent linguistically isolated households -0.190 -0.004 0.390 0.139 -0.102 0.605

Percent of tract poverty population who 

 are 65+ years old 0.252 -0.097 -0.031 0.044 0.022 -0.089

Percent of tract poverty population 

 who are children 0.014 -0.013 -0.172 0.069 0.155 -0.173

Percent vacant housing units -0.054 0.121 -0.113 0.117 0.127 -0.108

SES index of disadvantage -0.511 0.153 -0.171 0.582 0.451 -0.241

Persistent poverty county -0.052 -0.045 -0.150 0.044 -0.071 -0.141

Source: ERS estimates based on 2006 ERS-compiled directory of supermarkets and RAND’s Center for Population Health 

and Health Disparities Data Care.
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Table 3.4

Group Separation and Predictor Variables for Low-, Medium-, and High-Access to Supermarkets: 

Driving Proximity and Variety for Metro Core, Micropolitan / Small Town Core, and Rural Neighborhoods

Driving proximity Driving variety

Access group separation

Rural 

core

Micro/Smalltown coreMetro 

core Rural core

Micro/Smalltown coreMetro 

core

Region (NE, MW, S, W) 0.263 0.130 n/a 0.256 0.255 0.075

Segregation by race (dissimilarity) -0.027 0.026 n/a -0.125 -0.271 -0.013

Segregation by income (Gini) -0.085 0.033 n/a -0.190 -0.358 -0.008

Land area of tract in miles 0.725 0.900 n/a 0.640 0.584 0.693

Roadway connectivity (alpha) 0.334 -0.063 n/a 0.374 0.202 -0.154

Median value of owner-occupied housing -0.313 -0.033 n/a -0.266 -0.211 0.113

Percent rural population -0.005 0.105 n/a 0.099 0.071 0.483

Percent Black or African-American population -0.173 -0.041 n/a -0.178 -0.134 -0.060

Percent Hispanic population 0.191 0.055 n/a 0.147 0.307 -0.086

Percent linguistically isolated households 0.323 0.265 n/a 0.194 0.178 0.182

Percent of tract poverty population 

 who are 65+ years old -0.056 -0.025 n/a 0.035 0.081 0.122

Percent of tract poverty population 

 who are children 0.003 0.073 n/a 0.069 0.137 -0.080

Percent vacant housing units 0.182 0.133 n/a 0.179 0.257 0.413

SES index of disadvantage -0.077 -0.187 n/a 0.036 -0.092 0.040

Persistent poverty county 0.072 0.052 n/a 0.018 0.106 0.350

Driving proximity Driving variety

Access group prediction

Rural 

core

Micro/Smalltown coreMetro 

core Rural core

Micro/Smalltown coreMetro 

core

Region (NE, MW, S, W) 0.222 0.045 n/a 0.351 0.198 0.208

Segregation by race (dissimilarity) 1.251 -0.870 n/a 1.742 2.558 -0.362

Segregation by income (Gini) -1.316 0.958 n/a -1.921 -2.893 0.462

land area of tract in miles 0.591 0.953 n/a 0.514 0.529 0.575

Roadway connectivity (alpha) 0.356 -0.007 n/a 0.479 0.242 -0.048

Median value of owner-occupied housing -0.373 0.045 n/a -0.374 -0.258 0.050

Percent rural population -0.032 -0.088 n/a 0.095 0.017 0.309

Percent Black or African-American population -0.158 -0.271 n/a -0.232 -0.107 -0.114

Percent Hispanic population -0.035 -0.340 n/a -0.075 0.166 -0.393

Percent linguistically isolated households 0.350 0.237 n/a 0.243 0.156 0.455

Percent of tract poverty population who are 

65+ years old -0.052 0.111 n/a 0.058 0.187 0.088

Percent of tract poverty population who are 

children -0.169 0.101 n/a -0.037 0.071 0.020

Percent vacant housing units 0.302 0.032 n/a 0.401 0.283 0.429

SES index of disadvantage -0.016 -0.385 n/a -0.145 0.017 -0.034

Persistent poverty county 0.104 0.016 n/a 0.046 0.043 0.327

Source: ERS estimates based on 2006 ERS-compiled directory of supermarkets and RAND’s Center for Population Health 

and Health Disparities Data Care.
47

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United States Department of Agriculture

predictors was found; for example, in rural core areas segregation was not 

among the top predictors of walking accessibility. This fi nding lends some 

support to the need for situation-specifi c research and policy. 

Summary 

The fi ndings of this study indicate that low-access to supermarkets is 

most heavily infl uenced by characteristics of neighborhood and household 

socioeconomic environments, such as the extent of income inequality, racial 

segregation, transportation infrastructure, housing vacancies, household 

deprivation, and rurality. This lends support to the notion that there is 

indeed a socioeconomic “contextual effect” that should be considered when 

designing food access policy. In that vein, there is growing evidence that 

documents the success of non-health interventions that have had a positive 

impact on health (e.g., improvements to road networks and investments in 

public transportation options), particularly for those living in deprived areas 

(e.g., Cassady and Mohan, 2004; Wrigley et al., 2003). 

References

Apparicio, P., M.S. Cloutier, and Shearmur, R. (2007). “The Case of 

Montreal’s Missing Food Deserts: Evaluation of Accessibility of Food 

Supermarkets,” Int J Health Geogr, February 12, 6:4.

Bostock, L. (2001). “Pathways of Disadvantage? Walking as a Mode of 

Transport Among Low-Income Mothers,” Health Soc Care Community, 

January, 9(1): 11-8.

Burns, C.M., and A.D. Inglis (2007). “Measuring Food Access in 

Melbourne: Access to Healthy and Fast Foods by Car, Bus, and Food in an 

Urban Municipality in Melbourne,” Health Place, December, 13(4): 877-85.

Cassady, D., and V. Mohan (2004). “Doing Well by Doing Good? A 

Supermarket Shuttle Feasibility Study,” J Nutr Educ Behav, 36(2): 67-70.

Cummins, S., and S. Macintyre (2006). “Food Environments and Obesity—

Neighborhood or Nation? Int J Epidemiol, February, 35(1): 100-4.

Diez Roux, A.V., S.S. Merkin, D. Arnett et al. (2001). “Neighborhood of 

Residence and Incidence of Coronary Heart Disease,” N Engl J Med, 345(2): 

99-106.

Ford, P.B., and D.A. Dzewaltowski (2008). “Disparities in Obesity 

Prevalence Due to Variation in the Retail Food Environment: Three Testable 

Hypotheses, Nutr Rev, April, 66(4): 216-28.

Franco, M., A.V. Diez Roux, T.A. Glass, B. Caballero, and F.L. Brancati 

(2008). “Neighborhood Characteristics and Availability of Healthy Foods in 

Baltimore,” Amer J Prev Med, December 35(6): 561-7.

Galvez, M.P., K. Morland, C. Raines, J. Kobil, J. Siskind, J. Godbold, and 

B. Brenner (2008). “Race and Foodstore Availability in an Inner-City 

Neighborhood,” Public Health Nutr, June, 11(6): 624-31.
48

Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

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Gittelsohn, J., and S. Sharma (2009). “Physical, Consumer, and Social 

Aspects of Measuring the Food Environment Among Diverse Low-Income 

Populations,” Am J Prev Med, April, 36(4 Suppl): S161-5.

Hemphill, E., K. Raine, J.C. Spence, K.E. Smoyer-Tomic (2008). “Exploring 

Obesogenic Food Environments in Edmonton, Canada: The Association 

Between Socioeconomic Factors and Fast-Food Outlet Access.” American 

Journal of Health Promotion, July-August, 22(6): 426-432.

Huberty, C.J., and L.L. Lowman (1997). “Discriminant Analysis via 

Statistical Packages,” Educational and Psychological Measurement, 57: 

759-784.

Laraia, B.A., P. Kannan, B. Frizzelle, and S. Walsh (2007). “Infl uence of 

Supermarket Distance on Diet and Weight Status Among Pregnant Women,” 

Presentation, 135th APHA Annual Meeting and Exposition, November 30.

Larson, N.I., M.T. Story, and M.C. Nelson (2009). “Neighborhood 

Environments: Disparities in Access to Healthy Foods in the U.S.,” Am J 

Prev Med, January, 36(1): 74-81.

Macintyre, S., L. MacDonald, and A. Ellaway (2008). “Do Poorer People 

Have Poorer Access to Local Resources and Facilities? The Distribution of 

Local Resources by Area Deprivation in Glasgow, Scotland,” Soc Sci Med, 

September, 67(6): 900-14.

Morland, K, S. Wing, A. Diez Roux, and C. Poole (2002). “Neighborhood 

Characteristics Associated With the Location of Food Stores and Food 

Service Places,” Am J Prev Med, January, 22(1): 23-9.

Moore, L.V., and A.V. Diez Roux (2006). “Associations of Neighborhood 

Characteristics With the Location and Type of Food Stores,” Am J Public 

Health, February, 96(2): 325-31. 

Moore, L.V., A.V. Diez Roux, and S. Brines (2008). “Comparing 

Perception-Based and Geographic Information System (GIS)-Based 

Characterizations of the Local Food Environment,” J Urban Health, March, 

85(2): 206-16.

Odoms-Young, A.M., S. Zenk, and M. Mason (2009). Measuring Food 

Availability and Access in African-American Communities: Implications for 

Intervention and Policy,” Am J Prev Med, April, 36(4 Suppl): S145-50.

Pearce, J., R. Hiscock, T. Blakely, and K. Witten (2009). “A National Study 

of the Association Between Neighbhourhood Access to Fast-Food Outlets 

and the Diet and Weight of Local Residents,” Health Place, March, 15(1): 

193-7.

Sharkey, J.R. (20090). “Measuring Potential Access to Food Stores and 

Food Service Places in Rural Areas in the U.S.,” Am J Prev Med, April, 36(4 

Suppl): S151-5.
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Wrigley, N., D.L. Warm, and B.M. Margetts (2003). “Deprivation, Diet 

and Food Retail Access: Findings From the Leeds ‘Food Deserts’ Study,” 

Environment and Planning A., 35: 151-88.

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CHAPTER 4

Food Access and Its Relationship to Diet 

and Health Outcomes

Many factors contribute to an individual’s overall diet, body weight, and the 

risk of developing diet-related diseases, such as diabetes or cardiovascular 

disease. Individual factors can explain some but not all of the differences 

in the rates in which different population groups experience these problems. 

Focus on food access has increased as researchers try to better understand 

the factors besides individual behaviors that may lead to differences in diet 

and health outcomes (Diez-Roux, 2009). Interest in the relationship of food 

access to diet and health is also rooted in a substantial body of literature 

that shows disparities in many health outcomes across race, ethnicity, and 

socioeconomic status (Institute of Medicine, 2003; National Research 

Council, 2004). It is hypothesized that differences in food access across 

race, ethnicity, and socioeconomic status may contribute to or reinforce these 

health disparities (Diez-Roux, 2009). 

This chapter fi rst considers different conceptual and methodological 

approaches to understanding how food access can affect diet and diet-related 

outcomes. It then briefl y reviews what is known about the relationship 

between food access and diet, obesity, and diet-related problems. Evidence 

regarding what is known about effects of diet on health outcomes like 

obesity, diabetes, and other diet-related diseases is also considered. The 

chapter concludes with a discussion of research and data needs to advance 

knowledge of the effects of food access on diet and health. 

Conceptual Framework

In a simple conceptual model, it is hypothesized that individual (and family) 

characteristics as well as characteristics of the physical environment impact 

dietary decisions. Individual characteristics include demographics (age, sex, 

race/ethnicity), socioeconomic status (income, education, and employment), 

family characteristics (family size and composition, presence of children), 

and preferences for food and other goods. The physical environment 

includes the food environment (accessibility to stores and restaurants) along 

with characteristics of the built environment, such as parks, sidewalks, 

availability of public transportation, air pollution, and noise. It is also likely 

that the social environment faced by individuals and families (e.g., cultural 

and social norms, social support, and safety and violence) affects diet. Diet 

is a major determinant of BMI and obesity status, and it is also a factor in 

risks of such diseases as diabetes and cardiovascular disease. Some of the 

same individual factors and physical and social environments affect BMI and 

diet-related diseases as well. 

The conceptualized model is an effective starting point, but it is 

oversimplifi ed. Individuals are certainly impacted by their larger physical 

and social environments, but physical and social environments are also 

impacted by individuals. Individuals have some choice over which physical 

and social environments with which to interact. Diet-related outcomes and 

health conditions are affected by other factors besides diet, including genetic 
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makeup, exercise habits, and working conditions. The directions of these 

relationships are not necessarily one-way. Obese individuals may have 

diffi culty exercising or engaging in an active lifestyle. Obesity has also 

been tied to lower wages among women, which could affect their family’s 

economic situation (Averett and Korenman, 1996; Cawley, 2004). And 

health conditions themselves can impact diet; for example, those with high 

blood pressure or diabetes may need to adopt special diets.

These complicated relationships highlight the limitations of the crosssectional studies that have dominated the research in this area. Such studies 

are important because they illuminate how food access and the larger 

environment potentially impact diet and diet-related problems. But the 

results can not be interpreted causally. 

Literature Review

In reviewing the literature on food access and diet and health outcomes, 

one must fi rst distinguish between studies that attempt to examine causal 

links between food access and health outcomes and those that only consider 

cross-sectional correlations. Also, most of the studies examine the effects 

of food access on proximal outcomes, such as food shopping behavior and 

food consumption, often focusing on particular foods such as fruits and 

vegetables, whole grains, or low-fat milk. Other studies, however, focus 

on outcomes such as high BMI, obesity, and heart disease, which are not 

as proximate outcomes. The causes of these more distant outcomes are 

much broader than just food access, and, thus, other factors besides lack of 

access to some foods may help account for the rate of incidence of these 

health outcomes. To get a sense of this, the analysis includes a review 

of literature on the degree to which specifi c foods that may be lacking in 

some neighborhoods (such as fresh fruits and vegetables, low fat milk, 

and whole grains) are related to such health outcomes as obesity, diabetes, 

cardiovascular disease, and cancer. 

Food access and dietary intake

The majority of studies that have examined the relationship between store 

access and dietary intake fi nd that better access to a supermarket or large 

grocery store is associated with healthier food intakes (Larson et al., 2009). 

The relationship between the availability of restaurants (both fast food and 

full-service) and dietary intake has also been studied. In general, these 

studies have found that greater availability of fast food restaurants and lower 

prices of fast food restaurant items are related to poorer diet. Access to fullservice restaurants shows either no relationship or a positive relationship with 

healthy dietary intake. 

Only a few studies have used longitudinal data to measure how changes 

in access affect changes in diet. The few that exist focus on changes in 

shopping behavior and changes in dietary intake, not more distant outcomes 

such as obesity or other diet-related diseases. Two studies have examined 

the impact of the opening of a large supermarket in underserved areas in 

Leeds and Glasgow, UK (Wrigley et al., 2003; Cummins et al., 2005). The 

Leeds study used a pre-post intervention design, with survey interviews 

of participants about their shopping and food intake 5 months before and 
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United States Department of Agriculture

7 months after a Tesco supermarket opened in the area. The Glasgow 

study used a pre-post study design to assess change in shopping and food 

intake behaviors surrounding a new store opening, but it also considered 

a comparison area that had similar neighborhood characteristics but did 

not have a new store open in the area. The comparison area was added to 

determine if any changes in shopping or diet could be due to secular changes 

in diet that were not due to a new store opening. Results of both studies 

showed that shopping behavior was affected by the openings of new stores—

that is, a signifi cant number of sampled individuals from the neighborhood 

switched their shopping to the new store. Both studies also show that 

average fruit and vegetable intake increased among surveyed individuals, but 

that the average increase was small (just over one-third of a serving). The 

average increase in fruit and vegetable intake among those who switched 

their main food shopping to the new store was larger, but still under one-half 

of a full serving size. The increase in fruit and vegetable intake in Leeds was 

statistically signifi cant, but the increase in Glasgow was not. The Glasgow 

study, which used a control comparison area, shows that some of the increase 

in fruit and vegetable intake among sampled individuals could be due to 

overall increased consumption of these foods in both the control and study 

area—not due to the better accessibility to the store in the study area. Also 

noteworthy is that in both studies, respondents who switched to the new store 

reported better self-reported psychological health. 

In contrast to opening new supermarkets, some areas have implemented 

programs to improve what is offered in small corner or convenience stores in 

underserved areas. Rather than build a whole new store, the idea is to work 

within the existing infrastructure to offer more healthy options and fewer less 

healthy options. Some of these interventions have measured the impact on 

shopping, sales, and food intake. Overall, results from these studies show 

that stocking and promoting healthier food items increases sales of the items. 

Some studies have also shown increases in healthy food consumption (Ayala 

et al., 2009; Gittelsohn, 2009). One intervention stocked prepared packs 

of fruits and vegetables (washed, cut, and bagged) at two tiendas (small 

stores) that served primarily Latino customers in North Carolina. Fruit and 

vegetable intake for customers at these two tiendas was compared with the 

fruit and vegetable intake of customers at two control group tiendas that did 

not offer the fruit and vegetable packs (Ayala et al., 2009). The study found 

that customers who shopped at stores where the packs were sold increased 

fruit and vegetable intake by one full serving. Customers who shopped in the 

two control tiendas exhibited no change in consumption. 

These small store interventions show some promise, however, much of the 

research on the effectiveness of these interventions is formative. The studies 

are usually on very small and localized samples and often have very short 

followup periods from which changes can be observed. Further, there has 

also been little evaluative research to determine the cost effectiveness and 

sustainability of the changes in the stores and in consumers’ diets. 

Food access and obesity

Many studies have examined the link between store and restaurant access and 

BMI and obesity (see Larson et al., 2009, for a recent summary). In general, 

these studies fi nd that better access to a supermarket is associated with 
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reduced risk of obesity and better access to convenience stores is associated 

with increased risk of obesity. Results with respect to restaurants are mixed. 

Some show that fast food availability is associated with increased risk of 

obesity for adults and children, but others fi nd no association (Larson et al., 

2009). 

Currie et al. (2009) examine how school-level obesity rates among ninth 

graders in California are related to the distance between the school and fast 

food and full-service restaurants. The study also examines weight gain 

during pregnancy for women in Michigan, New Jersey, and Texas using 

Vital Statistics data and measuring distance from each woman’s home to fast 

food and full-service restaurants. Results vary across the two samples. They 

fi nd very localized effects in the sample of ninth graders in schools—the 

rate of obesity in the school increases 5.2 percent for schools located within 

0.10 of a mile of a fast food restaurant (relative to schools that are within 

0.25 of a mile). There is no relationship between the school obesity rate and 

distances of a quarter or half mile from a fast food restaurant and no effect 

of full-service food restaurant availability. For the sample of mothers, the 

studies fi nd that living within half a mile of a fast food restaurant increases 

the probability of gaining more than 20 kilograms during pregnancy by 2.5 

percent. The authors interpret the smaller effects on women as evidence that 

they are less constrained by travel than the ninth graders. 

Another study examined the relationship between proximity to fast food 

restaurants and supermarkets to BMI for a sample of individuals from Marion 

County, Indiana (Indianapolis) (Chen et al., 2009). Unlike most previous 

studies, this study attempted to control for the fact that an individual’s choice 

of where to live may be affected by the availability of different foodstores 

and restaurants.30 Results of this study show that proximity to fast food 

restaurants has a small positive impact on BMI (Chen et al., 2009). In 

contrast, proximity to a grocery store has a small negative impact on BMI. 

The sizes of the total effects were less than half of a BMI point but were 

larger for people who lived very close to a store or restaurant. 

Another study found that neighborhood environment could be an important 

determinant of BMI and obesity (Katz et al., 2007). The Moving-toOpportunity demonstration project from the U.S. Department of Housing and 

Urban Development (HUD) used a random assignment methodology to study 

the effects of different public housing policies on families. Families living 

in high poverty public housing projects in fi ve U.S. cities were randomly 

assigned to one of three groups: 1) an experimental group that received 

mobility counseling and a Section 8 public housing voucher that could only 

be used in census tracts with low poverty rates; 2) another experimental 

group that received a Section 8 voucher that could be used in the traditional 

way, without any geographic restriction; and 3) a control group that received 

no new assistance. Results showed that adults in the treatment groups had 

lower probabilities of obesity relative to the control group (Katz et al., 

2007). While this study indicates there may be some neighborhood effects 

on obesity, it does not directly show an effect of food access on obesity. The 

reduction in obesity could have been due to other neighborhood or personal 

effects that were correlated with the move to a better neighborhood (e.g., 

better access to parks or less psychological distress). 

30The study also accounts for spatial 

dependence across administrative units. 
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The Relationship Between Consumption of Specifi c 

Foods, Obesity, and Diet-Related Diseases

Part of the goal of improving access to healthy and affordable food is 

to reduce obesity and diet-related diseases among populations that are 

adversely affected by these health conditions. With respect to obesity, it 

may be counterintuitive to think that a lack of access to any food is related 

to obesity—clearly the problem is too much food. The hypothesized causal 

pathway between lack of access and body weight is that some populations 

cannot get healthy food options and thus rely on energy-dense options that 

may cause weight gain. If healthier food is as available and as inexpensive 

as energy-dense food, it is hypothesized that consumers will substitute 

away from energy-dense foods to healthier foods and reduce the risk of 

obesity. As noted earlier, there is scant causal evidence to either support 

or refute this hypothesis. There are, however, a number of studies that 

explore the relationship between consumption of specifi c foods (fruits and 

vegetables, whole grains, low-fat milk, and beverages) and obesity and dietrelated diseases. Since these specifi c foods are often the foods lacking in 

underserved areas (with the exception of beverages), this research is relevant 

to the question of how lack of access affects obesity and diet-related diseases. 

Consumption of specifi c foods and their effects on obesity 

It is hypothesized that because of their high fi ber content and, in the case 

of whole grains, their improved glycemic control, fruits, vegetables, and 

whole grains could increase satiety so that consumers who increased their 

intake of these foods would substitute away from other foods that may be 

more energy-dense. Such a substitution would either stabilize total caloric 

intake or possibly reduce it. With respect to the fat content of milk, it is 

hypothesized that consumers who choose low-fat milk instead of milks with 

higher fat content would have lower caloric intake and lower BMI. 

There is only weak support for these hypothesized relationships in the 

literature.31 In the case of fruit and vegetable consumption, cross-sectional 

data show that people who eat more fruits and vegetables have lower BMI. 

But cross-sectional data cannot distinguish whether consuming more fruits 

and vegetables causes lower BMI since those who eat more fruits and 

vegetables may be more health conscious, more likely to exercise, and more 

likely to have lower BMI relative to those who do not. Intervention and 

longitudinal studies have shown that increased fruit and vegetable intake may 

lead to small decreases in BMI, but some studies even show that increases in 

body weight can occur with increased consumption of fruits and vegetables 

because total caloric intake increased. 

The case of whole grains is similar to that of fruits and vegetables. Relative 

to refi ned grains, greater intake of whole grains provides little or no benefi t 

for weight management. 

Cross-sectional evidence with respect to consumption of low-fat versus 

whole milk shows differences in preference across race and ethnicity that 

do not seem to be explained by availability or price. Hispanic and Black 

consumers are more likely to drink whole milk and White consumers are 

more likely to drink low-fat milk. The evidence also suggests that low-fat 

31This section draws heavily on a 

review of the literature presented by 

Dr. Richard Mattes, Purdue University, 

at the IOM Workshop on the Public 

Health Effects of Food Deserts (Mattes, 

2009).
56

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milk consumption is not associated with lower BMI. In fact, among children, 

consumption of low-fat milk was linked to weight gain. 

The relationship between beverage consumption and obesity has also been 

extensively studied. Calories consumed from beverages as a portion of 

Americans’ total energy intake have almost doubled in the past 40 years, so 

much so that in 2002, 21 percent of total energy intake is from beverages 

(Duffey and Popkin, 2007). It is hypothesized that beverages provide 

less satiety than solid foods. As a result, increased calories consumed as 

beverages may not lead to reductions in calories from solid food and in fact 

may be consumed in addition to whatever calories come from solid foods 

leading to increased energy intake and weight gain. A summary of research 

suggests that beverage consumption is associated with increased energy 

intake, weight gain, and BMI. But there is less research from longitudinal 

studies or randomized controlled trials to establish the causality of beverage 

consumption and weight gain. 

Obesity is a complex problem with many causes. Evidence presented here 

suggests that while some studies fi nd a correlation between food accessibility 

and BMI and obesity, the causal pathways are not well understood. Lack 

of access to specifi c nutritious foods may be less important than relatively 

easy access to all other foods. “Food swamps” may better explain increases 

in BMI and obesity than “food deserts.”.32 Increasing access to specifi c 

foods like fruits and vegetables, whole grains, and low-fat milk alone may 

not make a dent in the obesity problem. Many of the stores that carry 

these nutritious foods at low prices also carry all the less healthy foods 

and beverages as well. Without also changing the dietary behaviors of 

consumers, interventions aimed at increasing access to healthy foods may not 

be successful in addressing obesity. 

Consumption of specifi c foods and their effects

 on diet-related disease 

Not all of the relationships between specifi c healthy foods and diet-related 

diseases like diabetes, cardiovascular disease and cancer are well-understood, 

but some broad conclusions can be drawn..33 First, plant-based foods like 

fruits, vegetables, and nuts and whole grains are linked to reduced risk 

of cardiovascular disease. Diets high in saturated fat, trans fat, or refi ned 

sugars are linked with higher risk of diabetes and cardiovascular disease. 

Sugar-sweetened beverages increase the risk of obesity, diabetes, and 

cardiovascular disease. The evidence of how these foods relate to risks of 

cancer are not as clear cut and varies across types of cancers. For example, 

consumption of nonstarchy vegetables and fruits probably protect against 

cancers of the mouth, pharynx, and larynx and of the esophagus and stomach 

(see IOM, 2009, for a more thorough discussion).

The link between plant-based foods and whole grains to lower risk of CVD 

is relevant to questions about food accessibility. The lack of these foods in 

consumers’ diets due to lack of access could plausibly contribute to increased 

risk of CVD, especially if other foods high in saturated and trans fat are 

relatively more accessible and inexpensive.34 The link between sugarsweetened beverages and increased risk of obesity, diabetes, and CVD may 

be less of a question about food access since these beverages are almost 

32Rose et al. (2009) introduced the 

term “food swamp” to characterize 

areas with an abundance of less healthy 

food options in contrast to “food 

deserts” that lack healthy food options.

33This section draws heavily from a 

presentation by Dr. Frank Hu, Harvard 

University, at the IOM Workshop on 

the Public Health Effects of Food 

Deserts. 

34This is a hypothesis that could be 

empirically tested. 
57

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omnipresent—in supermarkets, corner stores, vending machines, and many 

other food and nonfood retailers. 

Summary

There is clear evidence that the food environment is associated with the kinds 

of foods that people eat. But most studies are cross-sectional and cannot 

make causal links. A few studies have examined food intake before and after 

healthy options for food become available (either within existing stores or 

because new stores open). These studies show mixed results. Some show a 

small but positive increase in consumption of fruits and vegetables and other 

nutritious food, while others show no effect. 

There is little evidence that shows that increased consumption of healthy 

foods such as fruits and vegetables, low-fat dairy, or whole grains leads to 

lower BMI or reduced risk of obesity. Stronger evidence suggests that the 

consumption of beverages, especially sweetened beverages, is linked with 

increases in BMI and obesity. Several studies fi nd that the proximity of fast 

food restaurants and supermarkets are correlated with BMI and obesity, but 

most of these are cross-sectional studies. One study that attempts to control 

for the correlation between individual’s preferences for foods and their 

choice of residential location shows that the proximity to a large grocery 

store is negatively linked to BMI and the proximity to fast food restaurants 

is positively linked to BMI, but both of these effects are small. In the case 

of obesity, easy access to all food may be a more important factor than lack 

of access to specifi c relatively nutritious foods. Increased access to healthy 

foods alone, without decreased consumption of all other foods, will likely 

have little impact on obesity among subpopulations of concern. 

Studies that go beyond correlation and try to map out causal relationships 

between the food environment and diet and health outcomes are rare. In 

order to disentangle these relationships and to ultimately improve the design 

of interventions that may reduce the impact of access barriers, improvements 

in research are needed. Better models that relate the food environment to 

diet and health are needed to disentangle causal relationships and defi ne 

tests of which factors may be most important in explaining the relationships 

(e.g., availability or price). Experimental studies that can isolate the effects 

of changes in the food environment to diet and health outcomes could 

help. Taking advantage of natural experiments or quasi-experiments where 

naturally occurring comparison groups or areas can be used to uncover causal 

pathways would be useful. Longitudinal data that can be used to determine 

changes in diet and health over time are also needed to improve what is 

known about the relationships between food environment and dietary health. 

References

Averett, S., and S. Korenman (1996). “The Economic Reality of the Beauty 

Myth,” Journal of Human Resources, 31(2): 304-330. 

Ayala, G.X., B. Baquero, L. Linnan, B.A. Laraia, and P. Bloom (2009). 

“Working With Tiendas to Promote Healthy Eating,” Presentation at the 

IOM/NRC Workshop on the Public Health Effects of Food Deserts, January 

27, Washington, DC.
58

Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

United States Department of Agriculture

Cawley, J. (2004). “The Impact of Obesity on Wages,” Journal of Human 

Resources, 39(2): 451-474. 

Chen, S.E., R.J.G.M. Florax, and S.D. Snyder, (2009). “Obesity, Fast Food, 

and Grocery Stores: Evidence from Geo-referenced Micro Data,” National 

Poverty Center Working Paper, http://www.npc.umich.edu/news/events/

food-access/index.php 

Cummins S., A. Findlay, M. Petticrew, and L. Sparks (2005). “Healthy 

Cities: The Impact of Food Retail-led Regeneration on Food Access, Choice 

and Retail Structure,” Built Environ 31(4): 288-301.

Currie, J., S. DellaVigna, E. Moretti, and V. Pathania (2009). “The Effect of 

Fast Food Restaurants on Obesity,” National Bureau of Economic Research 

Working Paper No. 14721, http://www.nber.org/papers/w14721. 

Diaz-Roux, A. (2009). “The Local Food Environment and Health: Where 

Are We and Where Do We Go Next?” Presentation at the IOM/NRC 

Workshop on the Public Health Effects of Food Deserts, January 26, 

Washington, DC.

Duffey, K.J., and B.M. Popkin (2007). “Shifts in Patterns and Consumption 

of Beverages Between 1965 and 2002,” Obesity, 15: 2739-2747. 

Gittelsohn, J. (2009). “Overview of Efforts to Change the Food 

Environment,” Presentation at the IOM/NRC Workshop on the Public Health 

Effects of Food Deserts, January 27, Washington, DC.

Hu, F.B. (2009). “Effects of Selected Dietary Factors on CVD and Cancer, 

“Presentation at the IOM/NRC Workshop on the Public Health Effects of 

Food Deserts, January 26, Washington, DC.

Institute of Medicine (2003). Unequal Treatment: Confronting Racial 

and Ethnic Disparities in Healthcare, B.D. Smedley, A.Y. Stith, and A.R. 

Nelson (eds.), Committee on Understanding and Eliminating Racial and 

Ethnic Disparities in Health Care, Board on Health and Sciences Policy, 

Washington, DC: The National Academies Press. 

Jeffery, R.W., J. Baxter, M.McGuire, and J. Linde (2006). “Are Fast Food 

Restaurants an Environmental Risk Factor for Obesity?” International 

Journal of Behavioral Nutrition and Physical Activity, 3(2): 

Katz, L.F., J.R. Kling, and J.B. Leibman (2007). “Experimental Analysis of 

Neighborhood Effects,” Econometrica 75(1): 83-119. 

Larson, N.I., M.T. Story, and M.C. Nelson (2009). “Neighborhood 

Environments: Disparities in Access to Healthy Foods in the U.S.,” American 

Journal of Preventive Medicine, 36(1): 74-81.e10. 

Mattes, R. (2009). “Effects of Selected Dietary Factors on Obesity,” 

Presentation at the IOM/NRC Workshop on the Public Health Effects of 

Food Deserts, January 26, Washington, DC.
59

Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

United States Department of Agriculture

National Research Council (2004). Eliminating Health Disparities: 

Measurement and Data Needs, M. Ver Ploeg and E. Perrin (eds.), Panel 

on DHHS Collection of Race and Ethnicity Data, Committee on National 

Statistics, Division of Behavioral and Social Sciences and Education, 

Washington, DC: The National Academies Press. 

Rose, D., J.N. Bodor, C.M. Swalm, J.C. Rice, T.A. Farley, P.L. Hutchinson 

(2009). “Deserts in New Orleans? Illustrations of Urban Food Access and 

Implications for Policy”. National Poverty Center Working Paper. http://

www.npc.umich.edu/news/events/food-access/index.php.

Wrigley, N., D. Warm and B.Margetts (2003). “Deprivation, Diet, and FoodRetail Access: Findings From the Leeds ‘Food Deserts’ Study,” Environment 

and Planning A 35: 151-188.

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CHAPTER 5

Food Access and Its Relationship 

To Food Choice

Policymakers are concerned about people with limited access to healthy food 

because they believe it may infl uence food shopping and spending behavior, 

the prices of food faced by people in areas with limited access, and the types 

of foods purchased and consumed. This chapter examines these economic 

consequences of limited access. Food shopping behaviors for participants 

of the Supplemental Nutrition Assistance Program are summarized.35 The 

chapter also considers food spending behavior for SNAP participants with 

different levels of access to supermarkets, examining the types of foods that 

SNAP participants purchase based on their access to supermarkets. Finally, 

the chapter analyzes data on the price of selected similar foods across 

different food retail outlet types. 

Food Shopping Behavior for Participants of SNAP

SNAP serves as the foundation of America’s national nutrition safety net 

for low-income families. In November 2008, more than 31 million persons 

participated in the program and received an average benefi t of $115. Benefi ts 

are targeted to the purchase of food for home use and are redeemed through 

more than 175,000 authorized stores.

Access to a variety of high quality and affordable foods is essential to meet 

the program’s mission of improving food security, reducing hunger, and 

providing access to a healthful diet and nutrition education. Of particular 

concern are households who live in rural areas or low-income urban 

neighborhoods where access to stores that offer such quality and variety 

at reasonable cost may be limited. During the mid-1990s, USDA’s Food 

and Nutrition Service implemented a research agenda to address questions 

about food access among SNAP and other low-income households. While 

these data are from the 1990s, they provide a foundation for exploring store 

access. At the same time, care should be taken to view these fi ndings in the 

historical context in which they were generated. Several relevant changes 

have occurred during the last 15 years. For example, both the number and 

profi le of authorized stores have changed. At the same time, there has been 

an increase in the percentage of SNAP benefi ts used in superstores and 

supermarkets. SNAP eligibility rules with respect to vehicle ownership are 

now less restrictive, which may expand store access.

In order to participate in SNAP, stores must apply for authorization and 

demonstrate that they meet established eligibility criteria. These criteria 

address the nature and extent of food business conducted; the volume of 

SNAP sales that can be reasonably expected; as well as the business integrity 

of the store applicant (7 CFR 278.1 (b)). The fi rst criterion is operationally 

defi ned in terms of 1) a store’s food sales volume in relation to overall sales 

and/or 2) inventory of staple foods and the variety of products available 

within specifi ed staple food categories. 

35On October 1, 2008, the Food 

Stamp Program changed its name to 

the Supplemental Nutrition Assistance 

Program (SNAP). Because all of the 

research discussed in this section was 

conducted prior to the name change, 

most program references are to the 

Food Stamp Program.
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These broad criteria enable FNS to authorize a wide variety of store types 

and sizes in many locations so that participants have a range of food 

shopping options. Table 5.1 compares the percentage of authorized retailers 

and benefi ts redeemed by store type in Fiscal Years (FY) 1994 and 2008.

Store types are defi ned in terms of the dollar value of annual gross sales and 

product lines offered. Supermarkets are defi ned as foodstores that provide a 

full range of foods and have $2 million or more in annual gross sales. Large 

groceries have annual sales between $500,000 and $2 million, while small 

grocery stores have annual sales of less than $500,000.36 Convenience stores 

provide a more limited range of foods, usually excluding fresh produce. 

Specialty stores primarily sell one or two product lines, such as produce, 

meats, or baked goods. Examples of other store types include nonprofi t food 

buying co-op stores and combination grocery/other stores. 

The largest category of stores in both FY 1994 and FY 2008 is convenience 

stores; they accounted for 27 and 35 percent of all authorized stores, 

respectively. In contrast, the majority of program benefi ts are spent in 

supermarkets or other large stores37 – 77 percent in FY 1994 and 87 percent 

in FY 2008. These data exemplify the rise in the use of superstores for 

SNAP participants. On the other hand, redemptions at medium-sized grocery 

stores have decreased since1994. 

SNAP benefi t redemptions in relation to where participants live

Historically, much of the research on food deserts has focused on geographic 

proximity to food retailers. While this work offers one perspective on store 

access, another is to examine where low-income families actually shop. 

Mantovani and Welsh (1996) report that food stamp shoppers tended to use 

their benefi ts outside of the ZIP Code in which they live. This pattern was 

36The 2008 data include a category 

for medium sized stores. For this 

classifi cation, stores with sales of 

$1 million to $2 million are large, 

$250,000 to $1 million are medium, 

and less than $250,000 are small.

37In FY 1994, this category included 

supermarkets and large grocery stores. 

In FY 2008, the category includes 

supermarkets, superstores, and large 

grocery stores.

Table 5.1

Percentage of Authorized Retailers and SNAP Redemptions by Category FY 1994 Versus FY 2008

Authorized retailers Benefi ts redeemed

Store type FY 1994 FY 2008 FY 1994 FY 2008

Percent

Supermarkets 15 12 77 47

Superstores na 8 37

Large grocery stores1 2 2

Medium grocery stores 25 6 11 2

Small grocery stores2 9 2

Convenience stores 27 35 4 4

Combination stores3 16 17 3 2

Farmers’ markets <1 <1 <1 <1

All other 17 9 5 2

Total 100 98 100 98

1In FY 1994, data for large grocery stores and supermarkets was combined.

2In FY 1994, data for small and medium grocery stores was combined.

3This category includes stores such as independent drug stores, dollar stores, and general stores.

na =not applicable. 

Source: USDA, FNS calculations based on 1994 and 2008 administrative data on SNAP redemptions.
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Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

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more noticeable in rural areas, where recipients shopped in relatively larger 

population centers. Even in urban areas, however, households traveled 

beyond their neighborhood supermarkets to more affl uent areas and/or to 

other low-income ZIP Codes to access stores offering items of particular 

interest, such as fresh fruits and vegetables or ethnic products. 

FNS also surveyed nationally representative samples of participants, eligible 

nonparticipants, and near-eligible nonparticipants with questions about 

shopping patterns and access to stores in the National Food Stamp Program 

Survey (Ohls et al., 1999). Like other surveys of this kind, a disproportionate 

number of long-term participants were included, and determinations of 

eligibility could only approximate the criteria applied by the program. 

Ohls et al. (1999) reported that nearly 90 percent of each low-income group 

used supermarkets as their main foodstore. Even among participants who 

reported that they usually did not shop at supermarkets, all but 2 percent 

reported that they sometimes used such stores.

Among program participants, the average distance to the nearest supermarket 

was 1.8 miles. In contrast, the average number of miles to the store used 

most often by participants and eligible nonparticipants was 4.9 miles. A 

similar study that used electronic benefi ts transfer (EBT) redemption data in 

the State of Maryland also found that SNAP participants redeemed benefi ts 

at stores farther than the nearest SNAP food retailer (Cole, 1997). This study 

found that in Maryland, the average distance traveled to redeem SNAP 

benefi ts was 2.7 miles, but the average distance to the nearest store was 0.3 

miles. These data suggest low-income households typically bypassed nearby 

supermarkets to use stores farther from home. 

Thirty-eight percent of participants and 34 percent of eligible nonparticipants 

reported that they did not shop in their neighborhoods. About half of each 

group said this was because there was no store nearby. Average distance 

to the most frequently used store among those reporting no neighborhood 

retailers was higher than the average distances reported by the overall 

participant and eligible nonparticipant samples. The average reported 

distance to the most frequently used store was 9.2 miles for participants with 

no neighborhood stores, compared to an average of 4.9 miles for the overall 

sample of food stamp households.

EBT transaction patterns

FNS has continuously tracked benefi t redemption by store in the aggregate. 

With the introduction of EBT systems, it became feasible to examine 

shopping patterns at the household level. Maryland was the fi rst State 

to operate EBT Statewide and provided FNS an opportunity to track the 

frequency, location, dollar value, and timing of household food purchases 

(Cole, 1997). Supermarkets comprised just 17 percent of authorized food 

stamp stores in Maryland at the time of the study. However, 44 percent of 

Statewide food stamp purchases occurred in supermarkets, and 72 percent 

of benefi ts were used in supermarkets. On any given day in the month, the 

supermarket percentage of total daily redemptions throughout the State was 

roughly constant. The same pattern occurred for other store types. This 

confl icts with the expectation that recipients make their large purchases in 
64

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United States Department of Agriculture

supermarkets early in the month and fi ll in with smaller buys at other store 

types during the rest of the month.

More recently, FNS analyzed a national sample of EBT transaction data that 

was linked to store and household characteristics (Cole, 2005). Participants 

spent most of their food stamp benefi ts in supermarkets. Supermarkets 

accounted for 64 percent of all EBT purchases and 83 percent of the dollar 

value of food stamp benefi ts redeemed. Over 46 percent of food stamp 

households shopped exclusively at supermarkets, while less than 6 percent 

never shopped in supermarkets. The latter families were concentrated among 

households receiving the minimum monthly benefi t, $10 or less.

Shopping patterns did not vary substantially across community 

characteristics. The average number and dollar amount of purchases 

among households in counties with persistent poverty mirrored the national 

averages. The percentage of households with no supermarket purchases 

was almost the same in areas with persistent poverty (6 percent) as in 

areas without (5 percent). Similarly, the data show little difference across 

urban, suburban, and rural households. The percent of food stamp benefi ts 

redeemed in supermarkets ranged from 80 percent among rural families to 85 

percent among households in suburban areas.

The aggregated redemption data for FY 2008 show that a majority of benefi ts 

are spent in large stores: 87 percent of food stamp benefi ts were redeemed in 

superstores, supermarkets, or large grocery stores. Only 4 percent of benefi ts 

were redeemed in convenience stores, and another 4 percent were redeemed 

in small to medium grocery stores.

Spending on Different Food Groups 

and Access to Supermarkets

The analysis now addresses the questions of what foods people buy and 

how access may infl uence purchase behavior. Using national data from the 

NFSPS, Rose and Richards (2004) examined the effects of limited access 

to supermarkets on the amount of fruit and vegetable purchases. Access 

to a supermarket was defi ned by three variables—distance to store, travel 

time to store, and car ownership. The study found that limited access to a 

supermarket was negatively related to the purchase of fruits and vegetables, 

but only the effect on fruits was statistically signifi cant.

This analysis uses the same data used by Rose and Richards (2004) to 

examine the amount of food from different food groups purchased per week 

by SNAP participants. The study by Rose and Richards (2004) is extended 

in three ways. First, fruits and vegetables are separated into canned and 

noncanned forms. Limited access to a supermarket is hypothesized to exert 

greater effects on the purchase of noncanned produce than canned produce 

since many smaller grocery stores and convenience stores sell mostly canned, 

but not necessarily fresh fruits and vegetables. Additionally, potatoes 

and dried beans are treated as a separate vegetable category. The analysis 

also examines purchases of milk and other dairy products. Like fresh 

produce, milk and dairy products are perishable, so that milk purchases are 

hypothesized to be negatively affected by limited access to a supermarket. 
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Second, survey respondents were also asked whether they did their major 

food shopping at a supermarket as well as the frequency at which they 

shopped for food. The analysis combines these two variables to develop 

three mutually exclusive categories of access to a supermarket: major food 

shopping was not at a supermarket, no matter how frequently shopping was 

done; major food shopping was at a supermarket but shopping was infrequent 

(less than once in 2 weeks); and major food shopping was at a supermarket 

and was conducted at least once every 2 weeks. The measure of shopping 

frequency is intended to capture diffi culty in getting to a store—if stores 

are relatively close and the costs (both time and travel costs) are low, then 

it is expected that respondents will shop more frequently. But if costs to 

getting to a supermarket are high, we would expect respondents to make less 

frequent trips to the supermarket. 

Third, the extension to the Rose and Richards study accounts for the 

censored nature of food expenditures. In a given week, some households 

may not make any purchase of the food groups in question. To accommodate 

this data issue, the analysis employs the Tobit censored regression model, as 

discussed later. 

Data

Data for examining supermarket access on food purchases are drawn from 

the NFSPS, conducted by Mathematica Policy Research, Inc. for USDA’s 

FNS. The NFSPS employed computer-assisted personal interview (CAPI) 

methods to collect data on household food purchases among food stamp 

recipients between June 1996 and January 1997. Respondents reported their 

7-day food use (some households were asked to provide four-day records), 

which included data on both the quantities and prices of food used as well 

as expenditures on food at home and away from home. This is the only 

USDA survey in which household food use (quantity and expenditure) data 

were collected since the 1987 88 Nationwide Food Consumption Survey. 

Social, demographic, and economic characteristics of households were also 

collected. 

Data such as the National Health and Nutrition Examination Survey 

(NHANES) could be used to model food consumption, but the NHANES 

data do not include measures of food retail access. Further, it is not feasible 

to link NHANES data with geographically identifying data that could be 

used with more direct measures of access to food retailers (e.g. distance to 

supermarkets, number of supermarkets in the area, and the variety of food 

markets in the area). 

In total, 1,109 in-person interviews were completed from the SNAP list 

frame, and 1,069 households provided complete information on quantity and 

expenditure data. After excluding households with missing information, the 

fi nal sample totals 860 households.

There are more than 2,000 foods recorded in NFSPS, and they are aggregated 

according to the research focus of the project. Insuffi cient intake of foods 

rich in fi ber and calcium (such as fruits, vegetables, and dairy products) is 

a major dietary defi ciency facing Americans, especially the low-income 

subpopulation (Lin, 2005). The analysis hypothesizes that households with 
66

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limited access to supermarkets tend to spend proportionally less of their 

food budget on perishable foods, such as fresh fruits, fresh vegetables, 

and dairy products, than households that shop mainly at supermarkets. 

This study focuses on household purchases of fi ve food groups—dairy 

products, noncanned fruits, noncanned vegetables, canned fruits, and canned 

vegetables.

The NFSPS collected data on access to and shopping at supermarkets. 

NFSPS respondents were asked if they did their food shopping at 

supermarkets, whether they had private vehicles or public transportation 

for food shopping, the distance and travel time to the stores in which they 

shopped, and the frequency of food shopping. Most of these variables are 

highly correlated. Only 7 percent of the sample households (64 out of 860) 

indicated that they did not shop mainly at supermarkets (table 5.2). Among 

those who shopped mainly at supermarkets, 27 percent (218 out of 796) 

shopped less than once in 2 weeks. In this study, three mutually exclusive 

categories are specifi ed to signify limited access to a supermarket: major 

food shopping was not at a supermarket, no matter how frequently shopping 

was done; major food shopping was at a supermarket but shopping was 

infrequent (less than once in 2 weeks); and major food shopping was at a 

supermarket and was conducted at least once every 2 weeks. This three-part 

measure of access is an individual measure of access and not an area-based 

measure of access like those used in the previous chapter.

Tobit censored regression model

On any given week, some food stamp households did not purchase a 

particular food group. Therefore, a cluster of zero consumption values for 

a particular food group is observed in the data—making it necessary to 

estimate a censored regression model. Any statistical procedure that does 

not account for zero observations produces inconsistent parameter estimates. 

Tobin (1958) was the fi rst to propose a censored normal regression model 

(Tobit model) to deal with censored data in regression, which can be 

expressed as below

(1) 

if 0

0 if 0, 1, 2, , ,

i ii ii

i i

qx x

x in

= β +ε β +ε > ′ ′

= β +ε ≤ = ′ L

where qi denotes the endogenous variable, x is the vector of exogenous 

variables, and i ε is the error term. The Tobit procedure was used to estimate 

6 censored purchase equations. 

Results

Six separate Tobit equations are estimated to examine the relationship 

between limited access to a supermarket on household purchases of fruits, 

vegetables, and milk. Fruits are disaggregated into canned and non-canned 

forms; vegetables are disaggregated into canned, potatoes and beans, and 

noncanned vegetables. Supermarket access is represented by a set of 

categorical variables: major food shopping was not at a supermarket, no 
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Table 5.2

Descriptive statistics of the NFSPS respondents

Supermarket shopping

Total

Frequent 

shopper

Infrequent 

shopper Not shop at

Sample 860 578 218 64

Average purchase Pounds per week

 Noncanned vegetables 3.73 3.93 3.32 3.30

 Canned vegetables 2.06 2.05 2.14 1.79

 Potatoes and beans 2.95 3.05 2.92 2.10

 Noncanned fruits 5.92 6.23 5.38 4.94

 Canned fruits 1.59 1.66 1.51 1.19

 Milk and diary products 12.29 12.71 12.31 8.52

Proportion consuming Percent

 Noncanned vegetables 85 86 83 78

 Canned vegetables 69 67 73 70

 Potatoes and beans 82 83 81 81

 Noncanned fruits 83 85 80 78

 Canned fruits 42 42 43 36

 Milk and diary products 97 97 98 94

Mean values of explanatory variables:

 Per capita income ($/month) 305

 Meal number (# meals prepared from 

 food purchase) 48

Percent

Four day (sample report only 4-day purchase) 8

Asian (sample) 1

Black (sample) 39

Native American (sample) 1

Hispanic (sample) 13

White (sample) 46

Single-headed (sample with one head) 34

Child (sample with children under 18) 56

Elderly (sample with senior > 60) 27

Less than high school (head without HS diploma) 46

High school (head with HS diploma) 38

College (head attended college) 16

Rural (living in rural area) 15

West 38

South 20

Midwest 24

Northeast 18

Spring 24

Summer 24

Fall 29

Winter 23

Source: USDA, ERS calculations based on 1996-97 National Food Stamp Program Survey data.
68

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matter how frequently shopping was done; major food shopping was at a 

supermarket but shopping was infrequent (less than once in 2 weeks); and 

major food shopping was at a supermarket and was conducted at least once 

every 2 weeks (the reference group). The results are shown in table 5.3.

As expected, households that did not shop mainly at a supermarket tended to 

purchase signifi cantly smaller amounts of noncanned vegetables, noncanned 

fruits, and milk than households that shopped frequently at a supermarket. 

Households that did not shop at a supermarket also purchased less canned 

fruits and vegetables as well as potatoes and beans, but the differences are 

not statistically signifi cant at the 10-percent level. Compared to households 

that shopped frequently at a supermarket, infrequent supermarket shoppers 

purchased less of the six food categories in question, but the differences are 

not signifi cant.

As indicated earlier, underconsumption of fruits, vegetables, and milk 

is a major dietary defi ciency facing Americans, especially low-income 

Americans. The results suggest that food stamp recipients who did not shop 

at a supermarket purchased less of these already under-consumed foods than 

recipients who shopped frequently at a supermarket. 

The Tobit results also point to other important determinants of food 

purchases. The number of meals (number of people and number of 

occasions) prepared from the weekly food purchases, as expected, positively 

affects the purchased amounts of the six food groups. Household purchases 

of these six food groups appear to vary by race and ethnicity. Compared 

with Whites, Asian and Hispanic households tend to buy more noncanned 

vegetables and noncanned fruits but less of canned vegetables and potatoes 

and beans. Black households tend to purchase less milk and potatoes and 

beans than Whites. Households with children purchase less noncanned 

vegetables but more canned fruits and milk than households without children. 

Households with elderly individuals buy more noncanned vegetables than 

households without elderly members. Sample members are grouped into 

three education categories—less than high school, high school graduate, and 

attended college. No differences are found across these education levels. Per 

capita income (within the low-income sample of SNAP participants) is not 

associated with differences in the purchases of any of the six food categories.

Price Differentials and Store Format

Another important consequence of limited access to foodstores is that 

consumers may face higher prices for food at the retail outlets that are 

available. As Chapter 5 noted, higher prices in some stores or areas may 

be due to lower volume of sales, higher fi xed costs, or other reasons. This 

section compares prices of three selected goods—milk, ready-to-eat cereal, 

and bread—which are sold in almost all types of food retail outlets. There 

is not enough detailed information to compare prices in areas with limited 

access with those with better access. Instead, price variation is examined 

across store type—grocery, convenient, discount, and other stores. 
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Literature review 

Many studies have examined price disparities across income class, store 

format, and accessibility (Andreyeva et al., 2008; Block and Kouba, 2006; 

Broda et al., 2009; Chung and Myers, 1999; Hayes, 2000; Hendrickson et 

al., 2006; Latham and Moffat, 2007; Talukdar, 2008). A limitation of these 

studies is their use of observed prices in a regional setting rather than actual 

prices paid on a national level. Kaufman et al. (1997) provides a review of 

literature on food price disparity dating back to the 1960s and identifi es the 

complexities of undertaking such research. 

Table 5.3

Tobit results

Vegetables Fruits

Non

canned Canned

Potatoes 

Beans

Non

canned Canned Milk

Intercept 0.76 0.38 1.52 ** 0.79 -5.95 *** 7.05 ***

Meal number 0.06 *** 0.02 *** 0.04 *** 0.07 *** 0.04 *** 0.13 ***

Four day 0.55 -0.97 ** 0.27 -0.38 -0.97 -0.84 

Income per capita 0.00 0.00 0.00 0.00 0.00 0.00 

Asian 3.43 ** -2.91 ** -3.37 *** 6.76 *** -1.00 -5.50 *

Black 0.25 0.27 -1.07 *** 0.12 -0.60 -5.60 ***

Native American -0.01 -0.04 -0.60 0.13 -0.17 -3.61 

Hispanics 1.09 * -1.90 *** -1.15 ** 4.03 *** 0.24 -1.78 

Single-headed -0.23 0.41 0.34 0.17 -0.20 1.48 **

Child -1.20 ** 0.35 0.14 0.42 1.98 *** 4.33 ***

Elderly 0.98 ** -0.31 -0.36 0.84 -0.57 0.07 

High school -0.26 0.24 -0.21 0.17 0.52 -0.02 

College 0.68 -0.12 -0.22 1.30 * 0.71 -0.79 

Rural -0.43 0.04 0.14 0.18 -0.33 -1.25 

West 0.31 1.08 *** 1.09 *** -0.79 0.91 0.90 

South 0.06 -0.18 -0.94 ** -0.12 0.44 -0.47 

Midwest -1.00 * 0.16 -0.51 -1.27 0.35 -0.26 

Spring 0.42 -0.46 -1.12 ** 0.67 1.20 -1.92 *

Summer 1.07 * -0.10 -0.33 2.08 *** 0.19 -1.29 

Fall -0.44 -0.06 -0.18 -0.98 0.29 -1.27 

Did not shop at a supermarket -1.32 ** -0.07 -0.28 -1.88 ** -0.47 -2.54 **

Shopped infrequently at a 

 supermarket -0.60 -0.08 -0.28 -0.86 -0.13 -0.07 

Scale 4.72 3.13 3.78 7.12 5.97 9.59 

Signifi cance levels: ***, **, and * denote 1, 5, and 10 percent, respectively.

Source: USDA, ERS Tobit model estimations based on 1996-97 National Food Stamp Program Survey 

data. 
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Andreyeva et al. (2008) replicated a 1971 study of food availability and 

price in New Haven, Connecticut. Their fi ndings show improvement in 

availability and price since 1971. Findings indicate differences across 

store types—grocery stores were approximately 4 percent cheaper than 

convenience stores for a basket of goods. The study also found that highincome areas faced higher prices than low-income areas. 

Block and Kuoba (2006) compared prices for a market basket of goods in 

different types of stores in the Austin and Oak Park sections of Chicago. 

Austin is a lower-middle-class African-American community that borders 

Oak Park, an upper-middle-income suburb. They fi nd mixed results. 

Discount supermarkets showed the lowest prices. Independent grocery stores 

had higher prices for packaged goods than chain supermarkets, but lower 

prices for fresh items. 

Broda et. al (2009) analyzed actual consumer purchases and found that poor 

households pay less for food items they purchase than households with 

higher incomes—a 10-percent increase in income roughly induces a modest 

0.1-percent increase in prices paid per food item. They also found that poor 

households tend to shop more frequently at discount stores and supercenters. 

Even after controlling for household characteristics and product fi xed effects, 

the study found that poorer households pay a lower price even in stores of the 

same retail chain.

Chung and Myers (1999) conducted a survey in the Twin Cities metropolitan 

area to determine how store type (nonchain/convenience store versus chain/

supermarket) and neighborhood quality (measured by percent of households 

under the poverty level within a zip code) affects price of a food market 

basket. They conclude that store type is more important in driving price 

disparities than the geographic location of a household—the premium for 

shopping at a convenience and/or nonchain store outweighs the premium for 

shopping in a poor neighborhood. Limitations to their methodology include 

the way missing price values were treated. When price for a selected item is 

missing, the least expensive brand/size product (e.g., an in-store brand in its 

largest package size) was used. When a selected item was not available in 

the store, the sample mean price was used. Additionally, the use of regional 

data based on “sticker prices” (those listed on the shelf) as opposed to 

actual transaction prices at the national level do not control for promotional 

purchases (e.g., on-sale and coupon use). 

Hayes (2000) analyzed prices in New York City to establish if prices in 

low-income neighborhoods were indeed higher than those in more affl uent 

neighborhoods. The study concludes that the mean price for a market food 

basket is 2 percent higher in more affl uent neighborhoods although the means 

are not signifi cantly different. Even after controlling for the price of on-sale 

items and generic branding, the prices in low-income neighborhoods were 

not signifi cantly different than in more affl uent neighborhoods. The author 

does acknowledge that it is possible that the quality of food items purchased 

by the poor is below that of the items purchased by the more affl uent.

Hendrickson et al. (2006) studied prices of selected Thrifty Food Plan foods 

in four Minnesota communities with higher than average poverty (two rural 

and two urban). The study examined prices offered in grocery stores in 
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United States Department of Agriculture

these communities for the TFP foods and compared their prices with those 

of the TFP Market Basket Price (MBP). If a food was found in the grocery 

store, the price of the lowest price version of the food (price per pound) was 

recorded. The study found that in the two urban areas, 6 and 9 out of the 

19 foods studied were more expensive than the TFP MBP. In the two rural 

areas, 2 and 4 of the 19 foods studied were more expensive than the TFP 

MBP. Over all of these communities, the prices per pound of fresh produce 

were equal or less expensive than the TFP MBP price. Although this study 

uses the lowest price per pound product in the store for a selected food, it 

still only uses the available price instead of the actual paid price. Further, 

the TFP MBP is a national price average so it is not clear if the prices in the 

Minnesota communities studied are different from the TFP MBP because 

prices in the neighborhoods are different or because the State or region has 

different prices. 

Mantovani et al. (1997) examined information on MBP for each store in a 

national sample of stores authorized to redeem SNAP benefi ts. Market basket 

quality was measured in terms of the availability of acceptable items as 

guided by a USDA publication on buying quality food (1975). This analysis 

focused on product availability and cost in areas with different concentrations 

of poverty. In urban areas, market basket costs in supermarkets and large 

grocers were nearly equivalent across levels of poverty. Prices were less 

at “other” stores located in high-poverty areas than those in lower poverty 

areas. In rural areas, market basket costs were consistently similar in higher 

and lower poverty areas. 

Latham and Moffat (2007) study prices of a market basket of goods 

across store types in low and higher income neighborhoods of Hamilton, 

Ontario, Canada. They fi nd that prices at supermarkets in low income 

areas were similar to prices at areas that were not low income. Prices were 

higher, however, at variety stores, which offer fewer groceries but more 

nonprescription drugs, tobacco products, and other products, operating in 

low-income areas. 

Talukdar (2008) investigated prices faced by the poor for both food 

and nonfood items in Buffalo, New York, and surrounding suburban 

neighborhoods. The study found that the inner-city neighborhoods 

experience a weakened competitive market leading to cost-ineffi cient 

“corner stores” which have a 6-7 percent premium over regional or national 

chain grocery stores. Even after controlling for economies of scale and 

competitive environments, prices were 2 to 5 percent higher in the poorest 

neighborhoods.

This study extends the literature by analyzing actual consumer purchases at 

a national level, rather than observed “sticker prices” in certain localities. 

Furthermore, the study will focus on particular food items rather than 

representative “food baskets,” enabling the pricing models to control for 

specifi c product attributes such as milk fat and whole grain, as well as for 

market factors such as promotional on-sale prices and coupon use. The study 

also borrows from a recent analysis conducted in conjunction with ERS, 

which explores actual prices paid by consumers across different income 

levels (Broda et al., 2009).
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Table 5.4

Descriptive statistics of variables used in price analysis

Fluid milk RTE cereal Bread

Variables Defi nition

Convenience 

store

All 

other

Convenience 

store

All 

other

Convenience 

store

All 

other

Price

Unit value (expenditure net of any promotions divided by the corresponding 

quantity), cents per ounce

2.47 2.59 16.17 16.71 8.67 9.03

Income 

The ratio of household income over the 

federal poverty level; where income is 

the midpoint of the income class

3.57 3.83 3.59 3.73 3.18 3.73

Market shares (percent of purchase occasions)

Grocery store Purchase occasion at grocery store 73 67 72

Convenience store Purchase occasion at convenience or 

drug store 52 1

Discount store Purchase occasion at supercenter or 

club warehouse 17 20 17

Other stores Purchase occasion at other store 5 10 9

Promotional sale Purchase made on-sale or with a 

coupon

27 18 69 36 19 23

Spring Purchase in spring (Jan-Mar) 27 26 27 26 26 25

Summer Purchase in summer (Apr-Jun) 24 25 23 25 24 25

Fall Purchase in fall (Jul-Sept) 25 25 28 26 26 25

Winter Purchase in winter (Oct-Dec) 25 24 22 23 24 24

East Northeastern census region 28 20 26 18 22 20%

Central Midwestern census region 33 24 29 26 35 24

West Western census region 11 20 19 22 13 20

South Southern census region 29 35 26 35 29 36

Urban Purchase in urban area 81 77 85 77 78 77

Pint 16 oz. used as midpoint (0 - 24 oz.) 2 1 -- -- -- --

Quart 32 oz. used as midpoint (25 - 48 oz.) 4 6 -- -- -- --

Half gallon 64 oz. used as midpoint (49 - 96 oz.) 26 33 -- -- -- --

Gallon 128 oz. used as midpoint ( > 97 oz.) 68 60 -- -- -- --

Skim Less than 0.5g of fat* 18 24 -- -- -- --

Low-fat Less than 4.7g of fat (includes 0.5%, 

1%, 1.5%, 2%)* 58 54 -- -- -- --

Whole 8g of fat* 24 22 -- -- -- --

Size Continuous quantity (1 oz. - 81 oz.) -- -- 15.18 17.58 -- --

Whole-grain Identifi ed as a whole-grain product -- -- 60 52 13 21

Small size 16 oz. used as midpoint (0 - 18 oz.) -- -- -- -- 25 27

Medium size 20 oz. used as midpoint (19 - 22 oz.) -- -- -- -- 44 33

Large size 24 oz. used as midpoint ( > 22 oz.) -- -- -- -- 31 40

No. observations 55,000 978,414 14,759 660,650 9,873 876,944

1,033,414 675,409 886,817

Source: USDA, ERS calculations based on 2006 Nielsen Homescan Panel data.
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Data

The data source for investigating price differentials by store type is the 2006 

Nielsen Homescan panel data. The panelists constitute a random sample that 

is representative of the U.S. population and provides purchase information 

of food items for at-home consumption. Each household is supplied with 

a scanner device that the panelist uses at home to record grocery items 

purchased at all retail outlets. The household either scans the Uniform 

Product Code (UPC) or a designated code for random-weight purchases for 

each food item. Each purchase records the date, the quantity purchased, 

expenditures for that quantity, promotional information including whether or 

not the item is on sale, and detailed product characteristics. 

Total enrollment in the Homescan panel for 2006 was over 37,000 

households, but to avoid would-be data problems resulting from incomplete 

reporting, only those households that reported purchases for at least 10 

months were included. Panelists report total expenditures and the quantity 

of food purchased. Prices are derived as unit values – the ratio of reported 

expenditures, net of any promotional and sale discounts, to the reported 

quantities for each purchase record. 

Each purchase record is identifi ed by store type (grocery, convenience, 

discount, and others),38 day of purchase, whether the purchase was 

made with a discount, and an array of product attributes. Each panelist 

also provided data on his or her social, economic, and demographic 

characteristics, including income, household size, and place of residence.

This study examines the prices of the three most popular healthy food items 

purchased by Homescan panelists at convenience stores and also at grocery 

stores—fl uid milk, ready-to-eat cereals (RTE cereals hereafter), and bread.39

For milk, attributes include fat content (nonfat/skim, low-fat, or whole) 

and container size (pint, quart, half gallon, or gallon,). Buttermilk, soy, and 

fl avored milk are not included in the analysis. For cereals and bread, attribute 

data include container size and whether it is a whole grain bread or cereal. 

Rolls, buns, muffi ns, and other sweetened breads, such as cakes and donuts, 

are excluded from the data set. Although the three aforementioned foods 

examined in this study could be considered loss leader products,40 the model 

controls for in-store promotions and coupon use. Table 5.4 provides a list 

of variables constructed from the data to be used in the empirical estimation 

with descriptive statistics.

Hedonic pricing model

The analysis of price differences at grocery stores and convenience stores 

is carried out using the hedonic model, which is based on Lancaster’s 

(1966) characteristics demand theory that consumers derive utility from 

the characteristics or attributes inherent in a good or service. The price 

consumers pay for a good is the sum of the values consumers assign to the 

good’s attributes, as shown below.

(2) it s s sit r it r rit P = +∑ MKT +∑ PRO + e =1 =1 α0 α β

38Homescan does not differentiate 

between the sizes of grocery stores 

(i.e., large specialty grocers, such as 

Whole Foods, and large grocers, such 

as Publix, Giant, and Safeway, are all 

considered grocery stores). Discount 

stores include large supercenters such 

as Super Wal-Mart and Super Target 

and warehouse stores. 

39These are the most frequently 

purchased goods at convenience stores 

for all consumers, not just low-income 

consumers. We considered studying 

prices of fruits and vegetables and other 

“healthy” food options across store 

outlet type, but the sample of purchases 

was too small (i.e., not enough of 

these types of goods were purchased 

from convenience stores to study their 

prices). 

40Loss leader pricing of products is a 

marketing strategy in which retailers set 

low prices for particular “loss leader” 

products, typically below or at cost, to 

attract customers into the store to buy 

other products.
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Table 5.5

Hedonic results

Fluid milk RTE cereal Bread

Variable Coeffi cient

Standard

error Coeffi cient

Standard

error Coeffi cient

Standard

error

Constant 2.419*** 0.025 22.879*** 0.220 6.338*** 0.137

Income 0.024*** 0.002 0.334*** 0.016 0.317*** 0.008

Convenience 0.129*** 0.031 5.839*** 0.775 0.620*** 0.200

Discount -0.202*** 0.016 -0.720*** 0.114 -0.351*** 0.063

Other -0.188*** 0.030 -2.351*** 0.150 -1.334*** 0.061

On sale -0.508*** 0.016 -3.695*** 0.106 -1.669*** 0.065

Spring 0.026*** 0.005 -0.310*** 0.036 -0.314*** 0.017

Summer -0.035*** 0.005 -0.035 0.040 -0.262*** 0.016

Fall -0.034*** 0.004 -0.128*** 0.032 -0.190*** 0.016

East -0.417*** 0.044 0.297 0.210 0.640*** 0.120

Central -0.437*** 0.027 -0.226 0.171 -0.067 0.121

West -0.210*** 0.035 1.110*** 0.168 1.254*** 0.193

Urban 0.019*** 0.019 0.644*** 0.113 0.323*** 0.100

Income*conv -0.008 0.005 -0.289*** 0.043 -0.153*** 0.045

Pint 4.213*** 0.173 -- -- -- --

Quart 1.988*** 0.042 -- -- -- --

Half gallon 0.983*** 0.026 -- -- -- --

Skim -0.087*** 0.015 -- -- -- --

Low-fat -0.062*** 0.013 -- -- -- --

Pint*conv 0.896*** 0.181 -- -- -- --

Quart*conv 0.072 0.069 -- -- -- --

Halfgal*conv -0.429*** 0.056 -- -- -- --

Skim*conv -0.095** 0.045 -- -- -- --

Low-fat*conv -0.053** 0.027 -- -- -- --

Whole-grain -- -- 0.068 0.046 1.592*** 0.042

Size -- -- -0.366*** 0.009 -- --

Size*conv -- -- -0.373*** 0.045 -- --

Small -- -- -- -- 4.986*** 0.091

Medium -- -- -- -- 0.478*** 0.100

Small*conv -- -- -- -- -2.181*** 0.387

Medium*conv -- -- -- -- 0.404** 0.199

No. of obs. 1,033,414 675,409 886,817

R-squared 0.511 0.159 0.268

Source: USDA, ERS hedonic regression model estimations based on 2006 Nielsen Homescan Panel 

data.
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United States Department of Agriculture

where Pit is the price paid by the i-th household in time t; MKTit represents 

a set of market factors such as income (a measure of neighborhood store 

and product quality), type of store, promotional offering, season, region, 

and urbanicity of purchase; PROit represents product attributes; and eit is 

the error term. Interaction terms between convenience store purchases and 

product attributes, as well as income, allow for the testing of additional price 

differentiations observed in convenience stores.

Results

Milk, RTE cereals, and bread are three of the most frequently purchased 

items at both convenience stores and grocery stores by Homescan panelists. 

The hedonic model is specifi ed in linear functional form so that estimated 

coeffi cients represent price premiums or discounts. The hedonic results 

are summarized in table 5.5. The R-squared is 51 percent for milk, 16 

percent for RTE cereals, and 27 percent for breads. These goodness-of-fi t 

measures are quite high for cross-sectional studies, implying that the data 

fi t the model reasonably well. “Grocery” is treated as the reference store 

in the model so that the estimated coeffi cient for “Convenience” measures 

the price difference between the two types of stores. Specifi cally, a positive 

(negative) coeffi cient for “Convenience” store indicates that consumers pay 

a higher (lower) price at a convenience store than at a grocery store. Income 

is included to capture store, product, and neighborhood quality attributes 

unobserved in the data. In particular, a household’s income is expected to be 

associated with the quality of shopping venue and product offering, which, in 

turn, would be refl ected in the price paid. 

Fluid milk 

TThe price of milk is expressed as cents per fl uid ounce (128 ounces in a 

gallon). The estimated constant term suggests an average price of 2.42 cents 

per ounce (or $3.10 per gallon) for whole milk in a gallon container sold 

in a grocery store in the Southern United States in the winter. The results 

suggest that the same milk is sold at a price 0.13 cents per ounce higher at 

a convenience store, or about 5 percent above the grocery store price. As 

expected, consumers pay a lower price at discount stores, such as Wal-Mart, 

at an average of 0.2 cents per ounce below the grocery store price. The 

estimated coeffi cient for “Income” (a measure of store and product quality) 

is signifi cant but small, indicating that the price of milk in a grocery store 

increases only slightly with income. Additional variation in price associated 

with income in a convenience store (measured by the interaction between 

“Income” and “Convenience”) is found to be insignifi cant.

Differences in milk prices between convenience and grocery stores vary by 

container size and fat content, as indicated by some signifi cant coeffi cients 

for the interaction terms between convenience store and milk type. Adding 

the estimated coeffi cient for the container size “Pint” (i.e., 4.21 cents) to the 

constant term of 2.42 cents helps determine the average price (cents/oz) of a 

pint of whole milk sold in a grocery store in the Southern United States in the 

winter (6.63 cents/oz or $1.06/pint). The same pint of milk is sold for 1.03 

cents more (0.13 + 0.90) at convenience stores, or about 16 percent more 

than in a grocery store. A half gallon of whole milk was priced at 3.40 cents/
76

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United States Department of Agriculture

oz (2.42 + 0.98) at grocery stores, but there was actually a price discount (0.3 

cents/oz) for such milk sold at convenience stores. 

More than a quarter (27 percent) of milk purchases at convenience stores 

was associated with a promotion (i.e., sale or coupon use), compared with 

18 percent of all purchases recorded elsewhere (table 5.4). When milk was 

purchased under a promotion with a discount, the discount averaged about 

20 percent below the regular price. This price discount of 20 percent is quite 

large, compared with the 5-percent price premium for convenience store milk 

over grocery store milk, suggesting that the use of a coupon or promotional 

shopping habits could effectively lower the price of milk purchased at a 

convenience store or grocery 

RTE cereals

RTE cereals were priced at an average of 22.87 cents/oz in a grocery store 

(in the Southern United States and in the winter), or about $4 per box in its 

average container size reported in table 5.4. Consumers paid 5.84 cents/oz 

(25 percent) more at convenience stores. As expected, a lower unit price is 

associated with larger packaging. The interaction between the packaging 

size and a convenience store purchase modeled by the variable “Size*conv” 

(-0.37) in combination with “Size” (-0.37) indicates that price of cereal 

relative to packaging size falls twice as fast in a convenience store (-0.74) as 

in a grocery store (-0.37), signifying that size has more infl uence on price in 

a convenience store. 

Like milk, RTE cereal in a grocery store increases in price with the affl uence 

of the neighborhood, as measured by household income. However, the 

interaction term between income and convenience store is negative and 

signifi cant (-0.29) and must be interpreted in conjunction with the income 

variable (0.33). This suggests less price variation in convenience stores 

relative to store and product quality, as measured by income. 

In general, whole-grain cereals command a higher price, although the price 

differential is quite small. A surprisingly large proportion (69 percent) 

of convenience store RTE cereals were purchased under a promotional 

discounted price, compared with the share reported for those purchased 

elsewhere (table 5.4). The average discount is 3.69 cents/oz, which is 16 

percent of the regular price. Clearly, purchasing items on sale or using 

a coupon can effectively offset the higher price that consumers face at 

convenience stores.

Bread

Bread in its largest 24-ounce size was priced at an average of 6.34 cents/

oz at grocery stores and about 0.62 cents (about 10 percent) more at 

convenience stores. When bread was purchased on sale or with a coupon, 

the average price dropped to 4.67 cents/oz (26 percent of the regular price). 

Prices of bread also varied greatly by package size, and unlike whole-grain 

RTE cereals, whole-grain bread commanded a large and signifi cant price 

premium, averaging 1.59 cents/oz (or 25 percent of the price of non-wholegrain breads). Like the case of milk, price differentials between convenience 

store bread and grocery store bread varied by package size. The estimated 
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Access to Affordable and Nutritious Food: Measuring and Understanding Food Deserts and Their Consequences 

United States Department of Agriculture

coeffi cients indicate that medium- and large-sized bread (constituting 76 

percent of convenience store purchases) was priced higher in a convenience 

store than in a grocery store. Yet, bread sold in a small package size was 

priced lower in a convenience store. 

As with RTE cereal, the variable “Income” was used as a measure of 

neighborhood store and product quality. The results indicate that the effect 

of income on grocery store prices (0.32) is about twice that of the effect 

on convenience store prices (0.32 – 0.15). This again points to less price 

variation across neighborhood store and product quality (income) for 

convenience store purchases relative to grocery store purchases. Higher 

household income increases the probability that a household will choose 

to shop at a specialty foodstore, which tend to maintain higher prices on 

average.

Discussion

Grocery stores generally stock a multitude of product offerings that present 

consumers with choices of brand, size, quality, and other product attributes. 

This results in greater price disparity for particular food items. Convenience 

stores have more limited intra-product choices. Therefore, consumers face 

a relatively constricted price range in convenience stores as compared with 

grocery stores. Access to a grocery store allows consumers to choose from a 

wider array of products, thereby allowing consumers to choose items whose 

prices fall within their budgets.

The analysis of price variation for similar goods across different store types 

shows that prices are higher, on average, at convenience stores than they 

are at grocery stores, and this fi nding is confi rmed in the literature (Broda 

et al., 2009). Relatively easy access to convenience stores and smaller food 

retailers in some neighborhoods may lead to higher prices for food for people 

who live in those neighborhoods. But this argument assumes that people 

who live in these neighborhoods do not shop at large stores or search for sale 

items. Broda et al. (2009) use 2005 Nielsen Homescan data to address the 

effects of access to different types of stores on overall shopping expenditures 

and prices paid by lower income consumers. The analysis does not directly 

consider access to stores or shopping patterns but instead focuses on 

differences across household income levels. 

Broda et al. (2009) show that across all income levels, 52-57 percent of all 

food purchases are made at grocery stores. Spending on food at convenience 

stores is a very small portion of shoppers’ food budgets, even for those at 

the lowest income levels. Those with the lowest incomes (from $5,000 to 

$11,999) spend 2 to 3 percent of their total food expenditures in convenience 

stores, while the highest income consumers (annual income over $100,000) 

spend only 0.7 percent of their total food expenditures at convenience stores. 

Low-and middle-income households (incomes between $5,000 and $49,999) 

spend 20-22 percent of their food dollars at supercenters, where prices are 

lower. Households with incomes over $70,000 spend 13-17 percent of their 

food dollars at these types of stores. Clearly, lower income consumers shop 

at outlets offering lower prices.
78

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United States Department of Agriculture

Examining prices paid for specifi c goods (through the Universal Product 

Code of the good purchased) across household income level, Broda et al. 

(2009) fi nd that while households with incomes below $8,000 per year may 

pay 0.5 to 1.3 percent more for their groceries than households earning 

slightly more, those earning between $8,000 and $30,000 pay the lowest 

prices for groceries. Households with the highest incomes, with earnings 

over $100,000, pay the greatest amount for groceries, between 2-3 percent 

higher than poorer households. These results suggest that the poor do not 

pay higher prices for food. 

One caveat to these results is that the Broda et al. (2009) study does not 

directly address access issues as it is only approximated by household income 

level. The study does not have information about the costs consumers face 

to get to food retail outlets, which could be greater for those who live in areas 

with limited access. 

Summary

For SNAP to meet its mission, it is essential that participants have access to 

foodstores offering good quality, variety, and reasonable prices without the 

participants encountering undue burden. As a whole, participants live close 

to an authorized retailer, which is often a supermarket or large grocery store. 

More than 90 percent of participating households spend at least some of their 

benefi ts in a supermarket, and close to 90 percent of all benefi ts are redeemed 

in supermarkets or large grocery stores. Food stamp recipients reported 

being largely satisfi ed with the stores in which they shop most frequently. 

Studies of SNAP participant access and shopping patterns reviewed here 

indicate that most SNAP participants have access to supermarkets and large 

grocery stores. These fi ndings, however, do not eliminate the possibility that 

access may be challenging for some participants and nonparticipants in some 

places. 

The analysis of food purchases suggests that SNAP participants who did not 

shop mainly at a supermarket purchased less noncanned fruit, noncanned 

vegetables, and milk than SNAP participants who shopped frequently at a 

supermarket. SNAP participants who did not shop at a supermarket also 

purchased less canned fruits and vegetables than others, but the differences 

are not statistically signifi cant. Overall, the results suggest that lack of access 

to a supermarket is associated with lower levels of expenditures on some 

foods that are important for healthy diets.

This analysis of food purchasing behavior, like the majority of studies on 

the topic, only shows associations between access and food purchases, not 

whether access differences actually cause differences in food purchasing. 

It is possible that those who do not have access to supermarkets or other 

sources of healthy foods would not buy these foods if their access was better. 

To determine a causal impact, longitudinal data or information that is related 

to access but not to food purchasing behavior would be needed. 

It appears that only two studies have used longitudinal data to try to 

determine differences in purchasing behavior over time as the degree of 

access changed (Wrigley et al., 2003; Cummins et al., 2005). These studies 

were conducted in the UK, and both considered changes in shopping 
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behavior and food intake when a new supermarket was opened in an 

underserved area.41 Results from Wrigley et al. (2003) showed that a sizable 

number of residents in both studies switched their main shopping source to 

the new store, more walked to and from the store than before, and fewer took 

buses, taxis, or someone else’s car due to the change in access. Cummins et 

al. (2005) also found that when access improved, many shoppers switched 

to a new store, more walked to the new store, and fewer traveled to the store 

using someone else’s car.

This study’s demand analysis does not consider the relative food prices 

of these groups of foods that survey participants face. Those with limited 

access could face higher prices of noncanned fruits and vegetables, which 

could impact their purchase behavior. The analysis attempted to incorporate 

prices and estimate a demand system, but the results were not reasonable, 

possibly due to the rather limited size of the sample. 

Another limitation of the analysis of food purchasing behavior is that the 

sample includes only participants of SNAP. Not all those who are eligible 

for the program choose to apply and receive benefi ts. Those who do, tend to 

be poorer and have lower incomes than those who do not. But it is possible 

that some poor people choose not to participate because they do not have 

good access to stores where they can redeem benefi ts or to stores where they 

want to buy foods. Thus, the analysis could underestimate the differences 

in access on food purchasing behavior by not including those who do not 

participate in SNAP because of access limitations.

Households with limited access to supermarkets may rely more on fast 

food or carryout restaurants to satisfy their needs for foods. It has been 

documented that foods from fast food restaurants are generally lower in 

nutritional quality (Lin et al., 1999). Therefore, a greater reliance on fast 

food could adversely affect the diet and health of those who have limited 

access to supermarkets. Future research should be conducted to study the 

effect of limited access to supermarkets on food purchases at commercial 

foodservice outlets. 

The results for estimating price differentials between grocery stores and 

convenience stores are consistent with a priori expectations that consumers 

pay more for food at a convenience store than at a grocery store. Likewise, 

neighborhood quality, as measured by household income, affects prices 

positively, but to a lesser magnitude for those purchases at a convenience 

store. An important fi nding, however, is that on-sale purchases and 

coupon use are frequently reported at convenience stores. The price 

discount afforded by on-sale purchases or coupon use is quite large and 

can compensate for the higher price registered at convenience stores. 

Frugal shopping habits can effectively overcome the price disadvantage at 

convenience stores. Of course, frugal shopping habits can also be effective 

when shopping at grocery stores. 

Analysis by Broda et al. (2009) is consistent with the idea that frugal 

shopping habits can overcome high prices. This study fi nds small differences 

in expenditures at different food retailers across income levels. It also 

fi nds that, in general, the poor do not pay more for food. The study fi nds 

that households earning between $8,000 and $30,000 per year pay the least 

41Findings on food intake changes 

from these studies are summarized in 

Chapter 4. 
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for groceries, while the poorest consumers, those with household incomes 

below $8,000, pay between 0.5 to 1.3 percent more for their groceries than 

households earning slightly more. 

With respect to price analyses, Homescan panelists may under-report some 

items purchased at a convenience store due to on-the-go consumption (e.g., 

fresh produce and ready-to-eat snacks). Prices reported by Homescan 

panelists are nationally representative, but the results may not apply to 

local areas with specifi c characteristics, such as areas affected by Hurricane 

Katrina. 

References

Andreyeva, T., D.M. Blumenthal, M.B. Schwartz, M.W. Long, and K.D. 

Brownell (2008). “Availability and Prices of Foods Across Stores and 

Neighborhoods: The Case of New Haven, Connecticut,” Health Affairs, 

September/October, 27(5): 1381-1388. 

Block, D., and J. Kouba (2008). “A Comparison of the Availability and 

Affordability of a Market Basket in Two Communities in the Chicago Area,” 

Public Health Nutrition, 9(7): 837-845. 

Broda, C.E. Leibtag, and D.E. Weinstein (2009). “The Role of Prices in 

Measuring the Poor’s Living Standard,” Journal of Economic Perspectives, 

23 (2): Spring, 000-000.

Chung, C., and S.L. Myers (1999). “Do the Poor Pay Moor for Food? An 

Analysis of Grocery Store Availability and Food Price Disparities,” Journal 

of Political Economy, 91 (3): 480-93.

Code of Federal Regulations. Title 7 - Agriculture, Chapter 2: Subchapter C. 

Part 278.

Cohen, B., J. Ohls, M. Andrews, M. Ponza, L. Moreno, A. Zambrowski, 

and R. Cohen (1999). “Food Stamp Participants’ Food Security and 

Nutrient Availability,” Final Report to the Food and Nutrition Service, U.S. 

Department of Agriculture, Mathematica Policy Research. 

Cole, N. (1997) Evaluation of the Expanded EBT Demonstration in 

Maryland: Patterns of Food Stamp and Cash Welfare Benefi t Redemption, 

Report submitted to U.S. Department of Agriculture, Food and Nutrition 

Service, by Abt Associates, Inc., February.

Cole, N., and Lee, E. (2005) Analysis of EBT Redemption Patterns: Methods 

and Detailed Tables, Report submitted to U.S. Department of Agriculture, 

Food and Nutrition Service, by Abt Associates, Inc., November.

Cole, N., D. Hoaglin, and J. Kirlin, J. (2001). National Survey of WIC 

Participants,Report submitted to U.S. Department of Agriculture, Food and 

Nutrition Service, by Abt Associates, Inc., June.
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Cummins, S., A. Findlay, M. Petticrew, and L. Sparks (2005). “Healthy 

Cities: The Impact of Food Retail-Led Regeneration on Food Access, Choice 

and Retail Structure,” Built Environment, Vol. 31(4): 288-301.

Hayes, L.R. (2000). “Are Prices Higher for the Poor in New York City?” 

Journal of Consumer Policy, 23 (2): 127-52.

Hendrickson, D., C. Smith, and N. Eikenberry (2006). “Fruit and Vegetable 

Access in Four Low-Income Food Desert Communities in Minnesota,” 

Agriculture and Human Values, 23:371-383. 

Kaufman, P.R., J.M. MacDonald, S.M. Lutz, and D.M. Smallwood (1997). 

Do the Poor Pay More for Food? Item Selection and Price Differences Affect 

Low-Income Household Food Costs, Agricultural Economic Report No. 759, 

U.S. Department of Agriculture, Economic Research Service.

Koralek, R.D. (1996). Conference on Access to Food September 18 and 19, 

1995: Report of the Proceedings, U.S. Department of Agriculture, Food and 

Nutrition Service, February.

Lancaster, K. (1966). “A New Approach to Consumer Demand Theory,” 

Journal of Political Science 74 (April): 132-157.

Latham, J., and T. Moffat (2007). “Determinants of Variation in Food Dost 

and Availability in Two Socioeconomically Contrasting Neighbourhoods of 

Hamilton, Ontario, CA,” Health and Place, 13: 273-287. 

Lin, B. (2005). “Diet Quality Usually Varies by Income Status,” Amber 

Waves, Vol. 3, Issue 4, September. 

Mantovani, R.E., L. Daft, T.F. Macaluso, J. Welsh, and K. Hoffman (1997). 

Authorized Food Retailer Characteristics Study (Technical Report IV): 

Authorized Food Retailers’ Characteristics and Access Study, Report 

submitted to U.S. Department of Agriculture, Food and Nutrition Service, by 

Macro International, Inc.

Mantovani, R.E., and J. Welsh (1996). Authorized Food Retailer 

Characteristics Study (Technical Report III): Geographic Analysis of 

Retailer Access, report submitted to U.S. Department of Agriculture, Food 

and Nutrition Service, by Macro International, Inc.

Martinez, Steve, and Phil Kaufman (2008). “Twenty Years of Competition 

Reshape the U.S. Marketing System,” Amber Waves, Vol. 6, Issue 2, April. 

Ohls, J.C., M. Ponza, L. Moreno, A. Zambrowski, A., and R. Cohen (1999). 

Food Stamp Participants’ Access to Food Retailers, Report submitted to 

U.S. Department of Agriculture, Food and Nutrition Service, by Mathematica 

Policy Research, Inc.

Olander, C., E. Jones, and S. Carlson (2006). An Analysis of Food Stamp 

Redemption Patterns, U.S. Department of Agriculture, Food and Nutrition 

Service.
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Pearson T, J. Russell, M.J. Campbell, and M.E. Barker (2005). “Do “Food 

Deserts” Infl uence Fruit and Vegetable Consumption?--A Cross-Sectional 

Study,” Appetite 45(2): 195-7.

Rose, Donald and Rickelle Richards (2004). “Food Store Access and 

Household Fruit and Vegetable Use Among Participants in the US Food 

Stamp Program,” Public Health Nutrition 7(8): 1081-1088.

Talukdar, D. (2008). “Cost of Being Poor: Retail Price and Consumer Price 

Search Differences Across Inner-City and Suburban Neighborhoods,” 

Journal of Consumer Research, 35 (3): 457-71.

Tobin J. (1958). “Estimation of Relationships for Limited Dependent 

Variables,” Econometrica 26(1): 24-36.

U.S. Department of Agriculture (1975). How to Buy Food for Economy 

and Quality: Recommendations from the United States Department of 

Agriculture. New York: Courier Dover Publications.

Wrigley, N., D. Warm, and B. Margetts (2003). “Deprivation, Diet and 

Food-Retail Access: Findings From the Leeds ‘Food Deserts’ Study,” 

Environment and Planning A, 35: 151-188. 

Zenk, Shannon N. et al. (2005). “Fruit and Vegetable Intake in African 

Americans: Income and Store Characteristics,” American Journal of 

Preventive Medicine 29(1): 1-9.
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CHAPTER 6

The Economics of Supermarket 

and Grocery Store Location

There has been little consideration of the economics behind the variation 

in food access across areas. It is important to understand the economic 

conditions that may contribute to food deserts—that is, the costs that food 

retail businesses face and the choices available to consumers who want to 

buy foods. This chapter outlines an economic framework for considering 

food access and why some areas may have limited access. This framework 

considers the consumer and demand factors, business and supply factors, 

and the market conditions that interact to create differences in the food retail 

environment across areas and subpopulations. The chapter then provides 

a broad overview of the history of supermarket development that captures 

how food retailers have responded to different demand, supply, and market 

conditions. ERS, through the National Poverty Center at the University of 

Michigan, commissioned a paper to consider the economic framework of food 

deserts (Bitler and Haider, 2009). This chapter draws heavily from that paper. 

Consumer Behavior and Demand

Factors of Food Access

Food in general is considered to be a normal good—that is, demand for it 

increases as income increases. Given that food is a normal good, it should 

not be surprising that high-income areas have relatively more foodstores 

and restaurants than low-income areas. Price is also a major determinant 

of food demand. The higher the price of a food, the lower the quantity 

demanded. On the other hand, the higher the price of a substitute food, the 

higher demand will be for that food item. Given the budget constraints of 

low-income consumers and the prices of some specifi c foods, low-income 

consumers may substitute higher priced goods with lower priced goods (e.g., 

hamburger for steak or canned fruits for fresh fruits). 

Travel costs and time costs of acquiring food as well as the time costs of 

preparing foods are also likely to affect demand for particular foods. The 

convenience of eating restaurant food or a prepared meal versus eating at 

home may be an important part of demand for food. Even for foods prepared 

at home, there may be relatively greater time costs than those for prepared 

foods or takeout foods. Consumers may value the convenience of a fast food 

or prepared meal more because it does not require spending much time to 

prepare.

Demand for some foods could be affected if individuals do not know which 

foods are healthy or unhealthy or if individuals do not know how to use or 

prepare some foods. Tastes for different foods, or preferences as economists 

call them, may also drive demand and store location. For example, 

foodstores that sell many Asian or Hispanic food products are often located 

in areas where there are high concentrations of people of Asian or Hispanic 

descent, presumably because demand for those foods is high in those areas.42 

42This is a bit of a chicken and egg 

problem—people of a specifi c ethnic 

decent may choose to live in an area 

where they have relatively easy access 

to the foods they wish to consume. We 

discuss this further later in this chapter. 
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Consumers choose where to live and thus, choose the food environment 

available to them. Clearly, the choices of the poor, especially those living in 

extreme poverty, are constrained by their budgets. But even among the poor, 

there are choices in where to reside. This choice is an aspect of food access 

that is not well considered in the literature. Not accounting for residential 

choice limits the ability to assess whether limited access to affordable and 

nutritious food affects food shopping, diet, and health outcomes. If two 

people are equal in all other manners, the one who places a high value on 

access to a large supermarket is probably more likely to live in an area 

where those things are available relative to another who values food-related 

amenities less and perhaps other amenities more.

Food Retailer Behavior and Supply

Factors of Food Access

In general, supply is driven by the costs of input goods—in this case, the 

land, materials, machines, and labor needed to build and operate a grocery 

store and the costs of products to stock the shelves. As these costs increase, 

supply decreases. Economic theory considers fi xed costs (costs that do not 

change as the quantity of output changes, such as the land and materials 

needed to build a store) and variable costs (the costs of operating that change 

as the quantity of output changes). Differences in fi xed or variable costs 

across areas could impact the types of stores and products available. 

Food retailers that face higher fi xed costs will either need to charge a higher 

price for goods sold or limit the range of products sold (Bitler and Haider, 

2009). Fixed costs faced by food retailers probably vary greatly by the 

type of area. In dense urban areas, land prices may be higher and zoning 

requirements of local governments may be more cumbersome and costly 

to meet relative to less dense suburban and rural areas. Consequently, it is 

likely that the fi xed costs in urban areas are greater. Further, the volume 

of sales in a store could affect how much fi xed costs matter to fi rms. For 

example, stores that serve a higher volume of consumers (either in the 

number of consumers or in terms of the amount they purchase) will be able 

to spread fi xed costs over more people and prices may be lower than in 

stores that serve lower volumes of consumers. This could explain why some 

rural, less populated areas do not have stores or why some foods may not be 

stocked in low-volume stores, especially foods that perish. 

If fi xed costs drive store location decisions or the types of products sold, 

then the fi xed costs should be higher in low-income areas if these areas are 

underserved. Within urban areas, for example, low-income areas are likely 

to have lower land prices than high-income areas. In this case, it would be 

surprising to fi nd that land costs leave some poor areas without access. It is 

possible that zoning requirements or security costs are higher in poor areas 

and thus more important for stores to locate in those areas (regardless of size) 

(Bitler and Haider, 2009). 

In the United States, supermarket competition within a market region may 

be characterized by a small number of chain supermarkets accounting for 

a large share of the market sales and a large number of smaller “fringe” 

stores, which account for lower total market sales (Ellickson 2005 and 

2007; Smith 2004 and 2006). Ellickson (2005 and 2007) argues this is 
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consistent with an endogenous fi xed-cost economic framework, as when 

large food retailers incur (take on) fi xed costs to keep competitors from 

entering the market. Such fi xed costs could come in the form of offering a 

larger variety of products (increasing shelf space and improving distribution 

systems), carrying higher quality products (e.g., organic products), or paying 

advertising costs to differentiate from competitors. The smaller fringe stores 

do not incur these costs but survive by being more densely distributed (e.g., 

small corner stores that offer convenience over variety or quality) and/or 

by gaining business from consumers who may not value quality or variety 

as much, or who perhaps cannot afford to. Ellickson (2005 and 2007) fi nds 

evidence that this model may describe the supermarket landscape across the 

United States. 

One major variable cost for operating a foodstore is the cost of hiring 

workers. Poor areas are often areas with relatively lower wages, which 

should reduce the costs to operate a store, all else equal. King et al., (2004) 

fi nd that stores serving low-income shoppers (stores with greater shares 

of revenue from SNAP participants) have signifi cantly lower payroll costs 

as a percentage of sales than stores that do not serve as many low-income 

shoppers. 

King et al., (2004) fi nd that operating cost structures of stores with higher 

portions of total revenues from SNAP redemptions are different than those 

of other stores. For example, the stores had lower labor costs but also lower 

sales margins, and they were more likely to be supplied by wholesalers 

than to be part of a large chain with its own supply chain. However, overall 

operating costs for these stores were not different than other stores.43 

Economies of scale, economies of scope, and economies of agglomeration 

may also explain why product availability is differentially concentrated 

across areas or stores (Bitler and Haider, 2009). Economies of scale, which 

is when the costs of operating a store decrease as store size increases, and 

economies of scope, which is when the costs decrease as more product 

variety increases, suggests that larger stores that offer greater variety can do 

so and offer lower prices. Both factors may account for the ability of larger 

stores to survive more easily than smaller stores. For example, if wholesale 

costs to stock store shelves are lower for larger stores because they can buy 

in bulk and buy a greater variety of products more cheaply, larger stores may 

be able to lower prices and smaller stores may not be able to compete. This 

does not explain, however, why larger stores do not locate in low-income 

areas. 

In a competitive marketplace, fi rms selling the same products may have a 

disincentive to locate near each other. But this may not always be the case. 

Economies of agglomeration, where the costs of operating a store are lower 

when a store is located near other stores (e.g., because of roads or distribution 

systems), may explain why stores are concentrated in some areas and not in 

others. 

The Market

The market is where consumers and suppliers meet, their interactions 

determining the prices paid and the products bought and sold. The most 

43Fixed costs were not considered in 

this study. 
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basic economic models assume that markets are perfectly competitive and 

that food retailers and consumers do not have control over price. In such a 

case, it is possible that if demand is suffi ciently low, retailers would not be 

willing to supply products because they could not sell them or could only sell 

them at higher prices. 

It is possible that food retailers actually have some market power, especially 

in settings where there are few competitors. If so, then retailers would 

have an incentive to increase price and restrict quantities to increase profi ts. 

Supply-side conditions, such as high fi xed costs or economies of scale, could 

lead to food retailers having market power (Bitler and Haider, 2009). 

It is possible that the supply-and-demand conditions are such that the market 

does not operate effi ciently—that some intervention could be implemented 

that would improve circumstances for both retailers and consumers. A 

market failure could arise, if for example, there is poor information on the 

part of suppliers about the foods that consumers in an area will demand (for 

a given price). For example, if the models that supermarkets use to predict 

sales in an area do not adequately capture potential sales in low-income areas 

as some have suggested, better information on the potential sales could lead 

to more stores or products offered in those areas (Drake, 2009; Policy Link, 

2007). 

Supermarket “redlining,” which has been suggested as a possible reason 

some low-income or minority areas lack larger stores, could also constitute 

a market failure. Economic theory suggests that if markets are competitive, 

a retail fi rm that does not discriminate should have an incentive to locate in 

an area that is, except for its minority status, otherwise the same as any other 

area served by a supermarket. That is, the market would tend to reward fi rms 

that locate in otherwise underserved areas because there is less competition, 

at least in the short run. In the long run, nondiscriminating fi rms will enter 

the area until no more fi rms can be supported by the population. If fi rms lack 

good information on the true purchasing power or demand for food in areas 

with concentrations of racial or ethnic minorities, then this market failure is 

one potential reason why fi rms do not locate in these areas. 

 It is also important to note that housing market discrimination could limit 

the ability of minorities to move to areas that may have better access to food. 

Legal discrimination in housing and lending markets (which existed until 

the Fair Housing Act of 1968) and illegal discrimination since then (Turner 

and Skidmore, 1999; Turner et al., 2002a; Turner et al., 2002b; Wyly and 

Holloway, 1999) could potentially limit residential choice of affected groups, 

which could contribute to disparities in supermarket access. 

Economic theory suggests that either supply conditions or demand conditions 

could lead to variation across areas in store locations, the types of stores 

available, and the products and prices offered within stores. Unfortunately 

it is diffi cult to determine how much either supply conditions or demand 

conditions affect store location and the types of foods that are offered 

because they are determined simultaneously. Monitoring price could help. 

For example, if the price of some nutritious foods in underserved areas is 

high but the prices of other nutritious foods are cheaper, it is likely that 

demand-side factors are driving differences in availability (Bitler and Haider, 
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2009). Likewise, understanding more about cost differences of suppliers 

in different areas could also help determine if supply side factors are to 

blame. Finally, it may be also be useful to consider the markets for other 

goods and services in low-income areas. Many areas that are underserved 

by supermarkets may also be underserved by other businesses, such as banks 

or health care services. Understanding whether such businesses face similar 

costs or similar demand for products as food retailers might help explain 

the lack of supermarkets and other businesses in general (Bitler and Haider, 

2009). 

Broad Trends in the Supermarket and 

Food Retail Environments

Very broadly, grocery retail has gone through three major changes 

(innovations) in the past century: the rise of chain grocery stores over 

independently owned stores, the rise of supermarkets that offered an 

increased number and variety of products; and the rise of supercenters that 

continued the trend to even larger stores offering more and more products 

(Ellickson, 2005; Tedlow, 1990). In each of these cases, suppliers found 

ways to decrease the costs of production in order to bring lower prices to 

consumers. Chain grocery stores used integration of manufacturing and 

wholesaling to cut out middlemen and offer lower prices (Ellickson, 2005). 

The middle of the 20th century saw a rise in automobile use, interstate 

highways, and movement of residences and businesses to the suburbs where 

large tracts of land were available for relatively lower costs. Supermarkets 

grew in size and carried an increasing variety of products (Tedlow, 1990). 

The advent of computerized logistics and inventory systems integrated 

with the supermarkets themselves occurred during the 1980s and 1990s 

and provided the catalyst for the most recent trend toward supercenters. 

Ellickson (2005) shares evidence of this growth—in 1980, the average 

store carried 14,145 products; by 2004, the average store carried over 

30,000 products. Reliance on their own distribution and inventory systems 

along with larger store sizes allowed supercenters to charge lower prices. 

Wal-Mart, which is now the Nation’s largest retailer of grocery products, is 

one model of this type of format. 

Competition spurred by this latest trend to supercenters has contributed to 

other developments among more traditional supermarkets. Some retailers 

target middle and higher income consumers—often offering specialty 

products or primarily organic products (e.g., Whole Foods) (Drake, 2009). 

Other retailers have offered their own line of premium store brands and 

organic store brands to tap into the more health-conscious consumers (e.g., 

Kroger’s Naturally Preferred or Giant Food’s Nature’s Promise lines) 

(Martinez and Kaufman, 2008). 

Hard discount stores, which target bargain and low-income shoppers, have 

also developed. These stores are typically smaller than other supermarkets 

(typically between 10,000 to 14,000 square feet, compared with an overall 

average store size of 48,000 square feet), offer more private label brands and 

fewer national brands, and offer fewer product size options. Many of these 

stores operate in underserved or low-income areas. Examples include: 
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• Save-a-Lot (a subsidiary of SuperValu, Inc.) operates 1,200 stores, 

including 4 in Prince Georges County, Maryland, and 1 store in Coahoma 

County, Mississippi, a persistently poor county in the Mississippi Delta 

region. 

• ALDI stores (a German discount retailer which opened its fi rst U.S. 

stores in Southeastern Iowa) have 1,000 stores in 29 States. 

• Fresh and Easy Neighborhood Markets (a subsidiary of Tesco, the UK’s 

largest retailer) opened two stores last year in low-income areas of Los 

Angeles (Compton and Eagle Rock). 

• Food4Less (a subsidiary of the Kroger Co.) recently opened a store in 

Englewood, an impoverished neighborhood on the southwest side of 

Chicago. 

Food retail has also expanded to retailers that have not focused on food 

in the past. This “channel blurring” has occurred among pharmacies, 

which typically carry snack and convenience foods and increasingly carry 

refrigerated and frozen items, and among dollar stores (such as Family Dollar 

and Dollar Store) (Martinez and Kaufman, 2008; Sharkey and Horel, 2009). 

Warehouse clubs (e.g., Costco and Sam’s Club), which offer a limited 

variety of food products and larger or bulk sizes, have also expanded into 

food markets, serving primarily small businesses and middle-upper-income 

consumers (Martinez and Kaufman, 2008). 

Finally, grocery stores and supermarkets also compete with restaurants and 

other sources of food away from home. Almost half of all food spending 

in the United States is for food away from home (48.9 percent in 2006) 

(Martinez and Kaufman, 2008).

Given these competitive forces, why is it that some areas lack access to a 

supermarket or large grocery store? The range of reasons offered include 

some that are identifi ed in this chapter and others that have been summarized 

elsewhere..44 Many apply specifi cally to urban areas, but some apply to rural 

areas as well. Examples include the following: 

• Zoning and regulatory approval processes

• The need to secure land sites large enough for stores

• Environmental remediation and/or demolition of current structures

• Higher construction and operating (e.g., rent, real estate taxes, security 

costs) costs in urban areas

• Lower access (to customers and to distribution chains), visibility, or 

traffi c fl ow and less space for parking in urban areas relative to suburban 

areas

• Local politics, where offi cials and groups may have competing goals for 

development

Despite these potential hurdles, large, high-density populations in 

underserved urban areas and less competition are two factors that may make 

44See Drake, 2009; Policy Link, 

2008; Food Marketing Institute, 1998.
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underserved areas attractive for food retail development (Food Marketing 

Institute, 1998). 

Summary

Economic theory can help explain why some areas may have limited access. 

Consumer behavior and demand-side issues, such as lack of knowledge of 

the nutritional benefi ts of foods or food retailer behavior, and supply-side 

issues, such as different fi xed costs for developing a store in an underserved 

area, may explain variation across areas in the types of foods offered and 

the stores that offer them. Further research is needed to determine which 

factors may be driving differences in access. This research could explore 

how differences in fi xed and operating costs of different areas may account 

for variation in access to different types of stores or different products 

within stores. Research could also consider how consumer knowledge and 

preferences and the time cost tradeoffs affect consumer decisions of which 

foods to eat and whether to make or to buy prepared foods or restaurant 

meals. Research on price variation at the local level and spatial demand 

models could also be used to help determine which factors contribute to 

differences in access to food retailers. 

References

Bitler, Marianne, and Steven J. Haider (2009). “An Economic View of Food 

Deserts in the United States,” National Poverty Center Working Paper. http://

www.npc.umich.edu/news/events/food-access/index.php (Accessed 3/30/09). 

Drake, W. (2009). “Food Retailer Site Location Motivations, Methodologies 

and Behaviors,” Presentation at the IOM/NRC Workshop on the Public 

Health Effects of Food Deserts. January 27, 2009, Washington, DC.

Ellickson, Paul B. (2007) “Quality Competition in Retailing: A Structural 

Analysis,” International Journal of Industrial Organization 24(3), 521-540. 

Ellickson, Paul B. (2005). “Does Sutton Apply to Supermarkets?” RAND 

Journal of Economics. 38(1), 43-59. 

Food Marketing Institute (1998). Urban Supermarkets, Food Marketing 

Institute. 

King, Robert P., Ephraim S. Leibtag, and Ajay S. Behl (2004). “Supermarket 

Characteristics and Operating Costs in Low-Income Areas,” U.S. Department 

of Agriculture, Economic Research Service, Agricultural Economic Report 

No. 839. 

Martinez, Steve, and Phil Kaufman (2008). “Twenty Years of Competition 

Reshape the U.S. Marketing System,” Amber Waves, Vol. 6, Issue 2, April.

Neckerman, K.M., M. Bader, M. Purciel, P. Yousefzadeh (2009). 

“Measuring Food Access in Urban Areas,” National Poverty Center Working 

Paper, http://www.npc.umich.edu/news/events/food-access/index.php
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Policy Link, and LISC Bay Area (2007). Grocery Store Attraction 

Strategies, http://www.policylink.org/documents/groceryattraction_fi nal.pdf 

Rose, D., J.N. Bodor, C.M. Swalm, J.C. Rice, T.A. Farley, and P.L. 

Hutchinson (2009). “Deserts in New Orleans? Illustrations of Urban Food 

Access and Implications for Policy,” National Poverty Center Working 

Paper, http://www.npc.umich.edu/news/events/food-access/index.php.

Sharkey, J.R., and S. Horel (2009). “Characteristics of Potential Spatial 

Access to a Variety of Fruits and Vegetables in a Large Rural Area,” 

National Poverty Center Working Paper, http://www.npc.umich.edu/news/

events/food-access/index.php 

Smith, H. (2006). “Store Characteristics and Retail Oligopoly,” Rand 

Journal of Economics, 37(2): 416-430. 

Smith, H. (2004). “Supermarket Choice and Supermarket Competition in 

Market Equilibrium,” 71(1): 235-263. 

Sparks, A., N. Bania, L. Leete (2009). “Finding Food Deserts: Methodology 

and Measurement of Food Access in Portland, Oregon,” National Poverty 

Center Working Paper, http://www.npc.umich.edu/news/events/food-access/

index.php 

Tedlow, R.S. (1990). New and Improved: The Story of Mass Marketing in 

America, Cambridge, MA: Harvard Business School Press. 

Turner, M.A., F. Frieberg, E. Godfrey, C. Herbig, D.K. Levy, and R.R. Smith 

(2002a). All Other Things Being Equal: A Paired Testing Study of Mortgage 

Lending Institutions, U.S. Department of Housing and Urban Development. 

Turner, M.A. and F. Skidmore (1999). Mortgage Lending Discrimination: A 

Review of Existing Evidence, Urban Institute Monograph Series on Race and 

Discrimination, Urban Institute. 

Turner, M.A., S.L. Ross, G.C. Galster, and J. Yinger (2002b). 

Discrimination in Metropolitan Housing Markets: National Results from 

Phase I of HDS2000, U.S. Department of Housing and Urban Development. 

Wyly, E.K., and S.R. Holloway (1999). “The New Color of Money: 

Neighborhood Lending Patterns in Atlanta Revisited,” Housing Facts and 

Findings 1(2): 1-11. 
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CHAPTER 7

Community Food Projects

Nationwide analyses look for solutions that address large segments of 

the population. Community food projects (CFP) are projects in which 

low-income communities develop their food systems. CFP are by nature 

small and comprehensive within geographical places, with deep roots and a 

broad array of impacts on individual communities. The Community Food 

Projects Competitive Grant Program (CFPCGP) at USDA’s Cooperative 

State Research, Education and Extension Service funds innovative 

demonstration food projects that lead to food system changes unique to the 

communities they serve. These small projects can reduce the negative impact 

of areas with limited access to affordable and nutritious food, thus making 

community the appropriate unit of analysis.

Community Food Security

The CFPCGP was designed to address issues of “community food security.” 

Community food security incorporates the participation of local communities 

in developing innovative approaches that foster local solutions for feeding 

low-income families. This strategy encourages a greater role for the 

entire food system, including local agriculture, and represents a proactive 

approach to fi ghting hunger, economic and social justice, and environmental 

stewardship. 

Unlike the charity model, which provides emergency food as a short-term 

solution, the community food security concept addresses the long-term need 

of communities to obtain a safe, culturally acceptable, nutritionally adequate 

diet through a sustainable food system that maximizes community selfreliance and social justice (Hamm and Bellows, 2003). Community food 

security projects also address the broader well-being of the community, that 

is, its economic, social, and environmental issues.

Legislative authority

The CFPCGP was established through Authority Section 25 of the Food 

Stamp Act of 1977, as amended in 1996 and by Section 4125 of the Farm 

Security and Rural Investment Act of 2002 (Pub. L. No. 107-171) (7 U.S.C. 

2034), and further amended by Section 4402 of the Food, Conservation and 

Energy Act of 2008 and the Food and Nutrition Act of 2008 (Public Law No. 

110-234) (7 U.S.C. 2034). This law authorized a program of Federal grants 

to support the development of community food projects at a funding level of 

$5 million annually through FY 2012. 

Community food projects competitive grant program

Food is a very basic need. Seeing this need, community leaders all over the 

country have begun to organize their communities around food access. This 

is especially true in low-income communities where food sources often are 

most scarce. The primary goals of the CFPCGP are to:
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• Meet the food needs of low-income individuals; 

• Increase the food self-reliance of low-income communities; 

• Promote comprehensive responses to local food, farm, and nutrition 

issues; and 

• Meet specifi c State, local, or neighborhood food and agricultural needs. 

Projects can address needs relating to infrastructure improvement and 

development, planning for long-term solutions, or the creation of innovative 

marketing activities that mutually benefi t agricultural producers and 

low-income consumers

Current CFPCGP status

CFPCGP was funded for $1 million in 1996, $2.5 million for 1997-2001, and 

$5 million beginning in 2002 (current legislation). Since 1996, 290 projects 

have been funded in 48 States. Currently, three types of projects are funded 

each year, each type for 1 to 3 years:

• Planning projects, funded at $10,000 to $25,000, provide seed money for 

communities beginning to address their community food security issues. 

Planning projects are a separate type of project started in 2006 and 

represent 20 to 30 percent of the funded projects, but only 3 to 5 percent 

of the funding.

• Training and technical assistance projects, averaging $100,000 to 

$300,000, assist organizations in applying for community-based or 

planning projects and with running grants once awarded. T&TA projects 

represent about 15 to 20 percent of the funded projects and approximately 

10 percent of the funding.

• The community-based food projects are funded at $10,000 to $300,000. 

CFP projects represent about 50 to 60 percent of the projects funded and 

85 percent of the funds.

The community food projects

Because the community food projects are designed to solve local problems, 

they vary considerably based on local needs. Their specifi city is both a major 

strength and a challenge for evaluating them at a national level—and for 

describing how they contribute to addressing issues within areas with limited 

access to affordable and nutritious food. 

The following are examples of the types of issues that are addressed within 

CFP. These were extracted by Pothukuchi (2007) in a study of projects’ 

self-reported evaluation of performance on goals and objectives. The study 

refl ects summary data of grantee reports from 42 CFPs funded between 1999 

and 2003.

Issues addressed in Community Food Projects 

• Healthy food availability: Increase the availability of healthy, 

locally produced foods, especially in impoverished and underserved 
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neighborhoods, through food assistance programs, backyard and 

community gardens, grocery stores, farmers’ markets, Community 

Supported Agriculture shares, food buying clubs, and other resources.

• Healthy diets: Encourage the adoption of healthy diets by providing 

culturally and age-appropriate training and experiences for youth and 

adults in food production, preparation, and nutrition.

• Nutrition program participation: Enroll eligible residents in 

government nutrition programs such as SNAP, WIC (Special 

Supplemental Nutrition Program for Women, Infants, and Children), and 

the Farmers’ Market Nutrition Programs.

• Local food marketing: Increase local markets for small- and familyscale farms, including direct marketing and purchases by local 

institutions and businesses.

• Sustainable agriculture: Support agricultural practices that protect 

air, water, soil, and habitats; promote biodiversity; reduce energy use; 

promote reuse and recycling; and treat animals humanely.

• Food-related entrepreneurship: Support on- and off-farm value-added 

and processing enterprises, especially smaller operations and those 

owned by women and minorities.

Table 7.1

Community Food Projects Competitive Grant Program Activities, 2006

Percent

Youth/school gardening or agricultural project 81

Promoting local food purchases 73

Food access and outreach 57

Entrepreneurial food and agriculture 53

Community food assessment 47

Farm to cafeteria 47

Farmers’ market 47

Food policy council/network 47

Job skills training 47

Training and technical assistance 47

Community gardens 43

Community-supported agriculture 43

Micro-enterprise/Entrepreneur skill training 43

Emergency food collection and distribution 37

Farm/Grower cooperative 37

Restoration of traditional foods/agriculture 27

Community or incubator kitchen/Value-added 

 Production/Processing 26

Immigrant/Migrant farm project 16

Food-buying cooperative 14

Source: USDA, CSREES calculations based on Abi-nader, 2007. 
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• Farm worker conditions: Promote safe and fair working conditions 

for farmers, farm workers, and other food workers, such as those in 

processing plants and wholesale and retail operations.

• Food heritages: Honor and celebrate diverse food cultures and traditions 

in the community.

• Local food system awareness: Develop greater awareness and 

appreciation among residents of the value of local foods and food 

heritages to encourage more locally based eating.

• Integration of food in community processes: Systematically integrate 

food system issues into community and regional planning and other 

community institutions and processes to promote public health, economic 

vitality, social equity, and ecological sustainability.

• Food system participatory planning: Engage community residents and 

organizations in collaboratively assessing food needs, and devising and 

implementing actions to meet needs.

• Food democracy: Increase residents’ awareness of and voice in foodrelated decisions at different levels of government.

The goal of addressing these issues is to bring communities to a higher 

level awareness of their community food system so that awareness leads to 

action, defi cits and assets are assessed,; resources are examined, and potential 

solutions to problems are tried and tested. Community involvement can be a 

potent force in overcoming food access issues. 

One way to see how these issues are addressed within CFP is to look at the 

national level output reports. The CFPCGP works with grantees to develop 

and implement a program-wide evaluation. Currently, grantees voluntarily 

report common activities (Common Output Tracking Form) across their 

projects. The latest analysis from 2006 enumerated 19 project-type activities 

reported by 51 of a possible 65 projects (table 7.1). The projects are 

comprehensive and have many component activities. The percentages in the 

table refer to the share of projects that report having managed or participated 

in the activity. It is hoped that the combination of these activities leads to 

systems change within a community and that the multi-pronged approach to 

getting more food into the community increases food security for the long 

term. The net sum of these activities is to reduce the impact of limited access 

to food. Here are some examples of how the combinations of different 

activities have been implemented: 

“This kind of systems thinking guides the CFPCGP and is evident 

in the projects that have received funds. In places as different as 

Lubbock, Texas and Green Bay, Wisconsin, the CFP has played 

a key role in building comprehensive approaches to multiple 

problems. The South Plains Food Bank of Lubbock uses its 5 1⁄2 

acre farm to produce food for the food bank. The farm also serves 

as a demonstration site for sustainable farming practice, a youth 

training and job site, and a community–supported agriculture 

facility. In Green Bay, the Brown County Task Force on Hunger 

identifi ed the region’s large Hmong population as the group most 
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Examples of Community Food Projects by Type of Project

Food Assessments

Fresno Fresh Access Project, Fresno Metropolitan Ministry, Fresno, CA, 

www.fresnometroministry.org

Community Tradition, Foods, and Future, Legacy Cultural Learning Center, 

Muskogee, OK, www.legacycultural.org/artwellness

Food Policy Councils

Food Access and Food Justice in New Orleans: Rising above the Waterline,

New Orleans Food and Farm Network, www.noffn.org

Educating State Legislative Leaders and CFP Grantees about Policy Options 

that Support Community Food Systems, National Conference of State 

Legislatures, www.ncsl.org

Youth Programs

Healthy Harvests Initiative: Building Boston’s Food Security from the Ground Up, 

The Food Project, Lincoln, MA, www.thefoodproject.org

Food for Life, Youth Farm and Market Project, Minneapolis, MN, 

www.youthfarm.net

Entrepreneurial Food and Agriculture Activities

Appalachian FoodNet Project and Rural Food Centers Project, Appalachian 

Center for Economic Networks, Athens, OH, www.acenetworks.org

Building an Integrated Sustainable Food System, Appalachian Sustainable 

Development, Abington, VA, www.apsusdev.org

Urban Agriculture

Integrated Development Through Urban Agriculture, Nuestras Raices, Holyoke, 

MA, www.nuestras-raices.org

Neighborhood Urban Agriculture Coalition, Greensgrow, Philadelphia, PA, 

www.greensgrow.org

Rural Producers

Tribal Fish Market Connection Project, Ecotrust and Columbia River Inter-Tribal 

Fish Commission, Portland, OR, www.ecotrust.org

Patchwork Family Farms: Value-added Processing for Community Food 

Security, Missouri Rural Crisis Center, Columbia, MO, 

www.inmotionmagazine.com/rural.html

Community Gardens

Urban Detroit Agriculture and Education Project, Capuchin Soup Kitchen, 

Detroit, MI, www.cskdetroit.org.

For detailed descriptions of these projects, see appendix D. 
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at risk for food insecurity. The Hmong benefi t from small business 

and enterprise mentoring that allows them to develop farm- and 

food-related micro-businesses. Self-suffi ciency and self-help 

are putting people on the road to food security.” (Healthy Food 

Healthy Communities, 2006). 

Examples of community food projects

There are many types of CFPs, including food assessments, food policy 

councils, youth projects, entrepreneurial food and agriculture activities, urban 

agriculture, rural producers, and community garden projects. This section 

describes the general goals and structures of these types of CFPs (see box, 

“Examples of Community Food Projects by Type of Project”). Detailed 

descriptions of these projects are given in appendix D. 

Food assessments 

A community food assessment (CFA) is a way for a community to identify 

both its challenges and its resources around food and to use food as a tool 

for community development. Of 51 projects reporting on the COTF in 2006, 

24 percent had conducted CFAs. An assessment of the local food system 

is a way to bring the whole community together around a single issue that 

matters to everyone—food. A CFA can motivate people to make change 

to improve food access. It serves the goals of the CFPCGP by involving 

low-income community members in becoming aware of and analyzing the 

food system and by making community members part of the solution. Two 

food assessments funded by CFPCGP are listed in the box and detailed in 

appendix D. 

Food policy councils

Food policy councils (FPCs) may take many forms but typically are 

commissioned by State or local government, with participation from 

diverse stakeholders engaged in food and agriculture. They foster a more 

comprehensive and coordinated approach to addressing food system issues 

by bringing together stakeholders to develop and implement solutions. FPCs 

play various roles, including educating offi cials and the public, making 

policy recommendations, improving coordination between existing programs, 

and starting new programs. FPCs help identify ways that governments can 

mobilize existing resources or shape policy to improve the food system 

(Winne, 2008). 

FPCs have sometimes been the focus of applicants who see the need to 

address policy issues early on in their community food work. Other grantees 

have found that they need an FPC to address barriers confronted during 

the operation of their project. Projects have often run into local ordinances 

or legislative barriers and have formed formal and informal policy groups 

to deal with these issues. For example, a project in Washington State 

established a market garden on public land only to fi nd that legislation 

disallowed marketing from the land. The amendments to the CFPCGP 

legislation in 2002 put an added emphasis on funding planning and 

policy activities within the program. Examples of Food Policy Councils 

projects funded by CFPCGP include a food policy council in New Orleans, 
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Louisiana, and a nationwide policy project funded to educate State legislators 

about food systems issues and CFP grantees about policy issues.

Youth projects

Some CFPs are specifi cally targeted toward youth. The goals of these 

projects are often educational—providing agricultural, nutrition, meal 

preparation, and culinary training to young people. A basic idea of these 

projects is to strengthen the connections between youth and the foods 

they eat. Two examples of youth-focused CFPs are the Healthy Harvests 

Initiative and the Food for Life program. 

Entrepreneurial food and agricultural activity

The goals of these types of projects are to build the capacity of communities 

to develop and promote their own agricultural activities. The projects often 

involve entrepreneurial training, technical assistance in expanding local 

markets, developing and producing value-added products or infrastructure 

development. Examples of projects that have served rural and low-income 

communities include the Appalachian Center for Economic Networks 

in Athens, OH, and Building an Integrated Sustainable Food System in 

Abington, Virginia. 

Urban agriculture, rural producers, and community gardens 

These types of projects offer support for developing commercial gardens and 

food production for sale or for home consumption in underserved urban or 

rural areas. Funds are used for developing capacity to grow, sell, and market 

food for commercial enterprises or to grow and prepare food for at-home 

consumption. The projects cover a wide range of areas and communities. 

Lessons Learned

Annually, national experts are selected as peer reviewers for the CFPCGP. 

Reviewers select the most meritorious proposals through a competitive 

process for funding. The reviewers are people who run community food 

projects, work in academia, or do similar work and know what works in 

communities. Through the years, lessons have been learned about what tends 

to work in communities and what does not. These lessons include project 

development issues, challenges faced by CFPs, and general lessons about 

community food security work. Some of the lessons learned include the 

following:

Project Development

• Projects must be “community-based,” not “community-placed” to be 

successful in the long term. A number of projects in the early years of 

the program represented institutions’ “good ideas” about what would 

bring more food into the community. These “top-down” projects tend 

not to work. A classic version of a top-down project is building a 

community garden without gaining consensus from the community. 

Projects must come from the community and must be a solution to 

their perceived needs for improving food security in their community.
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• Three years is a very short time to develop a project that is going to be 

sustainable and economically viable. Often, more than three years is 

needed. 

• The program requires that projects become “self-sustaining” 

without defi ning the term. Sustainability has many defi nitions. The 

sustainability of projects must be interpreted loosely. In some cases, 

it can be interpreted as meaning economically self-sustaining. For 

example, a youth farmstand would have to make enough money from 

selling produce to allow the project to move into the future. But 

another way to view sustainability of a youth education program is 

that the skills learned by youth will stay with them into the future and 

increase their ability to be self-suffi cient.

• Feedback reviews from unsuccessful grant applications for CFPCGP 

have led to successful subsequent applications. Projects are improved 

considerably by acting on review comments.

• Small community organizations must build leadership within their 

organizations to ensure that food projects can continue beyond the 

guidance of one charismatic leader. 

Challenges

• In several projects, securing available land for community gardens was 

only the fi rst problem. Requirements to test the health of the soil and 

remediate soil for contaminants presented further challenges. One project 

turned this experience into a community outreach initiative. Youth 

gardeners became leaders in the community through their efforts to test 

neighbors’ soil so that their gardens would be free of contamination as 

well.

• Some projects unexpectedly encountered local ordinances that interfered 

with plans (see earlier Washington State example). 

• Land access for food production and transportation to gardens and 

foodstores were major hurdles for many projects. City-owned lands 

could be taken after soil remediation, sending projects back to the 

beginning.

Programmatic lessons

• Immigrant populations are a force in the community food security 

movement. Many groups come from agrarian backgrounds and eagerly 

approach the promise of growing familiar foods and marketing to fellow 

immigrants. Farmers’ markets create viable local food sources for 

the broader community and an economic foothold for new immigrant 

populations. The “new” fruits and vegetables introduced by immigrants 

are a nutritional asset for the American diet.

• Native American and Native Hawaiian projects are stimulating interest 

and reviving deep connections to traditional foods among these groups, 

and they are offering elements of native diets that may be healthier 

options than alternative foods currently consumed.
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• Projects employing youth in community food projects are fi nding that 

they are successfully engaging the future leaders in the community food 

security movement.

• Corner stores are seen as a potential tool for improving the diets of 

low-income residents of communities with limited food access. Corner 

stores tend to not have the resources to make the transition to selling 

more fresh foods. One project found that corner stores could not afford 

the refrigeration necessary, their regular suppliers did not handle fresh 

produce, and they lacked the expertise to handle fresh produce.

• Community food projects by nature are dynamic and change to adapt to 

the ever-changing environment. The CFPCGP has had to be fl exible to 

allow projects to adapt to change.

• Community food projects frequently have unexpected outcomes. One 

unexpected outcome is that community gardens have been seen to reduce 

crime in the vicinity of gardens. A project in a public housing unit was 

adopted by a park service adjacent to the garden. After the garden came 

into existence, crime and vandalism rates dropped signifi cantly in the 

park area, encouraging the park service to continue the project.

• Food can be a powerful community organizing tool. Ethnic foods 

can provide strong bonds between community members that help 

lead to unity in solving other community issues. A disintegrating 

infrastructure in Holyoke, Massachusetts, led community leaders to 

start building gardens to grow ethnic foods, which progressively led to 

farmstands, small farms, and ethnic festivals. Youth gardens in Boston, 

Massachusetts, fostered better relationships between generations and 

racial groups. Native American groups found power in advocacy as 

their awareness of traditional foods increased through community 

food assessments with elders and they learned how traditional foods 

could be restored in their communities. Community gardens can break 

down isolation and bring people out into the community for positive 

interaction, which often results in reduced crime rates.

Community food projects are small, community-driven attempts to address 

the problem of food access in low-income communities. CFP projects 

often grow from frustrations stemming from poor access to food sources. A 

frequent fi rst step is assessing what exists, as well as, what is missing in the 

community. Knowledge of what assets exist often strengthens a community’s 

collective confi dence to tackle problem solving. 

When communities come together to address their community food 

security issues, their solutions often go beyond food access to include 

social, economic, and environmental issues as well. Food is an attractive 

community development tool. A community food security approach to 

fi ghting food access problems is a viable way to make a difference in small, 

yet signifi cant ways for those individual communities that are affected by 

a lack of access to food. Modest grants for communities across the Nation 

have given people the incentive they need to build solutions that match the 

needs of the community. The goal of the CFPCGP has been to build capacity 

in communities to achieve food security and to demonstrate model programs 

that may be adopted in other communities across the country. 
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References

Abi-nader, J. (2007). “Community Food Security Coalition and National 

Research Council Common Output Tracking Form, Fiscal Year 2006 

Results” (Internal, unpublished document).

CRP Incorporated (1998). A Study of Access to Nutritious and Affordable 

Food, U.S. Department of Agriculture, Food and Nutrition Service.

First Nation’s Development Institute (2004). “Food Sovereignty Assessment 

Tool,” www.fi rstnations.org/publications

Fisher, A. (2009). Presentation at the IOM/NRC Workshop on the Public 

Health Effects of Food Deserts, January 27, Washington, DC.

FNS-314 (2000). Together We Can! A What, Why and How Handbook 

for Working to End Hunger in Your Community, U.S. Department of 

Agriculture, Food and Nutrition Service, January.

FNS-315 (2000). The National Nutrition Safety Net. Tools for Community 

Food Security, U.S. Department of Agriculture, Food and Nutrition Service, 

January. 

Hamm, M.W. and A.C. Bellows (2003). “Community Food Security and 

Nutrition Educators”. Journal of Nutrition Education and Behavior, 35(10): 

37-43. 

Healthy Food Healthy Communities: A Decade of Community Food Projects 

in Action, 2006 (developed in cooperation between USDA, CSREES, 

Community Food Security Coalition, and World Hunger Year), http://www.

foodsecurity.org/pubs.html#cfpdecade

Jessup, E., J. Hofer, P. Moua (2005). The Fresno Metro Ministry Fresno 

Fresh Access Community Food Assessment was staffed by the Hunger and 

Nutrition Project: Fresno Metro Ministry, www.fresnometroministry.org

Koralek, R.D. (1996). Conference on Access to Food September 18 and 

19, 1995, U.S. Department of Agriculture, Food and Nutrition Service, 

November.

O’Connor, J.J., and B. Abell (1992). Successful Supermarkets in Low-Income 

Inner Cities, U.S. Department of Agriculture, Food and Nutrition Service, 

August.

Pothukuchi, K., K. Seidenburg, and J. Abi-Nader (2007). Building 

Community Food Security: Lessons from Community Food Projects, 1999-

2003, October 2007, http://www.foodsecurity.org/pubs.html#building.

Winne, M. (2008). “Issue Brief: America’s Farms Feed America’s Children,” 

Community Food Security Coalition, www.foodsecurity.org. 

Winne, M. (2008). Personal communication, November 15, 2008.
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World Hunger Year (2009). “Community Food Projects Database,” Food 

Security Learning Center, http://www.yhunger.org/programs/fslc/features/

community-food-projects-database.html 

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CHAPTER 8

Policy Options

A variety of programs and policies to reduce the effects of limited access to 

affordable and nutritious food have been implemented or considered across 

the country. This chapter discusses broad categories of these programs 

and highlights examples that either have been implemented in an area or 

are under consideration. Many of the programs have been initiated at the 

community, local, or State levels, some with Federal support (as described in 

the previous chapter), but often only with support from localities, States, or 

private sources. Where available, evidence of the effectiveness of programs 

that have already been implemented is considered, although there is not a 

substantial literature from which such evaluations can be drawn. The chapter 

concludes with some broad guiding principals for setting policy priorities. 

It is suggested that a variety of demonstration projects might be a feasible 

way to begin efforts to decrease the effects of limited access to food and to 

simultaneously learn more about what works and what does not. 

Incentive Programs To Entice New Stores 

or Improve Existing Stores

The discussion at the end of Chapter 6 described broad trends in supermarket 

and foodstore markets. These trends have had major impacts on where 

supermarkets of different types locate and the prices that consumers face. 

The trends also exhibit the market forces that drive food retailers to adjust 

their store formats, production costs, and location decisions. For example, 

the rise of nontraditional retailers in food retail (supercenters and large 

discount stores like Wal-Mart, Costco, or Super Target), which offer foods 

at prices that are 8 to 27 percent lower than at large supermarket chains, 

has changed the competitive environment and has likely led to decreases 

in the average prices of foods for consumers (Leibtag, 2006). These stores 

are not typically located in urban areas and may not be accessible in all 

rural areas either. Thus, those outside of the reach of these large stores 

may be less affected by the price benefi ts of the stores. On the other hand, 

several major supermarkets have subsidiaries tailored specifi cally to cater 

to low-income and bargain shoppers, for example, Save-A-Lot, ALDI, and 

Food-4-Less stores. Often these stores locate in low-income neighborhoods 

or underserved rural areas. They serve as examples of market-driven 

alternatives--meaning that the retailers sees a gap in a market where they 

can profi tably operate and adjust their business models to fi t the needs of the 

consumers, in this case, low-income consumers and discount shoppers. 

Some traditional grocers have opened stores in more densely populated areas 

(although not necessarily low-income areas), adapting their store models to 

fi t into smaller spaces. Further, many other nontraditional retailers, such as 

pharmacies and dollar stores, have expanded into food retail as well (Sharkey 

and Horel, 2009). Smaller corner stores and convenience stores that already 

serve areas without access to a supermarket have also expressed interest in 

offering more nutritious foods in their stores (Gittelsohn, 2009). These stores 

are sometimes unwilling to take the risk of offering these foods when there is 

uncertainty about whether they can sell enough of them (Gittelsohn, 2009). 
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There are many other examples of grocery stores that have opened stores or 

expanded service in underserved areas (see, for example, Food Marketing 

Institute, 1998). 

As explained in Chapter 6, both cost factors for food retailers and demand 

factors for consumers may leave some areas underserved by food retail 

stores or types of food retail stores. If cost factors keep food retailers from 

developing new stores or expanding services in existing stores (e.g., if fi xed 

costs of obtaining land and permits for building a new store are higher in 

these areas), then efforts to reduce these costs or to subsidize development of 

new or expanded stores may be effective policy solutions. 

Many localities and some States have utilized a number of methods to entice 

new stores to open in these areas or to improve existing stores. These efforts 

range from fi nancing for new large-scale supermarkets, to small incentives 

offered to existing stores to stock healthier foods, such as gift cards at fruit 

and vegetable wholesalers. This section highlights a few of these efforts. 

Previously published studies provide very good detail about the potential 

benefi ts and the hurdles of different approaches and the strategies that can 

be used to ensure successful store development (Policy Link, 2007; Food 

Map 8.1

Locations of new or planned foodstores in Philadelphia 

through Pennsylvania Fresh Food Financing Initiative

Blue areas represent the areas of greatest need, which have low 

supermarket sales, low income, and high rates of deaths due to 

diet-related diseases. The orange dots represent stores that have 

been opened under the Pennsylvania Fresh Food Financing Initiative. 

The yellow dots represent stores funded by the program that are 

under construction. 

Source: Figure provided courtesy of Robert Wood Johnson Foundation and 

The Food Trust, http://www.rwjf.org/files/newsroom/profiles/foodtrust/
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Marketing Institute, 1998). Less has been published about the potential 

drawbacks of these approaches. Two examples of potential drawbacks 

that have not been explored are the use of tax revenues to encourage 

grocery stores when other uses of the revenues could be more benefi cial to 

low-income areas or the degree to which changes in competition faced by 

existing stores could affect the local market. 

One program to encourage development of new supermarkets or other 

grocery stores in underserved areas is the Pennsylvania Fresh Food Financing 

Initiative. This program provides grants of up to $250,000 or loans of up 

to $2.5 million per store when the infrastructure costs or credit needed 

to develop a new store in an underserved market are not available. The 

initiative is a public-private partnership involving the State of Pennsylvania, 

The Food Trust, the Greater Philadelphia Urban Affairs Coalition, and the 

Reinvestment Fund (TRF). The State appropriated $30 million and TRF 

gave $90 million for the program. Thus far, $41.8 million in grants and loans 

have funded 58 stores and 1.4 million square feet of retail space (Weidman, 

2009). Figure 8.1 shows the location of new or planned foodstores in 

Philadelphia funded through this partnership. A study to evaluate the 

impact of the opening of a large supermarket in one underserved area in 

Pennsylvania is underway. Illinois, Louisiana, New Jersey, and New York 

are also currently considering similar funding programs. 

A number of Federal fi nancing and incentive programs have been used 

to encourage new store development. These programs exist primarily to 

spur community development (not just supermarket development) in areas 

that lack mainstream investment (Federal Reserve System and Brookings 

Institution, 2008). The services provided through these programs include 

grants and low-interest fi nancing, tax incentives, and training or technical 

assistance in community development. The programs have varying goals, 

including improving labor market opportunities and housing options and 

spurring development in low-income areas. Often, they include fi nancing or 

cooperation with State and local governments. Examples of these programs 

include New Market Tax Credits, Community Development Block Grants 

(CDBG), the Empowerment Zone Program, the Environmental Protection 

Agency’s Brownfi eld program, and the Department of Housing and Urban 

Development’s Section 108 Loan program. For examples of how these 

programs have been leveraged to improve food availability in communities, 

see Policy Link (2007).45 

Improvements to already existing stores have also been used to address 

food accessibility in underserved areas. Modifi cations to such existing 

stores include increasing the availability of nutritious food, decreasing the 

availability of less healthy food, changing the relative prices of both of 

these types of foods, or changing the physical layout of foods within stores 

(Gittelsohn, 2009). These improvements are at a smaller scale than new 

store development, and, thus, can be less time consuming and much less 

expensive. Store improvements such as these have been applied in a number 

of settings (e.g., Baltimore Healthy Stores, Apache Healthy Stores, tiendas in 

North Carolina and California). 

New York City has recently implemented a program to improve food 

offerings within small stores or bodegas throughout the city, which are 

45This chapter also discusses the 

CDBG program in considering housing 

policy options. 
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often much more plentiful and convenient in underserved communities 

than large grocery stores or supermarkets. The Healthy Bodega Initiative 

recruited bodegas to increase their offerings of low-fat milk and, eventually, 

fruits and vegetables. The city also provided promotional and educational 

materials to entice consumers to purchase the new offerings and to encourage 

bodegas to participate. About 1,000 bodegas were recruited for the low-fat 

milk campaign, and 450 bodegas were recruited for the fruit and vegetable 

campaign. Participating bodegas experienced increases in sales of low-fat 

milk and of fruits and vegetables (Nonas, 2009). 

The changes in the WIC food packages currently being implemented in 

States may have an impact on the feasibility of increasing nutritious food 

options in small grocery stores and corner stores. The new food packages 

include greater incentives to purchase low-fat milk and whole grains, and 

vouchers for purchasing fruits and vegetables. Each State is required to 

adopt the new food packages by October 2009. The new food packages 

may provide increased and steady demand for these foods in stores in 

neighborhoods with high concentrations of WIC participants and may 

encourage operators of small stores to offer healthier food options. A pilot 

program implemented in New York State showed that the vegetable and fruit 

vouchers were popular among stores that accepted the WIC vouchers (New 

York State Department of Health, 2007). 

During the 1990s, USDA’s Food and Nutrition Service pursued a research 

agenda to address questions about food access among SNAP participants 

and other low-income households (see Chapter 5). The agency concurrently 

implemented three projects to identify strategies for improving access in 

underserved areas. Activities included a conference with experts who 

shared their ideas for increasing food access among low-income Americans 

(Koralek, 1996), along with two studies. The studies examined successful 

supermarkets in low-income, inner-city communities (O’Connor and Abell, 

1992) and identifi ed widely used means to improve consumer food access in 

underserved neighborhoods (CRP, 1998). 

Two key themes emerged from the conference and were reinforced by the 

two descriptive studies: 

• Proximity to a supermarket is generally regarded as critical to ensuring 

access to a variety of reasonably priced foods. 

• There are desirable alternatives to traditional supermarkets as a means of 

ensuring food access.

The conference focused on bringing supermarkets to low-income 

communities through careful, cooperative planning and troubleshooting 

that starts with sound market research (Koralek, 1996). Adequate funding 

was identifi ed as a key issue, and several private and public sources of 

support were discussed. These sources ranged from commercial banks with 

community development subsidiaries to grant and loan programs sponsored 

by Federal agencies. Participants in the conference, as well as fi ndings 

from an FNS study (O’Connor & Abell, 1992), emphasized the importance 

of community buy-in for supermarket development. Introduction of a 

supermarket not only provides new products and services but also creates 
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jobs and helps to keep money in the community. These points can be used 

to obtain support from a mayor or city council when it comes to handling 

zoning, tax, and other issues. Buy-in from consumers will affect the ultimate 

success of the business, so matters of location, preferred store type, and other 

consumer interests should be addressed up front.

Desirable alternatives to traditional supermarkets include food cooperatives, 

urban agriculture, farmers’ markets, public markets, smaller independent 

stores, and transportation hubs. Each has its own challenges but can 

contribute to a community’s capacity to provide its own food. 

Community-Level Interventions

Another type of intervention to increase the availability of healthy foods is 

through community-level programs, such as farmers’ markets, community 

gardens, or mobile carts or trucks that sell fruits and vegetables. These 

options for improving the food environment are often less expensive, require 

less space, and can be quicker to implement than programs that encourage 

new store development. These programs typically operate on a more limited 

scale (e.g., seasonally or only 1 day a week) or in the case of community 

gardens can require large time commitments on the part of consumers. The 

goal of these interventions is to give consumers more options for purchasing 

nutritious food by increasing the supply of these foods. Often these 

interventions include nutrition and food education components or incentives 

to consumers to increase demand for nutritious food. 

There are many, many examples of farmers’ markets, community gardens, 

mobile carts, or other similar community-level interventions implemented 

in underserved areas. This analysis cannot cover them all but highlights 

an example from New York City as one of the more comprehensive plans 

that has been implemented. Also discussed is the use of SNAP benefi ts at 

farmers’ markets, as it involves a component of the largest U.S. food and 

nutrition assistance program and could potentially be a lever with which the 

Federal efforts could increase access. 

In addition to the Healthy Bodegas initiative, the City of New York has 

implemented two other community-level initiatives to increase access to 

fruits and vegetables. One initiative is to increase the number of farmers’ 

markets in underserved neighborhoods and increase their use by residents 

through the Health Bucks program. The Health Bucks program offers $2 

coupons for the purchase of fresh fruits and vegetables at participating 

farmers’ markets. Health Bucks were used as a tool to introduce consumers 

to farmers’ markets, generating business for producers and helping reduce 

food access barriers for consumers. The program was expanded specifi cally 

for SNAP participants, with an additional $2 Health Buck offered for every 

$5 spent using EBT at the farmers’ market. Through this program, EBT 

sales at farmers’ markets more than doubled from $40,000 in 2007 to over 

$89,000 in 2008 (Nonas, 2009). The program is being expanded into upstate 

New York as the Fresh Bucks program. 

In addition to the farmers’ market and Health Bucks program, the city is also 

in the process of implementing a “Green Cart” program, which is intended 

to increase the number of mobile carts that sell fresh produce, especially 
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in underserved areas. Green Cart vendors are subject to the same permit 

process as other vendors, except they are restricted to operate in underserved 

areas and sell only fresh produce under the NYC Green Cart umbrella. The 

program is still recruiting vendors, a process that has been more diffi cult 

than expected (Nonas, 2009). To aid in the recruitment process, private 

foundation grants are being used to offer low-cost microloans to fi nance carts 

for vendors. 

Another mechanism to increase use of farmers’ markets among underserved 

communities and populations is through SNAP. The percent of SNAPauthorized stores comprised of farmers’ markets has been consistently low. 

With widespread implementation of EBT systems during the late 1990s, 

the presence of farmers’ markets among authorized retailers declined. This 

may be explained by the need for access to electricity in order to use the 

typical point-of-sale EBT equipment. Over the last several years, however, 

the number of participating farmers’ markets has grown. In FY 2008, there 

were 753 farmers’ markets authorized to participate in the program, up from 

253 in FY 2000. Several factors contributed to this increase, including 

the increased popularity of these markets among the general population. 

In addition, FNS has endorsed a number of local and State initiatives to 

provide access to benefi ts through wireless point-of-sale equipment or scrip 

alternatives. With provisions in the 2008 Farm Bill, USDA’s Agricultural 

Marketing Service will set aside 10 percent of the approximately $5 million 

in competitive grants funding for FY 2009 for new EBT projects at farmers’ 

markets.

Farmers’ markets and community gardens have sprung up in many places, 

including in underserved areas. There is much knowledge about how such 

programs can successfully operate and become sustainable for long periods 

(see, for example, Fisher, 2009 and Smiley, 2009). There are often benefi ts 

of the programs that are harder to measure, such as increasing community 

and social capital or keeping ethnic and minority populations in touch with 

their cultural culinary heritage. The programs can also serve as educational 

opportunities for teaching about horticulture, nutrition, and farming and 

growing practices. There is, however, little evaluation of their effectiveness 

in terms of changing shopping behavior, diet, and diet-related outcomes for 

consumers. 

Transportation-Related Improvements

In contrast to bringing the stores to people, an alternative approach is to 

bring the people to the stores. Programs that improve public transportation 

routes from areas with limited access or provide transportation subsidies to 

low-income individuals or subsidized supermarket shuttle services could be 

effective ways to reduce access problems. Transportation improvements 

such as these have some advantages, depending on characteristics of the 

areas. First, as the analysis in Chapter 2 showed, supermarket access 

may also be a problem for low-income people who live outside of low 

income areas. In cases where those with access problems are dispersed, 

transportation interventions may be more effi cient than new store 

development since targeting an area for a store to locate to reduce access 

problems for this group would be diffi cult (relative to areas where there are 

high concentrations of low-income people with access problems). If public 
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transportation in the area already exists, it may not be too expensive or take 

as much time to change or add routes that could be used to improve access 

to stores. A transportation subsidy given to those with limited access may 

enable consumers to access foodstores that better fi t their budgets. Such 

improvements could also help consumers access other services that may be 

lacking in their area (such as banks or health care providers). 

Using the NFSPS survey data and data from the Louisiana Neighborhood 

Environment and Consumption Survey (LANECS), Rose et al. (2009) 

estimated travel costs for different transportation modes and across areas in 

New Orleans that had poor or good access to supermarkets. This exploratory 

exercise considered both out-of-pocket travel costs and time costs for the 

different travel options for grocery shopping.46

Not surprisingly, a taxi was the most expensive option ($66.60 per month), 

while a bus was $38.70, a ride from a friend or relative was $21.90, and 

walking was $21.00.47 Driving one’s own car was the least expensive 

method for getting to the store ($5.90 per month). The study also considered 

differences in time costs for New Orleans residents living in areas with poor 

access to supermarkets (defi ned as census tracts more than 2 kilometers from 

a supermarket), compared with the time costs for those living in areas with 

good access (defi ned as census tracts within 2 kilometers of a supermarket) 

(Rose et al., 2009). The average difference in travel cost between areas with 

poor access and areas with good access was $10.58 per month—meaning 

that SNAP participants in poor-access areas of New Orleans had total travel 

costs (both time and out-of-pocket costs) that were on average (across mode 

of transportation) almost $11 higher than those in areas in New Orleans with 

good access. 

To improve access to supermarkets for low-income households, a 

transportation benefi t in conjunction with SNAP benefi ts for participants 

who do not own cars and who have poor access to a supermarket could be 

considered (provided a satisfactory method for assessing access levels for 

participants is already available and could be implemented). SNAP benefi ts 

for people who live in areas with poor access could include a transportation 

subsidy (on top of the food benefi t) that could be used to compensate for 

out-of-pocket costs to getting to a supermarket. For example, the city of 

Madison, WI, recently proposed to offer monthly bus passes at a discounted 

rate of $27.50 (originally $55) to people eligible for SNAP (Rose, 2009).48 ). 

Another option could be to deduct transportation costs from total income for 

applicants with limited access to supermarkets, which would effectively give 

a larger benefi t to these households (Rose et al., 2009).49 

Further exploration into the feasibility and costs and possible reactions of 

consumers to such transportation interventions is needed. If better public 

transportation options are made available, it would be useful to project how 

many people would use them. If transportation costs are deducted from 

income for SNAP applicants, it would also be useful to know the percentage 

of participants who would use their additional benefi ts to access better stores 

and more nutritious food, or who would simply buy more of the same foods 

from the same stores. 

46The time cost estimates use the 

hourly minimum wage to value time. 

See table 5 in Rose et al., 2009, for 

details. 

47Costs are to the nearest 

supermarket by mode of transport 

based on approach by Feather, 2003. 

Costs for each mode were weighted 

using the distributional data on the 

mode of transportation. 

48The program is partially intended 

to offset the effect of an increase in 

fares on low-income individuals. 

49This would not increase benefi ts 

for households that already receive the 

maximum benefi t.
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SNAP Retailer Policy

Retailers authorized to participate in SNAP must sell staple foods for home 

preparation and consumption. In addition, the store must offer for sale, on a 

continuous basis, three different varieties of foods in each of four staple food 

categories, with perishable foods in at least two categories; or have more than 

50 percent of total gross sales in staple foods. Most stores are authorized 

under criterion a. A store technically can meet this criterion with a small 

number of items that meet the variety and perishable requirements.

As part of an overall review of existing regulations, USDA will review the 

current provisions for retailer depth of stock. The goal remains to balance 

access to a suffi cient number of neighborhood stores with availability of a 

continuous supply of required foods.

There is some previous research on the relative tradeoffs of restricting small 

stores from SNAP authorization and from increasing access to large grocery 

stores or supermarkets (Feather, 2003). At the time of the study, (pre-EBT 

card implementation), there was concern about the amount of fraud in 

small stores authorized to receive SNAP benefi ts. The study, which valued 

and compared the benefi ts (costs) of restricting access for small stores and 

increasing access to large stores for participants, showed that access to a 

new store would result in a gain of $2.78 to $7.76 per participant, per month, 

depending on store location in relation to the participant location and the 

value of the cost of travel time. On the other hand, the loss to participants 

of restricting store access to only supermarkets and large grocery stores was 

estimated between $4.16 and $8.78 per participant, per month. 

Housing and Community Development-Related Policy

State and local governments make most of the zoning and land-use planning 

decisions that determine the relationship of residential to commercial land 

uses. Yet, research shows many planners do not actively or systematically 

plan their communities’ food access the way they plan access to services and 

facilities like transportation, parks, hospitals, or schools (Pothukuchi and 

Kaufman, 2000).

Some planning by units of local government is in direct response to Federal 

mandates and can be infl uenced by shifts in Federal policy. For example, 

when developing or rehabilitating public housing, public housing agencies 

are required by Federal regulation to choose sites that are accessible to 

commercial services. Chapter 3 of this study fi nds segregated, low-income 

areas are more likely to lack adequate food access than other areas. The 

characteristics of those areas are consistent with those of areas having 

large public housing developments, though further study is necessary to 

establish any potential links between low-access and public housing. If 

public housing and low-access are linked, the U.S. Department of Housing 

and Urban Development has existing regulatory authority to require future 

developments be sited closer to opportunities to access healthy food.

Another example of a Federal opportunity to encourage local governments 

to plan for communities’ food access is the Community Development Block 

Grant program. Federal regulations specifi cally lay out the following goals: 
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increased access to quality public and private facilities and services, and 

access to capital and credit for development activities that promote the longterm economic and social viability of the community. Communities could 

be encouraged to incorporate healthy food systems planning in the required 

Consolidated Plans they submit that lay out their plans for the use of grant 

funds. 

No changes to Federal rules governing programs like public housing and 

Community Development Block Grants would be needed to encourage food 

systems planning at the local level. Encouraging local governments to plan 

does not dictate specifi c policies, but instead allows communities to devise 

solutions specifi cally tailored to the community.

Anti-Poverty Policy

Chapter 3 provided analysis of the factors correlated with whether an area 

has limited access to affordable and nutritious food. Findings showed 

that some household characteristics are associated with greater risk of low 

access. Household characteristics such as low educational attainment, low 

employment levels, and reliance on public assistance were found to be 

the most infl uential determinants (in addition to low-income, upon which 

the analysis was conditioned). Federal anti-poverty programs, such as the 

Earned Income Tax Credit, Temporary Assistance for Needy Families; 

and food and nutrition assistance programs, such as SNAP, WIC, and the 

school meal programs are targeted to households with these characteristics. 

These programs often have goals to encourage and support employment 

and to offer income support, food, and better nutrition. Thus, some of the 

populations for which a food access policy intervention may be targeted 

already receive public assistance from these general anti-poverty programs. 

If these programs are successful in raising incomes, employment prospects, 

and educational opportunities, then low-income populations may be able to 

access better foods as well. 

Summary

This chapter outlined the types of policy options that could be considered 

to reduce the effects of limited access to affordable and nutritious foods. A 

formal evaluation of the benefi ts and costs of these policy interventions was 

beyond the scope of this study. There are, however, some broad overarching 

guidelines to consider if future policy interventions are needed. 

The analysis in chapter 2 shows that the number of people that are likely 

affected by limited access is small. Further, data on shopping patterns 

and prices actually paid show that many, but perhaps not all, low-income 

consumers have strategies for fi nding stores that offer the products they want 

at lower prices. Both of these fi ndings suggest that a large-scale, nationallevel program may have diffi culty addressing what are likely to be quite 

localized pockets of limited access. 

The analysis in Chapter 2 and from other studies (Sparks et al., 2009; 

Apparicio et al., 2007) also shows food access may be a problem for some 

low-income families who live outside of areas with high concentrations of 

poverty. This observation is also important for policy considerations because 
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the type of intervention that may be effective in areas with concentrated 

poverty are probably different than the type of interventions that may 

be effective if the population with limited access is more geographically 

dispersed. For example, improving existing stores or enticing new stores 

or farmers’ markets to areas where the population with limited access is 

concentrated may be an effective solution. On the other hand, if those 

who lack access are more dispersed, it may be more effective to improve 

transportation options or provide transportation subsidies for this population 

to travel to stores rather than encouraging new stores. 

It is diffi cult to discern the degree to which supply-side and demand-side 

factors contribute to differences in store access (Bitler and Haider, 2009). 

Yet, making this determination is critical for understanding the types of 

policy interventions that may be needed. 

On the demand side, the key question is if better access to affordable and 

nutritious food is provided, will people buy these foods? Evidence is mixed 

(see Cummins, 2005; Wrigley, 2003; Gittelsohn, 2009; Ayala et al., 2009). 

These studies show small increases or no changes in consumption of healthy 

foods in areas where access was improved. Related evidence suggests 

that price subsidies for fruits and vegetables can increase consumption 

of these foods for low-income consumers in general, but the effects are 

small and consumption is still below recommended levels (Dong and Lin, 

2009). It is possible that demand for more nutritious foods like fruits and 

vegetables may also be driven by the time costs associated with preparing 

and cooking them, especially considering the relative convenience and price 

of alternative sources of foods. It is also possible that some consumers 

may not know the nutritional benefi ts of such foods. Some may also lack 

familiarity with preparation and the taste of these foods. If either of these is 

true, then increasing access through supply-side factors alone (e.g., enticing 

new stores) will not change food purchasing and dietary habits. A public 

health campaign to promote healthier eating and cooking habits may be 

more effective. Or, a program that gives low-income consumers incentives 

to purchase more fruits and vegetables, such as the Healthy Incentive Pilot 

that is being planned by FNS, could be more effective in increasing demand 

for these foods. If the time-cost tradeoffs are the major determinants behind 

lower demand, then such products as already-cut-and-cleaned fruits and 

vegetables or healthier prepared food options may be useful. There are 

usually price premiums for these prepackaged alternatives, however. 

If supply-side factors, such as high fi xed costs for new store development 

in low-income areas or restrictive zoning policies, are an obstacle for larger 

food retailers, then local policies toward zoning or government programs 

(at the Federal, State, or local level) that subsidize these costs for stores 

may be effective. There is some evidence that overall operating costs of 

stores serving low-income consumers are similar to those of other stores 

(King et al., 2004). But it is still possible that high fi xed costs (as opposed 

to operating costs) could be barriers to entry for stores trying to locate in 

underserved areas. 

Either of these demand or supply factors could be a market failure—that is, 

the market is not operating effi ciently such that a government intervention 

could make the market operate more effi ciently in a way that everyone 
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benefi ts. But again, there is not enough information to tell if there is a market 

failure on either the demand or supply side (see Bitler and Haider (2009) for 

a more complete discussion). 

It is also important to differentiate between general issues faced by 

low-income neighborhoods and only those relevant to the supply of healthy 

foods. Many low-income neighborhoods may also lack access to banking 

services, health care services, and well-functioning public schools (Federal 

Reserve Bank and Brookings Institute, 2008). A supermarket can be a 

politically popular development for a low-income neighborhood because 

it provides for a most fundamental need—food (Rose et al., 2009). On 

the other hand, other services and businesses may be more important for a 

community (Bitler and Haider, 2009).

Chapter 4 summarized research on the effects of interventions on food 

shopping and consumption of specifi c foods. Given the rather modest 

fi ndings, it is important to have realistic expectations about the potential 

effects of policy interventions on diet and, particularly, on more distal 

outcomes such as high BMI, obesity, and diet-related diseases. More 

proximate outcomes, such as shopping patterns and food intake, are more 

likely to be affected by any policy to increase access than more distal 

outcomes like obesity because there are so many other factors that contribute 

to obesity and because increased consumption of healthy foods may not lead 

to weight loss. 

Given the lack of understanding about the actual causes of differences in 

access to food, it is tenuous to consider large-scale, national-level policy 

interventions. One possible approach, however, is to fund a number 

of demonstration projects throughout the country that use different 

approaches to reducing the effects of limited access to food. Projects could 

be appropriately tailored to fi t the access issues facing a community—

considering the specifi c needs of low-income people and the communities 

in which they live. The projects funded could vary in size and scope as 

appropriate—for example, incentives for the development of large-scale 

supermarkets or smaller projects like the Community Foods Projects that 

are already underway. Some of the demonstrations could operate through 

SNAP—for example, a transportation benefi t for SNAP participants or a 

deduction of transportation costs for participants in determining benefi t levels 

could be implemented in a variety of settings. Expansions to programs that 

focus on broader community development goals besides attracting food 

retailers could also be considered (such as CDBGs or New Market Tax 

Credits). 

Regardless of the types of projects that may be considered, an important 

component of the projects should include formal evaluations of the 

benefi ts and costs of interventions. In some cases, randomized trials could 

conceivably be conducted; for example, transportation subsidies could be 

randomly assigned to SNAP participants who face access barriers. In other 

cases, this kind of evaluation would not be possible. Instead, the collection 

of longitudinal data on changes in shopping behavior or dietary intake 

(or whatever the desired outcome change is) for affected populations is 

important. Also important to consider are opportunities to conduct natural 

experiments or quasi-experimental designs in which comparison areas 
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or populations are used as a benchmark control to the intervention area 

or population. It may also be possible to tailor interventions specifi cally 

to people who already have diet-related diseases and who have diffi culty 

accessing foods they need to manage their diseases. For example, diabetes 

patients under clinical care could be randomly assigned coupons for farmers’ 

markets or coupons for healthy options at supermarkets to determine if 

increasing access was an effective way to manage the disease. 

Chapter 4 and Rose et al, (2009) raised the possibility that food swamps and 

not food deserts may be more of a factor in BMI and obesity concerns. If 

it is the case that energy-dense foods are too easily accessed, then policy 

interventions may need to reduce the incentive to eat less healthful foods 

or reduce access to these foods. This could be much more diffi cult because 

even stores stocking the healthiest of options also stock the least healthy 

options. Taxing less healthy foods (e.g., soda or sweetened beverages) is 

one example of such a policy, but these taxes inherently have distributional 

tradeoffs that must also be considered. 

References

Apparicio, P., M. Cloutier, R. Shearmur (2007). The Case of Montreal’s 

Missing Food Deserts: Evidence of Accessibility to Food Supermarkets,” 

International Journal of Health Geographics, 6(1), 4. 

Ayala, G.X., B. Baquero, L. Linnan, B.A. Laraia, and P. Bloom (2009). 

“Working With Tiendas to Promote Healthy Eating,” Presentation at the 

IOM/NRC Workshop on the Public Health Effects of Food Deserts, January 

27, Washington, DC.Bitler, M., and S. Haider (2009). “An Economic View 

of Food Deserts in the United States,” National Poverty Center Working 

Paper, http://www.npc.umich.edu/news/events/food-access/index.php 

CRP Incorporated (1998). A Study of Access to Nutritious and Affordable 

Food, U.S. Department of Agriculture, Food and Nutrition Service, April.

Cummins S, A. Findlay, M. Petticrew, and L. Sparks (2005). “Healthy Cities: 

The Impact of Food Retail-Led Regeneration on Food Access, Choice and 

Retail Structure,” Built Environ 31(4): 288-301.

Dong, D., and B.H. Lin (2009). Fruit and Vegetable Consumption by 

Low-Income Americans: Would a Price Reduction Make a Difference?

Economic Research Report No. 70, U.S. Department of Agriculture, 

Economic Research Service, January.

Feather, P.M. (2003). “Valuing Food Store Access: Policy Implications for 

the Food Stamp Program,” American Journal of Agricultural Economics, 

Vol. 85(1): 162-172. 

Federal Reserve System, and the Brookings Institution (2008). The 

Enduring Challenge of Concentrated Poverty in America: Case Studies 

from Communities Across the U.S., A Joint Project of the Community 

Affairs Offi ces of the Federal Reserve System and The Metropolitan Policy 

Program at The Brookings Institution, Federal Reserve Bank of Richmond: 

Richmond, VA. 
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Fisher, A. (2009). “Community Level Food Environment” Presentation 

at the IOM/NRC Workshop on the Public Health Effects of Food Deserts, 

January 27, Washington, DC.

Food Marketing Institute (1998). Urban Supermarkets, Food Marketing 

Institute. 

FNS-314 (2000). Together We Can! A What, Why and How Handbook 

for Working to End Hunger in Your Community, U.S. Department of 

Agriculture, Food and Nutrition Service, January.

FNS-315 (2000). The National Nutrition Safety Net: Tools for Community 

Food Security, U.S. Department of Agriculture, Food and Nutrition Service, 

January. 

Gittlesohn, J. (2009). “Overview of Efforts to Change the Food 

Environment,” Presentation at the IOM/NRC Workshop on the Public Health 

Effects of Food Deserts, January 27, Washington, DC.

King, Robert P., Ephraim S. Leibtag, and Ajay S. Behl (2004). Supermarket 

Characteristics and Operating Costs in Low-Income Areas, Agricultural 

Economic Report No. 839, U.S. Department of Agriculture, Economic 

Research Service. 

Koralek, R.D. (1996). Conference on Access to Food September 18 and 

19, 1995. U.S. Department of Agriculture, Food and Nutrition Service, 

November.

Leibtag, E. (2006). “The Impact of Big-Box Stores on Retail Food Prices 

and the Consumer Price Index,” Economic Research Report No. 33, U.S 

Department of Agriculture, Economic Research Service. 

New York State Department of Health (2007). New York State WIC 

Program 2006 Vegetable and Fruit Demonstration Project, Bureau of 

Supplemental Food Programs Food Delivery Systems Unit, Division of 

Nutrition, Albany, NY. 

Nonas, C. (2009). “New York City: Healthy Food Access,” Presentation 

at the IOM/NRC Workshop on the Public Health Effects of Food Deserts, 

January 27, Washington, DC.

O’Connor, J.J., and B. Abell (1992). Successful Supermarkets in Low-Income 

Inner Cities, U.S. Department of Agriculture, Food and Nutrition Service, 

August.

Policy Link, and LISC Bay Area (2007). Grocery Store Attraction 

Strategies, http://www.policylink.org/documents/groceryattraction_fi nal.pdf 

Pothukuchi, K., and J. L. Kaufman (2000). “Longer View: The Food System: 

A Stranger to the Planning Field,” Journal of the American Planning 

Association, Vol. 66, Part 2.
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Rose, D. (2009). “Discount Bus Passes Proposed,” Wisconsin State Journal, 

Friday, February 20, http://www.madison.com/wsj/home/local//index.

php?ntid=431881 

Rose, D., J.N. Bodor, C.M. Swalm, J.C. Rice, T.A. Fraley, and P.L. 

Hutchinson (2009). “Deserts in New Orleans? Illustrations of Urban Food 

Access and Implications for Policy,” National Poverty Center Working 

Paper, http://www.npc.umich.edu/news/events/food-access/index.php 

Sharkey, J., and S. Horel (2009). “Characteristics of Potential Spatial Access 

to a Variety of Fruits and Vegetables in a Large Rural Area,” National 

Poverty Center Working Paper, http://www.npc.umich.edu/news/events/

food-access/index.php 

Smiley, A. (2009). Title Presentation at the IOM/NRC Workshop on the 

Public Health Effects of Food Deserts, January 27, Washington, DC.

Sparks, A., Bania, N., and L. Leete (2009). “Finding Food Deserts: 

Methodology and Measurement of Food Access in Portland, OR,” National 

Poverty Center Working Paper, http://www.npc.umich.edu/news/events/

food-access/index.php 

Weidman, J. (2009). “Improving Access to Nutritious Food,” Presentation 

at the IOM/NRC Workshop on the Public Health Effects of Food Deserts, 

January 27, Washington, DC.

Wrigley, N., D. Warm and B.Margetts (2003). “Deprivation, Diet, and 

Food-Retail Access: Findings From the Leeds “Food Deserts” Study,” 

Environment and Planning A 35: 151-188.
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CHAPTER 9

Future Research Needs

Progress has been made in characterizing the problem of food deserts and 

in describing their consequences for affected populations. Many aspects 

of food deserts, however, are not well defi ned or understood. This chapter 

outlines future research needs for each of these aspects of food deserts. It 

fi rst focuses on conceptual and measurement issues of food access. These 

were discussed in greater detail in Chapters 1 and 2. Next, it considers the 

research issues for determining the causes of limited access, which were 

discussed in Chapter 6. Finally, it highlights future research considerations 

for understanding the economic and health consequences of limited access. 

Conceptualizing and Measuring Food Access 

and the Food Environment

A variety of concepts and methods have been used to measure access to 

affordable and nutritious food. But because of the breadth of sources of both 

nutritious and less nutritious foods and the complexity with which consumers 

make choices about where to shop, what to eat, and how much time to devote 

to these and other food-related activities, it is very diffi cult to fully measure 

and characterize food access for consumers.

Existing studies almost exclusively consider access to stores from 

consumers’ homes and ignore the potential access to food that consumers 

have because they travel to school, work, or other activities. Future research 

should consider how people fi t grocery and food shopping into their daily 

activities and travel patterns. It will also be important to consider how these 

activities and patterns expose people to food environments outside of their 

neighborhoods and how this may affect their shopping and diet. 

Most of the research that has been conducted focuses on an area-based 

concept of access. Area-based concepts are important because crosssectional evidence suggests that the food and neighborhood environment has 

a strong correlation to food intake and to some diet-related health outcomes 

and because individual factors alone cannot explain all the differences in 

diet and health. The fi ndings from the national- level assessment of access 

to supermarkets and the discussion in Chapter 1 suggest that an individualbased concept of access may be as important. That is, some low-income 

people do not live in areas with concentrated poverty, but still may lack the 

resources or transportation to access affordable food. Further clarifi cation 

of both individual measures of access and area measures of access is needed. 

Studies that offer both types of measures could be useful. For example, 

it would be useful to understand the degree to which individuals who 

report access limitations (such as those in the Current Population SurveyFood Security Supplement CPS-FSS) live in areas that may be considered 

low-access areas. 

It is clear from the research that some areas and individuals have relatively 

less access to some food retailers than other areas and individuals. This is 

useful information which highlights issues of equity and areas where policy 
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interventions or private store development may be targeted. But it is not 

clear whether these relative deprivations in access to food constitute an 

absolute inadequacy of access to food. That is, just knowing that an area has 

less access to food than another area may not be enough to say that access is 

inadequate and thus, whether policy-interventions may be necessary. This 

is not to say that there are no areas where access is inadequate. Rather, 

such a designation has not yet been systematically considered to make such 

judgments. Further conceptual clarifi cation of the adequacy of food access is 

needed in order to do so. 

Actually measuring food access, even when the key conceptual constructs are 

well-defi ned, will require a great deal more data. National-level assessments, 

such as those conducted in Chapter 2, are important for monitoring broad 

characteristics of the food environment in the U.S. The use of supermarkets 

and large grocery stores as proxy measures of the availability of affordable 

and nutritious food is a feasible way to operationalize a measure of access 

on a broad national scale. Such stores typically offer competitive prices 

and have greater variety of foods. But examining only large supermarkets 

and grocery stores inevitably understates the sources of food available 

to consumers, since food is available from many other retailers and from 

restaurants and because consumers do not limit their food shopping to stores 

near their homes. Thus, national-level assessments can only go so far. 

It may be more feasible to collect indepth information on food availability, 

prices, and consumer shopping and travel behavior systematically across 

a few local areas so that concepts and measures of access can be further 

defi ned and studied. Tools like the Nutrition Environment Measures Survey 

and indepth studies that have included complete enumeration of stores in 

an area and even collected information on the contents of shelves of stores 

within an area (for example, Rose et al., 2009; Sharkey and Horel, 2009) 

could be used. Surveys of individual consumers’ food shopping, preparation, 

and consumption patterns could be a part of such studies. Such surveys 

may also include questions of access limitations like those collected in the 

CPS-FSS or the National Food Stamp Participant Survey. 

It would also help to know the prices of goods because while some goods 

may be available, their prices may be so high that they are not reasonably 

affordable. Data collected by the Bureau of Labor Statistics to compute the 

Consumer Price Index, or data from Nielsen’s Homescan panel could be 

linked to food availability data (Bitler and Haider, 2009). 

On a national level, it may be more feasible to use direct questions about 

food access and the food environment to understand the extent of the 

problem. Questions on whether households had enough to eat and if 

not, why, that were once a part of the Current Population Survey’s Food 

Security Supplement could again be included on the annual survey. These 

questions may need to be modifi ed to elicit more information about access 

limitations. But they could be useful in monitoring changes and trends in 

the number of Americans affected by limited access to food sources on a 

national level. The NFSPS is another model for collection of individual 

level measures of access to food. This survey collected much more extensive 

data on consumer shopping behavior and data on modes of travel to stores, 

travel times and distances, types of foods purchased, and consumer opinions 
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about food shopping options. Other surveys of individuals and households 

could consider asking more questions about food shopping and consumption 

behavior, including questions on the kinds of stores at which major shopping 

is conducted, the distance to the stores and the time and costs required to 

reach them, and the modes of transportation used to get the stores. Nationallevel surveys such as the National Health and Nutrition Examination Survey 

(NHANES) or the Consumer Expenditures Survey could be used for this 

purpose. NHANES also collects food intake data and information on BMI 

and other diet-related health outcomes for individuals and could be used 

to better understand the economic and health consequences of food access 

(a point discuss later in this chapter). Individual questions of mobility 

limitations (e.g., for the elderly or disabled) could also be considered. 

Understanding the Causes of Limited Access

It is possible that supply-side factors, such as the costs fi rms face to open 

new stores, or demand-side factors, such as a lack of information about the 

health benefi ts of some foods, or both could lead to variation in access across 

areas and in the types of retailers or types of foods sold in areas. Future 

studies should consider how supply or demand factors differ across areas in 

ways that could lead to differences in access to food retailers and restaurants. 

There is some research on the cost factors affecting food retailers (King et 

al., 2004). The operating costs of supermarkets that serve relatively larger 

portions of low-income consumers are not greater than those that serve fewer 

low-income consumers, but it is not clear if there may be differences in fi xed 

costs of developing supermarkets or stocking healthier food alternatives. In 

a similar way, further exploration into demand-side factors—why consumers 

choose the foods they buy and the stores and restaurants from which they buy 

could also help determine the causes of variation in food access across areas. 

Such studies would require going beyond the usual factors that economic 

studies consider in demand analysis, such as income, education, and price. 

Although data could be diffi cult to collect, more information is needed on 

consumer knowledge of nutrition, food preparation, tastes, and variations 

of such across different subpopulations. Understanding the time, price, and 

nutritional tradeoffs of cooking at home versus eating at restaurants or eating 

prepared meals would also be benefi cial for understanding how some areas 

may be saturated with some types of food outlets but lacking in others. 

Another potential approach to understanding product availability and market 

prices is to apply spatial demand models to food access. These models 

(Capozza and Van Order, 1978) consider not only the costs for suppliers 

but also the transportation costs for consumers to get to different stores as 

determinants of spatial distribution of stores and prices. Applying these 

models would require better data on transportation costs to stores (Bitler and 

Haider, 2009). 

Understanding the Economic, Diet, and Health 

Consequences of Limited Access

The causal pathways linking food access to shopping and consumption 

behavior and the effects of these behaviors on diet-related conditions like 

body weight, diabetes, or cardiovascular disease are not well understood. 
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Methodological and data improvements could make it possible to go beyond 

cross-sectional relationships to isolate the effects of access, or changes in 

access, on shopping and consumption behavior separately from the effects of 

individual preferences for foods or other goods and services, which also may 

affect shopping and food intake decisions. 

Natural experiments and quasi-experiments, such as the UK studies 

in Glasgow and Leeds, could be conducted in the United States where 

new stores or improved stores become available. Instrumental variable 

approaches that try to consider the effects of access separately from decisions 

about choices of where to live could also be used to better understand the 

effects of access on shopping, diet, and diet-related health outcomes (see, 

for example, Chen et al., 2009). Studies that use cross-sectional variation in 

prices of foods, individual characteristics, and neighborhood characteristics 

could help sort out how these factors interact to affect food shopping and 

food intake. Finally, there is little consideration of the effects of time costs 

on decisions of where to shop for food, what to eat, and where to eat it. More 

consideration of the time tradeoffs of different consumption and shopping 

choices is needed. 

Cross-sectional data sets like NHANES and the American Time Use 

Survey could be further exploited to uncover the causal links. Doing so 

may require linking geographical data to the individuals or households in 

these surveys so that aspects of food access and the food environment can 

be linked to food intake or time-use data.50 Localized data on food prices 

from Nielsen is being linked with the NHANES survey by National Center 

for Health Statistics staff and used by ERS staff to better understand how 

price variation affects food consumption decisions. The 1996 National 

Food Stamp Participant Survey collected a wealth of information on food 

access, food shopping behavior, travel costs and time costs of food shopping, 

and food use (see Chapters 2 and 5). This survey is over 10 years old. 

An updated version of the survey could be considered to help improve 

understanding of food access issues. In addition to providing more recent 

data, additional information on food access, food shopping, and food use 

could be collected in an updated round, including complete information 

for both SNAP participants and eligible nonparticipants. It may also be 

feasible to oversample individuals who live in areas that have low access to 

supermarkets to provide greater sample size to study those who live in areas 

with low access. 

Longitudinal data on both consumer shopping and food intake and on the 

food environment could help tease out the causal pathways between access 

and consumption by linking changes in the food environment to changes in 

shopping or food intake. Currently, none of the national-level surveys collect 

detailed longitudinal data on consumer shopping or food intake. Given the 

lack of national longitudinal data on food shopping and food intake, it may 

be more feasible to add questions on intake of a few important foods (e.g., 

the frequency of eating fruits and vegetables or whole grains) to existing 

longitudinal surveys such as the Panel Study of Income Dynamics, the 

National Longitudinal Survey of Adolescent Health, or the Early Childhood 

Longitudinal Surveys. These longitudinal surveys or others, such as the 

Survey of Income and Program Participation, could also ask basic questions 

50The estimates of time spent 

traveling to grocery stores across 

distances from supermarkets in tables 

2.8 and 2.9 used such linked data. 
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of whether households have enough food and if not, why, such as those 

questions that were at one time part of the CPS-FSS. 

In addition to understanding where stores choose to locate, it is also 

important to know why people choose to live where they live. Economic 

studies have considered the values consumers place on different amenities 

of neighborhoods, such as availability of parks or the quality of public 

schools. Not much is known about how consumers value such amenities as 

supermarkets or restaurants. Low-income individuals are more constrained 

in these choices than high-income individuals. Even less is known about 

how lower income and other subpopulations value different amenities and 

about how social networks may affect location decisions. Further research 

on residential choice could help further the understanding of how differences 

in income, preferences, and price affect the way consumers sort into areas 

with different levels of access (see Bayer et al., 2004). 

References

Bayer, P.J., R. McMillan, K.S. Rueben (2004). “An Equilibrium Model 

of Sorting in an Urban Housing Market,” National Bureau of Economic 

Research Working Paper No. W10865. 

Bitler, Marianne, and Steven J. Haider (2009). “An Economic View of Food 

Deserts in the United States,” National Poverty Center Working Paper, http://

www.npc.umich.edu/news/events/food-access/index.php. 

Capozza, D.R., and R. Van Order (1978). “A Generalized Model of Spatial 

Competition,” American Economic Review 68(5): 896-908. 

Chen, S.E., R.J.G.M. Florax, and S.D. Snyder (2009). “Obesity, Fast Food, 

and Grocery Stores: Evidence from Geo-referenced Micro Data,” National 

Poverty Center Working Paper, http://www.npc.umich.edu/news/events/

food-access/index.php 

King, Robert P., Ephraim S. Leibtag, and Ajay S. Behl (2004). “Supermarket 

Characteristics and Operating Costs in Low-Income areas Areas,” 

Agricultural Economic Report No. 839, U.S. Department of Agriculture, 

Economic Research Service. 

Rose, D., J.N. Bodor, C.M. Swalm, J.C. Rice, T.A. Farley, P.L. Hutchinson 

(2009). “Deserts in New Orleans? Illustrations of Urban Food Access and 

Implications for Policy,” National Poverty Center Working Paper. http://

www.npc.umich.edu/news/events/food-access/index.php.

Sharkey, J.R. and S. Horel (2009). “Characteristics of Potential Spatial 

Access to a Variety of Fruits and Vegetables in a Large Rural Area,” 

National Poverty Center Working Paper, http://www.npc.umich.edu/news/

events/food-access/index.php.

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Appendix A

Access to Affordable and Nutritious Food: 

Understanding Food Deserts

Workshop Agenda

Waugh Auditorium

Economic Research Service

1800 M Street, NW

October 9, 2008 

8:00 – 8:30 Check-in and continental breakfast

8:30 – 9:00 Welcome, Introductions, and Goals of the Workshop

 Laurian Unnevehr, Food Economics Division, ERS 

 Shelly Ver Ploeg, Food Assistance Branch, ERS

9:00 – 10:30 Session 1: Defi ning and Conceptualizing Areas of Low 

Access to Affordable and Nutritious Food

 Chair: Robert Gibbs, Farm and Rural Household WellBeing Branch, ERS

 Lois Morton, Iowa State University

 Diego Rose, Tulane University 

 Joel Gittelsohn, Johns Hopkins University

10:30 – 10:45 Break

10:45 – 12:00 Session 2: Implications of Low Access for Food and 

Nutrition Assistance

 Programs

 Chair: Steve Carlson, Food and Nutrition Service, 

USDA

 Carol Olander, Food and Nutrition Service, USDA 

 Anne Bartholomew, Food and Nutrition Service, USDA 

 Joel Berg, New York City Coalition Against Hunger

 Lorette Picciano, Rural Coalition

12:00 – 12:45 pm Lunch at ERS

12:45 – 1:30 Luncheon Address: Food Deserts and Diet and Health 

Outcomes

 Judith Wylie-Rosett, Albert Einstein College of Medicine 

and the American Heart Association

1:30 – 3:00 Session 3: Community Efforts to Reduce the Impact of 

Areas of Low Access

 Chair: Elizabeth Tuckermanty, Cooperative State 

Research, Education, and Extension Service, USDA 

 Joel Gittelsohn, Johns Hopkins University
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 LaDonna Redmond, Institute for Community Resource 

Development, Chicago, IL

 Pamela Roy, Farm to Table & the New Mexico Food and 

Agriculture Policy Council

 Ken Meter, Crossroads Resource Center, Minneapolis, 

MN

3:00 – 3:15 Break

3:15 – 4:45 Session 4: Roundtable Discussion of Program and Policy 

Options

 Chair: Neil Conklin, Farm Foundation 

 Tim Hammonds, Food Marketing Institute

 John Weidman, The Food Trust

 Andy Fisher, Community Food Security Coalition 

 Kami Pothukuchi, Wayne State University

4:45 Closing and Adjourn

 Neil Conklin, Farm Foundation 
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Appendix B

Box B.1

Measures of Access Used in Food Desert and Related Studies

1) Algert, Susan J., Aditya Agrawal, and Douglas S. Lewis (2006). "Disparities in 

Access to Fresh Produce in Low-Income Neighborhoods in Los Angeles." American 

Journal of Preventive Medicine 30(5): 365-370.

Area Studied

Pomona, CA

Measure of Access

Divided food stores in Pomona as having a “variety” of produce 

or “limited produce” based on onsite inspections, telephone 

interviews regarding produce content, or inference from store 

description. Stores selling four or fewer produce items were 

counted as “limited” while those serving more than four were 

counted as having a “variety” of produce. Access was measured by distance from a store offering a variety of produce. 

Addresses of food pantry clients were geocoded. Those living 

outside of a “walkable” distance to a store selling a variety of 

produce (0.8 km or about a 15 minute walk) were highlighted 

as not having access to a variety of produce.

2) Alwitt, Linda F., and Thomas D Donley (1997). "Retail Stores in Poor Urban

Neighborhoods." The Journal of Consumer Affairs 31(1): 139-164.

Area Studied

Chicago, IL

Measure of Access

Examined number of retail stores of different types and sizes 

in “poor” ZIP Codes. Poor ZIP Codes are defi ned as: 1) poverty 

rate in highest quartile of ZIP Codes in Chicago, 2) lowest 

quartile of high school graduation rate, 3) lowest quartile of labor force participation, and/or 4) highest quartile of unemployment rate. Store size was defi ned by the number of employees. 

3) Apparicio, Philippe, Marie-Soleil Cloutier, and Richard Shearmur (2007). "The 

case of Montréal's missing food deserts: Evaluation of accessibility to food supermarkets." International Journal of Health Geographics 6(4).

Area Studied

Montreal, Canada

Measure of Access

Measured access by proximity (nearest supermarket), diversity 

(number of supermarkets within 1,000 meters), and variety in 

terms of food and prices (mean distance to the three closest, 

different chain-name supermarkets). Supermarkets were defi ned as grocery stores associated with one of the seven major 

chains in Quebec.

4) Baker, Elizabeth A., et al. (2006). "The Role of Race and poverty in Access to 

Foods That Enable Individuals to Adhere to Dietary Guidelines." Preventing Chronic 

Disease: Public Health Research, Practice, and Policy 3(3).

Area Studied

St. Louis, MO

Measure of Access

Supermarket audits were used to assess “healthy food” availability in stores, where healthy food was defi ned by USDA 

dietary intake guidelines. A checklist of 78 canned, fresh, or 

frozen fruits and vegetables were used by auditors. Availability 

of lean, low-fat and fat-free meat, poultry, and dairy products 

was also considered. Composite score for each supermarket was developed based on these audits. The composite 

scores were then divided into tertiles of high, medium, and low 

availability. Census tracts were divided by racial composition 

(greater than 75% African-American, greater than 75% White, 

and mixed) and poverty level (less than 10% poor, 10-20% 

poor, and greater than 20% poor).

5) Berg, Nathan, and James Murdoch (2008). "Access to grocery stores in Dallas." 

International Journal of Behavioural and Healthcare Research 1(1): 22-37.
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Area Studied

Dallas County, TX

Measure of Access

Mapped all of the mainline chain grocery stores in Dallas 

County and then counted how many stores were within a 

1-mile radius of neighborhood Census block groups. The study 

linked this geospatial data with neighborhood income levels, 

poverty levels, and racial composition.

6) Blanchard, Troy C., and Thomas Lyson (2006). "Access to Low Cost Groceries in

Nonmetropolitan Counties: Large Retailers and the Creation of Food Deserts." Journal of Regional Studies.

Area Studied

Mississippi

Measure of Access

A food desert is classifi ed as having no supermarkets or 

supercenters within a 10-mile radius of a ZIP Code centroid 

(with exceptions for highways). A supermarket classifi cation 

was based on having 50 or more employees and a supercenter 

classifi cation was based on NAICS category 452910.

7) Horowitz, Carol R., et. al. (2004). "Barriers to Buying Healthy Foods for People 

With Diabetes: Evidence of Environmental Disparities." American Journal of Public 

Health 94(9): 1549-1554.

Area Studied

East Harlem and 

Upper East Side, 

New York City

Measure of Access

Classifi ed stores as desirable if they had at least one item of 

the following fi ve and undesirable if it had none of the items:

(1) Diet soda (1-L or 2-L size)

(2) 1% fat or fat-free milk (1-quart, half-gallon, or 1-gallon size)

(3) High-fi ber bread, low-carbohydrate bread, or both high-fi ber 

and low-carbohydrate bread (defi ned as 2 g or more fi ber, 10 g 

or less carbohydrate per slice, or both)

(4) Fresh fruits

(5) Fresh green vegetables or tomatoes.

8) Block, Daniel and Joanne Kouba (2005). “A comparison of the availability and affordability of a market basket in two communities in the Chicago area.” Public Health 

Nutrition 9(7): 837-845.

Area Studied

Austin and Oak 

Park in Chicago, IL

Measure of Access

The study compared item availability, price, and quality of food 

in 10 different types of foodstores in Austin (poor and AfricanAmerican) and Oak Wood (upper-middle class and diverse 

races). Item availability was calculated by how many items 

a store stocked from a food list based on the USDA’s Thrifty 

Food Plan. Price was calculated from the items in the food 

basket and the average price from all stores was assigned if 

an item was not sold at that store. Quality rating was subjective and only given two options, “satisfactory” or “poor.” The 10 

store types were national and regional chain markets, independent supermarkets, independent groceries, chain drug stores, 

gas stations, liquor stores with food, chain convenience stores, 

dollar stores, and specialty stores.

9) Block, JP, RA Scribner, KB DeSalvo (2004). "Fast food, race/ethnicity, and income: 

a geographic analysis." American Journal on Preventive Medicine 27(3): 211-7.
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Area Studied

New Orleans, LA

Measure of Access

The study geocoded all fast food restaurants and used a 1 

mile and 0.5-mile radius as buffers around Census tracts to 

determine “shopping areas” in each tract. Fast food restaurant 

density was calculated by the number of fast food restaurants 

per square mile. Fast food restaurants have two or more of the 

following characteristics: expedited food service, takeout business, limited or no wait staff, and payment tendered prior to 

receiving food. These data were then compared with neighborhood characteristics, such as percentage Black population and 

low income.

10) Bodor, J Nicholas, Donald Rose, Thomas A Farley, Christopher Swalm, and 

Susanne K Scott. "Neighborhood fruit and vegetable availability and consumption: 

the role of small food stores in an urban environment." Public Health Nutrition 11(4): 

413-420.

Area Studied

Four Census tracts 

in central-city New 

Orleans

Measure of Access

Adequate access was calculated by two components; distance 

from the household to a foodstore and the instore availability of 

healthy items. The study included all stores selling food within 

the four Census tracts and all supermarkets within 5 km of the 

tract borders. Trained observers determined instore availability by calculating the linear shelf space devoted to fruits and 

vegetables and the number of fresh produce varieties available. 

Neighborhood availability was then determined by summing all 

of the shelf space devoted to fresh fruits and vegetables in all 

small food stores within 100 m of the household residence.

11) California Center for Public Health Advocacy, PolicyLink, and UCLA Center for 

Health Policy Research (2008). Designed for Disease: The Link Between Local Food 

Environments and Obesity and Diabetes.

Area Studied

California

Measure of Access

Retail Food Environment Index (per adult): Total number of fast 

food restaurants and convenience stores divided by the total 

number of grocery stores and produce vendors within a given 

radius of the person’s home. The radius used was 0.5 miles for 

urban areas and 5 miles for rural areas.

12) Clarke, Graham (2002). "Deriving Indicators of Access to Food Retail Provision 

in British Cities: Studies of Cardiff, Leeds, and Bradford." Urban Studies 39(11): 

2041-2060.

Area Studied

Cardiff, Leeds/

Bradford (UK)

Measure of Access 

The study used GIS to map store locations. A circle with a 

500-meter radius was drawn around each multiple/co-op store, 

which represents the maximum walking distance. Potential 

food deserts were only the areas that met the Carstairs indices 

of multiple deprivation and only had a few, small independent 

stores. The Hansen accessibility and spatial interaction model 

take into account the location and size (30,000, 60,000, and 

90,000 square feet) of store in relation to its customers and 

social class structures.

13) Examining the Impact of Food Deserts on Public Health in Chicago. Mari Gallagher Research & Consulting Group (2006).
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Area Studied

Chicago, IL

Measure of Access 

Food Balance Score: average distance (blocks) to any mainstream food venue divided by the average distance to a fringe 

food venue (includes fast-food). A mainstream food venue is a 

grocery store and a fringe food venue is everything else, such 

as convenience stores and fast food restaurants. The study 

compared food ratio scores and their impact on Years of Potential Life Lost (YPLL) and BMI outcomes, holding education, 

income, and race constant.

14) Examining the Impact of Food Deserts on Public Health in Detroit. Mari Gallagher Research & Consulting Group (2007).

Area Studied

Detroit, MI

Measure of Access 

Food Balance Score: average distance (blocks) to any mainstream food venue divided by the average distance to a fringe 

food venue. A mainstream food venue is a grocery store and 

a fringe food venue is everything else, such as convenience 

stores and fast food restaurants. Researchers manually classifi ed store types based on appearance and items they sold. 

Diet-related death data are used to calculate Years of Potential 

Life Lost, a statistic that measures the total number of life years 

lost due to premature death per 100 people in a population 

from a certain cause. The Food Balance Score was then compared with the YPLL statistic in each neighborhood.

15) Guy, Cliff, Graham Clarke, and Heather Eyre (2004). “Food retail change and the 

growth of food deserts: as case study of Cardiff.” International Journal of Retail and 

Distribution Management 32(2): 72-88.

Area Studied

Cardiff, UK

Measure of Access 

The study measured the spatial distribution of multiple and coop grocery stores (healthy food stores) using electoral divisions 

as the geographical unit. Areas with a high deprivation are 

considered food deserts, and this was calculated by the Welsh 

Index of Multiple Deprivation (IMD). IMD includes income, 

employment, health, education, housing, and geographic 

components.

16) Hendrickson, Deja, Chery Smith, and Nicole Eikenberry (2006). "Fruit and vegetable access in four low-income food deserts communities in Minnesota." Agriculture and Human Values 23: 371-383.

Area Studied

Four urban and 

rural communities 

in Minnesota

Measure of Access 

The study used a survey based on the Thrifty Food Plan to 

measure the average price per pound of a food item, how 

much of the food item was offered, and the quality of the food 

item. Quality was determined by expiration dates on packaged food and by the opinion of surveyors for produce items. 

Consumer focus groups also revealed what residents thought 

of the availability, quality, and price of food.

17) Hosler, Akiko, et. al. (2008). "Assessing Retail Fruit and Vegetable Availability 

in Urban and Rural Underserved Communities." Preventing Chronic Disease 5(4). 

http://www.cdc.gov/pcd/issues/2008/oct/07_0169.htm. Accessed December 4, 2008.
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Area Studied

Urban and rural 

areas in New York 

State

Measure of Access 

Instead of categorizing stores based on business type (such 

as a gas station or supermarket), businesses were labeled as 

a fruit and vegetable stores if they stocked at least two types 

of fresh fruit and at least three types of fresh vegetables. There 

were three types of fruit and vegetable stores: super produce 

stores, year-round produce stores, and seasonal produce 

stores. Fruit-for-snack stores were stores that carried at least 

one type of ready-to-eat fresh fruit but didn’t meet the fruit and 

vegetable measure.

18) Sharkey, Joseph R. and Scott Horel (2008). "Neighborhood Socioeconomic 

Deprivation and Minority Composition Are Associated with Better Potential Spatial 

Access to the Ground-Truthed Food Environment in a Large Rural Area." The Journal of Nutrition 138(3): 620-627.

Area Studied

6-county rural 

region in Texas

Measure of Access 

The study calculated the distance to the nearest foodstore from 

the population-weighted center of each CBG and then examined associations among deprivation, minority composition, 

population density, and distance to foodstore.

19) Kaufman, Phil R. (1999). "Rural Poor Have Less Access to Supermarkets, Large 

Grocery Stores." Rural Development Perspectives 13(3): 19-25.

Area Studied

Lower Mississippi 

Delta (36 rural, 

high-poverty 

counties)

Measure of Access 

Net accessibility is the ratio of available large grocery store 

sales to potential food spending by households in a ZIP Codebased area (region broken up into quartiles). The stores in the 

study all have annual sales of $500,000 or more.

20) Morton, Lois Wright and Troy C. Blanchard (2007). "Starved for Access: Life in 

Rural America's Food Deserts." Rural Realities 1(4): 1-10.

Area Studied

Rural Iowa

Measure of Access 

Low access: 50% of population lives more than 10 miles from 

large foodstore (supermarkets or supercenters).

Food Desert: All residents live more than 10 miles from large 

foodstore

Large foodstores are large supermarkets and supercenter.

21) Short, Anne, Julie Guthman, and Samuel Raskin (2007). "Food Deserts, Oases, 

or Mirages? Small Markets and Community Food Security in the San Francisco Bay 

Area." Journal of Planning Education and Research 26: 352-364.

Area Studied

San Francisco, CA 

(Bayview, Mission, Central East 

Oakland)

Measure of Access 

The study measured accessibility by distance (mapping), 

affordability and nutritional adequacy (market basket analysis), cultural acceptability (types, variety and quality of goods 

carried and characteristics that may affect the shopping 

experience), and quality (produce observation) for small (less 

than 3,000 sq ft) full-service foodstores. Instead of measuring 

overall community access, the study measured whether small 

full-service stores can enhance access to food compared with 

chain and large supermarket availability.

22) Smoyer-Tomic, Karen E., John C. Spence, and Carl Amrhein (2006). "Food Deserts in the Prairies? Supermarket Accessibility and Neighborhood need in Edmonton, Canada." The Professional Geographer 58(3): 307-326.
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Area Studied

Edmonton, Canada

Measure of Access 

Edmonton was broken up into neighborhoods based on ZIP 

Code and accessibility was measured by distance (shortest 

route) and GIS mapping to the closest supermarket. A supermarket was defi ned as offering a full range of grocery items 

(dairy, fresh produce, meat, and baked goods) and having a 

minimum of 10 employees. Two areas within Edmonton were 

then compared. 

23) Wrigley, Neil, Daniel Warm, and Barrie Margetts (2003). "Deprivation, diet, and 

food-retail access: fi ndings from the Leeds 'food deserts' study." Environment and 

Planning A 35: 151-188.

Area Studied

Leeds, England

Measure of Access 

The study measured both the physical (distance and mode 

of transportation) and economic (income) access barriers to 

supermarkets before and after an intervention.

24) Zenk, Shannon, et. al. (2005). "Neighborhood Racial Composition, Neighborhood Poverty, and the Spatial Accessibility of Supermarkets in Metropolitan Detroit." 

American Journal of Public Health 95(4): 660-667.

Area Studied

Detroit, MI

Measure of Access 

The study measured the Manhattan block distance to nearest supermarket (only chain supercenters and chain full-line 

grocery stores) from the middle of the neighborhood for 869 

neighborhoods.

25) Zenk, Shannon N., et al. (2005). "Fruit and Vegetable Intake in African Americans: Income and Store Characteristics." American Journal of Preventive Medicine

29(1): 1-9.

Area Studied

Eastside Detroit, 

MI (97% African 

American)

Measure of Access 

The study measured the effects of sociodemographics and 

food-store characteristics on fruit and vegetable intake. Sociodemographics include age, education, and family income. 

The foodstore characteristics were location, type (classifi ed by 

defi nitions from the Food Marketing Institute), and perceived 

quality and availability of fruit and vegetables.

26) Blanchard, Troy C., and Thomas Lyson (2006). Food Availability and Food 

Deserts in the Nonmetropolitan South. Assistance Needs of the South’s Vulnerable 

Populations, Number 12.

Area Studied

South US

Measure of Access 

A food desert is when a certain percentage of a county’s 

population is not located within 10 miles of a large food retailer 

(supermarkets with 50 or more employees, supercenters, and 

wholesale clubs).

27) Powell, Lisa M, et. al. (2006). “Food store availability and neighborhood characteristics in the United States.” Preventive Medicine 44: 189-195.

Area Studied

28,050 ZIP Codes 

in United States 

Measure of Access 

The study compared the availability of chain and nonchain 

supermarkets, grocery stores, and convenience stores to 

neighborhood characteristics (race, ethnicity, and SES) at the 

ZIP Code level. “Availability” was defi ned as the number of 

each type of store in the ZIP Code.

28) Sturm, R., and A. Datar (2004). "Body mass index in elementary school children, metropolitan area food prices and food outlet density." The Royal Institute of 

Public Health 119: 1059-1068.

Area Studied

U.S. Kindergarten 

children

Measure of Access 

The study compared the change in the children’s BMIs (fi rst 

year and third year) with the prices for meat, fruits, dairy, and 

fast food and the per capita number of foodstores and restaurants in the residence ZIP Code.

29) Powell, Lisa M., et. al. (2007). "Associations Between Access to Food Stores 

and Adolescent Body Mass Index." American Journal of Preventive Medicine

33(4S): S301-S307.

Area Studied

U.S. adolescents

Measure of Access 

The study compared the BMI of adolescents to the distance 

and type of foodstores located near residence (ZIP Code). 

There were four types of stores: chain supermarkets, nonchain 

supermarkets, grocery stores, and convenience stores.

30) Rose, Donald and Rickelle Richards (2004). "Food store access and household 

fruit and vegetable use among participants in the US Food Stamp Program." Public 

Health Nutrition 7(8): 1081-1088.

Area Studied

US

Measure of Access 

Supermarket Access Variable: Distance of store, travel time, 

individuals’ car ownership, and type of store was used to 

measure household fruit use and household vegetable use 

(expressed in grams per male adult equivalent per day).

31) Shaw, Hillary J (2006). "Food Deserts: Towards the Development of a Classifi cation." Series B: Human Geography 88(2): 231-247.

Area Studied

UK

Measure of Access 

Urban areas were divided into 250 sq m blocks and rural 

areas were divided into 500 sq m blocks. Each neighborhood 

was ranked based on whether the area was residential, had a 

grocery store, and whether 10 or more kinds of fresh fruit and 

vegetables were sold (scale 1-4).

Locations of Food Access Studies

Local/ State: Regional/ Nation:

1-Pomona, CA 13-Chicago, IL 26-South USA

2-Chicago, IL 14-Detroit, MI 27-USA

3-Montreal, Canada 15-Cardiff, UK 28-USA

4-St. Louis, MO 16-Minnesota 29-USA

5-Dallas County, TX 17-New York 30-USA

6-Mississippi 18-Texas 31-UK

7-New York, NY 19-Lower MS Delta

8-Chicago, IL 20-Iowa * 26-31 not shown on 

maps

9-New Orleans, LA 21-San Francisco, CA 

10-New Orleans, LA 22-Edmonton, Canada

11-California 23-Leeds, UK

12-Cardiff, UK 24-Detroit, MI

 25-Detroit, MI

UK

23

22

3

16

20

17

6

12, 15

19

4

14, 24, 25 7

2, 8, 13

9, 10

1

21

11

Canada

USA
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Appendix C

Methods, Supporting Tables, and Maps for 

National-Level Analysis of Supermarket Access 

I. Methods

Store Directory Development. The directory of authorized SNAP 

foodstores was merged with the TDLinx listing of supermarkets to take 

advantage of the strengths found in each source. SNAP-authorized stores 

that did not match with the TDLinx directory were examined in detail. 

After reviewing SNAP stores classifi ed as either SM (supermarkets) or SS 

(superstores) in the 2006 listing, it was concluded that many of the SNAP 

superstores did not meet the same criteria as the industry supermarkets.51

To further sort through which of the SNAP stores met the defi nition of a 

supermarket, researchers examined the annual total sales and food sales of 

all SNAP stores classifi ed as SM and SS that did not match with TDLinx 

supermarkets. Stores that did not meet the annual $2 million in sales 

requirement or that did not have signifi cant food sales were eliminated. 

Researchers used the Trade Dimensions Marketing Guidebook (2008) 

to verify where food sales data were unavailable or not current. Online 

sources, such as company Web sites and other online sources for additional 

information about a store’s annual sales and the kinds of foods sold, were 

checked. By combining the two store listings and using outside sources 

for verifi cation, researchers obtained a more comprehensive national list of 

supermarkets and supercenters. 

The combined list of supermarkets was converted into a GIS-useable 

format by geocoding the street addresses into store point locations. In 

many instances, the two databases referenced the same store, while in 

other instances the geocoded location differed. To address near-matches, 

researchers employed a proximity analysis and an automated matching 

system to analyze similarities in store names and addresses and location. 

Some manual data analysis was also performed to identify points in the two 

data sets that were the same supermarket. The resulting data set included all 

of the more than 34,000 TDlinx store locations, as well as an additional 6,000 

SNAP store locations (all of which had annual sales above $2 million and did 

not match a TDlinx store). The fi nal combined data set included locations 

for 40,108 supermarkets and supercenters nationwide.

Walking and Driving Distances Measures of Access. Walking access 

measures a range of distances for which it is feasible to walk to a 

supermarket, while drivable distance measures a range of distances for 

which it is feasible to drive to a supermarket. Researchers developed a timebased distance measure equivalent for both walking and driving. A walking 

speed of 2 miles per hour was assumed; thus, “high” access would equal a 

15-minute walk, or one-half mile in distance. For drivability, researchers 

assumed a point-to-point driving speed of 40 miles per hour; thus, a walking 

distance equivalent to “high” access based on driving is 10 miles (40mph 

x 0.25 hours). These measures were extended to obtain driving timeequivalent distances for “medium” and “low” access.

51 SNAP-authorized stores were, for 

the most part, self-classifi ed by the 

applicant. A large number of SNAP 

superstores are known to carry some 

packaged foods but are less likely to 

include fresh fruit and vegetables or 

fresh meat products.
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The walkability range is categorized as high, if a supermarket is within a 

half mile; medium, if a supermarket is between one-half and 1 mile; and 

low, if the nearest supermarket is more than a mile away. Obviously, 

whether walking to a supermarket is feasible or not depends on more than 

just distance—it could also depend on whether the individual is capable of 

walking that distance, whether there are safe sidewalks on which to walk and 

controlled intersections, and whether there are other barriers, such as crime 

that may make walking to a store dangerous. Furthermore, the measures of 

distance are all Euclidian, or straight-line distance, which may not represent 

the actually distance that must be walked to access a supermarket. Despite 

these limitations, this defi nition of walkability is grounded in the literature. 

Algert, Agrawal, and Lewis (2006) defi ned access to stores selling a variety 

of produce as a walkable distance of 0.8 km., or about a 15-minute walk. 

Apparicio et al. (2007) measured supermarket access proximity by the 

number of stores within 1,000 meters, or about 0.6 miles. In a study by 

the California Center for Public Health Advocacy and the UCLA Center 

for Health Policy Research (2008), a store radius of 0.5 miles was used to 

determine adequate access in urban areas. 

In rural areas, a drivability measure of access is also used. Drivability is 

categorized as either high, if a supermarket is within 10 miles; medium, 

if a supermarket is between 10 and 20 miles; and low, if a supermarket is 

greater than 20 miles away. The drivability range is not as well grounded 

in the literature as is the walkability range. Sharkey and Horel (2009) used 

a walking distance of 1 mile, and driving distances of 3, 5, and 10 miles to 

measure foodstore proximity in six rural counties in Texas. Morton and 

Blanchard used any distance outside of 10 miles to describe areas with 

limited access (2007). Kaufman (1999) found access to a supermarket 

involved a trip of more than 30 miles for 70 percent of low-income 

households in a 36-county area of Mississippi, Louisiana, and Arkansas. 

Researchers know of no other studies that have systematically characterized 

rural areas as having access to stores or not. The categorization is admittedly 

somewhat arbitrary, but is not thought to be unreasonable for illustrating 

national-level trends. 

Use of a Kernel Density Function to Defi ne Low-Income Areas. Two 

criteria were used to identify low-income neighborhoods and communities: 

a household income had to equal 200 percent or less of the Federal poverty 

threshold, and, for a given geographic area, at least 40 percent of the 

population had to meet that criterion. Because the geographic areas consisted 

of uniform 1-sq.-km. grids, a systematic search criterion was used, where for 

each grid, the population of adjacent grids extending 3 km. in all directions 

was used to test whether at least 40 percent of the total population within the 

search area met the poverty threshold requirement. This procedure is a type 

of the kernel density function. Its primary purpose here is to test each grid 

within the context of adjacent grids for meeting the low-income area criteria. 

The kernel density function also serves to smooth the observed variation in 

population income, resulting in contiguous (less fragmented) low-income and 

non-low-income geographic areas, in which the population within these areas 

is more similar than dissimilar with respect to the income thresholds applied.
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While the choice of search area to use is not empirically derived, researchers 

experimented with different search areas and observed the resulting plots of 

low-income areas. An effort was made to avoid applying search criterion 

less than 3 km., in which results gave highly fragmented (pixilated) areas, 

interspersed with many higher income areas. Conversely, the use of a large 

search criterion, such as 5 km., resulted in very large low-income areas that 

included many higher income populations within it. In determining the 

kernel density search area, these factors and outcomes were considered.

II. Tables.

Table C.1

National levels of access households living in higher income areas: walking and driving distances1

Access level 

Number

of squarekilometer grids

Share of 

total square 

kilometer 

grids 

Total 

higher-income 

area persons,

by access level2

Share of total 

higher-income 

area persons, 

by access level

Number of 

low-income 

persons in 

higher-income 

areas, by 

access level 

Share of total 

low-income 

persons in 

higher-income 

areas, by 

access level 

Percent Millions Percent Percent

Walking:

 High 57,209 1.0 47.6 22.8 10.6 24.4

 Medium 148,359 2.6 68.0 32.6 13.9 32.2

 Low 5,433,305 96.4 92.8 44.5 18.8 43.3

Subtotal 5,638,873 100.0 208.3 100.0 43.3 100.0

Driving:

 High 3,776,567 67.0 205.2 98.5 42.4 97.9

 Medium 1,292,894 22.9 2.8 1.3 0.8 1.9

 Low 569,412 10.1 0.3 0.1 0.1 0.2

Subtotal 5,638,873 100.0 208.3 100.0 43.3 100.0

1Includes low-income households living in higher-income areas.

2Areas defi ned according to ERS criteria. See text for details.

Sources: Census of Population, 2000 and ERS-compiled 2006 supermarket directory.
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III. Figures.

Measuring Access Using Cumulative Density Functions (CDFs). In the 

area-based analysis, researchers fi rst separated the U.S. population into one 

of three urbanicity types (urban, urban clusters, and rural) to compare access 

to supermarkets among areas having similar levels of built environment. For 

each urbanicity, low-income areas were compared with non-low income 

areas, as well as for three additional vulnerable subpopulations. Within 

each urbanicity-subpopulation class, distance was measured to the nearest 

supermarket for each grid. All grids were ranked according to distance, 

from closest to farthest. The corresponding populations of the ranked grids 

were tallied and accumulated. The resulting distances and corresponding 

cumulative share of total population were plotted in a cumulative density 

function (CDF) chart, in which for any point on the curve(s), distance 

(measured on the horizontal axis) and its corresponding cumulative share of 

population (measured on the vertical axis) can be determined. 

Figure C.1. plots separate CDFs for each of the three urbanicities: urban 

areas, urban clusters, and rural areas. The vertical axis shows the cumulative 

share of the total U.S. population, with the horizontal axis indicating distance 

to the nearest supermarket. Using CDFs, one can examine the CDF curves 

of two or more subpopulations for potential access inequalities. Large 

differences in access appear as gaps between the individual CDF curves. 

The overall shape of individual curves also refl ects the range of access 

experienced for a given share of the population or subpopulation. Access 

curves initially having very steep slopes and gradually fl attening at the top 

indicate superior access relative to access curves that have a more gradual 

slope extending to the upper right corner of the chart. These differences 

can be seen in fi gure C.1., where dotted vertical lines demarcate distances 

of 0.5 and 1.0 miles, indicating high, medium, and low access for each of 

the three urban categories. The intersections of the horizontal lines on each 

curve indicate the corresponding share of the total population for each urban 

category. Accordingly, 74.4 percent of the urban population was within 1 

mile of the nearest supermarket. Access in urban clusters was only slightly 

lower, where 64.9 percent of the population had a supermarket within 1 mile. 

In contrast, rural populations had considerably lower levels of access. Only 

12.4 percent of the rural population was within 1 mile of a supermarket. 

This result is not unexpected, due to the much greater spatial dispersion 

of the rural population and its greater dependence on owned-vehicle 

transportation.52 

Similar CDF charts are presented below for selected urbanicitysubpopulation combinations. 

52In the analysis of rural area access to 

supermarkets, we employ both walking 

and driving distance criteria. We do not 

show the drivable distance on fi gures 

that include urban and urban cluster 

areas because very few people in these 

areas are outside of 10 miles of a supermarket (in fact, almost all are within 4 

miles, as shown in fi gure 3.1. 
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0

0.0

0.2

0.4

0.6

0.8

1.0

0.5 1 2 3 4

Distance to nearest supermarket (mi)

Cumulative share of population (%)

Urban areas

Urban clusters

Rural areas

Figure C.1

Supermarket access by urbanicity for the total population

0

0.0

0.2

0.4

0.6

0.8

1.0

1234

Distance to nearest supermarket (mi)

Cumulative share of low income population (%)

Figure C.2

Access to supermarkets by urbanization level for non low income areas

Low income area

Non−low income areas
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United States Department of Agriculture

0

0.0

0.2

0.4

0.6

0.8

1.0

1234

Distance to nearest supermarket (mi)

Cumulative share of low income population (%)

Figure C.3

Supermarket access for low-income individuals in low-income and 

non-low-income areas within urban areas

Low income area

Non−low income areas

Distance to nearest supermarket (mi)

0.0

0.2

0.4

0.6

0.8

1.0

Cumulative share of households without a vehicle

Figure C.4

Supermarket access for households without vehicle by low-income and 

non-low-income areas within rural areas

0 15 20 25 5 10

Low income area

Non−low income 

areas
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United States Department of Agriculture

Distance to nearest supermarket (mi)

0.0

0.2

0.4

0.6

0.8

1.0

Cumulative share of non−White population

Figure C.5

Supermarket access for non-White individuals by low-income and 

non-low-income areas within rural areas

0 15 20 25 5 10

Distance to nearest supermarket (mi)

0.0

0.2

0.4

0.6

0.8

1.0

Cumulative share of people 65 and older

Figure C.6

Supermarket access for elderly households by low-income and non-lowincome areas within rural areas

0 15 20 25 5 10
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Methodology for Average Time Spent 

in Travel to Grocery Shopping

The estimates of average time spent in travel to grocery shopping were 

made using the pooled 2003-2007 American Time Use Survey (ATUS) 

data. The U.S. Bureau of Labor Statistics’ ATUS is a continuous survey 

that began in 2003. One individual age 15 or older from each sampled 

household is interviewed about his or her use of time for the 24-hour period 

from 4 a.m. the day before the interview to 4 a.m. on the interview day. The 

time diary information includes where the respondent was and whom the 

respondent was with for each activity. The ATUS also collects information 

on the respondent’s household, labor force participation, and demographic 

characteristics. (For more information on the ATUS, see www.bls.gov/tus).

The pooled 2003-2007 ATUS microdata fi les contain 72,922 completed 

interviews. Of those, 11,726 observations are of respondents who grocery 

shopped on their diary day.

Measuring travel time can be diffi cult and complex. Individuals string 

together activities with travel in between (“trip chaining”), making it 

diffi cult to separate out travel specifi c for a single activity. For example, an 

individual may leave from home and travel some distance, stop to buy coffee, 

then continue traveling to the work location, work a full day, then travel to 

the dry cleaners, pick up dry cleaning, travel to the grocery store, grocery 

shop, travel to a restaurant, eat at the restaurant, and, fi nally, travel home. 

In this case, neither commuting to or from work or traveling to or from the 

grocery store is a single travel occurrence.

The ATUS time diary data contain extensive information about Americans’ 

travel. The data specify travel as an activity, and record mode of 

transportation and whom the respondent was with when traveling. If the 

travel was by vehicle, the data include whether the respondent was the driver 

or passenger. Travel is defi ned as moving from one location (or address) to 

another.

To deal with the complexities of trip chaining, ATUS codes the travel 

activities as to their purpose, looking ahead to the next activity and location. 

For example, the time a respondent travels from home to work is coded 

as 180501, “Travel related to working.” If the respondent went grocery 

shopping after work, the time spent traveling to the store is coded as 180701, 

“Travel related to grocery shopping.” The exception to the “looking ahead” 

rule is when the respondent is traveling from one location to home, in which 

case the purpose of the travel is coded as a previous activity. As a result, 

calculating travel time to the grocery store is complicated by the fact that 

some diaries will have only one “side” of travel related to grocery shopping 

coded as travel related to grocery shopping and others will have both 

sides—the going and coming home—of the trip coded as travel related to 

grocery shopping. 

To best deal with trip chaining and the ATUS coding, researchers decided 

to estimate average time to the grocery store as follows. For each time 

diary with grocery shopping as an activity in the respondent’s time diary, 

researchers added all legs of travel from home to the grocery store and then 
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added all legs of travel from the grocery store to home. The total travel time 

home-to-shopping was compared with the total travel time shopping-tohome, and the shorter total time was chosen as the “time distance” to grocery 

shopping. In doing this, researchers did not have to consider the coded 

purpose of the travel, and they also did not have to consider the “dwell time,” 

that is, the time spent on an activity between two travel occurrences. All the 

characteristics of travel to grocery shopping, such as mode of transportation, 

were characteristics of the shorter travel side. In cases where the respondent 

did not start the day at home or did not end the day at home, researchers only 

had information for one side (home to grocery shopping or grocery shopping 

to home). In these cases, the total travel time for that side was used as the 

time distance to the grocery shopping. 

For 6.4 percent of the grocery shoppers in the ATUS data, the shortest time 

distance is actually from work and not from home. It was decided that the 

work location is a relevant means of access to grocery shopping, so for these 

respondents the travel time is work-to-store or store-to-work. Consequently, 

the average times presented use two “anchors,” home and work.

Grocery shopping was defi ned as the ATUS activity 070101 with the 

location of grocery store, restaurant or bar, other store/mall, outdoors away 

from home, or other place. Grocery shopping with the locations home, 

work, church, and library were not included so as to exclude online grocery 

shopping. If the respondent was not at home for any activity in the 24-hour 

time diary, that observation was not included. If the mode of travel was by 

airplane, the observation was excluded as the grocery shopping was likely in 

an airport during out-of-town travel. Thirty-seven observations with grocery 

shopping were excluded for the following reasons: it was determined that 

the respondent was out of town when the grocery shopping took place; the 

time diary was not clear as to what the respondent did on the diary day, 

usually due to “can’t remember” or “none of your business” responses; 

or the respondent had a large number of errands or other activities on the 

diary day such that the total travel time to grocery shopping would likely 

be an overestimate of the time distance to the grocery store. The resulting 

dataset used to calculate the estimates contains 11,569 observations. A small 

number of extreme cases were included—respondents who had zero minutes 

travel to grocery shopping, which is possible if the store is in the same 

building as the respondent’s previous activity, and respondents who had 

120 minutes or longer travel to grocery shopping. Additional details about 

variable coding and defi nitions are available on request.

The Bureau of Labor Statistics (BLS) and the Census Bureau calculated 

the estimates to ERS specifi cations to have estimates by whether or not the 

respondent was in a low-access area. Without BLS and Census cooperation, 

this analysis could not have been done as detailed geographical information 

is not publicly available in the ATUS data fi les. 

The analysis used 90 percent confi dence intervals to determine whether two 

average time estimates were statistically different.

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Appendix D

Examples of Community Food Projects 

by Type of Project

Food Assessments

The Fresno Fresh Access Project was funded at $200,000 for 2 years. 

The grantee organization was the Fresno Metropolitan Ministry, Fresno, 

California. The purpose of this community food assessment was to identify 

key factors in food availability, accessibility, affordability, and quality in 

Fresno County. The objectives were to assess a number of districts, involve 

local residents and community volunteers in the survey process, empower 

community members to make food policy recommendations to local offi cials, 

and create an action plan from data collected during the assessment.

The Fresno Metropolitan Ministry trained more than 80 local neighborhood 

leaders, conducted more than 850 survey-assessments of consumers, and 

surveyed 131 retail stores. Numerous activities and several different formats 

were used to collect data in an effort to assess area food needs. Activities 

included surveys, use of assessment software, GIS mapping, and local task 

forces. These methods provided different perspectives on food and nutrition 

needs in the county. Using software and GIS mapping tools, a base map of 

the food system in Fresno County was developed.

Some of the fi ndings included the following (Jessup, 2005):

• Low-income people bought fresh produce at fl ea markets, but some 

lacked funds to buy fresh food;

• Healthy, culturally appropriate foods were not available in some 

neighborhoods; 

• About one-third of weekly meals consisted of fast food;

• Food resources in the Fresno area that could contribute to both food 

access and economic development around food production and 

distribution were abundant; and

• Food acquisition habits varied by ethnicity.

Web site: www.fresnometroministry.org

The Community Tradition, Foods, and Future, an assessment project, 

was funded at $13,895 for 1 year. The organization funded was Legacy 

Cultural Learning Center, Muskogee, OK. The food assessment planned by 

the Legacy Cultural Learning Center was guided by a publication produced 

through another CFPCGP grant with First Nations Development Institute 

(FNDI). FNDI was awarded a grant for training and technical assistance 

called the Food Sovereignty Assessment Tool (FSAT) (2004). The FSAT 

publication provides an introduction to the food security movement in 

Indian country and is a resource for thinking about food systems in Native 

communities and what can be done to regain control of Native food systems. 
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The FSAT has been widely used by Native American groups in assessing 

their food systems.

The Legacy Cultural Learning Center’s project goal was to create the 

fi rst organized collection of data focused on the food, diet, and traditional 

agriculture issues of the Muskoke People and their neighbors. The project 

assessed the food assets, strengths, needs, and defi ciencies of rural, 

low-income communities in the Tribal Nation. They conducted meetings 

and surveys in 17 of the 23 Muskoke communities and in one neighboring 

Seminole Nation community to assess food system assets, strengths, needs, 

and defi ciencies. There was good participation in discussions focused on 

diet-related health problems such as diabetes, heart trouble, and high blood 

pressure.

Data was collected from written surveys and also from more than 17 personto-person interviews with tribal and community leaders, academics, and 

project collaborators. Through the surveys, the project learned that the Tribal 

Elderly Nutrition Service operates 10 fully equipped kitchens and dining 

facilities serving more than 18,000 meals monthly, including an extensive 

home delivery network. The surveys uncovered a heavy reliance on fast 

food, a preference for local food but limited access to it, and signifi cant 

concerns about rising food costs. Approximately one-third of respondents 

have gardens. Surveys found a signifi cant positive consumer response to the 

availability of locally produced foods available directly from farmers and 

farmers’ markets. However, there are few local growers and small farmers do 

not have the ability to supply the quantities needed. 

Following the surveys and interviews, project staff developed a draft plan 

of action to address the most urgent needs and concerns, including Elderly 

Nutrition Service purchases of local products, initiation of community 

gardens on donated land, purchase of a large RV to serve as a mobile 

resource center, and establishment of a Native Foods restaurant (The Legacy 

Learning Center, Muskogee, Oklahoma, 2006). 

Web site: www.legacycultural.org/artwellness

Food Policy Councils

The project, Food Access and Food Justice in New Orleans: Rising 

Above the Waterline, was funded in 2006 for $265,000 for 3 years. 

The organization funded was the New Orleans Food and Farm Network 

(NOFFN). It was designed to assist in the Hurricane Katrina recovery. 

The project supported the development of sustainable food projects 

and neighborhood capacity by facilitating innovative linkages at the 

neighborhood level. In brought together diverse groups including public 

health, social work, urban agriculture, and food security sectors via an active 

food system coalition. 

The fi rst component of NOFFN’s project was to increase food justice 

awareness in New Orleans through expanding the membership and 

representation of “Grow New Orleans,” the local food system coalition. 

Grow New Orleans mobilized and connected food system stakeholders 
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and community members, and created a formalized food policy network to 

inform public policy and private development. 

The project helped to rebuild the food system by developing a food access 

and food justice action plan and by creating neighborhood food access 

maps. These inform both the general public and those involved in rebuilding 

efforts. To further include and mobilize the public, NOFFN involved 

communities and leaders to conduct neighborhood-based participatory food 

assessments. They plan to create sustainable community food projects based 

on neighborhood appraisals and regional assets, which contribute to local 

food security. NOFFN plans to facilitate three to fi ve targeted community 

food projects, which will lead to a sustainable and just food system (World 

Hunger Year, 2009; Food Security Learning Center).

Web site: www.noffn.org

Educating State Legislative Leaders and CFP Grantees About Policy 

Options That Support Community Food Systems was a project funded 

in 2005 for 2 years at $200,000. The organization funded was the National 

Conference of State Legislatures (NCSL). The goal of this training and 

technical assistance project was to broaden the CFP program’s impact on 

State and local policy by sponsoring site visits and forums for legislators, 

legislative staff, and community representatives, and by hosting a national 

training session for CFP grantees to learn about legislative processes, to 

build capacity of CFP grantees to implement policy initiatives as part 

of their projects. Policy training on such topics as food policy councils, 

direct marketing, and procurement policy were offered. In recognition of 

the role legislators have in crafting policy, NCSL educated them and their 

staff about the legislative and programmatic components of creating a 

sustainable community food system and enhancing access to fresh produce. 

This component had the added benefi t of building relationships between 

legislators and CFP grantees. 

The project consisted of two components: site visits and training. Three 

2-day site visits provided a forum for participants to discuss problems 

and progress in implementing community food systems with agriculture, 

transportation, land-use, nutrition, food service, food security, and education 

experts. Participants, including legislators, legislative staff, CFP grantees 

and local stakeholders, visited sites ranging from small farms, distribution 

centers, schools, or other State institutions that purchase local produce, or 

farmers’ markets. Preference for selection of programs for the site visits were 

given to past or current CFP grantees. The second component was a 1-day 

pre-conference training at the 2006 Community Food Security Coalition 

(CFSC) annual conference to educate CFP grantees about the legislative 

process and about how to incorporate policy into current or future programs. 

NCSL partnered with the CFSC throughout this entire project (World Hunger 

Year, 2009). 

Web site: http://www.ncsl.org
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Youth Programs

Healthy Harvests Initiative: Building Boston’s Food Security from the 

Ground Up was funded in 2000 for 2 years at $150,000. The organization 

funded was The Food Project in Lincoln, MA. The Food Project made 

substantial progress in demonstrating the economic viability of a sustainable 

metropolitan food system by linking food production with youth and 

community-based enterprise. It opened and equipped a 1,500-square foot 

urban center that included a commercial kitchen and involved youth in 

business startup and management by working with them to research, design, 

and test a value-added food enterprise: farm-fresh salsa. It also tripled the 

membership in its Community Supported Agriculture program and increased 

both its urban and rural farmland.

The kitchen has become an integral part of The Food Project’s work. Youth 

use the kitchen to process and prepare produce for value-added products, 

a catering business, and weekly food deliveries. All of the programs 

incorporate culinary and nutrition training, expanding the scope of the 

involvement of youth in the creation of a local food system. Children from 

local elementary and middle schools have classes in the kitchen, which is 

also available to local growers for canning food grown in their gardens.

Healthy food production and consumption skills were further modeled in the 

community through land remediation, education, and outreach. Under the 

grant, an additional urban lot was cleared, cleaned, and put into production 

growing herbs and vegetables. Youth further began a series of regular 

community lunches and now prepare 11 lunches annually serving over 800 

people. The Food Project has also established an education center with a 

library of cookbooks and resources on food enterprise development. The 

project also runs two farmers markets a week for the entire local community 

where youth are employed in marketing and selling the produce they grow 

(World Hunger Year, 2009). 

Web site: http://www.thefoodproject.org

“Food for Life” was funded in 2001 at $180,000 for 3 years. The 

organization funded is the Youth Farm and Market Project (YFMP) in 

Minneapolis, MN. A central objective of the grant to the YFMP was to 

help youth both understand and strengthen the local food system (i.e., to 

learn to grow food, to plan and prepare nutritious meals, and to increase 

community awareness, health, and diversity). To that end, YFMP collected 

recipes that emphasized nutritional content, as well as other cooking class 

materials. They created and implemented a comprehensive, cultural nutrition 

curriculum, which called for the participation of parents and local chefs. 

YFMP has grown a variety of cultural foods, specifi cally Asian and Latin 

American, for summer lunches. Meal plans emphasizing the importance of 

balance were also designed. 

YFMP worked with ethnic farmers of the Twin Cities area to plan purchases 

and distribution in low-income housing developments. YFMP staff also met 

with school offi cials to discuss securing more local, organic produce for 

school meals. They worked to sell produce to several local businesses and 
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restaurants and to secure culturally appropriate foods that would supplement 

project-grown foods for summer lunches. Program participants had at least 

three opportunities to prepare a meal alongside local chefs. Youth-led harvest 

festivals provided recognition, community meals, and awards for project 

participants. 

Another objective of the grant was to make a wider variety of foods available 

to low-income consumers via public housing markets. They assisted 

immigrant growers who were able to sell a variety of vegetables and prepared 

foods to the project. They also worked with a number of schools on gardens 

and on getting local foods into school meals. They ran a youth farmstand at a 

senior public housing high rise, which was very popular with senior residents 

(World Hunger Year, 2009). 

Web site: http://www.youthfarm.net

Entrepreneurial Food and Agriculture Activities

Appalachian Center for Economic Networks is an organization in 

Athens, OH, that has been funded for two separate projects, both involving 

entrepreneurial training: “Appalachian FoodNet Project” in 2003 and “Rural 

Food Centers Project” in 2005. The total amount funded was $200,000. 

These projects enhanced the food system in a three-county area of 

southeastern Ohio by developing linkages between low-income residents, 

food businesses, consumers, and nonprofi t and faith-based organizations. The 

project developed a gleaning and food processing program, provided training 

and assistance to food-related businesses started by low-income people, 

expanded a regional branding program, and convened a local food congress. 

These projects responded to the goals of the Appalachian Food Congress 

by establishing three new food centers in isolated rural communities to help 

meet long-term food and economic needs, provide food processing and 

gardening training, and expand markets for local products and low-income 

entrepreneurs. 

The project provided direct technical assistance in bringing the kitchen 

up to commercial codes, developing strategic uses for the kitchens, and 

providing basic equipment needed for the planned uses. Once operational, 

these kitchens were able to offer food processing and training to low-income 

residents hoping to start small food businesses to supplement family income. 

The kitchens will also be able to process gleaned crops from neighboring 

fi elds to assist local food pantries that are housed in their facilities. The 

commercial kitchens can also be utilized for hosting summer feeding 

programs for low-income children during the summer months (World 

Hunger Year, 2009). 

Web site: http://www.acenetworks.org

Building an Integrated Sustainable Food System was funded in 2005 

for $210,000 for 3 years. The organization funded was the Appalachian 

Sustainable Development, Abington, Virginia.
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This project promoted a “fi eld to table” strategy in southern Virginia and 

eastern Tennessee to stimulate the development of food and farm-based 

economic opportunities for limited resource farmers and entrepreneurs, to 

increase access to local foods for low-income households, to enhance the 

value-added infrastructure, and to educate the public about a sustainable food 

system. 

The main needs this project addressed were poor or inadequate nutrition; the 

decline in farm incomes and family farming; lack of access to larger, better 

paying markets for local farm products; the lack of infrastructure needed 

to add value to local farm products; the lack of affordable, locally raised 

organic and sustainable farm products; and inadequate research, education, 

and technical assistance available to farmers attempting new enterprises and/

or production practices. The overall purpose of this project is to help create a 

more just and sustainable food system in southwestern Virginia and northeast 

Tennessee, one that can serve as a model for other rural regions around 

the Nation (World Hunger Year, 2009; Food Security Learning Center, 

Community Food Projects Database). 

Web site: http://www.appsusdev.org

Urban Agriculture

Integrated Development Through Urban Agriculture was funded in 2002 

and 2005 for a total of $184,000. The organization funded was Nuestras 

Raices in Holyoke, Massachusetts. The grantee built an urban agriculture 

center with classrooms and a community gathering space, as well as a bakery 

and restaurant that were later spun off to private entrepreneurs. The grantee 

conducted market research and promotion of Puerto Rican specialty produce, 

assisted experienced community gardeners in establishing commercial 

gardens, and aided youth in establishing a market garden. 

A second CFP grant in 2002 allowed Nuestras Raices to build on and expand 

its agriculture-based community development work. The organization took 

a leadership role in the development of the Holyoke Food Policy Council, 

a partnership of 30 members from various sectors of the food system. 

The council conducted a visioning/mapping exercise and interviewed 

stakeholders to complete a study of the local food system. Nuestras Raices 

established two new community gardens at public housing projects with 

plots for 6 families and 40 youth. An existing 17-plot community garden 

was transformed into a market garden where adults and youth have begun 

farming commercially. The organization also conducts a Spanish-language 

promotion program for two farmers’ markets, supports a youth-managed 

stand at one of the markets, provides technical assistance and shared tools to 

residents growing Puerto Rican specialty crops not available elsewhere, and 

is developing seed sources for those crops to sell at farmers markets. 

To increase the agricultural space in the community, CFP grant funding 

allowed Nuestras Raices to look for farmland for purchase. Two plots 

were identifi ed and in late 2004, with State and foundation funding, the 

organization was able to buy a 4-acre stretch of land on the Connecticut 

River near downtown Holyoke. The land, called the Tierra de Oportunidades, 
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now includes six new market farms, nature trails, an outdoor stage, tropical 

fl owers and crops, and a farmstand. There are plans for a youth center and 

an offi ce on the land as well, in part to refl ect the substantial involvement of 

community youth in the efforts to purchase the land and to draft a business 

plan for the farm operation (World Hunger Year, 2009; Food Security 

Learning Center, Community Food Projects Database). 

Web site: http://www.nuestras-raices.org

Neighborhood Urban Agricultural Coalition was funded in 2002 for 

3 years at $200,000. Greensgrow in Philadelphia, Pennsylvania, was 

funded for this project. The project provided a comprehensive, practical 

approach to improving access to fresh food in low-income communities 

through the establishment of small, locally owned urban farms that use 

vacant land and “brownfi elds,” sponsored and developed by communitybased organizations and community development corporations. The effort 

developed neighborhood cooperatives (CSA shares) to be supplied by both 

urban producers and rural farmers. 

The grant helped Greensgrow to establish the Neighborhood Urban 

Agriculture Coalition to increase access to healthy food in the surrounding 

neighborhood. The farm has started a twice-weekly farmstand on its property 

that sells its own produce along with products from other nearby farms and 

producers. Over 20 farms are part of the project, including Amish farms and 

some from nearby New Jersey. All food is labeled with place of origin and 

any special growing practices. The stand accepts food stamps and WIC and 

Senior Farmers’ Market Nutrition Program coupons, and operates a satellite 

stand at the nearby Senior Center. Development of the Greensgrow nursery 

and CSA were also facilitated by CFP funding. Greensgrow is established 

as a group of producers and farmers who make value-added product and sell 

them at both the Greensgrow farmstand and to the Reading Terminal Market, 

a major public market in Philadelphia. The farm has been able to expand into 

two additional small lots, now used for heirloom tomatoes and bee colonies. 

Farm staff teach low-income urban consumers about the complexity of 

community food issues. The staff also brings concerns of consumers to the 

producer group. Greensgrow also is working with local high schools and 

Temple University, whose students are learning about the Greensgrow model 

for urban agriculture.

Greensgrow aims to encourage other similar brownfi eld land redevelopment 

in Philadelphia. It is involved in local zoning issues and has developed a 

preliminary site selection protocol for other groups to use (World Hunger 

Year, 2009). 

Web site: http://www.greensgrow.org

Rural Producers

The Tribal Fish Market Connection Project was funded in 2003 for 

$125,000 for 3 years. The organization funded was Ecotrust, Portland, OR. 

The project works with the Columbia River Inter-Tribal Fish Commission 

(CRITFC) on wild salmon marketing and helps provide more diverse sources 
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of revenue and employment to the 400 tribal fi sher families on the Columbia 

River. The project helped build a business plan and product development 

capacity for the four Columbia River treaty fi shing tribes represented by 

CRITFC—the Confederated Tribes of the Umatilla Indian Reservation, the 

Confederated Tribes of the Warm Springs Reservation, the Yakama Nation, 

and the Nez Perce Tribe. The tribes are brought together by the struggle to 

save the salmon and by shared spiritual traditions. Fishers involved in the 

project are selling their “treaty right tribal caught fi sh” at Portland Farmers’ 

Market, with a devoted fan base and the capacity to increase their income 

fourfold. With Ecotrust spreading the word, consumers are quickly learning 

that there are plenty of high-quality regional foods that not only taste great 

but can help maintain a way of life closely tied to the identity and spirit of 

tribal nations (World Hunger Year, 209). 

Web site: http://www.ecotrust.org

Patchwork Family Farms: Value-Added Processing for Community 

Food Security was a project funded for $183,898 through the Missouri Rural 

Crisis Center, Columbia, Missouri. The project built the capacity of two 

ongoing programs, “Patchwork Family Farms” and the “Food Cooperative,” 

to become self-suffi cient through value-added processing activities. The 

project increased processing capacity by 20 percent, increasing profi tability 

per hog by fi nding uses for less popular cuts of meat. The project 

successfully produced and marketed additional value-added products. 

Expanding on a food cooperative system already in place, the project 

increased the diversity and volume of local products available to cooperative 

members in 23 rural counties in the middle of the State. 

Patchwork Family Farms is composed of 15 independent family hog farmers. 

When consumers purchase a Patchwork product, the money directly supports 

Missouri farm families. Patchwork Family Farms has been recognized 

by USDA, the Environmental Protection Agency, and Oxfam America 

for their efforts to help the environment and sustain the traditions of rural 

communities. Meat was cooperatively sold to African-American churches in 

St. Louis and Kansas City at reasonable prices.

During 1999, when hog prices for Missouri farmers averaged 28 cents per 

pound, Patchwork producers received 43 cents per pound. With the grant, 

Patchwork Family Farms was able to double their product line, test valueadded marketing, and provide a “best sellers” product list. To achieve the 

goal of expanding the food cooperative system, this project grew the capacity 

for marketing and distribution of locally raised produce, meat, and other 

local products to low- and moderate-income families in Mid-Missouri. This 

expanded marketing capacity allows for local growers and producers to 

supply the enlarging cooperative system. Also, the project worked to increase 

the number of farmers and grantee chapter members who are growing for 

themselves and for sale through the coop program. 

To grow the coop system, through increased demand, the Missouri Rural 

Crisis Center will develop an outreach campaign targeting rural Missourians 

that explains the economic, environmental, social, and public health benefi ts 

of community-based food systems. To further the public’s appreciation of 
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local foods and increase or continue interest in coops and local products, they 

will also provide education and resources pertaining to the preparation and 

processing of locally raised food and related products (World Hunger Year, 

2009; Food Security Learning Center, Community Food Projects Database). 

Web site: http://www.inmotionmagazine.com/rural.html

Community Gardens

Urban Detroit Agriculture and Education Project received funding of 

$150,000 in 2003 for 2 years. The organization funded was Capuchin Soup 

Kitchen, Detroit, Michigan. The project provides a haven for low-income 

people to socialize with their neighbors, gives access to affordable produce, 

and educates people about nutrition and the origins of their food. The project 

has evolved to focus on four objectives: launching a Garden Resource 

Program, organizing educational opportunities for community and backyard 

gardeners, developing the Romanowski Farm Park (a public park centered 

on a 5-acre farm in Detroit), and increasing access to nutritious food for all 

Detroiters. The project works with 80 community gardens connected to many 

grassroots organizations that are critical to the success of the program. This 

Detroit project is an example of the extraordinary types of collaborations that 

make community food projects work. The project works with the following 

collaborators: The Greening of Detroit, Detroit Agriculture Network, 

Michigan State University Extension, O.W. Holmes Elementary School, 

American Indian Health and Family Services, City of Detroit Recreation 

Department, Michigan Association of Community Gardeners, Michigan 

Department of Agriculture, Gleaners Food Bank, Michigan Department 

of Community Health, Catherine Ferguson Academy, Detroit 4H Center, 

Foundation for Agriculture Resources in Michigan and Michigan State 

University.

The Garden Resource Program encourages communication and collaboration 

among gardeners working in the same areas (called clusters) of the city. 

In the program’s fi rst year (2004), it offered several incentives to facilitate 

the efforts of the 300 households working in 33 community gardens and 

47 family gardens. The program tilled or plowed gardens; tested soil for 

nutrients and heavy metals; delivered compost and woodchips to gardeners; 

distributed coffee bags for use as a weed barrier; provided tools; distributed 

containers of vegetable, herb, and fl ower seedlings ready for planting; 

and organized hundreds of volunteers. At meetings in each of the four 

participating city clusters, gardeners have chosen a neighborhood leader 

and a location for a neighborhood resource center. In addition, the Garden 

Resource Program will use the Soup Kitchen’s Earth Works Garden 

program as a model for future market expansion for its own participants. 

Last year, Earth Works program gardeners sold produce, honey, black 

raspberry-currant, and gooseberry jams at four low-income health clinics. 

They marketed their products at a local farmers’ market in the spring and 

planned to harvest vegetables from the farm in the summer. Through all its 

efforts, the program improves communication, services, and resources for 

participants and ultimately increases access to affordable, nutritious food.
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The Capuchin Soup Kitchen and its partners have increased food security 

throughout Detroit by increasing the number of urban gardeners citywide 

and by seeking new partners. The Urban Detroit Agriculture and Education 

project grant allowed project partners to form the Detroit Agriculture 

Network, which will become its own nonprofi t organization focused on food 

security (World Hunger Year, 2009). 

Web site: http://www.cskdetroit.org/