Abstract

Recent revisions to the food packages provided by the Special Supplemental Nutrition Program for Women, Infants, 

and Children (WIC) added healthy foods and required WIC-authorized stores to make these foods available. This 

study examined the availability, variety, and prices of healthy foods before and after implementation of the food 

package revisions in 252 convenience and nonchain grocery stores in Connecticut. The findings provide strong 

evidence that stores responded to the food package revisions by improving the availability and variety of healthy 

foods in both urban and suburban settings. Most of these improvements occurred in WIC-authorized convenience 

and grocery stores, especially those in low-income neighborhoods. Some positive changes in the availability of 

whole-grain products were also observed in non-WIC convenience and grocery stores. The policy change, which 

targets WIC participants, improved access to healthy foods for both WIC and non-WIC consumers.

By Tatiana Andreyeva, Joerg Luedicke, Ann E. Middleton, Michael W. Long, and 

Marlene B. Schwartz, Rudd Center for Food Policy and Obesity, Yale University

Changes in Access to Healthy Foods 

after Implementation of the WIC Food 

Package Revisions

Contractor and Cooperator Report No. 66

April 2011

This study was conducted by Rudd Center for Food Policy and Obesity, Yale 

University under a cooperative research agreement with USDA’s Economic 

Research Service (ERS) Food Assistance and Nutrition Research Program 

(FANRP): agreement number 59-5000-8-0105 (ERS project representative: 

Elizabeth Frazao). The views expressed are those of the authors and not 

necessarily those of ERS or USDA.
2 

Changes in Access to Healthy Foods after Implementation of the WIC 

Food Package Revisions

Tatiana Andreyeva, PhD 

Joerg Luedicke, MS

Ann E. Middleton, MPH

Michael W. Long, MPH 

Marlene B. Schwartz, PhD

Corresponding author

Tatiana Andreyeva, PhD

: 

Director of Economic Initiatives

Associate Research Scientist

Yale University

Rudd Center for Food Policy & Obesity

309 Edwards Street 

New Haven, CT 06520-8369 

(203) 432-8432 

tatiana.andreyeva@yale.edu
3

Executive Summary 

Policy changes that target demand and supply of healthy foods could be one strategy to 

improve access to healthy food in at-risk communities. In December 2007, the USDA 

revised the WIC food packages to offer participants foods that better reflect the Dietary 

Guidelines for Americans. These revisions can increase demand for healthy foods from 

millions of WIC participants and also require WIC-authorized stores to stock these foods. 

The policy change targeting WIC participants has the potential to improve access to

healthy foods for all customers in at-risk communities.

What Did the Study Find? 

Implementation of the WIC food package revisions significantly increased the 

availability and variety of key healthy foods in WIC-authorized convenience and grocery 

stores in the state of Connecticut. When facing new government regulations to stock 

certain healthy foods, Connecticut convenience and grocery stores found ways to deliver 

healthy foods that were previously lacking in their stores and communities. Most 

improvements in the provision of fruit and vegetables, whole grain products and lowerfat milk occurred in WIC-authorized convenience and grocery stores. Some positive 

changes in the availability of whole grain products were also observed in non-WIC 

convenience and grocery stores, potentially due to better access to new WIC healthy 

foods in supply chains. If the experience in Connecticut is typical of other states, national 

food policy that incentivizes demand and requires changes in stores can improve the food 

environment in at-risk communities. This could occur at no additional cost to taxpayers 

as the WIC food package revisions were designed to be cost-neutral. 

The WIC food package revisions had a greater impact on the availability and variety of 

healthy foods in low-income communities, which is particularly important given the 

limited transportation options and lack of supermarkets in many low-income 

neighborhoods. In comparison to WIC stores serving higher-income neighborhoods, WIC 

stores in lower-income areas had greater improvements in the availability and variety of 

fruit and vegetables and whole grain products. The same was true for increased shelfspace for lower-fat milk, which helped reduce the large gap by neighborhood income 

before the revisions. We found no neighborhood differences in prices of three of the most 

commonly available foods -- eggs, whole milk and canned vegetables. Prices of these 

products in convenience and grocery stores were unaffected by the WIC revisions. 

Store size and SNAP and WIC-authorization status of convenience and grocery stores 

were positively related to better availability and variety of fruits and vegetables and 

whole grain products. Larger and WIC-authorized convenience and grocery stores also 

had more shelf-space for lower-fat milk. Although market competition from other small 

stores and fast food outlets had a limited effect on the availability, variety and pricing of 

healthy foods in convenience and grocery stores in our sample, increasing distance from 

supermarkets predicted greater availability and variety of fruit and vegetables and whole 

grain products. Given existing literature on the negative impact of convenience stores on 
4

diet quality and obesity risk, this information may be useful to policymakers considering 

zoning restrictions on these stores as a strategy for improving the community food 

environment. In the absence of nearby supermarkets, this study found that convenience 

and non-chain grocery stores serve an important role as suppliers of healthy foods. 

How Was the Study Conducted? 

This study assessed predictors of access to healthy food, focusing on the impact of the 

WIC food package revisions on the availability of healthy foods in convenience and 

grocery stores in Connecticut. Using a standardized questionnaire to assess availability, 

variety, quality, and prices of WIC-approved foods, the study conducted a systematic 

inventory of all food stores in five diverse Connecticut towns. The assessment was 

conducted before and after the introduction of new WIC food packages in October 2009. 

The policy effect of the WIC food package revisions was measured by differential 

changes in multiple measures of the provision of healthy foods in252 WIC-authorized 

and non-WIC convenience and grocery stores. To account for non-random group 

assignment of the natural experiment setting, the study estimated multilevel random 

intercept models that isolated contributions of neighborhood, market, and store 

characteristics. The study further assessed if the WIC food package revisions had a 

differential impact in communities of various neighborhood income levels. 

Acknowledgements

The study was funded by a grant from USDA, Economic Research Service. The authors 

thank Elizabeth Frazao, Shelly Ver Ploeg, Phillip Kaufman, Joel Gittelsohn and Jamie 

Chriqui for comments on the earlier version of the paper. We are grateful to the 

considerable number of stores taking part in the study. We also thank Alissa Roman for 

research support and acknowledge all research assistants participating in data collection. 

Finally, we appreciate assistance provided by the WIC Program at Connecticut State 

Department of Public Health. 
5

Introduction 

Prior research has shown that environments in which people make food decisions can 

affect their diet quality and health. Limited availability of healthy foods can contribute to 

increased risk for poor nutrition (Booth et al 2001), chronic illnesses like obesity (Larson 

et al 2009) and heart disease (Diez 2003). The food environment is influenced by 

multiple economic, social, retail and policy factors. Understanding each factor and their 

potential interaction is important for creating effective policy change that improves food 

environments in at-risk communities. In this study we considered several factors that 

could affect the food environment: (a) the WIC food package revisions as an example of 

a food policy change, (b) neighborhood attributes, (c) food market characteristics, and (d) 

store characteristics. 

Overview of the WIC Food Package Revisions: One of the nation’s largest food 

assistance programs, the Supplemental Nutrition Program for Women, Infants, and 

Children (WIC) provides supplemental healthy foods, nutrition education, and medical 

referrals to approximately 50% of infants, 25% of children less than 5 years of age, 29% 

of pregnant and 26% of postpartum women in the U.S. In 2009, 9.12 million women, 

infants, and children were enrolled in WIC nationwide, spending $4.6 billion dollars 

annually (USDA 2010). In 2007, following recommendations from the Institute of 

Medicine (IOM, 2005), the USDA issued regulations to revise and implement new WIC 

food packages by October 1, 2009. A major focus of the revisions was to align WIC 

benefits with the 2005 Dietary Guidelines for Americans and to offer low-income 

mothers, children and infants foods that reflect dietary recommendations and promote 

healthy weight (Oliveira and Frazao, 2009). 

Noteworthy changes included additions of new whole grain products and cash-value 

vouchers for fruits and vegetables as well as reductions in the fat content of WIC 

approved dairy foods. The revisions also reduced quantities of eggs and cheese as well as

eliminated juice from the infant food packages and reduced juice quantities for children 

and women. A number of incentives to support breastfeeding were included. For 

example, the new food package delays the introduction of complementary foods to 

infants from the child’s 4th to 6th month; includes jarred baby meats for fully breastfed 

babies and jarred fruit and vegetables for all babies at 6 months (Oliveira and Frazao, 

2009). These revisions will likely increase demand for healthy foods from millions of 

WIC participants, and lead WIC authorized stores to increase availability of these foods. 

As food stores are the main delivery system of WIC-approved foods (Oliveira and 

Frazao, 2009) and required to carry new healthy foods, revisions in the WIC food 

packages have the potential to improve access to healthy foods for all customers. 

Neighborhood Income Effects: While most individuals in the U.S. have reasonable 

access to healthy food, those with poor access (approximately 2-8% of the U.S. 

population) are from low-income households, and tend to live in lower-income 

neighborhoods, marked by racial segregation or low population density (USDA, 2009). 

Areas with low median household income and minority predominance often have poor 
6

access to healthy foods and higher neighborhood food prices (Horowitz, et al., 2004; 

Inagami, et al., 2006; Morland, et al., 2002; Sloane, et al., 2003), and, in comparison to 

suburban and higher income areas, less access to supermarkets and higher densities of 

fast food outlets and convenience stores (Larson, et al., 2009). Access to a variety of 

affordable healthy foods is especially problematic for low-income consumers lacking 

transportation to retailers outside their communities and forced to shop in small local 

markets (Morland, et al., 2002; Sloane, et al., 2003). Although lower-income families on 

average spend less on the same foods than high-income families by shopping in 

supermarkets and large groceries (Broda, et al., 2009, USDA, 2009), the lack of access to 

affordable healthy foods in many low-income communities is a significant problem. 

Economies of scale, technological advancements and increasing market competition drive 

down prices in supermarkets relative to smaller stores. More recently, nontraditional 

stores such as mass merchandisers, supercenters, club warehouse and dollar stores have 

begun to offer even bigger discounts. Compared to traditional supermarkets their 

expenditure-weighted price discounts in 2004-2006 were on average 7.5 percent ranging 

from 3 to 28 percent across products (Leibtag, et al., October 2010). About 90% of the 

Supplemental Nutrition Assistance Program (SNAP) benefits are already spent in large 

grocery stores and supermarkets (USDA, 2009). Distance to stores does not appear to be 

a major barrier for low-income shoppers looking for affordable and preferred foods – on 

average SNAP recipients travel further than the nearest grocery store to purchase food 

(Ohls, 1999). 

Market Competition: Consistent with economic theory, a number of studies have found 

that increased local market competition leads to lower food prices (Abe and Kawaguchi, 

2010; Basker and Noel, 2009; Binkley and Connor, 1998; Cotterill, 1999; Marion, 1998; 

Stiegert and Sharkey, 2007). A survey of supermarket fresh produce buyers found that 

local competitive pricing was one of the most common pricing tactics in this volatile 

product category (McLaughlin, 2004). Still, only a few studies have considered how local 

competition across store types impacted food pricing and availability. For example, 

Binkley et al (1998) found supermarket prices to be lower in the face of competition from 

other supermarkets and warehouse stores, but unrelated to the concentration of small 

stores. Further, competition from fast food outlets led to lower supermarket prices for 

some goods (e.g., frozen foods, canned goods, baked goods) and higher prices for others 

(e.g., fresh produce, fresh meat). 

Few studies have assessed how market competition influences the broader food 

environment and, in particular, small food stores. One study provided empirical support 

for a theory of market segmentation resulting in a natural oligopoly of high-quality 

supermarkets providing a broad range of high-quality products and a competitive market 

of small grocers providing low-price and low-quality products (Ellickson, 2006). 

However, we did not identify any studies that examined availability and prices of food 

categories in small food stores based on competition from other small stores, 

supermarkets or fast food outlets. Because small stores like convenience and non-chain 

grocery stores are a particularly important part of the food environment for residents of 

low-income communities who have limited access to transportation to supermarkets, 
7

additional research is needed on the role of market competition in shaping food 

availability, prices and quality in these stores.

Store Characteristics: Store size and type may be important predictors of availability and 

prices of healthy foods. For example, store type differences across regions were shown to 

account for most of the regional and market variation in food prices in the U.S. (Leibtag, 

2005, 2008). Controlling for store type and product availability eliminated all differences 

in food prices by household income (Leibtag, 2005, 2008). Variation in food costs to 

suppliers by store type influence food availability, quality and prices. Large supermarkets 

have economies of scale and can use more direct distribution channels. As the retail food 

market has consolidated in recent years, supermarkets have moved away from supplying 

fruits and vegetables from regional wholesalers to directly obtaining goods from large 

product shippers distributing bulk quantities of products (Cook, 2004). This shift may 

leave small stores with fewer regional direct wholesalers and higher expenses in offering 

fruit and vegetables (Kaufman, et al., 2000). Few available suppliers could make it harder 

for small stores to find specific product sizes and brands at affordable wholesale prices, 

including those specified in the WIC food packages (Oliveira and Frazao, April 2009). 

Store surveys comparing market food baskets show that prices are usually higher and 

healthy foods are less available in convenience versus grocery stores (Glanz, et al., 2007; 

Kaufman, et al., 1997; Liese, et al., 2007). Chain affiliation also predicts better food 

availability and lower prices of healthy foods (Chung and Myers, 1999). Grocery stores 

have higher availability and quality of produce than convenience stores (Glanz, et al., 

2007), which is more strongly predictive of fruit and vegetable consumption than 

perceived price (Zenk, et al., 2005). Better food availability, prices and quality have led 

low-income customers to redeem most of their food assistance benefits in supermarkets 

(Cole, 1996), and low-price supercenters (USDA, 2009). However, convenience stores 

remain an important food source for the approximately 10% of the U.S. population who 

have no access to suitable transportation and live in neighborhoods with poor food retail 

access (5%) or low-income areas located further than one mile from a supermarket (4%) 

(USDA, 2009). 

Study Objectives and Components: This study’s goal was to evaluate multiple 

determinants of access to healthy foods. Using a pre-post natural experiment design 

enabled by the WIC food package revisions, we evaluated multiple domains of healthy

food access in representative suburban and urban areas in Connecticut. 

The study included five tasks: 

1. Refine an existing store inventory instrument to focus on WIC-approved foods; 

2. Collect and test data for differences in food environments across stores and 

neighborhoods; 

3. Assess the role of food market competition in access to healthy food; 

4. Evaluate the impact of the WIC food package revisions on the availability, variety, 

prices and quality of healthy foods; 

5. Track WIC vendor participation in the state of Connecticut.
8

A supplement to this study was interviewing of managers and owners of convenience and 

grocery stores about their perceptions, beliefs and attitudes as well as the effects of the 

WIC revisions on these stores (Andreyeva, et al., 2011a). Structured in-person interviews 

were completed before and after the revisions implementation in a subsample of retailers 

assessed via store inventories. 

Methods

Data

Sampling, Store Selection and Classification: Our sample included food stores operating 

in five Connecticut towns. To maximize the diversity of food environments surveyed 

within a limited study budget, we sampled towns from the 24 most populated towns in 

the state and included five towns of diverse residential income and food store density. 

Stores located within half-a-mile of each town’s boundaries were also included. 

Residents of the five sampled towns included about 20% of WIC participants in 

Connecticut in 2008 (USDA, 2010). 

We obtained a list of food retailers (stores, delis, fast food outlets and restaurants) from a 

commercial database provider InfoUSA, Inc. (InfoUSA, 2008). InfoUSA uses a 

proprietary modified 6-digit Standard Industrial Classification (SIC) code to classify 

establishments. We identified food stores using several selected SIC codes: Convenience 

Stores, Delicatessens, Food Markets, Food Products-Retail, Grocers-Retail, Health and 

Diet Foods, Fruits and Vegetables-Retail. Specialized stores like bakeries and meat 

markets were excluded due to limited range of foods. Pharmacies were excluded as they 

accept WIC benefits only for infant formula. Store locations were geocoded using 

ArcGIS 9.2 and Environmental Systems Research Institute (ESRI) Census 2000 

TIGER/Line® data for designated towns and census tracts and InfoUSA latitude and 

longitude data for store coordinates (ESRI, 2000). 

We extended a list of 371 food outlets identified through InfoUSA with 12 stores from

WIC administrative records. At baseline in Spring 2009, we excluded 99 locations based 

on site visits: 35 were not found at InfoUSA-provided addresses, 19 were closed/out of 

business, 28 were delis/restaurants, 9 specialties, and 8 other businesses. During field 

work, we identified 29 more stores; all found in census tracts with median household 

income above $39,200 (considered as higher-income neighborhoods in our study). There

was no difference by neighborhood income in the misclassification errors (e.g., delis with 

SIC classification as stores). The final sample included 313 stores; 10 stores (3%) were 

dropped from the analysis after refusing participation. In Spring 2010, we successfully 

assessed 289 of the baseline 303 stores. Between the two assessments, 13 stores closed, 

including 6 supermarkets due to the statewide closure of a supermarket chain. One store 

refused participation at follow-up. 

Stores were classified in four categories according to the definitions used in the validated 

Nutrition Environment Measures Survey in Stores (NEMS-S) instrument (NEMS, 2007): 
9

1. Convenience stores (stores with longer operating hours and offering a medium 

variety of canned goods, dairy products, and other groceries in limited amounts), 

2. Food-marts (convenience stores associated with a gas station),

3. Grocery stores (non-chain markets like “mom and pop” grocery stores, bodegas 

and stores with a greater food variety than in convenience stores but less 

availability compared to supermarkets), and 

4. Supermarkets (regional or national chain grocery stores). 

Store WIC authorization status was based on store reports verified in administrative data

from the state WIC agency. SNAP authorization status was store-reported. 

Data Collection: We visited stores in March-June 2009 (pre-revision) and April-July 

2010 (post-revision) with many 2009 visits completed in March (63%) and 2010 visits in 

May (56%). Assessments were conducted between 9am and 4pm on weekdays by two 

trained raters. Training for raters consisted of a 4-hour presentation and a 2-week field

work period with a research staff member. During the field training both raters completed 

assessments independently and reviewed forms to improve performance. After the 

training, each rater completed half of the measures. For inter-rater reliability testing, two 

raters conducted all assessments independently in about 20% of stores chosen at random 

(e.g., first 2 stores out of 10 scheduled for daily assessment). Data from the rater with the 

highest number of completed items were used in analysis. 

The study was exempt by the university institutional review board (IRB). Raters 

requested permission to do a store inventory upon entering the store premises. They 

introduced themselves, showed university identification, asked to talk to the store 

manager and, if the manager was not available, presented a letter describing the study and 

discussed it with a cashier. On occasion, raters had to come back to obtain permission 

from the manager if they were unavailable during the initial visit. The average time to 

obtain permission and complete the assessment by two raters varied from 17 (SD 9.2) 

minutes at food-marts to 24 (SD 11.6) minutes at convenience stores, 31 (SD 15) minutes 

at non-chain groceries and 47 (SD 14.2) minutes at supermarkets.

Measures

Store Instrument Development and Measures: We adapted a store inventory instrument 

from the validated NEMS-S measure (Glanz, et al., 2007) and focused it on WIC 

authorized foods as proxies for healthy foods (Appendix A). The instrument included 

products from the pre- and post-revision WIC food packages approved in the state of 

Connecticut: milk, eggs, cheese, baby foods, juice, cereal, bread, tortillas, rice, tofu, soy 

milk, canned fish, peanut butter, dry beans, and fresh, canned, frozen fruit and vegetables. 

Each food category had multiple products including 5 types of milk (skim, 1%, 2%, and 

whole cow’s plain milk, and soy milk), most popular according to national consumption 

data (USDA, 2006) fresh fruits and vegetables (10 each), frozen fruits and vegetables (3 

each), and canned fruit and vegetables (3 each). For rice, breads and cereals we 

considered whole grain and non-whole grain products including for comparison non-
10

authorized foods such as white rice, white flour bread and tortillas. Altogether, the 

instrument had 65 unique food items.

The store assessment started with general store characteristics such as verification of the 

store name and address, date and time of assessment, number of cash registers and aisles, 

and WIC and SNAP authorization status. Food assessment included product availability, 

price, quality, and variety. Each product was recorded as available if the store had it in 

stock during our inventory. Prices were recorded in dollars per unit for the least 

expensive brands or supermarket brands for most products and for reference brands of 

select items (e.g., WIC-approved brands of infant formula). Produce quality was assessed 

for all available ten fresh fruits and vegetables. The quality indicator was based on a oneto-five scale ranging from “A”: Excellent Quality (no molded, wrinkled, shriveled, 

bruised, wilted items), “A-”: Very Good Quality (few molded, bruised, etc.), “B”: Good 

Quality (several/ less than a quarter molded, bruised, wrinkled), “B-”: Fair Quality (a 

quarter to a third molded, wrinkled, bruised) to “C”: Poor Quality (majority molded, 

wrinkled, bruised). 

 

In convenience and non-chain grocery stores (i.e., non-supermarkets), raters assessed 

milk shelf-space and counted varieties of fruit and vegetables and whole grains. Measures 

of milk shelf-space were based on procedures in the NEMS-S and other food availability 

surveys (Cheadle, et al., 1991; Glanz, et al., 2007) to include a count of gallons and halfgallons of plain lower-fat (skim, 1%, 2%) and whole milk of any brand. Raters also 

evaluated varieties of fresh, frozen and canned fruit and vegetables eligible for purchase 

with WIC fruit and vegetable vouchers (excluding white potatoes, canned or frozen fruit 

and vegetables with added sugar, cream, and oils). They counted unique varieties of WIC 

approved fruit and vegetables. For example, two brands of frozen broccoli were counted 

as one frozen vegetable; Navel and Valencia oranges added one fresh fruit to the count. 

To familiarize raters to some cultural and ethnic fruit and vegetables, we provided an 

extended checklist of produce and showed pictures of many exotic fruit and vegetables. 

As brand role is likely to play an important role in consumer selection of breads, rice and 

heavily-marketed cereals, we incorporated brand diversity in counting varieties of whole 

grain products. Raters counted across brands and types of brown rice but not package 

sizes. They used the same approach for whole wheat and whole grain breads, but 

included two counts: all sizes and all WIC-approved 16-ounce loaves. A count of whole 

grain tortillas included varieties and brands of whole wheat and corn soft tortillas. Whole 

grain cereals were counted using a checklist of eight most popular whole grain cereals 

based on 2007 national sales data from Nielsen Company (The Nielsen Company, 2008). 

Given an uncertain definition of whole grain cereals and frequent product reformulation, 

we considered it impractical to collect data on availability of all whole grain cereals. The 

eight cereals in our count represented 58% of 2007 national sales of cereals to all 

households and 46% of sales to households with children (The Nielsen Company, 2008). 

Dependent Variables: We measured food availability as the percentage of stores that 

offered that food for purchase during our inventory. Composite variety measures were 

calculated for each store other than supermarkets and included a total count of varieties of 
11

fresh, frozen, canned fruit and vegetables as well as a count of varieties of whole grain 

products (brown rice, whole grain cereal, whole wheat/ whole grain breads and whole 

grain tortillas). Milk shelf-space was measured as a ratio of available gallons of lower-fat 

milk to gallons of whole milk with half-gallons converted into gallons. A produce quality 

rating was calculated as the mean of available quality ratings for fresh fruit and 

vegetables. Finally, we developed a composite scale of healthy food supply, discussed 

elsewhere (Andreyeva, et al., 2011b). 

Prices were converted to standard units, which varied by product. Prices of fresh produce 

available by piece or bunch were converted to prices per pound using conversion factor 

data from the USDA’s National Nutrient Database for Standard Reference (USDA, 

2007). We adjusted 2009 price data for inflation between March 2009 and May 2010 

using U.S. city average consumer price index (CPI) for corresponding food categories 

(Bureau of Labor Statistics, 2008). Price comparisons across stores and neighborhoods 

are challenging due to significant differences in availability of foods. This prevented us 

from constructing a representative market basket of healthy foods that would be equally 

available across stores and neighborhoods. Instead, we compared inflation-adjusted 

prices of individual foods and used a composite price index for three most commonly 

available foods. The basket included products available in at least 90% of stores in both 

years: whole milk, eggs, and canned vegetables. 

Independent Variables: The set of store-level characteristics included store size --

measured by number of cash registers (1, 2, or 3 registers and above) -- and indicators of 

SNAP and WIC authorization status. A set of market characteristics contained measures 

of competition for food stores and fast food chains. Food store competition was assessed 

for each store except supermarkets using data on the density of other non-supermarket 

stores in the area. We calculated the kernel density of non-supermarket stores within halfa-mile radius of the store coordinates. The kernel density gives a greater weight to stores 

closer in space within the search radius; a store across the street has a greater impact on 

the score than a store half-a-mile away. Another market competition measure was 

distance to the nearest supermarket assessed for each non-supermarket store (in 

thousands of feet). To account for differences in customer traffic, we included population 

density measured as number of census tract residents within the store tract. We

categorized census tracts with median household income under $39,200 (American 

Community Survey 2006-2008 estimates) as low-income neighborhoods and the rest as 

higher-income areas. Our income threshold was based on 2008-2009 income eligibility 

for WIC for a family of four people (Food and Nutrition Service, 2008). Town indicators 

were not included due to lack of independent town variation in healthy food access. 

We also assessed competition for consumer food dollars from fast food chains and 

included measures of their density within half-a-mile radius of each convenience and 

grocery store. We selected the top 20 national fast food restaurants with the highest U.S. 

sales in 2009 (QSR Magazine, 2009). The restaurants, in order of sales, included 

McDonald’s, Subway, Burger King, Starbucks, Wendy’s, Taco Bell, Pizza Hut, Dunkin’ 

Donuts, KFC, Sonic, Arby’s, Jack in the Box, Domino’s, Chick-fil-A, Panera Bread, 

Dairy Queen, Papa John’s, Hardee’s, Quizno’s, and Popeye’s. Collectively, they totaled 
12

$117 billion in 2009 sales accounting for 85% of the top 50 chain restaurants sales. There 

were 459 outlets belonging to one of the top 20 fast food chains within half-mile 

boundaries of the study towns. We further added 74 establishments from local fast food 

chains. Using Arc GIS10 we constructed a separate kernel density of fast food chain 

outlets within a half-mile radius of each non-supermarket store. Of note, focusing on 

national and regional fast food chains leaves out other sources of prepared food, which in 

some low-income urban areas have been shown to be more prevalent than fast food 

restaurants (Lee, et al., 2010). 

 

Missing Data Imputations: We applied a propensity score matching approach to impute

missing data (Enders, 2010). Missing values were fairly infrequent ranging in prevalence 

from 0.3% to 9.4% with the median of 2.1% in 2009 and 2.8% in 2010. The imputation 

approach used information from the assessed stores with valid data and closest in 

background characteristics to those missing data. Similarity in certain characteristics of 

the matched stores makes them known as nearest neighbors. In the first step, for each

food item missing data we estimated a Probit regression model with a binary dependent 

variable indicating whether information was missing. Using this model we predicted 

propensity scores for all stores conditional on the store type, size and location in a lowversus higher-income neighborhood. In the second step, we matched stores with missing 

values with their nearest neighbors with valid data (stores with similar propensity scores) 

and replaced the missing value with the value from the nearest neighbor.

Data Analysis

Descriptive Data Analysis: We computed descriptive statistics and conducted chi-square 

and t-tests in store comparisons by WIC status, year and neighborhood income. 

Descriptive analyses were conducted for convenience and non-chain grocery stores with 

observations in both years (N=259). We isolated supermarkets as a unique group not 

expected to change due to the WIC revisions (N=36 in 2009 and N=30 in 2010). The 

inter-rater reliability analyses were completed by calculating percent agreement and 

kappa statistics for stores with two independent assessments (Dilorio, 2005; Landis and 

Koch, 1977). 

Growth Models of the WIC Policy Change: We used multilevel regression modeling 

techniques to account for data structure with multiple sources of variation. There are two 

reasons for employing a multilevel estimation approach (Hox, 2002). First, our data is 

hierarchically structured and observations are not independent of each other but clustered 

or “nested” within groups. At the lowest level of analysis (Level 1) we have 2009 and 

2010 observations that are grouped within a store. Stores, referred as Level 2 in the data

hierarchy, are grouped within neighborhoods or census tracts (Level 3). Stores located in

the same neighborhood are more similar to each other and their error terms are correlated. 

Second, regression modeling is essential in light of the quasi-experimental nature of the 

WIC revisions. There was no random assignment of stores to the experiment (WIC) and 

control (non-WIC) groups, and WIC stores are likely different from non-WIC stores. For 

example, WIC stores more likely locate in low-income areas where WIC participants 
13

live, and neighborhood characteristics affect food availability. A multivariate approach is 

needed to account for non-random group assignment.

Multilevel models are also known as mixed-effects models for their simultaneous 

estimation of fixed and random effects. In a 3-Level linear random intercept model we 

can formulate a regression equation on each level. The basic application is an intercept 

only model without covariates for which the Level 1 equation can be written as: 

 Yijk π 0 += eijkjk (Equation 1) 

where Yijk is the dependent variable for an observation i in a store j in a census tract or 

neighborhood k, π 0 jk is the mean of the 2009 and 2010 measurements for a given store j

in a neighborhood k, and eijk is the normally distributed error term for an observation i in 

a store j in a neighborhood k. On the second level, we can allow the predicted means of 

the dependent variable to vary across stores:

 0 jk βπ k += r 000 jk (Equation 2) 

where the predicted mean in each store π 0 jk is regarded as the dependent variable so that 

another intercept β 00k can be estimated for all stores clustered in a neighborhood k. The 

term r 0 jk denotes the error term for stores j in neighborhoods k and indicates the 

variation across stores. Finally, we formulate the third-level model and estimate the 

variation of the store level means across neighborhoods: 

 

 00k γβ 000 += u00k (Equation 3) 

Here, γ 000 is the highest-level intercept and the error term u00k indicates variation of the 

store means across neighborhoods. By substituting the equation terms, we can formulate 

the model in a single mixed model equation: 

 ijk γ 0000 jk 00k +++= eurY ijk (Equation 4) 

This model estimates a fixed intercept and three random effects that can be interpreted as 

variance components. To estimate the unadjusted effect of the WIC food package 

revisions, we included two variables and their cross-level interaction effect in the 

baseline model: an indicator variable for the year of assessment was added on Level 1, an 

indicator for the store WIC authorization status on Level 2, and their interaction was 

added across levels. We can estimate the following equation on Level 1: 

 Yijk += ππ 10 jkjk yearijk )( + eijk (Equation 5) 

where the coefficient of the time variable π1 jk is an estimated parameter of the difference 

in the dependent variable between the two measurement points for each store j in 

neighborhood k, and the intercept π 0 jk becomes the predicted value of the dependent 

variable in a given store at Time 1 (2009). 
14

On the second level, we can formulate two equations. First, we predict the baseline 

difference between WIC and non-WIC stores as follows: 

 0 jk 00 += ββπ 01kk jk )( + rWIC 0 jk (Equation 6) 

Second, we predict the difference between two measurement points by WIC authorization 

status to assess the differential change over 2009-2010 for WIC and non-WIC stores:

 

 1 jk 10 += ββπ 11kk WICjk )( (Equation 7) 

Given that WIC authorization status is measured at the store level and the time variable is 

measured at the observation level, Equation 7 represents a cross-level interaction effect. 

The difference between 2009 and 2010 is directly modeled as the dependent variable on 

the next level predicted by WIC status. Finally, we formulate the Level 3 equation that

provides the intercept to our model of the predicted mean of the dependent variable for 

all non-WIC stores at Time 1 (2009): 

 00k γβ 000 += u00k (Equation 8) 

We can formulate a single equation by substituting the equation terms: 

Yijk 000 += γγ 010 WICjk +γ 100 yearijk +γ 110 WICjk yearijk )(*)()()( 0 jk 00k +++ eur ijk

 (Equation 9) 

This specification has seven estimation parameters: four fixed effects and three random 

effects. The fixed effects provide the point estimates of interest. The intercept can be 

interpreted as the predicted mean outcome for non-WIC stores at Time 1. The estimated 

parameter γ 010 represents the difference between WIC and non-WIC stores at Time 1, 

γ 100 can be interpreted as the difference between Time 1 and Time 2 for non-WIC stores 

and γ 110 gives a point estimate of the change between Time 1 and Time 2 for WIC stores

or the baseline rate of change. Hence, γ 110 represents a point estimate of the effect of the 

WIC food package revisions. 

After fitting the model to estimate an unadjusted effect, we included a third-level 

indicator for neighborhood income. We predicted the change for WIC stores in low- and 

higher-income neighborhoods yielding a three-way interaction spanning all levels of data, 

which could determine whether the revisions had a differential impact by neighborhood 

income. This model can be written as follows: 

k jk ijk jk k ijk

ijk k jk ijk jk ijk

WIChiinc year eur

Y hiinc WIC year WIC year

+++

+= + + + +

111 0 00

000 001 010 100 110

)(*)(*)_(

)(*)()()()_(

γ

γγ γ γ γ

 (Equation 10) 
15

Finally, we included other covariates on all levels for control purposes. All models were 

estimated applying a Maximum Likelihood method (Full Maximum Likelihood, cf. (Hox, 

2002)). Some of our dependent variables were not continuous and normally distributed. 

For example, we estimated multilevel Poisson regression models for two count measures

of whole grain products and fruit and vegetable varieties. The same modeling logic as 

with multilevel linear models was applied for Poisson models. 

All regressions were estimated for a sample of convenience and grocery stores with prepost data and unchanged WIC-authorization status in 2009 and 2010. Stores that changed 

their groups from experiment (WIC) to control (non-WIC) and vice versa violated the 

natural experiment setting of the WIC food package revisions. This additional restriction 

excluded six stores of which five stores lost WIC-authorization between 2009 and 2010 

and one was a new WIC vendor (N=252). 

Cross-Sectional Models of Food Access: Using baseline 2009 data for 267 convenience 

and grocery stores we estimated a set of multilevel linear and count models to assess 

predictors of healthy food access. These cross-sectional models were two-level (store and 

neighborhood/census tract) and included the same covariates as in the growth models. 

Our cross-sectional approach aimed to estimate the role of various contributors to food 

access independent of the effect of the WIC food package revisions. 

Store Participation in WIC: To inform the debate on whether stores drop from the WIC 

program as a result of the package revisions, we tracked changes in WIC store 

participation in Connecticut between May 2008 and October 2010. We used 30 months of 

data from the state WIC agency on stores authorized to accept WIC benefits. The 

monthly list provided the store name, contact information (address and phone number), 

and local WIC agency. All phone numbers were checked for accuracy and used in 

merging of authorized stores across months. Each store was classified as a pharmacy, 

supermarket chain, or grocery/convenience/food mart. Stores that prepare and dispense 

drugs and medicines were grouped into ‘Pharmacy’ based on the store name (e.g., CVS). 

Supermarket chains were identified by regional and national chain names and by 

selecting stores with more than one establishment under the same name. The remaining 

stores were grouped into non-chain grocery stores, convenience stores, and food marts. 

Changes in participation were assessed monthly for the total number of stores and 

separately for each group.

Results

Sample Description: In 2009, we assessed 135 convenience stores, 81 food-marts, 51 

grocery stores, and 36 supermarkets; with the same type distribution in 2010 (Table 1).

About two-thirds of stores were authorized to accept SNAP benefits although their

percentage varied from 35-40% in food-marts to 100% in supermarkets. WIC

authorization was less common, with about 15% of non-supermarket stores and 81% of

supermarkets accepting WIC benefits in 2009. Only 7% of stores in higher-income areas 
16

were WIC authorized versus 26% in low-income areas. There was large variation in store 

type across different towns (Table 2). For example, supermarkets represented 20% of all

stores in the sample’s wealthiest town but only about 5% of stores in the poorest town. 

There were large differences by store type in low- and higher-income areas. For example, 

36% of all stores in wealthier neighborhoods were food-marts that carry very limited 

varieties of foods. In contrast, in low-income areas 11% of stores were classified as foodmarts. As shown in earlier studies (Larson, et al., 2009), the share of supermarkets was 

higher in wealthier neighborhoods (14 vs. 8% in 2009). 

Reliability Analysis: Inter-rater reliability data were collected in 16% (48/303) of the 

stores in 2009 and 26% (75/289) of the stores in 2010. Agreement on product availability 

was consistently high in both years, ranging from 80-100% in 2009 (Mean=96%) and 88-

100% in 2010 (Mean=97%) with kappa statistics in the range of 0.64-1.00 (Table 3). In 

2009, only a few products had percent agreement on availability as low as 80-90%, 

including canned salmon (80%) and cheese, dry lentils, and cereals (88-89%). To 

calculate fruit and vegetable quality reliability, the five-point quality score was collapsed 

to three levels (1, 2=level 1, 3=level 2, 4, 5=level 3). Inter-rater reliability of fruit and 

vegetable quality rating was lower relative to percentage agreement on food availability, 

especially in 2010. In 2009, percent agreement on quality ratings ranged from 60-100% 

(Mean=80%) with about half of assessed fruit and vegetables in the range of 60-80% 

(Table 4). In 2010, agreement on produce quality was lower, ranging from 30-86% 

(Mean=59%). Kappa statistics could not be calculated if the particular fruit or vegetable 

was not offered in a sufficient number of stores. Most fruits and vegetables scored in the 

upper range of the scale leading to low and sometimes negative kappa statistics when 

agreement was high but variation was low (e.g., 2009 oranges ratings agreed 71% of the 

time, but the kappa was 0.08). 

Descriptive Analysis for Convenience and Grocery Stores

Food Availability: Baseline food availability in convenience and grocery stores was the 

lowest for soy products (<10%) and the best for whole milk, eggs, and canned vegetables 

(≥90%). Many of important healthy foods had limited availability: about half of nonsupermarket stores did not carry any fresh fruit and vegetables and about 80% had no 

whole wheat/ whole grain bread and tortillas in 2009. Furthermore, while 69% of stores 

stocked white rice, only 17% offered brown rice (Table 5). Availability significantly

improved for a few healthy foods in 2010, doubling for brown rice and almost doubling 

for whole wheat/whole grain bread. Few improvements in the total sample mask 

differential patterns for WIC and non-WIC convenience and grocery stores. For example, 

fresh fruit was available in about half of WIC and non-WIC stores in 2009; this remained 

unchanged in non-WIC stores at follow-up but rose to 100% in WIC stores (Table 6). 

The availability of fresh and frozen vegetables also increased significantly in WIC stores, 

with no change seen in non-WIC stores. The availability of whole wheat bread and brown 

rice went up in both WIC and non-WIC stores although the increase was several times 

larger in WIC stores. Improvements in non-WIC convenience and grocery stores that are 

not required to change following the WIC package revisions might reflect broader secular 
17

changes in demand for whole grain products and/or improved access to suppliers carrying 

new products for their WIC stores. 

Food Prices: Compared to 2009, a number of food products had lower inflation-adjusted 

prices in Connecticut convenience and grocery stores in 2010 (Table 7). For example, a 

gallon of whole milk went down from $4.13 to $3.92; a similar rate of reduction was seen 

in lower-fat milk. Of note, U.S. consumer price index (CPI) for milk during this period 

was 1.9% and the Northeast CPI for food consumed at home was 0.6% (Bureau of Labor 

Statistics, 2008). Other significantly cheaper products in 2010 were whole grain cereals, 

white-flour bread, oranges, orange juice, and jarred baby fruit/vegetables. In contrast, 

whole wheat bread, white rice, eggs and some vegetables were on average more 

expensive in 2010. Using the price index of three most commonly available foods (whole 

milk, eggs, and canned vegetables) we observed no change as whole milk prices went 

down while egg prices increased. 

Comparisons by WIC status (Table 8) suggest that the total cost of three most commonly 

available foods increased on average by $0.04 in non-WIC stores but went down by 

$0.07 in WIC convenience and grocery stores. A larger reduction in the cost of whole 

milk in WIC vs. non-WIC stores explains the difference ($0.27 or 7% versus $0.19 or 5% 

respectively). Of note, similar changes with greater price reductions in WIC stores were 

seen for lower-fat milk, which suggests that the WIC food package revisions that reduced 

whole milk and increased lower-fat milk are unlikely to explain the observed milk price 

patterns in Connecticut convenience and grocery stores. Price change comparisons by 

WIC status for many healthy foods are difficult due to a very low number of WIC 

convenience and grocery stores offering these foods at baseline. For example, only 3 

WIC stores had whole wheat/whole grain bread in 2009 and 27 stores in 2010. 

Milk Shelf Space: The 2009 share of lower-fat milk in milk shelf-space was larger in 

non-WIC than WIC convenience and grocery stores (46.9% and 33.4%, p<0.01). As 

expected, lower-fat milk share increased significantly to 41.5% in WIC convenience and 

grocery stores in 2010 (p=0.05). The reduction to 44.4% in non-WIC stores was not 

significant (Table 9). 

Variety Count: Following the WIC food package revisions, both non-WIC and WIC 

convenience and grocery stores had significant increases in available varieties of some 

whole grain products (Table 9). For example, non-WIC stores had more varieties of 

whole grain cereals, brown rice and whole wheat/corn tortillas in 2010 even though they 

had no program requirements to change their inventory. In total, their count of whole 

grain products increased from 3.0 to 3.8 (p<0.05). In comparison, WIC stores had a 

significant increase in the number of available varieties of brown rice (from 0.3 to 1.6, 

p<0.001), whole grain cereals (from 2.4 to 3.1, p<0.05), and whole wheat bread (from 0.4 

to 0.9, p<0.10). Taking into account whole wheat/corn tortillas, the total whole grain 

count increased from 3.6 to 6.6 (p<0.01). As a result of the 2010 changes, the gap in 

available varieties of whole grain products between WIC and non-WIC convenience and 

grocery stores more than tripled from 0.8 to 2.9. 
18

Improvements in available varieties of fruit and vegetables were smaller in WIC 

convenience and grocery stores and lacking in non-WIC stores (with an exception of 

canned unsweetened fruit). After the package revisions, WIC stores offered a 

significantly greater variety of fresh fruit (from 2.6 to 4.6, p<0.10) and canned 

unsweetened fruit (1.1. to 1.6, p<0.05), but had no changes in any vegetables and frozen 

fruit varieties. The count of fresh, frozen and canned fruit and vegetables in non-WIC 

convenience and grocery stores was on average 14.2 varieties in both 2009 and 2010. In 

comparison, WIC stores had on average 18.6 varieties at baseline and 22.1 varieties a 

year later. The high prevalence of food-marts among non-WIC stores could explain the 

difference in the baseline variety of fruit and vegetables between WIC and non-WIC 

stores (although not significant). Food-marts rarely carry fruit and vegetables and could 

bias results for all non-WIC convenience and grocery stores. 

Produce Quality: Significant differences in the percentage of inter-rater agreement on 

produce quality between 2009 and 2010 and low agreement on quality in 2010 do not 

allow us to directly compare changes in produce quality over two years. Our raw quality 

ratings suggest that fruit and vegetable average quality declined in 2010. However, 

variation in perceptions of produce quality among different raters collecting data in 2009 

and 2010 (despite exposure to the same training) likely explains the observed effect. One 

confirmation of this hypothesis is a reduction in the average quality of produce in 

supermarkets, including those located in higher-income areas. These stores have 

consistently high quality of produce (data not shown), so the observed effect is 

questionable. To address the problem, we z-standardized produce ratings with a mean of 

10 and a standard deviation of 1 in both years. Comparisons for WIC and non-WIC 

convenience and grocery stores revealed no difference for either fresh fruit or vegetables: 

2009 standardized mean for fruit (vegetables in parentheses) was 9.85 (9.83) in WIC 

stores vs. 9.67 (9.71) in non-WIC stores. There was no change in standardized quality 

ratings between 2009 and 2010 in either WIC or non-WIC convenience and grocery 

stores.

Cross-Sectional Models of Food Access in Convenience and Grocery Stores

Food Prices: We found few significant predictors of the price index of three most 

commonly available foods in convenience and grocery stores (Table 10). The price index 

was positively related to higher population density in the store census tract, which 

suggests that demand is higher at each price level in more populated neighborhoods. 

Better customer traffic allows stores to charge somewhat higher prices. We found no 

price difference between stores located in low- and higher-income neighborhoods, which 

is different from some earlier studies (Block and Kouba, 2006; Chung and Myers, 1999; 

Hendrickson, et al., 2006; Kaufman, et al., 1997; Kunreuther, 1973). 

Milk Shelf Space: In contrast to price models, there were many significant associations 

between lower-fat milk share and store, market and neighborhood characteristics. Larger 

stores had a significantly higher share of lower-fat milk in milk shelf-space, by about 

14.3 percentage points in stores with two cash registers (p<0.001) and by 16.4 percentage 
19

points in stores with at least three cash registers (p<0.10), as compared to stores with one 

cash register (Table 11). There was no difference in milk shelf-space by WIC status, but 

SNAP authorized stores had about 7 percentage points lower share (p<0.05). As 

expected, lower-fat milk was available in relatively larger quantities in stores located in 

higher-income neighborhoods, by about 14.1 percentage points after including all 

covariates (p<0.01). Of note, the variance components analysis suggests that milk shelf 

space had a sizable variation by neighborhood income. Food market competition 

measures did not correlate with milk shelf-space with one exception of marginally 

significant density of other food stores (-0.54 percentage points, p<0.10). 

Variety Count: Poisson models for fruit and vegetable variety predicted a positive 

association with store size (Table 12). In comparison with smallest stores with one cash 

register, stores with two cash registers had about three times more varieties (IRR=2.736, 

p<0.001) and stores with at least three cash registers had seven times as many varieties of 

fruit and vegetables (IRR=7.212, p<0.001). WIC and SNAP authorization status were 

both independently related to greater variety of fruit and vegetables, by around 13.3% 

(IRR=1.133, p<0.05) and 76.3% (IRR=1.763, p<0.001) respectively. Stores in wealthier 

neighborhoods had 31.2% (IRR=0.688, p<0.10) lower variety of fruit and vegetables. 

The only market competition measure related to more fruit and vegetable variety was 

distance to the nearest supermarket (IRR=1.076, p<0.001). Similar results were found for 

the whole grain variety (Table 13), with the exception of SNAP status. Store size, WIC

authorization status, neighborhood income, and distance to the nearest supermarket were 

again significant predictors of greater variety of whole grain products in Connecticut 

convenience and grocery stores. One new measure with predictive power was population 

density that suggested a greater variety of whole grain products in more denselypopulated areas (IRR=1.117, p<0.05). 

Growth Models of the WIC Revisions Effects 

Food Prices: There was no baseline difference in food prices for three most commonly 

available foods (whole milk, eggs, and canned vegetables) between WIC and non-WIC 

stores. For this basket we detected a reduction of about $0.05 (p<0.05) in 2010 prices. 

This change was observed before and after adjusting for covariates, neighborhood and 

WIC revisions (Table 14). As discussed above, this effect is driven by significantly lower 

prices of whole milk. It is unknown why milk prices in Connecticut convenience and 

grocery stores went down between 2009 and 2010. This could reflect reductions in 

demand among WIC customers (due to the package revisions) and/or non-WIC 

customers (due to a trend to reduce fat intake) or other unobserved factors that influenced

the milk market. There was no effect of the WIC package revisions on prices of three 

most commonly available foods. 

 

Milk Shelf-Space: Estimation results for the share of lower-fat milk revealed no baseline 

difference between WIC and non-WIC convenience and grocery stores (Table 15). This 

significantly changed after the WIC food package revisions: non-WIC stores had a 

slightly lower share (by 2.42 percentage points, p<0.10) while WIC stores notably 
20

increased the share of lower-fat milk in 2010. Specifically, the unadjusted effect was an 

increase of 9.39 percentage points for all WIC stores, which grew to 13.46 percentage 

points for WIC stores located in low-income neighborhoods in the models accounting for 

neighborhood income and a smaller 12.13 percentage points increase for these stores 

after including covariates. The significant interaction of income, WIC and time (-14.56 

percentage points, p<0.05) indicates the amount by which this increase was smaller for 

WIC stores located in higher-income areas. This suggests no change with respect to milk 

shelf space in WIC stores in higher income areas and a reduction in the baseline gap in 

milk shelf-space by neighborhood income. Differences in milk shelf-space by 

neighborhood income were substantial: 21.67 percentage points higher in high-income 

areas before covariate adjustment and 11.09 percentage points after including controls. 

Store size and density of fast food chains were positively related to a higher share of

lower-fat milk while the opposite was true for SNAP authorization status and density of 

convenience and grocery stores. 

Variety Count: Fruit and vegetable variety in WIC convenience and grocery stores was 

significantly higher than in non-WIC stores, by 74% (p<0.01) before adjusting for 

covariates (Table 16). After the WIC package revisions, fruit and vegetable varieties in 

WIC stores increased by 23.8% (p<0.01) in lower-income neighborhoods whereas nonWIC stores saw a reduction of 5.4% (p<0.10). The effect for WIC stores located in 

higher-income areas was essentially zero (IRR of 1.238* IRR of 0.774 = 0.96, indicating 

a decrease of 4% within these stores). Larger store size, SNAP authorization status and a 

further distance from the closest supermarket predicted greater variety of fruit and 

vegetables in Connecticut convenience and grocery stores. 

Available varieties of whole grain products (Table 17) were higher in WIC compared to 

non-WIC convenience and grocery stores (by 56.5%, p<0.05). They increased further 

after the WIC package revisions, by 42.6% (p<0.01) for all WIC stores. After adjusting 

for neighborhood income and other covariates, the effect was a 61.6%-increase (p<0.001) 

for WIC stores located in low-income neighborhoods, but no significant change for WIC 

stores in higher-income areas. Spillovers from WIC to non-WIC stores might explain the 

increased variety of whole grain products in non-WIC convenience and grocery stores in 

2010, by 24.6% (p<0.001). As with fruit and vegetable varieties, store size, SNAP 

authorization status and distance to the nearest supermarket were significantly related to 

greater available varieties of whole grain products in Connecticut convenience and 

grocery stores. 

Analysis of Supermarkets: As supermarkets were not expected to change following the 

WIC food package revisions and offered large varieties of healthy foods at baseline, we 

focused their analysis on price and produce quality comparisons by neighborhood income 

and overtime. As we expected, produce quality was somewhat worse in supermarkets in 

lower-income neighborhoods in both years, but the only significant difference was for 

fresh fruit in 2010 (9.34 vs. 10.2, p<0.05). Limited statistical power due to a small sample 

size of supermarkets may explain a lack of significant results. We observed no significant 

difference in supermarket prices of three most commonly available foods by 

neighborhood income. In contrast to price changes in convenience and grocery stores, 
21

milk prices did not decrease in supermarkets. The only significant change was a price 

reduction for canned vegetables, which did not affect the price index of three most 

commonly available foods. 

Store Participation in WIC: The number of stores authorized to accept WIC benefits in 

the state of Connecticut went down from 605 in May-September 2008 to 538 in JuneOctober 2010 (Table 18). The reduction was driven by dropouts among convenience, 

non-chain grocery stores and food-marts; their participation during this period declined 

by 16% versus 8% for supermarkets and 5% for pharmacies. The biggest reductions in 

the total number of WIC-authorized stores occurred in October 2008 (N=25), October 

2009 (N=14), and April 2010 (N=20). The latter reflected a temporary drop due to one 

chain closure with many stores resuming work and WIC participation under another 

chain ownership. Significant changes in October participation reflect the authorization

cycle that uses October 1 as the timeline for renewals and new authorizations in 

Connecticut. Most of the reduction in WIC store participation occurred before the WIC 

package implementation in October 2009, including 36 stores in the fourth quarter of 

2008, of which 30 were convenience and grocery stores. In comparison, the number of 

participating stores between October 2009 and October 2010 declined by 11 stores (1 

convenience store and 12 pharmacies dropped and 2 supermarkets were added). Given 

this pattern of change in store participation, it does not appear that Connecticut 

convenience and non-chain grocery stores stopped participating in the WIC program as a 

result of the WIC food package revisions. 

Discussion

The implementation of the revised WIC food packages significantly increased the 

availability and variety of key healthy foods in WIC convenience and grocery stores in 

Connecticut. Utilizing a natural experiment design to observe WIC and non-WIC stores 

before and after the revisions implementation, this study showed strong evidence of the 

positive impact of the WIC food package revisions on healthy food availability and 

variety in urban and suburban settings. When facing new demand from WIC customers 

and new government regulations to stock certain healthy foods, Connecticut convenience 

and grocery stores found ways to deliver healthy foods that were previously lacking in 

their stores and communities. If the experience in Connecticut is typical of other states, 

national food policy that incentivizes demand and requires changes in stores can improve 

the food environment for all customers in at-risk communities. This could occur at no 

additional cost to taxpayers as the WIC food package revisions were designed to be costneutral.

While most significant changes occurred in WIC-authorized convenience and grocery 

stores, non-WIC stores also showed improvements in the availability and variety of 

whole grain products. Such improvements could be due to better access to new WIC 

healthy foods in supply chains. Wholesalers serving small stores usually work with both 

WIC and non-WIC stores so that the latter group also gets access to new foods that 

wholesalers carry for WIC stores. Alternatively, secular changes in customer demand for 
22

healthier foods could encourage all food stores to provide those foods. As both WIC and 

non-WIC customers get exposed to new healthy foods in WIC stores, they may start 

asking for them in non-WIC stores as well. Increased provision of healthy foods, 

especially whole grain products, may also suggest that there was unmet demand before 

the WIC package revisions. However, based on interviews with managers and owners of 

convenience and grocery stores, it appears that stores can find ways to provide food if 

there is customer demand (Andreyeva, et al., 2011a). 

The WIC food package revisions had a greater impact on the availability and variety of 

healthy foods in low-income communities, which is particularly important given the 

limited transportation options and lack of supermarkets in many low-income 

communities. Improvements in the availability and variety of fruit and vegetables and 

whole grain products were significantly larger in WIC stores located in lower-income 

neighborhoods as compared to WIC stores serving higher-income areas. The same was 

true for increased shelf-space for lower-fat milk, which helped reduce the large gap 

observed by neighborhood income before the WIC revisions. It is interesting that while 

observing poorer availability of lower-fat milk in low-income areas, we saw the opposite 

effect for available varieties of whole grain products and fruit and vegetables. It is 

possible that a considerable prevalence of food-marts (convenience stores at gas stations) 

in higher-income neighborhoods explains these results. Food-marts always have milk but 

a very limited inventory of other foods, especially fruit and vegetables.

We found no neighborhood-attributable differences in prices of three most commonly 

available foods in convenience and grocery stores, which is different from some earlier 

studies (Block and Kouba, 2006; Chung and Myers, 1999). A limited range of food 

products and exclusion of supermarkets from price comparisons could explain the 

difference in findings. In fact, only higher population density made a detectable 

difference in prices of most commonly available foods. It is notable that the WIC food 

package revisions had no effect on price changes for several most commonly available 

foods that underwent significant changes in the new packages (reduced amounts of eggs 

and whole milk and authorization of canned vegetables). 

In addition to the significant beneficial effect of the WIC food package revisions on the 

availability and variety of major healthy foods, we have identified several consistent 

predictors of healthy food availability and variety in Connecticut convenience and 

grocery stores. Store size was always related to greater varieties of major healthy foods 

and shelf-space of lower-fat milk. SNAP- and WIC-authorization of convenience and 

grocery stores generally suggested similar patterns (besides milk for SNAP), in part 

because WIC requires authorized stores to carry a minimum amount of certain healthy 

foods. This result also reflects the fact that many food-marts do not accept SNAP or WIC 

benefits. Due to their location, these stores most likely rely more heavily than other 

convenience stores on sales of cigarettes, lotteries, and snack foods to customers stopping 

for gas. From a policy prospective, we may think of encouraging convenience and 

grocery stores to participate in SNAP and especially WIC that sets nutritional standards 

on food packages. Program participation of these stores could increase access to healthy 

food in the communities they serve. 
23

Although market competition from other small stores and fast food chain outlets had a 

limited effect on the availability and pricing of healthy foods in convenience and grocery 

stores in our sample, increasing distance from supermarkets predicted greater availability 

and variety of fruit and vegetables and whole grain products. Given existing literature on 

the negative impact of convenience stores on diet quality and obesity risk, this 

information may be useful to policymakers considering zoning restrictions on these stores 

as a strategy for improving the community food environment. In the absence of nearby 

supermarkets, this study found that convenience and non-chain grocery stores serve an 

important role as suppliers of healthy foods. 

Study Strengths and Limitations: Our study has a number of unique strengths, 

including collection of multiple observations on a large number of food stores for many 

healthy foods; analysis of multiple dimensions of food access such food availability, 

variety, pricing, and quality; and use of a natural experiment setting of the WIC food 

package revisions. In combination with in-depth interviews of stores (Andreyeva, et al., 

2011a), this study provides a comprehensive assessment of the supply side effects of the 

WIC food package revisions. 

Out study is not without limitations. First, we assessed food availability, variety, pricing 

and quality and their changes after the WIC revisions in one state, Connecticut. States 

differed in the revisions implementation (e.g., minimum stocking requirements, 

authorized foods), which could influence the results. Market characteristics and their role 

in food access might also vary across states. For example, we did not assess rural areas 

that may have unique problems in the revisions implementation and/or the role of other 

access predictors. Second, food availability is only one of many determinants of food 

choices and diet quality. We evaluated multiple measures of access to healthy foods, but 

did not assess customer food choices. No conclusions can be made on whether and how 

changes in the availability of healthy foods affected dietary outcomes of WIC participants 

and low-income populations in general. Third, we used approximate measures of market 

competition due to lack of sales data for food stores. Further, we did not assess changes 

in availability of unhealthy foods, which are part of the food environment. 

Directions for Future Research: This study provides an in-depth assessment of the 

supply-side impacts of the WIC food package revisions, but does not address the impact 

on food demand among WIC and non-WIC customers. Future studies should assess 

whether the WIC food package revisions led to changes in consumer purchases, 

improvements in nutritional quality of diet, weight and health outcomes. Further 

monitoring of the impact of the WIC revisions implementation is important to ensure that 

the initial beneficial effects outlined in this study are sustained in the long term. 

Conclusion: Our results suggest that policies aimed at increasing demand for healthy 

foods can improve the availability and variety of healthy foods in at-risk communities. 

Recent revisions to the WIC food packages significantly increased the availability and 

variety of fruits and vegetables, whole grain products and lower-fat milk in WICauthorized convenience and grocery stores, especially in low-income neighborhoods. 
24

Through new demand for healthy foods from WIC participants and new stocking 

requirements in WIC stores, the WIC food package revisions led to beneficial changes

with healthy food availability within six to eight months of implementation. The policy 

change that targeted WIC participants has factually improved the food environment for 

all customers and potentially at no additional cost to taxpayers. 
25

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28

Table 1: Types of Stores and WIC/SNAP Authorization Status

2009 2010

Type of Store N % WIC 

authorized

% SNAP 

authorized

N % WIC 

authorized

% SNAP 

authorized

Convenience store 135 21.5 63.7 129 21.7 72.1

Food mart/ gas station 81 4.9 34.6 80 6.3 42.5

Grocery store 51 11.8 62.8 50 14.0 66.0

Supermarket 36 80.6 100.0 30 73.3 100.0

Total 303 22.4 60.1 289 21.5 65.7

Source: Authors’ calculations
29

Table 2: Sample Distribution by Town and Store Type

Town N Convenience 

Store

Food mart/gas 

station

Grocery Store Supermarket

2009 Percentages

Bristol 38 34.2 47.4 0.0 18.4

Danbury 49 36.7 22.5 26.5 14.3

Manchester 37 37.8 43.2 2.7 16.2

New Haven 135 61.5 14.1 19.3 5.2

West Hartford 44 15.9 38.6 25.0 20.5

Total 303 44.6 26.7 16.8 11.9

2010

Bristol 36 33.3 50.0 0.0 16.7

Danbury 48 35.4 22.9 27.1 14.6

Manchester 33 39.4 45.5 3.0 12.1

New Haven 130 62.3 14.6 19.2 3.9

West Hartford 42 14.3 40.5 26.2 19.1

Total 289 44.6 27.7 17.3 10.4

Source: Authors’ calculations
30

Table 3: Inter-rater Reliability for Food Availability Assessment

Type of Food

2009

% Agreement Kappa

2010

% Agreement Kappa

Fruit (average) 97 0.91 97 0.93

Apples 100 1.00 100 1.00

Grapes 100 1.00 100 1.00

Oranges 100 1.00 100 1.00

Pineapple 100 1.00 100 1.00

Frozen Blueberries 100 1.00 100 1.00

Canned Pineapple 100 1.00 97 0.94

Tropicana Orange Juice - 1 gallon (2010) 100 1.00 99 0.97

Juicy Juice, Plastic Bottle, 64 fl oz 98 0.88 96 0.91

Canned Peaches 97 0.89 95 0.80

Bananas 97 0.93 93 0.80

Cantaloupe 97 0.91 100 1.00

Peaches 97 0.90 98 0.93

Strawberries 97 0.89 100 1.00

Watermelon 97 0.87 98 0.92

Lemons 94 0.87 93 0.87

Juicy Juice, Canned (2009) 93 0.86 -- --

Tropicana Orange Juice - 1/2 gallon 91 0.79 97 0.94

Frozen Peaches 90 0.74 100 1.00

Frozen Strawberries 90 0.74 94 0.85

Canned Peaches2 (2010) -- -- 98 0.93

Canned Pineapple2 (2010) -- -- 95 0.88

Other, less expensive 64 fl oz bottle -- -- 90 0.79

Vegetables (average) 99 0.96 97 0.94

Green Cabbage 100 1.00 98 0.95

Whole Carrots 100 1.00 95 0.90

Celery 100 1.00 98 0.95

Cucumber 100 1.00 100 1.00

Yellow Onions 100 1.00 95 0.87

Frozen Broccoli 100 1.00 100 1.00

Frozen Green Beans 100 1.00 96 0.90

Frozen Sweet Corn 100 1.00 100 1.00

Canned Corn 100 1.00 99 0.97

Canned Green Beans 98 0.93 100 1.00

Canned Peas 98 0.88 99 0.97

Broccoli 97 0.91 100 1.00

Baby Carrots 97 0.90 98 0.95

Lettuce 97 0.93 100 1.00

Peppers 97 0.94 98 0.95

Tomatoes 97 0.90 98 0.92

Canned Corn2 (2010) -- -- 94 0.88

Canned Green Beans2 (2010) -- -- 97 0.94

Canned Peas2 (2010) -- -- 88 0.76

Milk (average) 98 0.95 100 0.98

Whole Milk 100 ** 100 1.00

Soy Milk 100 1.00 100 1.00

Skim Milk 98 0.95 100 1.00

Reduced Fat Milk 98 0.95 100 1.00
31

Low-fat Milk 96 0.91 99 0.96

Soy Milk2 (2010) -- -- 99 0.94

Protein (average) 94 0.81 96 0.90

American Cheese 100 1.00 98 0.97

Eggs - large white 100 1.00 99 0.88

Sardines, any size, any brand 98 0.88 98 0.96

Peanut Butter, Smooth 97 0.65 100 1.00

Peanut Butter, Chunky 97 0.94 95 0.90

Dry Beans* 97 0.66 100 1.00

Barley, whole (2009) 97 0.79 -- --

Tuna 6oz. (2009) 95 0.90 -- --

Tuna 5oz. 95 0.90 92 0.79

Barley, pearled (2009) 91 0.72 -- --

Cheese – low-fat 90 0.66 100 1.00

Dry Lentils* 89 0.68 91 0.79

Cheese - regular 88 0.76 94 0.86

Salmon1 (2010) -- -- 91 0.81

Salmon2 (2010) -- -- 95 0.80

Grains 96 0.88 95 0.87

Bread

Arnold German Dark Wheat (2009) 100 1.00 -- --

Arnold Bakery Light (2009) 100 1.00 -- --

Pepperidge Farm Light (2009) 100 1.00 -- --

Weight Watchers 16oz. (2009) 100 1.00 -- --

Arnold Whole Wheat (2009) 95 0.64 -- --

Arnold, other (2009) 95 0.73 -- --

Wonder Bread - White (2009) 93 0.86 -- --

Other, Less expensive white bread (2010) -- -- 95 0.89

Whole Grain 16oz (2010) -- -- 97 0.94

Whole Grain 24oz (2010) -- -- 97 0.92

Wonder Bread - White (20oz) (2010) -- -- 94 0.87

Tortillas

Any WG tortillas available (y/n) (2009) 100 1.00 -- --

White Flour Tortillas 96 0.92 100 1.00

White Corn Tortillas ** ** 100 1.00

Whole Wheat Tortillas ** ** 97 0.92

Rice

Brown Rice - Uncle Ben's 95 0.88 100 1.00

White Rice - Uncle Ben's 88 0.74 93 0.86

Brown Rice - Other least exp. (2010) -- -- 98 0.97

White Rice - Other least exp. (2010) -- -- 97 0.84

Cereal

Whole grain cereal

Kashi Go Lean Crunch 98 0.90 94 0.74

Post Raisin Bran 98 0.90 89 0.60

Post Honey Bunches of Oats 95 0.86 97 0.89

GM Honey Nut Cheerios 95 0.89 92 0.84

Plain Cheerios 95 0.90 97 0.92

Oatmeal unflavored (2009) 95 0.90 -- --

Non-whole grain cereal - Rice Krispies 93 0.83 97 0.94

Quaker Life 91 1.00 92 0.72

Kellogg’s Raisin Bran 88 0.70 88 0.70

Frosted Mini Wheats (2010) -- -- 97 0.93

Corn Flakes (2010) -- -- 94 0.87
32

Formula/Baby Food 96 0.91 98 0.95

Gerber Soy Plus 2 100 1.00 97 0.92

Beachnut Fruit/Vegetables 100 1.00 97 0.92

Gerber Good Start Gentle Plus (powder) 97 0.94 100 1.00

Gerber Soy Plus 97 0.93 97 0.94

Gerber Fruit/Vegetables 97 0.93 97 0.92

Gerber Good Start Gentle Plus (liquid) 94 0.89 100 1.00

Gerber Good Start Soy Plus (liquid) 94 0.88 100 1.00

Gerber Meat 94 0.87 100 1.00

Plain infant cereal (Gerber) 94 0.82 94 0.89

Beachnut Meat 91 0.80 97 0.94

Plain infant cereal (Beachnut) -- -- 100 100

Average Across All Items 96 0.90 97 0.92

Min 80 0.64 88 0.60

Max 100 1.00 100 1.00

-- not asked in data collection wave, or an 'optional' variable 

* Missing data and distribution cause kappa problems

** too few rating categories

Source: Authors’ calculations
33

Table 4: Inter-rater Reliability for Produce Quality

Produce 2009 2010

% Agreement Kappa % Agreement Kappa

Fruit

Watermelon 100 -- 40 -0.36

Peaches 100 1.00 86 0.70

Cantaloupe 86 0.00 30 -0.25

Pineapple 83 0.00 78 0.55

Strawberries 80 0.00 33 -0.20

Apples 76 0.14 52 0.18

Grapes 75 0.00 69 0.14

Oranges 71 0.08 59 0.37

Lemons 69 0.16 52 0.28

Bananas 65 -0.02 58 0.31

Vegetable

Celery 100 -- 73 0.18

Whole Carrots 100 1.00 69 0.48

Baby Carrots 86 0.00 55 0.07

Broccoli 86 0.00 60 -0.18

Green Cabbage 78 -0.06 69 0.29

Tomatoes 76 0.50 63 0.37

Yellow Onions 75 0.45 57 0.28

Lettuce 65 0.31 55 0.26

Cucumber 64 0.15 62 0.42

Peppers 60 0.21 63 0.43

Average 80 0.22 59 0.21

Min 60 -0.06 30 -0.36

Max 100 1.00 86 0.70

Source: Authors’ calculations
34

Table 5: Food Availability in Convenience and Grocery Stores

Food 2009

N=259

2010

N=259

Diff. in %

Percentages

Whole milk 93 93 0

Eggs 90 89 -1

Canned plain vegetables 90 91 1

Lower-fat milk 77 78 1

Canned tuna/salmon/sardines 76 75 -1

White flour bread 75 78 4

Non-whole grain cereal 73 80 10 *

Peanut butter 72 77 7

White rice 69 76 10 *

Whole grain cereal 68 73 7

Orange juice 64 54 -16 **

Cheese 61 65 7

Canned unsweetened fruit 58 65 12 *

Fresh fruit 54 59 9

Dry beans/lentils 53 55 4

Fresh vegetables 47 49 4

Jarred baby foods 29 28 -3

Infant formula 25 31 24

Frozen vegetables 24 30 25

Whole wheat/whole grain bread 22 40 82 ***

Whole wheat/corn tortillas 19 26 37 **

Brown rice 17 35 106 ***

Frozen fruit 12 14 17

White flour tortillas 10 14 40

Soy milk 9 11 22

Tofu 7 8 14

Note: *p<0.10, **p<0.05, ***p<0.01 for difference between 2009 and 2010.

Source: Authors’ calculations
35

Table 6: Food Availability in Convenience and Grocery Stores by WIC Authorization Status

Non-WIC stores WIC authorized stores

Food 2009 2010 2009 2010

N=220 N=225 N=39 N-34

Percentages

Whole milk 92 92 100 100

Eggs 88 87 100 100

Canned plain vegetables 88 89 100 100

Lower-fat milk 75 75 90 100

Canned tuna/salmon/sardines 72 71 97 100

White flour bread 71 75 95 97

Non-whole grain cereal 69 76 * 97 100

Peanut butter 67 74 97 97

White rice 66 72 90 100

Orange juice 65 52 *** 64 71 ***

Whole grain cereal 62 69 97 100

Cheese 55 60 97 100

Canned unsweetened fruit 54 62 * 79 88

Fresh fruit 54 53 51 100 ***

Dry beans/lentils 45 48 97 100

Fresh vegetables 43 44 67 85 *

Whole wheat/whole grain bread 24 34 ** 8 79 ***

Frozen vegetables 23 27 28 50 *

Jarred baby foods 22 18 69 94 ***

Brown rice 16 26 ** 21 94 ***

Whole wheat/corn tortillas 15 22 ** 38 53

Infant formula 13 20 ** 92 100 *

Frozen fruit 11 13 15 21

Soy milk 10 12 3 9

White flour tortillas 9 12 15 26

Tofu 8 9 3 5

Note: *p<0.10, **p<0.05, ***p<0.01 for difference between 2009 and 2010.

Source: Authors’ calculations. 
36

Table 7: Food Prices in Convenience and Grocery Stores

2009^ 2010

Product N Mean, $ N Mean, $

Whole milk, gallon 231 4.13 227 3.92***

Eggs, dozen 226 2.07 226 2.26***

Canned vegetables, 15oz 217 1.48 216 1.52

Canned corn, 15oz 192 1.47 200 1.55**

White flour bread, 16oz 191 2.41 200 1.94***

Non-whole grain cereal, 16oz 185 5.45 205 5.44

Lower-fat milk, gallon 183 3.83 185 3.69**

Peanut butter, 18oz 183 3.50 198 3.55

White rice, 16oz 175 1.78 197 2.04***

Whole grain cereal, 16oz 163 5.73 188 5.19***

Dry beans/lentils, 1lb 135 1.43 142 1.57***

Canned pineapple, 15oz 130 1.75 141 1.85

Onions, 1lb 102 1.06 102 1.39***

American cheese, 1lb 99 4.81 96 4.86

Tomatoes, 1lb 96 1.93 99 2.11*

Bananas, 1lb 92 1.42 110 1.55

Apples, 1lb 78 1.88 99 1.86

Lettuce, 1lb 75 1.41 84 1.53*

Oranges, 1lb 68 2.30 93 2.02**

Baby jarred fruit/vegetables, 4oz 67 1.01 71 0.93* 

Orange juice, gallon 64 5.61 84 4.45***

Infant formula powder, 12oz 57 15.30 74 15.65

Whole wheat/whole grain bread, 16oz 54 2.44 103 2.60**

Canned sardines, 3.75oz 48 1.50 52 1.52

Whole wheat/corn tortillas, 16oz 48 1.24 68 1.47

Brown rice, 16oz 43 2.19 90 2.21

Frozen corn, 16oz 34 2.13 41 2.41

White flour tortillas, 16oz 25 2.50 34 2.85

Tofu, 16oz 18 2.27 21 2.34

Soy milk, 64oz 14 4.12 27 3.40*

Frozen strawberries, 16oz 12 4.22 15 4.08

Note: *p<0.10, **p<0.05, ***p<0.01 for difference between 2009 and 2010.

Source: Authors’ calculations

^2009 prices are adjusted for inflation using US city average CPI for corresponding food categories
37

Table 8: Food Prices in Convenience and Grocery Stores by WIC Authorization Status

Non-WIC stores WIC authorized stores

2009^ 2010 2009^ 2010

N Mean, $ N Mean, $ N Mean, $ N Mean, $

Whole milk, gallon 193 4.08 194 3.89 *** 38 4.34 33 4.07 ***

Eggs, dozen 187 2.08 192 2.29 *** 39 2.00 34 2.10

Canned vegetables, 15oz 179 1.52 183 1.54 38 1.29 33 1.39

Canned corn, 15oz 159 1.50 169 1.58 * 33 1.31 31 1.42

White flour bread, 16oz 155 2.44 167 1.99 *** 36 2.26 33 1.61 ***

Non-whole grain cereal, 16oz 147 5.55 171 5.58 38 5.07 34 4.75 **

Lower-fat milk, gallon 148 3.73 153 3.62 * 35 4.22 32 4.03 *

Peanut butter, 18oz 145 3.68 165 3.70 38 2.82 33 2.81

White rice, 16oz 140 1.78 163 2.09 *** 35 1.81 34 1.82

Whole grain cereal, 16oz 128 5.94 154 5.31 *** 35 4.93 34 4.63 *

Dry beans/lentils, 1lb 97 1.47 108 1.62 ** 38 1.33 34 1.43

Canned pineapple, 15oz 101 1.79 116 1.86 29 1.61 25 1.82

Onions, 1lb 79 1.05 79 1.36 *** 23 1.10 23 1.51 ***

American cheese, 1lb 66 4.82 67 4.94 33 4.79 29 4.69

Tomatoes, 1lb 77 1.84 80 2.10 ** 19 2.30 19 2.14

Bananas, 1lb 79 1.52 88 1.54 13 0.87 22 1.57 ***

Apples, 1lb 69 1.93 69 1.88 9 1.46 30 1.81 *

Lettuce, 1lb 62 1.41 64 1.51 13 1.39 20 1.59 *

Oranges, 1lb 62 2.34 69 2.07 6 1.91 24 1.86

Baby jarred fruit/vegetables, 4oz 42 1.10 39 0.99 25 0.87 32 0.86

Orange juice, gallon 50 5.51 64 4.42 *** 14 5.94 20 4.51 ***

Infant formula powder, 12oz 21 14.78 40 15.62 36 15.60 34 15.69

Whole wheat/whole grain bread, 16oz 51 2.44 76 2.59 3 2.38 27 2.64

Canned sardines, 3.75oz 35 1.54 31 1.60 13 1.40 21 1.39

Whole wheat/corn tortillas, 16oz 33 1.29 50 1.62 15 1.12 18 1.06

Brown rice, 16oz 35 2.20 58 2.19 8 2.17 32 2.24

Frozen corn, 16oz 29 2.22 33 2.49 5 1.61 8 2.08 **

White flour tortillas, 16oz 19 2.73 26 3.03 6 1.76 8 2.27

Tofu, 16oz 17 2.27 20 2.28 1 2.29 1 3.42

Soy milk, 64oz 13 4.11 24 3.44 1 4.21 3 3.09

Note: *p<0.10, **p<0.05, ***p<0.01 for difference between 2009 and 2010.

Source: Authors’ calculations
38

Table 9: Variety of Healthy Foods in Convenience and Grocery Stores by WIC Authorization Status

Non-WIC stores WIC authorized stores

Food 2009 2010 2009 2010

N=220 N=225 N=39 N=34

Milk shelf-space

 Share of lower-fat milk 46.9 44.4 33.4 41.5 *

Variety count 

Total whole grain products 3.0 3.8 ** 3.6 6.6 ***

 Whole grain cereal 1.6 2.0 ** 2.4 3.1 **

 Whole wheat/whole grain bread 0.9 1.0 0.4 0.9 *

 Brown rice 0.3 0.4 * 0.3 1.6 ***

 Whole wheat/corn tortillas 0.2 0.4 *** 0.6 1.0

Total fruits and vegetables 14.2 14.2 18.6 22.1

 Canned plain vegetables 5.2 4.7 8.2 7.5

 Fresh vegetables 4.0 3.8 4.7 5.1

 Fresh fruit 2.5 2.8 2.6 4.6 *

 Frozen vegetables 1.3 1.4 1.3 2.4

 Canned unsweetened fruit 0.7 0.9 ** 1.1 1.6 **

 Frozen fruit 0.5 0.6 0.7 0.9

Note: *p<0.10, **p<0.05, ***p<0.01 for difference between 2009 and 2010. 

Source: Authors’ calculations
39

Table 10: Price Index in Convenience and Grocery Stores, 2009 Linear Models

Model 1 Model 2 Model 3 Model 4 Model 5

Store size (# of cash registers, ref.: 1 register)

2 registers -0.015 -0.021 -0.019 -0.019 -0.039

3 or more registers -0.027 -0.037 -0.022 -0.022 -0.042

WIC authorization status (1=yes) -0.077 -0.088 -0.089 -0.058

SNAP authorization status (1=yes) 0.024 0.012 0.012 0.014

Median household income > $39,200 -0.043 -0.042 -0.081

Distance to nearest supermarket (in 1000ft) -0.001 -0.006

Density of convenience and grocery stores 0.000

Density of fast food chains 0.007+

Population density (in 1000) 0.068**

Constant 2.787*** 2.787*** 2.821*** 2.824*** 2.733***

Std Dev between districts 0.038 0.032 0.024 0.026 0.000

Std Dev between stores 0.318 0.318 0.318 0.318 0.311

Number of observations 192 192 192 192 192

+ p<0.1, * p<0.05, ** p<0.01, *** p<0.001

Source: Authors’ calculations
40

Table 11: Lower-Fat Milk Share in Milk Shelf-Space in Convenience and Grocery Stores, 2009 

Linear Models

Model 1 Model 2 Model 3 Model 4 Model 5

Store size (# of cash registers, ref.: 1 register)

2 registers 15.233*** 15.318*** 15.544*** 15.518*** 14.305***

3 or more registers 17.856+ 21.385* 18.041* 18.043* 16.383+

WIC authorization status (1=yes) 1.547 3.103 3.107 4.277

SNAP authorization status (1=yes) -9.568** -7.309* -7.318* -6.994*

Median household income > $39,200 20.253*** 20.177*** 14.141**

Distance to nearest supermarket (in 1000ft) 0.054 -0.064

Density of convenience and grocery stores -0.544+

Density of fast food chains 0.454

Population density (in 1000) 1.416

Constant 44.904*** 49.529*** 34.027*** 33.889*** 39.327***

Std Dev between districts 16.399 15.179 12.653 12.641 10.824

Std Dev between stores 21.744 21.731 21.592 21.595 21.872

Number of observations 252 252 252 252 252

+ p<0.1, * p<0.05, ** p<0.01, *** p<0.001

Source: Authors’ calculations
41

Table 12: Count of Fruit and Vegetable Varieties in Convenience and Grocery Stores, 2009 Poisson 

Models

Model 1 Model 2 Model 3 Model 4 Model 5

Store size (# of cash registers, ref.: 1 register)

2 registers 2.552*** 2.646*** 2.641*** 2.712*** 2.736***

3 or more registers 8.856*** 6.973*** 7.038*** 7.201*** 7.212***

WIC authorization status (1=yes) 1.140* 1.135* 1.143* 1.133*

SNAP authorization status (1=yes) 1.753*** 1.742*** 1.768*** 1.763***

Median household income > $39,200 0.749+ 0.650* 0.688+

Distance to nearest supermarket (in 1000ft) 1.078*** 1.076***

Density of convenience and grocery stores 0.999

Density of fast food chains 0.996

Population density (in 1000) 0.936

Std Dev between districts 0.703 0.651 0.641 0.678 0.678

Number of observations 267 267 267 267 267

IRR reported as coefficients

+ p<0.1, * p<0.05, ** p<0.01, *** p<0.001

Source: Authors’ calculations
42

Table 13: Count of Whole Grain Varieties in Convenience and Grocery Stores, 2009 Poisson Models

Model 1 Model 2 Model 3 Model 4 Model 5

Store size (# of cash registers, ref.: 1 register)

2 registers 2.909*** 3.022*** 3.014*** 2.996*** 2.906***

3 or more registers 7.072*** 6.840*** 7.009*** 7.089*** 6.865***

WIC authorization status (1=yes) 1.440** 1.420** 1.412** 1.485**

SNAP authorization status (1=yes) 1.151 1.132 1.133 1.139

Median household income > $39,200 0.821 0.745+ 0.670*

Distance to nearest supermarket (in 1000ft) 1.054** 1.052*

Density of convenience and grocery stores 0.996

Density of fast food chains 1.016+

Population density (in 1000) 1.117*

Std Dev between districts 0.535 0.540 0.530 0.507 0.452

Number of observations 267 267 267 267 267

IRR reported as coefficients

+ p<0.1, * p<0.05, ** p<0.01, *** p<0.001

Source: Authors’ calculations
43

Table 14: Price Index in Convenience and Grocery Stores, 2009-2010 Growth Linear Models

+ p<0.1, * p<0.05, ** p<0.01, *** p<0.001

Source: Authors’ calculations

Model 1 Model 2 Model 3 Model 4

WIC authorization status (1=yes) -0.054 -0.058 -0.055 -0.032

Time of measurement (1=2010) -0.064** -0.064** -0.061* -0.053*

WIC x Time 0.023 0.021 0.021 0.017

Median household income > $39,200 -0.013 -0.013 -0.027

Income x WIC x Time 0.006 -0.003 0.018

Store size (# of cash registers, ref.: 1 register)

2 registers 0.04 0.027

3 or more registers -0.039 -0.06

SNAP authorization status (1=yes) -0.028

Distance to nearest supermarket (in 1000ft) -0.005

Density of convenience and grocery stores 0.003

Density of fast food chains 0.004

Population density (in 1000) 0.059**

Constant 2.798*** 2.806*** 2.799*** 2.698***

Std Dev between districts 0.084 0.084 0.080 0.054

Std Dev between stores 0.202 0.202 0.203 0.201

Std Dev within stores 0.197 0.197 0.197 0.198

Number of observations 356 356 356 356
44

Table 15: Lower-Fat Milk Share in Milk Shelf-Space in Convenience and Grocery Stores, 2009-2010 

Growth Linear Models

Model 1 Model 2 Model 3 Model 4

WIC authorization status (1=yes) -1.922 0.73 1.239 5.192

Time of measurement (1=2010) -2.736* -2.731* -2.541+ -2.416+

WIC x Time 9.388** 13.353*** 13.462*** 12.130**

Median household income > $39,200 21.675*** 21.246*** 11.093*

Income x WIC x Time -14.555* -16.580* -14.496*

Store size (# of cash registers, ref.: 1 register)

2 registers 8.349** 7.466**

3 or more registers 9.826 8.645

SNAP authorization status (1=yes) -6.120*

Distance to nearest supermarket (in 1000ft) 0.022

Density of convenience and grocery stores -0.749*

Density of fast food chains 0.616*

Population density (in 1000) 2.180

Constant 48.437*** 32.951*** 31.320*** 42.098***

Std Dev between districts 15.380 12.53 12.399 9.617

Std Dev between stores 18.633 18.527 17.895 17.967

Std Dev within stores 13.612 13.485 13.530 13.480

Number of observations 473 473 473 473

+ p<0.1, * p<0.05, ** p<0.01, *** p<0.001

Source: Authors’ calculations
45

Table 16: Count of Fruit and Vegetable Varieties in Convenience and Grocery Stores, 2009-2010 

Growth Poisson Models

Model 1 Model 2 Model 3 Model 4

IRR

WIC authorization status (1=yes) 1.740** 1.589* 1.590* 1.331

Time of measurement (1=2010) 0.998 0.998 1.007 0.946+

WIC x Time 1.110+ 1.150* 1.170* 1.238**

Median household income > $39,200 0.672* 0.667* 0.710+

Income x WIC x Time 0.88 0.833 0.774*

Store size (# of cash registers, ref.: 1 register)

2 registers 1.201** 1.270***

3 or more registers 1.260+ 1.400**

SNAP authorization status (1=yes) 1.410***

Distance to nearest supermarket (in 1000ft) 1.036***

Density of convenience and grocery stores 1.007

Density of fast food chains 0.984

Population density (in 1000) 1.025

Constant 8.447*** 11.080*** 10.681*** 7.422***

Std Dev between districts 0.404 0.365 0.360 0.403

Std Dev between stores 0.929 0.929 0.897 0.816

Number of observations 504 504 504 504

+ p<0.1, * p<0.05, ** p<0.01, *** p<0.001

Source: Authors’ calculations
46

Table 17: Count of Whole Grain Varieties in Convenience and Grocery Stores, 2009-2010 Growth 

Poisson Models

Model 1 Model 2 Model 3 Model 4

IRR

WIC authorization status (1=yes) 1.565* 1.466* 1.487* 1.435*

Time of measurement (1=2010) 1.259*** 1.259*** 1.330*** 1.246***

WIC x Time 1.426** 1.598** 1.592** 1.616***

Median household income > $39,200 0.794 0.756* 0.675*

Income x WIC x Time 0.69 0.594* 0.599*

Store size (# of cash registers, ref: 1 register)

2 registers 1.977*** 1.937***

3 or more registers 4.257*** 3.948***

SNAP authorization status (1=yes) 1.208*

Distance to nearest supermarket (in 1000ft) 1.032*

Density of convenience and grocery stores 0.994

Density of fast food chains 1.008

Population density (in 1000) 1.104+

Constant 2.067*** 2.412*** 2.097*** 1.635*

Std Dev between districts 0.328 0.310 0.316 0.269

Std Dev between stores 0.764 0.768 0.601 0.590

Number of observations 504 504 504 504

+ p<0.1, * p<0.05, ** p<0.01, *** p<0.001

Source: Authors’ calculations
47

Table 18: WIC Vendor Participation in Connecticut 2008-2010

5/2008-

9/2008

10/2008-

2/2009

3/2009-

7/2009

8/2009-

12/2010

1/2010-

5/2010

6/2010-

10/2010

Number of Stores

(1) Pharmacy 112 110 111 111 110 107

(2) Supermarket chain 213 208 205 198 192 196

(3) Grocery, convenience stores 

and food marts

280 252 239 236 237 235

Total Stores 605 570 555 545 539 538

Source: Authors’ calculations
1

FOOD STORE ASSESSMENT FORM 

FOOD OUTLET COVER PAGE 

 

Rater ID: | Store ID: - - | | | 

 STORE NAME: ____________________________ DATE: __ __/ __ __ / 2010 

IS NAME DIFFERENT?___________________ DAY OF THE WEEK: __________ 

ENTERED BY: _________________ START TIME __ __:__ __ AM/PM 

CHECKED BY: _________________ END TIME __ __:__ __ AM/PM 

Type of store

 Convenience store 

 Food mart/gas station 

 Grocery store 

 Supermarket

Vendor survey

 

 Store to be interviewed 

 Interview schedule (date/time) ____________________________ 

 Name/Phone #________________________________________ 

Store features

 Number of cash registers 

 Number of aisles 

 WIC Authorization 

 Food Stamp Authorization

  1  2  3  4-9  10+ 

 

___________________ 

 

 Authorized  Not authorized  Pending  Used to be WIC 

 

 Authorized  Not authorized  Pending 

Observation status

 Completed 

 Denial 

 Out of business 

 Store not found 

 Other___________________

 

 Freezer(s) available 

How is produce stored? (check all that apply): 

 

 Does not sell produce 

 Cooler/refrigerated bins 

 Cases/bins (room temp) 

 Directly on the floor 

 At the cash register/point of purchase 

 Case with water spray 

 Other_______________________ 

 
2

Food Store Assessment Form 

Measure 1: MILK 

 

**STORE DOES NOT SELL MILK | 

Not 

Available Price/0.5 gallon Price/gallon No price label Comments 

1. Skim milk (fat free 0%) 

Least expensive brand (store 

brand in supermarkets) 

Brand___________________ 

 

 

 

 $ . | 

 

 $ . |  ______________ 

2. Low fat milk (1%) 

Least expensive brand (store 

brand in supermarkets) 

Brand___________________ 

 

 $ . | 

 

 $ . | 

 

 ______________ 

3. Reduced fat milk (2%) 

Least expensive brand (store 

brand in supermarkets) 

Brand___________________ 

 

 

 

 

 $ . | $ . | 

 

 ______________ 

4. Whole milk 

Least expensive brand (store 

brand in supermarkets) 

Brand___________________ 

 

 $ . | 

 

 $ . | 

 

 ______________ 

5. Soy milk (unflavored) 

Brand: 8TH Continent 

NO ‘LITE’. 

Other least expensive brand

 

 

 

 

 

 

 

 Half-gallon 

 $ . | 

 $ . | 

Size (oz) if not 

standard 

 | | 

 | 

 

 

 

 

 

 

 

______________ 

______________ 

Number of half-gallons of whole milk – all brands (skip if supermarket): | | 

Number of gallons of whole milk – all brands (skip if supermarket): | | 

Number of half-gallons of 2%, 1% and skim milk – all brands (skip if supermarket): | | 

Number of gallons of 2%, 1% and skim milk – all brands (skip if supermarket): | | 
3

 Food Store Assessment Form 

Measure 2: PROTEIN 

A. CHEESE 

**STORE DOES NOT SELL CHEESE | 

If Cheddar is not available, use the cheapest American, Mozzarella, Monterrey Jack, Colby or Swiss and note in 

Other. Remember about restrictions. In case 16oz is not available, use the closest size available and note it

Item Not 

Available Price/lb Oz/package If not standard 

No price 

label 

 Comments 

1. Reduced fat cheddar 

 Unflavored in block 

 Least expensive brand, 16oz

 $ . | 

 

 | |  ___________ 

2. Regular cheddar

 Unflavored in block

Least expensive brand, 16oz 

 $ . | 

 

 | |  ___________ 

3. American Slices 

 from deli, 1lb  $ . | 

 

 | |  ___________ 

B. EGGS 

**STORE DOES NOT SELL EGGS | 

Item Not 

Available Price/dozen No price label Comments 

Eggs, dozen 

 Large white, least expensive 

 Large brown, least expensive 

 

 

 

 

 

 $ . | 

 $ . | 

 

 

 

______________

______________ 

 C. TOFU 

**STORE DOES NOT SELL TOFU | 

 

Item Not 

Available Price/package Oz/package 

If not standard

No price 

label 

 Comments 

Tofu 

 

Nasoya All Lite and Regular 

Varieties, 14-16oz 

 

Other calcium-set tofu_________

Any other tofu________________

 

 

 

 

 

 

 

 

 

 

$ . | 

$ . | 

$ . | 

 | | 

 

 | | 

 | | 

 

 

 

 

 

 

 

 

 

 

___________

___________ 

___________ 
4

 Food Store Assessment Form 

Measure 3: 100% JUICE 

**STORE DOES NOT SELL JUICE | 

Reminder: Assess 2nd item in Italics if 1st item is not available 

Item Not Available Price/package No price label Comments 

1. 100% Orange Juice 

Half gallon plastic bottle, 

no cartons, 64oz only 

 Least expensive brand 

1 gallon plastic bottle, 128oz only 

 Least expensive brand 

 

 

 

 

 

 

 

 

 

 

 $ . | 

 

 $ . | 

 

 

 

 

 

 

 

 

______________ 

______________ 

2. 100% juice or 100% juice 

blends 

 Any flavor, plastic bottle 

Nestle Juicy Juice, 64oz 

 Any 100% juice, plastic bottle 64oz 

 Least expensive brand 

 

 

 

 

 

 

 

 

 $ . | 

 

 $ . | 

 

 

 

 

 

 

______________ 

______________ 
5

Food Store Assessment Form 

Measure 4: FRESH FRUIT 

**STORE DOES NOT SELL FRUIT | 

Produce Item Not 

Available Price # 

Unit 

pc lb 

Quality 

A A - B B- C 

No 

price 

label

Organic 

1. Apples O Red 

delicious 

O ________

 

  $ . | pc lb A A- B B- C  

2. Bananas 

  $ . | pc lb A A- B B- C  

3. Cantaloupe 

  $ . | pc lb A A- B B- C  

4. Grapes O Red 

seedless 

O ________

 

  

 

$ . | pc lb A A- B B- C  

5. Lemons 

  

 

$ . | pc lb A A- B B- C  

6. Oranges O Navel 

O ________ 

 

  

 

$ . | pc lb A A- B B- C  

7. Peaches 

  

 

$ . | pc lb A A- B B- C  

8. Pineapple 

  

 

$ . | pc lb A A- B B- C  

9. Strawberries 

  $ . | pc lb A A- B B- C  

10. Watermelon O Seedless 

O ________ 

 

  

 

$ . | pc lb A A- B B- C  

Count varieties of ALL fresh, whole or cut fruit (skip if supermarket)*: | | 

*Count 2 kinds of 1 fruit as 1 variety (e.g. Navel and Valencia oranges count as 1 variety of fruit) 

Quality Scale: 

 A: Excellent (outstanding quality, perfectly ripe-no molded, wrinkled/shriveled, bruised, wilted) 

 A-: Very good (very good quality, ripe–few molded, wrinkled/shriveled, bruised, wilted) 

 B: Good (good quality – several/less than a quarter molded, wrinkled/shriveled, bruised, or wilted) 

 B-: Fair (a quarter to a third molded, wrinkled/shriveled, bruised, or wilted) 

 C: Poor (majority molded, wrinkled/shriveled, bruised, or wilted) 

Comments:
 Food Store Assessment Form 

Measure 5: FRESH VEGETABLES 

 

**STORE DOES NOT SELL FRESH VEGETABLES |

Produce Item Not 

Available Price #

Unit 

pc lb 

Quality 

A B C 

No 

price 

label

 Organic

1. Broccoli O Bunch 

O _______

 

  $ . | pc lb A A- B B- C  

2. Green 

Cabbage 

O Head 

O _______

 

  $ . | pc lb A A- B B- C  

3. Whole 

Carrots 

O 1 lb bag 

  $ . | pc lb A A- B B- C  

4. Baby 

Carrots 

O 1 lb bag 

  

 

$ . | pc lb A A- B B- C  

5. Celery 

  

 

$ . | pc lb A A- B B- C  

6. Cucumber O Regular 

O _______

 

  

 

$ . | pc lb A A- B B- C  

7. Lettuce O Iceberg 

O _______ 

 

  

 $ . | pc lb A A- B B- C  

8. Yellow 

Onions 

O Loose 

O 

________ 

 

  

 

$ . | pc lb A A- B B- C  

9. Peppers O Green bell 

O _______ 

 

  $ . | pc lb A A- B B- C  

10. Tomatoes O Loose 

O _______

 

  

 

$ . | pc lb A A- B B- C  

Count varieties of ALL fresh whole or cut vegetables, except white potatoes (skip if supermarket): | | 

Quality Scale: 

 A: Excellent (outstanding quality, perfectly ripe-no molded, wrinkled/shriveled, bruised, wilted) 

 A-: Very good (very good quality, ripe–few molded, wrinkled/shriveled, bruised, wilted) 

 B: Good (good quality – several/less than a quarter molded, wrinkled/shriveled, bruised, or wilted) 

 B-: Fair (a quarter to a third molded, wrinkled/shriveled, bruised, or wilted) 

 C: Poor (majority molded, wrinkled/shriveled, bruised, or wilted) 

Comments:

6
7

 Food Store Assessment Form 

 Measure 6: FROZEN VEGETABLES & FRUIT 

Reminder: Assess fruits/vegetables without added sugars, fats, creams or oils (i.e. plain) 

A. FROZEN VEGETABLES 

**STORE DOES NOT SELL FROZEN VEGETABLES | 

Item Not 

Available Price/package

Oz/package 

If not 

standard

No price 

label 

 Comments 

1. Broccoli 

Least expensive brand, 

plain, 16oz 

 $ . | | |  ______________ 

2. Green beans 

Least expensive brand, 

plain, 16oz 

 $ . | 

 

 | | 

 

 ______________ 

3. Sweet corn 

Least expensive brand, 

not on the cob, 16oz

 $ . | 

 

 | | 

  ______________ 

Count varieties of ALL frozen vegetables, except white potatoes (skip if supermarket): | | 

B. FROZEN FRUITS 

**STORE DOES NOT SELL FROZEN FRUITS | 

Item Not 

Available Price/package Oz/package 

if not standard 

No price 

label Organic 

1. Blueberries 

Least expensive brand, 

plain, 16oz 

 

 

 

 

$ . | 

 

 | | 

 

 

 

 

 

 

 

 

2. Peaches 

Least expensive brand, 

plain, 16oz

 

 

$ . | | |  

 

 

 

3. Strawberries 

Least expensive brand, 

plain, 16oz

 

 

 

$ . | 

 

 | | 

 

 

 

 

 

 

Count varieties of ALL frozen fruit without added sugars (skip if supermarket): | | 
8

 Food Store Assessment Form 

 Measure 7: CANNED VEGETABLES & FRUIT 

Reminder: Assess fruits/vegetables in metal cans without added sugars, fats or oils. 

If the standard size is not available, attempt to use the closest size to 15oz, ideally within 14-16oz. 

A. CANNED VEGETABLES 

**STORE DOES NOT SELL CANNED VEGETABLES | 

Item Not 

Available Price/can

Oz/can 

If not 

standard 

No price 

label 

 Comments 

1. Sweet corn 

Del Monte, 15oz can 

Other least expensive 

brand, 15oz can

 

 

 

 

$ . | 

$ . | 

 

 | | 

 | | 

 

 

 

 

______________

______________ 

 2. Green beans 

Del Monte, 15oz can 

Other least expensive 

brand, 15oz can

 

 

 

 

$ . | 

$ . | 

 

 | | 

 | | 

 

 

 

 

______________

______________ 

3. Peas 

Del Monte, 15oz can 

Other least expensive 

brand, 15oz can

 

 

 

 

$ . | 

$ . | 

 | | 

 | | 

 

 

 

______________

______________ 

Count varieties of ALL canned plain vegetables except white potatoes (skip if supermarket): | | 

B. CANNED FRUITS 

**STORE DOES NOT SELL CANNED FRUIT | 

Item Not 

Available Price/can

Oz/can 

If not 

standard

No price 

label 

 Comments 

1. Peaches (in water or 

100% juice, no syrup)

Del Monte, 15oz can 

Other least expensive 

brand, 15oz can 

 

 

 

 

 

$ . | 

$ . | 

 

 | | 

 | | 

 

 

 

 

______________

_______________ 

2. Pineapple (in water 

or 100% juice, no syrup)

Dole, 15oz can 

Other least expensive 

brand, 15oz can 

 

 

 

 

 

$ . | 

$ . | 

 

 | | 

 | | 

 

 

 

 

 

______________

______________ 

Count varieties of ALL canned fruit without added sugars (skip if supermarket): | |
9

 Food Store Assessment Form 

 Measure 8: PROTEIN 

A. CANNED FISH 

**STORE DOES NOT SELL CANNED FISH | 

Item Not 

Available Price/can

Size (oz) 

if not 

standard 

No price 

label 

 Comments 

1. Chunky Light Tuna in Water

Least expensive brand, 5oz 

 

 $ . | 

 

 

 | | 

 

 

 ______________ 

2. Salmon in Water 

Least expensive brand, 6-7.5oz 

Other least expensive brand, 

>7.5oz

 

 

 

 

 $ . | 

 $ . | 

 | | 

 

 | | 

 

 

 

 

______________ 

______________ 

3. Sardines in Water 

 Least expensive brand, 3.75oz 

  $ . | 

 

 

 | |  ______________

B. PEANUT BUTTER 

**STORE DOES NOT SELL PEANUT BUTTER | 

If 16.3oz is not available, try to use size 16.3-18oz. if no 16.3-18oz, assess the closest available size.

Item Not 

Available Price/jar Size (oz) No price label 

 Comments 

1. Unflavored smooth

Least expensive brand 16.3-18oz  

 $ . | 

 

 

 | |  ______________

2. Unflavored chunky 

Least expensive brand 16.3-18oz  $ . | | | 

 

 

 

 

______________

C. DRY BEANS AND LENTILS

**STORE DOES NOT SELL DRY BEANS AND LENTILS | 

Item Not Available Price No price 

label 

 Comments 

1. Dry beans 

 

Least expensive type and brand, 

1lb bag 

 

 $ . |  _____________

2. Dry lentils/peas 

Least expensive type and brand, 

1lb bag

 

 

 

 $ . | 

 _____________ 
10

 Food Store Assessment Form 

 Measure 9: BREAD 

**STORE DOES NOT SELL BREAD | 

Definition of Whole Wheat Bread – whole wheat is listed first on the ingredients list (primary ingredient by weight 

Beware of packaging saying “wheat bread” – this is not whole wheat bread) 

Item Not 

Available Price/package Size (oz) 

If not standard

No price 

label 

 Comments 

1. Whole grain / 100% whole 

wheat bread 

Least expensive brand, 16oz 

Brand____________________

Least expensive type and 

brand, 24 oz 

Brand____________________ 

 

 

 

 

 

 

 $ . | 

 

 $ . | 

 

 | | 

 | | 

 

 

 

 

 

______________ 

______________ 

2. White Bread 

 Wonder family size, 20oz 

 Least expensive brand, 20oz 

 

 

 

 

 

 

 $ . | 

 $ . | 

 

 | | 

 | | 

 

 

 

 

 

 

______________ 

______________ 

 

Count varieties of ALL whole wheat* and whole grain breads (skip if supermarket): | | 

Count varieties of ALL 1 lb whole wheat* and whole grain breads (skip if supermarket): | | 

 B. TORTILLAS 

**STORE DOES NOT SELL TORTILLAS | 

Item Not 

Available Price/package Size (oz) 

If not standard

No price 

label 

 Comments 

1. Whole wheat tortillas 

 Least expensive brand, 

1lb package 

Brand_________________

 

 $ . | 

 

 | |  

______________ 

2. White corn tortillas 

Least expensive brand, 

1lb package 

Brand_________________

 

 

 $ . | 

 

 | | 

 

 ______________ 

3. White flour tortillas 

Least expensive brand, 1lb 

package

 

 $ . | 

 

 | | 

 

 ______________ 

Varieties of ALL whole wheat and white corn tortillas* (skip if supermarket): | | 
11

Food Store Assessment Form 

Measure 10: GRAINS 

A. RICE 

**STORE DOES NOT SELL RICE | 

Item Not 

Available Price/package Size (oz) 

If not standard

No price 

label 

 Comments 

1. Brown rice unflavored 

 Uncle Ben’s, 1lb bag 

 Least expensive brand, 

1lb bag 

Brand:________________ 

 

 

 

 

 

 

 $ . | 

 $ . | 

 

 

 | | 

 | | 

 

 

 

 

______________

______________

2. White rice unflavored 

 Uncle Ben’s, 1lb bag 

Least expensive brand, 

1lb bag 

 

 

 

 $ . | 

 $ . | 

 

 | | 

 | | 

 

 

 

______________

______________ 

Count varieties of ALL brown rice (skip if supermarket): | | 

B. CEREAL

**STORE DOES NOT SELL CEREAL | 

Other Available Whole Grain Cereals (check box if available in any size, SKIP IN SUPERMARKET): 

 Post Honey Bunches of Oats  General Mills Honey Nut Cheerios  Kellogg’s Raisin Bran  

 Kashi Go Lean Crunch  Post Raisin Bran  Quaker Life  

Item Not 

Available Price/package Size (oz) 

If not standard

No price 

label 

 Comments 

1. Whole grain cereal 

Cheerios, 18oz 

Frosted Mini Wheats, 18oz 

Other, 14-18oz_________ 

Other, any size_________ 

 

 

 

 

 

 

 

 

 

 $ . | 

 $ . | 

 $ . | 

 

 $ . | 

 

 | | 

 | | 

 

 | | 

 | | 

 

 

 

 

 

 

 

 

______________ 

______________ 

______________ 

2. Non-whole grain 

cereal

 Rice Krispies, 18oz 

 Corn Flakes, 18oz 

 Special K, 18oz 

 Other ________________ 

 

 

 

 

 

 $ . | 

 $ . | 

 $ . | 

 $ . | 

 | | 

 | | 

 | | 

 | | 

 

 

 

 

 

______________

______________ 

______________ 
12

 Food Store Assessment Form 

 Measure 11: BABY FOOD 

**STORE DOES NOT SELL BABY FOOD Note: May see Gerber formula listed as Nestle Formula

Item Not 

Available Price/package Size (oz) 

If not standard

No price 

label 

 Comments 

1. Infant formula 

powder

Gerber Good Start Gentle 

Plus, 12oz 

Gerber Good Start Soy Plus, 

12oz 

Gerber Good Start 2 Soy 

Plus 2, 12oz

 

 

 

 

 

 

 

 

 

 

 $ | . | | 

 

 $ | . | | 

 $ | . | | 

 

 | | 

 

 | | 

 | | 

 

 

 

 

 

 

 

 

 

 

______________

______________ 

______________ 

2. Infant formula 

concentrate (liquid) 

Gerber Good Start Gentle 

Plus (formerly Good Start 

Supreme DHA & ARA), 13oz 

Gerber Good Start Soy Plus, 

13oz

 

 

 

 

 

 

 $ . | 

 $ . | 

 | | 

 

 | | 

 

 

 

 

 

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3. Plain infant cereal 

 Beech-Nut 8oz 

 Gerber 8oz

 

 

 

 

 $ . | 

 $ . | 

 | | 

 

 | | 

 

 

 

 

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4. Baby food meat (Stage 1: Beef & Beef Broth/Chicken & Chicken Broth/ Turkey &Turkey Broth) 

 Beach-Nut, 2.5oz 

 Gerber, 2.5 oz, similar 

 flavors 

 

 

 

 

 

 $ . | 

 

 $ . | 

 

 | | 

 

 | | 

 

 

 

 

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5. Baby food fruit/ 

vegetables (plain) 

(Stage 2: Butternut Squash, Tender Golden Sweet Potatoes, Tender Sweet Carrots, 

Tender Sweet Peas, Tender Young Green Bean, Applesauce, Chiquita Bananas, Pears) 

 Beach-Nut, 4oz 

Gerber, 4oz, similar flavors 

 

 

 

 

 

 $ . | 

 $ . | 

 | | 

 

 | | 

 

 

 

 

 

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