ERS has compiled supermarket scanner data of meat prices into a
unique searchable database that
contains monthly average retail price data for selected cuts of
beef, pork, chicken, turkey, lamb, and veal. In addition to prices
of various cuts of meat, the database includes information on the
volume sold and the volume of "featured" products sold
under retailers' weekly advertised specials and frequent shopper
discounts. These data are collected at the point of sale by supermarkets
using electronic scanners in checkout lines. The data set reflects
information from stores representing 20 percent of supermarket sales
in the United States.
The database allows users to create custom
tables in three different formats based on selected time periods
(beginning in January 2001) and specific types and cuts of meat.
The resulting tables report the average price of cuts of meat sold
during the month, the volume of sales (indexed for 2001=100), and
the percentage of volume sold under featuring
(discounting). Other items on the web page include documentation
of methodology, frequently
asked questions, and descriptions of planned research.
Sales respond to discounting and
season
Meat prices vary according to type of cut, season, and relative
price of competing meats. Examining the impact of discounting
and seasonality on the volumes sold of different cuts will
help us understand the demand for meat and thus the forces
shaping the livestock market.
When stores feature, or discount,
a particular meat product, its volume of sales rises. The sales
volume for chicken leg quarters, for example, appears to be
quite responsive to featuring.
Conversely, if featuring activity
is low, as it is for beef liver, the volume changes very little.
Seasons and holidays affect meat
purchases even when prices are not discounted. For example,
ground beef and steaks are popular during the summertime, but
ham is heavily purchased around Easter, Thanksgiving, and Christmas.
How Are the Data Compiled?
Only supermarkets with annual sales of $2 million or more
and that voluntarily provide their information are included
in the study. These data do not include sales from fast food
shops or restaurants, butcher shops, warehouse clubs, convenience
stores, institutions, mail order firms, or food distributors
that choose not to provide their data for commercial use.
Commercial sources combine the data from retailers to protect
confidentiality. Scanner data are grouped according to standardized
categories. Then, after adjusting for feature discounts, prices
are weighted according to volume to calculate the average
price for a category (for example, round roast, USDA Choice
boneless) for that month.
Scanner data for meat are particularly difficult to compile.
Aggregation is complex because meat is sold in randomly sized
packages and, unlike most other packaged foods, does not have
uniform product codes (UPC) for each cut. In addition, stores
can provide a name and code for a meat cut that is unique
to that store, that geographic area, or that franchise. Because
of the difficulties in assigning an average price to a given
standardized cut, no one has used scanner data before for
analysis of meat prices.
How Do Scanner Data Differ From BLS
Price Data?
The Bureau of Labor Statistics (BLS) estimates the Consumer
Price Index using retail food price data gathered by data
collectors who visit a wide variety of stores. The new scanner
data supplement BLS data by including more cuts of meat (for
example, veal and lamb), data on volume of meat sold, and
the effects of featuring (discounting).
The datasets differ in many other ways, as well. BLS data
are based on a large sample and include stores that may not
discount meat prices. BLS measures prices as snapshots in
time every month. Scanner data, on the other hand, continuously
capture purchase prices throughout the month. In addition,
the BLS data may not capture the extent of supermarket featuring,
which is assumed to be widespread. ERS data for many meat
cuts show lower prices than BLS data by better capturing volume-weighted
featuring and price variability.