How Much Lower Are Prices at Discount Stores? An Examination of Retail Food PricesWebsite Administrator
Economic Research Report No. (ERR-105) 51 pp, October 2010
What Is the Issue?
Food prices vary across different parts of the United States.
One factor that may lead to differences in food prices is the types
of stores in a given market or neighborhood. Nontraditional
discount food retailers, including supercenters, mass
merchandisers, wholesale club stores, and dollar stores, have
gained a substantial portion of the retail food market over the
past 15 years. Previous studies have shown that prices for some
items are lower in nontraditional than in traditional stores. But
those earlier studies were generally limited in the number of items
compared, the detail level of comparison, and the geographic areas
studied. This study compares prices for a wide range of foods at a
finer level of detail than earlier studies, at both the national
and geographic market levels, in order to quantify the difference
in food prices across store formats.
What Are the Major Findings?
Nationally, 86 percent of broad food groups had lower prices in
nontraditional stores than in traditional stores. Even after
controlling for differences in brand and package size by comparing
identical Universal Product Code (UPC) items, prices were lower for
82 percent of UPC products.
Expenditure-weighted average prices were 7.5 percent lower in
nontraditional stores at the UPC level, with prices for individual
food items ranging from 3 to 28 percent lower in nontraditional
stores. This indicates that factors other than brand and package
size, such as differences in store costs and pricing strategies,
play a role in explaining price differences between store
At the market level, price differences between traditional and
nontraditional stores were smaller and less frequent in areas with
a high market share of nontraditional retailers. Atlanta and San
Antonio-which are cities with a high share of nontraditional
retailers-had the fewest products with significantly lower prices
in nontraditional stores and an average price discount of just 5.3
percent, while cities with a low share of nontraditional retailers
(Philadelphia and the New York City metro area) had an average
price discount of 11.5 percent.
Smaller price differences between store types may be due to some
higher priced traditional retailers' exiting markets in which
nontraditional retailers gain a large market share, with the
remaining traditional retailers' lowering their prices in response
to increased competition. Such an outcome would result in a
decrease in average prices in traditional stores. Alternatively,
the smaller differences could be due to nontraditional retailers'
raising prices once they have a
large enough market share to do so.
Results for specific food groups and items include:
• Meat items had the largest average price discounts in
nontraditional stores, while grain-based products had the greatest
variation in price differences between nontraditional and
• All canned products were priced significantly lower in
nontraditional stores, even at the UPC level.
• Private-label (store-brand) items had larger price differences
between store types than did national-brand goods.
How Was the Study Conducted?
The study analyzed 2004-06 Nielsen Homescan data, which includes
all food-at-home purchases for about 40,000 households in 52
markets and selected nonmetropolitan areas. (Nielsen defines
"nonmetropolitan areas" as areas outside the 52 largest
metropolitan areas in the United States). The study compared price
differences at the national and market level for four broad food
groups-dairy, meat, fruits and vegetables, and grains. These food
divided into four levels of aggregation for each year with the
most commonly purchased products compared at each aggregation
1. broad food categories, such as low-fat milk
2. products of the same brand or a narrower subgroup of the broad
categories (for items that do not have national brands, such as
most fresh fruit)
3. products with the same individual package sizes, such as
6-ounce containers of yogurt
4. products with the same UPC
A linear regression model was used to control for other factors
that may influence the average price for a given food item or group
of foods, such as region and calendar quarter when purchased. When
estimating food price differences between store types at the market
level, we focused on six markets: Philadelphia and New York (with
low shares of nontraditional retailers); Chicago and
Baltimore/Washington (with medium shares of nontraditional
retailers); and Atlanta and San Antonio (with high shares of