United States 

Department of 

Agriculture

Economic 

Research 

Service

Economic 

Research 

Report 

Number 88

November 2009

Comparing Two Sources of 

Retail Meat Price Data

William Hahn, Janet Perry, and Leland Southard
Data on Meat Price Spreads

www.ers.usda.gov/data/meatpricespreads/

Data on Retail Scanner Prices for Meat

www.ers.usda.gov/data/meatscanner/

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Recommended citation format for this publication:

Hahn, William, Janet Perry, and Leland Southard. Comparing Two 

Sources of Retail Meat Price Data, ERR-88, U.S. Dept. of Agri., 

Econ. Res. Serv. November 2009.

Hahn, William

Comparing two sources of retail meat price data.

(Economic research report (United States. Dept. of 

Agriculture. Economic Research Service); no. 88)

1. Meat—Prices—United States—Statistics, 2. United 

States—Statistical services. I. Perry, Janet E.

 II. Southard, Leland. III. United States. Dept. of Agriculture. 

Economic Research Service. IV. Title.

HD9415
United States

Department

of Agriculture

www.ers.usda.gov

A Report from the Economic Research Service

William Hahn

whahn@ers.usda.gov

Janet Perry

jperry@ers.usda.gov

Leland Southard*

Comparing Two Sources of 

Retail Meat Price Data

Economic

Research

Report

Number 88

November 2009

Abstract 

The livestock industry uses information on meat prices at different stages in the marketing 

system to make production decisions. When grocery stores began using electronic scanners 

to capture prices paid for meat, it was assumed that the livestock industry could capitalize on 

having these point-of-sale data available as a measure of the value of its products. This report 

compares scanner price data with publicly available data collected by the U.S. Department of 

Labor’s Bureau of Labor Statistics (BLS). Of the two data types, scanner data provide more 

information about retail meat markets, including a wider variety of meat-cut prices, multiple 

measures of an average price, the volume of sales, and the relative importance of discounted 

prices. The scanner data sample, however, is not statistically drawn, and complicated 

processing requirements delay its release, which makes scanner data less useful than BLS data 

for analyzing current market conditions. 

Keywords: price spreads, meat, meat pricing, scanner data, retail prices, retail meat prices, 

farm-to-retail

Acknowledgments

The authors would like to acknowledge Bill Cook and Mark Bowman of the U.S. Department 

of Labor’s Bureau of Labor Statistics for reviewing early drafts of this report. We would also 

like to thank those in USDA’s Economic Research Service (ERS) who provided technical 

reviews: Ephraim Leibtag, Steven Zahniser, Barry Krissoff, and Daniel Pick, as well as two 

anonymous outside reviewers. The authors thank John Weber and Curtia Taylor of ERS for 

editorial and design assistance. 

*Leland Southard, now retired from ERS, was the project manager for the development of the retail meat scanner 

database, and his contributions to the development and documentation of the database were essential to the success 

of the program.
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Contents

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

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

Scanner Data to the Rescue (Or Not). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Comparing BLS and Scanner Prices and Price Spreads . . . . . . . . . . . . . . . . 7

Statistical Modeling With Scanner and BLS Data. . . . . . . . . . . . . . . . . . . . 12

BLS Prices, Sales-Weighted Scanner Prices, and 

 the “Ideal” Measure of Retail Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Data Sources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

Appendix – Dynamic-Adjustment, State-Space 

Model: Equations, Specifi cations, and Tests . . . . . . . . . . . . . . . . . . . . . . . . 28
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Summary

USDA’s Economic Research Service has a long history of calculating meat 

prices at different stages in the marketing system. ERS uses data collected by 

the U.S. Department of Labor’s Bureau of Labor Statistics (BLS) as the basis 

of its measure of retail meat prices. The livestock industry uses this information to make production decisions. 

What Is the Issue?

Recent legislation required USDA to investigate the use of an alternative 

source of data on retail meat prices. The purpose of the legislation was to 

address livestock industry concerns regarding the quality of retail meat price 

data used by ERS. This report compares average retail prices calculated using 

data from BLS with data from grocery stores using point-of-purchase scanners to record prices. It analyzes the value of both data sets in forecasting 

near-term market conditions. 

What Did the Study Find?

Both BLS data and scanner data have relative strengths. BLS data have 

several advantages over scanner data:

• BLS uses statistical sampling to select retail outlets to survey. By 

contrast, scanner data are volunteered by stores and may exclude certain 

retailers that BLS makes efforts to include.

• Because BLS uses sampling to select outlets, statistical theory implies 

that price averages derived from BLS data ought to be unbiased. To the 

extent that retailers whose price history is not captured in the scanner 

data set have different price structures than retailers who volunteer their 

data, scanner averages may or may not be biased. 

• BLS data are generally available 12-20 days after the end of the month 

they are gathered. Because of processing issues, 7-8 weeks are required 

before scanner data become available. 

Scanner data, in turn, have several advantages over BLS data: 

• Scanner data include more meat cuts. The latest iteration of scanner data 

showed 188 cuts, including domestic and imported lamb. By contrast, the 

BLS database lists 32 meat and poultry cuts, some of which have been 

discontinued. 

• Scanner data provide some quantity measures. BLS data provide none. 

Scanner data provide an index that compares a month’s sales of a particular meat cut with average monthly sales of the same cut for a base year. 

Scanner data also show the share of meat cuts sold at a discounted price. 

• Scanner data provide a wider range of price-related statistics than the 

BLS data. For example, scanner data are weighted by actual sales and can 

be used to calculate standard deviations, not just average prices. Standard 

deviations are a measure of the variation in prices. 
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In their current form, scanner data provide a different, but not necessarily 

better, view of retail meat markets than BLS-based data. Given the present 

production lag, scanner data for meat do not appear to contribute value to the 

analysis. 

How Was the Study Conducted?

ERS routinely uses BLS data to calculate retail composites for Choice beef, 

pork, broilers, whole chickens, and whole frozen turkeys. This study calculated the same composites using scanner data and compared the two data 

sets. This study also used an econometric analysis comparing scanner and 

BLS prices to determine why scanner prices are more volatile than BLS 

prices. Both scanner data and BLS data were then examined for their value in 

analyzing current market conditions.
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Introduction

Economics is the study of the interaction of supply and demand, with 

prices transmitting information to producers and consumers about where 

they should allocate their resources. Gathering information on prices can 

be costly. As a public service, the Federal Government has programs that 

report prices. USDA’s Agricultural Marketing Service (AMS) collects and 

publishes livestock and wholesales meat price data. USDA’s Economic 

Research Service (ERS) uses AMS data in its analysis of livestock markets. 

To supplement its analysis, ERS relies on data collected by the U.S. 

Department of Labor’s Bureau of Labor Statistics (BLS) to calculate retail 

meat prices and farm-to-wholesale-to-retail price spreads. 

Recent legislation required USDA to investigate the use of an alternative 

source of retail meat price data.1 The purpose of this provision was to alleviate producers’ concerns regarding the quality of the retail meat price and 

price spread data used by ERS.2 This report compares the calculations of 

average retail prices using data from BLS and data from grocery stores using 

point-of-sale scanners to record prices. The report offers a statistical analysis 

of the relative value of the two data sets in forecasting near-term market 

conditions.

Why Do Grocery Store Prices Matter?

Meat producers, consumers, and policymakers make decisions based on price 

information from the livestock, meat, and poultry industries. AMS collects 

and disseminates a wide range of information on livestock and meat prices, 

with some prices reported twice daily. Other Federal Government agencies 

also collect and publish a wide range of meat and poultry quantity estimates 

tracing meat production and trade, providing a continuous time series for 

these statistics. Data for the fi nal stages of marketing—specifi cally, on meat 

sold through foodservice and retail stores—are much less complete. 

Consumers are spending an increasing share of their food dollars on awayfrom-home food. Still, a survey analyzed by Davis and Lin (2005) revealed 

that 78 percent of all pork and 65 percent of all beef were purchased in 

grocery stores. This fi nding suggests that grocery store prices are important 

measures of the value of meat to consumers.

To measure activity in the retail sector, ERS uses meat- and poultry-cut 

prices gathered by BLS (to calculate its cost-of-living indices) to calculate 

retail values for pork, beef, and poultry (see box, “Bureau of Labor Statistics 

Retail Price Data”). Hahn (2004) discusses ERS procedures for calculating 

retail, wholesale, and farm prices for beef and pork and their associated price 

spreads. The price spread is the difference between the value of the animal 

and its meats at different levels in the marketing system. Hahn also provides 

evidence that BLS-based retail prices can be useful leading indicators of farm 

and wholesale price changes for beef and pork. High (low) retail prices in 

one month are often followed by higher (lower) wholesale and farm prices in 

the following months. 

1 The Livestock Mandatory 

Reporting Act of 1999 (LMRA) required USDA to acquire an alternative 

measure of retail meat prices independent of the Bureau of Labor Statistics 

data. Part of the LMRA required ERS 

to continue the use of its pre-existing 

price-spread procedures so that the two 

data sources could be compared. Prior 

to the Act, the National Cattlemen’s 

Beef Association had purchased and 

analyzed data from a grocery store data 

syndicate. ERS developed a pilot project to expand coverage to include additional retail cuts of beef, pork, poultry, 

and lamb and then began purchasing 

this type of data as well.

2 The main purpose of the 

legislation was to revamp USDA’s 

Market News program, which aims 

to aid the effi cient marketing of 

agricultural commodities by providing 

the public with price and sales 

information drawn from transactions 

around the country. Fundamental 

changes in livestock industries had 

called into question the effectiveness 

of Market News reporting for livestock 

and led to a major redesign of the 

program through the LMRA. A 

recent ERS report (Perry et al., 2005) 

reviews developments leading up to 

the LMRA and assesses its impact on 

cattle markets after its implementation 

in 2001. GAO (2005) indicates that 

further work is required for the prices 

to refl ect market transactions. The 

text of the legislation can be seen at: 

www.ers.usda.gov/data/meatscanner/

livestockmandatoryreportingact.pdf 
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BLS’s use of statistical sampling in selecting the outlets it surveys helps 

ensure the quality of the data it collects. However, BLS procedures have a 

well-known weakness: they are a better measure of the prices that consumers 

observe rather than the prices that consumers actually pay. Consumers 

generally buy more of an item when its price is lower. Economists have 

long recognized that consumer reaction to price changes complicates the 

BLS estimation of cost-of-living indices. Shapiro and Wilcox (1996) estimate that failing to account for consumer responses results in an average 

annual increase of 0.7 percent to BLS’s Consumer Price Index (CPI). This 

may make the CPI overstate the actual infl ation rate. Measuring the prices 

consumers actually pay would require a weighted average price—one calculated by weighting each price by the volume of sales for that price. 

Thus, use of the BLS data may lead to ERS overstating its retail meat price 

estimates and the associated retail price spread. ERS-supported research 

in 1970 (Degner) calculated that switching from a simple-average price to 

a weighted-average price would reduce estimated retail beef prices by 7.5 

percent. In 1999, the General Accounting Offi ce (GAO, now the Government 

Accountability Offi ce) estimated that ERS’s pork retail value overstated the 

consumer price of pork in December 1998 by about 6 percent, or 14 cents per 

pound. Grocery store scanners provide data on prices consumers pay rather 

than on prices they observe. A sales-weighted average may provide a more 

accurate measure of what consumers actually pay for their meat cuts. Using 

point-of-sale prices and quantities sold at the various prices may result in 

estimates of average prices that more closely match market activity.

Bureau of Labor Statistics Retail Price Data 

The U.S. Department of Labor’s Bureau of Labor Statistics (BLS) 

collects data on a wide range of prices paid by U.S. consumers for 

various products from various outlets. BLS uses the data to calculate 

various versions of the Consumer Price Index (CPI). The CPI is a 

measure of infl ation focused on the buying habits of urban consumers. 

On its website, BLS states that “the all urban consumers group represents about 87 percent of the total U.S. population.” The items and 

outlets sampled by BLS are based on periodic surveys of consumer 

purchases. These surveys occur roughly every 10 years. BLS uses a 

rotating sample of randomly selected outlets. The price data that BLS 

collects are then used to calculate national averages that are reported 

each month. BLS publishes prices only when it has enough observations for a statistically reliable estimate. It contacts outlets to collect 

price information every business day, either via phone or through 

onsite visits. It surveys tens of thousands of outlets, although it does 

not collect all possible prices from each outlet. BLS reports monthly 

average retail price data on its website, http://www.bls.gov/data/. Data 

for a given month are released between the 12th and the 20th of the 

following month. 
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Scanner Data to the Rescue (Or Not)

Scanner data are now available for food products and offer new opportunities for empirical research. Stores use these data to track price and inventory movement and to control product movement along their supply chains. 

Scanner data allow the gathering of information at the point of sale, which 

could potentially lead to improved market information.

ERS purchased grocery store scanner data to compare the value of the 

scanner data with that of the current retail price data from BLS. The scanner 

data come from retail grocery stores with annual sales of $2 million or more. 

The scanner data on meat sales come from national and regional chains that 

account for about 20 percent of the value of all U.S. supermarket sales. It is 

not known how much of total U.S. meat sales are covered by these stores, 

however. 

The stores in the scanner data set share common characteristics. All are 

supermarkets that process their receipts using electronic scanners, sell products through the traditional supermarket retail meat case, and voluntarily 

provide their scanner data to commercial data fi rms. Processing of the data 

is conducted by a private fi rm that buys the data from a syndicate, which 

purchases and collects the data for all products from the stores within the 

syndicate. Confi dentiality requirements prohibit ERS from directly accessing 

the raw scanner data.

Confi dentiality requirements also limit ERS’s knowledge of the geographic 

coverage of the data. Some of the chains that provide scanner data may have 

stores in rural areas or small towns that the BLS data do not cover. Scanner 

data do not include data on sales from butcher shops, warehouse clubs, mail 

order fi rms, selected big-box food distributors, and other retailers that choose 

not to provide data for third-party use. BLS samples a wider variety of outlets 

than those included in the scanner data, so BLS prices provide more comprehensive outlet coverage.

BLS uses statistical sampling to determine the outlets that are surveyed; 

consequently, one could use statistical theory to evaluate price averages. 

Conversely, the scanner data come from stores that volunteer their data. 

To the extent that retailers whose price history is not in the data set have 

different price structures for their products than the remaining stores, the 

average scanner price could be biased. If averages are biased, one cannot 

determine whether an average price constructed from the scanner data is 

higher or lower than the “true” average price that consumers pay.

Scanner data provide a wider range of price-related statistics than the BLS 

data set. The scanner data set contains both a simple average price (by stores) 

and a sales-weighted average price for all the meat cuts. And, because this 

average price is weighted by actual sales, the data set also includes the standard deviation associated with each average, a measure of the variation in 

prices. The advantages of the scanner data in this case, however, are offset by 

the lack of statistical sampling, which reduces users’ confi dence in the data.

The scanner data also provide measures of meat sales quantity: the share of 

the selected meat cut sold at a “featured,” or discounted, price and an index 
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that measures total sales for the cut. The index compares a month’s sales 

for the selected meat cut with average monthly sales for the base year 2001. 

These indices show how a meat cut’s sales vary seasonally or year-to-year, 

but they do not allow one to compare relative volumes of meat cuts (e.g., 

pounds of ground beef versus pounds of bone-in chicken breast).

The use of quantity indices helps ensure the confi dentiality of the stores 

supplying the data. Management of participating stores had expressed 

concerns that revealing the total pounds of cuts sold by the syndicate 

might allow competing stores to estimate meat sales by fi rm. In hindsight, 

one could have protected total sales-volume information by reporting the 

percentage of the tonnage of meat-case sales accounted for by each cut and 

an index for total tonnage. This type of breakout on quantities would protect 

stores’ business interests while providing analysts and the public with more 

valuable information.3

Improved scanner data will not answer all the questions regarding the quantities of meat purchased because grocery stores are not the only sources of beef 

and pork for consumers. Restaurants and other foodservice establishments 

sell large amounts of meat. Further, the scanner database does not include 

all the meat sold through grocery stores. The scanner database includes only 

random-weight meat products, including whole beef and pork muscle cuts, 

ground beef, ham, lamb, veal, turkey, certain frozen items (e.g., patties, 

whole birds), and random-weight packages of bacon and sausage. The database does not include the following:

• Fixed-weight items sold in standard package sizes, such as luncheon 

meat,4 most branded sausage products, packaged bacon, or precooked 

items

• The meat in other processed foods, such as soups and frozen dinners

• Deli meats, including rotisserie beef, pork, and chicken

The BLS data cover a much narrower range of meat cuts than scanner data. 

BLS’s database lists only 34 meat and poultry cuts, some of which have 

been discontinued. Others may be reported irregularly. By contrast, the latest 

iteration of scanner data shows prices for 188 cuts, including domestic and 

imported lamb. 

Scanner Data Issues

Developing a database using scanner data for meat cuts requires complex 

processing that delays publishing until 7-8 weeks after the end of the month 

the data are gathered. AMS releases some of the data needed to calculate 

wholesale prices daily; all of it is available weekly. BLS data are available 12-20 days after the end of the month they are gathered. For example, 

January’s scanner data are available in late March, and January’s BLS 

data are available in February. To forecast January’s prices at the end of 

December, one would use December’s AMS wholesale data, November’s 

BLS data, and October’s scanner data. 

Two features of scanner data account for the length of the processing time: 

insuffi cient standardization of Universal Product Codes (UPCs)5 and a lack 

3 A tonnage index that addresses 

this issue is planned for the next iteration of scanner data. 

4 Market analysis can benefi t from 

the ability to trace the volume of each 

species’ meat that goes into processing, 

but in many cases, the fi rms may not 

divulge product recipes. 

5 Meat may be cut and packaged 

in the back of a store or at a central 

meat-cutting facility owned by the 

foodstore chain itself. Sources report 

that centralized cutting now dominates 

retail meat production. However, this 

centralized cutting may be specialized 

to the store’s requirements. 
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of data on featured prices. Grocery store scanners rely on UPCs to record 

product types and prices based on information supplied by the store’s database. Efforts are under way to standardize meat-cut descriptions and UPCs 

using the Uniform Retail Meat Identity Standards (URMIS) (see box, “UPC 

and UMRIS Codes”). 

The volume of scanner data generated by an individual store is huge, and 

the data that stores share with the syndicate are only a subset (and summary) 

of the available data. Each store in the syndicate shares a weekly database 

providing the following information for each purchase: 

• UPC

• Description of the item

• Number of packages sold with that UPC

• Total dollar amount of sales of that item

• Regular price

The downloaded data do not include the weight of the packages or their 

actual price per pound. If the database had just one of these additional items, 

one could calculate the other item from the sales volume, which is expressed 

in dollars. Why are package weights and the actual price excluded from the 

database? Syndicated data were developed largely for the benefi t of packaged-goods manufacturers who are not selling random-weight products like 

meat. In this instance, the UPC is enough to identify the weight or size of the 

scanned package. The average price of an item is the dollar value of sales 

divided by the total number of packages. Comparing the calculated average 

price to the regular price enables one to determine whether the item was on 

sale and what customers paid, on average, for the item. 

The syndicate’s data are further processed by a contractor hired by ERS. 

The contractor has access to another database, not generated by store scanners, that lists all newspaper-advertised special prices. This database enables 

advertised prices to be matched to individual stores. A series of programmed 

“exception-checking” fl ags automatically marks those cases where features 

may occur. Based on certain criteria, the advertised price replaces the regular 

price. Exception-fl agging may compare the calculated average weight using 

the regular price with its past average. When items are on sale, the average 

package weight, based on regular prices, will decline. 

The use of exception-checking could lead analysts to understate the average 

price. Many stores represented in this data set use shopper loyalty cards. 

Stores estimate that over 90 percent of shoppers use loyalty cards. Some of 

the meat that is assigned the sale price based on exception-checking may 

actually have been sold at the regular price because the consumer did not 

have or did not use a loyalty card. If and when this happens, the feature price 

may be lower than the store’s true weighted-average price for the cut, and the 

estimated sales volume (expressed in pounds) may be too high. 

The processing time for scanner data may be shortened by modifying the 

programs that stores use to summarize and share their data with the syndicate. For example, stores could split the fi elds for price, dollar volume, and 
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packages sold into regular and discount fi elds. However, because the primary 

users of the data are packaged-goods manufacturers, the syndicate has little 

incentive to modify its procedures to account for random-weight products.

The delay in processing time may make scanner data less valuable. Gropp 

and Kadareja (2006) found that stale public information might contribute to 

large spikes in price volatility. They conclude that timelier, higher quality 

public information results in a closer alignment of market activity because 

players are acting on the same information rather than incorporating various 

beliefs about the validity of the data. The potential for access to higher 

quality data may lead one to consider using scanner data for developing a 

weighted-average price, but the delay in processing the data could negate any 

added value. 

UPC and URMIS Codes

The scanner data’s descriptions for each grocery store are carefully 

matched to the corresponding Uniform Retail Meat Identity Standards 

(URMIS). First established in 1973 by the Industry-wide Cooperative 

Meat Identifi cation Standards Committee (ICMISC), the URMIS 

system was developed to provide an identifi cation system for retail 

meat cuts and a standardized nomenclature for every retail red meat 

item (beef, veal, lamb, and pork). The URMIS system was revised in 

2003 and is available at http://www.beefretail.org/uDocs/urmis/start.

pdf. Before the advent of URMIS, a specifi c retail cut may have had 

several different names depending on the store or region where it was 

sold. For example, Kansas City strip, New York strip, and beef loin 

steak are all the same cut of meat. URMIS was established to eliminate consumer confusion caused by the proliferation of names used to 

describe retail meat cuts. In October 1984, a guideline for assigning 

retail identifi cation numbers to URMIS descriptions was approved by 

GS1 US (formally, the Universal Code Council), thus creating UPCs 

for random-weight meat products. UPCs have been developed for all 

URMIS codes and poultry products. Because the guidelines remain 

voluntary, chains, and even stores within a chain, may not consistently 

use URMIS codes for their random-weight meats. For this study, 

both URMIS codes and product descriptions from the packages were 

used to determine the product type so that average prices could be 

developed for consistent groups of meat cuts. For more information 

about the UPC system, see http://www.gs1us.org/. For specifi c UPC 

numbers and descriptions for meat and poultry, see http://www.meattrack.com/index.php/.
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Comparing BLS and Scanner Prices and 

Price Spreads

This analysis presents basic fi ndings about retail meat prices using scanner 

data, it compares the scanner-based price series to the BLS-based price 

series, and it provides statistical analysis of the market information provided 

by both series.6 This analysis also includes wholesale prices and the wholesale-to-retail price spreads implied by the different measures of retail prices. 

The wholesale-to-retail price spread refl ects the difference between the retail 

price of a product and its wholesale price. ERS calculates other price spreads; 

however, this study examines only the wholesale-to-retail price spread, 

referred to here as the “price spread,” or “spread.” 

Scanner data include two types of average prices. The store-weighted 

average price averages the price from each store. BLS uses a similar process 

to calculate its averages. The sales-weighted average price uses volume sold 

at particular prices to weight the average. As noted earlier, this may provide 

a more accurate measure of the average prices consumers pay for the various 

meat cuts. ERS publishes retail prices and price spreads for fi ve different 

meats: a Choice beef composite, a pork composite, a broiler composite, 

whole chickens, and whole frozen turkeys. This analysis used scanner data 

to develop both sets of price averages (store-weighted and sales-weighted) to 

calculate retail prices for the above products, which were then compared with 

BLS-based retail prices for January 2001-August 2005.7 

There are two reasons to expect that a sales-weighted scanner average would 

be lower than the store-weighted average. First, stores that featured a product 

at a lower price would be expected to have more sales and a larger weight in 

the average than stores that did not feature the product. Second, high-salesvolume stores tend to have lower prices, and high-volume stores will have 

a greater weight in the sales-weighted average than they would have in the 

store-weighted average. One might also expect that sales-weighted prices 

would be lower than BLS prices. It is unclear as to whether BLS prices 

should be higher or lower than store-weighted scanner prices. 

Table 1 shows the average difference between BLS prices and storeweighted scanner prices, and between store-weighted scanner prices and 

sales-weighted prices during the sample period January 2001-August 2005. 

The store-weighted scanner prices for beef were generally lower than the 

BLS-based prices. For the other four meats, the store-weighted scanner prices 

were higher. For all fi ve meats, the sales-weighted scanner prices averaged 

lower than store-weighted scanner prices. Scanner data reveal that when 

stores lower prices to feature a particular cut, consumers react by increasing 

their purchases, which lowers the average price that consumers pay for meat. 

The sales-weighted average prices were less than 90 percent of the storeweighted average prices, and lower prices were particularly noticeable for 

whole chickens at scanner-data stores. Using the sales-weight prices implies 

lower average price spreads. Figures 1-5 show the three average retail prices 

and the wholesale prices for the fi ve meats over the study period.

Store-weighted scanner prices are expected to be higher than sales-weighted 

scanner prices because weighting prices by sales volume should favor the 

6 This analysis is based on scanner 

data for January 2001-August 2005. 

The LMRA lapsed briefl y in 2004, 

but President George W. Bush signed 

legislation (P.L. 108-444) extending the 

program through September 30, 2005. 

The LMRA was renewed in late 2006, 

extending the law until September 30, 

2011 (P.L. 109-296). Because of these 

lapses and other budgetary issues, data 

after 2005 became available after ERS 

fi nished the analysis in this report. 

7 The database is documented at 

www.ers.usda.gov/data/meatscanner/. 

The sales-weighted price corresponds 

to the price labeled “weighted average, feature-weighted” in the online 

database. The store-weighted price 

here corresponds to the “mean featureweighted price” in the database. 
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lower prices. The only exception is for turkey8 (see fi g. 4), but only for a 

few months in the sample. When the sales-weighted average was above the 

store-weighted average, there were a large number of stores with low prices 

and sales and a few stores with high prices and sales.9 High volume and high 

price is unusual as high-volume outlets generally have lower prices (Leibtag, 

2005). 

For the Choice beef composite the BLS-based price and the store-weighted 

scanner price were remarkably similar for the fi rst 49 months, January 

2000-January 2004 (see fi g. 1). In February 2004, however, the two prices 

diverged, with the store-weighted scanner price staying below the BLS-based 

price for the remainder of the sample period.

The divergence may stem from the December 2003 reports of the fi rst 

confi rmed U.S. case of bovine spongiform encephalopathy (BSE, also known 

as mad cow disease) in a dairy cow. An immediate reaction was that all 

U.S. beef export markets were closed. Some consumers in the United States 

chose to stop eating beef—at least for a time. All retail prices for meat (not 

just beef) for December 2003 were lower than prices for November, and the 

price decline in January 2004 was even larger. Beef prices were reduced to 

encourage domestic consumption of beef already on the market because this 

product could not be exported. In February 2004, the decline in the scanner 

average prices was much larger than the decline in the BLS-based prices. 

Starting in February 2004, the store-weighted scanner prices dropped below 

the BLS-based prices and maintained that position. Based on analysis of 

ACNielsen Homescan data, Kuchler and Tegene (2006) found that deviations 

from established purchase patterns following the BSE announcements varied 

across beef products but were limited to no more than 2 weeks in all cases. 

Pritchett et al. (2007) analyzed the scanner data’s price and quantity data and 

found a similarly small and transient effect of BSE on consumer demand for 

meat. 

Could the apparent break in Choice beef prices be evidence that stores in the 

scanner data’s sample reacted differently to BSE than the stores in BLS’s 

8 A special effort is made to 

include turkeys that stores give away as 

seasonal incentives in exchange for the 

purchase of a specifi ed dollar amount 

of other groceries. These turkeys are 

given a price of $0 in the scanner data, 

whereas BLS assigns the regular price 

to those turkeys.

9 For example, suppose that one 

were averaging prices from three 

stores—two small stores and one large 

store. The large store could have twice 

the sales volume of the two small stores 

combined. The large store’s volume 

would give it twice the weight of 

the other two combined in the salesweighted case, but all three stores 

would have the same weight in the 

store-weighted case. If the large store 

has lower prices than the two small 

stores, the sales-weighted price will be 

lower than the store-weighted price. If 

the large store has higher prices, the 

store-weighted price will be lower than 

the sales-weighted price.

Table 1

Comparison of retail prices, January 2001-August 2005

Average price differences

Product

BLS-based price 

minus store-weighted 

scanner price

Store-weighted 

scanner price minus 

sales-weighted 

scanner price 

Cents per pound

Choice beef composite 10.5 40.8 

Pork composite -26.3 48.9 

Whole chickens -41.3 53.4 

Whole, frozen turkeys -11.1 16.2 

Broiler composite -10.6 44.4 

Source: USDA, Economic Research Service using electronic scanner data for meat. For summary statistics, see www.retail-lmic.info/CD/default.asp?ERSTab=2. BLS-based price composites are available at www.ers.usda.gov/data/meatpricespreads/.
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0

50

100

150

200

250

300

350

400

450

500

Oct-00 Apr-01 Nov-01 May-02 Dec-02 Jun-03 Jan-04 Aug-04 Feb-05 Sep-05 Mar-06

Cents per pound

BLS Sales-weighted scanner Store-weighted scanner Wholesale

Figure 1

Choice beef composite prices

Source: USDA, Economic Research Service calculations.

Cents per pound

Figure 2

Pork composite prices

Source: USDA, Economic Research Service calculations.

0

50

100

150

200

250

300

350

Oct-00 Apr-01 Nov-01 May-02 Dec-02 Jun-03 Jan-04 Aug-04 Feb-05 Sep-05 Mar-06

BLS Sales-weighted scanner Store-weighted scanner Wholesale
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Cents per pound

Figure 3

Whole chicken prices

Source: USDA, Economic Research Service calculations.

0

20

40

60

80

100

120

140

160

180

Oct-00 Apr-01 Nov-01 May-02 Dec-02 Jun-03 Jan-04 Aug-04 Feb-05 Sep-05 Mar-06

BLS Sales-weighted scanner Store-weighted scanner Wholesale

Cents per pound

Figure 4

Whole turkey prices

Source: USDA, Economic Research Service calculations.

0

20

40

60

80

100

120

140

Oct-00 Apr-01 Nov-01 May-02 Dec-02 Jun-03 Jan-04 Aug-04 Feb-05 Sep-05 Mar-06

BLS Sales-weighted scanner Store-weighted scanner Wholesale
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sample? Consider the price data for pork, chicken, and broilers (see fi gs. 

2, 3, and 5). For each of these meats, both scanner-based prices dropped 

during the fi rst part of 2004, unlike BLS-based prices. The downward trend 

was most notable for the sales-weighted prices. For broilers, scanner prices 

remained lower than BLS-based prices after the fi rst quarter of 2004, whereas 

pork and chicken prices returned to their previous levels. 

For pork and chicken, the store-weighted scanner price was consistently 

above the BLS-based price, whereas the sales-weighted price was consistently below the BLS-based price. The store-weighted price for broilers 

started out above the BLS price, but after the fi rst quarter of 2004, it began to 

track the BLS-based price. 

Throughout the sample period, both the store-weighted and sales-weighted 

scanner prices for beef were signifi cantly lower than the BLS-based prices, 

while scanner prices for the other meats were signifi cantly higher than 

BLS-based prices. This pattern demonstrates that the stores providing 

scanner data are somehow different from those in BLS’s sample. Locations 

of the scanner-data stores skew toward medium-sized cities. By contrast, 

BLS’s sample is concentrated in metropolitan areas. Stores vary in their 

approaches to pricing and sales; scanner-data stores tend to have high regular 

prices for most meats, at least when compared with stores in BLS’s sample. 

It is possible that the beef-pricing strategies of scanner-data stores changed in 

2004, relative to those in BLS’s sample.

Cents per pound

Figure 5

Broiler composite prices

Source: USDA, Economic Research Service calculations.

BLS Sales-weighted scanner Store-weighted scanner Wholesale

0

20

40

60

80

100

120

140

160

180

200

Oct-00 Apr-01 Nov-01 May-02 Dec-02 Jun-03 Jan-04 Aug-04 Feb-05 Sep-05 Mar-06
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Statistical Modeling With Scanner 

and BLS Data

As part of its analysis, ERS developed several questions about the data series 

being compared:

• Which price series best represents the national average retail price? 

• What contributes to the differences in the month-to-month variability of 

the price series?

• In light of the expense, complex processing requirements, and reporting 

lag times, do scanner data suffi ciently increase the understanding of meat 

markets?

To address these questions, ERS developed and tested a series of statistical models and applied these models to each of the fi ve meats. ERS used 

the same basic statistical structures for all fi ve of the meats. This structure 

attempts to account for the interaction between a meat’s wholesale price, its 

BLS price, and its sales-weighted scanner (SWS) price. The technical details 

on the statistical structures are outlined in the appendix of this report. All fi ve 

sets of prices were analyzed separately.

ERS used a combination of “dynamic-adjustment modeling” and “statespace” techniques to analyze the differences in the two data sources for meat 

prices. Dynamic-adjustment models were used to analyze Choice beef and 

pork composites (see Hahn, 2004). Dynamic adjustment implies that it takes 

time for the market to react to changes. One of the implications of dynamic 

adjustment is that the current month’s prices may be affected by the previous 

month’s prices. 

State-space is a specifi c type of statistical estimation that is appropriate in 

this analysis, particularly for dynamic adjustment models that have missing 

or unobserved data (Durbin and Koopman, 2001). (The missing and unobserved variables are called state variables.) State-space uses the variables one 

sees to estimate the unseen variables. The word estimate is important here; 

these estimates are likely to be inaccurate. Part of state space is measuring 

the accuracy of the estimates. These estimated states are used to predict the 

next period’s state and observed variables. Once one observes the next period’s information (whatever it is), one can improve the estimates of the state 

variables and make the forecast for the following month. Generally speaking, 

as one gains more information, estimates of the state variables improve and 

forecasts are more accurate. The ability of state-space techniques to handle 

missing data is helpful in this analysis. ERS had a limited amount of scanner 

data at the start of the analysis. SWS data ran from January 2001 to August 

2005, or 56 months. BLS and WHL data pre-date SWS data and continued 

after the end of the scanner data. SWS data are missing prior to January 2001 

and (initially) after August 2005. If SWS data are helpful for explaining 

meat-market conditions, state-space techniques allow the analyst to account 

for SWS data effects on the BLS and WHL data when SWS is not available. 

State space also allows one to measure the degree to which observing SWS 

will improve the accuracy of forecasts and analysis.
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Which price series best represents the national average retail price? The 

rationale behind the legislation requiring the publication of scanner prices 

was that BLS prices did not adequately refl ect meat prices. Without correct 

pricing signals, producers may make incorrect production decisions, so it 

was assumed that having prices that more accurately refl ect consumers’ 

preferences would be of value. ERS tested whether BLS or SWS prices best 

measure the retail price. One of the unobserved variables in the state-space 

analysis is the best measure of the retail price (BMRP). Note that BMRP 

does not include the term average. BLS prices are based on simple-averages. 

SWS prices are sales-weighted averages. It may well be the case that neither 

simple averages nor sales-weighted averages are ideal. One extreme possibility is that there is a single fi rm that the other fi rms follow—that fi rm’s 

price could be the BMRP. 

Neither BLS nor SWS prices by themselves cover the entire at-home meat 

market. Stores that are outside either the BLS or the SWS sample may have 

an effect on the market. The average price from these excluded outlets is 

called some other price (SOP). SOP is another unobserved variable. SOP 

could also include the infl uences of foodservice and export markets. The 

state-space model makes BMRP a weighted average of the BLS, SWS, and 

SOP. Part of the estimation process is fi nding the weights for BLS, SWS, and 

SOP prices that give the most accurate forecasts for WHL, BLS, and SWS 

prices. One can test to determine if any of these weights is 1; that would 

make its price the BMRP. If BLS is the BMRP, that would mean that retail 

meat pricing is determined by sales in urban markets. If SWS is the BMRP, 

then grocery stores with scanners drive retail pricing. If SOP is BMRP, then 

neither BLS nor SWS are driving meat market pricing. However, data on 

BLS and SWS prices will still be valuable for analysts in this case as these 

two prices can provide information with regards to SOP/BMRP. 

What contributes to the differences in the month-to-month variability of the 

price series? Sales-weighted scanner prices show more variation month 

to month than BLS prices. The statistical model can estimate and test two 

sources of this additional volatility. The fi rst source of a price-volatility 

differential is variation in adjustment speeds. 

Market conditions change daily. Economists have observed that short-term 

reactions to market conditions usually differ from long-term reactions. For 

example, cutting beef production by 5 percent for a month has a different 

effect on prices than cutting beef production by 5 percent for a year. 

Economists have developed the dynamic-adjustment model to examine this 

type of phenomenon. 

The dynamic-adjustment model for this analysis estimates the long-term, 

or “full-adjustment,” effects of changes in economic conditions on the fi ve 

prices in the model: WHL, BLS, SWS, (the three that are observed sometimes) SOP, and BMRP. The full-adjustment effects are often called target 

values. The current month’s prices are a function of the current month’s 

targets and the previous month’s prices. If the current month’s price is 

somewhere between the previous month’s price and the target, the effect 

is partial adjustment. The current month’s price may equal its target (full 

adjustment) or it may overadjust. ERS has used dynamic-adjustment models 

to measure adjustment patterns in the Choice beef and pork composites (see 
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Hahn, 2004). This research revealed that price adjustment was dynamic, 

and mostly “partial.” When there is partial adjustment, the target prices 

vary more month-to-month than do the actual prices. As prices adjust more 

quickly, volatility increases. Hahn’s research implies that wholesale Choice 

beef and pork values adjust more quickly than (BLS-based) retail prices. 

Consequently, wholesale values are more volatile than BLS-retail values. 

The higher volatility of scanner data may mean that the scanner prices are 

rapidly adjusting to changes in the market. For technical reasons, ERS tested 

faster adjustment speed by SWS in two phases. In the fi rst phase, the adjustment speeds for SWS and BLS were checked to determine if they were the 

same. If the speeds were not the same, ERS used model simulations to determine which of the two retail prices adjusted faster. 

The second source of a differential in price volatility is referred to as a transient effect. The partial-adjustment model uses the current month’s prices to 

forecast the next month’s prices. A transient effect is that part of the current 

month’s price change that does not help forecast the next month’s price. 

Transient effects are purely random. The average or expected transient effect 

for a month will be 0; the “size” of a transient effect is measured by its standard deviation. The standard deviation is a statistical measure that is never 

negative. Larger transient-effect standard errors produce more volatile prices. 

Model estimates were tested to determine if the standard-deviations of the 

BLS and SWS prices’ transient effect were the same. (ERS also tested model 

estimates to determine if prices have transient effects at all.) If the difference 

between the transient effects was statistically signifi cant, the estimates were 

compared to determine which of the two was larger. 

There are two potential sources for transient effects; either or both could be 

present in the data. The fi rst is that transient effects could be a fundamental 

part of the pricing processes. The second is that the transient effects are actually the result of statistical problems. Both data sources are based on samples. 

BLS does not sample all prices in all stores every day. The scanner data 

do not include all supermarkets with scanners. Statistical theory holds that 

the averages one derives from a sample will differ from the average based 

on the entire population and that larger, randomly selected samples tend to 

increase the accuracy of sample averages as estimates of the true average. 

Statistical theory also requires that the samples be independently drawn. 

Many individual stores are owned by large, multi-outlet fi rms, and pricing 

within such fi rms is likely to be coordinated. According to the U.S. Census 

Bureau (2005), the top four grocery fi rms in 2002 controlled 31 percent of 

U.S. grocery sales. The number of independent prices (associated with separate fi rms) in both the scanner and BLS-based data sets was much smaller 

than the number of individual stores, raising the potential for sampling error. 

Finally, it also may be the case that the best possible measure of a retail price 

would use neither a simple average (BLS) nor a sales-weighted average 

(SWS). 

Is it possible to determine the causes of transient effects? The raw data for 

WHL, BLS, and SWS prices come from different sources using different 

data-collection methods. If the transient effects were purely the result of 

measurement errors, one might expect these errors to be independent of one 

another—their correlations should be zero. True transient effects seem more 
15

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likely to be correlated. ERS tested the correlations of the transient effects to 

determine if they differ from zero. Correlated transient effects would seem to 

rule out pure measurement errors. 

Separating the transient changes from the nontransient changes is diffi cult, 

as transient effects are another set of unobserved variables. The statistical 

model requires state variables for WHL and BLS prices to account for these 

transient effects. The WHL and BLS state variables are not actually the transient effects; they are the prices minus the transient effects. The SWS state 

variable is also basically the price minus its transient effect. These observedprice states are those parts of the prices that are useful for predicting future 

price movements. If a price has transient effects, then its state can only be 

estimated. Even if the price is observed, the state will be measured with error. 

Do scanner data suffi ciently increase the understanding of meat markets?

This analysis formalizes the process of forecasting by using state-space 

techniques to measure the degree to which SWS data may improve analysis 

and near-term forecasting of meat-market conditions. ERS used the current 

month’s prices to forecast the following month’s prices and state variables. 

If SWS are not available, one has to estimate their value and apply that estimate to the forecast, which affects the accuracy of the forecast. If SWS data 

are available, their use will improve the estimates of the other state variables, 

BMRP and SOP, further improving the forecasts. The degree to which SWS 

data improve forecasts is a measure of their value. Part of the estimation of 

state-space models is the calculation of the accuracy of the forecasts and 

state-variable estimates under different scenarios. 
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BLS Prices, Sales-Weighted Scanner Prices, 

and the “Ideal” Measure of Retail Prices

Table 2 provides estimates of the averaging parameters. These parameters 

transform the BLS, SWS, and SOP into an estimate of the BMRP. If a price’s 

averaging parameter is one, that price is the BMRP. Neither the BLS price 

nor the SWS price is the BMRP in any of the fi ve meats. In three of the 

fi ve meats—beef, pork, and whole chickens—the weight for SOP is exactly 

one. An SOP weight equal to one implies that the BLS and SWS prices are 

reacting to WHL and SOP; SOP and WHL do not react to BLS and SWS. 

The primary value of BLS and/or SWS in market analysis in this case is 

their value in measuring SOP. The weight for Turkey for SOP is close to 

one; however, the small weight for the BLS price is statistically signifi cant. 

Broiler is the one meat for which SOP has a weight of zero. For the broiler 

composite, the BMRP is an average of the SWS and BLS prices. The weight 

for broilers for SWS is close to one; again, the small weight on the BLS price 

is statistically signifi cant. For the broiler composite, a combination of the 

SWS and BLS prices appears to make the BMRP.

Volatility of Sales-Weighted Scanner Prices

Two factors may account for the higher volatility of SWS: faster price 

adjustment and larger transient effects. Table 3 shows estimated standard 

deviations for the transient effects for each meat product. Three of the fi ve 

meats show transient effects for only two of the prices. Two of the meats 

show transient effects for all three prices. All fi ve meats show transient 

effects for SWS. These SWS transient effects are considerably larger than 

those of WHL or BLS. Those transient effects that are not estimated to be 

zero are statistically signifi cant. Also, the BLS and SWS transient effects are 

statistically different for all fi ve meats. The SWS transient standard deviation 

is the largest of the three standard deviations for all meats. In fact, the 

smallest SWS standard deviation, broiler composite, is larger than the largest 

standard for any other meat or price, Choice beef’s wholesale price. The 

large transient effects account for the SWS prices being more volatile than 

the BLS or WHL prices.

Table 2

Estimates of parameters that transform retail prices into the best 

measure of the retail price

Price or state

Product

BLS-based 

retail price

Sales-weightedscanner retail priceSome other 

retail price 

Averaging parameter

Choice beef composite 0 0 1.0000

Pork composite 0 0 1.0000

Whole chickens 0 0 1.0000

Whole, frozen turkeys 0.0208 0 0.9792

Broiler composite 0.0353 0.9647 0

Source: USDA, Economic Research Service using state-space model estimates.
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Table 4 shows correlation estimates. Correlations come in pairs and cannot 

be calculated if one or both members of the pair have a standard deviation of 

zero. Consequently, three of the prices have only one correlation while two 

prices have a full set. The hypotheses that the meats’ transient effects are all 

uncorrelated can be rejected. As mentioned earlier, measurement errors are 

a source of transient effects. Pure measurement errors are likely to make the 

transient effects uncorrelated. The signifi cant correlations are evidence of 

there being no pure measurement errors. Also, evidence of the fact that the 

BLS price for Choice beef and the WHL prices for chickens and turkeys have 

zero transient effects shows that these variables are not measured with error. 

Transient effects are a source of SWS volatility. Differences in adjustment 

speeds of prices are another potential source. All fi ve meats were tested 

using model simulations to determine if BLS and SWS adjust at the same 

rate. This hypothesis was accepted for pork and rejected for the other meats. 

To determine how BLS and SWS prices differ in their adjustment speeds, 

the various models’ estimates were simulated. The model simulations made 

the target prices for WHL, BLS, SWS, SOP, and BMRP 1 cent per pound 

higher than the starting prices. The target prices were fi xed for 12 months, 

and the models were simulated without transient or other random effects. In 

the real world, the target values vary monthly and there are other unpredictable effects on prices. A month’s simulated price is a function of the target 

and the previous month’s simulated price. For ease of comparisons, all fi ve 

variables were started at zero. The closer the price is to the target of one, the 

more fully adjusted it is.

Figure 6 illustrates the price adjustments for the Choice beef composite. The 

wholesale price initially over-reacts to changes in the target, whereas the 

BLS-based retail price does not react at all. The scanner-based retail price 

reacts slightly at fi rst. The wholesale price then over-corrects in the second 

month, whereas the two retail prices adjust slowly. For the Choice beef 

composite, the simulated scanner price is generally closer to its full-adjustment value than the BLS-based price, indicating quicker adjustment.

Figure 7 illustrates the price adjustments for the pork composite category. 

In this case, adjustments are the same for the two observed retail prices. The 

wholesale price is always closer to its full-adjustment value than either of the 

retail prices.

Table 3

Estimated standard errors of the transient effects on meat prices

Product

Wholesale 

price

BLS-based 

retail price

Sales-weighted, 

scanner-based price

Cents per pound

Choice beef composite 1.12 * 8.86

Pork composite 0.39 0.18 10.45

Whole chickens * 0.19 3.94

Whole, frozen turkeys * 0.35 7.13

Broiler composite 0.01 0.34 2.59

*Denotes estimates that hit the lower bound of zero.

Source: USDA, Economic Research Service using state-space model estimates.
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Figure 8 shows the price adjustment for whole chickens. For this product, 

the BLS-based retail price adjusts more rapidly than the scanner-based retail 

price, whereas the wholesale price adjusts more rapidly than either retail 

price.

Frozen turkey is the only meat of the fi ve that has a strong cyclical adjustment pattern. As shown in fi gure 9, it appears that the BLS-based retail price 

adjusts faster than the scanner-based retail price, which, in turn, adjusts faster 

than the wholesale price.

Table 4

Estimated correlations of transient effects

Product

Wholesale price and 

BLS-based retail price

Wholesale price and salesweighted, scanner-based priceBLS-based retail price and salesweighted, scanner-based price

Correlation estimates

Choice beef composite NA -0.11 NA

Pork composite 0.63 -0.50 0.36

Whole chickens NA NA -0.23

Whole, frozen turkeys NA NA 0.20

Broiler composite -0.64 1.00 -0.59

NA=not available, cannot be calculated as one or both of the standard errors of the price is 0. 

Source: USDA, Economic Research Service using state-space model estimates.

Cents per pound

Figure 6

Twelve months of price adjustment for the Choice beef composite

Source: USDA, Economic Research Service using state-space model estimates.

Wholesale BLS retail Scanner retail

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
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Cents per pound

Figure 7

Twelve months of price adjustment for the pork composite

Source: USDA, Economic Research Service using state-space model estimates.

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Wholesale BLS retail Scanner retail

Cents per pound

Figure 8

Twelve months of price adjustment for whole chickens

Source: USDA, Economic Research Service using state-space model estimates.

BLS retail

Scanner retail

Wholesale

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
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For the broiler composite, the BLS-based retail price initially shows almost 

complete adjustment, while the scanner-based retail price and the wholesale 

price barely move (fi g. 10). However, feedback from the wholesale price and 

the scanner-based price pulls the BLS-based price away from full adjustment. 

The scanner-based price and the wholesale price show similar adjustments. 

The BLS-based retail price is closer to its target level than either the scannerbased retail price or the wholesale price. 

The Choice beef composite is the only product for which the adjustment of 

the scanner-based retail price is statistically different from and faster than 

the adjustment of the BLS-based retail price. The difference between the 

adjustments of BLS and SWS prices was only large in the second month of 

adjustment. It appears that most of the volatility of SWS prices is due to their 

larger transient effects.

Using Scanner Data To Analyze Current 

Market Conditions

One result from the state-space models was a state value for the BMRP. Both 

BMRP and SOP have to be estimated; however, one cannot estimate the transient parts of either. The state variables for BMRP and SOP are those prices 

that are useful for forecasting future conditions. Table 5 shows the steadystate value of the standard error of the BMRP state’s measurement given four 

different patterns of data availability. The lower the standard error, the more 

accurate the measurement.

Cents per pound

Figure 9

Twelve months of price adjustment for whole, frozen turkeys

Source: USDA, Economic Research Service using state-space model estimates.

Wholesale BLS retail Scanner retail

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
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Cents per pound

Figure 10

Twelve months of price adjustment for the broiler composite

Source: USDA, Economic Research Service using state-space model estimates.

Wholesale BLS retail Scanner retail

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Table 5

Standard errors for predicting the value of the best measure of the retail price (BMRP) state

Product

Observed prices Timing

Choice beef 

composite

Pork 

composite

Whole 

chickens

Whole, frozen 

turkeys

Broiler 

composite

Cents per pound

WHL and BLS All available at 

end of month 0.819 1.653 1.918 0.907 4.182

Actual release 

pattern 3.729 1.783 1.999 1.087 4.246

WHL, BLS, and 

SWS

All available at 

end of month 0.809 1.483 1.594 0.884 1.488

Actual release 

pattern 3.728 1.639 1.951 1.069 3.895

Percent improvement adding scanner data

Prices all available at end of month 0.58 5.45 9.21 1.29 47.51

Actual release pattern 0.01 4.21 1.21 0.84 4.31

Percent improvement for making release dates more timely

WHL and BLS 63.99 3.79 2.07 9.02 0.76

WHL, BLS, and SWS 64.32 5.02 10.06 9.47 44.71

Note: WHL is wholesale price, BLS represents prices from the Bureau of Labor Statistics, and SWS is the series of prices from sales-weighted 

scanner data.

Source: USDA, Economic Research Service using state-space model estimates.
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Does the timing of data releases affect the value of the estimates? As noted 

earlier, newer data provide the most value, so the delay in the release of BLS 

and SWS data reduces their value in near-term forecasting—the state-space 

method allows one to measure how changing the timing of SWS data affects 

their value. The four patterns of data availability come from combining the 

two sets of observed prices with the two data release schedules—the actual 

release pattern, and a second estimate of the contribution of having access to 

all three types of data at the end of the month. The cases in which all the data 

are available at the end of the month are also relevant to historical analysis, 

when all the data for a specifi c time period are available. Adding scanner data 

to the mix (that is, having all three types of data available at the end of the 

month) should improve the accuracy of the models, reducing their standard 

errors. 

The pattern of data release is especially important for analysis of current 

market conditions and forecasts. Wholesale prices are released daily, and 

by month’s end, the entire month’s set of daily wholesale prices is available. By contrast, BLS’s data are released 2-3 weeks after the end of the 

month, and scanner data are released 7-8 weeks after the end of the month. 

Thus, at month’s end, monthly wholesale price data are current, but the 

latest monthly observation of the BLS-based retail prices is 1 month old, 

and the latest monthly observation of scanner-based retail prices is 2 months 

old. Not surprisingly, the results reported in table 5 indicate that having all 

three prices available at the end of the month would provide more accurate 

measurements than the current pattern of data release. 

Because of this timing issue, the scanner data contribute little to the price 

analysis for four of the fi ve meat products. The broiler composite is the 

exception; scanner data greatly improve the estimates of the national average 

retail price, but only in the analysis of historical data. The 7-8 week lag in the 

availability of scanner data makes this data “stale” and of little value to the 

analysis of current market conditions in this context.
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Conclusion

Scanner data have a number of advantages over BLS data. Scanner data 

cover a wider selection of meat cuts and animal species, and they provide 

information that is not offered by BLS, such as the share of each cut sold 

at a sale price, several different measures of “average” prices, and quantity 

indices for cuts and aggregates. With additional coverage and refi nements to 

the scanner data, regional estimates and the relationship of price to quantities purchased could be developed—but at additional costs. Importantly, 

analysis of scanner data demonstrates that switching from averages based on 

fi xed weights per outlet to sales-weighted averages almost invariably leads to 

lower estimated average prices and lower estimated price spreads. This analysis did not have access to quantities of meat sold, only to quantity indices. 

The indices allow one to demonstrate that the price affects the quantity 

purchased and a lower price stimulates buying. The indices do not allow one 

to compare the relative volume of one meat cut sold with another. Relative 

volume measures would greatly improve the quantity data. 

BLS-based data have several advantages over scanner data. First, BLS’s data 

are available to the public free of charge as a byproduct of other BLS activities, whereas access to scanner data must be purchased. Second, BLS selects 

stores and products using statistically representative sampling techniques, 

whereas scanner data are based on a voluntarily assembled group of participating stores. To conduct this analysis, ERS assumed that the coverage of the 

scanner data is adequate to provide statistical reliability, but there is no way 

to assess the quality of the data. Scanner data exclude certain retailers that 

BLS makes efforts to include, which may affect average prices.

BLS-based data are released more quickly than scanner data—about 2 weeks 

after the end of the month. The lag of 7-8 weeks for scanner data stems from 

data collection and processing issues. Scanner data are a byproduct of data 

collected on packaged goods from data collection systems designed for packages, not random-weight products. Scanner-data collection procedures could 

be modifi ed to speed the processing of random-weight data; these modifi cations might not improve the analysis of package-goods data.

It is expected that speeding up the release of scanner data may enhance their 

value. More current data may improve the analysis of current retail price 

conditions. ERS models allowed for the estimation of the additional information that scanner data provide when added to pre-existing data on wholesale 

and BLS-based retail prices. For most of the meats studied, the addition of 

scanner data contributed little to the analysis of market prices. Given the 

present production lag, scanner data for meat do not appear to contribute 

value to the analysis. 

All the meat price data used in this study are byproducts of other activities. 

ERS calculates wholesale composites using data that AMS publishes on 

wholesale prices in its Market News program. ERS estimates of retail meat 

prices are based on the prices that BLS collects to measure infl ation. Scanner 

data for random-weight meat packages are a byproduct of data collected for 

packaged goods companies. Processing of scanner data for random-weight 

products will likely improve only if demand for the data increases. While 
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demand has recently grown, it is unclear if the change has affected the cost of 

the data. In their current form, scanner data provide a different, but not necessarily better, view of retail meat markets than BLS-based data.
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References

Baumol, William J. Economic Dynamics, Macmillan Publishing Company, 

New York, 1970.

Davis, Christopher G., and Biing-Hwan Lin. Factors Affecting U.S. Pork 

Consumption, Outlook Report No. LDP-M-130-01, U.S. Department of 

Agriculture, Economic Research Service, May 2005, www.ers.usda.gov/

publications/ldp/may05/ldpm13001/ldpm13001.pdf.

Davis, Christopher G., and Biing-Hwan Lin. Factors Affecting U.S. Beef 

Consumption, Outlook Report No. LDP-M- 135-02, U.S. Department of 

Agriculture, Economic Research Service, October 2005, www.ers.usda.

gov/publications/ldp/oct05/ldpm13502/ldpm13502.pdf.

Degner, Robert L. Impact of Price Specials on Estimates of Retail Meat 

Prices, Master’s thesis, Texas A&M University, Department of 

Agricultural Economics and Rural Sociology, December 1970.

Durbin, J., and S.J. Koopman. Time Series Analysis by State Space Methods, 

Oxford University Press, Oxford, UK, 2001.

Gropp, Reint, and Arjan Kadareja. Stale Information, Shocks and Volatility, 

European Central Bank, Working Paper Series No. 686, October 2006, 

http://www.ecb.eu/pub/pdf/scpwps/ecbwp686.pdf 

Hahn, William F. Beef and Pork Values and Price Spreads Explained, 

Outlook Report No. LDP-M-118-01, U.S. Department of Agriculture, 

Economic Research Service, May 2004, www.ers.usda.gov/publications/

ldp/apr04/ldpm11801/

Kuchler, Fred, and Abebayehu Tegene. Did BSE Announcements Reduce 

Beef Purchases? Economic Research Report No. 34, U.S. Department of 

Agriculture, Economic Research Service, December 2006, www.ers.usda.

gov/publications/err34/ 

Leibtag, Ephraim S. “Where You Shop Matters: Store Formats Drive 

Variation in Retail Food Prices,” Amber Waves, U.S. Department of 

Agriculture, Economic Research Service, November 2005, www.ers.usda.

gov/amberwaves/november05/pdf/featurewhereyoushopnovember2005.

pdf. 

Mathews, K.H., Jr., M. Vandeveer, and R.A. Gustafson. An Economic 

Chronology of Bovine Spongiform Encephalopathy in North America, 

Outlook Report No. LDP-M-143-01, U.S. Department of Agriculture, 

Economic Research Service, June 2006, www.ers.usda.gov/publications/

ldp/2006/06jun/ldpm14301/

Perry, Janet, James MacDonald, Ken Nelson, William Hahn, Carlos Arnade, 

and Gerald Plato. Did the Mandatory Requirement Aid the Market?—

Impact of the Livestock Mandatory Reporting Act, Outlook Report No. 

LDP-M-135-01, U.S. Department of Agriculture, Economic Research 
26

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Service, September 2005, www.ers.usda.gov/publications/ldp/sep05/

ldpm13501/ldpm13501.pdf

Pritchett, James, Kamina Johnson, Dawn Thilmany, and William Hahn. 

“Consumer Response to Recent BSE Events,” Journal of Food 

Distribution Research, 38:2:57-68, July 2007. 

Shapiro, Matthew D., and David W. Wilcox. “Mismeasurement in the 

Consumer Price Index: An Evaluation,” Macroeconomics Annual, 11:93-

142, 1996.

U.S. Department of Commerce, U.S. Census Bureau. Sector 44: 

Retail Trade: Subject Series—Establishment & Firm Size: 

Concentration by Largest Firms for the United States: 2002,

November 25, 2005, http://factfi nder.census.gov/servlet/

IBQTable?_bm=y&-ds_name=EC0244SSSZ6.

U.S. General Accounting Offi ce (GAO). Pork Industry: USDA’s Reported 

Prices Have Not Refl ected Actual Sales, GA0/RCED-00-26, December 

1999, http://www.gao.gov/new.items/rc00026.pdf

U.S. Government Accountability Offi ce (GAO). Livestock Market Reporting: 

USDA Has Taken Some Steps To Ensure Quality, But Additional Efforts 

Are Needed, GAO-06-202, December 2005, http://www.gao.gov/new.

items/d06202.pdf
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Data Sources

ERS calculated the retail values of the Choice beef, pork, and broiler 

composites using BLS average prices for meat cuts. Data are available on the 

ERS website at www.ers.usda.gov/data/meatpricespreads/

The beef and pork tables are available with the most recent data for the 

Choice beef and pork composites. The retail-prices table contains the recent 

broiler composite. “Older” data may be accessed in the historical data 

worksheet. 

Whole chicken and whole frozen turkey prices were taken directly from BLS 

data. In the BLS database, these items are identifi ed as APU0000706111 and 

APU0000706311, respectively. Data are available on the BLS website at 

http://data.bls.gov/cgi-bin/srgate.
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Appendix: Dynamic-Adjustment, State-Space 

Model: Equations, Specifi cations, and Tests

State-space models are generally specifi ed using two sets of equations: one 

for the unobserved state variables and one for the observed variables. The 

three observed variables in this study are the wholesale price (WLS), the 

BLS-based retail price (BLS), and the sales-weighted scanner-based retail 

price (SWS). There are three state variables associated with these three 

prices. Two more state variables are meant to measure the effects of the 

two unseen retail prices, the best measure of the retail price (BMRP), and 

the third retail price (some other price, or SOP) that is averaged with the 

BLS-based retail price and scanner-based price to make the BMRP. Two 

more states are added to improve the models’ fi t and provide more pricespread effects. 

The three observed prices, as noted above, are WHL, BLS, and SWS. The 

two unobserved price states are BMRP and SOP. The last two states are the 

wholesale price driver (WPD) and the retail price driver (RPD).

The observation equations, written in scalar form equations (1a-c), are:

(1a) t WHL t WHL t WPD t WHL y s s e , = , + , + ,

(1b) t BLS t BLS t RPD t BLS y s s e , = , + , + ,

(1c) t SWS t SWS t RPD t SWS y s s e , = , + , + ,

In equations 1a-c, y is an observed price, s is the state variable, and e is the 

transient error term. All the terms in equation 1a-c have two subscripts; the 

“t” subscript stands for a particular month and the state or observed variable 

defi ned above. The WPD state affects the wholesale price; the RPD state 

drives the two retail prices. 

The dynamic adjustment of prices takes place in the fi rst fi ve states (WHL, 

BMRP, BLS, SWS, and SOP). In the dynamic-adjustment model, the current 

states are a function of this month’s full-adjustment values, the previous 

month’s states, and random components. ERS specifi ed the full-adjustment 

values as linear functions of observed variables “x.” Appendix table 1 

provides the variables’ names and descriptions.

This study uses some of the same features in the dynamic adjustment model 

from previous research on beef and pork price spreads (Hahn, 2004). One of 

the commonalities is an assumption about the relationship between the fulladjustment wholesale and retail prices. When fully adjusted, the retail price 

is the wholesale price plus a price spread, which is independent of the wholesale price. For example, if the full-adjustment wholesale price is $2 and the 

full-adjustment price spread is $1 per pound, then, the full-adjustment retail 

price is $3. If the full-adjustment wholesale price rises 10 cents and the fulladjustment price spread does not change, then the full-adjustment retail price 

also goes up 10 cents. One can compare this type of behavior to the case 

where the retail price is always some percentage markup over the wholesale 

price, say 100 percent. If there is a 100-percent markup, doubling the wholesale price will double the retail price. 
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The assumed wholesale-retail price relationship with full price adjustment 

makes sense economically if:

• A fi xed proportion relationship exists between wholesale meat inputs and 

retail meat output 

• Meat retailing is reasonably competitive with constant returns to scale 

The x variables are divided into three subsets. The fi rst set is the “l” (level) 

subset. It includes beef, pork, chicken, and turkey production, factors that 

would shift the supply of meats, such as the cost of feedstuffs, and factors 

that would shift the demand for meat, such as the prices of other foods. This 

subset also includes variables meant to measure the effects of the discovery 

of bovine spongiform encephalopathy (BSE), fi rst in Canada and then in the 

United States. Canada’s BSE cases have had major repercussions on U.S. 

beef imports, while the discovery of BSE in the United States caused a severe 

Appendix table 1

Exogenous variables in the state-space models

Name Description Subset

BeefQ Log of beef production Xl

ChicQ Log of chicken production

PorkQ Log of pork production

TurkQ Log of turkey production

Corn Corn price from USDA, Agricultural 

Marketing Service (AMS) - #2 Yellow, 

Central Illinois

Soy Soy meal price from USDA, AMS - High 

protein, Central Illinois

AOG Consumer Price Index (CPI) for all items 

less food and shelter

Fish CPI for fi sh and seafood

Milk CPI for dairy and related products

CAN0, CAN1 CAN0 measures the immediate effect of 

the bovine spongiform encephalopathy 

(BSE) outbreak in Canada, CAN1 the 

ending effects 

USA0, USA1 USA0 is the immediate effect of BSE 

in the United States, USA1 the ending 

effects

CAN0 and USA0 go to CAN1 and USA1 

over 6 months

x0, x1, x2 Intercept, trend, and trend-squared, post 

scanner (January 2001-August 2005)

Xr

xb1, xb2 Trend and trend-squared, prescanner 

(January 1998-December 2000)

JAN, FEB, MAR, APR, 

MAY, JUN, JUL, AUG, 

SEP, OCT, NOV, DEC

Twelve monthly dummies for seasonal 

effects

Xr

, Xd

Source: USDA, Economic Research Service.
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drop in U.S. beef exports (Mathews et al., 2006). The BSE-related variables 

are in the “Xl” subset and will not affect target price spreads. 

The next set of variables is the “r” (“rest” of the variables) subset, which 

includes the intercept, trend, trend squared, and monthly dummies. As noted 

previously, it is assumed that the full-adjustment values of the retail prices 

are to be the wholesale price plus a price spread. Similar to Hahn (2004), it 

is assumed that the full-adjustment wholesale price is a function of all the x 

variables and that the full-adjustment price spreads are a function of the fi rst 

subset. 

Based on visual examination, seasonality is important in the turkey price 

spreads. November invariably has the year’s lowest retail prices, highest 

wholesale prices, and lowest price spread. Other meats may have seasonal 

price spreads; steaks, chops, and ribs tend to be featured heavily during the 

summer grilling season, as is ham during Christmas and Easter. Therefore, 

seasonality variables are added to the previous Hahn model. The seasonal 

dummies are added in the “r” subset and a “d” subset is included for the 

seasonal dummies exclusively.

The equations for the full-adjustment values of the states can be written:

(2a) t WHL l t l r t r f , = b x , + b x ,

(2b) t j l t l r r j t r f , , , , = b x + (b + c )x for j={BLS, SWS, SOP, BMRP}

In equations 2a and 2b, the “f’s” are the full-adjustment values for the fi rst 

fi ve states. The term “b” is a vector of coeffi cients that make the wholesale 

price’s full adjustment value, and “xt

” is the vector of exogenous variables 

for month “t.” Both b and x are split into their “l” and “r” subsets. The “cr,j” 

is a set of vectors that make the full-adjustment price spreads for the four 

retail prices. Equation (2b) can be used to explain the wholesale price also by 

setting the cr,WHL vector to all 0s.

The xr

 set contains an intercept and all 12 of the monthly dummies. These 13 

variables are perfectly collinear, which means multiple sets of coeffi cients 

will give the same ending results. The coeffi cients are made unique by the 

requirement that the seasonal dummy coeffi cients sum to 0. The dummies’ 

coeffi cients then represent their month’s typical deviation from the annual 

average.

The BMRP state is defi ned as a weighted average of the BLS, SWS, and SOP 

states. The xl

 effects on all fi ve full-adjustment values are the same, so the 

price spreads are constrained as follows:

(3) r,PBMRP BLS

* r,BLS SWS* r,SWS SOP* r,SOP c =α + c +α c α c

The Greek letter alpha, j is a positive weight used to average the BLS, 

scanner, and SOP states up to the BMRP state. The three alphas sum to 1. 

In the dynamic adjustment model, the current states are a function of the 

current value of the full-adjustment values, the previous months’ states, and 
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random components. Equation 4 is a general version of the dynamic-adjustment model:

(4) [ ] t t A Ft Ut S = A*S −1 + I − * +

In equation 4, Ft is a vector of full-adjustment values, St is the vector of state 

variables, (St-1 is the previous month’s vector), A is an adjustment matrix, I

the identity matrix, and Ut

 a vector of random error terms. Equation 4 is set 

up so that if all the errors are 0, and if St-1 equals Ft

, then St equals Ft. If the 

matrix A implies partial adjustment, then the current state will be somewhere 

between the previous month’s state and the current month’s full-adjustment 

value. In most state-space applications, what is referred to as the “adjustment 

matrix” is generally called the transition matrix. 

The desire is for the transition matrix to be “stable.” If it is stable, Ft

 is fi xed 

for a long period of time, and all the error terms are made zero, simulating 

the process, and St

 will get closer and closer to Ft.

The averaging constraint from equation 3 imposes restrictions on the adjustment matrix, A, and on the random components, the Ut

. These issues are 

addressed later. Now, equation 4 is made simpler and the observation equations (1a-c) are made more complicated. Equation 4 is simple enough, but it 

is complicated fi rst by Ft

 being replaced with the function of the exogenous 

variables. These are split into two groups:

(5) t t [ ] A [ ] 1 bl xt l C xt r Ut S = A*S −1 + I − * [ ]* * , + * , +

In equation 5, the term [1] is a fi ve-row, one-column vector of ones, and C is 

a matrix of coeffi cients defi ned by stacking up the br

 and cr:

(6) 

⎥

⎥

⎥

⎥

⎥

⎥

⎦

⎤

⎢

⎢

⎢

⎢

⎢

⎢

⎣

⎡

+

+

+

+

=

r r SOP

r r MPR

r r BLS

r r BMRP

r

,

,

,

,

b c

b c

b c

b c

b

C

Equations 4 and 5 are linear difference equations. One of the strategies in 

solving these types of systems is to strategically separate them. Baumol 

shows several examples on how this is done using the linear structure of 

the model. The strategy here is to separate those parts associated with the xr

variables. 

Dropping the random portion and xl

 from equation 5 leaves 7:

(7) [ ] t * t 1 A *C*xt,r S = A S − + I −

Baumol shows specifi cally in cases where the x consists of intercepts, trends, 

and squared trends that a solution for St

 will take the form:

(8) St = D*xt,r , 
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where D is a matrix of coeffi cients whose values depend on the A and C. 

This type of procedure works because the xr

 variables are simple functions of 

time. The X effects are much more complicated, and simple solutions such as 

equation 8 do not exist. 

The set of exogenous variables in equation 8 also contains monthly dummies 

for each month t. Baumol’s procedures can be generalized to include monthly 

dummies. Solving by parts, as is done here, doubles (at least) the state 

variables. 

Returning to equation 5, some terms are not used in equation 6, such as:

(9) t t v l t l Ut S = A*S −1 + a *b *x , + where

(10) a = [I − A]*[1] v

A specifi cation like equation 10 has the full-adjustment values of all fi ve state 

variables with the same value, bl

*xt,l. In the model, that common, full-adjustment value is called the “target” value. Having the same full-adjustment 

value for these fi ve states makes programming the model easier. The “split” 

state variables can be incorporated into the observation equation by defi ning 

the st,i as in equation 5e and writing equation 1a-c as:

(11a) t WHL t WHL t WPD WHL t r t WHL y s s e , , , , , = + + d *x +

(11b) t BLS t BLS t RPD BLS t r t BLS y s s e , = , + , + d *x , + ,

(11c) t MPR t MPR t RPD MPR t r t MPR y s s e , = , + , + d *x , + ,

The term di is the appropriate row from the matrix D. The states, st,i now just 

account for the xl

 and random effects, while the di*xt,r account for xr effects. 

Because the states share the same full-adjustment value, dynamic adjustment tends toward making the states similar to one another, which makes 

the prices similar. Some may adjust more quickly. The di

*xt,r part makes the 

observed prices different from one another. 

The D matrix is based on the C matrix, which has fi ve rows, one a function of the other three. Thus, there are only three rows of the D matrix in the 

observation equations. However, the coeffi cients of the C matrix cannot be 

calculated given what is observed from the D coeffi cients. This is the other 

reason the intercept, trend, and seasonal terms are removed from the state 

equations. Even if the intercept, trends, and seasonal factors are kept in the 

state equations, there would have been multiple versions of the C matrix 

consistent with the observed data.

In equations 11a-c, observed wholesale-retail price spreads are driven by 

four factors: the difference in adjustment speed of the three prices (st,i), price 

spread adjustment (di

*xt,r), the difference between WPD & RPD, and the 

transient effects (et,i). The intercept, trend, and trend-squared parts of pricespread adjustment will give a smooth pattern to interpret. The price-driver 

states, WPD and RPD, are introduced to allow for some “nonsmoothness,” 

or unpredictability, of price spreads. WPD and RPD are not driven by exogenous variables; they have no explicit full-adjustment values. 
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Until this point, the description of the structure of the adjustment matrix has 

been vague. The seven state variables require seven equations. Many of the 

elements of the A matrix to zero are restricted. This makes it easier to show 

the state-equations a few equations at a time. The fi rst two equations are 

written for the RPD and WPD states. These two states interact only with one 

another:

(12) t WPD t WPD t RPD ut WPD s a s a s , = 1,1

* −1, + 1,2* −1, + ,

(13) t RPD t WPD t RPD ut RPD s a s a s , = 2,1* −1, + 2,2* −1, + ,

The double-subscripted, small-case “a” in equations 12 and 13 and following 

equations are coeffi cients from the A matrix. The double-subscripted lowercase “u” is the individual element of the random vector Ut

. Both of these 

state variables are driven by random factors and their lagged values. If their 

coeffi cients imply a stable relationship, these states will tend to adjust toward 

0. Remember, RPD affects both retail prices, and WPD affects the wholesale 

price. The structure of these two terms allows for general interaction between 

retail and wholesale prices; one price is not specifi ed as a leader nor is the 

other specifi ed as a follower.

WHL and BMRP states interact with one another exclusively. These two 

states are also affected by the exogenous variables in xl

:

(14) 

t WHL t WHL t BMRP a a l t l ut WHL s a s a s , 3,3 1, 3,4 1, 3,3 3,4

* * , , = * − + * − + (1− − ) b x +

(15) 

t BMRP t WHL t RBMRP a a l t l ut TNA s a s a s , 4,3 1, 4,4 1, 4,3 4,4

* * , , = * − + * − + (1− − ) b x +

Just as in equations 9 and 10, this specifi cation makes no assumptions about 

either price being leader or following the other. The last three equations 

determine the BLS, SWS, and SOP states. These share a common structure:

(16) 

l t l t BLS

t BLS t WHL t BMRP t BLS t SWS t SOP

a a a a a u

s a s a s a s a s a s

4,3 4,4 5,5 5,6 5,7 , ,

, 4,3 1, 4,4 1, 5,5 1, 5,6 1, 5,7 1,

(1 )* *

* * * * *

+ − − − − − +

= − + − + − + − + −

b x

(17) 

l t l t MPR

t SWS t WHL t BMRP t BLS t SWS t SOP

a a a a a u

s a s a s a s a s a s

4,3 4,4 6,5 6,6 6,7 , ,

, 4,3 1, 4,4 1, 6,5 1, 6,6 1, 6,7 1,

(1 )* *

* * * * *

+ − − − − − +

= − + − + − + − + −

b x

(18) 

l t l t SOP

t SOP t WHL t TBMRP t BLS t SWS t SOP

a a a a a u

s a s a s a s a s a s

4,3 4,4 7,5 7,6 7,7 , ,

, 4,3 1, 4,4 1, 7,5 1, 7,6 1, 7,7 1,

(1 )* *

* * * * *

+ − − − − − +

= − + − + − + − + −

b x

Equations 16-18 “recycle” the a4,3 and a4,4 coeffi cients from equation 15. The 

coeffi cients for the previous month’s WHL and BMRP states are the same as 
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those in the BMRP equation. This keeps all four retail prices relatively close 

to one another. Also, as noted earlier, the desire if for the TNS state to be a 

weighted average of the other three “retail” price states. Reusing the a4,3 and 

a4,4 coeffi cients means that the lagged WHL and RET affects the average 

automatically. In addition, the following side constraints are used on the “a” 

coeffi cients and “u” random errors: 

(19) 0 = α + BLS

*a5,i +α MPR*a6,i αSOP*a7,i , for i=(5, 6, and 7)

(20) ut,RET =α + BLS*ut,BLS +αSOP*ut,SOP α MPR*ut,MPR

All the models are estimated using maximum-likelihood estimation. The 

general model is specifi ed earlier, and more constrained models, are specifi ed later. The special cases impose constraints on the models, and these 

constraints will not make the likelihood-value higher. Testing hypotheses 

involves comparing the estimated likelihood of constrained and unconstrained models. Twice, the likelihood difference generally has an asymptotic 

chi-square distribution. Some of the hypothesis tests will have odd properties, as discussed later.

Model Restrictions: Restrictions on the 

Averaging Parameters

The averaging parameters—the alphas—determine how the individual retail 

prices add up to the BMRP state. Tests determine if either the BLS-based 

price or the store-weighted, scanner-based price represents the true national 

average retail price. Also, data are checked to determine if an average of only 

the BLS and SWS prices (not “some other price,” SOP) approximates the 

national average. The fi rst two cases mentioned are a special case of this last 

one. Making the RET not include SOP means setting the alpha for SOP equal 

to 0. This imposes one restriction on the model. Moving from the model 

where SOP is not part of BMRP to either the BLS-is-BMRP or the SWS-isBMRP cases requires one more restriction. The three “alpha” must sum to 1, 

so if two are fi xed, the value of the remaining parameter is known. 

What would SOP do in the model if its alpha coeffi cient is 0? Making alpha 

equal to 0 will not change the basic structure of equations 16-18, just how the 

constraint operates. The previous month’s value of SOP will affect both BLS 

and SWS, however, so the averaging constraint will cancel out these effects 

in BMRP and will prevent SOP from affecting the next month’s wholesale 

price. 

The alpha parameters are required to be positive, which complicates the tests 

somewhat. Most statistical theory is based on testing parameters that are not

constrained in size or sign. For example, suppose the true alpha for one of 

the states is one. In unconstrained estimation, some of the estimates may be 

larger than one; however, the chances that an estimate will be exactly one 

are practically zero. Once the estimates are constrained to be no larger than 

one, there is a chance that an estimate will be exactly one. In models without 

bounds on variables, imposing a constraint always decreases the likelihood. 

Putting the upper level of all the “alphas” at one means that occasionally 

an “alpha” is estimated to be one in the otherwise unconstrained model. In 

this case, the more- and less-constrained likelihoods are the same. The same 
35

Comparing Two Sources of Retail Meat Price Data / ERR-88

Economic Research Service/USDA

process applies on the lower bound of zero. An unconstrained, estimated 

alpha could be negative; the chances of an unconstrained estimate being 

exactly zero are also effectively zero. Because all the estimated alphas are 

required to be positive, there is a chance that some of the estimates are zero. 

The alpha-related tests will have a chance of being exactly 0 if the hypothesis is true (or close to being true). What the statistical tables call a 5-percent 

value would occur less frequently than 5 percent of the time. Rejecting alpharelated restrictions with large test statistics is a somewhat more comfortable 

choice than accepting those with small test statistics. 

Model Restrictions: Requiring BLS-based and Scanner Prices To Have the 

Same Speed of Adjustment

One of the special models shows the BLS-based and scanner prices adjusting 

at the same rate, which requires additional side restrictions to equations 13 

and 14. Both the BLS-based and scanner states will have the same reaction to 

the lagged SOP state by imposing:

(21) a4,7 = a5,7

In addition to equation 21, there is the following constraint:

(22) 4,4 4,5 5,4 5,5 a + a = a + a

These two restrictions do not have any associated sign or size constraints. 

The test-statistic for the “same-speed-of-adjustment” hypothesis ought to be 

asymptotically chi-square. 

Model Restrictions: Constraining Transient Effects

The random effects (“e”) terms in the observation equations are transient 

effects. Forcing BLS and scanner data to have the same-sized transient effect 

means that their e variances have to be constrained to be equal. This is one 

restriction. Making all the e uncorrelated means restricting each of the three 

covariance terms to 0. All three variances must be positive, which complicates tests for the transient effects in some cases. 

All variances of e must be positive. It is possible that the optimal estimate for 

an e variance is zero. If it is, then the observation equation actually has no e

terms. If two or more of the observation equations have no e, then all of them 

are uncorrelated automatically. Making them uncorrelated when two or all 

are zero is not restrictive. If the unconstrained BLS and SWS variances are 

zero, then the “BLS-and-SWS-have-the-same-transient-effects” model also 

have the same likelihood as the unconstrained variances.

Appendix table 2 provides the results of the tests. Only two of the more 

restricted models passed. Pork’s BLS and SWS have the same speed of 

adjustment, and broiler’s BMRP is a weighted average of the BLS and SWS 

prices. It happens to be the case that broiler’s SOP weight hit its lower bound 

of zero. Consequently, requiring that the BMRP be an average of BLS and 

SWS is not actually restrictive. 
36

Comparing Two Sources of Retail Meat Price Data / ERR-88

Economic Research Service/USDA

Using State-Space Estimates To Measure the Value 

of Scanner Data

The dynamic-adjustment model was a state-space model in which the accuracy of predicting the states improved as the data set lengthened. There is a 

limit to the accuracy of these predictions—the measurement/forecast accuracy of the states/observed variables approaches what is called a steady-state 

value. The estimates tend to move toward their steady-state values fairly 

quickly; however, side analysis helps ensure that the steady-state values are 

actually calculated. These calculations are the basis of the “all-data-releasedat-the-same-time” results. 

To estimate the “actual-release-pattern” results for instances in which there 

are only BLS and WHL data, the analysis starts with the steady state results 

and then calculates the effects of adding just the wholesale price. This 

provides the result when there is a long series of both prices, but one more 

observation on the wholesale price. For the “see-all-three-prices” results; the 

analysis starts with the steady state, adds a month’s worth of BLS and WHL, 

and then adds a new month of WHL exclusively.

Appendix table 2

Signifi cance levels for the hypothesis test results based on an asymptotic chi-square distributions

Hypothesis

Model degrees 

of freedom

Choice beef 

composite

Pork 

composite

Whole 

chickens

Whole, frozen 

turkeys

Broiler 

composite

Signifi cance level (percent)

BLS-based price and 

scanner-based price have 

same speed of adjustment

2 0.0 21.3 0.0 0.1 0.0

BLS-based price equals 

national average 2 0.0 0.0 0.0 0.0 0.0

Scanner-based price equals 

national average 2 0.0 0.0 0.0 0.0 0.0

An average of the BLSbased and scanner prices is 

the national average (SOP2 

has a weight of 0.)

1 0.0 0.0 0.0 0.0 100.0

BLS-based and scanner 

prices have the same 

transient variance

1 0.0 0.0 0.0 0.0 0.0

All three prices’ transient 

effects are uncorrelated 3 0.0 0.0 0.0 0.0 0.0

Note: Highlighted cells are not statistically signifi cant.

Source: USDA, Economic Research Service comparison of results from alternative state-space models.