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1. What legislation mandates the collection
of this data?
2. What is electronic scanner data, and how
is the confidentiality of individual stores protected in the ERS
data set?
3. What does "featuring" mean?
4. What are the categories of meat included
in the database?
5. How were the categories of meat determined?
6. What are URMIS codes and how is ERS using
them?
7. Which stores are included in the scanner
data?
8. How are data weighted to arrive at a national
average price?
9. What types of difficulties arise in scanner
data collection for meat and how are they addressed?
10. How does the BLS collect meat price data?
11. How do the ERS scanner data compare with
the BLS data?
12. How are meat price data used in market
and policy analysis?
13. How are meat price spreads calculated?
14. What geographic coverage is provided by
the scanner data?
What legislation mandates the collection
of this data?
The Livestock
Mandatory Reporting Act of 1999 (P.L 106-78, Title IX, Section
257—Publication of Information on Retail Purchase Prices for
Representative Meat Products) requires the compilation and publication
of retail purchase prices for "representative food products
made from beef, pork, chicken, turkey, veal, or lamb." ERS
is responsible for publishing retail meat purchase prices and quantity
measures for these representative meat products. In addition, USDA's
Agricultural Marketing Service has released background
information on the rule (which implements the program).
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What is electronic scanner data, and how is the confidentiality of individual stores protected
in the ERS data set?
Data are collected at the point of sale by supermarkets using electronic
scanners in check-out lines. Stores may use bar codes attached to
the product package or store codes typed into the register to record
the product type and price. Supermarkets are retail grocery stores
with dairy, produce, fresh meat, packaged food, and nonfood departments
and annual sales of $2 million or more. While not based on a random
sample, the raw data underlying the database are from supermarkets
across the United States that account for approximately 20 percent
of U.S. supermarket sales.
Supermarkets that use electronic scanners may provide
the information to commercial data firms (i.e., syndicated data
suppliers). These firms combine point-of-sale transaction data from
supermarkets. They process and categorize the data and sell information
to both supermarket chains and manufacturers for inventory, revenue
control, and general marketing purposes.
To ensure confidentiality of the meat retail scanner
data, a third-party cooperator (to ERS) obtains and processes the
retail scanner data and provides ERS with summary statistics. Store-
and chain-level data are not provided to ERS in raw form nor can
it be constructed from the data published on the ERS website. No
data related to individual store- and/or chain-level sales are obtained
or maintained by ERS. The summary data are delivered to ERS every
month by our third-party cooperator, reviewed by ERS staff for consistency
and quality, and posted to the ERS website.
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What does "featuring" mean?
Featuring refers to the price discounts offered to consumers through
retailers' weekly feature advertisements. These discounts likely
have an effect on the quantity of meat sold.
In preparation of the data (by ERS' third-party cooperator), information
on featuring activity is matched and compared to the price provided
in the retail scanner data. Where differences in the recorded price
and the feature price are observed, the feature price is used to
represent the price of the product to the consumer. For example,
the regular price of Choice T-bone steak in supermarket X is $7.50
per pound. In the second week of May, the advertised price is $6.50
per pound. Depending upon the supermarket's data management system,
this feature price may or may not be recorded as the purchase price.
(Sometimes item discounts are recorded at the bottom of a sales
receipt and are subtracted from the total sale.) In this example,
the advertised feature price for supermarket X's Choice T-bone steak
would replace the recorded price for that item in the database.
Processes have been created and iterations performed to ensure that
the feature price adjustment for individual items are valid and
performed in an appropriate and consistent manner.
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What are the categories of meat included
in the database ?
Average monthly retail scanner prices are reported for beef, pork,
poultry, lamb, and veal. Only random-weight
items that are species-specific and sold in the fresh meat department
of traditional supermarkets are included in ERS' meat retail scanner
database. Multi-species items, canned meats, products containing
meat (such as frozen dinners), and deli products are not included.
Although most bacon and sausage are sold in fixed-weight packages,
the database does contain information on random-weight bacon and
sausage.
The data from individual point-of-sale transactions are aggregated
into the following categories:
Beef
Ground chuck
Ground beef, 100-percent beef
Lean and extra lean ground beef
All uncooked ground beef
Chuck roast, USDA Choice, boneless
Chuck roast, graded and ungraded but not choice or prime
Round roast, USDA Choice, boneless
Round roast, graded and ungraded but not choice or prime
All uncooked beef roasts
Steak, T-bone USDA Choice, bone-in
Steak, rib eye USDA Choice
Steak, round, USDA Choice
Steak, round, graded and ungraded but not choice or prime
Steak, sirloin USDA Choice, boneless
Steak, sirloin, graded and ungraded but not choice or prime
All uncooked beef steaks
Beef for stew, boneless
All uncooked other beef not veal (such as beef briskets and ribs)
All beef
Pork
Bacon, sliced
Chops, center cut, bone in
Chops, boneless
All pork chops
Ham, boneless not canned
All ham (not canned or sliced)
Sausage, fresh, loose
All other pork excluding canned and sliced (such as pork roast and
ribs)
All pork
Poultry
Chicken, fresh whole
Chicken, breast, bone-in
Chicken, legs, bone-in
All chicken
Turkey, frozen whole
All turkey
Other meat
All lamb
All veal
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How were the categories of meat determined?
After consultation with industry groups, ERS chose to base the product
groupings in the meat retail scanner database on those defined by
the URMIS industry standard and BLS. Initially,
ERS is publishing weighted-average prices from the retail scanner
data side-by-side with matching BLS price data. After further observation
and evaluation of the retail scanner data, ERS plans to report more
detailed meat-cut categories.
Currently, BLS reports about 30 meat-cut categories, excluding
lamb and veal, for the entire fresh meat department (one of the
five standard departments within a supermarket). Many meat cuts
are aggregated in the BLS data into a combined category. For example,
items listed as chuck roast, arm pot roast, shoulder pot roast,
and 7-bone pot roast are combined into the chuck roast category.
Because the ERS data are based on URMIS codes, the system can
accommodate more exacting item descriptions and, thus, more specific
retail prices by group than the BLS data. Combined with industry
agreed-upon carcass yields, the scanner data could translate into
a more accurate "rebuild" of the carcass and give us better
information for calculating price spreads.
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What are URMIS codes and how is ERS using them?
The Uniform Retail Meat Identity Standards (URMIS) codes were established
in 1973 by the National Cattlemen's Beef Association. The system
was developed to provide a retail-meat-cut identification system
and a standardized nomenclature for every retail red meat item (beef,
veal, lamb, and pork).
The goal of URMIS is to eliminate consumer confusion caused by
the proliferation of names used to describe retail meat cuts. Before
URMIS, a specific retail cut had several different names depending
on the store or region of the country in which it was sold. For
example, a Kansas City strip, New York strip, and beef loin steak
are all the same cut. While the URMIS standards have been part of
the industry for several decades, the program is strictly voluntary
and has seen mixed levels of implementation.
ERS is using URMIS codes to categorize descriptions of different
cuts of meat so the ERS and BLS data are comparable. First, items
in retailers' point-of-sale systems—that are represented in
the meat retail scanner database—are matched (by ERS' third-party
cooperator) to an URMIS code. Second, URMIS codes are assigned to
the appropriate scanner data category. (See item
groupings by scanner data category for a list of categories
in the retail scanner database and examples of individual meat cuts
that are in those categories. See scanner
and BLS categories for the scanner data categories that correspond
to the BLS meat categories. Both files are in *.xls format).
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Which stores are included in the scanner data?
Retail scanner data are from supermarkets that: 1) process
their receipts by electronic scanners, 2) sell products through
the traditional supermarket retail meat case, and 3) voluntarily
provide their data to commercial data firms. Supermarkets are defined
as retail grocery stores with dairy, produce, fresh meat, packaged
food, and nonfood departments and annual sales of $2 million or
more. Not included in retail scanner data are sales from butcher
shops, warehouse clubs, convenience stores, fast-food establishments,
and restaurants; at institutions (e.g., hospitals and schools);
through mail order; or by food distributors that choose not to provide
their data for third-party use.
ERS' third-party cooperator obtains the retail scanner data from
a commercial data firm. ERS does not have information about the
stores whose data are included in the database, but we know that
the information covers approximately 20 percent of U.S. supermarket
sales.
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How are data weighted to arrive at a national average price?
ERS' third-party cooperator obtains retail scanner data at the chain
level by item from a commercial data firm. While not based on a
random sample, the raw data underlying the database are from supermarkets
across the United States that account for approximately 20 percent
of U.S. supermarket sales. Because of the variation in item codes
for random-weight meats, ERS' cooperator standardizes item codes
across stores and retailers. After adjusting for feature discounts,
items are classified into appropriate cut and aggregate categories
based on the item description and background information. The weighted-average
price for each category is computed by dividing total dollar sales
for the month by the volume sold (in pounds).
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What types of difficulties arise in
scanner data collection for meat and how are they addressed?
Meat sold in random-weight
packages requires special data processing procedures that differ
from those used for other retail food items that have manufacturers'
universal product codes (UPC bar codes). Random-weight foods may
be labeled with UPC bar codes (meat more often than produce), but—for
the same item—the code may vary among supermarket chains and
among stores within a chain. As a result, for the retail scanner
database, item codes are standardized (by ERS' third-party cooperator)
across stores and retailers.
Once codes for items are standardized, item prices are checked
for feature activity. Featuring refers to the price discounts offered
to consumers through retailers' weekly feature advertisements. These
discounts likely have an effect on the quantity of meat sold. In
preparation of the data (by ERS' third-party cooperator), information
on featuring activity is matched and compared to the price provided
in the retail scanner data. Where differences in the recorded price
and the feature price are observed, the feature price is used to
represent the price of the product to the consumer.
For example, the regular price of Choice T-bone steak in supermarket
X is $7.50 per pound. In the second week of May, the advertised
price is $6.50 per pound. Depending upon the supermarket's data
management system, this feature price may or may not be recorded
as the purchase price. (Sometimes item discounts are recorded at
the bottom of a sales receipt and are subtracted from the total
sale.) In this example, the advertised feature price for supermarket
X's Choice T-bone steak would replace the recorded price for that
item in the database. Processes have been created and iterations
performed to ensure that the feature price adjustment for individual
items are valid and performed in an appropriate and consistent manner.
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How does the BLS collect meat price data?
Meat prices are included in the information that the Bureau of Labor
Statistics collects for development of the Consumer Price Index
(CPI). The CPI represents all goods and services purchased for consumption.
BLS has classified expenditure items into more than 200 categories,
arranged into eight major groups. Food and beverages—items
such as breakfast cereal, milk, coffee, chicken, wine, full-service
meals, and snacks—are in one major group.
For each of the more than 200 categories, BLS has chosen samples
of items to represent the thousands of varieties available in the
marketplace. For example, in a given supermarket, the Bureau may
choose a plastic bag of golden delicious apples, U.S. extra fancy
grade, weighing 4.4 pounds to represent the "apples" category.
Each month, BLS data collectors visit or call thousands of retail
stores all over the United States to obtain price information on
thousands of items used to track and measure price changes in the
CPI. These prices represent a scientifically selected sample of
the prices paid by consumers for goods and services purchased.
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How do the ERS scanner data compare
with the BLS data?
The ERS retail scanner data supplements BLS data in three ways.
First, the ERS database contains an index of volume sold (with the
average monthly volume for 2001 equaling 100). BLS does not collect
information on the volume of meat sold. Second, it provides additional
specie coverage for lamb and veal. Third, BLS collects a "snapshot"
of prices from sample stores once a month. This may not capture
the full amount of featuring done by the store. Since featuring
influences the volume sold and the ERS scanner database reflects
featuring for the entire month, it is hypothesized that the ERS
data may report lower prices.
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How are meat price data used in market
and policy analysis?
Meat prices from BLS are used to develop
farm-to-retail
price spread information that measures the relative contributions
of farm production, food manufacturing, wholesaling, and retailing
firms. Changing consumer preferences are one of the driving forces
behind changing food selections. As preferences change, so do the
marketing services needed to transform live animals to finished
products. The recent strong economy raised incomes and allowed more
consumers to pay for convenience. Families have lifestyles that
include limited time for preparing food at home, raising the demand
for quick, easy-to-prepare food and the accompanying marketing services.
Price-spread information captures both changes in the relative prices
of inputs used by food marketing firms and changes in consumer demand
for marketing services and convenience.
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How are meat price spreads calculated?
ERS calculates the difference (spread) between the value of live
animals at the farm, carcasses at wholesale levels, and meat products
at retail levels. ERS analysts calculate farm values for Choice
steers and slaughter hogs, and wholesale and retail values for the
meat produced by Choice steers, slaughter hogs, and broilers to
determine price spreads. Meat Price Spreads: Documentation outlines the
methodology ERS uses to determine farm, wholesale, and retail values
for meat. ERS is required by the Livestock Mandatory Reporting Act
of 1999 to continue using BLS retail prices in calculating price
spreads for 2 years after the first release of the retail scanner
prices for meat.
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What geographic coverage is provided by the scanner data?
At first, ERS' meat retail scanner database will provide national
coverage. While not based on a random sample, the raw data underlying
the database are from supermarkets across the United States that
account for approximately 20 percent of U.S. supermarket sales.
In the future, price reporting by region (Northeast, Midwest, South,
and West) may be added to the database.
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