Economic

Research

Service

Economic 

Research

Report

Number 195

September 2015

United States Department of Agriculture

Food Choices and Store Proximity

Ilya Rahkovsky and Samantha Snyder
Economic Research Service 

www.ers.usda.gov

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Rahkovsky, Ilya and Snyder, Samantha. Food Choices and Store Proximity, ERR-195, U.S. 

Department of Agriculture, Economic Research Service, September 2015.
United States Department of Agriculture

Economic 

Research 

Service

Economic

Research

Report

Number 195

September 2015

Food Choices and Store Proximity

Ilya Rahkovsky and Samantha Snyder

Abstract

In 2010, 9.7 percent of the U.S. population lived in low-income areas more than 1 mile 

from the nearest supermarket. The diet quality of these consumers may be compromised by their food environment. Some may be unable to reach supermarkets regularly 

or without effort, instead buying food from nearer stores that offer less healthy food 

products. This report investigates the correlation between households that live in lowincome, low-access (LILA) areas and their purchases of 14 major food groups that vary in 

dietary quality. The report finds a modest negative effect, particularly among urban LILA 

consumers, and this effect is only slightly alleviated when LILA consumers travel farther 

from their homes to purchase food. 

Keywords: access to supermarkets, food, transportation, food environment, low-income, 

diet and health

Acknowledgments

The authors thank Shelly Ver Ploeg, Economic Research Service; Richard Volpe, 

California Polytechnic State University; and Susan Chen, University of Alabama for 

helpful review comments as part of the peer review process. We also thank Ryan 

Williams, Chris Dicken, and Vince Breneman from ERS for helping with our use of the 

TDLinx data and ERS editor Dale Simms and ERS designer Lori A. Fields for highquality editing and design of this manuscript. 
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Contents

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

Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Food Prices. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Store Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Food Environment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Descriptive Statistics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Low-Income, Low-Access Areas and Distance to Retail Foodstores. . . . . . . . . . . . . . . . . . . . . 13

Urban and Rural LILA Areas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Poverty or Low Access as Greater Factor in Food Purchases: Disaggregation of LILA. . . . . . . 17

Store Channels and Their Effect on Food Purchases. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Explaining Food Disparities: Access or Preferences? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

Conclusion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Appendix. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
What Is the Issue?

In 2010, 9.7 percent of the U.S. population lived in low-income areas more than 1 mile from 

the nearest supermarket. The diet quality of these consumers may be compromised by their 

food environment. Some may be unable to reach supermarkets regularly or without effort, 

instead buying food from nearer stores that offer less healthy food products. Retailers may 

be discouraged from locating in low-income areas due to insufficient demand, and poverty 

can prevent the residents of these areas from obtaining lower priced and better quality products far (over 1 mile for urban consumers, 10 miles for rural) from their homes. 

Understanding how access to supermarkets affects the healthfulness of food purchases may 

help policymakers determine the value of attracting supermarkets to underserved areas. 

USDA’s Economic Research Service conducted two large-scale studies to define low-foodaccess areas and to identify the population living in those areas. This report investigates the 

correlation between living in low-income, low-access (LILA) areas and the purchase of 14 

major food groups in order to estimate the effect on diet quality of living in LILA areas. The 

researchers accounted for the prices consumers face and their demographic characteristics. 

What Did the Study Find?

Living in a LILA area has only a modest negative effect on the healthfulness of food 

purchases—a difference too small to explain much of the national disparities in diet quality 

and obesity—and this effect is only slightly alleviated when LILA consumers travel farther 

from their homes to purchase food. Even after traveling to stores farther from their home, 

LILA area consumers tend to buy less healthy food. Thus, as the effect of living in LILA 

areas on diets is modest, improvements in dietary quality are likely only with a multipronged policy approach that addresses hardwired shopping and eating habits in addition to 

retail coverage. Among the report’s findings:

• Food prices paid by LILA and non-LILA consumers were very similar. There is no 

evidence that LILA consumers are paying more for healthful foods.

• Consumers living in LILA areas bought 4.5 percent less fruit, 2.7 percent fewer 

vegetables, and 10.8 percent fewer low-fat milk products than consumers not residing 

in LILA areas. At the same time, they bought 8.9 percent more red meat, 5.0 percent 

more diet (soda) drinks, and 3.3 percent more nondiet drinks.

United States Department of Agriculture

A report summary from the Economic Research Service

ERS is a primary source 

of economic research and 

analysis from the U.S. 

Department of Agriculture, 

providing timely information on economic and policy 

issues related to agriculture, 

food, the environment, and 

rural America. www.ers.usda.gov

Economic

Research

Service

Economic 

Research

Report

Number 195

September 2015

United States Department of Agriculture

Food Choices and Store Proximity

Ilya Rahkovsky and Samantha Snyder

September 2015

Find the full report at 

www.ers.usda.gov/

publications/erreconomic-researchreport/err195

Food Choices and Store Proximity

Ilya Rahkovsky and Samantha Snyder
• LILA consumers travel slightly farther from home to purchase their food. Almost all LILA households 

traveled to stores more than 1 mile from their home, the distance commonly used to delineate low food 

access. Thus, of the 7.7 percent of households in the Homescan sample who lived in LILA areas, very 

few limited their food purchases to the LILA areas. 

• LILA consumers who travel farther to buy food purchase more fruits, vegetables, fish, and poultry and 

fewer drinks (diet and nondiet), but the magnitudes of these effects are small and they cannot explain 

large nutritional disparities observed in the population. 

• Separating the LILA measure into low-access areas and low-income areas shows that low income is 

more strongly associated with the purchases of unhealthful food than is living in an area with limited 

access to supermarkets.

The study has several limitations. First, the survey, which measures consumer food purchases, underrepresents poor consumers. Similarly, the data on store locations lack some small independent stores, 

which are prevalent in poor areas. Third, we disregard food choices in schools and restaurants, so results 

pertain only to food purchased at grocery stores and other purchases for at-home consumption. Given 

these limitations, the results of this study may not apply to very poor households or to individuals who 

primarily eat away from home. 

How Was the Study Conducted?

This report uses household food purchase and characteristics data from the 2010 Nielsen Homescan Panel 

Survey; food retail locations data from the TDLinx retail database; census tract locations and demographic data from the U.S. Census Bureau; and food-access data from the ERS Food Access Research 

Atlas. These data were used to estimate a demand model of 14 food groups, which cover most food products eaten at home. We selected groups with maximum nutritional differences to establish a measure of 

dietary quality and to measure consumer preferences for healthy foods. 

The researchers estimated food product demand elasticities for consumers in LILA areas. With information on 

household and shopping locations, we calculate the distance traveled by each household on each shopping trip 

and the items purchased on that trip. With information on both residential location and shopping outlet location, we are able to determine whether there is a relationship between distance traveled for shopping and the 

healthfulness of the items purchased on a given shopping trip. 

www.ers.usda.gov
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Food Choices and Store Proximity

Introduction

Much research in recent years has investigated the links between household access—specifically 

in low-income low-access (LILA) areas—to food stores and subsequent purchases, consumption 

patterns, and health outcomes. There has been extensive investigation into the presence of LILA 

areas and their characteristics. Wilde and colleagues (2014) find a negative correlation between an 

area’s average income and store density, while Dutko and colleagues (2012) find that, among lowincome areas, there were fewer stores in areas with very poor or minority consumers. 

Store density is primarily determined by population density, which varies in both high- and lowincome areas. Moore and Diez Roux (2006) studied census tracts in Maryland, New York, and 

North Carolina and found that large supermarkets were more likely to be located in low-income 

than high-income tracts and less likely to be in tracts with large minority populations. Morland and 

colleagues (2002)—using data from Mississippi, North Carolina, Maryland, and Minnesota—found 

fewer stores in poor and minority areas. 

Cummins and colleagues (2014) found no increase in the consumption of fruits and vegetables after 

a new grocery opened in a low-access area of Philadelphia. This suggests that exact store location 

may be inconsequential to consumers in the habit of shopping at favored stores. Edin and colleagues 

(2013) found that poor consumers are quite aware of food prices in different stores and are willing 

to travel far to access the lower prices, a finding corroborated by studies in Minneapolis, New York 

City, and Chicago (Chung and Myers 1999; Horowitz et al., 2004; Block and Kouba, 2006). 

Studies have linked a poor food environment to negative health outcomes, such as the prevalence 

of overweight/obesity and associated diseases. Most have used rates of overweight and obesity 

as indicators of health and pointed to higher prevalence of these as proof that the food environment impacts residents’ health outcomes. Holsten (2009), in reviewing a number of these studies, 

contends that the food environment-obesity relationship found is often undermined by inadequate 

data or research design. Ford and Dzewaltowski (2008) focus on disadvantaged populations and find 

that the disparities in the food environment along socioeconomic and demographic lines coincide 

with the gradient in obesity prevalence along the same factors. Morland and colleagues (2006) find 

a negative relationship between the presence of supermarkets and rates of obesity/overweight and a 

positive relationship between access to convenience stores and the same health indicators. However, 

significant links between store access and diabetes, cholesterol levels, and high blood pressure were 

not observed. Chen and colleagues (2010, 2013) show that the presence of nearby grocery stores is 

associated with lower body mass index (BMI) and the presence of fast-food restaurants with higher 

BMI. The second Chen study exploits detailed data and econometric techniques to identify a causal 

relationship between fast-food restaurants and BMI. 
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To reasonably connect food environment with obesity and overweight, researchers must assume 

the existence of an intermediate step, specifically differences in behavior resulting from foodaccess disparities that ultimately result in observable health outcomes. It is within this body of 

literature that the research presented here best fits. Rose and Richards (2004), using 1996-97 

National Food Stamp Program Survey data, found that access to supermarkets among lowincome consumers increased their consumption of fruits and vegetables (only fruit consumption was significantly higher, however). Using the same data, Lin and colleagues (2014) found 

store access to have a very small effect on purchases, though households with very limited store 

access spent more on canned meats and less on beverages. Morland and colleagues (2002) also 

noted negative and significant effects of low food access on the purchases of fruits and vegetables for both Black and White residents. Notably, the increase in purchases for Blacks was triple 

the increase for Whites. 

Focusing specifically on rural seniors, Sharkey and colleagues (2010) found that both objective 

and perceived low access to food stores were correlated with poorer diets in terms of fruits and 

vegetables consumed. Using two separate composite measures of diet quality, Moore and colleagues 

(2008) confirmed that good access to grocery stores (measured in three ways) exhibited a strong 

relationship with healthy dietary patterns. However, these results are not universal across studies. 

Examining the components of LILA areas (low access, low incomes, and high food prices) both 

separately and together, Pearson and colleagues (2005) found no relationship between diet quality 

(specifically fruit and vegetable consumption) and living in or near a LILA area. 

The challenges of limited access to healthful food and the consequences of poor food choices— 

both social and physical—have led some to press for governments subsidizing the entry of grocery 

retailers into underserved areas. The Food, Conservation, and Energy Act of 2008 authorized a 

Federal study (by USDA) of food desert areas (Ver Ploeg et al., 2009). The Agricultural Act of 

2014 introduced $125 million in spending over 5 years for the Healthy Food Financing Initiative to 

promote healthy food access in underserved communities (Aussenberg, 2014). In addition, many 

States introduced separate programs to support food access. These attempts to attract supermarkets 

to the underserved areas generally were part of larger community revitalization schemes. From 

2001 to 2011, 11 States enacted legislation to address these issues and 7 States attempted legislation (CDC, 2012). And in 2014, Maryland passed legislation to provide $1 million in assistance—

including State loans and grants—to food desert areas (Maryland House Bill 451, 2014). 

This report investigates the correlation between living in low-access, low-income areas and food 

purchases made for at-home consumption. We are able to trace the effect of food environment on 

consumer purchases due to the unique datasets used. With information on household and shopping locations, we calculate the distance traveled by each household on each shopping trip and the 

items purchased on that trip. To our knowledge, previous studies have looked only at the availability of shopping outlets at the household level. For the most part, this characteristic may be 

quantified by the number of grocery/convenience stores or restaurants in a household’s ZIP Code, 

census tract, or defined radius. Attempts are then made to relate either purchasing decisions or 

health outcomes to these measures of access. 

With information on both residential location and shopping outlet location, we are able to 

determine whether there is a relationship between distance traveled for shopping and the 

healthfulness of the items purchased on that trip. This examination is different in a few very 

important ways. First, we are not concerned with characterizing the food environment for 
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each household, per se. While we do use information on whether a household is located in a 

LILA area to refine our model, we allow for the fact that households likely travel outside of 

their immediate surroundings to shop for food. Second, because we observe households over 

an entire year (2010) and for multiple shopping trips, our model captures how households 

vary their purchases over distance. Since we observe a household’s purchasing decisions and 

the distance it travels to its chosen retail food store, we are better able to predict whether 

improved access to supermarkets would, in fact, improve the nutritional profile of low-income 

households. Because of these differences, our results may be more policy relevant.
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Data

The study of food demand by level of food access in LILA areas requires information on 

consumer purchases, the local food environment, and shopping behavior. Our information 

on consumer purchases is from the 2010 Nielsen Homescan Survey, the largest nationwide 

survey of food purchases. Households are asked to record all food purchases from all outlets 

for in-home consumption. (Food purchased in restaurants and fast-food places for consumption 

outside the home does not appear in the survey.) Nielsen households are recruited via mail and 

Internet ads and are awarded with points for prizes and sweepstakes. Low-income consumers 

are underrepresented in the survey (Einav et al., 2008 and 2010). 

The Homescan purchase record provides information on date, price, quantity, product, and 

store, as well as whether the product was purchased at a discount. For most products, consumers 

need to scan the Universal Product Code (UPC), enter a date, and provide information about the 

place of purchase and any discounts using a special hand-held device given for this purpose. If 

Nielsen collects sales information from the store where a product was purchased, the price of 

the product from the store record and detailed product information (size, product type) from a 

UPC dictionary is matched to the household’s reported purchase in Homescan. If Nielsen does 

not have price information about a product, then a Homescan panelist is asked to manually enter 

the price. For random-weight items without a UPC code, such as fresh tomatoes, consumers 

identify the product from a catalog, enter its code, weight, and expenditures. 

Households are sampled from 52 markets in the 48 contiguous States.1 A random-weight panel 

(27,418 households in 2010) records all food purchases, including items without UPC codes that 

are priced per unit of weight. A second panel (33,217 households) records just UPC purchases. 

Prior to 2007, Homescan participants recorded detailed information, such as weight and expenditures, for a finely differentiated category (e.g., tomatoes). After 2007, to reduce participant 

burden, Nielsen stopped recording the weight of random-weight items and aggregated product 

categories (e.g., from tomatoes to vegetables). Participants must provide consistent information on 

their purchases for at least 9 months to appear in the final dataset, after which they can stay in the 

survey as long as they want, and some do for many years.

The Homescan survey also includes detailed demographic information on the panelists, which is 

updated once a year. Projection weights, based on participants’ demographics, are also provided 

by Nielsen to mimic demographics across the U.S. population. The survey provides census tract 

information for participating households, from which we extrapolate latitude and longitude of the 

household residence. 

Underreporting of food purchases is a serious problem in many household studies. To alleviate this 

problem, we dropped households that spent less than $250 on groceries per quarter. This can alter 

the sample’s composition by dropping not only the underreporting panelists but also panelists who 

mostly eat away from home, which is less of an issue in our at-home food investigation. 

We started with 27,418 households in the Homescan random-weight panel, but dropped households for 3 reasons. First, we dropped 28 households with missing random-weight prices and 

 1The 52 Nielsen market areas mostly correspond to metropolitan areas. Some market areas are rural and can span 

several States.
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purchase dates, leaving 27,390 households in the sample. Then, as this study requires matching 

food purchases with food environment data, we dropped from the sample 906 households that we 

were unable to match to the census. (The dropped households have similar characteristics to the 

households remaining in the sample.) Finally, we dropped 737 households that purchased less than 

$250 of groceries per quarter, leaving 25,747 households in our analysis sample. 

Food Prices

Estimation of the demand models requires aggregating thousands of food products into a small 

number of food groups, 14 here characterized by descriptive similarity (grains vs. meat) and by 

nutritional similarity (low-fat meats vs. regular meats): fruits, vegetables, sweets, oil and nuts, eggs, 

regular meats, low-fat meats, fish and poultry, processed foods, grains, regular milk products, low-fat 

milk products, diet drinks, and nondiet drinks. This classification helps to assess the healthfulness of 

consumer choices while maintaining food groups that align with consumer perceptions (see table 2). 

For random-weight items such as fresh produce and refrigerated meats, 2010 Homescan data 

provide only total expenditures, not prices and quantities. However, in the 2005 and 2006 

Homescan data, consumers provided this information. We use these 2 years (with prices 

converted to 2010 dollars using the Food at Home Consumer Price Index) as a base to impute 

prices and quantities for random-weight items purchased in 2010. This imputation introduces a 

measurement error in the price data, but avoids the more troublesome bias in prices from omitting the random-weight products. 

Many household demand studies face problems related to omitted price information (if the 

consumer did not purchase a particular product) and aggregation of food groups, which can mask 

substantial differences across consumers in the quality purchased within food groupings. Some 

consumers may choose to buy higher quality products at higher prices and using unit values for 

these consumers fails to capture average prices of all product offerings. Also, some consumers 

may be better at finding lower prices and this aptitude may be correlated with their preferences for 

particular food products. 

To address this “selectivity” problem, we use the superlative Fisher ideal price index based on 

UPC-level prices and quantities, which alleviates the missing quality information as suggested by 

Diewert (1976):

p

p q p q

p q p q hjt

kht k kht kht

k k k kht

= ∑ ∑

∑ ∑

0

0 0 0

, 

where 

phjt = ∑ ∑

∑ ∑

0

0 0 0

0

0

and 

phjt = ∑ ∑

∑ ∑

0

0 0 0

0

0

are prices and quantities in household h in time t for UPC k,

phjt = ∑ ∑

∑ ∑

0

0 0 0

0

0 is the national 

average for the quantity of UPC k, and 

phjt = ∑ ∑

∑ ∑

0

0 0 0

0

0

is the average price of UPC k.

As some Nielsen households did not purchase a single product in a food group that quarter, we 

need a price measure based on the prices paid by other consumers. To do this, we calculated 

consumer-specific market-area prices based on all consumer purchases made in a Nielsen market. 
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In the index, we weighted the prices other Nielsen consumers paid by the inverse Euclidian 

distance between them and the (nonpurchasing) consumer for whom we calculate the prices. 

When a consumer has no purchases of a particular food group, we assign a consumer-specific area 

price to this consumer for the missing food group prices.

Store Data

For store locations, we used TDLinx, a dataset of food retailers collected by Nielsen with information on retailer location, size, and characteristics. We matched TDLinx with Nielsen Household 

data using a key provided by Nielsen. Homescan surveys provide the census tract of each household, so we assigned household location to the population-weighted centroid of the census tract. 

Using the key, we were able to link most but not all of the stores. When Homescan provided ZIP 

Codes of the missing stores, we assigned store locations to the centroids of these ZIP Codes. In 

addition, we searched TDLinx data for stores with the same name as the stores in Homescan 

and assigned a location to the store (with the same name) nearest the consumer residence. A few 

recorded distances were very far, possibly because panelists were traveling that far, were living in 

another home at the time of the survey, or because of a simple panelist-store mismatch. To reduce 

the bias from implausibly long distances, we dropped all distances greater than 40 miles. 

Food Environment

We use the USDA definition of low-income low-access (LILA) area as a census tract identified in the 

Food Access Research Atlas. The tract is classified as low access if at least 500 people or 30 percent 

of residents live more than 1 mile from a supermarket in urban areas (10 miles in rural areas). We 

used the census classification of rural and urban tracts to determine the relevant distance. A tract 

is classified as low-income using the U.S. Department of Treasury’s New Markets Credit program, 

which applies to any tract where the poverty rate is above 20 percent or median family income is 

less than 80 percent of the statewide (or metropolitan area) median. 

The 2010 Nielsen Homescan data provide information on consumer food purchases. The survey 

records home locations based on census tract definitions from 2000. To match these tracts to food 

environment information, we used access measures defined in the Food Desert Locator created by 

USDA’s Economic Research Service, which is also based on 2000 census tracts.2 We used Food 

Desert Locator data for the disaggregated measures of food environment (i.e., both the low-income 

and low-access criteria for LILA areas). The information on distance to stores is provided by 

USDA’s Economic Research Service, which compiled a supermarket directory in 2006.

According to 2010 census numbers, an estimated 32 percent of urban and 42 percent of rural residents lived in low-income areas, although they are not necessarily poor themselves. In our Nielsen 

data, 25 percent of urban and 39 percent of rural consumers lived in low-income areas. So the 

Homescan survey may undersample consumers in poor areas, but these differences are small.

2The current version of this Food Access Research Atlas (USDA, 2015) is impossible to match precisely with 2000 

Census tracts as it is based on 2010 Census tracts.
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Descriptive Statistics

Household quarterly purchases are the unit of observation. Lower levels of aggregation such as 

individual shopping trips or even household-store combinations did not work because many shopping trips were inconsequential and the food spending of households was highly concentrated in 

one or two favorite stores. Therefore, we collapsed all shopping trips in a quarter to one observation 

and all shopping trip information was aggregated to the quarterly level. In particular, we created 

an expenditure-weighted average distance measure, which averaged the distances from a panelist’s 

home to various stores visited during the quarter. Similarly, we created indicators for store formats/

channels—such as groceries, supercenters, or convenience stores—that reflect the share of quarterly 

food expenditures in these channels. Just a few stores dominated food expenditure for both LILA 

and non-LILA consumers. The most frequented store, for example, accounts for 58 percent of food 

expenditure (fig. 1). 

In our sample, 7.8 percent of households lived in the areas designated as LILA in 2010. These 

consumers have lower income and education and are more likely to be Black. LILA households 

also tend to travel slightly farther to purchase food. The average expenditure-weighted trip of LILA 

households was 7.5 miles, versus 5.6 miles for non-LILA households (table 1). 

0

 10

 20

 30

 40

 50

 60

 70

Store 1 Store 2 Store 3 Store 4 Store 5

Figure 1

Share of food expenditures in top 5 stores

Non-LILA consumers LILA consumers

Percent

LILA = Low-income, low-access.

Source: USDA, Economic Research Service.
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We used the Nielsen Homescan classification of food retailers to divide all stores into five groups. 

Supermarkets—including natural and health food stores, warehouse stores, and military commissaries—accounted for about 57 percent of the food dollars spent by LILA and 63 percent by nonLILA households. Supercenters were the second most popular retail channel, with 15.9 percent of 

non-LILA food spending and 22.1 percent of LILA spending. A supercenter (for example, Walmart 

supercenter, Super Target, and Meijer) is usually more than 50,000 square feet, and sells both food 

and nonfood products. Volpe and colleagues (2013) found that consumers tend to purchase less 

healthy products from supercenters. Club stores, such as Costco and Sam’s, were the third most 

popular channel with 8.6 of non-LILA food expenditures and 7.0 percent of LILA spending. Stores 

Table 1

Descriptive statistics for LILA and non-LILA households (percent)

Non-LILA area LILA area

Variable Mean S.D. Mean S.D.

Household size 2.66 1.44 2.63 1.64

Age female head 41.8 15.34 42.25 16.65

Age male head 39.65 16.59 38.6 18.66

Household income ($) 65,534 50,017 47,574 40,883

Children 0-6 years old 0.13 0.34 0.12 0.35

Children 7-12 years old 0.20 0.40 0.21 0.43

Children 13-17 years old 0.16 0.37 0.18 0.41

No children 0.64 0.48 0.65 0.51

Black 0.11 0.31 0.18 0.41

White 0.86 0.35 0.81 0.42

Asian 0.031 0.172 0.018 0.142

Hispanic 0.12 0.32 0.11 0.34

Less than high school education 0.045 0.208 0.080 0.292

High school graduate 0.73 0.44 0.78 0.45

College graduate 0.22 0.42 0.14 0.37

Share of supermarkets 0.63 0.2989 0.57 0.3266

Share of drug and convenience stores 0.020 0.0529 0.021 0.0593

Share of mass merchandise stores 0.026 0.0699 0.023 0.0854

Share of club stores 0.086 0.1624 0.070 0.1644

Share of supercenters 0.159 0.2512 0.221 0.2909

Share of other stores 0.080 0.1572 0.091 0.1776

Average distance to store weighted by the purchase 5.62 4.81 7.45 7.23

Live in census tract >1 mile from a supermarket 0.42 0.39 0.62 0.34

Live in census tract >10 mile from a supermarket 0.009 0.059 0.085 0.220

Low-income tract 0.22 0.41 1 0

Urban 0.82 0.38 0.68 0.50

Number of households 23,890 1,857

Bold denotes statistical significant difference at 5% level. LILA = Low-income, low-access.

Source: Nielsen Homescan, 2010; Nielsen TDLinx, 2010; U.S. Census, 2000. 
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not classified by Nielsen, mostly small independent stores, are responsible for 8.0 percent of food 

spending among non-LILA consumers and 9.1 percent among LILA consumers. Mass merchandise 

stores and convenience stores were responsible for less than 3 percent of food spending in 2010. 

LILA and non-LILA consumers exhibit no difference in the number of stores they visit; on average, 

a LILA consumer visits 10.9 stores a year and a non-LILA consumer visits 11 stores per year. 

Consumers who lived in LILA and non-LILA areas faced similar food prices in 2010, with prices 

in non-LILA areas about 1 percent higher. LILA consumers, on average, spent $698 per quarter 

on food, while non-LILA consumers spent $730 (table 2).3 By category, LILA consumers spent 29 

percent less on low-fat milk products, 13 percent less on fruits, 8 percent less on vegetables, 13 percent 

more on red meats, and 5 percent more on nondiet drinks. 

LILA consumers purchase more food, despite spending less on it and facing similar prices. Thus, 

LILA consumers were purchasing more low-price food products in almost every food category than 

non-LILA consumers. Measured by weight, LILA consumers purchased 21 percent more red meats 

(regular and low-fat), 13 percent more nondiet drinks, and 9 percent more fish and poultry. Only in 

three categories—fruits, sweets, and low-fat milk products—did non-LILA consumers purchase 

significantly more food than LILA consumers.

Regional studies on whether the poor face higher food costs are inconclusive (Beaulac et al. 2009; 

Ver Ploeg et al., 2009) Kristjansson et al. 2009, USDA, Ver Ploeg et al. 2009. Even less evidence 

exists on the prices faced by consumers in LILA areas. The poor tend to purchase similar products 

at lower prices than other consumers (Broda et al., 2009). So prices in the immediate neighborhood 

may not provide useful information if consumers are willing to travel for lower prices. Our results 

suggest that LILA consumers face similar prices, but choose to buy cheaper products (within a food 

group) than non-LILA consumers. 

3This is lower than the $906 quarterly expenditures estimated by the Consumer Expenditure Survey in 2010. The difference is probably due to the differences in sample composition and participants’ reporting practices in the two surveys 

(U.S. Department of Labor, 2011).
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Table 2

Descriptive statistics for food purchases in LILA and non-LILA markets

Non-LILA area LILA area

Variable name Mean S.D. Mean S.D. Price of (percent)

Fruits 1.00 0.14 1.00 0.15

Vegetables 1.00 0.13 1.00 0.13

Sweets 1.01 0.11 1.00 0.11

Oils, nuts 1.01 0.15 1.00 0.14

Eggs 1.01 0.17 1.01 0.17

Regular meats 1.01 0.15 1.00 0.15

Low-fat meats 1.01 0.16 1.01 0.17

Fish, poultry 1.01 0.17 1.00 0.18

Processed foods 1.01 0.10 1.00 0.10

Grains 1.01 0.11 1.00 0.13

Regular milk products 1.01 0.11 1.00 0.11

Low-fat milk products 1.01 0.16 1.01 0.17

Diet drinks 1.01 0.14 1.00 0.16

Nondiet drinks 1.01 0.18 1.00 0.20

Quantity of (oz)

Fruits 897 873 825 871

Vegetables 788 680 789 723

Sweets 883 1594 848 951

Oils, nuts 128 158 134 185

Eggs 108 119 112 132

Regular meats 165 192 210 299

Low-fat meats 30 53 32 62

Fish, poultry 67 102 73 117

Processed foods 2,353 2,320 2,414 2,297

Grains 454 335 456 390

Regular milk products 806 896 866 1019

Low-fat milk products 483 787 389 794

Diet drinks 1,462 2,095 1,447 2,288

Nondiet drinks 1,560 1,918 1,802 2,505

Expenditures on ($)

Fruits 53.6 52.7 47.4 48.2

Vegetables 53.4 48.1 49.3 41.5

Sweets 88.7 88.1 84.0 71.4

Oils, nuts 18.9 22.7 17.9 22.4

Eggs 6.7 7.3 6.9 8.2

Regular meats 29.1 29.2 33.3 36.0

Low-fat meats 7.1 12.2 7.1 12.3

Fish, poultry 13.5 19.9 13.5 21.1

Processed foods 257.9 183.8 247.1 178.3

Grains 51.6 36.9 48.4 40.7

continued—
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Expenditures 

on ($)

Regular milk products 59.0 44.7 56.6 48.2

Low-fat milk products 19.4 25.7 15.1 23.3

Diet drinks 35.7 42.1 33.5 42.0

Nondiet drinks 34.9 42.8 37.3 47.6

Total expenditures 729.6 397.1 697.5 396.6

Bold indicates statistically significant difference at 5% level. LILA = Low-income, low-access.

Source: Nielsen Homescan Data 2010. 

Exact Affine Stone Index Demand Model

We estimate demand using the censored Exact Affine Stone Index (EASI) Implicit 

Marshallian Demand system (Lewbel and Pendakur 2009; Zhen et al., 2013). The EASI model 

is an improvement over traditional demand models like the Almost Ideal Demand model as 

it allows easy incorporation of demographic variables in the demand system, accounting for 

unobserved preference heterogeneity, and highly nonlinear Engel curves. The original EASI 

model was modified by Zhen and colleagues (2013) to allow for censoring of households with 

zero consumption of some products. 

Let a consumer maximize his/her utility by choosing over a vector of J-products that 

results in the budget shares w = [w1,...,wJ]. The choice is constrained by the vector of prices 

p = [p1,...,pJ] and by the total amount of nominal expenditures x. The consumer minimizes a cost function C(p,u) = x to reach a target utility level u. From this minimization 

of cost, we can derive a Hicksian budget share demand function that depends on utility and 

prices: wj = wj

(p,u). Hicksian demand is useful in measuring welfare changes as it allows 

the measurement of utility. However, the utility is difficult to observe in reality where we 

usually observe paired quantities and prices. The relationship between the prices and quantities is described by a Marshallian demand function where the consumer maximizes utility 

while being constrained by prices and expenditures wj = wj

(p,x). The Implicit Marshallian 

demand proposed by Lewbel and Pendakur (2009) depends not only on prices and expenditures, but also on budget shares: wj = wj

 (p,x,w). The implicit Marshallian demand function 

is flexible enough to allow for nonlinear Engel curves and random utility parameters, which 

account for preference heterogeneity. The price of this flexibility is that there are budget 

shares on both sides of the demand function. This introduces endogeneity in the model, 

which requires instruments to address. 

The Hicksian demand wj = wj

 (p,u) is derived from Shephard’s Lemma wj(p,u)=

∂C(p,u)/∂pj

. We can preserve the general structure of the Hicksian demand, while substituting a function of prices, expenditures, and budget shares for the generally unobserved 

utility. Lewbel and Pendakur (2009) propose a simple and flexible Hicksean budget share 

demand function that is unrelated to goods: wj(p,u) = m j (u). Then the cost function should 

be ln C(p,u) = u + mj

(u) ln pj. Knowing the budget shares, we can present utility as u 

= ln x - wj

ln pj. If we substitute this utility function back into the Hicksean budget share 

demand, we get an implicit Marshallian demand wj = mj

(ln x - wjln pj). This demand 

continued—
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uses the Hicksian demand structure with variables from the Marshallian demand. To differentiate this unobserved explicit utility, we denote implicit utility as y = ln x - wj

ln pj.

After enhancing the model with flexible Engel curves, we need to add unobserved preference 

heterogeneity  = [1,...,J], where  has a mean of zero. We want  to enter the budget share 

equation as an additive element: wj(p,u,)= m j (u) + j

. Hence, the error term in the model 

can be interpreted as an unobserved taste heterogeneity parameter. To comply with Shepherd’s 

Lemma,  should enter the cost function as C(p,u,)= u + mj

(u) ln pj + j ln pj, where 

we substitute explicit utility, u, with implicit utility, y. 

Next, we need to incorporate prices, p, and the demographic characteristics of the 

consumers, z = [z1,...,zT] (in our specification, living in a LILA area will be one of the z’s), 

where z1 = 1 serving as a constant term. This exercise produces an Exact Affine Store Index 

with a cost function:

ln C(p,y,z,) = y +

g

mj

(y,z) ln pj+ 

g

a jk ln pj ln pk + g ztln pj

+

g

j ln pj

and a Hicksian budget share equation:

whjt* = 

g

aij ln phjt +

g

+ g zhkt + uhit

h=1,...,H;j=1,...,J;t=1,...,T

where whjt* is the latent budget share, which is related to the observed share as 

whjt = max(whit*,0), and phjt is the a price index of product j for household h in time period t. H

is the total number of households, J is the number of food groups, and the Jth good is used as 

a numeraire. yht is a Stone price index of food expenditures yht = ln xht – whjt ln phjt. The 

model allows for non-linear Engel curves by choosing L >1. After a few tries, we chose L=3; 

for values of L>3, the estimation failed because of nonpositive definite variance-covariance 

matrix. We believe that a cubic polynomial is sufficiently flexible to measure the Engel curve. 

zhkt is a set of household characteristics k for household h in time period t. zhkt is a demand 

shifter such as demographics or food environment; one of the demand shifters is a constant 

term. We follow Zhen et al. (2010) by using Amemiya’s generalized least squares, AGLS estimator for censored equation (Amemiya, 1979; Newey, 1987). We choose L=3 since any higher 

order utility polynomial introduces multi-collinearity. The formulas to calculate demand elasticities are presented in the appendix.
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Results

In table 3, we present results of the estimated elasticities of the baseline model. All own-price elasticities are negative, validating the demand estimation. The own-price elasticities ranged from -0.46 

for grains to -0.76 for nondiet drinks. Own-price elasticities for LILA and non-LILA consumers, 

estimated separately, were very similar and not presented here. 

Low-Income, Low-Access Areas and Distance to Retail Foodstores

In table 4, we present our baseline model of the demand elasticities with respect to unit changes in 

the explanatory variables. All coefficients in the model are statistically significant, so we will discuss 

magnitudes only. Given the structure of the z-variables, our baseline household is non-Hispanic 

White or Asian, lives in a rural non-LILA area, has no children, and has a high school education. 

Living in a LILA area is associated with a less healthy diet. Consumers who lived in these areas 

purchased 4.3 percent fewer fruits, 2.4 percent fewer vegetables, 8.6 percent more red meat, 10.4 

percent less low-fat milk products, 5.2 percent more diet drinks, and 2.6 percent more nondiet drinks 

in 2010. While statistically significant, the magnitudes of the differences between LILA and nonLILA consumers are modest. 

Other demographic variables have much larger effects on food purchases than LILA residence. For 

example, consumers with children under age 7 purchased 23 percent more fruits than consumers 

with no children. Black consumers bought 31 percent more red meat and 35 percent more nondiet 

drinks than White/Asian consumers. College-educated consumers purchased 19 percent more fruit 

than consumers who only graduated from high school; urban consumers purchased 17 percent less 

red meat than rural consumers. 

Table 5 presents the effect of expenditure-weighted average shopping trip distance on food 

purchases. Specification 1 presents results from the baseline model discussed earlier. Specification 

2 adds average distance traveled to the store, with a column showing the effect on food purchases of 

increasing the average distance traveled in the (reporting) quarter by 1 mile. Traveling farther has 

only a modest effect on purchases, the largest being an increase in purchases of oils/nuts by 0.77 

percent/mile traveled and a decrease in purchases of low-fat red meats by 0.77 percent per mile. 

In the third specification, we interact the LILA indicator with the distance variable to test whether 

the diet of LILA consumers improves (more than the diet of non-LILA consumers would) by travelling farther from home. This hypothesis is weakly supported by the results. If LILA households 

travel 1 extra mile to the store, they tend to increase purchases of fruits (0.42 percent/mile), vegetables (0.55 percent/mile), low-fat milk products (0.61 percent/mile) and fish/poultry (0.67 percent/

mile). At the same time, LILA consumers reduce their purchases of diet drinks (-0.56 percent/mile), 

nondiet drinks (-0.33 percent) and low-fat red meats (-0.40 percent/mile). We conclude that LILA 

consumers improve their diet modestly as they travel farther from home, and the magnitude of the 

effect is small. Given that LILA consumers, on average, travel 7.5 miles to a foodstore, it may be 

difficult for an average LILA consumer to travel even farther. 

The EASI model is relatively complicated, so to show its robustness, we present a simple OLS 

(Ordinary Least Squares) regression of the budget shares for fruits and vegetables. Appendix table 

1 presents estimated changes to fruit and vegetable purchases (in percent) associated with living in 

LILA areas. The estimated coefficients are similar to the ones presented in table 4.
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Table 3

Estimated elasticities of demand across 14 food groups (percent)

Fruits

Vegetables Sweets Oils, nuts Eggs Red meats Low fat red meats Fish, poultry Processed foods Grains Milk products

Low-fat

milk 

products

Diet 

drinks

Non-diet 

drinks

Fruits

-0.61 -0.01 -0.03 0.01 0.00 -0.06 0.00 -0.01 -0.14 -0.01 -0.06 0.04 -0.04 -0.03

(0.07) (0.01) (0.01) (0.01) (0.00) (0.01) (0.01) (0.01) (0.03) (0.01) (0.01) (0.01) (0.00) (0.00)

Vegetables

-0.01 -0.69 -0.04 -0.05 0.00 -0.04 -0.02 -0.01 -0.09 0.00 -0.02 0.00 0.00 -0.01

(0.01) (0.05) (0.05) (0.03) (0.00) (0.01) (0.00) (0.00) (0.03) (0.01) (0.01) (0.02) (0.01) (0.02)

Sweets

-0.02 -0.02 -0.59 0.00 -0.01 -0.01 -0.01 -0.01 -0.10 -0.07 -0.03 0.00 -0.07 -0.03

(0.01) (0.03) (0.03) (0.01) (0.00) (0.01) (0.00) (0.00) (0.03) (0.01) (0.01) (0.00) (0.01) (0.01)

Oils, nuts

0.03 -0.10 -0.01 -0.59 -0.02 -0.01 0.00 -0.03 -0.20 -0.05 -0.01 0.00 0.02 0.01

(0.02) (0.07) (0.02) (0.08) (0.00) (0.01) (0.00) (0.01) (0.03) (0.01) (0.01) (0.01) (0.01) (0.01)

Eggs

0.00 0.02 -0.06 -0.06 -0.57 -0.03 0.03 -0.01 -0.19 0.01 0.02 0.01 0.04 -0.05

(0.03) (0.03) (0.05) (0.02) (0.08) (0.02) (0.01) (0.01) (0.07) (0.06) (0.03) (0.02) (0.04) (0.01)

Red meats

-0.10 -0.07 -0.03 -0.01 -0.01 -0.54 0.00 0.00 -0.08 -0.05 -0.03 -0.06 0.03 0.00

(0.01) (0.03) (0.02) (0.01) (0.00) (0.07) (0.02) (0.00) (0.08) (0.01) (0.02) (0.01) (0.01) (0.01)

Low-fat red meats

-0.02 -0.06 -0.04 0.01 0.02 0.00 -0.63 -0.01 -0.26 -0.03 0.01 -0.01 0.09 -0.04

(0.06) (0.02) (0.02) (0.01) (0.00) (0.05) (0.09) (0.01) (0.06) (0.02) (0.01) (0.01) (0.03) (0.04)

Fish, poultry

-0.04 -0.04 -0.05 -0.03 -0.01 0.00 -0.01 -0.65 -0.08 -0.04 -0.10 0.02 0.02 -0.02

(0.02) (0.01) (0.02) (0.01) (0.00) (0.01) (0.01) (0.05) (0.06) (0.03) (0.04) (0.02) (0.01) (0.02)

Processed foods

-0.04 -0.03 -0.05 -0.03 -0.01 -0.02 -0.02 -0.01 -0.71 -0.05 -0.05 -0.03 -0.03 -0.01

(0.01) (0.01) (0.00) (0.00) (0.00) (0.01) (0.00) (0.00) (0.04) (0.00) (0.01) (0.00) (0.01) (0.00)

Grains

0.00 0.01 -0.11 -0.02 0.00 -0.03 -0.01 -0.01 -0.16 -0.46 0.00 -0.02 -0.02 -0.03

(0.02) (0.01) (0.01) (0.00) (0.01) (0.01) (0.00) (0.01) (0.04) (0.09) (0.04) (0.00) (0.01) (0.00)

Milk products

-0.05 -0.02 -0.04 0.00 0.00 -0.02 0.00 -0.03 -0.16 0.00 -0.53 -0.04 -0.01 0.00

(0.01) (0.01) (0.01) (0.00) (0.00) (0.01) (0.00) (0.01) (0.04) (0.03) (0.07) (0.01) (0.00) (0.02)

Low-fat milk products

0.08 0.02 0.02 0.00 0.00 -0.06 0.00 0.02 -0.16 -0.03 -0.08 -0.64 -0.02 0.00

(0.02) (0.04) (0.01) (0.01) (0.01) (0.02) (0.00) (0.01) (0.04) (0.01) (0.01) (0.07) (0.00) (0.01)

Diet drinks

-0.06 0.00 -0.15 0.01 0.01 0.02 0.03 0.01 -0.16 -0.03 -0.02 -0.02 -0.65 0.01

(0.01) (0.02) (0.02) (0.00) (0.01) (0.01) (0.01) (0.00) (0.05) (0.01) (0.00) (0.00) (0.05) (0.02)

Non-diet drinks

0.00 0.03 0.02 0.02 0.00 0.02 0.00 0.00 0.18 0.00 0.03 0.01 0.04 -0.76

(0.03) (0.03) (0.05) (0.01) (0.01) (0.01) (0.01) (0.02) (0.10) (0.03) (0.04) (0.01) (0.03) (0.03)

Coefficients statistically significant at 1% level are marked bold. Source: Computed by USDA, Economic Research Service.
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Table 4

The effect of demographic factors on food purchases

LILA

Household size

Children 

0-6 years 

old

Children 

7-12 

years old

Children 

13-17

 years old Black Hispanic

Less than

high 

school

College 

graduate Urban

Household 

income ($)

Fruits

-4.34 -7.92 22.65 7.14 -2.42 11.27 9.27 -13.67 19.45 7.77 1.32

(0.0017) (0.0031) (0.0088) (0.0028) (0.0009) (0.0044) (0.0036) (0.0053) (0.0075) (0.0030) (0.2448)

Vegetables

-2.43 -2.01 -4.58 -8.43 -11.01 -5.82 3.16 -5.81 5.10 3.70 0.78

(0.0017) (0.0014) (0.0033) (0.0061) (0.0079) (0.0042) (0.0023) (0.0042) (0.0037) (0.0027) (0.1972)

Sweets

0.02 -1.63 -6.03 0.33 -0.22 -3.79 -6.38 2.23 -6.21 -1.06 -0.88

(0.0000) (0.0013) (0.0049) (0.0003) (0.0002) (0.0031) (0.0052) (0.0018) (0.0051) (0.0009) (0.1076)

Oils, nuts

0.21 -5.74 -11.97 -11.55 -11.33 7.31 2.44 -14.48 15.02 -3.64 0.46

(0.0002) (0.0049) (0.0103) (0.0099) (0.0097) (0.0063) (0.0021) (0.0124) (0.0129) (0.0031) (0.3085)

Eggs

3.37 5.24 -0.45 -7.15 -9.34 16.41 16.11 11.05 0.90 -3.76 -1.01

(0.0011) (0.0016) (0.0001) (0.0022) (0.0029) (0.0051) (0.0050) (0.0035) (0.0003) (0.0012) (0.3819)

Red meats

8.55 8.46 -2.92 -1.44 -2.61 31.03 -1.61 19.27 -9.96 -16.97 -1.42

(0.0089) (0.0089) (0.0031) (0.0015) (0.0027) (0.0325) (0.0017) (0.0202) (0.0104) (0.0178) (0.2356)

Low-fat red 

meats

4.01 2.84 13.80 19.08 8.49 -20.29 26.15 3.66 7.73 -6.26 -0.81

(0.0103) (0.0073) (0.0356) (0.0492) (0.0219) (0.0523) (0.0674) (0.0094) (0.0199) (0.0162) (0.6247)

Fish, 

poultry

1.25 3.15 -13.78 -12.45 -5.36 60.46 7.79 -11.86 7.33 -0.25 -0.19

(0.0029) (0.0073) (0.0318) (0.0287) (0.0124) (0.1395) (0.0180) (0.0274) (0.0169) (0.0006) (0.2397)

Processed 

foods

0.18 -0.11 -3.23 0.83 2.96 6.66 0.63 1.19 -4.04 3.50 0.11

(0.0005) (0.0003) (0.0095) (0.0024) (0.0087) (0.0196) (0.0019) (0.0035) (0.0119) (0.0103) (0.0592)

Grains

-2.71 5.62 1.66 6.46 3.98 -8.28 0.03 -0.17 3.93 -2.64 -0.25

(0.0030) (0.0061) (0.0018) (0.0071) (0.0043) (0.0090) (0.0000) (0.0002) (0.0043) (0.0029) (0.1459)

Milk 

products

0.97 3.84 17.91 -2.01 0.65 -28.12 -0.08 -4.89 0.35 -7.29 -0.35

(0.0008) (0.0033) (0.0156) (0.0017) (0.0006) (0.0244) (0.0001) (0.0042) (0.0003) (0.0063) (0.1150)

Low-fat 

milk 

products

-10.37 -5.44 30.46 22.76 9.15 -66.73 -18.27 -28.50 27.88 8.22 1.72

(0.0089) (0.0047) (0.0262) (0.0195) (0.0079) (0.0573) (0.0157) (0.0245) (0.0239) (0.0071) (0.3184)

Diet drinks

5.23 -4.49 -21.30 -10.40 -12.52 -36.59 -8.57 8.87 -5.25 -3.74 1.03

(0.0065) (0.0056) (0.0264) (0.0129) (0.0155) (0.0453) (0.0106) (0.0110) (0.0065) (0.0046) (0.1456)

Nondiet 

drinks

2.61 6.24 -5.92 -3.44 10.99 35.25 0.07 24.74 -14.19 -6.36 -1.46

(0.0284) (0.0679) (0.0643) (0.0374) (0.1194) (0.3832) (0.0007) (0.2689) (0.1543) (0.0691) (0.1488)

All coefficients are statistically significant at 5% level, except the underlined coefficients. LILA - Low-income, low-access.

Source: Computed by USDA, Economic Research Service.
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Table 5

Effect on food purchases of distance travelled to stores 

(1) (2) (3)

LILA LILA distance LILA distance LILA*distance

Fruits

-4.34 -4.08 -0.21 -7.26 -0.26 0.42

(0.0017) (0.0016) (0.0001) (0.0028) (0.0001) (0.0002)

Vegetables

-2.43 -2.54 0.09 -6.71 0.02 0.55

(0.0017) (0.0018) (0.0001) (0.0048) (0.0000) (0.0004)

Sweets

0.02 0.13 -0.10 1.64 -0.07 -0.20

(0.0000) (0.0001) (0.0001) (0.0013) (0.0001) (0.0002)

Oils, nuts

0.21 -0.72 0.77 -3.05 0.73 0.30

(0.0002) (0.0006) (0.0006) (0.0026) (0.0006) (0.0003)

Eggs

3.37 3.44 -0.05 5.95 -0.01 -0.33

(0.0011) (0.0011) (0.0000) (0.0019) (0.0000) (0.0001)

Red meats

8.55 8.33 0.19 10.63 0.23 -0.30

(0.0089) (0.0087) (0.0002) (0.0111) (0.0002) (0.0003)

Low-fat red meats

4.01 4.92 -0.77 7.90 -0.72 -0.40

(0.0103) (0.0127) (0.0020) (0.0204) (0.0018) (0.0010)

Fish, poultry

1.25 0.69 0.44 -4.40 0.36 0.67

(0.0029) (0.0016) (0.0010) (0.0101) (0.0008) (0.0015)

Processed foods

0.18 0.24 -0.05 0.97 -0.04 -0.10

(0.0005) (0.0007) (0.0001) (0.0029) (0.0001) (0.0003)

Grains

-2.71 -2.65 -0.06 -2.49 -0.05 -0.02

(0.0030) (0.0029) (0.0001) (0.0027) (0.0001) (0.0000)

Milk products

0.97 0.74 0.18 -0.46 0.16 0.16

(0.0008) (0.0006) (0.0002) (0.0004) (0.0001) (0.0001)

Low-fat milk products

-10.37 -9.90 -0.37 -14.53 -0.44 0.61

(0.0089) (0.0086) (0.0003) (0.0126) (0.0004) (0.0005)

Diet drinks

5.23 4.99 0.22 9.19 0.29 -0.56

(0.0065) (0.0061) (0.0003) (0.0113) (0.0004) (0.0007)

Non-diet drinks

2.61 2.66 -0.04 5.19 0.00 -0.33

(0.0284) (0.0289) (0.0004) (0.0564) (0.0000) (0.0036)

All coefficients are statistically significant at 1%. LILA - Low-income, low-access.

Source: Computed by USDA, Economic Research Service.
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Urban and Rural LILA Areas

Rural and urban areas need to pass different distance thresholds to be classified as LILA areas 

(10 miles and 1 mile, respectively). The effect of travelling an extra mile to the store may be more 

modest for rural residents who typically have to travel longer distances for services and shopping. 

The effect on food purchases of living in a rural LILA area is much weaker than the effect of living 

in an urban one (table 6). Living in an urban LILA area is associated with fewer purchases of 

fruits (-6.7 percent), vegetables (-4.9 percent), and low-fat milk products (-14.6 percent) and greater 

purchases of red meats (10.1 percent) and nondiet drinks (5.4 percent). (The urban column shows the 

average difference in purchases between urban and rural consumers.) We find that urban consumers 

have more healthful diets, purchasing more fruits, vegetables, and low-fat milk products and fewer 

red meats and nondiet drinks. This general difference makes the diet of urban LILA and rural nonLILA consumers roughly similar. 

Next, we interact average shopping distance travelled with the indicators for rural and urban LILA 

areas. For consumers living in urban LILA areas, travelling 1 more mile to the store is associated 

with increased purchases of vegetables (0.79 percent), fruits (0.55 percent), oils/nuts (0.89 percent), 

low-fat red meats (1.33 percent), and fish/poultry (0.56 percent), and reduced purchases of nondiet 

drinks (-0.32 percent) (table 7). For rural LILA consumers, a 1-mile increase in distance travelled 

is associated with an increase in purchases of milk products (0.29 percent) and a decrease in the 

purchase of oils/nuts (-0.69 percent) and sweets (-0.54 percent). 

Poverty or Low Access as Greater Factor in Food Purchases: 

Disaggregation of LILA

For a census tract to be classified as LILA, it needs to have both low-income households and limited 

access to retail foodstores. In this section, we consider these criteria separately to assess their relative importance. 

Depth of poverty and urban residence have larger impacts on consumers’ diets than the distance to 

supermarkets. Consumers living in poor areas purchase less fruit (-5.7 percent), fewer vegetables 

(-2.7 percent), more sweets (2.6 percent), more red meats (6 percent), fewer low-fat milk products 

(-15.1 percent), and more nondiet drinks (9.9 percent) than consumers in rural non-LILA areas (table 

8). On the other hand, living in an urban census tract that is not a LILA area is associated with a 

healthier diet. Consumers in urban tracts buy 11.3 percent more fruits, 4.4 percent more vegetables, 

21.6 percent less red meats, and 9.7 percent more low-fat milk products.

Curiously, residents of rural areas more than 10 miles from a supermarket and residents of rural 

LILA areas have healthier diets than rural residents closer to supermarkets. Seemingly, the benchmark of 10-mile distance from a supermarket does not equate to a disadvantaged rural population. 

Longer distance in an urban context has a stronger effect on diet. Urban consumers who are more 

than 1 mile from a supermarket purchase 7.3 percent fewer fruit and 11.1 percent more red meats 

(table 8). On the positive side, they purchase 6.8 percent fewer nondiet drinks and 5.1 percent more 

fish and poultry. The joint effect of low income and low access in urban areas (Urban LILA indicator) is associated with the purchase of 2.7 percent fewer vegetables, 3.6 percent more red meats, 

and 5.5 percent less fish and poultry. 
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Table 6

Effect on food purchases of living in rural and urban low-income, 

low-access areas 1 

Rural LILA Urban LILA Urban

Fruits

0.62 -6.69 8.49

(0.0002) (0.0026) (0.0033)

Vegetables

2.83 -4.92 4.47

(0.0020) (0.0035) (0.0032)

Sweets

0.72 -0.32 -0.95

(0.0006) (0.0003) (0.0008)

Oils, nuts

8.05 -3.51 -2.52

(0.0069) (0.0030) (0.0022)

Eggs

-0.09 5.00 -4.26

(0.0000) (0.0016) (0.0013)

Red meats

5.25 10.12 -17.45

(0.0055) (0.0106) (0.0183)

Low-fat red meats

-1.45 6.62 -7.05

(0.0037) (0.0171) (0.0182)

Fish, poultry

8.62 -2.26 0.82

(0.0199) (0.0052) (0.0019)

Processed foods

-1.90 1.18 3.19

(0.0056) (0.0035) (0.0094)

Grains

-1.39 -3.34 -2.45

(0.0015) (0.0036) (0.0027)

Milk products

2.10 0.42 -7.13

(0.0018) (0.0004) (0.0062)

Low-fat milk products

-1.50 -14.56 9.49

(0.0013) (0.0125) (0.0082)

Diet drinks

-2.59 8.94 -4.88

(0.0032) (0.0110) (0.0060)

Nondiet drinks

-3.31 5.40 -7.17

(0.0360) (0.0587) (0.0779)

All coefficients are statistically significant at 1% level. LILA - Low-income, low-access.

Source: Computed by USDA, Economic Research Service.
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Table 7

Effect on food purchases of living in rural and urban low-income, low-access areas 2

Urban

Rural 

LILA Urban LILA

Rural 

LILA *

distance

Urban LILA 

* distance distance

Urban * 

distance

Fruits

12.37 4.01 -9.52 -0.30 0.55 0.16 -0.62

(0.0049) (0.0016) (0.0037) (0.0001) (0.0002) (0.0001) (0.0002)

Vegetables

3.71 0.43 -9.14 0.20 0.79 -0.04 0.11

(0.0027) (0.0003) (0.0066) (0.0001) (0.0006) (0.0000) (0.0001)

Sweets

-0.01 7.47 0.20 -0.54 -0.09 0.04 -0.15

(0.0000) (0.0061) (0.0002) (0.0004) (0.0001) (0.0000) (0.0001)

Oils, nuts

3.24 13.59 -8.28 -0.69 0.89 0.96 -0.32

(0.0027) (0.0115) (0.0070) (0.0006) (0.0008) (0.0008) (0.0003)

Eggs

-5.71 -0.18 7.73 0.04 -0.52 -0.13 0.18

(0.0018) (0.0001) (0.0024) (0.0000) (0.0002) (0.0000) (0.0001)

Red meats

-22.47 5.94 9.22 0.02 0.16 -0.27 0.74

(0.0234) (0.0062) (0.0096) (0.0000) (0.0002) (0.0003) (0.0008)

Low-fat red 

meats

-22.17 10.89 -0.38 -0.50 1.33 -1.70 1.51

(0.0573) (0.0281) (0.0010) (0.0013) (0.0034) (0.0044) (0.0039)

Fish, poultry

5.42 3.46 -5.23 0.23 0.56 0.62 -0.38

(0.0125) (0.0080) (0.0120) (0.0005) (0.0013) (0.0014) (0.0009)

Processed 

foods

2.11 -4.34 2.62 0.22 -0.27 -0.12 0.11

(0.0062) (0.0128) (0.0077) (0.0007) (0.0008) (0.0003) (0.0003)

Grains

-4.34 -1.79 -1.59 0.08 -0.33 -0.18 0.21

(0.0047) (0.0020) (0.0017) (0.0001) (0.0004) (0.0002) (0.0002)

Milk 

products

-7.30 -1.91 0.24 0.29 0.03 0.09 0.11

(0.0064) (0.0017) (0.0002) (0.0002) (0.0000) (0.0001) (0.0001)

Low-fat milk 

products

8.20 -2.91 -14.18 0.21 -0.06 -0.38 -0.06

(0.0071) (0.0025) (0.0123) (0.0002) (0.0001) (0.0003) (0.0001)

Diet drinks

-1.22 0.56 9.20 -0.37 -0.05 0.47 -0.32

(0.0015) (0.0007) (0.0113) (0.0005) (0.0001) (0.0006) (0.0004)

Nondiet drinks

-3.82 -3.51 7.14 -0.06 -0.32 0.26 -0.43

(0.0415) (0.0382) (0.0776) (0.0006) (0.0035) (0.0029) (0.0047)

All coefficients are statistically significant at 1% level. LILA - Low-income, low-access.

Source: Computed by USDA, Economic Research Service.
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Table 8

Effect of distance and low income on food purchases 

Urban Poor Mile 1/urban Mile 10/rural LILA/urban LILA/rural

Fruits 11.28 -5.72 -7.25 6.21 -0.96 3.42

(0.0045) (0.0023) (0.0029) (0.0025) (0.0004) (0.0014)

Vegetables 4.40 -2.71 -0.45 0.25 -2.69 4.73

(0.0032) (0.0019) (0.0003) (0.0002) (0.0019) (0.0034)

Sweets -1.28 2.59 0.14 -12.89 -2.41 1.72

(0.0010) (0.0021) (0.0001) (0.0104) (0.0020) (0.0014)

Oils, nuts

-2.26 -1.58 1.09 21.90 -2.40 4.30

(0.0019) (0.0013) (0.0009) (0.0187) (0.0021) (0.0037)

Eggs -4.85 0.37 0.87 -6.46 4.59 1.05

(0.0015) (0.0001) (0.0003) (0.0020) (0.0014) (0.0003)

Red meats

-21.61 5.99 11.09 0.77 3.63 0.67

(0.0223) (0.0062) (0.0114) (0.0008) (0.0037) (0.0007)

Low-fat red meats

-12.50 0.97 12.14 -6.19 4.10 -0.87

(0.0321) (0.0025) (0.0312) (0.0159) (0.0105) (0.0022)

Fish, poultry

0.24 3.10 5.05 31.83 -5.54 -0.77

(0.0005) (0.0071) (0.0116) (0.0733) (0.0127) (0.0018)

Processed foods

3.60 -0.09 -1.14 -1.73 1.42 -1.44

(0.0106) (0.0003) (0.0034) (0.0051) (0.0042) (0.0042)

Grains

-4.47 -2.90 3.91 -1.20 -1.59 0.95

(0.0049) (0.0032) (0.0043) (0.0013) (0.0017) (0.0010)

Milk products

-7.49 1.07 2.54 15.19 -0.82 -2.06

(0.0065) (0.0009) (0.0022) (0.0132) (0.0007) (0.0018)

Low-fat milk products

9.68 -15.07 -4.23 -3.09 -1.66 10.13

(0.0083) (0.0129) (0.0036) (0.0026) (0.0014) (0.0087)

Diet drinks

-6.60 2.42 4.23 -3.53 6.37 -3.60

(0.0081) (0.0030) (0.0052) (0.0043) (0.0078) (0.0044)

Nondiet drinks

-3.39 9.93 -6.83 -5.96 -1.69 -9.08

(0.0368) (0.1075) (0.0740) (0.0645) (0.0183) (0.0984)

All coefficients are statistically significant at 1% level. LILA - Low-income, low-access.

Source: Computed by USDA, Economic Research Service.
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Store Channels and Their Effect on Food Purchases

There is little difference in the retail channels patronized by LILA and non-LILA consumers (LILA 

consumers purchase more from supercenters and non-LILA consumers purchase more from supermarkets), but it is possible that the same retail channels offer different products in LILA areas. In 

this section, we investigate whether LILA and non-LILA consumers buy similar products in the 

same retail channels. We include five retail channel indicators in the estimation, with grocery stores 

(the most popular channel) the omitted category. 

Retail channel or format has a large effect on food purchases. Purchases in supercenters were 

associated with less healthful purchases. When consumers increase the share of expenditures 

in supercenter stores by 10 percentage points, this change is associated with an increases in 

purchases of low-fat meats (16 percent) and red meats (4.4 percent) and a decrease in purchases 

of fruits (2.5 percent) and vegetables (2.6 percent). Other store channels have average expenditure shares less than 10 percent, so we can reliably estimate only small changes to the expenditure share of these channels. 
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Explaining Food Disparities: Access or Preferences?

LILA consumers living far from supermarkets with limited means of transportation may face a food 

environment that makes it difficult to purchase healthy foods. In this report, we measured the effect 

of living in LILA areas on consumer food purchases. Consumers living in LILA areas consume 

fewer fruits, vegetables, and low-fat milk products and more red meats and drinks (diet and nondiet). 

The magnitudes of the differences are small and, even if eliminated, would not translate into significant dietary or health improvements. The effects of demographic variables—such as race, education, 

and income—on food purchases are much larger than the effect from living in a LILA area. 

LILA consumers travel slightly farther from home to purchase their food. Almost all LILA households travel to stores more than 1 mile from their home, the distance commonly used to delineate 

low food access. Thus, of the 7.7 percent of households in the Homescan sample who lived in LILA 

areas, very few limited their food purchases to the LILA areas. 

LILA consumers who travel farther to buy food purchase more fruits, vegetables, fish, and poultry 

and fewer drinks (diet and nondiet). Perhaps, stores close to LILA consumers sell a variety of products similar to those offered at convenience stores, such as drinks, milk, sweets, and limited meats 

and produce. When LILA consumers travel farther to retail stores that offer a greater variety of 

foods and they are unconstrained in their choices, LILA consumers exhibit stronger preferences for 

fish, poultry, and produce, but the magnitudes of these effects are small and they cannot explain 

large nutritional disparities observed in the population. 

The LILA definition requires that a rural area be at least 10 miles from the nearest supermarket to 

be classified as a low food-access area. However, even with this generous parameter, food demand 

in rural LILA areas is hardly different from other rural areas. Rural residents generally tend to buy 

fewer healthy food products than urban consumers. 

For an area to classify as LILA, it must have both limited access to supermarkets and low income 

levels. The poverty level of a census tract, we find, is a more important factor for unhealthy food 

purchases than is access to stores. Distance to retail foodstores has almost no effect on purchases in 

rural areas, but a small negative effect in urban areas.

If LILA consumers are restricted in their choice of stores and are compelled to shop in stores with 

high prices and a scarcity of healthy food, we would expect to see differences in the retail channels 

where LILA and non-LILA consumers shop. However, our results do not bear this out. Instead, the 

descriptive statistics of retail channels (table 1) show that the difference in retail channels patronized by LILA and non-LILA households is small. Thus, if we assume that stores in the same retail 

channel offer a similar variety of healthy foods, then there is little difference in the availability of 

healthy foods to LILA and non-LILA consumers. Nor do we find that LILA consumers are facing 

higher prices; in fact, they are facing slightly lower prices for most food products (see table 2). 

After controlling for the retail channel where consumers shop (table 9), the baseline difference 

in food purchases between LILA and non-LILA households decreases by half. LILA consumers 

purchased less healthful food in all retail channels except supercenters compared to non-LILA 

consumers. On the other hand, LILA consumers purchased less healthful products in supermarkets 

and club stores, which tend to have the most healthful product offerings, suggesting lower preferences for these products among LILA consumers. 
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The relatively poor diet of LILA consumers cannot be explained by their inability to reach supermarkets or by different relative prices in supermarkets where they shop. Two likely explanations are 

that LILA consumers have different preferences than non-LILA consumers, or that LILA consumers 

shop in stores that offer a different variety of products than the stores where non-LILA consumers 

shop, even if these stores belong to the same retail channel. Future research examining the variety of 

products offered in the stores where LILA consumers shop may illuminate this issue. 
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Conclusion

Our results begin to shed light on whether the quality of food choices in low-access areas is a 

demand or supply problem. Do consumers in LILA areas value supermarkets less or do those 

retail outlets, for some reason, avoid these areas? We find that LILA area consumers purchase 

unhealthier food products than consumers living elsewhere. The difference in the healthfulness of 

their purchases, however, is quite small. Also, LILA area consumers tend not to purchase much 

healthier items farther from home, suggesting that these consumers exhibit less demand for such 

items. Households in LILA areas must travel unrealistically long distances to erase even the small 

disparity with non-LILA households in healthfulness of food purchases. Thus, as the effect of 

living in LILA areas on diets is modest, the dietary effect of a policy attracting supermarkets to 

these areas will be similarly modest. 

There are three important caveats to our results. First, Nielsen HomeScan data underrepresent poor 

consumers. We correct this problem somewhat by using population weights, but this remedy cannot 

help if the poor in Homescan are systematically different from the poor not sampled by it. We 

suspect that the coverage of the very poor may be particularly lacking. Second, our match of store 

locations to consumers is imperfect, which introduces a measurement error to our distance variable 

for the unmatched stores. Finally, we observe the food purchased in stores only to the extent that 

participants correctly report it, and we don’t have information on food consumed away from home in 

restaurants, schools, and other places, which are important components of overall food consumption. 

While these caveats may be daunting, this is the best data available at the moment that links food 

purchases with consumer and store locations. In addition, this study benefits from a large consumer 

panel with frequent and detailed information on food purchases. The fact that this is the first large 

national study of food purchases in LILA areas illustrates the data difficulties faced by researchers. 

Future research might combine a better measurement of food consumed away from home with better 

coverage of poor consumers. USDA’s National Household Food Acquisition and Purchase Survey 

(FoodAPS), recently available, combines both and is an attractive avenue for future research on food 

access and food purchases. 

Our results show that living in LILA areas does not have major adverse effects on the food 

purchases for most residents of these areas. At the same time, given our data limitations, it is 

possible that living in LILA areas is a large problem for a small minority of particularly disadvantaged consumers. Future research with a better coverage of the very poor consumers can shed 

light on this question. 
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Appendix 

Calculating Elasticity: Price Elasticity of Demand

In the fully interactive EASI model we estimate 

j k T J

g ln

Hicksian price semi-elasticity

From Hicksian semi-elasticity we can calculate Hicksian elasticity:

Then the Hicksian elasticity is 

Where 1ij = 1 if i = j and 1ij = 0 if i  j.

(2)

(3)
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From equation (2) we can derive Marshallian expenditure semi-elasticity (see details in the 

Appendix of Lewbel and Pendakur (2009)) where xm xm is the Marshallian expenditures. 

From the semi-elasticity we can derive elasticity:

From Slutsky’s equation for elasticities

we can derive Marshallian price elasticities:

Elasticity with respect to Z’s

Lewbel and Pendakur (2009) present a general formula for budget semi-elasticity:

From (2) we know that: 

g

g

 and ..
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Then a budget semi-elasticity with respect to z’s is:

g

g

Next we find the quantity elasticity of demand with respect to z’s:

g

g

As total spending and market prices do not depend on z variables:

g

g

Then the quantity elasticity of demand with respect to z’s is:

g

g

Appendix table 1

Robustness check: OLS estimated expenditures (percent) 

Budget share of fruits Budget share of vegetables

LILA -2.93*** -5.31***

(0.77) (0.93)

LILA -5.92*** -7.36***

(1.17) (1.41)

Dist_w -0.012 -0.34***

(0.042) (0.051)

LILA * distance 0.39 0.34**

(0.18) (0.15)

Coefficients statistically significant at 1% level are marked bold. LILA = Low-income, 

low-access. OLS = Ordinary Least Squares.

Source: Computed by USDA, Economic Research Service.
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