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United States General Accounting Office:
GAO:
Report to the Honorable Tom Daschle, U.S. Senate.
March 2002:
Economic Models Of Cattle Prices:
How USDA Can Act to Improve Models to Explain Cattle Prices:
GAO-02-246:
Contents:
Letter:
Executive Summary:
Purpose:
Background:
Results in Brief:
Principal Findings:
Agency Comments:
Chapter 1: Introduction:
The Cattle and Beef Industry Consists of Several Interlocking Pieces:
Structural and Technological Changes in the Cattle and Beef Industry:
Beef’s Competition from Other Meats:
International Trade in Beef and Cattle Is Growing:
The Cattle Cycle Is an Important Feature of Supply:
Modeling the Cattle and Beef Industry Can Take Different Forms:
Objectives, Scope, and Methodology:
Chapter 2: The USDA and ITC Models Were Not Designed to Answer
Questions about Structural Change:
USDA’s Models Project Cattle Prices under Baseline Conditions:
Short-Term Projections Rely on Analysts’ Judgments:
Long-Term Projections Are Based on USDA’s Livestock Model:
The Livestock Model Has Not Been Reestimated, Documented, or Validated:
ITC’s Models Lack Industry Specifics Needed to Predict Prices:
Research Is Inconclusive on How Structural Change Affects Domestic
Cattle Prices:
Conclusions:
Recommendations for Executive Action:
Agency Comments and Our Evaluation:
Chapter 3: Many Factors Determine Cattle Prices and Producers’ Incomes:
Cattle Demand and Supply, International Trade, and Structural Change:
Consumer Demand for Beef Influences Demand for Cattle:
Several Considerations Shape Producers’ Decisions to Supply Cattle:
International Trade Affects Domestic Prices and Producers’ Incomes:
Structural Change Is Relevant:
Conclusions:
Recommendations for Executive Action:
Agency Comments and Our Evaluation:
Chapter 4: Building a Comprehensive Model Depends on Resolving Modeling
and Data Issues:
Analyzing How Demand and Supply Link Producers to Consumers Is
Important:
Obtaining Better Data to Analyze Consumer Demand Is Important:
Aspects of Cattle Supply and Prices Are Relevant:
International Trade Issues:
Overarching Issues Related to Modeling Scope:
The Panel’s Priority Items for Government Action:
Conclusions:
Recommendations for Executive Action:
Agency Comments and Our Evaluation:
Appendixes:
Appendix I: Objectives, Scope, and Methodology:
Appendix II: USDA’s Livestock Model:
The Cattle and Beef Sector:
The Hog and Pork Sector:
The Chicken Sector:
The Turkey Sector:
The Consumption Section of the Model:
The Demand Section of the Model:
The Price Section of the Model:
Cost and Returns Section of the Model:
Appendix III: Our Survey Phases and Methodology:
Appendix IV: The Panel’s Ratings of Problems and Issues in Developing an
Adequate Model:
Appendix V: Summary of Phase III of Our Survey:
Panelists’ Responses on Structural Change:
Panelists’ Responses on International Trade:
Issues Facing Comprehensive Analysis:
Specific Actions the Federal Government Should Take:
Appendix VI: Our Panel of Experts:
Appendix VII: Comments from the U.S. Department of Agriculture:
GAO Comments:
Appendix VIII: GAO Contacts and Staff Acknowledgments:
GAO Contacts:
Staff Acknowledgments:
Glossary:
Tables:
Table 1: What Detailed Analysis Requires for Adequate Cattle Price
Modeling:
Table 2: Inadequate Retail Data and Quantification Factors Influencing
Consumer Demand Pose Challenges to Modeling:
Table 3: Cattle Cycle, Expectations of Profits, and Long-Term Variables
Pose Challenges to Modeling:
Table 4: Vertical Coordination Poses Challenges to Modeling:
Table 5: Quantifying International Trade Factors Is an Issue for
Modeling:
Table 6: The Relevance of a Model’s Purpose and Scope:
Table 7: The Five Problems Most Important for Government Action in
Developing a Comprehensive Analysis:
Table 8: The Panel’s Comments on Data Needs That the Government Can
Address:
Table 9: The Panel’s Comments on the Government’s Role in Data and
Modeling Issues:
Table 10: The Number of Panelists Participating in the Study’s Three
Phases:
Table 11: Descriptive Statistics on Factors Rated in the Phase II
Questionnaire:
Table 12: Descriptive Statistics on Issues and Problems Rated in the
Phase II Questionnaire:
Table 13: Issues the Panel Recommended the Federal Government Act On:
Figures:
Figure 1: Cattle Being Fed in a Feedlot Prior to Slaughter:
Figure 2: Cattle Demand and Supply Relationships Linking Producers and
Consumers:
Figure 3: Cattle Eating at a Feedlot Trough:
Figure 4: The Beef and Cattle Industry from Animal Breeding to
Consumption:
Figure 5: Prices Signal Changes Along the Demand and Supply Chain
between Producers and Consumers:
Figure 6: Retail Beef, Boxed Beef, and Slaughter Steer Price Movements,
1974–99:
Figure 7: The Rise in Steer and Heifer Slaughter, Accounted for by the
Four Largest U.S. Meatpackers, Selected Years 1980–99:
Figure 8: U.S. Per Capita Retail Beef Consumption Fell in the 1970s and
1980s and Leveled Off in the 1990s:
Figure 9: U.S. Retail Beef Prices Were Higher Than Chicken and Pork
Prices, 1970–99:
Figure 10: U.S. Beef Exports Have Generally Risen Since 1980:
Figure 11: U.S. Beef Exports Rose as a Percentage of U.S. Consumption,
1970–99:
Figure 12: U.S. Beef Imports Varied as a Percentage of Commercial
Production, 1970–99:
Figure 13: U.S. Cattle Imports Exceeded Exports, 1970–2000:
Figure 14: U.S. Cattle Imports Rose as a Percentage of Slaughter,
1970–2000:
Figure 15: The Cattle Cycle: Rising and Falling Cattle Inventories,
1930–2000:
Figure 16: How Cattle Inventories Peaked Before Beef Production,
1970–99:
Figure 17: The Cyclical Movement of Cattle Prices, 1970–99:
Figure 18: The Opposite Movement of Cattle Prices and Commercial
Slaughter, 1974–2000:
Figure 19: Domestic Cattle Demand and Supply Are More Important Than
Other Factors:
Figure 20: The Panelists’ Assessment of Structural Change and
International Trade Varied:
Figure 21: Consumer Preferences, Prices of Beef Substitutes, and Health
Concerns Are More Important Than Other Factors Influencing Consumer
Demand:
Figure 22: Capacity Use at Meatpacking Plants and Retailing Beef Costs
Are More Important Than Other Factors Influencing Meatpackers’ and
Retailers’ Demand for Cattle and Beef:
Figure 23: Supply Factors Vary in Importance:
Figure 24: Beef Is More Important in International Trade Than Cattle:
Figure 25: International Trade Will Be More Important 5 Years from Now:
Figure 26: Various Aspects of Structural Change Influence Cattle Prices
and Producers’ Incomes:
Figure 27: Structural Change Will Be More Important 5 Years from Now:
Abbreviations:
AMS: Agricultural Marketing Service:
BLS: Bureau of Labor Statistics:
CGE: computable general equilibrium:
COMPAS: Commercial Policy Analysis System:
CPI: consumer price index:
ERS: Economic Research Service:
FAPSIM: Food and Agricultural Policy Simulator:
FI: federal inspection, federally inspected:
GIPSA: Grain Inspection Packers and Stockyards Administration:
ICEC: Interagency Commodity Estimates Committee:
ITC: U.S. International Trade Commission:
LMR: livestock mandatory reporting:
NAFTA: North American Free Trade Agreement:
NASS: National Agricultural Statistics Service:
USDA: U.S. Department of Agriculture:
[End of section]
United States General Accounting Office:
Washington, D.C. 20548:
March 15, 2002:
The Honorable Tom Daschle:
United States Senate:
Dear Senator Daschle:
We are pleased to respond to your request that we review economic models
of the U.S. Department of Agriculture and U.S. International Trade
Commission, especially their treatment of competition, marketing
practices, and international trade effects on U.S. cattle prices and
producers’ incomes. In this report, we address three research
questions.
* To what extent do these models incorporate structural changes—
specifically, market concentration in the meatpacking sector and the use
of marketing agreements, forward contracts, and imports?
* What are the most important factors that affect cattle prices and
producers’ incomes?
* What are the most significant data and modeling issues to be
considered in developing a more comprehensive model, or logical
framework, to explain cattle prices and producers’ incomes?
We make several recommendations to the secretary of agriculture about
how to resolve issues and problems regarding cattle price modeling.
As agreed with your office, unless you publicly announce its contents
earlier, we plan no further distribution of this report until 30 days
after its issue date. We will then send copies to the appropriate
congressional committees; the secretary of agriculture; the chairman,
U.S. International Trade Commission, and the director, Office of
Management and Budget. We will also make copies available to others
upon request.
If you have any questions about this report or would like to discuss it
further, I can be reached at (202) 512-2700. Key contributors to the
report are listed in appendix VIII.
Sincerely yours,
Signed by:
Nancy Kingsbury:
Managing Director, Applied Research and Methods:
[End of letter]
Executive Summary:
Purpose:
Cattle prices and the livelihood of those who raise cattle in the
United States are influenced by many factors, ranging from weather to
consumer taste. In addition, a number of structural changes are
occurring in the cattle and beef industry. All these elements, and
more, could be considered in developing a logical framework to explain
cattle prices and producers’ incomes.
There is some concern that economic models that the U.S. Department of
Agriculture (USDA) and the U.S. International Trade Commission (ITC) use
do not account for all the factors that affect cattle prices and
producers’ incomes. At the request of Senator Tom Daschle, GAO
addressed the following questions: (1) To what extent do these models
incorporate structural changes—specifically, market concentration in
the meatpacking sector, the use of marketing agreements and forward
contracts, and imports? (2) What are the most important factors that
affect cattle prices and producers’ incomes? (3) What are the most
significant data and modeling issues that need to be considered in
developing a more comprehensive model, or logical framework, to explain
cattle prices and producers’ incomes?
Background:
Market concentration is a measure of total sales or purchases of the
largest firms in a specific market or industry. Today, the four largest
meatpacking firms handle more than 80 percent of all steer and heifer
slaughter (fig. 1). Twenty years ago, market concentration was less
than half as great. Meatpacking firms purchase cattle for slaughter and
produce meat items for sale to wholesalers and retailers. Some cattle
producers are worried that greater market concentration has meant that
fewer meatpackers bid for their cattle and that they do so at lower
prices. Other industry observers hold that technological change and
cost economies are the most important factors driving the meatpacking
sector and that market concentration has played a relatively minor role
in determining cattle prices.
Figure 1: Cattle Being Fed in a Feedlot Prior to Slaughter
(photograph):
[See PDF for image]
[End of figure]
Cattle were traditionally bought and sold in spot or cash markets, where
prices are determined in an auction setting.[Footnote 1] Today, cattle
are also being bought and sold by means of direct marketing agreements
between meatpackers and producers, sometimes in the form of contracts.
An agreement may stipulate the number of cattle to be delivered to the
meatpacker, their quality, and a pricing formula to determine the price
to be paid for the cattle. Some industry analysts believe that such
marketing arrangements can result in a less competitive market for
cattle and lower prices, while others believe that producers benefit
from such arrangements.
Although the United States is the largest beef producer in the world,
it is a net beef importer, buying more beef from other nations than it
sells to them. Most U.S. beef exports are choice cuts, while most
imports are used for ground beef. The United States also imports a
greater volume of cattle than it exports. Some U.S. cattle producers
believe that imports of live cattle have resulted in lower U.S. cattle
prices, but some industry analysts believe that international trade has
benefited producers and consumers.
To determine the extent to which USDA and ITC models incorporate market
concentration in the meatpacking sector, marketing agreements and
forward contracts, and imports, GAO obtained the models’ documentation
and discussed the models with agency officials. To identify the most
important factors affecting cattle prices and producers’ incomes, GAO
undertook a Web-based survey of a panel of 40 experts (named in app.
VI). This panel, which reflected a broad range of expertise in
agricultural economics, also identified the most significant data and
modeling issues that need to be addressed if a more comprehensive
modeling framework is to be developed. Appendix I contains a detailed
description of this methodology.
Results in Brief:
USDA and ITC models include imports but do not incorporate market
concentration, marketing agreements, and forward contracts because they
were not designed to answer questions about these factors. USDA uses
various methods to predict cattle prices. Its long-term livestock model
projects annual cattle prices over a 10-year period and consists of many
mathematical relationships describing the U.S. livestock sector. In
addition, a committee of USDA officials meets monthly to analyze market
data and to forecast monthly cattle prices up to 18 months into the
future. ITC’s model, called the Commercial Policy Analysis System
(COMPAS), has been used to calculate the effects of dumping imports of
live cattle on U.S. cattle prices. ITC has other models that were
designed mainly to assess the broader effects of international trade on
sectors of the economy. ITC’s models lack specific details on the
cattle and beef industry and cannot be readily modified to include
market concentration, marketing agreements, and forward contracts.
In GAO’s review of USDA’s livestock model to determine whether it
incorporates imports, market concentration, marketing agreements, and
forward contracts, several issues arose involving best modeling
practices. The entire model has not been reestimated in more than a
decade, even though much of the data used to estimate it predate the
rapid rise of meatpacking concentration during the 1980s, the growing
popularity of marketing agreements and forward contracts, technological
change, and shifting consumer preferences. Thus, it is not clear to
what extent the estimated values of model parameters would change and
lead to different projections of cattle prices if newer data were used.
Moreover, data sets used to estimate the model have been lost, along
with standard measures of statistical goodness of fit and other
diagnostics of model performance.[Footnote 2] This information is
critical to model evaluation. USDA offered several reasons for this
lack of documentation. Foremost was that budgetary cuts have led to a
lack of resources needed to provide better documentation and to replace
lost data.
GAO’s expert panel identified many important factors influencing cattle
prices and producers’ incomes. Some, but not all, of these factors are
included in USDA’s livestock model. The panel believed that domestic
cattle demand and supply are the fundamental forces driving cattle
prices and producers’ incomes. It agreed less about the importance of
international trade and structural changes that include market
concentration, marketing agreements, and forward contracts.
The panel identified a number of important data and modeling issues to
be addressed in developing a comprehensive modeling system to predict
cattle prices and producers’ incomes. It cited collecting better data to
quantify a number of important factors not included in the model. It
also would like to see a more complete characterization of the supply
and demand relationships connecting the cattle producer to the final
consumer. The panel’s emphasis on a more complete characterization of
the cattle and beef industry underscores the idea that the demand for
cattle is ultimately driven by consumer demand for beef and other
demand and supply forces linking cattle producers to feedlots,
meatpackers, and retailers.
Principal Findings:
Models Account for International Trade but Were Not Designed to Answer
Questions about Market Concentration, Marketing Agreements, and Forward
Contracts:
USDA uses various methods to project cattle prices. Its long-term
livestock model projects annual cattle prices over a 10-year period and
consists of many mathematical relationships describing the U.S.
livestock sector. In addition, a committee of USDA officials meets each
month to analyze market data and forecast monthly cattle prices up to
18 months into the future.
USDA’s livestock model focuses on a number of fundamental factors that
influence cattle prices, including animal numbers, commercial beef
production, and meat demand. Besides generating USDA’s livestock long-
term forecast, it is used by USDA’s Economic Research Service (ERS) to
project the effect of legislative policy and other events, such as
changing feed costs, on the livestock sector.
The livestock model was estimated initially with 1960–88 data, and it
does not incorporate market concentration, marketing agreements, and
forward contracts. The model was not designed to address these kinds of
questions. USDA’s research on these structural changes is inconclusive
on their effect on cattle prices paid to cattle producers. Similarly,
USDA’s short-term forecasting committee does not explicitly account for
concentration, marketing agreements, and forward contracts.
Both the livestock model and the short-term forecasting committee
explicitly account for imports and exports of beef and cattle in their
projections of cattle prices. The model uses values of beef imports and
exports that are based on the projections of another set of USDA models
that focus on international trade. Likewise, USDA’s short-term
forecasting committee considers the latest information on beef imports
and exports. Values of imports and exports of live cattle are
determined outside the livestock model. Cattle imports and exports are
considered in short-term, monthly forecasting.
ITC has a sweeping mandate to assess possible injury to any U.S.
industry from imports, and it uses COMPAS to measure the effects of
unfair or underpriced imports on U.S. industry. For example, COMPAS has
been used to calculate the effects of such imports of live cattle on
U.S. cattle prices.
ITC also maintains other models, including a multisector model to
estimate the impact of broad trade initiatives such as the North
American Free Trade Agreement (NAFTA). While this model is designed to
estimate effects of these initiatives on all sectors, it is not
detailed enough to estimate the effects of cattle imports on U.S.
cattle prices. None of these models explicitly accounts for
concentration, marketing agreements, and forward contracts.
USDA’s livestock model has not been reestimated in more than a decade,
even though much of the data used to estimate it predate the rapid rise
of meatpacking concentration during the 1980s, the growing popularity of
vertical alliances, technological changes, and shifting consumer
preferences. Thus, it is unclear to what extent the estimated values of
model parameters would change and lead to different projections of
cattle prices if newer data were used. In addition, the data sets used
to estimate the model have been lost, along with standard measures of
statistical goodness of fit and other diagnostics of model performance.
This information is critical to model evaluation, and its maintenance
simply constitutes good housekeeping.
According to USDA, budgetary cuts have led to a lack of resources needed
to provide better documentation and replace lost data. An assistant
administrator of ERS acknowledged that reestimating the model with
current data makes sense and should include back casting, a standard
validation practice comparing model projections with actual results.
To help ensure that models USDA uses to project cattle prices are
properly maintained and reflect the most current information on the
cattle and beef industry, GAO recommends that the secretary of
agriculture direct ERS to periodically reestimate and validate the
livestock model. To ensure that models USDA uses to project cattle
prices are properly documented, GAO recommends that the secretary of
agriculture direct ERS to provide basic documentation on these models.
This would include documenting (1) the data set used to estimate the
model, (2) standard measures of statistical goodness of fit and other
diagnostics of model performance, and (3) any changes made to improve
or otherwise update the model.
GAO’s Panel Identified the Most Important Factors Affecting Cattle
Prices and Producers’ Incomes and Some Are Included in USDA’s Livestock
Model:
The first step GAO’s expert panel took was to identify the most
important factors affecting cattle prices and producers’ incomes; the
range they enumerated was wide. GAO then asked each panel member to
vote on the importance of all the factors and tallied the votes. The
panel judged domestic supply and demand for cattle more important than
international trade and structural change as explanations for cattle
price and income movements.
The panel identified many demand factors. For instance, the panelists
pointed to an array of factors linking cattle prices to consumer and
retailer demand for beef and to meatpacker demand for cattle. Chief
among the factors affecting consumer demand for beef were consumer
preferences, especially for quality and convenience, and prices of
substitutes for beef, notably poultry and pork. The panelists also
highlighted consumers’ health concerns about food safety and diet.
The panel also identified numerous supply factors, including the cattle
cycle and input costs, especially the costs of feed and forage. Weather
is an important factor influencing both feed and forage costs. The
cattle cycle, referring to increases and decreases in herd size over
time, is determined by expected cattle prices and the time needed to
breed, birth, and raise cattle to market weight, among other things.
Expected prices are important because the relatively long biological
cycle for cattle makes it necessary for producers to make decisions
about herd size months and even years before animals are sold and
prices are known. Cattle quality was another factor that scored
relatively high in importance. Grade and yield were cited as important
quality characteristics. Cattle quality is also a factor affecting the
demand for cattle and is linked to consumer demand for quality beef
products.
Structural change and international trade were generally viewed as
somewhat less important, although there was less agreement among the
panel. Structural change and international trade, depending on the
element, can be a demand or supply factor affecting cattle prices and
producers’ incomes. The panel identified the most important elements
associated with structural change in the cattle and beef industry as
economies of scale and technological change. Economies of scale refers
to cost savings from operating larger plants, which have become more
prevalent with consolidation in the meatpacking sector. Economies of
scale and technological change were judged more important in
meatpacking than in retailing and feedlots. Some examples of
technological change are developments in packaging and processing.
Vertical coordination also scored relatively high in importance among
structural change factors. Within vertical coordination, value-based
marketing and pricing scored the highest in importance. Efficiency of
the supply chain—the distribution system used to move products beyond
the farm gate to the final point of consumption—is another aspect of
structural change that received more votes from the panel. In
international trade, exports of beef were identified as the most
important factor, with trade barriers having the most influence on net
beef exports, the difference between beef exports and imports.
A number of factors the panel judged important are included in USDA’s
livestock model, such as feed costs and cattle inventory features of the
cattle cycle. The model does not explicitly cover other important
factors, such as product quality and convenience aspects of consumer
preferences and grade and yield characteristics of cattle quality. The
panel also believed that international trade and structural change will
become more important in coming years, with implications for future
modeling.
It is not clear to what extent the livestock model indirectly captures
the effects of factors that it does not include but that influence
cattle prices. For example, in the model, the retail price of beef and,
therefore, cattle prices are influenced by beef, pork, and poultry
consumption, which depend on consumer preferences. Similarly, the
effects of economies of scale and market concentration may be hidden in
the relationship between boxed beef prices, which represent prices
meatpackers receive for their products, and cattle prices. However,
because the model does not explicitly account for these factors, it is
not equipped to shed light on their relative importance in explaining
and projecting cattle prices. There is no ready way to know how
important these excluded factors are in the model’s cattle price
projections.
To improve USDA’s ability to answer questions about the current and
future state of the cattle and beef industry, GAO recommends that the
secretary of agriculture direct ERS to (1) review the findings of GAO’s
expert panel regarding important factors affecting cattle prices and
producers’ incomes and (2) prepare a plan for addressing these factors
in future modeling analyses of the cattle and beef industry.
The Panel Identified the Most Important Data and Modeling Issues:
The panel identified a number of important data and modeling issues to
be addressed in developing a comprehensive modeling system to predict
cattle prices and producers’ incomes. It cited the need to collect
better data to quantify important factors, particularly on the consumer
demand side, such as tastes and health concerns, which are not included
in USDA’s livestock model. The panel also favored a more complete
characterization of the supply and demand relationships connecting the
cattle producer to final consumer. The model is more detailed
“upstream” in its representation of cattle production than it is
“downstream” in its representation of the packer, retailer, and
consumer. The panel’s emphasis on a more complete representation of the
cattle and beef industry reflects that the demand for cattle is
ultimately driven by consumer demand for beef and other demand and
supply forces linking cattle producers to feedlots, meatpackers, and
retailers.
The panel also emphasized that a model’s purpose is critical in
determining the factors to include in a model; it noted that what is
appropriate to include in a short-term forecasting model differs from
what is appropriate in a model designed for longer-term projections and
policy simulation. Moreover, the panelists questioned the feasibility
of constructing one all-encompassing model to address the wide variety
of questions that may arise.
The panel recommended that the government take a number of actions to
facilitate the development of a more comprehensive modeling framework
for explaining and projecting cattle prices and producers’ incomes.
These actions focus primarily on the need for better data.
To improve USDA’s ability—and that of the research community as a
whole—to answer questions about the current and future state of the
cattle and beef industry, GAO recommends that the secretary of
agriculture direct ERS to (1) review the findings of GAO’s expert panel
regarding important data and modeling issues and, (2) in consultation
with other government departments or agencies responsible for
collecting relevant data, prepare a plan for addressing the most
important data issues that the panel recommended for government action,
considering the costs and benefits of such data improvements, including
tradeoffs in departmental priorities and reporting burdens.
Agency Comments:
We provided a draft of this report to the U.S. International Trade
Commission and the U.S. Department of Agriculture for their review and
comment. ITC generally agreed with the report and offered several
points of clarification. USDA identified some changes and points of
clarification. See appendix VII for USDA’s comments and our evaluation.
[End of Executive Summary]
Chapter 1: Introduction:
The livelihood of cattle producers depends fundamentally on the price
they receive for their product and their cost to produce it. But behind
this simple arithmetic are a host of demand and supply factors that
influence cattle prices and the costs of raising cattle. For instance,
the outcome for producers depends on how consumer tastes affect the
demand and price for beef. Producers’ fortunes also hinge on how
weather affects the supply and cost of forage and feed grains. The long
biological cycle for cattle means that producers have to make supply
decisions about herd size long before animals are sold and prices are
known. International trade in cattle and beef, competition from
poultry, pork, and other protein sources for a place in the consumer’s
shopping cart, and household income are also among the many demand
factors that influence cattle prices and producers’ incomes.
In addition, structural changes that have been reshaping segments of the
industry are affecting cattle demand and supply. The four largest
meatpacking firms now slaughter more than 80 percent of all steers and
heifers, compared with 36 percent 20 years ago. Agreements between
producers and meatpackers stipulating prices, number of cattle, and
quality considerations are becoming more commonplace. Technological
changes now enable packers to deliver shelf-ready products to grocers.
Information technology is being used to conduct live-cattle auctions on
the Internet. All these developments and more potentially influence the
demand and supply of cattle, directly or indirectly affecting cattle
prices and producers’ incomes.
Many demand and supply factors can be considered in developing a model,
or logical framework, to explain cattle prices and producers’ incomes.
Which of these factors to include depends on the model’s purpose or the
specific questions it is intended to answer. Data availability and the
results of testing how well various factors explain prices and incomes
also determine which factors to include in a model. Modeling frameworks
can range from highly complex mathematical formulations to less formal
meetings of the mind among a panel of experts.
The Cattle and Beef Industry Consists of Several Interlocking Pieces:
A series of demand and supply relationships links consumer preferences
for beef to producers’ decisions to raise cattle.[Footnote 3]
Circumstances at any link in the chain, such as a change in consumer
preferences for beef, can affect other links and can result in changes
in cattle prices and producers’ incomes. Figure 2 shows how this chain
of supply and demand works. For instance, consider a situation in which
consumers signal an increased preference for beef through their meat
counter selections and menu choices and their willingness to pay higher
prices for beef. In turn, higher retail beef prices provide an
incentive for retailers to supply more beef to consumers. To supply
consumers with these extra products, grocers and food service providers
respond by placing more orders for ready-to-consume beef products,
which processors and wholesale distributors supply. To meet the greater
demand, the processors place more orders for boxes of larger meat cuts
to be supplied by meatpackers, which they convert into smaller cuts
ready for consumption at the retail level. Increasingly, packers supply
these smaller cuts, having integrated meat processing into their
plants. Greater orders for beef at the wholesale level lead to upward
pressure on wholesale beef prices and boxed-beef prices. To provide
more beef, packers place orders for more cattle supplied by feedlots,
which puts upward pressure on cattle slaughter prices.
Figure 2: Cattle Demand and Supply Relationships Linking Producers and
Consumers:
[See PDF for image]
This figure is an illustration of the cattle demand and supply
relationships linking producers and consumers, and includes the
following information:
Supply:
Seedstock, cow-calf producers, and stockers;
Feedlots;
Packers and processors.
Demand:
Retailers and food service;
Consumers.
[End of figure]
Feedlots specialize in feeding steers and heifers a concentrated diet
of corn and other grains before the animals are slaughtered at the
meatpacking plant. Typically, animals remain in feedlots until they
weigh 950 to 1,250 pounds. Greater demand for these fed cattle,
resulting from increased demand for beef, has a ripple effect
throughout other cattle production stages. To supply more cattle to
meatpackers, feedlots need more cattle from stocker or growing
operations, which in many cases are integrated with cow-calf producers.
Most of the calves that cow-calf producers supply for beef production
are placed in these growing operations, where they take on weight while
they pasture on grass and other forages. These feeder cattle are sent
to feedlots when they weigh between 500 and 750 pounds (fig. 3 shows
such cattle feeding at a feedlot trough). Increased demand for these
feeder cattle by feedlots puts upward pressure on feeder cattle prices.
Figure 3: Photograph: Cattle Eating at a Feedlot Trough:
[See PDF for image]
[End of figure]
In the face of increased demand, cow-calf producers raise more calves,
sometimes relying on seedstock operators, who supply more breeding
stock, such as bulls. Calves are usually weaned from cows when they
weigh about 500 pounds. Figure 4 traces the movement of animals from
breeding to processing and consumption. Thus, as the effects of an
increase in consumer demand for beef unfold, prices, signaling this
change in demand, eventually rise along the chain, depending on the
strength of demand and the availability of supply, as depicted in
figure 5. Figure 6 outlines the changes in retail beef, boxed beef, and
slaughter prices from 1974 through 1999.
Figure 4: The Beef and Cattle Industry from Animal Breeding to
Consumption:
[See PDF for image]
This figure is an illustration of the beef and cattle industry from
animal breeding to consumption. The following information is presented:
Cow-calf sector (5 to 11 months): Bulls, Heifers; retained for
breeding;
Stocker-yearling sector 12 to 20 months): Buys calves and supplies
feeder cattle to feedlot sector; (Overlap ownerships in first two
sectors);
Feedlot sector (3 to 5 months): Buys feeder cattle and supplies fed
cattle to beef packing houses;
Beef packing houses: Buys fed cattle and supplies beef to wholesalers,
retailers, and other processors; Culls: trimmings.
Wholesalers, retailers, and other processors: Buys beef; Imports;
Hamburger.
Cow-calf sector: Steer:
* gestation period 9 months;
* Raised by mother 6 to 10 months;
* Weaned 6 to 10 months at about 500 pounds;
* If 600 pounds or more, or preconditioned and sent directly to
feedlots:
- Weight gain 125-150 pounds;
- preconditioning lots (high0intensive medical and nutritional program
for 1-1.5 months);
Stocker-yearling sector: Fed on forage, wheat pasture, and silage;
Steer of heifer sent to feedlots when 750 pounds;
Feedlot sector: Fed until 950 to 1,250 pounds, 15 to 24 months old; fed
high-energy rations of corn and protein supplements and roughage
Beef packing houses: Produces boxed beef; Subprimal cuts (top round,
tenderloin, sirloin); smaller consumer cuts;
Wholesalers, retailers, and other processors: smaller consumer cuts to
grocery chains, hotels, restaurants, institutions; exports.
[End of figure]
Figure 5: Prices Signal Changes Along the Demand and Supply Chain
between Producers and Consumers:
[See PDF for image]
This figure is an illustration of prices signal changes along the
demand and supply chain between producers and consumers, as follows:
Seedstock, cow-calf producers and stockers;
Feedlots:
* Feeder price;
* Slaughter or fed price;
Packers and processors:
* Boxed beef or wholesale beef price;
Retailers and food service:
* retail beef price;
Consumers:
[End of figure]
Figure 6: Retail Beef, Boxed Beef, and Slaughter Steer Price Movements,
1974–99:
[See PDF for image]
This figure is a multiple line graph depicting retail beef, boxed beef,
and slaughter steer price movements, 1974–99. The following are
indicated in dollars per 100 pounds:
Retail Price;
Wholesale boxed beef value;
Slaughter steer price.
Source: USDA, Agricultural Marketing Service, ERS.
[End of figure]
Important connections exist also between the cattle and beef industry
and other sectors of the economy. Some of the closest connections are
with products that compete with beef, such as poultry and pork. Other
close connections are with critical inputs to the cattle and beef
industry, such as feed grains. Because the cattle and beef industry is
a major user of feed grains, beef production is also affected by grain
supplies and prices. Feed is a major cost component in cow-calf
production. In addition, foreign demand and supply of beef and cattle
interact with domestic demand and supply in determining cattle prices
and producers’ incomes.
Structural and Technological Changes in the Cattle and Beef Industry:
The demand and supply relationships connecting various segments of the
cattle and beef industry are changing in a number of ways. Some of the
structural changes relate to how meatpackers procure cattle.
Historically, cattle were bought and sold in a spot market. Most sales
occurred at terminal markets and auctions with cattle ready for
delivery on sale. More recently, this activity has shifted to feedlots,
where packers purchase cattle directly from cattle owners or feedlot
managers. Cattle procurement no longer relies solely on the spot market
and now involves closer ties between packers and feedlots. Three
procurement methods involving such closer ties are marketing
agreements, forward contracts, and packer fed cattle.
In a marketing agreement, a feedlot may sell cattle to a packer
according to a prearranged schedule and price. Such agreements
generally involve ongoing relationships between feedlots and packers
for the sale of cattle rather than a single transaction. Prices paid
for cattle are often determined by a formula, which may be based on
prices paid for other cattle slaughtered at the meatpacker’s plant or
publicly reported prices. In addition, price premiums and discounts may
be paid that are based on cattle quality.
In a forward contract, the packer and seller agree on future delivery of
cattle, typically using a formula based on futures prices or publicly
reported prices to set the contract’s base price. When the price is
based on futures prices, the parties agree on a differential from
futures prices, called the price basis. Premiums and discounts are
applied for differences in cattle quality. Typically, feedlots and
packers agree on delivery month, specific cattle to be delivered,
cattle quality standards, and the price basis.
Packers also slaughter cattle that they own themselves and feed in
feedlots. Packers may also share ownership of cattle with individuals
or feedlots where the cattle are fed. This arrangement, called vertical
integration, goes a step further, supplanting the coordinated exchange
relationship between feedlots and packers that characterizes marketing
agreements and forward contracts with the meatpacker’s outright
ownership of the cattle. Vertical integration also occurs when a single
entity has ownership control of animal production, processing, and
marketing beef products.
Tying cattle prices to quality is called value-based pricing. It
derives from the belief that traditional cattle pricing, relying on
animal weight, does not adequately relay consumer preferences for
quality and attendant price signals to producers. Grade and yield
pricing is frequently used, which applies price premiums and discounts
to a predetermined base price according to carcass attributes. Another
slight variation is grid pricing, in which a base price is determined
after the transaction between buyer and seller has been negotiated. In
addition, some beef packers use the wholesale value of beef to
determine the price they are willing to pay for cattle.
What effect vertical coordination—through marketing agreements and
forward contracts, vertical integration, and value-based pricing—is
having on cattle prices and producers’ incomes has been debated by
various industry analysts. For instance, some believe that marketing
agreements and forward contracts have adversely affected prices paid
for cattle bought in the spot market, while others hold that producers
benefit from these arrangements. Some research suggests that rising
levels of vertical coordination and integration can be traced to
consolidation in the meatpacking and feedlot sectors.
Another feature of structural change in the cattle and beef industry has
been the consolidation of the meatpacking sector into fewer firms
operating large production facilities able to slaughter half a million
or more steers and heifers per year. Large plants accounted for less
than 25 percent of steer and heifer slaughter in 1980 but more than 75
percent in 1995. A recent USDA study found that economies of scale help
explain this increase in consolidation and market concentration in the
meatpacking sector.[Footnote 4] USDA also found that large facilities
are fabricating more meat products because they can do so at lower cost
than meat wholesalers and retailers, the traditional carcass buyers.
Market concentration measures total sales of the largest firms in a
specific market or industry. The four largest meatpacking firms
accounted for 36 percent of total commercial slaughter in 1980, 72
percent in 1990, and 81 percent in 1999, as seen in figure 7, which
therefore can be seen as illustrating a rise in market concentration in
the meatpacking sector over that period of time. Some analysts are
concerned that greater concentration has led to fewer meatpackers
bidding for cattle and offering lower prices. Others hold that
technological change and cost economies are the most important factors
driving the meatpacking sector and that market power associated with
concentration has played a relatively minor role in determining cattle
prices.
Figure 7: The Rise in Steer and Heifer Slaughter, Accounted for by the
Four Largest U.S. Meatpackers, Selected Years 1980–99:
[See PDF for image]
This figure is a vertical bar graph depicting the following
approximated data:
The Rise in Steer and Heifer Slaughter, Accounted for by the Four
Largest U.S. Meatpackers, Selected Years 1980–99:
Year: 1980;
Percent of slaughter: approximately 38%;
Year: 1985;
Percent of slaughter: approximately 50%;
Year: 1990;
Percent of slaughter: approximately 75%;
Year: 1995;
Percent of slaughter: approximately 80%;
Year: 1998;
Percent of slaughter: approximately 78%;
Year: 1999;
Percent of slaughter: approximately 80%.
Source: USDA, Grain Inspection, Packers and Stockyard Administration.
[End of figure]
Technological changes in the cattle and beef industry, according to
USDA, are becoming an underlying cause of economies of scale in
meatpacking. In a development directly affecting packers, retailers,
and consumers, packaging and processing technology has enabled
meatpackers to move from supplying boxed beef to firms that specialize
in further processing to directly supplying case-ready meats,
convenience products, often seasoned and marinated, and precooked
products for immediate retail sale. In contrast, in the early 1970s,
meatpacking plants were typically engaged only in slaughter, sending
carcasses to wholesalers and retailers for processing into retail
products. Packers have also begun marketing their products
electronically.
Another technological development that affects packers and producers
directly is the electronic measurement of animal carcass quality,
making it easier for packers to determine the grade and other
characteristics of carcasses. In another development affecting
producers and packers, cattle marketing has begun on the Internet.
Cattle feeding through feed additives and computerized onsite feedmills
and feeding operations represents yet more technological innovation.
Beef’s Competition from Other Meats:
The consumption of beef and other meats has changed over time. A USDA
study concluded that decreased demand for beef was a major reason for
the larger increase in market concentration in the beef industry than
in the pork industry.[Footnote 5] According to USDA, decreased demand
for beef was an important incentive for meatpacking firms to seek cost
savings through larger plants. As shown in figure 8, per capita beef
consumption began falling in the mid-1970s but leveled off in the
1990s.[Footnote 6] During these two decades, per capita poultry
consumption rose steadily while per capita pork consumption remained
relatively stable. Meanwhile, retail beef prices were higher and
remained higher than chicken and pork prices, as shown in figure 9.
Figure 8: U.S. Per Capita Retail Beef Consumption Fell in the 1970s and
1980s and Leveled Off in the 1990s:
[See PDF for image]
This figure is a multiple line graph depicting U.S. per capita retail
beef consumption from 1970 through 1999. Lines represent the
consumption of beef, pork, and chicken.
Source: USDA, ERS.
[End of figure]
Figure 9: U.S. Retail Beef Prices Were Higher Than Chicken and Pork
Prices, 1970–99:
[See PDF for image]
This figure is a multiple line graph depicting U.S. per capita retail
beef prices from 1970 through 1999. Lines represent the prices of beef,
pork, and chicken.
Source: USDA, ERS.
[End of figure]
International Trade in Beef and Cattle Is Growing:
Although the United States is the largest beef producer in the world,
and although its exports of beef to other nations have grown more
rapidly than its imports, it is a net beef importer, as depicted in
figure 10. Most beef exports from the United States are choice cuts,
while most imports into the United States are used for ground beef.
Beef exports rose from less than 1 percent of U.S. beef consumption in
1970 to 9 percent in 1999, seen in figure 11. Beef imports, in
contrast, have ranged between 7 percent and 11 percent of U.S.
commercial production since 1970, seen in figure 12.
Figure 10: U.S. Beef Exports Have Generally Risen Since 1980:
[See PDF for image]
This figure is a multiple line graph depicting U.S. beef exports in
millions of pounds from 1970 through 1999. Lines represent exports,
imports, and net imports.
Source: USDA, ERS.
[End of figure]
Figure 11: U.S. Beef Exports Rose as a Percentage of U.S. Consumption,
1970–99:
[See PDF for image]
This figure is a multiple line graph depicting U.S. beef exports in
millions of pounds from 1970 through 1999 as a percentage of U.S.
consumption. Lines represent U.S. consumption and exports as a
percentage of U.S. consumption.
Source: USDA, ERS.
[End of figure]
Figure 12: U.S. Beef Imports Varied as a Percentage of Commercial
Production, 1970–99:
[See PDF for image]
This figure is a multiple line graph depicting U.S. beef imports in
millions of pounds from 1970 through 1999 as a percentage of commercial
production. Lines represent U.S. commercial production and imports as a
percentage of commercial production.
Source: USDA, ERS.
[End of figure]
The United States imports more cattle than it exports, as seen in
figure 13. The nations from which it imports cattle—Canada and
Mexico—are, for all practical purposes, the same nations to which it
exports cattle. Imports of cattle also made up a greater percentage of
cattle slaughtered in the United States during the 1990s, as seen in
figure 14.
Figure 13: U.S. Cattle Imports Exceeded Exports, 1970–2000:
[See PDF for image]
This figure is a multiple line graph depicting U.S. cattle imports
exceeded exports in thousand head. Lines represent imports, net
imports, and exports.
Source: USDA, National Agricultural Statistics Service, ERS.
[End of figure]
Figure 14: U.S. Cattle Imports Rose as a Percentage of Slaughter,
1970–2000:
[See PDF for image]
This figure is a multiple line graph depicting U.S. cattle imports as a
percentage of slaughter. Lines represent imports as a percentage of
cattle slaughtered and imports as a percentage of cattle and calves.
Source: USDA, National Agricultural Statistics Service, ERS.
[End of figure]
The Cattle Cycle Is an Important Feature of Supply:
Cattle have the longest biological cycle of all meat animals. The cattle
cycle (illustrated for 1930–2000 in fig. 15) refers to increases and
decreases in herd size over time and is determined by expected cattle
prices and the time needed to breed, birth, and raise cattle to market
weight, among other things. The actions of individual producers to
“time the market” by building up their herds in advance of expected
cyclical peaks in cattle prices can also shape the cattle cycle. As
figure 16 shows, cattle inventories have at times reached peak numbers
before associated peaks in beef production, and while the number of
cattle has fallen, beef production has risen. Figure 17 illustrates the
cyclical movement that cattle prices have exhibited over time. They
tend to move in a direction opposite to that of commercial cattle
slaughter, as shown in figure 18.
Figure 15: The Cattle Cycle: Rising and Falling Cattle Inventories,
1930–2000:
[See PDF for image]
This figure is a line graph depicting the rise and fall of cattle
inventory in thousand head. The line represents U.S. cattle inventory
on January 1 of each year from 1930 to 2000.
Source: USDA.
[End of figure]
Figure 16: How Cattle Inventories Peaked Before Beef Production,
1970–99:
[See PDF for image]
This figure is a multiple line graph depicting how cattle inventory
peaked before beef production in thousand head. The lines represent
U.S. cattle inventory on January 1 of each year and U.S. commercial
beef production.
Source: USDA, ERS.
[End of figure]
Figure 17: The Cyclical Movement of Cattle Prices, 1970–99:
[See PDF for image]
This figure is a multiple line graph depicting the cyclical movement of
cattle prices in dollars per 100 pounds. The lines represent:
Slaughter steer 1,100-1,300 lbs (Nebraska)[A];
Feeder steer 750-800 lbs (Oklahoma City)[B];
Slaughter cows, commercial (Sioux Falls).
[A] The slaughter steer price indicated is for quality grades choice
2–4. Choice is one of eight quality grade designations for steers and
heifers: prime, choice, select, standard, commercial, utility, cutter,
and canner. Quality grades are based on an evaluation of factors
related to the palatability of the lean meat. Yield grades 2–4 are
three of five (1–5), of which yield grade 1 represents the highest
degree of cutability, or the yield of closely trimmed retail cuts.
[B] The feeder steer price indicated is for medium number 1. For feeder
steers, medium number 1 means medium frame, number 1 thickness.
According to USDA: “Variations in frame size among feeder cattle
primarily affect the composition of their gain in weight. The gain in
weight of a larger framed feeder animal of a given degree of thickness
normally will consist of more muscle and bone but less fat than a
smaller framed animal. There are three frame classifications: large,
medium, and small. Variations in thickness are reflected in differences
in ribeye area and, therefore, relate primarily to the ultimate yield
grade of the carcass that a feeder animal will produce.”
Source: USDA, U.S. Standards for Grades of Slaughter Cattle Washington,
D.C.: USDA, AMS, Livestock and Seed Division, July 1, 1996), p. 3, and
U.S. Standards for Grades of Feeder Cattle (Washington, D.C.: USDA,
AMS, Livestock and Seed Program, October 1, 2000), pp. 1–2. See also
[hyperlink,
http://www.ers.usda.gov/data/sdp/view/asp?f=livestock/94006/] (Jan. 16,
2002).
[End of figure]
Figure 18: The Opposite Movement of Cattle Prices and Commercial
Slaughter, 1974–2000:
[See PDF for image]
This figure is a multiple line graph depicting the opposite movement of
cattle prices and commercial slaughter in dollars per 100 pounds and
thousand head. The lines represent:
Slaughter steer 1,100-1,300 lbs (Nebraska)[A];
Commercial cattle slaughter.
[A] The slaughter steer price indicated is for quality grade choice
2–4. Choice is one of eight quality grade designations for steers and
heifers: prime, choice, select, standard, commercial, utility, cutter,
and canner. The quality grades are based on an evaluation of factors
related to the palatability of the lean meat. Yield grades 2–4 are
three of five (1–5), of which yield grade 1 represents the highest
degree of cutability or the yield of closely trimmed retail cuts.
Source: USDA, U.S. Standards for Grades of Slaughter Cattle
(Washington, D.C.: USDA, AMS, Livestock and Seed Division, July 1,
1996), p. 3. See also [hyperlink,
http://www.ers.usda.gov/data/sdp/view/asp?f=livestock/94006/] (Jan. 16,
2002).
[End of figure]
Modeling the Cattle and Beef Industry Can Take Different Forms:
Economic modeling of the beef and cattle industry can take a variety of
forms, depending on the questions asked. These questions define the
purpose of a model.
The purpose of modeling the cattle and beef industry can range from
wanting accurate short-term forecasts of cattle prices to seeking
information on how farm policy affects cattle producers. Models can also
be designed to answer questions about the effects of structural change
and international trade, to name two.
Another critical issue determining the type of modeling has to do with
judgments about how successful a model will be in answering relevant
questions. Success depends on the availability and cost of acquiring
reliable data to estimate key supply and demand relationships in the
cattle and beef industry. In some cases, it also depends on the ability
to isolate cause and effect in the model—for instance, being able to
pinpoint what caused the decline in per capita beef consumption. Being
able to accurately define and estimate cause and effect in a model is
complicated by the possibility of multiple causes and the challenge of
isolating each one’s effect. Limited knowledge about the processes
being studied and changes in demand and supply relationships over time
are important hurdles, as well. Success is also contingent on the
quality of previous research.
Models can consist of a single equation representing the link between
current and past values of a variable for short-term forecasting
purposes to frameworks consisting of many interrelated equations. The
parameters of these equations—measuring, for example, how sensitive
herd expansion is to rising feed costs—may be estimated by the
statistical analysis of historical data in the course of building the
model. Alternatively, parameter values may be based on the results of
previous research or may be calibrated to replicate the data of a
chosen benchmark year. The results of previous empirical research or
calibration are often relied on when data are unavailable.
Regardless of how simple or complex the modeling is, projections of key
variables, such as cattle prices, typically reflect more than just
running the model. An analyst’s judgment concerning the plausibility
and consistency of a model’s results also plays an important role in
deciding what projections to report. A pronounced example of this is
the instance in which the modeling framework consists solely of an
expert panel meeting periodically to reach consensus forecasts on
variables of interest, after considering a variety of relevant
information sources.
Objectives, Scope, and Methodology:
Concerned that current models the government uses do not fully account
for how some marketing practices and trade affect prices U.S. cattle
producers receive for their livestock, Senator Daschle asked us to
determine:
* the extent to which economic models that USDA and ITC incorporate
imports, concentration in the U.S. meatpacking industry, and marketing
agreements and forward contracts in predicting domestic cattle prices;
* the most important factors affecting cattle prices and producers’
incomes; and;
* the most important data and modeling issues in developing a
comprehensive analysis to project cattle prices and producers’ incomes.
To determine the extent to which USDA’s and ITC’s economic models
incorporate imports, market concentration, and marketing agreements and
forward contracts, we obtained documentation on their relevant models.
We also met with USDA and ITC officials to discuss these models. We
examined the structure and specification of the models, including
estimated equations, methods of estimation, estimation results, and
information on data used for estimation.
To address the second and third objectives, we convened a virtual panel
on the Internet of 40 agricultural experts. We asked them (1) what the
most important factors affecting cattle prices and producers’ incomes
are and (2) what the most important data and modeling issues would be
for developing a comprehensive analysis to project cattle prices and
producers’ incomes.
In selecting the panel, we generated a prospective list of experts,
based on a literature review, referrals from USDA and ITC officials, and
congressional sources. Of 48 experts we contacted, 42 agreed to
participate. Forty experts completed all phases of our panel survey.
To structure and gather opinions from the expert panel, we employed a
modified version of the Delphi method.[Footnote 7] The Delphi method
can be used in a number of settings, although when first developed at
the RAND Corporation in the 1950s, it was applied in a group-discussion
forum. One of the strengths of the Delphi method is its flexibility.
Rather than employing face-to-face discussion, we used a version that
incorporated an iterative and controlled feedback process,
administering a series of three questionnaires over the Internet. We
used this approach to eliminate the potential bias associated with live
group discussions. The biasing effects of live discussions can include
the dominance of individuals and group pressure for conformity.
Moreover, by creating a virtual panel, we were able to include many
more experts than we could have with an actual panel. This allowed us
to obtain the broadest possible range of opinion.
In the first questionnaire, in phase I, we asked the experts three open
ended questions:
* During the past few years, what were the most important factors or
variables affecting (a) the prices received by domestic cattle producers
and (b) producers’ incomes?
* If you were to conduct a comprehensive analysis of domestic cattle
prices and producers’ incomes, are there other factors or variables not
listed in question 1 that you would include?
* What problems or issues would you face in developing a comprehensive
and reliable analysis to estimate domestic cattle prices and producers’
incomes?
After they completed the first questionnaire, we analyzed their
responses in order to compile a list of the most important factors
affecting cattle prices and producers’ incomes, as well as key problems
or issues facing analysis of prices and incomes. We combined the
responses to the first two questions, organizing them into four
categories—(1) domestic demand for cattle, (2) domestic supply of
cattle, (3) international trade, and (4) structural change. While the
last two categories overlapped the first two to some degree, we broke
them out to directly link our first objective regarding USDA and ITC
models to the experts’ responses. For the list of key problems or
issues, we organized each item under either a data or a modeling issue.
In the questionnaire in the second phase, experts rated the importance
of each of the factors identified during the first phase. Our analysis
of the data produced a ranking of most important factors and level of
agreement about each factor’s importance (see app. III).
During the second phase, we also asked the experts to evaluate issues
facing the development of a comprehensive analysis identified during the
first phase. They identified 41 data and modeling related issues (see
app. IV). We asked the experts to rate each of these data and modeling
issues by answering the following questions:
* How important is it to address this problem or issue for purposes of
modeling cattle prices and/or producers’ incomes?
* How feasible is it to overcome or implement the solution for this
problem or issue for purposes of modeling cattle prices and/or
producers’ incomes?
During the third phase, we presented the panel with the results of the
questionnaires from phases I and II, including a summary of findings and
descriptive statistics on the importance of the factors and the
importance and feasibility ratings of the 41 data and modeling issues.
We asked the experts to consider these results and give their opinions
of why there was a greater divergence of opinion on the importance of
structural change and international trade (see app. V for excerpts from
their statements of opinion).
After the panel members examined the results and considered the reasons
for the variance of opinion on international trade and structural
change, we offered the experts the opportunity to change their original
assessments. Two panelists changed their opinions on structural change,
and five changed their ratings on international trade.
Regarding data and modeling issues, we asked each expert whether the
federal government should take action to help overcome these issues. We
asked those who believed that government action was warranted to select
up to 5 issues from the 41 issues that had been identified. (The list
of rank-ordered issues recommended for federal action is in app. V.)
To ensure that the wording of the initial questions was unambiguous,
three panel members pretested a paper version of the first
questionnaire, and we made relevant changes before we deployed the
first questionnaire on the Internet. We did not pretest subsequent
questionnaires because they were based on the panel’s answers to
preceding questionnaires. We did, however, review them before we
deployed them.
Some of the panelists may have cooperative agreements or other ongoing
relationships with the federal government, trade groups, individual
companies, or other organizations within the agricultural industry. In
addition, some panel members may want to develop such relationships in
the future. Therefore, to mitigate potential conflict of interest, the
panel we convened was large enough to have a wide range of experience
and views in the subject area. None of the panel members were
compensated for their work on this project.
[End of chapter]
Chapter 2: The USDA and ITC Models Were Not Designed to Answer
Questions about Structural Change:
USDA and ITC have several models for analyzing the cattle and beef
industry. These models account for imports but do not incorporate market
concentration, marketing agreements, and forward contracts because they
were not designed to answer questions about these aspects of structural
change. USDA’s models include a variety of domestic and international
supply and demand variables to project U.S. cattle prices. One is a
short-term model projecting up to 18 months into the future, and the
other is a long-term model projecting up to 10 years. ITC’s models are
used to investigate injury claims resulting from imports that sell in
the United States at less than fair value or are subsidized and to
conduct broad economic studies. USDA separately monitors and conducts
research on how structural changes involving market concentration,
marketing agreements, and forward contracts affect the cattle and beef
industry.
USDA’s Models Project Cattle Prices under Baseline Conditions:
Each year, USDA publishes an agricultural baseline report with
projections for the livestock sector, including cattle and beef.
[Footnote 8] Changes in market concentration, marketing agreements, and
forward contracts are not explicitly considered in making these
projections. The baseline projections reflect a composite of results
from various economic models and judgmental analysis. The projections
of the livestock industry in the baseline are estimated by using USDA’s
short-term and long-term livestock models. They are based on specific
assumptions about the economy, agricultural policy, and international
developments. They assume normal weather patterns.[Footnote 9] Current
baseline projections also assume the continuation of the Federal
Agricultural Improvement and Reform Act of 1996.
As a result, these projections are a description of what to expect,
given assumptions defining a baseline scenario. Commodity projections
in the baseline are used to estimate the cost of farm programs needed
to prepare the president’s budget. Baseline projections are also used
to determine the incremental effects of proposed changes in
agricultural policy.
Short-Term Projections Rely on Analysts’ Judgments:
USDA’s Interagency Commodity Estimates Committee (ICEC) for meat
animals makes short-term cattle price projections. The committee uses a
data set that includes beef and cattle imports and exports but does not
contain information on changes in market concentration, marketing
agreements, and forward contracts. The committee consists of an official
from the World Agricultural Outlook Board, who serves as the chair, and
other members.[Footnote 10] Analysts from ERS make initial projections
that the committee reviews. Consensus is reached, and final projections
are included as the World Agricultural Supply and Demand Estimates
forecast in USDA’s agricultural baseline report.
In making initial projections, ERS starts by updating a historical
database, compiling the most current information on production, prices,
and trade statistics for the livestock industry. Monthly data are
collected on the production of beef, veal, pork, lamb, and poultry and
slaughter of steers, heifers, beef and dairy cows, broilers, hogs, and
turkeys. Most data are obtained from USDA’s Agricultural Marketing
Service (AMS) and National Agricultural Statistics Service (NASS). ERS
supplements these monthly data with the latest information from daily
and weekly releases, using numerous public and private sources. This
data set, combined with the latest release on cattle inventories, class
breakouts, and live and wholesale and retail prices, is used to make
projections.
The next step involves entering the updated data into a spreadsheet to
simulate possible short-term scenarios for the livestock industry.
Analysts’ judgments of current trends in the industry are used to
select one scenario and corresponding projections to present at the
monthly ICEC meeting.
Committee members meet monthly to review ERS’ initial projections; they
discuss whether recent information or developments related to weather,
the national and industry economic outlook, and international trade
suggest a need to revise these projections. The May meeting produces
quarterly and annual projections through the following year. Meetings in
subsequent months review projections approved the previous month that
are then revised as needed. The committee’s chairperson sees his role
as helping committee members reach consensus; however, the chair has
overall responsibility for approving projections and will impose a
decision if consensus cannot be reached. Projections from the October
meeting are used in the 10-year baseline report.
The most current available data on beef and cattle imports and exports
are used in arriving at the short-term projections. However, these trade
statistics are not as current as other data, being 6 weeks out of date
when the Department of Commerce releases them. An ERS analyst said that
to lessen the effect of this lag, it adjusts its trade forecasts by
using the most recent releases and information on important trading
partners and competitors, including currency rates, and changing supply
conditions in other countries. Information on market concentration,
marketing agreements, and forward contracts, while not part of the data
set analyzed, we believe can be implicitly included in committee
discussions.
Long-Term Projections Are Based on USDA’s Livestock Model:
ERS uses its livestock model to make annual projections of the cattle
and beef industry as well as the hog and poultry industries. It includes
international trade in beef and cattle in the model but not market
concentration, marketing agreements, and forward contracts. These
projections are included in USDA’s baseline report. This model consists
of equations specifying supply and demand relationships that affect the
livestock sector. It was estimated initially with 1960–88 data.
Production sectors supplying beef, pork, and poultry are modeled, along
with demand for them. The demand sector consists of a consumer demand
component, which determines retail prices, and another component
derived from consumer demand, which determines wholesale and producer
prices. Feedback from demand to production takes place through the
effect of producer prices on returns to cow-calf producers. Production,
supply, and demand variables are determined within the system of
equations making up the model, while macroeconomic, trade, and feed
variables are determined outside the model. An official from USDA who
helped build the model said that emphasis was placed more on modeling
production than on demand. Appendix II describes the model in detail.
The largest component of the livestock model deals with the cattle and
beef industry, including the size and composition of the cattle herd,
commercial slaughter, beef production and consumption, and retail,
wholesale, and cattle prices.
For herd size and composition, the model contains equations explaining
inventories of beef cows, calves, steers, heifers, and bulls. The
inventory of beef cows is the main driver of the cattle and beef
sector, helping determine the number of calves, steers, heifers, and
slaughter. The number of animals slaughtered plus cattle imports and
exports determine beef production.
Domestic beef consumption is computed by first adding beef imports and
beef inventories at the beginning of the year to beef production during
the year and then subtracting from this beef exports and beef
inventories at the end of the year. Beef, pork, and poultry consumption
help determine retail beef prices.[Footnote 11] Retail beef prices are
critical in explaining prices that meatpackers and cattle producers
receive, which, in turn, are an important component of returns to cow-
calf producers in the model. Returns to cow-calf producers help explain
the number of beef cows and calves, beef cows slaughtered, and heifers
added to the beef cow herd or slaughtered.
The cost of feed comes into play at several places in the model. For
example, hay and corn prices help explain the number of heifers added to
the beef cow herd and the number of beef cows slaughtered. Feedlot costs
also explain the number of steers slaughtered and feeder steer prices.
In addition, feed and other input costs are used in determining returns
to cow-calf producers. Feed cost projections come from USDA’s Food and
Agricultural Policy Simulator (FAPSIM).[Footnote 12]
Changes in market concentration, marketing agreements, and forward
contracts are not explicitly included in any of these modeled
relationships. International trade in beef and cattle is included,
although values for these trade variables are determined outside the
livestock model. Beef export and import projections are based on USDA’s
link system model.[Footnote 13]
The Livestock Model Has Not Been Reestimated, Documented, or Validated:
USDA has not reestimated the livestock model in its entirety since 1990,
when it was first developed. Much of the data used in the original
estimation are from the 1960s and 1970s, before rapid consolidation in
the meatpacking sector and increased use of marketing agreements and
forward contracts. Reestimating the model using the most current data
available would better reflect structural and other changes and would
reveal whether estimated values of key model parameters change and
result in different projections of cattle prices.
Originally published in 1990, documentation for the livestock model
contained estimation results, including standard errors for parameter
estimates, T ratios, and R squares, described as “vital statistics of
the model”.[Footnote 14] Including these statistics in model
documentation is standard practice. Since the model was first
estimated, some components of the model in the production and demand
sectors have been modified. According to USDA officials familiar with
the model, it was last modified about 1994. However, there is no
documentation on how such vital statistics may have changed as a result
of these modifications.
The 1990 documentation also described the validation of the livestock
model, noting that individual parameter estimates were obtained for
1960–86 to test its forecasting ability during 1987–89. Validation
measures such as mean percentage error and Theil’s relative change U1
statistics were reported, and the authors concluded that on the basis
of these results, the model forecasted reasonably well. Since then, the
model has not been further validated. An assistant administrator for
ERS said that validating, or back casting, the current version of the
model makes sense.
Current documentation of the livestock model includes a listing of the
equations and values for estimated parameters, seen in appendix II. USDA
officials said that other documentation of the livestock model,
including the data set used to estimate it, along with standard
measures of statistical goodness of fit and other diagnostics of the
model’s performance described above, were lost during a move to a new
location. They also said that budgetary cuts led to a lack of resources
needed to provide better documentation of the model, as well as to
replace lost data. USDA officials said that lack of resources has also
negatively affected the quality of documentation for FAPSIM and the
link system model.
ITC’s Models Lack Industry Specifics Needed to Predict Prices:
ITC uses two types of models to analyze the cattle and beef industry.
One type is a model to support its mandate to investigate domestic
injury claims resulting from imports being subsidized or selling in the
United States at less than fair value. The second type is a sector-
specific model used to carry out broad economic studies, including
those related to trade liberalization efforts.[Footnote 15] Neither
type of model is detailed enough to project cattle prices or address
the effects of structural changes associated with market concentration,
marketing agreements, and forward contracts in the cattle and beef
industry.
When investigating domestic injury claims, ITC economists use COMPAS, a
partial equilibrium model.[Footnote 16] COMPAS was designed to estimate
how importers’ selling of a specific product below its fair price would
affect price, sales, and revenue of that product in the competing
domestic sector. Selling imports at less than fair value is sometimes
referred to as dumping.[Footnote 17] COMPAS is also used to estimate
the effects of governments’ subsidizing exports. To do so, COMPAS uses
a standardized methodology, beginning with a supply and demand
framework and assuming less than perfect substitutability between
domestic and imported products.[Footnote 18] Values of demand and
supply parameters needed to assess the effects of dumping are often
obtained from other researchers’ estimates. ITC typically uses a range
of estimated values for these parameters to reflect uncertainty. ITC
commissioners may consider the results of this analysis in their
deliberations. However, according to ITC officials, commissioners rely
on the specifics of legal statutes and the record of facts collected
during ITC’s investigation in reaching their decisions rather than on
model results in assessing injury.
ITC injury investigations involving dumping and subsidies must adhere to
specific statutory criteria, procedures and time periods.[Footnote 19]
The process starts with an interested party filing a petition with ITC
and the Department of Commerce. For both dumping and subsidies
investigations, ITC must make a preliminary determination of whether
there is a “reasonable indication” that an industry is materially
injured or threatened with material injury by the imports in question.
If ITC’s determination is negative, the investigation ends. If it is
affirmative, the investigation continues and Commerce makes a
preliminary determination of whether there has been dumping or
subsidies and, if so, a preliminary calculation of what the dumping or
subsidy margin would be. Commerce continues the investigation,
regardless of its preliminary findings, and makes a final determination
of dumping or subsidies and a final calculation of margins. If
Commerce’s final determination is affirmative, ITC continues its
investigation and makes a final determination of material injury or
threat of material injury.
Recently, COMPAS was used, in response to a 1998 petition by the
Ranchers–Cattlemen Action Legal Foundation and others, to investigate
Canadian and Mexican cattle alleged to have been sold in the United
States at less than fair value. ITC staff used a range of estimates
representing supply, demand, and product substitution relationships in
the U.S. cattle market. These estimates, along with data on market
share, Commerce’s dumping margins, transportation costs, and tariffs,
were incorporated in COMPAS to analyze the likely effects of unfair
pricing of cattle imports on the U.S. cattle industry. In the absence
of dumping, ITC estimated U.S prices would have been between 0.2
percent and 1.8 percent higher, U.S. cattle producers’ revenue would
have been from 0.3 percent to 1.8 percent higher, and U.S. cattle
producers’ output would have been between 0 and 0.4 percent higher. The
commissioners determined that the industry was not materially injured
or threatened with material injury by these imports.[Footnote 20]
This 1998 investigation reveals some limitations in the COMPAS model for
analyzing problems in the cattle and beef industry. ITC’s estimates of
the effects of these imports relied on the value of the dumping margin
Commerce determined and on supply and demand price elasticities
(parties to the investigation are requested to provide feedback on these
values and other expert sources are consulted).[Footnote 21] In the
absence of a dumping investigation and data on a dumping margin, COMPAS
cannot be readily applied to assess the effect of an import quantity
surge. Furthermore, while COMPAS can be used to estimate the effect of
price changes in the cattle or beef sector, the model does not
explicitly link downstream beef-sector effects to the upstream cattle
sector.[Footnote 22] COMPAS also does not explicitly account for
changes in concentration in the meatpacking industry, marketing
agreements, and forward contracts.[Footnote 23]
The ITC 1998 investigation reveals other analytical issues. To account
for uncertainty about the values of key parameters used in COMPAS, such
as price elasticity or sensitivity of U.S. demand and supply of cattle
and the extent to which imported cattle can be substituted for U.S.
cattle, ITC used a fairly wide range of estimates for the parameters.
In addition, while ITC was informed that imports affected some U.S.
producers and regions more than others, published data at this level of
detail are often unavailable, and most studies that have estimated
price sensitivities used national data.
ITC uses various models to carry out other economic studies examining
the effects of broad trade policy changes, such as NAFTA. For example,
ITC issued a study in 1997 on the effect of NAFTA and the Uruguay Round
on U.S. trade of cattle and beef with Canada and Mexico, using an
econometric model that estimated effects on trade volume, but did not
estimate or predict effects on U.S. cattle prices.[Footnote 24] ITC has
also used computable general equilibrium (CGE) models to assess the
likely effects on various sectors of the U.S. economy from major trade
liberalization.[Footnote 25]
CGE models are generally not specific enough to predict cattle prices
or to address structural changes associated with market concentration,
marketing agreements, and forward contracts.
Research Is Inconclusive on How Structural Change Affects Domestic
Cattle Prices:
The models that USDA and ITC use do not explicitly account for the
structural changes occurring in the industry from greater concentration
in the meatpacking industry and greater use of marketing agreements and
forward contracts. According to USDA, its current research on these
structural changes is inconclusive about how they affect cattle prices
paid to cattle producers.
USDA and others have conducted research on the effects of these
structural changes on domestic cattle prices. Overall, research
conducted by or for the Grain Inspection Packers and Stockyards
Administration (GIPSA), a USDA agency, has not found conclusive
evidence linking these changes to domestic cattle price changes.
[Footnote 26] For example, GIPSA reported in 1996 that the findings of
an extensive literature review were inconclusive concerning the effects
of concentration, primarily because of limitations in methods or data
in the research reviewed.[Footnote 27] This report also stated that
while the body of evidence from the literature was insufficient to
support a finding of noncompetitive behavior, GIPSA also could not
conclude that the industry is competitive. The study recommended that
future research focus more directly on data disaggregation at the firm
and plant levels to provide a better understanding of the dynamics of
individual firm behavior and rivalry between firms.
Assessing competitiveness from available data was also difficult in an
ERS study on the causes and effects of consolidation and concentration.
[Footnote 28] While this analysis did not support conclusions about the
exercise of market power by beef packers, even though no other
manufacturing industry showed as large an increase in concentration
since the U.S. Bureau of the Census began regularly publishing
concentration data in 1947, it also concluded that models need to be
improved to more fully incorporate relevant determinants of company
behavior. Difficulty in assessing the competitiveness from available
data held true for another study entitled Effects of Concentration on
Prices Paid for Cattle, contracted for by GIPSA. The study’s summary
states: “The analysis did not support any conclusions about the
exercise of market power by beef packers. It appears that improved
models are needed to more fully incorporate relevant determinants of
firms’ behavior”.[Footnote 29]
The ERS study, using data from the Census of Manufacturers for 1963–92,
found that meatpackers had shifted toward larger plants that annually
slaughtered at least half a million steers and heifers. The study found
that scale economies were modest but extensive. The largest meatpacking
plants maintained only small cost advantages (1 to 3 percent) over
smaller plants, but these modest scale economies appeared to extend
throughout all sizes of 1992 plants. According to ERS, if larger
meatpackers realize lower costs, then concentration, by reducing
industry costs, can lead to improved prices for consumers and livestock
producers.[Footnote 30] However, because meatpackers face fewer
competitors, they could reduce prices paid to livestock producers, and
they might be able to raise meat prices charged to wholesalers and
retailers.
Another study, sponsored by GIPSA, examined the underlying cost
relationship believed to motivate packer behavior.[Footnote 31] This
study used monthly cost and revenue data for 1992–93 from a GIPSA
survey of the 43 largest U.S. beef packing plants. Estimates from this
study indicated significant cost economies and little if any depression
of cattle prices or excess profitability in the meatpacking industry.
GIPSA has also studied the effects on cattle prices of the greater use
of marketing agreements and forward contracts. Some of these studies
have found an inverse or negative relationship between captive
supplies, which encompass marketing agreements and forward contracts,
and spot market prices, but none has yet shown that captive supplies
cause low spot or cash market prices. For example, GIPSA entered into a
cooperative agreement in March 1998 with economists from two
universities.[Footnote 32] The agreement was to conduct an econometric
analysis of Texas cattle data to determine whether marketing agreements
and other contracting methods for procuring cattle (captive supplies)
had an adverse effect on the prices paid for cattle on the spot
market.[Footnote 33] The researchers said that their statistical
analysis did not support the notion that reducing captive supply
purchases or increasing spot market purchases would result in an
increase in the spot price.
Conclusions:
Cattle production is an important part of American agriculture. Industry
participants rely on USDA data and modeling results when they base their
future decisions on how best to plan and operate their businesses.
However, the primary model USDA uses for projecting critical information
that the industry needs has not been well maintained. The model has not
been reestimated in its entirety and has not been validated by
comparing its projections with actual results since its construction in
1989, despite significant changes in the structure of the industry.
Data sets used to estimate the livestock model along with standard
measures of statistical goodness of fit and other diagnostics of model
performance have been lost, and USDA has no plans to replace them.
Statistical goodness of fit and other diagnostics are also unavailable
for USDA’s link system and FAPSIM models, which provide key information
for the livestock model. This information is critical to model
evaluation, and its maintenance simply constitutes good housekeeping.
This lack of transparency carries with it the risk that projections
will be perceived as emanating from a black box.
Recommendations for Executive Action:
To help ensure that models USDA uses to project cattle prices are
properly maintained and reflect the most current information on the
cattle and beef industry, we recommend that the secretary of
agriculture direct ERS to periodically reestimate and validate the
livestock model. To ensure that models USDA uses to project cattle
prices are properly documented, we recommend that the secretary of
agriculture direct ERS to provide basic documentation on these models.
This would include documenting (1) the data set used to estimate the
model, (2) standard measures of statistical goodness of fit and other
diagnostics of model performance, and (3) any changes made to improve
or otherwise update the model.
Agency Comments and Our Evaluation:
See appendix VII.
[End of Chapter 2]
Chapter 3: Many Factors Determine Cattle Prices and Producers’ Incomes:
The expert panel we convened to identify the most important factors
affecting cattle prices and producers’ incomes listed numerous demand
and supply factors, including market concentration, marketing
agreements, forward contracts, and international trade. Many of the
most important factors cause consumer demand for beef to move up or
down, in turn pulling cattle prices and producers’ revenues up or down.
On the supply side, the most important factors motivate producers to
contract or expand herd size, in turn pushing cattle prices up or down.
The panel enumerated key input costs, which, together with producers’
revenues, determine incomes. Other important demand and supply factors
underscore the effects that feedlots, meatpackers, and retailers may
have on cattle prices and producers’ incomes. The panel also identified
key international trade factors that affect cattle demand and supply.
Appendix III contains a complete list of how the 40 panelists scored
all factors in importance.
Cattle Demand and Supply, International Trade, and Structural Change:
The factors the panel identified can be summarized under four broad,
overlapping headings: domestic cattle demand, domestic cattle supply,
international trade, and structural change. Structural change includes
changes in market concentration and growing use of marketing agreements
and forward contracts, all of which have been associated with
industrialization in the agricultural sector. A characteristic of
industrialization is a trend toward standardized methods of production
and economies of scale, as when production costs decline as plant size
increases.
The panel believed that domestic cattle demand and supply are the
fundamental forces driving cattle prices and producers’ incomes. Ninety-
five percent or more considered that these demand and supply factors
were important or most important (see fig. 19). (We had asked the
panelists to rate each factor as least important, somewhat important,
moderately important, important, or most important.) The panelists
agreed less about the importance of international trade and structural
change (fig. 20). While 31 percent of the panel designated structural
change important or most important, 30 percent believed it somewhat or
least important. Forty percent rated structural change moderately
important. A similar result held for international trade, with 28
percent rating it important or most important and 41 percent judging it
somewhat or least important.
Figure 19: Domestic Cattle Demand and Supply Are More Important Than
Other Factors:
[See PDF for image]
This figure is a series of pie-charts depicting the following
information:
Domestic demand:
Important or most important: 98%.
Domestic supply:
Important or most important: 95%.
Structural change:
Important or most important: 31%;
International trade:
Important or most important: 28%.
[End of figure]
Figure 20: The Panelists’ Assessment of Structural Change and
International Trade Varied:
[See PDF for image]
This figure is a series of pie-charts depicting the following
information:
Domestic demand:
Most important: 40%;
Important: 58%;
Moderately important: 3%;
Somewhat important: 0;
Least important: 0.
Domestic supply:
Most important: 65%;
Important: 30%;
Moderately important: 5%;
Somewhat important: 0;
Least important: 0.
Structural change:
Most important: 13%;
Important: 18%;
Moderately important: 40%;
Somewhat important: 15%;
Least important: 15%.
International trade:
Most important: 0;
Important: 28%;
Moderately important: 33%;
Somewhat important: 33%;
Least important: 8%.
[End of figure]
Consumer Demand for Beef Influences Demand for Cattle:
The panel pointed out a number of important factors that influence
consumer demand for beef, which has a cascading effect on the demand for
cattle. As consumer demand for beef rises or falls, so does the demand
for cattle. Changes in the demand for cattle directly affect cattle
prices and cattle sales revenues, an important source of producers’
income. Figure 21 shows that more than half the panel believed that
consumer preferences, the prices of substitutes for beef, and health
concerns tied to food safety and diet were important or the most
important determinants of cattle prices and producers’ incomes as they
affected consumer demand. Ninety-five percent of the panel viewed
product quality and 79 percent saw product convenience as important or
most important in driving consumer preferences. Poultry and pork were
the most significant substitutes for beef, with nearly 80 percent of
the panel rating poultry and pork prices important or most important.
Figure 21: Consumer Preferences, Prices of Beef Substitutes, and Health
Concerns Are More Important Than Other Factors Influencing Consumer
Demand:
[See PDF for image]
This figure is a vertical bar graph depicting the following data:
Consumer preferences:
Percent of panel judging import or most important: 82%.
Relative prices of substitutes:
Percent of panel judging import or most important: 70%;
Health concerns:
Percent of panel judging import or most important: 55%.
Income:
Percent of panel judging import or most important: 50%.
Seasonality:
Percent of panel judging import or most important: 25%.
[End of figure]
The panelists also identified a number of other factors in the retail
and meatpacking sectors that influence cattle prices and producers’
incomes through their effect on the demand for cattle and beef. The
majority of the panel believed that the degree to which meatpacking
plants were being used—packer capacity utilization—and the costs of
retailing beef products were important or most important through their
influence on meatpackers’ demand for cattle and retailers’ demand for
beef (see fig. 22). Forty percent of the panel believed that by-product
values, such as hides, were important or most important, while 29
percent judged that the wages meatpackers paid were important or most
important.[Footnote 34] We asked the panelists to judge the importance
of these factors separately from any effects that related structural
change, such as economies of scale, might have.
Figure 22: Capacity Use at Meatpacking Plants and Retailing Beef Costs
Are More Important Than Other Factors Influencing Meatpackers’ and
Retailers’ Demand for Cattle and Beef:
[See PDF for image]
This figure is a vertical bar graph depicting the following data:
Packer capacity utilization:
Percent of panel judging import or most important: 75%.
Costs of retailing beef:
Percent of panel judging import or most important: 57%.
By-product value:
Percent of panel judging import or most important: 40%.
Wages in packing:
Percent of panel judging import or most important: 30%.
[End of figure]
Several Considerations Shape Producers’ Decisions to Supply Cattle:
The panel pointed out a number of important factors that influence
producers’ decisions about how many cattle to supply to the market.
Changes in the supply of cattle directly affect cattle prices. Figure 23
suggests that producers’ decisions are set by how much it costs to
produce cattle with certain quality characteristics and by the prices
they expect to receive for those cattle. Producers’ incomes are
determined after subtracting input costs from sales revenues. Expected
prices are important because the relatively long biological cycle of
cattle makes it necessary for producers to make decisions about herd
size months and even years before they sell animals or know their
prices.
Figure 23: Supply Factors Vary in Importance:
[See PDF for image]
This figure is a vertical bar graph depicting the following data:
Cattle cycle:
Percent of panel judging important or most important: 77%.
Input costs:
Percent of panel judging important or most important: 72%.
Cattle quality:
Percent of panel judging important or most important: 70%.
Expected prices:
Percent of panel judging important or most important: 65%.
Future prices:
Percent of panel judging important or most important: 45%.
Technological changes in production:
Percent of panel judging important or most important: 40%.
Technological changes in marketing:
Percent of panel judging important or most important: 28%.
Risk management:
Percent of panel judging important or most important: 25%.
Dairy prices:
Percent of panel judging important or most important: 7%.
[End of figure]
The cattle cycle, referring to increases and decreases in herd size
over time, is determined by expected cattle prices and the time it
takes to breed, birth, and raise cattle to market weight, among other
things. The underlying risk in producers’ decisions leads producers to
use risk management techniques and participate in futures markets,
where producers can lock in futures prices as a hedge against the
possibility of receiving prices lower than they expect.
Technological changes have also been a factor. Growth hormones and new
methods of measuring carcass quality are examples of production
technology. Advances in computer technology have meant enhanced
marketing capabilities.
The panel believed that feeding cattle was the most significant input
cost, with 100 percent rating feed costs and 53 percent rating forage
costs important or most important. Eighty-three percent of the panel
viewed weather and 73 percent saw grain and oilseed policies as
important or most important in their influence on feed costs. Eighty-
one percent of the panel judged weather to be important or most
important in affecting forage costs. Ninety percent of the panel judged
grade and 81 percent saw yield as important or most important factors
affecting cattle quality.
International Trade Affects Domestic Prices and Producers’ Incomes:
The panel believed that exports and imports of beef and live cattle
affect domestic prices and producers’ incomes. Seventy-one percent
regarded beef exports as important or most important (fig. 24). These
exports, representing foreign demand for U.S. beef, affect cattle
demand and prices through their effect on beef prices. An increase in
beef exports raises beef prices, which in turn increase the demand for
cattle and raise cattle prices. Beef imports, representing the foreign
supply of beef, also affect domestic demand for cattle through their
effect on beef prices. For example, an increase in beef imports causes
beef prices to fall, which in turn reduces the domestic demand for
cattle and causes cattle prices to fall. Exports of live cattle,
representing foreign demand for U.S. cattle, and imports of live
cattle, representing the foreign supply of cattle to the United States,
directly affect cattle prices.
Figure 24: Beef Is More Important in International Trade Than Cattle:
[See PDF for image]
This figure is a vertical bar graph depicting the following data:
Beef exports:
Percent of panel judging important or most important: 70%.
Beef imports:
Percent of panel judging important or most important: 35%.
Cattle imports:
Percent of panel judging important or most important: 18%.
Cattle exports:
Percent of panel judging important or most important: 15%.
[End of figure]
As for the components of international trade, the panelists agreed more
about the importance of beef exports than about the importance of beef
imports and cattle exports and imports. Seventy-one percent rated beef
exports important or most important, with 8 percent voting somewhat
important and none checking least important. In contrast, 32 percent
believed beef imports were important or most important, while 32 percent
believed they were somewhat or least important. Seventy-eight percent of
the panel believed exports of live cattle were somewhat or least
important, while 8 percent rated cattle exports important or most
important. Forty-seven percent believed cattle imports were somewhat or
least important, while 16 percent believed they were important or most
important.
We also asked the panel to assess the importance of international trade
20 and 10 years ago and 5 years from now in determining cattle prices
and producers’ incomes. Most panelists believed that international
trade was less important 20 years ago than 10 years ago and believed
that it will be more important 5 years from now (fig. 25). For
instance, nearly half the panel thought that international trade will
be important or most important 5 years from now. In contrast, 95
percent believed that international trade was somewhat or least
important 20 years ago.
Figure 25: International Trade Will Be More Important 5 Years from Now:
[See PDF for image]
This figure is a vertical bar graph depicting the following data:
20 years ago:
Percent of panel judging important or most important: 0.
10 years ago:
Percent of panel judging important or most important: 8%.
5 years from now:
Percent of panel judging important or most important: 45%.
[End of figure]
In addition, the panel pointed out several factors that influence how
much U.S. beef other nations buy compared with how much foreign beef the
United States buys. They thought trade barriers were the most
significant factor determining the difference between beef exports and
imports, with 81 percent of the panel regarding these barriers as
important or most important. The majority of the panel viewed currency
exchange rates, foreign income, disease, and the use of hormones as
important or most important in affecting net imports of beef. The panel
also thought trade barriers were the most significant determinant of
trade in live cattle between the United States and other nations, with
65 percent rating it important or most important. Fifty-five percent
assessed disease as important or most important in determining trade in
live cattle.
Structural Change Is Relevant:
The panelists identified numerous factors that may have altered the
structure of the demand and supply relationships that link the prices
and incomes that cattle producers receive to the actions that
meatpackers, retailers, and consumers take. We have already discussed
some of these factors, such as growing consumer awareness of health and
food safety issues and greater emphasis on product convenience. The
panelists also cited the consolidation of the meatpacking sector into
fewer firms operating larger plants and vertical coordination among
meatpackers, producers, and retailers. Figure 26 lists a number of
factors that researchers have (1) scrutinized in recent years for their
potential effect on cattle prices and producers’ incomes and (2)
associated with structural change; the figure shows how important the
panel believed these factors are.
Figure 26: Various Aspects of Structural Change Influence Cattle Prices
and Producers’ Incomes:
[See PDF for image]
This figure is a vertical bar graph depicting the following data:
Economies of scale:
Percent of panel judging important or most important: 72%.
Technological change:
Percent of panel judging important or most important: 65%.
Efficiency of supply chain:
Percent of panel judging important or most important: 60%.
Vertical coordination:
Percent of panel judging important or most important: 50%.
Thin spot market:
Percent of panel judging important or most important: 45%.
Economies of scope:
Percent of panel judging important or most important: 40%.
Vertical integration:
Percent of panel judging important or most important: 37%.
Industry concentration:
Percent of panel judging important or most important: 35%.
Economies of agglomeration:
Percent of panel judging important or most important: 20%.
[End of figure]
Economies of scale is the most significant factor associated with
structural change in the cattle and beef industry—72 percent of the
panel viewed it as important or most important. It was viewed as
especially important in meatpacking, where 85 percent of the panel
judged it to be important or most important. Some researchers believe
that economies of scale and other types of cost economies have been
important factors driving the meatpacking sector and that market power
associated with concentration has played a relatively minor role in
determining cattle prices. Technological change, sometimes associated
with economies of scale, is also important, especially in meatpacker
production, where 76 percent of the panel viewed it as important or
most important. The panel judged concentration to be more important in
the meatpacking sector, where the majority thought it important or most
important. The panel judged it less important in the retail and feedlot
sectors.
Efficiency of the supply chain—another factor sometimes associated with
structural change and referring to the distribution system that moves
products beyond the farm gate to the final point of consumption—is also
important. Sixty percent of the panel rated it important or most
important. Some believe that greater efficiency in the distribution
system has an upward effect on cattle prices. Almost half the panel
thought that vertical coordination, involving the use of marketing
agreements and forward contracts as well as value-based marketing and
pricing, was important or most important. Value-based marketing and
pricing scored highest in importance among this type of coordination,
with 70 percent of the panel rating it important or most important.
Debate has been considerable about what effect vertical coordination has
on cattle prices. Some believe that thin spot markets for cattle result
from increased vertical coordination between meatpackers and cattle
producers, leading to lower spot prices for cattle and, through pricing
formulas, to lower prices in marketing agreements and forward contracts.
Other analysts disagree. Forty-three percent of the panel viewed thin
spot markets as important or most important. Thinness in markets
generally refers to a relatively small volume of market transactions
and relatively high price volatility.
In assessing structural change, the panelists agreed less about the
importance of industry concentration and thin spot markets than about
the importance of economies of scale. While 35 percent believed that
concentration was important or most important, 43 percent believed it
somewhat or least important. Similarly, 43 percent believed thin spot
markets were important or most important, while 38 percent labeled them
somewhat or least important. In contrast, 72 percent of the panel
assessed economies of scale as important or most important, 8 percent
somewhat important, and none least important.
We asked the panel to assess the importance of structural change 20
years ago, 10 years ago, and 5 years from now in determining cattle
prices and producers’ incomes. Most panelists believed that structural
change was less important 20 years ago than 10 years ago and believed
that it will be more important 5 years from now (fig. 27). For
instance, nearly half the panel thought that structural change will be
important or most important 5 years from now. In contrast, nearly half
the panel believed that structural change was somewhat or least
important 20 years ago.
Figure 27: Structural Change Will Be More Important 5 Years from Now:
[See PDF for image]
This figure is a vertical bar graph depicting the following data:
20 years ago:
Percent of panel judging important or most important: 13%.
10 years ago:
Percent of panel judging important or most important: 35%.
5 years from now:
Percent of panel judging important or most important: 46%.
[End of figure]
Conclusions:
The expert panel we convened identified numerous demand and supply
factors that it believed to be important determinants of cattle prices
and producers’ incomes. The panel’s findings underscore the importance
of demand and supply relationships throughout the cattle and beef
industry, from cow-calf producer to retail consumer. Some factors that
the panel scored relatively high in importance are included in USDA’s
livestock model—such as feed costs and cattle inventory features of the
cattle cycle—while others—such as product quality and the convenience
aspects of consumer demand and grade and yield characteristics of
cattle quality—are not explicitly covered. Economies of scale, capacity
utilization in meatpacking, costs of retailing beef products, and value-
based marketing are some of the other factors that the panel scored
relatively high in importance but that the livestock model does not
specifically address. The panel also believed that international trade
and structural change will become more important in the future, with
implications for future modeling.
For factors not included in the livestock model, it is unclear to what
extent their influence is captured indirectly. For example, in the
livestock model, the retail price of beef and, therefore, cattle prices
are influenced by the consumption of beef, pork, and poultry, which
depends on consumer preferences. Similarly, the effects of economies of
scale and market concentration may be hidden in the relationship
between boxed beef prices, which represent prices meatpackers receive
for their products, and cattle prices. However, because the livestock
model does not explicitly account for these factors, it is not equipped
to shed light on their relative importance when it attempts to explain
and project cattle prices. There is no ready way to know how important
these excluded factors are in the cattle price projections of the
livestock model.
Recommendations for Executive Action:
To improve USDA’s ability to answer questions about the current and
future state of the cattle and beef industry, we recommend that the
secretary of agriculture direct ERS to (1) review the findings of our
expert panel regarding important factors affecting cattle prices and
producers’ incomes and (2) prepare a plan for how to address these
factors in future modeling analyses of the cattle and beef industry.
Agency Comments and Our Evaluation:
See appendix VII.
[End of Chapter 3]
Chapter 4: Building a Comprehensive Model Depends on Resolving Modeling
and Data Issues:
When we asked the expert panel to identify problems in developing a
comprehensive and reliable analysis for projecting the most important
factors that affect cattle prices and producers’ incomes, the panel
mentioned many modeling and data issues. Some pointed to a web of
demand and supply connections that tie producers to packers, retailers,
and consumers and to gaps in how much we know about how these
connections affect cattle producers. Much of what the panel pointed to
deals directly or indirectly with structural change. Other panel members
pointed to the need for better data for analyzing consumer demand. They
cited a number of problems regarding cattle supply and prices and
international trade.
An overarching issue was whether one all-encompassing model can
adequately address the variety of questions that policymakers and
stakeholders raise. Altogether, the panel identified 41 modeling and
data issues. Appendix IV lists them all and their scores by importance
and feasibility of resolution. From this list, the panel identified a
number of actions it believed the government should take to advance our
knowledge in this area; the actions focus primarily on the need for
better data. Good data are basic to any comprehensive analysis of
cattle prices and producers’ incomes. In the absence of good data, the
most sophisticated method of analysis is likely to produce questionable
results.
Analyzing How Demand and Supply Link Producers to Consumers Is
Important:
The panel indicated that analyzing cattle prices and producers’ incomes
extends beyond the confines of cow-calf producers, stockers, and
feedlots. Table 1 lists modeling and data issues emphasizing the
interrelated nature of the cattle and beef industry and, with it, the
role of structural change. The panel’s comments suggested that
policymakers, stakeholders, and others concerned about the industry now
have a limited ability to analyze structural change and assess how it
affects cattle prices and producers’ incomes. A majority of the panel
believe that the unavailability of or inaccessibility to detailed data
linking information on producers, processors, and retailers is an
important problem in conducting a comprehensive analysis of changes to
the cattle and beef industry.
Table 1: What Detailed Analysis Requires for Adequate Cattle Price
Modeling:
What adequate analysis requires: Detailed knowledge of food chain
relationships;
What modeling now lacks[A]: Because relationships between the levels of
the food chain are changing, it is difficult to establish the driving
factors and their results:
* Rank: 5;
* Important or most important: 56%;
* Somewhat or least important: 18%;
What data now lack[A]: Disaggregated cost and revenue data linking
ranchers, feeders, packers, and retailers are not available:
* Rank: 2;
* Important or most important: 64%;
* Somewhat or least important: 20%.
What adequate analysis requires: Complete understanding of the cattle
cycle;
What modeling now lacks[A]: Prices and producers’ incomes vary
significantly at different stages of the cycle, but industry
restructuring has meant greater reliance on contracts and proprietary
data; it has become more difficult to assess how economic incentives
and incomes vary over time and space. It is not clear who benefits the
most from the evolving structure and how benefits are distributed (if at
all) among producers, processors, retailers, and consumers:
* Rank: 7;
* Important or most important: 45%;
* Somewhat or least important: 21%;
What data now lack[A]: Confidential data on farmers, processors, and
retailers are not accessible:
* Rank: 6;
* Important or most important: 54%;
* Somewhat or least important: 26%.
What adequate analysis requires: Detailed cost and demand data; data at
the transaction and micro levels;
What modeling now lacks[A]: Most models focus on isolated detail or try
to do more general equilibrium analysis with assumptions too simplistic
to capture what is actually happening:
* Rank: 14;
* Important or most important: 58%;
* Somewhat or least important: 32%;
What data now lack[A]: Publicly available government data do not
contain information over a given period at the transaction or micro
levels:
* Rank: 13;
* Important or most important: 51%;
* Somewhat or least important: 36%.
[A] Rank is based on the average ratings that the panelists assigned to
the importance of addressing the modeling and data issues they
identified. For example, according to the panel’s assessment, it is more
important to address an issue with a rank of 1 than an issue with a
rank of 41. Appendix IV lists the ranking of all 41 data and modeling
issues the panel identified.
[End of table]
The U.S. Census Bureau collects data on establishments and firms for
parts of the cattle and beef industry, including animal slaughtering and
processing, grocery and related product wholesalers, retail food
stores, and restaurants. Every 5 years, the bureau conducts a census
that it supplements monthly and annually by sample surveys. For
instance, the census of manufacturing, which includes animal
slaughtering and processing, collects data on the value of shipments,
payroll and employment by location, products shipped, the cost of
materials, inventories, capital expenditures and depreciable assets,
fuel and energy costs, hours worked, payroll supplements, and rental
payments. Fewer data are collected from the censuses on wholesale and
retail trade and food services. In addition, the monthly and annual
surveys contain less information than the 5-year census. Individual
panelists’ remarks suggest that these censuses do not contain
sufficiently detailed information on the cattle and beef industry.
Obtaining Better Data to Analyze Consumer Demand Is Important:
The panel believed that poor retail data and the difficulty of
quantifying factors that influence consumer demand hinder making
accurate model projections (see table 2). Given the importance that the
panel gave to consumer demand for beef, including the role of consumer
preferences, product convenience, and health concerns, making progress
in this area could improve model projections of cattle prices and
producers’ incomes.
Table 2: Inadequate Retail Data and Quantification Factors Influencing
Consumer Demand Pose Challenges to Modeling:
Issue: Lack of data;
Problem: Retail and consumption data are very poor;
Importance[A]:
* Rank: 3;
* Important or most important: 62%;
* Somewhat or least important: 13%.
Issue: Lack of data;
Problem: While consumers set retail value, quantity-weighted retail
prices are lacking;
Importance[A]:
* Rank: 16;
* Important or most important: 36%;
* Somewhat or least important: 28%.
Issue: Lack of data;
Problem: Data to quantify the impact of convenience on beef demand are
lacking;
Importance[A]:
* Rank: 19;
* Important or most important: 50%;
* Somewhat or least important: 35%.
Issue: Quantification;
Problem: Key long-term variables such as trends in health concerns are
hard to quantify conceptually, much less to get good data for;
Importance[A]:
* Rank: 10;
* Important or most important: 52%;
* Somewhat or least important: 23%.
Issue: Quantification;
Problem: Many factors such as consumer tastes and preferences needed
for incorporating in a model are difficult to quantify;
Importance[A]:
* Rank: 22;
* Important or most important: 45%;
* Somewhat or least important: 31%.
[A] Rank is based on the average ratings that the panelists assigned to
the importance of addressing the modeling and data issues they
identified. For example, according to the panel’s assessment, it is more
important to address an issue with a rank of 1 than an issue with a
rank of 41. Appendix IV lists the ranking of all 41 data and modeling
issues the panel identified.
[End of table]
Individual panelists’ remarks indicate that retail data may lack
consistent retail-level micro detail on prices and sales of fresh
meats. Some private sources of retail data, such as Information
Resources, Inc., offer data on sales and pricing, collected weekly from
supermarkets across the United States. These data, from grocery store
scanners, reflect actual consumer purchases at both regular and sale
prices.[Footnote 35]
In addition, USDA reports retail prices for beef, but these prices
reflect not actual purchases by consumers but, rather, an average of
selected beef cuts offered for sale, without regard to the amount
purchased. USDA first obtains average retail prices from the Bureau of
Labor Statistics, which collects them to calculate the consumer price
index (CPI). The bureau collects regular and sales prices from grocery
stores and averages these prices, regardless of the amount purchased at
each price. Then, USDA weights these prices by each cut’s proportion of
a cattle carcass. As a result, USDA does not report retail prices on
the basis of actual consumer purchases of beef products. The lack of
current-period quantity-weighted retail prices, which the panel cited,
has been a problem in the pork industry, too.[Footnote 36]
Aspects of Cattle Supply and Prices Are Relevant:
The panel identified several issues important in modeling cattle supply
related to the cattle cycle, expectations, and long-term variables
dealing with technological change and policy changes in feed crops (see
table 3). In addition, it cited problems with cattle prices, suggesting
that vertical coordination in the form of contracts and value-based
marketing is reducing how representative reported prices are (see table
4). The panel also pointed to problems with cattle price data not being
adjusted for volume and grade—a cattle quality consideration we noted
in chapter 3. We have discussed similar problems with hog prices.
[Footnote 37]
Table 3: Cattle Cycle, Expectations of Profits, and Long-Term Variables
Pose Challenges to Modeling:
Issue: Cattle cycle;
Problem: Appropriate modeling of dynamics in prices;
Importance[A]:
* Rank: 4;
* Important or most important: 52%;
* Somewhat or least important: 21%.
Issue: Expectations of profits;
Problem: Since current supply is a function of profits producers
expected to receive when they started production, analysts must use a
proxy for expectations, which measures the underlying concept with
error;
Importance[A]:
* Rank: 9;
* Important or most important: 57%;
* Somewhat or least important: 23%.
Issue: Long-term variables;
Problem: Key long-term variables, such as technical change and policy
changes (e.g., in feed crops) are hard to quantify conceptually, much
less to get good data for;
Importance[A]:
* Rank: 10;
* Important or most important: 52%;
* Somewhat or least important: 23%.
[A] Rank is based on the average ratings that the panelists assigned to
the importance of addressing the modeling and data issues they
identified. For example, according to the panel’s assessment, it is more
important to address an issue with a rank of 1 than an issue with a
rank of 15. Appendix IV lists the ranking of all 41 data and modeling
issues the panel identified.
[End of table]
Table 4: Vertical Coordination Poses Challenges to Modeling:
Issue: Reported cattle prices;
Problem: If the cattle prices the NASS reports no longer represent
prices actually paid to producers, it is difficult to use them for
meaningful analysis;
Importance[A]:
* Rank: 11;
* Important or most important: 51%;
* Somewhat or least important: 33%.
Issue: Available cattle price data;
Problem: Cattle price data are questionable because they are not
weighted for volume, grade, and so on;
Importance[A]:
* Rank: 21;
* Important or most important: 41%;
* Somewhat or least important: 26%.
Issue: Reported market prices;
Problem: Reported market prices may not indicate true prices received
because of extensive contracting and pricing quality grid differences;
Importance[A]:
* Rank: 12;
* Important or most important: 53%;
* Somewhat or least important: 38%.
[A] Rank is based on the average ratings that the panelists assigned to
the importance of addressing the modeling and data issues they
identified. For example, according to the panel’s assessment, it is more
important to address an issue with a rank of 1 than an issue with a
rank of 15. Appendix IV lists the ranking of all 41 data and modeling
issues the panel identified.
[End of table]
In April 2001, USDA’s AMS began collecting and reporting cattle and
other livestock market data, including prices, under the livestock
mandatory reporting (LMR) program, as required by the Livestock
Mandatory Price Reporting Act of 1999. Unlike AMS’s previous voluntary
market news program, which relied on industry cooperation to obtain
information on negotiated or cash sales, LMR is collecting data from
meatpackers on purchase prices in forward contracts and other
transactions using price formulas, such as those found in marketing
agreements. Under the LMR program, AMS is also collecting data on the
quantity of cattle purchased on a live weight and carcass basis, cattle
weight, the quality grade of cattle, and price premiums or discounts.
[Footnote 38] These data may help in future modeling efforts.
International Trade Issues:
The panel identified international trade issues, such as the difficulty
of quantifying the effects of trade barriers, as a factor in modeling
(see table 5). Difficulty quantifying the effects of trade barriers
could be significant in light of the panel’s assessment of their
importance in determining beef net exports and trade in live cattle.
Table 5: Quantifying International Trade Factors Is an Issue for
Modeling:
Issue: Trade barriers;
Problem: Data to quantify liberalization are lacking;
Importance[A]:
* Rank: 15;
* Important or most important: 44%;
* Somewhat or least important: 21%.
Issue: Importing countries;
Problem: Data to quantify purchasing power are lacking;
Importance[A]:
* Rank: 36;
* Important or most important: 24%;
* Somewhat or least important: 59%.
Issue: International effects;
Problem: International effects such as from Australia, Canada, Mexico,
New Zealand, and the Pacific Rim countries have not been integrated;
Importance[A]:
* Rank: 23;
* Important or most important: 34%;
* Somewhat or least important: 34%.
[A] Rank is based on the average ratings that the panelists assigned to
the importance of addressing the modeling and data issues they
identified. For example, according to the panel’s assessment, it is
more important to address an issue with a rank of 1 than an issue with
a rank of 15. Appendix IV lists the ranking of all 41 data and modeling
issues the panel identified.
[End of table]
Overarching Issues Related to Modeling Scope:
Table 6 presents important questions the panelists raised about the
purpose of modeling cattle prices and producers’ incomes and the
feasibility of developing a “one size fits all” model. This is relevant
in evaluating USDA’s and ITC’s models because they were not designed to
answer questions about the effects of market concentration, marketing
agreements, and forward contracts. In addition, these models are
national in scope and were not designed to analyze regional effects.
Table 6: The Relevance of a Model’s Purpose and Scope:
Issue: The purpose of modeling;
Problem: To keep misspecification as small as reasonable and to make
the cattle price model most useful, its purpose should be defined
before it is developed. A model whose purpose is short-term forecasting
should differ markedly from a model designed to answer policy
questions;
Importance[A]:
* Rank: 1;
* Important or most important: 84%;
* Somewhat or least important: 8%.
Issue: One all-purpose model versus several types of models;
Problem: Attempting to come up with one all-encompassing model may be
problematic, because issues may differ from state to state or region to
region. Separate models and perhaps more than one type of modeling and
analysis may be needed;
Importance[A]:
* Rank: 8;
* Important or most important: 53%;
* Somewhat or least important: 24%.
[A] Rank is based on the average ratings that the panelists assigned to
the importance of addressing the modeling and data issues they
identified. For example, according to the panel’s assessment, it is
more important to address an issue with a rank of 1 than an issue
with a rank of 15. Appendix IV lists the ranking of all 41 data and
modeling issues the panel identified.
[End of table]
The Panel’s Priority Items for Government Action:
Eighty-five percent of the panelists believed that government action is
needed to resolve the data and modeling issues they identified as
problems in developing a comprehensive and reliable analysis of cattle
prices and producers’ incomes. All who recommended government action
pointed to the need for better data for conducting analysis. The
panelists expressed concern about the availability of and access to
data at all levels of the demand and supply chain that links producers
to consumers. They also stressed that the quality of the data that are
now being collected on the cattle and beef industry could be improved,
citing the need for more representative, reliable, and consistent data.
These data issues are important because, as one panelist succinctly
said: “The results of the models are only as good as the data used to
estimate them.” Table 7 lists the top five issues that the panelists
believed warrant government action. Ninety-four percent of those who
cited the need for government action selected one or more of the data
issues in table 7. Appendix V presents the panelists’ own descriptions
of their beliefs about these issues.
Table 7: The Five Problems Most Important for Government Action in
Developing a Comprehensive Analysis:
Rank: 1;
Issue: Access to data on farmers, processors, and retailers is lacking
because the data are confidential;
Panelists recommending government action: Number[A]: 19;
Panelists recommending government action: Percent[B]: 56%.
Rank: 2;
Issue: Reported market prices are likely not to indicate
true prices received because of extensive
contracting and pricing quality grid differences
Panelists recommending government action: Number[A]: 16;
Panelists recommending government action: Percent[B]: 47%.
Rank: 3;
Issue: Disaggregated cost and revenue data linking
ranchers, feeders, packers, and retailers are not
available
Panelists recommending government action: Number[A]: 14;
Panelists recommending government action: Percent[B]: 41%.
Rank: 4;
Issue: Retail and consumption data are very poor;
Panelists recommending government action: Number[A]: 13;
Panelists recommending government action: Percent[B]: 38%.
Rank: 5;
Issue: If the cattle prices the NASS reports no longer
represent prices actually paid to producers, it is
difficult to use them for meaningful analysis
Panelists recommending government action: Number[A]: 10;
Panelists recommending government action: Percent[B]: 29%.
[A] The total number of panelists who believed the federal government
should take action was 34 of 40.
[B] Percentages are calculated based on the 34 panelists who believed
that the federal government should take action.
[End of table]
Proprietary or confidential data, the first issue in table 7 and the one
receiving the most votes for government action, is relevant to the
second and fifth issues in table 7, dealing with cattle prices, because
of contracting for cattle. It is an issue that the Livestock Mandatory
Price Reporting Act addresses, under which USDA is required to publish
data on cattle prices in a manner that protects the identity of those
who report them and preserves the confidentiality of proprietary
transactions.
USDA has tried to preserve confidentiality by reporting data only if at
least three reporting entities supply the information and no single
entity is responsible for reporting 60 percent or more of the data.
According to USDA, this resulted in the withholding of nearly 30
percent of the daily swine and cattle reports from publication, because
of confidentiality, between April 2 and June 14, 2001. To reduce the
amount of data being withheld, USDA recently announced a new
confidentiality guideline; it believes that had this guideline been in
place earlier, less than 2 percent of the daily swine and cattle
reports would have been withheld from publication during that period.
[Footnote 39]
The panelists also offered general and specific comments about how the
government can help address the issues it identified in table 7. Table 8
enumerates some of these comments. Appendix V presents excerpts of all
the panelists’ comments.
Table 8: The Panel’s Comments on Data Needs That the Government Can
Address:
Issue: Data access;
Comment: Only the federal government can provide access to data, since
most are proprietary; To take advantage of existing but unavailable
data, allow researchers to use data in-house under a confidentiality
agreement, as the Census Bureau does; The federal government can make
processor data available to researchers with a protective order
agreement that prohibits them from making data on firms public; GIPSA
has very good data on packers but is not readily available to outside
researchers; data at other levels of the market channel are much
poorer[A].
Issue: Retail price data;
Comment: Volume-weighted, representative price data are needed; It is
not clear whether ERS will provide detailed prices on meat cuts for
better demand analysis; Retail prices should reflect "featuring" and
"club-card" discounts, using scanning data.
Issue: Cattle price data;
Comment: Data representing all quality levels of cattle should be
collected; Better ways to summarize quality-adjusted fed cattle prices
are needed; Price reporting should be revised to include contracting,
requiring access to private market data; Price data and detail on the
grade and quality of export shipments are not available.
Issue: Overall data;
Comment: The quality and quantity of data, from farm to retail level,
need to be improved; cooperative research using experts should be
conducted, dividing the work according to their expertise; The primary
issue is availability of reliable, consistent data on firms and
markets; Competitive grants should be established for primary data
collection; Additional surveys should be undertaken; Data are often too
aggregate and nonspatial; better data is the key to better analysis
The primary issue, after defining the questions, is data availability
and quality. The importance of supply factors calls for detailed cost
analyses to assess cost economies, with data on plants over time. The
importance of consumer demand calls for tracking quality variations.
Data availability should be enhanced, and studies should be encouraged
or commissioned.
[A] GIPSA publishes an annual statistical report on the meat packing
industry based on data from meatpackers and others, dealing with packer
procurement practices, changing plant size, concentration ratios,
financial performance, and other matters. GIPSA also collects detailed
data for investigation work, but its access to this data is limited to
pursuing the investigation. According to one panelist, GIPSA has
accumulated very good data on feedlot-packer transactions, including
prices paid, types of contractual arrangements, and characteristics of
the lot transacted, but this data is not accumulated routinely.
[End of table]
The panelists expressed a range of views about the federal government’s
primary role in addressing the question of what the government should do
about data and modeling issues. Some panelists commented that the
government should emphasize data collection, while others saw the need
for more government analysis as well. Table 9 presents some of their
specific comments.
Table 9: The Panel’s Comments on the Government’s Role in Data and
Modeling Issues:
Issue: Data collection versus modeling;
Comment: Collecting and disseminating data would have a greater effect
than modeling; Resources should be devoted more to data collection than
to data analysis; The government’s role should be collecting data.
Issue: Data improvement;
Comment: Improving data is the primary role the government can and
should play; Government's direct role should be limited to improving
the way it generates data and the types of data it makes available to
researchers.
Issue: Quantification;
Comment: Quantify the effect of government actions such as recalls,
nutritional guidelines, the effects of the cattle cycle and supply and
demand on prices, and the effect of feed grain policy on calf prices
Provide more public data on market structure, such as Lerner indexes
and local market Herfindahls[A].
Issue: Funding;
Comment: The government needs to provide long-term funding for research
on all issues that motivated this survey, supporting the research
infrastructure at land grant universities.
Issue: Leadership in research and modeling;
Comment: The government should support a team of leading academic and
government experts to come together to design the modeling and
implementation process; The government should support a research focus
on such issues as structural change and cattle cycle that include
researchers from government and academia; The government should
stimulate research on key priorities identified in this survey, using a
mini-grant competition and bilateral agreements between USDA and other
institutions, as well as within USDA; Issues other than data involve
setting an agenda to have a set of policy models that account for market
structure across the various levels of the marketing system.
[A] The Herfindahl-Hirshman index is equal to the sum of each firm’s
squared percentage share of the total market and is a measure of market
concentration. Lerner indexes refer to the spreads between prices and
the marginal costs of production in product markets and to the
percentage markup of price over marginal cost. In a perfectly
competitive market, price is equal to marginal cost. Applied to input
markets, this concept translates to differences between the values of
marginal product and the prices paid for a factor of production. In a
perfectly competitive market, the value of marginal product equals the
price paid for the factor of production. Lerner indexes measure market
power.
[End of table]
Conclusions:
The expert panel we convened identified numerous data and modeling
issues that need to be addressed if a more comprehensive analysis of the
cattle and beef industry is to be conducted. However, the panel
emphasized the importance of carefully defining the questions for which
answers are to be sought before an ambitious data collection and
modeling effort is embarked on. The majority of the panel believed that
the federal government should take steps to improve the quantity and
quality of data that are available to researchers so that their
understanding of the factors that explain cattle prices and producers’
incomes will be better.
Recommendations for Executive Action:
To improve USDA’s ability—and that of the research community as a
whole—to answer questions about the current and future state of the
cattle and beef industry, we recommend that the secretary of
agriculture direct AMS, ERS, GIPSA, and NASS to (1) review the findings
of our expert panel regarding important data and modeling issues and,
(2) in consultation with other government departments or agencies
responsible for collecting relevant data, prepare a plan for addressing
the most important data issues that the panel recommended for
government action, considering the costs and benefits of such data
improvements, including tradeoffs in departmental priorities and
reporting burdens.
Agency Comments and Our Evaluation:
See appendix VII.
[End of Chapter 4]
Appendix I: Objectives, Scope, and Methodology:
We were asked the following questions:
1. To what extent do the economic models that USDA and ITC use
incorporate imports, market concentration in the U.S. meatpacking
industry, and marketing agreements and forward contracts in predicting
domestic cattle prices?
2. What are the most important factors affecting cattle prices and
producers’ incomes?
3. What are the most important data and modeling issues that need to be
addressed in developing a comprehensive analysis to project cattle
prices and producers’ incomes?
To answer the first question, we obtained documentation on several
models that USDA and ITC use, and we met with USDA and ITC officials to
discuss these models. We examined the models’ structure and
specification, including estimated equations, methods of estimation,
estimation results, and information on data used for estimation. We
were not able to fully evaluate USDA’s models because information on
statistical goodness of fit and other statistical diagnostics were not
available.
To address the second and third questions, we convened a virtual panel
on the Internet of 40 experts selected for their knowledge of the
cattle and beef industry. To help identify these experts, we reviewed
the extensive literature on cattle markets and the economics of the
cattle and beef industry, including studies USDA commissioned. To
structure and gather expert opinion from the panel, we employed a
modified version of the Delphi method.[Footnote 40] The Delphi method
can be employed in a number of settings, although when first developed
at the RAND Corporation in the 1950s, it was applied in a group
discussion forum. One of the strengths of the Delphi method is its
flexibility. We used a version that incorporated an iterative and
controlled feedback process rather than a committee or face-to-face
discussion method of obtaining expert opinion.
We administered a series of three questionnaires to the virtual panel
over the Internet. This approach helped minimize potential biasing
effects often associated with live group discussions. Biasing effects
of live expert discussion sessions may include the dominance of
individuals and group pressure for conformity.[Footnote 41] The former
bias would tend to limit the input of less dominant individuals, and
the latter bias would tend to suppress true opinion, particularly on
more controversial issues. Moreover, by creating a virtual panel we
were able to include many more experts than we could have if we had
convened a live panel. This allowed us to obtain the broadest possible
range of opinion on these matters.
On the first questionnaire (phase I), we asked the experts the
following two open-ended questions.
“During the past few years, what were the most important factors or
variables affecting (a) the prices received by domestic cattle
producers and (b) producers’ incomes?
“What problems or issues would you face in developing a comprehensive
and reliable analysis to estimate domestic cattle prices and producers’
incomes?”
After the first questionnaire was completed, we performed a content
analysis on the open-ended responses to compile a list of the most
important factors, as well as the various points of view the panel held
on the data and modeling issues facing analysis of prices and incomes.
Applying basic principles of economics and relying on published
articles, we were able to categorize the numerous factors the panelists
identified as domestic cattle demand and supply, international trade,
and structural change. The challenge at this stage was to organize the
very large number of factors the panelists enumerated into a smaller
list that was more tractable for the panelists’ further analysis yet
remained as consistent as possible with the basic economics of the
cattle and beef industry.
During the second phase of the study, the panel evaluated and rated the
importance of each of the factors it had generated during the first
phase. This step was the first component of the feedback process. In
the second questionnaire, also administered on the Internet, we
presented the panel with the list of factors identified in the first
phase, explaining that the list was produced by the experts’ peers
during phase I. We gave the expert panelists the opportunity to assess
the importance of those factors, even if an individual expert did not
mention the factor in the first round. We organized the factors into
four main categories, each with subcategories. Factors were rated on
importance at each category level. Analysis of the data, based on
descriptive statistics, produced a relative rank-ordering of the most
important factors and also indicated the level of agreement, based on
the standard deviation, within the panel about the level of importance
for each factor (see app. III).
During the second phase, we also asked experts to evaluate data and
modeling issues in developing a comprehensive analysis the panel
identified during the first phase. We presented to the expert panel a
total of 41 unique data and modeling-related issues, derived from the
phase I questionnaire responses (see app. IV). We asked the experts to
rate each issue on two dimensions—importance and feasibility—by
answering the following questions for each issue listed.
“How important is it to address this problem or issue for purposes of
modeling cattle prices and/or producers’ incomes?
“How feasible is it to overcome or implement the solution for this
problem or issue for purposes of modeling cattle prices and/or
producers’ incomes?”
During the final phase of the study, we presented the panelists with the
results of the two questionnaires in the form of two HTML tables
embedded within a third Internet questionnaire. The results included a
summary interpretation of the findings and descriptive statistics on
the importance of the factors affecting cattle prices and producers’
incomes, as well as the importance and feasibility ratings of the 41
data and modeling issues in developing a comprehensive analysis (the
tables we presented to the panel were essentially tables 11 and 12 in
apps. III and IV). The importance ratings for the factors associated
with international trade and structural change were more diverse than
they were for the categories of domestic demand for cattle and domestic
supply of cattle. We asked the panel to consider these results and
explain why there might be a relatively greater divergence of opinion
on the importance of structural change and international trade. These
responses are reproduced in appendix V.
After the panel members examined the results and considered the reasons
for the variance of opinion on international trade and structural change
factors, we offered them the opportunity to change their original
assessments of the importance of these factors. Two of the 40
respondents changed their opinions slightly on structural change, and 5
changed their ratings on international trade.
The second part of the phase III questionnaire pertained to data and
modeling issues in developing a comprehensive analysis. We were
interested in knowing whether the panel believed the government should
take any action to address any of these issues to advance our state of
knowledge. We asked each panelist who believed government action was
warranted to select up to 5 issues from the 41 identified that he or she
would recommend the federal government take action on (the list was
presented in order of the average importance rating from the responses
to the phase II questionnaire). Of the 40 panelists, only 3 selected
more than 5 issues (one selected 6, another selected 9, and the last of
the 3 selected 19). Another 6 panelists opted not to select any issues
for recommendation. We rank ordered the list of issues by the number of
votes the panel offered. For the rank ordering of issues that the panel
recommended for federal action, see appendix V.
Initially, 42 experts agreed to participate in the panel. Forty
panelists actually completed the first questionnaire, making the
response rate 95 percent for the phase I questionnaire. There was no
attrition on the two subsequent phases, as all 40 experts who completed
phase I also completed questionnaires for phases II and III (see table
10).
Table 10: The Number of Panelists Participating in the Study’s Three
Phases:
Experts selected who agreed to participate: 42;
Experts responding to questionnaire, Phase I: 40 (95%);
Experts responding to questionnaire, Phase II: 40 (95%);
Experts responding to questionnaire, Phase III: 40 (95%).
[End of table]
We pretested a paper version of the first questionnaire with three of
the panel members and made changes based on the pretests before we
deployed the first questionnaire. We did not pretest the second and
third questionnaires because their content was derived from respondent
answers to preceding questionnaires. They were reviewed before
deployment. We did conduct usability tests of all three versions of the
questionnaires for the Internet to ensure operability.
[End of Appendix I]
Appendix II: USDA’s Livestock Model:
USDA’s livestock model is a series of mathematical equations describing
the cattle and beef industry as well as the pork, poultry, and turkey
sectors. Annual data were used in the model’s statistical estimation.
The model’s largest component describes the cattle and beef industry.
Within this component, several major parts deal with herd size and
composition, commercial slaughter and beef production, beef consumption
and demand, and prices.
The livestock model contains equations explaining inventories of beef
cows, calves, steers, heifers, and bulls. The inventory of beef cows is
a major factor influencing the cattle and beef industry in the model.
Several key relationships illustrate how. First, the number of beef
cows helps determine the number of calves. In turn, the number of
calves helps determine the number of steers and heifers and how many
are slaughtered. The number of beef cows is also a factor explaining
how many beef cows and bulls are slaughtered. Animals slaughtered, plus
cattle imports and exports, determine beef production.
Beef production is added to inventories of beef at the beginning of each
year, along with beef imports, and from this sum are subtracted beef
exports and inventories at the end of the year to derive beef
consumption for each year. Beef consumption, along with pork, poultry,
and turkey consumption and several other factors, is used to explain
retail beef prices in an analytical framework called inverse demand,
indicating the price at which consumers buy given quantities of beef.
Retail beef prices help determine the prices that meatpackers, feedlots,
stockers, and producers receive, including boxed beef prices and prices
for cow carcasses, steers, heifers, feeder steers, and cows.
Feeder steer prices and cow prices play a role in determining returns to
cow-calf producers. These returns help explain the number of beef cows
and calves, beef cows slaughtered, and heifers added to the beef cow
herd or slaughtered.
The cost of animal feed comes into play at several places in the model.
For example, hay and corn prices help explain the number of heifers
added to the beef cow herd, as well as the number of beef cows
slaughtered. Similarly, feedlot costs are a factor explaining the
number of steers slaughtered. Feed costs for a fed steer, dependent on
corn, soybean meal, and hay prices, help explain feeder steer prices.
Finally, feed costs as well as other input costs are used in
determining returns to cow-calf producers.
This appendix lists the equations making up the livestock model, along
with the estimated values for their parameters. No measures of
statistical goodness of fit are available for this model.
The Cattle and Beef Sector:
Beef Cow Inventory on Hand January 1:
Changes in the number of beef cows reflect both the present and future
production capacity of the cattle and beef sector. Beef cow inventory
(cbcijus) is a function of previous numbers of beef cows, net returns to
cow-calf producers adjusted for inflation (rrct), previous heifers kept
for herd replacement (hfcbjus), and previous beef cows slaughtered
(cwkgnbe). The estimated equation is:
cbcijus = ca10 + ca11*lag(cbcijus) + ca12*lag2(rrct) +
ca14*lag(hfcbjus) + ca15*lag(cwkgnbe).
The values for estimated coefficients are:
ca10 = 457.591670:
ca11 = 0.790458:
ca12 = 17.758247:
ca14 = 1.301077:
ca15 = –0.351960:
Calf Crop:
Calves can be slaughtered about 1.5 to 2 years after birth, or they can
be used for herd replacement. Calf crop (ccrop) is a function of beef
cow inventory (cbcijus) and dairy cow inventory (cmcijus) and previous
real returns to cow-calf producers (rrct). The average calving rate is
around 90 percent, and previous returns measure changes at the margin
in breeding decisions. The estimated equation is:
ccrop = ca20 + ca21*(cbcijus + cmcijus) + ca23*lag1(rrct).
The values for estimated coefficients are:
ca20 = –459.150520:
ca21 = 0.909530:
ca23 = 15.559700:
Steers Larger Than 500 Pounds:
The number of steers weighing more than 500 pounds is used to project
total cattle inventory but not beef production. Steers larger than 500
pounds (stcijus) are a function of previous numbers of calves (ccrop),
adjusted for how many were slaughtered as calves (cvkcnus), cattle
imported (cimport), and exported (cexports). The estimated equation is:
stcijus = ca30 + ca31*lag(ccrop – cvkcnus + cimport – cexports).
The values for estimated coefficients are:
ca30 = 4944.79:
ca31 = 0.231615:
Heifers Larger Than 500 Pounds:
The number of heifers weighing more than 500 pounds is also used to
project total cattle inventory. Heifers larger than 500 pounds
(hfcijus) are a function of previous numbers of calves (ccrop),
adjusted for how many were slaughtered as calves (cvkcnus), cattle
imported (cimport) and exported (cexports), a ratio of hay prices
(rhayp) to corn prices (rcornp), and a time trend. The ratio of hay
prices to corn prices measures pasture conditions. If forage prices
rise relative to corn prices, there is pressure on the pasture.
The estimated equation is:
hfcijus = ca40 + ca41*lag(ccrop – cvkcnus + cimport – cexports) +
ca42* lag(rhayp/rcornp) + ca43*t.
The values for estimated coefficients are:
ca40 = 11444.70:
ca41 = 0.127433:
ca42 = –52.518250:
ca43 = 80.385386:
Other Heifers Larger Than 500 Pounds:
A number of heifers weighing more than 500 pounds are destined for the
feedlot or slaughter, not cow replacement. They are also used in
projecting total cattle inventory. Other heifers larger than 500 pounds
(hfcojus) are a function of previous numbers of calves (ccrop),
adjusted for how many were slaughtered as calves (cvkcnus), cattle
imported (cimport) and exported (cexports), a ratio of hay prices
(rhayp) to corn prices (rcornp), and real returns to cow-calf producers
(rrct). The estimated equation is:
hfcojus = ca50 + ca51*lag(ccrop – cvkcnus + cimport – cexports) +
ca52*lag(rhayp/rcornp) + ca53*lag(rrct).
The values for estimated coefficients are:
ca50 = 3700.42:
ca51 = 0.027243:
ca52 = 94.656956:
ca53 = –7.532166:
Heifers Larger Than 500 Pounds Kept for Beef Cow Replacements:
The number of heifers weighing more than 500 pounds kept for beef cow
replacement represent new additions to the breeding herd for beef
cattle. Heifers larger than 500 pounds kept for beef cow replacements
(hfcbjus) are a function of beef cow inventory (cbcijus), the ratio of
previous hay prices to corn prices (rhayp/rcornp), and previous real
returns to the cowcalf producer (rrct). The estimated equation is:
hfcbjus = ca60 + ca61*cbcijus + ca62* lag(rhayp/rcornp) +
ca63*lag(rrct).
The values for estimated coefficients are:
ca60 = –787.962926:
ca61 = 0.205469:
ca62 = –25.668633:
ca63 = 2.652821:
Bulls Larger Than 500 Pounds:
The number of bulls weighing more than 500 pounds is also used to
project total cattle inventory. Bulls larger than 500 pounds (blcijus)
are a function of the number of beef and dairy cows (cbcijus + cmcijus)
and a time trend. The estimated equation is:
blcijus = ca70 + ca71*(cbcijus + cmcijus) + ca72*t.
The values for estimated coefficients are:
ca70 = –1122.50:
ca71 = 0.064177:
ca72 = 14.276446:
Calves Smaller Than 500 Pounds:
The number of calves weighing less than 500 pounds is used to project
total cattle inventory. Calves smaller than 500 pounds (cvcijus) are a
function of previous numbers of calves (ccrop), adjusted for how many
were slaughtered as calves (cvkcnus), cattle imported (cimport) and
exported (cexports), and hay prices (rhayp). The estimated equation is:
cvcijus = ca80 + ca81*lag(ccrop – cvkcnus + cimport – cexports) +
ca82*lag(rhayp).
The values for estimated coefficients are:
ca80 = –6199.43:
ca81 = 0.562424:
ca82 = 148.097736:
Federally Inspected Steer Slaughter:
The number of steers slaughtered under federal inspection (FI) is used
in projecting beef production. When slaughter is federally inspected
(FI), the resulting meat products can move between states. If not, meat
products must be sold in the state where slaughter took place. The
proportion of FI slaughter has been increasing and is now about 98
percent of all slaughter. FI steer slaughter (stkgnus) is a function of
previous numbers of calves (ccrop), adjusted for how many were
slaughtered as calves (cvkcnus), cattle imported (cimport) and exported
(cexports), feedlot costs (rfedcost), and the FI slaughter ratio
(firatio). The estimated equation is:
stkgnus = ca90 + ca91*lag(ccrop – cvkcnus + cimport – cexports) +
ca93*rfedcost + ca94*lag(ccrop – cvkcnus + cimport – cexports)*
(1 – firatio).
The values for estimated coefficients are:
ca90 = 4846.86:
ca91 = 0.368034:
ca93 = –14053.28:
ca94 = –0.172560:
Federally Inspected Heifer Slaughter:
The number of heifers slaughtered under FI is also used in projecting
beef production. FI heifer slaughter (hfkgnus) is a function of
previous numbers of calves (ccrop), adjusted for how many were
slaughtered as calves (cvkcnus), cattle imported (cimport) and exported
(cexports), the change in dairy cow inventory (cmcijus), real returns
to cow-calf producers (rrct), and the FI slaughter ratio (firatio).
The estimated equation is:
hfkgnus = ca100 + ca101*lag(ccrop – cvkcnus + cimport – cexports) +
ca102*dif(cmcijus) + ca104*lag(rrct) + ca105*lag(ccrop – cvkcnus +
cimport –cexports)*(1 – firatio).
The values for estimated coefficients are:
ca100 = 6057.44:
ca101 = 0.142822:
ca102 = –1.148699:
ca104 = –11.557551:
ca105 = –0.795383:
Federally Inspected Beef Cow Slaughter:
The number of beef cows in the beef breeding herd that are slaughtered
is used in projecting beef production. There are two main reasons for
slaughtering beef cows—declines in productivity as the cow ages and
adjustments for profitability and forage conditions. FI beef cow
slaughter (cwkgnbe) is a function of the beef cow inventory (cbcijus),
previous returns to the cow calf producers (rrct), the hay price to
corn price ratio (rhayp/rcornp), and the FI slaughter ratio (firatio).
The estimated equation is:
cwkgnbe = ca130 + ca131*cbcijus + ca132*lag(rrct) +
ca134*rhayp/rcornp + ca135*(cbcijus)*(1 – firatio).
The values for estimated coefficients are:
ca130 = 2767.41:
ca131 = 0.085020:
ca132 = –9.450633:
ca134 = –44.259710:
ca135 = –0.359632:
Federally Inspected Bull Slaughter:
FI bull slaughter (blkgnus) measures the slaughter of the male component
of the beef and dairy breeding herd. It is a function of beef and dairy
cow herds and bulls larger than 500 pounds. The estimated equation is:
blkgnus = ca140 + ca141*(cwkgnbe + cwkgnda) + ca142*blcijus.
The values for estimated coefficients are:
ca140 = –879.305602:
ca141 = 0.044822:
ca142 = 0.502197:
Cattle Slaughter Weight:
Cattle slaughter weight (cekcaus) is used in computing beef production
and is based on the historical growth rate in slaughter weight.
For years after 2000, cekcaus = 743 + ((year2000)*2).
Before 2000, cekcaus = 707.
Commercial Beef Production:
The model projects beef produced and sold commercially in the United
States under federal and state inspection. Commercial beef production
(bescpus) is the sum of FI steer slaughter (stkgnus), FI heifer
slaughter (hfkgnus), FI beef cow slaughter (cwkgnbe), FI dairy cow
slaughter (cwkgnda), and FI bull slaughter (blkgnus), multiplied by
average dressed weights (cekcaus) and divided by the FI slaughter ratio
(firatio). The identity is:
bescpus = (cekcaus*(stkgnus + hfkgnus + cwkgnbe + cwkgnda +
blkgnus)*1/firatio)/1,000.
The Hog and Pork Sector:
Sows Farrowing Sows farrowing (swfalt) is a measure of the breeding
herd in the hog production sector of the model. This equation is
estimated as a change equation (this year minus last year)(dswfalt).
The data for the dependent variable in this equation is on a July-to-
June year. A July year was used to reflect the time lag in the
production of pork. It takes about 6 months to finish a pig for
slaughter. The variables in this equation are a dummy variable for 1975
and lagged hog net returns (rhogrec). The estimated equation is:
dswfalt = hog10 + hog11*d75 + hog12*lag(rhogrec) + hog13*lag2(rhogrec):
swfalt = lag(swfalt) + dswfalt:
The values for estimated coefficients are:
hog10 = –700:
hog11 = 656.392756:
hog12 = 85.174465:
hog13 = 39.336416:
The Pig Crop Pig crop (pigcalt) is an identity that is the product of
sow farrowings (swfalt) and pigs per litter (pslalt). Pigs per litter
is determined outside the model. The identity is:
pigcalt = swfalt*pslalt:
Federally Inspected Barrow and Gilt Slaughter:
Barrow and gilt slaughter (bgkgnus) is the equivalent of steer and
heifer slaughter in cattle and is the main source of pork production
(about 95 percent). Barrow and gilt slaughter is a function of the pig
crop (pigcalt), the FI slaughter ratio (firatio), and net returns to
hog production (rhogrec).
Net returns to hog production reflects the ability of hog producers to
retain gilts as profitability increases. The estimated equation is:
bgkgnus = hog20 + hog21*pigcalt + hog24*pigcalt*(1 – (firatio)) +
hog25*(rhogrec).
The values for estimated coefficients are:
hog20 = 12015.42:
hog21 = 0.775401:
hog24 = –1.385406:
hog25 = –122.936289:
Federally Inspected Sow Slaughter:
Sow slaughter (swkgnus) is the culling of the hog breeding herd. Sow
slaughter is less than 5 percent of total hog slaughter. It is a
function of sow farrowings (swfalt) and the FI slaughter ratio
(firatio). The estimated equation is:
swkgnus = hog30 + hog31*swfalt + hog34*swfalt*(1 – (firatio)).
The values for estimated coefficients are:
hog30 = –692.784442:
hog31 = 0.369626:
hog34 = 0.809428:
Boar Slaughter:
Boars (bskgnus) are the male component of the breeding herd and make up
less than 1 percent of slaughtered animals. Bskgnus is a function of net
returns to hog production (rhogrec). The estimated equation is:
bskgnus = hog40 + hog41*rhogrec.
The values for estimated coefficients are:
hog40 = 808.838566:
hog41 = –16.163353:
Hog Slaughter Weights:
Hog slaughter weights (hokcaus) are an identity:
hokcaus = 194 + 0.25*(year2000).
Commercial Pork Production:
Commercial pork production (poscpus) is an identity. It is the sum of
barrow and gilt (bgkgnus), sow (swkgnus), and boar slaughter (bskgnus),
times slaughter weights (hokcaus), adjusted for the FI slaughter ratio
(firatio). The identity is:
poscpus = (hokcaus*(bgkgnus + swkgnus + bskgnus)*1/firatio)/1,000.
The Chicken Sector:
Broiler Hatchery Supply Flock:
Broiler hatchery supply flock (chpbrhsf) is the breeding herd
equivalent of beef cows and sows. It is a function of lagged hatchery
supply flock (chpbrhsf) and lagged broiler net returns (rbroilnr). The
estimated equation is:
chpbrhsf = brf0 + brf1*lag(chpbrhsf) + brf2*lag(rbroilnr).
The values for estimated coefficients are:
brf0 = 0:
brf1 = 0.99:
brf2 = 280.514419:
Broiler Chicks Hatched:
Broiler chicks hatched (chiscbr) is a measure of the number of chickens
available for slaughter. It is a function of the hatchery supply flock
(chpbrhsf) times the number of eggs per layer (eggaa), which is
determined outside the model, net returns to broiler production
(rbroilnr), and a time trend. The estimated equation is:
chiscbr = brc0 + brc1*chpbrhsf* eggaa/100 + brc2*rbroilnr + brc3*t.
The values for estimated coefficients are:
brc0 = 190813.86:
brc1 = 0.402329:
brc2 = 76853.16:
brc3 = 16532.47:
The Average Dressed Weight of Broilers:
The average dressed weight of broilers (cykdgaus) is a trend equation:
cykdgaus = brd0 + brd2*t + brd3*t*t.
The values for estimated coefficients are:
brd0 = 2.425356:
brd2 = 0.011888:
brd3 = 0.00045267:
Broiler Slaughter:
Broiler slaughter (chikiyo) is a function of chicks hatched (chiscbr)
and a time trend. The estimated equation is:
chikiyo = brs0 + brs1*chiscbr + brs2*(t).
The values for estimated coefficients are:
brs0 = 20526.26:
brs1 = 33181.54:
brs2 = 0.756102:
Broiler Production:
Broiler production (chiaiyo) is an identity and is the product of
broiler slaughter (chikiyo) and average dressed weight (cykdgaus). The
identity is:
chiaiyo = chikiyo*cykdgaus.
The Turkey Sector:
The turkey component of the model is a single equation. In the original
model, there were equations for supply flocks and eggs hatched. However,
much of these data were discontinued.
Turkey Production:
Turkey production (turai) is estimated as a change equation. It is a
function of lagged net returns (rturknr). The estimated equation is:
dturai = tp0 + tp3*lag(rturknr).
The values for estimated coefficients are:
tp0 = 0.023609:
tp3 = 0.0047:
The Consumption Section of the Model:
Consumption is a residual, and the markets are cleared through a price-
dependent demand equation. Consumption for each of the meats is
production plus beginning stocks plus imports minus exports and ending
stocks.
Beef Consumption:
For beef consumption (bcn), the identity is:
bcn = (bescpus + becitus + besmtus – beuxtus – becotus)/(popa)*0.700;
where bescpus is beef production, becitus is beginning beef stocks,
besmtus is beef imports, beuxtus is beef exports, becotus is ending beef
stocks, and popa is population.
Pork Consumption:
For pork consumption (pcn), the identity is
pcn = (poscpus + pocitus + posmtus – pouxtus – pocotus)/(popa)*0.776;
where poscpus is pork production, pocitus is beginning pork stocks,
posmtus is pork imports, pouxtus is pork exports, pocotus is ending pork
stocks, and popa is population.
Broiler Consumption:
For broiler consumption (brcn), the identity is:
brcn = (chiaiyo + chiazyo + chihtyo – chimxyo – chihtyoe)/(popa*1,000);
where chiaiyo is broiler production, chiazyo is beginning broiler
stocks, chihtyo is broiler imports, chimxyo is broiler exports,
chihtyoe is ending broiler stocks, and popa is population.
Turkey Consumption:
For turkey consumption (tucn), the identity is:
tucn = (turai + turaz + turht – turmx – turhte)/(popa*1,000);
where turai is turkey production, turaz is beginning turkey stocks,
turht is turkey imports, turmx is turkey exports, turhte is ending
turkey stocks, and popa is population.
The Demand Section of the Model:
Demand equations for beef, pork, broilers, and turkey look alike. For
each meat, the percentage change in the CPI is a function of the
percentage changes in beef consumption (dbcn), pork consumption (dpcn),
broiler consumption (dbrcn), and turkey consumption (dtucn). It is also
a function of consumer expenditures less durables (drceldpc) and
consumer expenditures on nondurables less meats and energy (dqlfd),
services (dqcesp), and energy (dqcengp).
Beef Demand:
For beef, the estimated equation is:
drcpibv = f10 + f11*dbcn + f12*dpcn + f13*dbrcn + f14*dtucn + f15*dqlfd
+ f16*drceldpc + f17*dqcesp + f18*dqcengp + f19*dqcedp:
dbcn = (dif(bcn)/lag(bcn)):
dpcn = (dif(pcn)/lag(pcn));
dbrcn = (dif(brcn)/lag(brcn));
dtucn = (dif(tucn)/lag(tucn));
The values for estimated coefficients are:
f10 = –0.012032:
f11 = –1.195495:
f12 = (0.0056/0.01)*f21 – 0.0132750*(f16 – f26):
f13 = (0.0055/0.01)*f31 – 0.0047744*(f16 – f36):
f14 = (0.001/0.01)*f41 – 0.0015217*(f16 – f46):
f15 = (0.16501/0.0281963)*f51 – 0.16501*(f16 – f56), where f51 =
–0.038531 and f56 = 1:
f16 = 1:
f17 = (0.462395/0.0281963)*f71 – 0.462395*(f16 – f76), where f71 =
0.00971957 and f76 = 1:
f18 = (0.0353225/0.0281963)*f81 – 0.0353225*(f16 – f86), where f81 =
0.361559 and f86 = 1:
f19 = (0.1379945/0.0281963)*f91 – 0.1379945*(f16 – f96), where f96 = 1:
Pork Demand:
For pork, the estimated equation is:
drcpipo = f20 + f21*dbcn + f22*dpcn + f23*dbrcn + f24*dtucn + f25*dqlfd
+ f26*drceldpc + f27*dqcesp + f28*dqcengp + f29*dqcedp:
dbcn = (dif(bcn)/lag(bcn)):
dpcn = (dif(pcn)/lag(pcn)):
dbrcn = (dif(brcn)/lag(brcn)):
dtucn = (dif(tucn)/lag(tucn)):
The values for estimated coefficients are:
f20 = –0.019802:
f21 = –0.409412:
f22 = –1.088128:
f23 = –0.129141:
f24 = –0.025320:
f25 = –0.205671:
f26 = 1:
f27 = (0.462395/0.0132750)*f72 – 0.462395*(f26 – f76), where f72 =
0.00645992 and f76 = 1:
f28 = (0.0353225/0.0132750)*f82 – 0.0353225*(f26 – f86), where f82 =
0.230693 and f86 = 1:
f29 = (0.1379945/0.0132750)*f92 – 0.1379945*(f26 – f96), where f96 = 1:
Broiler Demand:
For broilers, the estimated equation is:
drcpibr = f30 + f31*dbcn + f32*dpcn + f33*dbrcn + f34*dtucn + f35*dqlfd
+ f36*drceldpc + f37*dqcesp + f38*dqcengp + f39*dqcedp:
dbcn = (dif(bcn)/lag(bcn)):
dpcn = (dif(pcn)/lag(pcn)):
dbrcn = (dif(brcn)/lag(brcn)):
dtucn = (dif(tucn)/lag(tucn)):
The values for estimated coefficients are:
f30 = –0.00354035:
f31 = –0.947073:
f32 = (0.0056/0.0055)*f23 – 0.0132750*(f36 – f26):
f33 = –1.55:
f34 = (0.001/0.0056)*f43 – 0.0015217*(f36 – f46):
f35 = (0.16501/0.0047744)*f53 – 0.16501*(f36 – f56), where f53 =
0.029685 and f56 = 1:
f36 = 1:
f37 = (0.462395/0.0047744)*f73 – 0.462395*(f36 – f76), where f73 =
–0.00027045 and f76 = 1:
f38 = (0.0353225/0.0047744)*f83 – 0.0353225*(f36 – f86), where f83 =
0.043442 and f86 = 1:
f39 = (0.1379945/0.0047744)*f93 – 0.1379945*(f36 – f96), where f96 = 1:
Turkey Demand:
For turkey, the estimated equation is:
drcpitu = f40 + f41*dbcn + f42*dpcn + f43*dbrcn + f44*dtucn + f45*dqlfd
+ f46*drceldpc + f47*dqcesp + f48*dqcengp + f49*dqcedp:
dbcn = (dif(bcn)/lag(bcn)):
dpcn = (dif(pcn)/lag(pcn)):
dbrcn = (dif(brcn)/lag(brcn)):
dtucn = (dif(tucn)/lag(tucn)):
The values for estimated coefficients are:
f40 = –0.011060:
f41 = –0.956750:
f42 = (0.0056/0.001)*f24 – 0.0132750*(f46 – f26):
f43 = –0.443534:
f44 = –0.667360:
f45 = 1.604581:
f46 = 1:
f47 = (0.462395/0.0015217)*f74 – 0.462395*(f46 – f76), where f74 =
–0.00523878 and f76 = 1:
f48 = (0.0353225/0.0015217)*f84 – 0.0353225*(f46 – f86), where f84 =
0.027265 and f86 = 1:
f49 = (0.1379945/0.0015217)*f94 – 0.1379945*(f46 – f96), where f96 = 1:
The Price Section of the Model:
Boxed Beef Price:
The boxed beef price (drbxbwp) is an average of the wholesale cuts of
beef and is a change equation. It is a function of the change in the
CPI for beef and the percentage of steer and heifer beef production and
exports of beef to total beef production.
The estimated equation is:
drbxbwp = be10 + be11*(drcpibv) + be14*(dif((stkgnus*stkgaus +
hfkgnus*hfkgaus + beuxtus)/bescpus)/lag((stkgnus*stkgaus +
hfkgnus*hfkgaus + beuxtus)/bescpus)):
The values for estimated coefficients are:
be10 = 0.00388167:
be11 = 1.252152:
be14 = –1.177702:
Cow Carcass Price:
The cow carcass price (drcwp) is the wholesale price for cull breeding
animals. It is also a change equation. Cow carcass price is a function
of the change in the CPI for beef and the percentage change in the
amount of beef production that is made up of cow beef production and
imports. The estimated equation is:
drcwp = be20 + be21*(drcpibv) + be24*dif(((cwkgnbe + cwkgnda)*
cwkgaus + besmtus)/bescpus)/lag(((cwkgnbe + cwkgnda)* cwkgaus +
besmtus)/bescpus):
The values for estimated coefficients are:
be20 = 0.00615177:
be21 = 1.447117:
be24 = –0.396987:
Steer Price:
The steer price (drstpom) is a function of the change in the boxed beef
price and is also a change equation. The estimated equation is:
drstpom = be30 + be31*drbxbwp:
The values for estimated coefficients are:
be30 = –0.00167894:
be31 = 0.868567:
Heifer Price:
The heifer price (drhfpom) is a function of the change in the boxed beef
price and is also a change equation. The estimated equation is:
drhfpom = be40 + be41*drbxbwp:
The values for estimated coefficients are:
be40 = –0.00086034:
be41 = 0.826819:
Cow Price:
The cow price (drcwpom) is a function of the change in the cow carcass
price and is a change equation. The estimated equation is:
drcwpom = be50 + be51*drcwp:
The values for estimated coefficients are:
be50 = –0.00169167:
be51 = 0.891149:
Feeder Steer Price:
The feeder steer price (rfstp) is a function of the steer price, feed
costs for a fed steer (corn price (rcornp), soybean meal price (rsbmp),
and hay price (rhayp)), and the change in the lagged calf crop. The
estimated equation is:
rfstp = fst10 + fst11*(rstpom/0.649) + fst12*(rcornp*(248/56) +
rsbmp*(20/2000) + rhayp*(38/2000)) + fst13*(dif(lag(ccrop)));
The values for estimated coefficients are:
fst10 = –11.109730:
fst11 = 1.036045:
fst12 = –1.599263:
fst13 = –0.00212560:
Barrow and Gilt Price:
Barrow and gilt price (drbg7mp) is a change equation and is a function
of the CPI for pork and the year-over-year change in pork production.
The estimated equation is:
drbg7mp = sph10 + sph11*(drcpipo) + sph12*dif(poscpus)/lag(poscpus).
The values for estimated coefficients are:
sph10 = 0.010541:
sph11 = 1.174368:
sph12 = –1.099576:
Broiler Price:
The broiler price (drchip) is a change equation and is a function of the
change in the broiler CPI and the change in broiler production. The
estimated equation is:
drchip = rbrs0 + rbrs1*(drcpibr)+ rbrs2*dif(brcn)/lag(brcn).
The values for estimated coefficients are:
rbrs0 = 0.017798:
rbrs1 = 1.223751:
rbrs2 = –0.570622:
Turkey Price:
The turkey price (drerturp) is a function of the change in the retail
CPI for turkey. The estimated equation is:
drerturp = rertys0 + rertys1*(drcpitu).
The values for estimated coefficients are:
rertys0 = 0.00277665:
rertys1 = 1.155973:
In the equations above for beef, pork, broiler, and turkey prices,
rcpibv = lag(rcpibv)*(1 + drcpibv):
rcpipo = lag(rcpipo)*(1 + drcpipo):
rcpibr = lag(rcpibr)*(1 + drcpibr):
rcpitu = lag(rcpitu)*(1 + drcpitu):
rbxbwp = lag(rbxbwp)*(1 + drbxbwp):
rcwp = lag(rcwp)*(1 + drcwp):
rbg7mp = lag(rbg7mp)*(1 + drbg7mp):
rerturpr = lag(rerturpr)*(1 + drerturpr):
Cost and Returns Section of the Model:
Fed Cattle Returns:
Fed cattle returns (fedret) are the ratio of the output price (rstpom
or real steer price) to feeding costs (real corn price (rcornp), real
soybean meal price (rsbmp), real hay price (rhayp), and real feeder
steer price (rfstp)). The identity is:
fedret = rstpom/(rcornp*(248/56) + rsbmp*(20/2000) + rhayp*(38/2000) +
0.649*rfstp).
Cattle Returns:
Cattle returns (rrct) are generated by using the cost and returns
survey data that ERS collects. Gross returns to the cow-calf operator
are indexed by the real feeder steer price (rfstp) and the real cow
price (rcwp). Costs (cattcc) are determined outside the model from cost
and returns data that ERS collects. The identity is:
rrct = (((77.71 + 46.27 + 61.52 + 40.30)*(rfstp*cpi/100)/64.56*
(1 + (0.01*(year1996))) + ((28.64*rcwp*cpi/100)/38.29)
(1 + ((year1995)*0.01))) – (cattcc – (55)))/cpi*100.
Hog Returns:
Hog returns (rhogrec) are generated using the cost and returns survey
data at ERS. Gross returns to the hog operator are indexed by the real
hog (rbg7mp) price. Costs—total costs (httcc) minus economic costs
(hcostf))—are determined outside the model, using ERS cost-and-returns
data. The identity is:
rcpitu = lag(rcpitu)*(1 + drcpitu):
rbxbwp = lag(rbxbwp)*(1 + drbxbwp):
rcwp = lag(rcwp)*(1 + drcwp):
rbg7mp = lag(rbg7mp)*(1 + drbg7mp):
rerturpr = lag(rerturpr)*(1 + drerturpr):
Cost and Returns Section of the Model:
Fed Cattle Returns:
Fed cattle returns (fedret) are the ratio of the output price (rstpom
or real steer price) to feeding costs (real corn price (rcornp), real
soybean meal price (rsbmp), real hay price (rhayp), and real feeder
steer price (rfstp)). The identity is:
fedret = rstpom/(rcornp*(248/56) + rsbmp*(20/2000) + rhayp*(38/2000) +
0.649*rfstp).
Cattle Returns:
Cattle returns (rrct) are generated by using the cost and returns
survey data that ERS collects. Gross returns to the cow-calf operator
are indexed by the real feeder steer price (rfstp) and the real cow
price (rcwp). Costs (cattcc) are determined outside the model from cost
and returns data that ERS collects. The identity is:
rrct = (((77.71 + 46.27 + 61.52 + 40.30)*(rfstp*cpi/100)/64.56*
(1 + (0.01*(year1996))) + ((28.64*rcwp*cpi/100)/38.29)*
(1 + ((year1995)*0.01))) – (cattcc – (55)))/cpi*100.
Hog Returns:
Hog returns (rhogrec) are generated using the cost and returns survey
data at ERS. Gross returns to the hog operator are indexed by the real
hog (rbg7mp) price. Costs—total costs (httcc) minus economic costs
(hcostf))—are determined outside the model, using ERS cost-and-returns
data. The identity is:
rhogrec = ((44.20*(rbg7mp*cpi/100)/44.76) – (httcc – hcostf –
((0 + (year2000)*0.5)*cpi/136)))/cpi*100.
Broiler Net Returns:
Broiler net returns (rbroilnr) are wholesale broiler price minus broiler
costs (brtc). The identity is:
rbroilnr = rchip – (brtc/cpi*100) – 1.
Broiler Feed Costs:
Broiler feed costs (brfeedc) are calculated by using a formula ERS
developed by using survey data. The exogenous data are corn price
(cornp), soybean meal price (sbmp), and a broiler feed conversion factor
(brfcv). The identity is:
brfeedc = ((((((cornp + 0.4*(cpi/124.0))/56*2,000)*0.70) + ((sbmp +
19.5*(cpi/124.0))*0.30)))*1.09 + (10.5*cpi/124.0))/2,000*brfcv*100).
Broiler Total Cost:
Broiler total cost (brtc) is a formula based on ERS survey data. The
identity is:
brtc = (brfeedc/0.75 + ((8*(cpi/124.0)*0.9))/0.75 +
11.4*(cpi/124.0)*0.9)).
Turkey Net Returns:
Turkey net returns (rturknr) are turkey price (rerturpr) minus turkey
costs (tutc). The identity is:
rturknr = rerturpr – (tutc/cpi*100) + 5.
Turkey Feed Costs:
Turkey feed costs (brfeedc) are calculated by using a formula ERS
developed by using survey data. The exogenous data are corn price
(cornp), soybean meal price (sbmp), and a turkey feed conversion factor
(tufcv). The identity is:
tufeedc = (((cornp)/56*2000)*0.70 + ((sbmp)*0.30))/2,000*tufcv*100.
Turkey Total Cost:
Turkey total cost (tutc) is a formula based on ERS survey data. The
identity is:
tutc = (tufeedc + 8.50*cpi/118.3)/(0.80) + 43.
[End of Appendix II}
Appendix III: Our Survey Phases and Methodology:
In the questionnaire in phase I of our Web-based survey, we asked the
panel of experts to identify the most important factors, or variables,
that affected the prices that domestic cattle producers received and
producers’ incomes over the past few years. We compiled a list of the
factors that the experts identified and we categorized them by groups.
We then presented the categories to the panelists in the questionnaire
in phase II of the survey. In phase II, we asked the experts to rate
each factor on a five-point scale, ranging from “least important” to
“most important” (we also gave the experts the option of responding
“don’t know/no opinion”).
In preparing for the phase III questionnaire, we calculated basic
descriptive statistics on the factors that the experts had rated in the
phase II questionnaire. These statistics consisted of the mean
(average), median, standard deviation, and frequency distribution and
are presented in table 11.
Table 11: Descriptive Statistics on Factors Rated in the Phase II
Questionnaire:
Main category:
(1) Factor[A]: 1. Domestic demand for cattle;
Rating: (2) Mean: 4.38;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 0.54;
Rating: (5) Least important (%): 0%;
Rating: (6) Somewhat important (%): 0%;
Rating: (7) Moderately important (%): 3%;
Rating: (8) Important (%): 58%;
Rating: (9) Most important (%): 40%;
Rating: (10) Number of respondents: 40.
(1) Factor[A]: 2. Domestic supply of cattle;
Rating: (2) Mean: 4.60;
Rating: (3) Median: 5;
Rating: (4) Standard deviation: 0.59;
Rating: (5) Least important (%): 0;
Rating: (6) Somewhat important (%): 0;
Rating: (7) Moderately important (%): 5%;
Rating: (8) Important (%): 30%;
Rating: (9) Most important (%): 65%;
Rating: (10) Number of respondents: 40.
(1) Factor[A]: 3. International trade;
Rating: (2) Mean: 2.80;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 0.94;
Rating: (5) Least important (%): 8%;
Rating: (6) Somewhat important (%): 33%;
Rating: (7) Moderately important (%): 33%;
Rating: (8) Important (%): 29%;
Rating: (9) Most important (%): 0;
Rating: (10) Number of respondents: 40.
(1) Factor[A]: 4; Structural change;
Rating: (2) Mean: 2.98;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 1.21;
Rating: (5) Least important (%): 15%;
Rating: (6) Somewhat important (%): 15%;
Rating: (7) Moderately important (%): 40%;
Rating: (8) Important (%): 18%;
Rating: (9) Most important (%): 13%;
Rating: (10) Number of respondents: 40.
Subcategory:
1. Domestic demand for cattle; Consumer demand items: 1.1 Income;
Rating: (2) Mean: 3.38;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 0.93;
Rating: (5) Least important (%): 0;
Rating: (6) Somewhat important (%): 20%;
Rating: (7) Moderately important (%): 33%;
Rating: (8) Important (%): 38%;
Rating: (9) Most important (%): 10%;
Rating: (10) Number of respondents: 40.
1. Domestic demand for cattle; Consumer demand items: 1.2 Relative
prices of substitutes;
Rating: (2) Mean: 3.90;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 0.98;
Rating: (5) Least important (%): 0;
Rating: (6) Somewhat important (%): 10%;
Rating: (7) Moderately important (%): 23%;
Rating: (8) Important (%): 35%;
Rating: (9) Most important (%): 33%;
Rating: (10) Number of respondents: 40.
1. Domestic demand for cattle; Consumer demand items: 1.2 Relative
prices of substitutes; a. Poultry;
Rating: (2) Mean: 4.10;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 0.97;
Rating: (5) Least important (%): 0;
Rating: (6) Somewhat important (%): 10%;
Rating: (7) Moderately important (%): 10%;
Rating: (8) Important (%): 38%;
Rating: (9) Most important (%): 41%;
Rating: (10) Number of respondents: 39.
1. Domestic demand for cattle; Consumer demand items: 1.2 Relative
prices of substitutes; b. Pork;
Rating: (2) Mean: 4.05;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 0.83;
Rating: (5) Least important (%): 0;
Rating: (6) Somewhat important (%): 5%;
Rating: (7) Moderately important (%): 15%;
Rating: (8) Important (%): 49%;
Rating: (9) Most important (%): 31%;
Rating: (10) Number of respondents: 39.
1. Domestic demand for cattle; Consumer demand items: 1.2 Relative
prices of substitutes; c. Seafood;
Rating: (2) Mean: 2.00;
Rating: (3) Median: 2;
Rating: (4) Standard deviation: 0.89;
Rating: (5) Least important (%): 31;
Rating: (6) Somewhat important (%): 46%;
Rating: (7) Moderately important (%): 15%;
Rating: (8) Important (%): 8%;
Rating: (9) Most important (%): 0%;
Rating: (10) Number of respondents: 39.
1. Domestic demand for cattle; Consumer demand items: 1.2 Relative
prices of substitutes; d. Lamb;
Rating: (2) Mean: 1.64;
Rating: (3) Median: 1;
Rating: (4) Standard deviation: 0.81;
Rating: (5) Least important (%): 54;
Rating: (6) Somewhat important (%): 31%;
Rating: (7) Moderately important (%): 13%;
Rating: (8) Important (%): 3%;
Rating: (9) Most important (%): 0%;
Rating: (10) Number of respondents: 39.
1. Domestic demand for cattle; Consumer demand items: 1.2 Relative
prices of substitutes; e. Plant protein source;
Rating: (2) Mean: 1.45;
Rating: (3) Median: 1;
Rating: (4) Standard deviation: 0.69;
Rating: (5) Least important (%): 66;
Rating: (6) Somewhat important (%): 24%;
Rating: (7) Moderately important (%): 11%;
Rating: (8) Important (%): 0%;
Rating: (9) Most important (%): 0%;
Rating: (10) Number of respondents: 38.
1. Domestic demand for cattle; Consumer demand items: 1.3 Consumer
preferences;
Rating: (2) Mean: 4.18;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 0.84;
Rating: (5) Least important (%): 0;
Rating: (6) Somewhat important (%): 5%;
Rating: (7) Moderately important (%): 13%;
Rating: (8) Important (%): 43%;
Rating: (9) Most important (%): 40%;
Rating: (10) Number of respondents: 40.
1. Domestic demand for cattle; Consumer demand items: 1.3 Consumer
preferences; a. Product quality;
Rating: (2) Mean: 4.30;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 0.72;
Rating: (5) Least important (%): 0;
Rating: (6) Somewhat important (%): 5%;
Rating: (7) Moderately important (%): 0%;
Rating: (8) Important (%): 55%;
Rating: (9) Most important (%): 40%;
Rating: (10) Number of respondents: 40.
1. Domestic demand for cattle; Consumer demand items: 1.3 Consumer
preferences; b. Product variety;
Rating: (2) Mean: 3.47;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 0.76;
Rating: (5) Least important (%): 0;
Rating: (6) Somewhat important (%): 11%;
Rating: (7) Moderately important (%): 37%;
Rating: (8) Important (%): 47%;
Rating: (9) Most important (%): 5%;
Rating: (10) Number of respondents: 38.
1. Domestic demand for cattle; Consumer demand items: 1.3 Consumer
preferences; c. Product convenience;
Rating: (2) Mean: 3.97;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 0.93;
Rating: (5) Least important (%): 3;
Rating: (6) Somewhat important (%): 5%;
Rating: (7) Moderately important (%): 13%;
Rating: (8) Important (%): 51%;
Rating: (9) Most important (%): 28%;
Rating: (10) Number of respondents: 39.
1. Domestic demand for cattle; Consumer demand items: 1.3 Consumer
preferences; d. Product promotion;
Rating: (2) Mean: 2.55;
Rating: (3) Median: 2;
Rating: (4) Standard deviation: 0.99;
Rating: (5) Least important (%): 13;
Rating: (6) Somewhat important (%): 43%;
Rating: (7) Moderately important (%): 23%;
Rating: (8) Important (%): 23%;
Rating: (9) Most important (%): 0%;
Rating: (10) Number of respondents: 40.
1. Domestic demand for cattle; Consumer demand items: 1.4 Health
concerns;
Rating: (2) Mean: 3.55;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 0.89;
Rating: (5) Least important (%): 0;
Rating: (6) Somewhat important (%): 13%;
Rating: (7) Moderately important (%): 32%;
Rating: (8) Important (%): 42%;
Rating: (9) Most important (%): 13%;
Rating: (10) Number of respondents: 38.
1. Domestic demand for cattle; Consumer demand items: 1.4 Health
concerns; a. Dietary;
Rating: (2) Mean: 3.65;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 1.14;
Rating: (5) Least important (%): 3;
Rating: (6) Somewhat important (%): 19%;
Rating: (7) Moderately important (%): 14%;
Rating: (8) Important (%): 41%;
Rating: (9) Most important (%): 24%;
Rating: (10) Number of respondents: 37.
1. Domestic demand for cattle; Consumer demand items: 1.4 Health
concerns; b. Food safety;
Rating: (2) Mean: 3.88;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 0.99;
Rating: (5) Least important (%): 0;
Rating: (6) Somewhat important (%): 10%;
Rating: (7) Moderately important (%): 25%;
Rating: (8) Important (%): 33%;
Rating: (9) Most important (%): 33%;
Rating: (10) Number of respondents: 40.
1. Domestic demand for cattle; Consumer demand items: 1.5 Seasonality;
Rating: (2) Mean: 2.68;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 1.10;
Rating: (5) Least important (%): 20;
Rating: (6) Somewhat important (%): 20%;
Rating: (7) Moderately important (%): 33%;
Rating: (8) Important (%): 28%;
Rating: (9) Most important (%): 0%;
Rating: (10) Number of respondents: 40.
Retailer demand and packer demand items separate from any structural
change effects.
1. Domestic demand for cattle; Consumer demand items: 1.6 Cost of
retailing beef products;
Rating: (2) Mean: 3.41;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 1.04;
Rating: (5) Least important (%): 5;
Rating: (6) Somewhat important (%): 15%;
Rating: (7) Moderately important (%): 23%;
Rating: (8) Important (%): 46%;
Rating: (9) Most important (%): 10%;
Rating: (10) Number of respondents: 39.
1. Domestic demand for cattle; Consumer demand items: 1.7 By-product
value;
Rating: (2) Mean: 3.11;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 1.10;
Rating: (5) Least important (%): 8;
Rating: (6) Somewhat important (%): 22%;
Rating: (7) Moderately important (%): 30%;
Rating: (8) Important (%): 32%;
Rating: (9) Most important (%): 8%;
Rating: (10) Number of respondents: 37.
1. Domestic demand for cattle; Consumer demand items: 1.8 Packer
capacity utilization;
Rating: (2) Mean: 3.90;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 0.85;
Rating: (5) Least important (%): 0;
Rating: (6) Somewhat important (%): 8%;
Rating: (7) Moderately important (%): 18%;
Rating: (8) Important (%): 51%;
Rating: (9) Most important (%): 23%;
Rating: (10) Number of respondents: 39.
1. Domestic demand for cattle; Consumer demand items: 1.9 Wages in
packing;
Rating: (2) Mean: 2.95;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 0.84;
Rating: (5) Least important (%): 3;
Rating: (6) Somewhat important (%): 29%;
Rating: (7) Moderately important (%): 39%;
Rating: (8) Important (%): 29%;
Rating: (9) Most important (%): 0%;
Rating: (10) Number of respondents: 38.
2. Domestic supply of cattle: 2.1 Cattle cycle;
Rating: (2) Mean: 4.08;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 0.80;
Rating: (5) Least important (%): 0;
Rating: (6) Somewhat important (%): 3%;
Rating: (7) Moderately important (%): 20%;
Rating: (8) Important (%): 45%;
Rating: (9) Most important (%): 33%;
Rating: (10) Number of respondents: 40.
2. Domestic supply of cattle: 2.2 Cattle quality;
Rating: (2) Mean: 3.64;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 0.84;
Rating: (5) Least important (%): 0;
Rating: (6) Somewhat important (%): 13%;
Rating: (7) Moderately important (%): 21%;
Rating: (8) Important (%): 56%;
Rating: (9) Most important (%): 10%;
Rating: (10) Number of respondents: 39.
2. Domestic supply of cattle: 2.2 Cattle quality; a. Weight;
Rating: (2) Mean: 3.79;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 0.87;
Rating: (5) Least important (%): 0;
Rating: (6) Somewhat important (%): 8%;
Rating: (7) Moderately important (%): 26%;
Rating: (8) Important (%): 45%;
Rating: (9) Most important (%): 21%;
Rating: (10) Number of respondents: 38.
2. Domestic supply of cattle: 2.2 Cattle quality; b. Yield;
Rating: (2) Mean: 4.00;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 0.82;
Rating: (5) Least important (%): 3;
Rating: (6) Somewhat important (%): 0%;
Rating: (7) Moderately important (%): 16%;
Rating: (8) Important (%): 57%;
Rating: (9) Most important (%): 24%;
Rating: (10) Number of respondents: 37.
2. Domestic supply of cattle: 2.2 Cattle quality; c. Grade;
Rating: (2) Mean: 4.38;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 0.68;
Rating: (5) Least important (%): 0;
Rating: (6) Somewhat important (%): 0%;
Rating: (7) Moderately important (%): 11%;
Rating: (8) Important (%): 41%;
Rating: (9) Most important (%): 49%;
Rating: (10) Number of respondents: 37.
2. Domestic supply of cattle: 2.3 Input costs;
Rating: (2) Mean: 3.67;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 0.96;
Rating: (5) Least important (%): 3;
Rating: (6) Somewhat important (%): 13%;
Rating: (7) Moderately important (%): 13%;
Rating: (8) Important (%): 59%;
Rating: (9) Most important (%): 13%;
Rating: (10) Number of respondents: 39.
2. Domestic supply of cattle: 2.3 Input costs; a. Interest rates;
Rating: (2) Mean: 3.03;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 0.96;
Rating: (5) Least important (%): 0;
Rating: (6) Somewhat important (%): 36%;
Rating: (7) Moderately important (%): 33%;
Rating: (8) Important (%): 23%;
Rating: (9) Most important (%): 8%;
Rating: (10) Number of respondents: 39.
2. Domestic supply of cattle: 2.3 Input costs; b. Land;
Rating: (2) Mean: 2.74;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 0.88;
Rating: (5) Least important (%): 8;
Rating: (6) Somewhat important (%): 31%;
Rating: (7) Moderately important (%): 41%;
Rating: (8) Important (%): 21%;
Rating: (9) Most important (%): 0%;
Rating: (10) Number of respondents: 39.
2. Domestic supply of cattle: 2.3 Input costs; c. Taxes;
Rating: (2) Mean: 2.34;
Rating: (3) Median: 2;
Rating: (4) Standard deviation: 0.85;
Rating: (5) Least important (%): 13;
Rating: (6) Somewhat important (%): 50%;
Rating: (7) Moderately important (%): 26%;
Rating: (8) Important (%): 11%;
Rating: (9) Most important (%): 0%;
Rating: (10) Number of respondents: 38.
2. Domestic supply of cattle: 2.3 Input costs; d. Regulations;
Rating: (2) Mean: 2.92;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 1.05;
Rating: (5) Least important (%): 5;
Rating: (6) Somewhat important (%): 32%;
Rating: (7) Moderately important (%): 37%;
Rating: (8) Important (%): 18%;
Rating: (9) Most important (%): 8%;
Rating: (10) Number of respondents: 38.
2. Domestic supply of cattle: 2.3 Input costs; e. Transportation;
Rating: (2) Mean: 2.79;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 0.98;
Rating: (5) Least important (%): 8;
Rating: (6) Somewhat important (%): 36%;
Rating: (7) Moderately important (%): 26%;
Rating: (8) Important (%): 31%;
Rating: (9) Most important (%): 0%;
Rating: (10) Number of respondents: 39.
2. Domestic supply of cattle: 2.3 Input costs; f. Labor;
Rating: (2) Mean: 2.73;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 0.87;
Rating: (5) Least important (%): 5;
Rating: (6) Somewhat important (%): 35%;
Rating: (7) Moderately important (%): 43%;
Rating: (8) Important (%): 14%;
Rating: (9) Most important (%): 3%;
Rating: (10) Number of respondents: 37.
2. Domestic supply of cattle: 2.3 Input costs; g. Feed;
Rating: (2) Mean: 4.79;
Rating: (3) Median: 5;
Rating: (4) Standard deviation: 0.41;
Rating: (5) Least important (%): 0;
Rating: (6) Somewhat important (%): 0%;
Rating: (7) Moderately important (%): 0%;
Rating: (8) Important (%): 21%;
Rating: (9) Most important (%): 79%;
Rating: (10) Number of respondents: 39.
2. Domestic supply of cattle: 2.3 Input costs; g. Feed; (i) Grain and
oilseed policies;
Rating: (2) Mean: 3.76;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 1.12;
Rating: (5) Least important (%): 5;
Rating: (6) Somewhat important (%): 11%;
Rating: (7) Moderately important (%): 11%;
Rating: (8) Important (%): 49%;
Rating: (9) Most important (%): 24%;
Rating: (10) Number of respondents: 37.
2. Domestic supply of cattle: 2.3 Input costs; g. Feed; (ii) Weather;
Rating: (2) Mean: 3.50;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 0.98;
Rating: (5) Least important (%): 0;
Rating: (6) Somewhat important (%): 18%;
Rating: (7) Moderately important (%): 29%;
Rating: (8) Important (%): 37%;
Rating: (9) Most important (%): 16%;
Rating: (10) Number of respondents: 38.
2. Domestic supply of cattle: 2.3 Input costs; h. Forage; (i) Weather;
Rating: (2) Mean: 4.11;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 0.95;
Rating: (5) Least important (%): 3;
Rating: (6) Somewhat important (%): 3%;
Rating: (7) Moderately important (%): 14%;
Rating: (8) Important (%): 42%;
Rating: (9) Most important (%): 39%;
Rating: (10) Number of respondents: 36.
2. Domestic supply of cattle: 2.4 Risk management;
Rating: (2) Mean: 2.86;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 0.92;
Rating: (5) Least important (%): 5;
Rating: (6) Somewhat important (%): 30%;
Rating: (7) Moderately important (%): 41%;
Rating: (8) Important (%): 22%;
Rating: (9) Most important (%): 3%;
Rating: (10) Number of respondents: 37.
2. Domestic supply of cattle: 2.5 Expected prices;
Rating: (2) Mean: 3.62;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 1.16;
Rating: (5) Least important (%): 5%;
Rating: (6) Somewhat important (%): 14%;
Rating: (7) Moderately important (%): 19%;
Rating: (8) Important (%): 38%;
Rating: (9) Most important (%): 24%;
Rating: (10) Number of respondents: 37.
2. Domestic supply of cattle: 2.6 Futures prices;
Rating: (2) Mean: 3.14;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 1.00;
Rating: (5) Least important (%): 3%;
Rating: (6) Somewhat important (%): 29%;
Rating: (7) Moderately important (%): 26%;
Rating: (8) Important (%): 37%;
Rating: (9) Most important (%): 6%;
Rating: (10) Number of respondents: 35.
2. Domestic supply of cattle: 2.7 Technological changes in production;
Rating: (2) Mean: 3.19;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 1.05;
Rating: (5) Least important (%): 0%;
Rating: (6) Somewhat important (%): 32%;
Rating: (7) Moderately important (%): 30%;
Rating: (8) Important (%): 24%;
Rating: (9) Most important (%): 14%;
Rating: (10) Number of respondents: 37.
2. Domestic supply of cattle: 2.8 Technological changes in marketing;
Rating: (2) Mean: 2.97;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 1.08;
Rating: (5) Least important (%): 11%;
Rating: (6) Somewhat important (%): 17%;
Rating: (7) Moderately important (%): 44%;
Rating: (8) Important (%): 19%;
Rating: (9) Most important (%): 8%;
Rating: (10) Number of respondents: 36.
2. Domestic supply of cattle: 2.9 Dairy prices;
Rating: (2) Mean: 1.72;
Rating: (3) Median: 2;
Rating: (4) Standard deviation: 0.85;
Rating: (5) Least important (%): 47%;
Rating: (6) Somewhat important (%): 39%;
Rating: (7) Moderately important (%): 8%;
Rating: (8) Important (%): 6%;
Rating: (9) Most important (%): 0%;
Rating: (10) Number of respondents: 36.
3. International trade; 3.1 Exports of beef;
Rating: (2) Mean: 3.95;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 0.93;
Rating: (5) Least important (%): 0%;
Rating: (6) Somewhat important (%): 8%;
Rating: (7) Moderately important (%): 21%;
Rating: (8) Important (%): 39%;
Rating: (9) Most important (%): 32%;
Rating: (10) Number of respondents: 38.
3. International trade; 3.2 Imports of beef;
Rating: (2) Mean: 3.00;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 1.14;
Rating: (5) Least important (%): 11%;
Rating: (6) Somewhat important (%): 21%;
Rating: (7) Moderately important (%): 37%;
Rating: (8) Important (%): 21%;
Rating: (9) Most important (%): 11%;
Rating: (10) Number of respondents: 38.
3. International trade; 3.3 Exports of cattle;
Rating: (2) Mean: 1.80;
Rating: (3) Median: 1.5;
Rating: (4) Standard deviation: 0.98;
Rating: (5) Least important (%): 50%;
Rating: (6) Somewhat important (%): 28%;
Rating: (7) Moderately important (%): 14%;
Rating: (8) Important (%): 8%;
Rating: (9) Most important (%): 0%;
Rating: (10) Number of respondents: 36.
3. International trade; 3.4 Imports of cattle;
Rating: (2) Mean: 2.47;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 1.01;
Rating: (5) Least important (%): 21%;
Rating: (6) Somewhat important (%): 26%;
Rating: (7) Moderately important (%): 37%;
Rating: (8) Important (%): 16%;
Rating: (9) Most important (%): 0%;
Rating: (10) Number of respondents: 38.
3. International trade; 3.5 Net imports of cattle; a. Currency exchange
rates;
Rating: (2) Mean: 3.45;
Rating: (3) Median: 3.5;
Rating: (4) Standard deviation: 1.01;
Rating: (5) Least important (%): 0%;
Rating: (6) Somewhat important (%): 21%;
Rating: (7) Moderately important (%): 29%;
Rating: (8) Important (%): 34%;
Rating: (9) Most important (%): 16%;
Rating: (10) Number of respondents: 38.
3. International trade; 3.5 Net imports of cattle; b. Trade barriers;
Rating: (2) Mean: 3.66;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 1.17;
Rating: (5) Least important (%): 3%;
Rating: (6) Somewhat important (%): 21%;
Rating: (7) Moderately important (%): 11%;
Rating: (8) Important (%): 39%;
Rating: (9) Most important (%): 26%;
Rating: (10) Number of respondents: 38.
3. International trade; 3.5 Net imports of cattle; c. Foreign income;
Rating: (2) Mean: 2.61;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 1.13;
Rating: (5) Least important (%): 22%;
Rating: (6) Somewhat important (%): 19%;
Rating: (7) Moderately important (%): 36%;
Rating: (8) Important (%): 19%;
Rating: (9) Most important (%): 3%;
Rating: (10) Number of respondents: 36.
3. International trade; 3.5 Net imports of cattle; d. Foreign
competition;
Rating: (2) Mean: 2.94;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 1.04;
Rating: (5) Least important (%): 8%;
Rating: (6) Somewhat important (%): 28%;
Rating: (7) Moderately important (%): 28%;
Rating: (8) Important (%): 33%;
Rating: (9) Most important (%): 3%;
Rating: (10) Number of respondents: 36.
3. International trade; 3.5 Net imports of cattle; e. Disease;
Rating: (2) Mean: 3.50;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 1.29;
Rating: (5) Least important (%): 11%;
Rating: (6) Somewhat important (%): 11%;
Rating: (7) Moderately important (%): 24%;
Rating: (8) Important (%): 29%;
Rating: (9) Most important (%): 26%;
Rating: (10) Number of respondents: 38.
3. International trade; 3.5 Net imports of cattle; f. Use of Hormones;
Rating: (2) Mean: 2.59;
Rating: (3) Median: 2;
Rating: (4) Standard deviation: 1.34;
Rating: (5) Least important (%): 24%;
Rating: (6) Somewhat important (%): 35%;
Rating: (7) Moderately important (%): 5%;
Rating: (8) Important (%): 27%;
Rating: (9) Most important (%): 8%;
Rating: (10) Number of respondents: 37.
3. International trade; 3.5 Net imports of cattle; g. Trade promotion;
Rating: (2) Mean: 1.89;
Rating: (3) Median: 2;
Rating: (4) Standard deviation: 0.98;
Rating: (5) Least important (%): 44%;
Rating: (6) Somewhat important (%): 31%;
Rating: (7) Moderately important (%): 17%;
Rating: (8) Important (%): 8%;
Rating: (9) Most important (%): 0%;
Rating: (10) Number of respondents: 36.
3. International trade; 3.6 Net imports of beef; a. Currency exchange
rates;
Rating: (2) Mean: 3.63;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 0.97;
Rating: (5) Least important (%): 0%;
Rating: (6) Somewhat important (%): 16%;
Rating: (7) Moderately important (%): 24%;
Rating: (8) Important (%): 42%;
Rating: (9) Most important (%): 18%;
Rating: (10) Number of respondents: 38.
3. International trade; 3.6 Net imports of beef; b. Trade barriers;
Rating: (2) Mean: 4.16;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 0.72;
Rating: (5) Least important (%): 0%;
Rating: (6) Somewhat important (%): 0%;
Rating: (7) Moderately important (%): 18%;
Rating: (8) Important (%): 47%;
Rating: (9) Most important (%): 34%;
Rating: (10) Number of respondents: 38.
3. International trade; 3.6 Net imports of beef; c. Foreign income;
Rating: (2) Mean: 3.72;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 0.88;
Rating: (5) Least important (%): 0%;
Rating: (6) Somewhat important (%): 8%;
Rating: (7) Moderately important (%): 31%;
Rating: (8) Important (%): 42%;
Rating: (9) Most important (%): 19%;
Rating: (10) Number of respondents: 36.
3. International trade; 3.6 Net imports of beef; d. Foreign
competition;
Rating: (2) Mean: 3.43;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 0.90;
Rating: (5) Least important (%): 0%;
Rating: (6) Somewhat important (%): 16%;
Rating: (7) Moderately important (%): 35%;
Rating: (8) Important (%): 38%;
Rating: (9) Most important (%): 11%;
Rating: (10) Number of respondents: 37.
3. International trade; 3.6 Net imports of beef; e. Disease;
Rating: (2) Mean: 3.39;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 1.26;
Rating: (5) Least important (%): 8%;
Rating: (6) Somewhat important (%): 21%;
Rating: (7) Moderately important (%): 16%;
Rating: (8) Important (%): 34%;
Rating: (9) Most important (%): 21%;
Rating: (10) Number of respondents: 38.
3. International trade; 3.6 Net imports of beef; f. Use of Hormones;
Rating: (2) Mean: 3.44;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 0.99;
Rating: (5) Least important (%): 0%;
Rating: (6) Somewhat important (%): 23%;
Rating: (7) Moderately important (%): 23%;
Rating: (8) Important (%): 41%;
Rating: (9) Most important (%): 13%;
Rating: (10) Number of respondents: 39.
3. International trade; 3.6 Net imports of beef; g. Trade promotion;
Rating: (2) Mean: 2.53;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 1.00;
Rating: (5) Least important (%): 17%;
Rating: (6) Somewhat important (%): 31%;
Rating: (7) Moderately important (%): 39%;
Rating: (8) Important (%): 11%;
Rating: (9) Most important (%): 3%;
Rating: (10) Number of respondents: 36.
4. Structural change; 4.1 Industry concentration;
Rating: (2) Mean: 2.90;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 1.34;
Rating: (5) Least important (%): 18%;
Rating: (6) Somewhat important (%): 25%;
Rating: (7) Moderately important (%): 23%;
Rating: (8) Important (%): 20%;
Rating: (9) Most important (%): 15%;
Rating: (10) Number of respondents: 40.
4. Structural change; 4.1 Industry concentration; a. National packer
level;
Rating: (2) Mean: 3.30;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 1.33;
Rating: (5) Least important (%): 11%;
Rating: (6) Somewhat important (%): 24%;
Rating: (7) Moderately important (%): 8%;
Rating: (8) Important (%): 38%;
Rating: (9) Most important (%): 19%;
Rating: (10) Number of respondents: 37.
4. Structural change; 4.1 Industry concentration; b. Regional packer
level;
Rating: (2) Mean: 3.57;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 1.07;
Rating: (5) Least important (%): 3%;
Rating: (6) Somewhat important (%): 16%;
Rating: (7) Moderately important (%): 22%;
Rating: (8) Important (%): 41%;
Rating: (9) Most important (%): 19%;
Rating: (10) Number of respondents: 37.
4. Structural change; 4.1 Industry concentration; c. Local packer
level;
Rating: (2) Mean: 3.33;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 1.31;
Rating: (5) Least important (%): 14%;
Rating: (6) Somewhat important (%): 14%;
Rating: (7) Moderately important (%): 14%;
Rating: (8) Important (%): 42%;
Rating: (9) Most important (%): 17%;
Rating: (10) Number of respondents: 36.
4. Structural change; 4.1 Industry concentration; d. National retailer
level;
Rating: (2) Mean: 2.91;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 1.21;
Rating: (5) Least important (%): 12%;
Rating: (6) Somewhat important (%): 30%;
Rating: (7) Moderately important (%): 21%;
Rating: (8) Important (%): 27%;
Rating: (9) Most important (%): 9%;
Rating: (10) Number of respondents: 33.
4. Structural change; 4.1 Industry concentration; e. Regional retailer
level;
Rating: (2) Mean: 2.62;
Rating: (3) Median: 2.5;
Rating: (4) Standard deviation: 1.26;
Rating: (5) Least important (%): 24%;
Rating: (6) Somewhat important (%): 26%;
Rating: (7) Moderately important (%): 21%;
Rating: (8) Important (%): 24%;
Rating: (9) Most important (%): 6%;
Rating: (10) Number of respondents: 34.
4. Structural change; 4.1 Industry concentration; f. Local retailer
level;
Rating: (2) Mean: 2.58;
Rating: (3) Median: 2;
Rating: (4) Standard deviation: 1.60;
Rating: (5) Least important (%): 42%;
Rating: (6) Somewhat important (%): 9%;
Rating: (7) Moderately important (%): 15%;
Rating: (8) Important (%): 15%;
Rating: (9) Most important (%): 18%;
Rating: (10) Number of respondents: 33.
4. Structural change; 4.1 Industry concentration; g. National feedlot
level;
Rating: (2) Mean: 2.47;
Rating: (3) Median: 2;
Rating: (4) Standard deviation: 1.19;
Rating: (5) Least important (%): 26%;
Rating: (6) Somewhat important (%): 26%;
Rating: (7) Moderately important (%): 24%;
Rating: (8) Important (%): 21%;
Rating: (9) Most important (%): 3%;
Rating: (10) Number of respondents: 34.
4. Structural change; 4.1 Industry concentration; h. Regional feedlot
level;
Rating: (2) Mean: 2.54;
Rating: (3) Median: 2;
Rating: (4) Standard deviation: 1.22;
Rating: (5) Least important (%): 23%;
Rating: (6) Somewhat important (%): 31%;
Rating: (7) Moderately important (%): 20%;
Rating: (8) Important (%): 20%;
Rating: (9) Most important (%): 6%;
Rating: (10) Number of respondents: 35.
4. Structural change; 4.1 Industry concentration; i. Local feedlot
level;
Rating: (2) Mean: 2.53;
Rating: (3) Median: 2;
Rating: (4) Standard deviation: 1.56;
Rating: (5) Least important (%): 35%;
Rating: (6) Somewhat important (%): 26%;
Rating: (7) Moderately important (%): 9%;
Rating: (8) Important (%): 9%;
Rating: (9) Most important (%): 21%;
Rating: (10) Number of respondents: 34.
4. Structural change; 4.2 Vertical integration;
Rating: (2) Mean: 2.79;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 1.24;
Rating: (5) Least important (%): 21%;
Rating: (6) Somewhat important (%): 21%;
Rating: (7) Moderately important (%): 23%;
Rating: (8) Important (%): 31%;
Rating: (9) Most important (%): 5%;
Rating: (10) Number of respondents: 39.
4. Structural change; 4.3 Vertical coordination;
Rating: (2) Mean: 3.41;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 1.09;
Rating: (5) Least important (%): 8%;
Rating: (6) Somewhat important (%): 8%;
Rating: (7) Moderately important (%): 36%;
Rating: (8) Important (%): 33%;
Rating: (9) Most important (%): 15%;
Rating: (10) Number of respondents: 39.
4. Structural change; 4.3 Vertical coordination; a. Marketing
agreements;
Rating: (2) Mean: 3.59;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 0.98;
Rating: (5) Least important (%): 0%;
Rating: (6) Somewhat important (%): 16%;
Rating: (7) Moderately important (%): 27%;
Rating: (8) Important (%): 38%;
Rating: (9) Most important (%): 19%;
Rating: (10) Number of respondents: 37.
4. Structural change; 4.3 Vertical coordination; b. Forward contracts;
Rating: (2) Mean: 3.39;
Rating: (3) Median: 3.5;
Rating: (4) Standard deviation: 1.03;
Rating: (5) Least important (%): 3%;
Rating: (6) Somewhat important (%): 18%;
Rating: (7) Moderately important (%): 29%;
Rating: (8) Important (%): 37%;
Rating: (9) Most important (%): 13%;
Rating: (10) Number of respondents: 38.
4. Structural change; 4.3 Vertical coordination; c. Value-based
marketing and pricing;
Rating: (2) Mean: 3.86;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 1.08;
Rating: (5) Least important (%): 3%;
Rating: (6) Somewhat important (%): 11%;
Rating: (7) Moderately important (%): 16%;
Rating: (8) Important (%): 38%;
Rating: (9) Most important (%): 32%;
Rating: (10) Number of respondents: 37.
4. Structural change; 4.4 Horizontal integration;
Rating: (2) Mean: 2.68;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 1.14;
Rating: (5) Least important (%): 16%;
Rating: (6) Somewhat important (%): 32%;
Rating: (7) Moderately important (%): 26%;
Rating: (8) Important (%): 21%;
Rating: (9) Most important (%): 5%;
Rating: (10) Number of respondents: 38.
4. Structural change; 4.5 Economies of scale;
Rating: (2) Mean: 3.95;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 0.92;
Rating: (5) Least important (%): 0%;
Rating: (6) Somewhat important (%): 8%;
Rating: (7) Moderately important (%): 21%;
Rating: (8) Important (%): 41%;
Rating: (9) Most important (%): 31%;
Rating: (10) Number of respondents: 39.
4. Structural change; 4.5 Economies of scale; a. Packer;
Rating: (2) Mean: 4.31;
Rating: (3) Median: 5;
Rating: (4) Standard deviation: 0.95;
Rating: (5) Least important (%): 3%;
Rating: (6) Somewhat important (%): 3%;
Rating: (7) Moderately important (%): 10%;
Rating: (8) Important (%): 31%;
Rating: (9) Most important (%): 54%;
Rating: (10) Number of respondents: 39.
4. Structural change; 4.5 Economies of scale; b. Retailer;
Rating: (2) Mean: 3.18;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 1.19;
Rating: (5) Least important (%): 10%;
Rating: (6) Somewhat important (%): 21%;
Rating: (7) Moderately important (%): 21%;
Rating: (8) Important (%): 38%;
Rating: (9) Most important (%): 10%;
Rating: (10) Number of respondents: 39.
4. Structural change; 4.5 Economies of scale; c. Feedlot;
Rating: (2) Mean: 3.72;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 0.86;
Rating: (5) Least important (%): 0%;
Rating: (6) Somewhat important (%): 5%;
Rating: (7) Moderately important (%): 38%;
Rating: (8) Important (%): 36%;
Rating: (9) Most important (%): 21%;
Rating: (10) Number of respondents: 39.
4. Structural change; 4.6 Economies of scope;
Rating: (2) Mean: 3.09;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 1.09;
Rating: (5) Least important (%): 11%;
Rating: (6) Somewhat important (%): 14%;
Rating: (7) Moderately important (%): 34%;
Rating: (8) Important (%): 34%;
Rating: (9) Most important (%): 6%;
Rating: (10) Number of respondents: 35.
4. Structural change; 4.6 Economies of scope; a. Packer;
Rating: (2) Mean: 3.27;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 1.23;
Rating: (5) Least important (%): 15%;
Rating: (6) Somewhat important (%): 9%;
Rating: (7) Moderately important (%): 18%;
Rating: (8) Important (%): 48%;
Rating: (9) Most important (%): 9%;
Rating: (10) Number of respondents: 33.
4. Structural change; 4.6 Economies of scope; b. Retailer;
Rating: (2) Mean: 3.59;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 0.99;
Rating: (5) Least important (%): 6%;
Rating: (6) Somewhat important (%): 6%;
Rating: (7) Moderately important (%): 24%;
Rating: (8) Important (%): 53%;
Rating: (9) Most important (%): 12%;
Rating: (10) Number of respondents: 34.
4.7 Economies of agglomeration;
Rating: (2) Mean: 2.30;
Rating: (3) Median: 2;
Rating: (4) Standard deviation: 1.14;
Rating: (5) Least important (%): 33%;
Rating: (6) Somewhat important (%): 22%;
Rating: (7) Moderately important (%): 26%;
Rating: (8) Important (%): 19%;
Rating: (9) Most important (%): 0%;
Rating: (10) Number of respondents: 27.
4.8 Efficiency of supply chain;
Rating: (2) Mean: 3.47;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 1.08;
Rating: (5) Least important (%): 5%;
Rating: (6) Somewhat important (%): 16%;
Rating: (7) Moderately important (%): 18%;
Rating: (8) Important (%): 47%;
Rating: (9) Most important (%): 13%;
Rating: (10) Number of respondents: 38.
4.9 Technological change;
Rating: (2) Mean: 3.59;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 1.01;
Rating: (5) Least important (%): 3%;
Rating: (6) Somewhat important (%): 16%;
Rating: (7) Moderately important (%): 14%;
Rating: (8) Important (%): 54%;
Rating: (9) Most important (%): 14%;
Rating: (10) Number of respondents: 37.
4.9 Technological change; a. Packer production;
Rating: (2) Mean: 3.95;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 0.87;
Rating: (5) Least important (%): 0%;
Rating: (6) Somewhat important (%): 8%;
Rating: (7) Moderately important (%): 16%;
Rating: (8) Important (%): 50%;
Rating: (9) Most important (%): 26%;
Rating: (10) Number of respondents: 38.
4.9 Technological change; b. Packer marketing;
Rating: (2) Mean: 3.03;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 1.01;
Rating: (5) Least important (%): 8%;
Rating: (6) Somewhat important (%): 19%;
Rating: (7) Moderately important (%): 41%;
Rating: (8) Important (%): 27%;
Rating: (9) Most important (%): 5%;
Rating: (10) Number of respondents: 37.
4.9 Technological change; c. Retailer production;
Rating: (2) Mean: 2.84;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 1.14;
Rating: (5) Least important (%): 19%;
Rating: (6) Somewhat important (%): 14%;
Rating: (7) Moderately important (%): 35%;
Rating: (8) Important (%): 30%;
Rating: (9) Most important (%): 3%;
Rating: (10) Number of respondents: 37.
4.9 Technological change; d. Retailer marketing;
Rating: (2) Mean: 3.19;
Rating: (3) Median: 3.5;
Rating: (4) Standard deviation: 1.35;
Rating: (5) Least important (%): 17%;
Rating: (6) Somewhat important (%): 14%;
Rating: (7) Moderately important (%): 19%;
Rating: (8) Important (%): 33%;
Rating: (9) Most important (%): 17%;
Rating: (10) Number of respondents: 36.
4.10 Thin spot market;
Rating: (2) Mean: 3.03;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 1.45;
Rating: (5) Least important (%): 22%;
Rating: (6) Somewhat important (%): 16%;
Rating: (7) Moderately important (%): 19%;
Rating: (8) Important (%): 24%;
Rating: (9) Most important (%): 19%;
Rating: (10) Number of respondents: 37.
4.10 Thin spot market; a. Price discovery;
Rating: (2) Mean: 4.00;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 1.06;
Rating: (5) Least important (%): 3%;
Rating: (6) Somewhat important (%): 10%;
Rating: (7) Moderately important (%): 10%;
Rating: (8) Important (%): 40%;
Rating: (9) Most important (%): 38%;
Rating: (10) Number of respondents: 40.
4.10 Thin spot market; b. Information transparency;
Rating: (2) Mean: 3.62;
Rating: (3) Median: 4;
Rating: (4) Standard deviation: 1.11;
Rating: (5) Least important (%): 8%;
Rating: (6) Somewhat important (%): 10%;
Rating: (7) Moderately important (%): 10%;
Rating: (8) Important (%): 56%;
Rating: (9) Most important (%): 15%;
Rating: (10) Number of respondents: 39.
4.10 Thin spot market; c. Bidding procedures ;
Rating: (2) Mean: 3.08;
Rating: (3) Median: 3;
Rating: (4) Standard deviation: 1.27;
Rating: (5) Least important (%): 17%;
Rating: (6) Somewhat important (%): 14%;
Rating: (7) Moderately important (%): 25%;
Rating: (8) Important (%): 33%;
Rating: (9) Most important (%): 11%;
Rating: (10) Number of respondents: 36.
[A] Experts mentioned these items in response to the following question
in phase I: “During the past few years, what were the most important
factors/variables affecting (a) the prices received by domestic cattle
producers and (b) producers’ incomes?” Percentages may not add to 100
because of rounding.
[End of table]
The basic question on the importance of each factor or variable varied
slightly, depending on the category or subcategory being rated. The
question for the four main categories—items 1 through 4—was:
“During the first phase of this study we asked you to identify ‘the
most important factors or variables affecting (a) the prices received
by domestic cattle producers and (b) producers’ incomes.’ The panel
identified many unique factors. We have organized those factors under
four main categories:
1. Domestic Demand for Cattle;
2. Domestic Supply of Cattle;
3. International Trade;
4. Structural Change;
“In this section, we ask that you rate the importance of each of the
main categories relative to the other main categories. In subsequent
sections, we will ask you to rank the relative importance of the
factors listed within each of the main categories.
“How important are each of the following main categories of factors in
affecting (a) the prices received by domestic cattle producers and (b)
producers’ incomes?”
Following this question, we listed each main category factor, and
experts rated each factor on a five-point scale, ranging from “least
important” to “most important,” as shown in column heads 5–9. We gave
the experts the option of responding “don’t know/no opinion”; the
default response on the Web-based questionnaire was “no response.” When
rating a factor, the experts had to actively de-select the “no
response” option. The question for the subcategory factors (for
example, 1.1, 1.2, ... 1.9 and 2.1, 2.2, ... 2.9), was:
“In this section we ask that you rate the importance of the factors
related to [main category factor—for example, ‘Domestic supply of
cattle’] that affect (a) the prices received by domestic cattle
producers and (b) producers’ incomes.
“How important is each of the following factors?”
We listed each of the subcategory factors following this question, and
the experts rated them on the same five-point scale, ranging from “least
important” to “most important.”
Finally, we probed further within some of the subcategories; they are
listed under subcategories and are preceded by lower-case letters (for
example, items 1.2a through 1.2e). To obtain a rating of importance
from experts on these factors, we asked:
“Within the subcategory [subcategory factor—for example, ‘relative
prices of substitutes’], how important are each of the following
factors?”
The experts also rated each subcategory factor on the same five-point
scale described above.
During phase III, we offered experts the opportunity to change their
original assessments of the importance of structural change and
international trade factors. Two of the 40 respondents changed their
opinions on some of the structural change factors, and 5 changed their
ratings on some of the international trade factors. The numbers in
table 11 reflect the changes the panelists made. The factors in table
11 affected by these changes are 3, 3.2, 3.3, 3.4, 4.8, and 4.10.
[End of Appendix III]
Appendix IV: The Panel’s Ratings of Problems and Issues in Developing
an Adequate Model:
In the phase I Web-based questionnaire, we asked the panel of experts to
identify any problems or issues that would be faced in developing a
comprehensive and reliable analysis to estimate domestic cattle prices
and producers’ incomes. We compiled a list of the issues and problems
they identified and then presented that list back to the panelists as
part of the phase II questionnaire. In the phase II questionnaire, we
asked the experts to rate each issue and problem identified in phase I
on two dimensions. First, we asked them to assess how important it
would be to address the issue or problem and, second, we asked how
feasible it would be to overcome it.
For our analysis (and in preparation for the phase III questionnaire),
we calculated basic descriptive statistics on these issues and problems
the experts rated in the phase II questionnaire. These statistics
consisted of the mean (average), median, standard deviation, and
frequency distribution. These statistics are presented in table 12.
Table 12: Descriptive Statistics on Issues and Problems Rated in the
Phase II Questionnaire:
Rank: 1;
(1) Issue or problem[A]: One very important question to answer to
develop a model, keep misspecification as small as reasonable, and
provide some usefulness is “What is the purpose of the cattle price
model?” If the purpose is short-term forecasting, the answer will differ
markedly from policy modeling or something else.
Importance:
(2) Mean: 4.05;
(3) Median: 4;
(4) Standard deviation: 0.85;
(5) Least important or feasible (%): 0%;
(6) Somewhat important or feasible (%): 8%;
(7) Moderately important or feasible (%): 8%;
(8) Important or feasible (%): 54%;
(9) Most important or feasible (%): 30%;
(10) Number of experts: 37;
Feasibility:
(2) Mean: 3.47;
(3) Median: 3.5;
(4) Standard deviation: 1.06;
(5) Least important or feasible (%): 6%;
(6) Somewhat important or feasible (%): 8%;
(7) Moderately important or feasible (%): 36%;
(8) Important or feasible (%): 33%;
(9) Most important or feasible (%): 17%;
(10) Number of experts: 36.
Rank: 2;
(1) Issue or problem[A]: Disaggregated cost and revenue data linking
ranchers, feeders, packers, and retailers are unavailable.
Importance:
(2) Mean: 3.75;
(3) Median: 4;
(4) Standard deviation: 1.25;
(5) Least important or feasible (%): 6%;
(6) Somewhat important or feasible (%): 14%;
(7) Moderately important or feasible (%): 17%;
(8) Important or feasible (%): 28%;
(9) Most important or feasible (%): 36%;
(10) Number of experts: 36;
Feasibility:
(2) Mean: 2.46;
(3) Median: 2;
(4) Standard deviation: 1.15;
(5) Least important or feasible (%): 23%;
(6) Somewhat important or feasible (%): 37%;
(7) Moderately important or feasible (%): 11%;
(8) Important or feasible (%): 29%;
(9) Most important or feasible (%): 0;
(10) Number of experts: 35.
Rank: 3;
(1) Issue or problem[A]: Retail and consumption data are very poor.
Importance:
(2) Mean: 3.57;
(3) Median: 4;
(4) Standard deviation: 1.09;
(5) Least important or feasible (%): 8%;
(6) Somewhat important or feasible (%): 5%;
(7) Moderately important or feasible (%): 24%;
(8) Important or feasible (%): 46%;
(9) Most important or feasible (%): 16%;
(10) Number of experts: 37;
Feasibility:
(2) Mean: 3.16;
(3) Median: 3;
(4) Standard deviation: 1.04;
(5) Least important or feasible (%): 5%;
(6) Somewhat important or feasible (%): 22%;
(7) Moderately important or feasible (%): 32%;
(8) Important or feasible (%): 32%;
(9) Most important or feasible (%): 8%;
(10) Number of experts: 37.
Rank: 4;
(1) Issue or problem[A]: A challenge is the appropriate modeling of
dynamics in prices due to the cattle cycle.
Importance:
(2) Mean: 3.47;
(3) Median: 4;
(4) Standard deviation: 1.08;
(5) Least important or feasible (%): 3%;
(6) Somewhat important or feasible (%): 18%;
(7) Moderately important or feasible (%): 26%;
(8) Important or feasible (%): 34%;
(9) Most important or feasible (%): 18%;
(10) Number of experts: 38.
Feasibility:
(2) Mean: 3.32;
(3) Median: 3;
(4) Standard deviation: 0.97;
(5) Least important or feasible (%): 3%;
(6) Somewhat important or feasible (%): 19%;
(7) Moderately important or feasible (%): 30%;
(8) Important or feasible (%): 41%;
(9) Most important or feasible (%): 8%;
(10) Number of experts: 37.
Rank: 5;
(1) Issue or problem[A]: The relationships between the different levels
of the food chain are changing, and it is difficult to establish both
driving factors and results.
Importance:
(2) Mean: 3.47;
(3) Median: 4;
(4) Standard deviation: 0.99;
(5) Least important or feasible (%): 3%;
(6) Somewhat important or feasible (%): 15%;
(7) Moderately important or feasible (%): 26%;
(8) Important or feasible (%): 44%;
(9) Most important or feasible (%): 12%;
(10) Number of experts: 34;
Feasibility:
(2) Mean: 3.00;
(3) Median: 3;
(4) Standard deviation: 0.89;
(5) Least important or feasible (%): 3%;
(6) Somewhat important or feasible (%): 26%;
(7) Moderately important or feasible (%): 41%;
(8) Important or feasible (%): 26%;
(9) Most important or feasible (%): 3%;
(10) Number of experts: 34.
Rank: 6;
(1) Issue or problem[A]: Confidential data on farmers, processors, and
retailers are inaccessible.
Importance:
(2) Mean: 3.41;
(3) Median: 4;
(4) Standard deviation: 1.16;
(5) Least important or feasible (%): 5%;
(6) Somewhat important or feasible (%): 21%;
(7) Moderately important or feasible (%): 21%;
(8) Important or feasible (%): 36%;
(9) Most important or feasible (%): 18%;
(10) Number of experts: 39;
Feasibility:
(2) Mean: 2.25;
(3) Median: 2;
(4) Standard deviation: 1.11;
(5) Least important or feasible (%): 28%;
(6) Somewhat important or feasible (%): 39%;
(7) Moderately important or feasible (%): 17%;
(8) Important or feasible (%): 14%;
(9) Most important or feasible (%): 3%;
(10) Number of experts: 36.
Rank: 7;
(1) Issue or problem[A]: A better understanding of the cattle cycle is
needed, because prices and producers' incomes vary significantly at its
different stages. This is especially important if the cattle cycle is
changing significantly with restructuring of the industry. With
increased reliance on contracts, it has become more difficult to assess
how economic incentives and incomes vary over time and space. It is not
clear who benefits most from the newly evolving structure and how
benefits are distributed (if at all) among producers, processors,
retailers, and consumers.
Importance:
(2) Mean: 3.37;
(3) Median: 3;
(4) Standard deviation: 1.05;
(5) Least important or feasible (%): 3%;
(6) Somewhat important or feasible (%): 18%;
(7) Moderately important or feasible (%): 34%;
(8) Important or feasible (%): 29%;
(9) Most important or feasible (%): 16%;
(10) Number of experts: 38;
Feasibility:
(2) Mean: 2.94;
(3) Median: 3;
(4) Standard deviation: 1.12;
(5) Least important or feasible (%): 11%;
(6) Somewhat important or feasible (%): 25%;
(7) Moderately important or feasible (%): 28%;
(8) Important or feasible (%): 31%;
(9) Most important or feasible (%): 6%;
(10) Number of experts: 36.
Rank: 8;
(1) Issue or problem[A]: Any attempt to come up with one all-
encompassing model may be problematic because problems may differ in
the states and regions. Separate and perhaps more than one type of
modeling and analysis may be needed.
Importance:
(2) Mean: 3.37;
(3) Median: 4;
(4) Standard deviation: 1.17;
(5) Least important or feasible (%): 8%;
(6) Somewhat important or feasible (%): 16%;
(7) Moderately important or feasible (%): 24%;
(8) Important or feasible (%): 37%;
(9) Most important or feasible (%): 16%;
(10) Number of experts: 38;
Feasibility:
(2) Mean: 3.31;
(3) Median: 3;
(4) Standard deviation: 1.08;
(5) Least important or feasible (%): 6%;
(6) Somewhat important or feasible (%): 17%;
(7) Moderately important or feasible (%): 29%;
(8) Important or feasible (%): 37%;
(9) Most important or feasible (%): 11%;
(10) Number of experts: 35.
Rank: 9;
(1) Issue or problem[A]: Current supply is a function of profits that
producers expected to receive when they started production. Analysts
must use a proxy for expectations that measures the underlying concept
with error.
Importance:
(2) Mean: 3.36;
(3) Median: 4;
(4) Standard deviation: 1.18;
(5) Least important or feasible (%): 10%;
(6) Somewhat important or feasible (%): 13%;
(7) Moderately important or feasible (%): 21%;
(8) Important or feasible (%): 44%;
(9) Most important or feasible (%): 13%;
(10) Number of experts: 39;
Feasibility:
(2) Mean: 3.08;
(3) Median: 3;
(4) Standard deviation: 1.12;
(5) Least important or feasible (%): 11%;
(6) Somewhat important or feasible (%): 21%;
(7) Moderately important or feasible (%): 24%;
(8) Important or feasible (%): 39%;
(9) Most important or feasible (%): 5%;
(10) Number of experts: 38.
Rank: 10;
(1) Issue or problem[A]: Many key long-term variables—technical change,
policy changes (e.g., in feed crops), and trends in health concerns—are
hard to quantify conceptually, much less to get good data for.
Importance:
(2) Mean: 3.26;
(3) Median: 4;
(4) Standard deviation: 1.12;
(5) Least important or feasible (%): 10%;
(6) Somewhat important or feasible (%): 13%;
(7) Moderately important or feasible (%): 26%;
(8) Important or feasible (%): 44%;
(9) Most important or feasible (%): 8%;
(10) Number of experts: 39;
Feasibility:
(2) Mean: 2.50;
(3) Median: 2.5;
(4) Standard deviation: 1.16;
(5) Least important or feasible (%): 24%;
(6) Somewhat important or feasible (%): 26%;
(7) Moderately important or feasible (%): 32%;
(8) Important or feasible (%): 12%;
(9) Most important or feasible (%): 6%;
(10) Number of experts: 34.
Rank: 11;
(1) Issue or problem[A]: If cattle prices NASS reports no longer
represent prices actually paid to producers for cattle, it is difficult
to use these series for meaningful analysis.
Importance:
(2) Mean: 3.24;
(3) Median: 4;
(4) Standard deviation: 1.28;
(5) Least important or feasible (%): 11%;
(6) Somewhat important or feasible (%): 22%;
(7) Moderately important or feasible (%): 16%;
(8) Important or feasible (%): 35%;
(9) Most important or feasible (%): 16%;
(10) Number of experts: 37;
Feasibility:
(2) Mean: 2.95;
(3) Median: 3;
(4) Standard deviation: 1.08;
(5) Least important or feasible (%): 8%;
(6) Somewhat important or feasible (%): 30%;
(7) Moderately important or feasible (%): 27%;
(8) Important or feasible (%): 30%;
(9) Most important or feasible (%): 5%;
(10) Number of experts: 37.
Rank: 12;
(1) Issue or problem[A]: Reported market prices are likely not to
indicate true prices received because of extensive contracting and
pricing quality grid differences.
Importance:
(2) Mean: 3.21;
(3) Median: 4;
(4) Standard deviation: 1.30;
(5) Least important or feasible (%): 10%;
(6) Somewhat important or feasible (%): 28%;
(7) Moderately important or feasible (%): 8%;
(8) Important or feasible (%): 38%;
(9) Most important or feasible (%): 15%;
(10) Number of experts: 39;
Feasibility:
(2) Mean: 3.08;
(3) Median: 3;
(4) Standard deviation: 1.12;
(5) Least important or feasible (%): 11%;
(6) Somewhat important or feasible (%): 18%;
(7) Moderately important or feasible (%): 32%;
(8) Important or feasible (%): 32%;
(9) Most important or feasible (%): 8%;
(10) Number of experts: 38.
Rank: 13;
(1) Issue or problem[A]: Publicly available government data do not
contain information over a given period at the transaction or micro
level.
Importance:
(2) Mean: 3.19;
(3) Median: 4;
(4) Standard deviation: 1.33;
(5) Least important or feasible (%): 14%;
(6) Somewhat important or feasible (%): 22%;
(7) Moderately important or feasible (%): 14%;
(8) Important or feasible (%): 35%;
(9) Most important or feasible (%): 16%;
(10) Number of experts: 37;
Feasibility:
(2) Mean: 2.44;
(3) Median: 3;
(4) Standard deviation: 1.30;
(5) Least important or feasible (%): 36%;
(6) Somewhat important or feasible (%): 11%;
(7) Moderately important or feasible (%): 31%;
(8) Important or feasible (%): 17%;
(9) Most important or feasible (%): 6%;
(10) Number of experts: 36.
Rank: 14;
(1) Issue or problem[A]: Most models focus on one piece of the puzzle
in isolation or try to do a more general equilibrium type of analysis
with assumptions far too simplistic to capture what is actually
happening. Detailed models of the cost and demand structure at each
level, as well as their connections, are important for understanding
these patterns.
Importance:
(2) Mean: 3.18;
(3) Median: 4;
(4) Standard deviation: 1.27;
(5) Least important or feasible (%): 16%;
(6) Somewhat important or feasible (%): 16%;
(7) Moderately important or feasible (%): 11%;
(8) Important or feasible (%): 50%;
(9) Most important or feasible (%): 8%;
(10) Number of experts: 38;
Feasibility:
(2) Mean: 2.83;
(3) Median: 3;
(4) Standard deviation: 1.08;
(5) Least important or feasible (%): 11%;
(6) Somewhat important or feasible (%): 28%;
(7) Moderately important or feasible (%): 33%;
(8) Important or feasible (%): 22%;
(9) Most important or feasible (%): 6%;
(10) Number of experts: 36.
Rank: 15;
(1) Issue or problem[A]: Data to quantify liberalization of trade
barriers are lacking.
Importance:
(2) Mean: 3.18;
(3) Median: 3;
(4) Standard deviation: 0.97;
(5) Least important or feasible (%): 8%;
(6) Somewhat important or feasible (%): 13%;
(7) Moderately important or feasible (%): 36%;
(8) Important or feasible (%): 41%;
(9) Most important or feasible (%): 3%;
(10) Number of experts: 39;
Feasibility:
(2) Mean: 3.08;
(3) Median: 3;
(4) Standard deviation: 1.10;
(5) Least important or feasible (%): 5%;
(6) Somewhat important or feasible (%): 29%;
(7) Moderately important or feasible (%): 29%;
(8) Important or feasible (%): 26%;
(9) Most important or feasible (%): 11%;
(10) Number of experts: 38.
Rank: 16;
(1) Issue or problem[A]: With consumers setting value at the retail
level, a lack of quantity-weighted retail prices poses problems.
Importance:
(2) Mean: 3.17;
(3) Median: 3;
(4) Standard deviation: 1.03;
(5) Least important or feasible (%): 3%;
(6) Somewhat important or feasible (%): 25%;
(7) Moderately important or feasible (%): 36%;
(8) Important or feasible (%): 25%;
(9) Most important or feasible (%): 11%;
(10) Number of experts: 36;
Feasibility:
(2) Mean: 3.25;
(3) Median: 3;
(4) Standard deviation: 1.08;
(5) Least important or feasible (%): 8%;
(6) Somewhat important or feasible (%): 11%;
(7) Moderately important or feasible (%): 39%;
(8) Important or feasible (%): 31%;
(9) Most important or feasible (%): 11%;
(10) Number of experts: 36.
Rank: 17;
(1) Issue or problem[A]: The theory to model structural change is not
very strong and is especially difficult to model since it is not
typically measured.
Importance:
(2) Mean: 3.13;
(3) Median: 3;
(4) Standard deviation: 1.20;
(5) Least important or feasible (%): 5%;
(6) Somewhat important or feasible (%): 21%;
(7) Moderately important or feasible (%): 28%;
(8) Important or feasible (%): 18%;
(9) Most important or feasible (%): 18%;
(10) Number of experts: 39;
Feasibility:
(2) Mean: 2.77;
(3) Median: 3;
(4) Standard deviation: 1.31;
(5) Least important or feasible (%): 20%;
(6) Somewhat important or feasible (%): 29%;
(7) Moderately important or feasible (%): 14%;
(8) Important or feasible (%): 29%;
(9) Most important or feasible (%): 9%;
(10) Number of experts: 35.
Rank: 18;
(1) Issue or problem[A]: Specifying cost functions is notoriously
difficult because of the lack of data and knowledge about response
functions by type of operations.
Importance:
(2) Mean: 3.11;
(3) Median: 3;
(4) Standard deviation: 1.07;
(5) Least important or feasible (%): 5%;
(6) Somewhat important or feasible (%): 24%;
(7) Moderately important or feasible (%): 35%;
(8) Important or feasible (%): 24%;
(9) Most important or feasible (%): 11%;
(10) Number of experts: 37;
Feasibility:
(2) Mean: 2.56;
(3) Median: 2;
(4) Standard deviation: 1.11;
(5) Least important or feasible (%): 18%;
(6) Somewhat important or feasible (%): 35%;
(7) Moderately important or feasible (%): 24%;
(8) Important or feasible (%): 21%;
(9) Most important or feasible (%): 3%;
(10) Number of experts: 34.
Rank: 19;
(1) Issue or problem[A]: Data to quantify the impact of convenience on
beef demand are lacking.
Importance:
(2) Mean: 3.11;
(3) Median: 3.5;
(4) Standard deviation: 1.16;
(5) Least important or feasible (%): 11%;
(6) Somewhat important or feasible (%): 24%;
(7) Moderately important or feasible (%): 16%;
(8) Important or feasible (%): 45%;
(9) Most important or feasible (%): 5%;
(10) Number of experts: 38;
Feasibility:
(2) Mean: 2.78;
(3) Median: 3;
(4) Standard deviation: 1.06;
(5) Least important or feasible (%): 8%;
(6) Somewhat important or feasible (%): 41%;
(7) Moderately important or feasible (%): 19%;
(8) Important or feasible (%): 30%;
(9) Most important or feasible (%): 3%;
(10) Number of experts: 37.
Rank: 20;
(1) Issue or problem[A]: Prices are made up of a very large number of
determinants whose importance changes over time, suggesting that model
misspecification is always present.
Importance:
(2) Mean: 3.11;
(3) Median: 3;
(4) Standard deviation: 1.20;
(5) Least important or feasible (%): 13%;
(6) Somewhat important or feasible (%): 18%;
(7) Moderately important or feasible (%): 21%;
(8) Important or feasible (%): 39%;
(9) Most important or feasible (%): 8%;
(10) Number of experts: 38;
Feasibility:
(2) Mean: 2.63;
(3) Median: 3;
(4) Standard deviation: 1.24;
(5) Least important or feasible (%): 26%;
(6) Somewhat important or feasible (%): 13%;
(7) Moderately important or feasible (%): 39%;
(8) Important or feasible (%): 13%;
(9) Most important or feasible (%): 8%;
(10) Number of experts: 38.
Rank: 21;
Cattle price data are questionable because they are not weighted for
volume, grade, etc.
Importance:
(2) Mean: 3.10;
(3) Median: 3;
(4) Standard deviation: 0.99;
(5) Least important or feasible (%): 8%;
(6) Somewhat important or feasible (%): 18%;
(7) Moderately important or feasible (%): 33%;
(8) Important or feasible (%): 38%;
(9) Most important or feasible (%): 3%;
(10) Number of experts: 39;
Feasibility:
(2) Mean: 3.42;
(3) Median: 3;
(4) Standard deviation: 0.98;
(5) Least important or feasible (%): 3%;
(6) Somewhat important or feasible (%): 13%;
(7) Moderately important or feasible (%): 37%;
(8) Important or feasible (%): 34%;
(9) Most important or feasible (%): 13%;
(10) Number of experts: 38.
Rank: 22;
Many factors such as consumer tastes and preferences needed to
incorporate in a model are difficult to quantify.
Importance:
(2) Mean: 3.03;
(3) Median: 3;
(4) Standard deviation: 1.13;
(5) Least important or feasible (%): 13%;
(6) Somewhat important or feasible (%): 18%;
(7) Moderately important or feasible (%): 24%;
(8) Important or feasible (%): 42%;
(9) Most important or feasible (%): 3%;
(10) Number of experts: 38;
Feasibility:
(2) Mean: 2.68;
(3) Median: 3;
(4) Standard deviation: 1.28;
(5) Least important or feasible (%): 24%;
(6) Somewhat important or feasible (%): 21%;
(7) Moderately important or feasible (%): 26%;
(8) Important or feasible (%): 21%;
(9) Most important or feasible (%): 8%;
(10) Number of experts: 38.
Rank: 23;
One needs to integrate international effects such as those from
Australia, Canada, Mexico, New Zealand, and the Pacific Rim countries.
Importance:
(2) Mean: 3.00;
(3) Median: 3;
(4) Standard deviation: 1.01;
(5) Least important or feasible (%): 5%;
(6) Somewhat important or feasible (%): 29%;
(7) Moderately important or feasible (%): 32%;
(8) Important or feasible (%): 29%;
(9) Most important or feasible (%): 5%;
(10) Number of experts: 38;
Feasibility:
(2) Mean: 3.38;
(3) Median: 3;
(4) Standard deviation: 1.09;
(5) Least important or feasible (%): 5%;
(6) Somewhat important or feasible (%): 16%;
(7) Moderately important or feasible (%): 27%;
(8) Important or feasible (%): 38%;
(9) Most important or feasible (%): 14%;
(10) Number of experts: 37.
Rank: 24;
Although the demand for beef and other meats has been analyzed
extensively, there is little consensus as to the fundamental own-price
and cross-price elasticities of demand.
Importance:
(2) Mean: 3.00;
(3) Median: 3;
(4) Standard deviation: 1.03;
(5) Least important or feasible (%): 5%;
(6) Somewhat important or feasible (%): 32%;
(7) Moderately important or feasible (%): 22%;
(8) Important or feasible (%): 38%;
(9) Most important or feasible (%): 3%;
(10) Number of experts: 37;
Feasibility:
(2) Mean: 3.45;
(3) Median: 4;
(4) Standard deviation: 0.86;
(5) Least important or feasible (%): 3%;
(6) Somewhat important or feasible (%): 11%;
(7) Moderately important or feasible (%): 32%;
(8) Important or feasible (%): 50%;
(9) Most important or feasible (%): 5%;
(10) Number of experts: 38.
Rank: 25;
Properly accounting for changes in market structure makes it more
difficult to estimate prices.
Importance:
(2) Mean: 2.95;
(3) Median: 3;
(4) Standard deviation: 1.16;
(5) Least important or feasible (%): 13%;
(6) Somewhat important or feasible (%): 24%;
(7) Moderately important or feasible (%): 24%;
(8) Important or feasible (%): 34%;
(9) Most important or feasible (%): 5%;
(10) Number of experts: 38;
Feasibility:
(2) Mean: 2.89;
(3) Median: 3;
(4) Standard deviation: 0.95;
(5) Least important or feasible (%): 6%;
(6) Somewhat important or feasible (%): 31%;
(7) Moderately important or feasible (%): 36%;
(8) Important or feasible (%): 25%;
(9) Most important or feasible (%): 3%;
(10) Number of experts: 36.
Rank: 26;
There are data constraints regarding what types of nonprice market
power may be exercised, such as controlling the flow of supplies to
particular plants or the effects of requirements retailers place on the
industry.
Importance:
(2) Mean: 2.94;
(3) Median: 3;
(4) Standard deviation: 1.18;
(5) Least important or feasible (%): 12%;
(6) Somewhat important or feasible (%): 29%;
(7) Moderately important or feasible (%): 18%;
(8) Important or feasible (%): 35%;
(9) Most important or feasible (%): 6%;
(10) Number of experts: 34;
Feasibility:
(2) Mean: 2.32;
(3) Median: 2;
(4) Standard deviation: 1.01;
(5) Least important or feasible (%): 26%;
(6) Somewhat important or feasible (%): 29%;
(7) Moderately important or feasible (%): 32%;
(8) Important or feasible (%): 13%;
(9) Most important or feasible (%): 0%;
(10) Number of experts: 31.
Rank: 27;
A system analysis should examine the marketing channel from cow-calf
producer to retail.
Importance:
(2) Mean: 2.94;
(3) Median: 3;
(4) Standard deviation: 1.32;
(5) Least important or feasible (%): 15%;
(6) Somewhat important or feasible (%): 32%;
(7) Moderately important or feasible (%): 9%;
(8) Important or feasible (%): 32%;
(9) Most important or feasible (%): 12%;
(10) Number of experts: 34;
Feasibility:
(2) Mean: 3.28;
(3) Median: 3.5;
(4) Standard deviation: 1.11;
(5) Least important or feasible (%): 3%;
(6) Somewhat important or feasible (%): 28%;
(7) Moderately important or feasible (%): 19%;
(8) Important or feasible (%): 38%;
(9) Most important or feasible (%): 13%;
(10) Number of experts: 32.
Rank: 28;
The data to calculate Lerner ratios and quantify the impact of packer
concentration on live cattle prices exist, but GIPSA has not made them
available.
Importance:
(2) Mean: 2.92;
(3) Median: 3;
(4) Standard deviation: 1.38;
(5) Least important or feasible (%): 25%;
(6) Somewhat important or feasible (%): 8%;
(7) Moderately important or feasible (%): 31%;
(8) Important or feasible (%): 22%;
(9) Most important or feasible (%): 14%;
(10) Number of experts: 36;
Feasibility:
(2) Mean: 3.39;
(3) Median: 3;
(4) Standard deviation: 1.27;
(5) Least important or feasible (%): 9%;
(6) Somewhat important or feasible (%): 15%;
(7) Moderately important or feasible (%): 27%;
(8) Important or feasible (%): 24%;
(9) Most important or feasible (%): 24%;
(10) Number of experts: 33;
Rank: 29;
Complicated dynamic feedback relationships in the cattle sector suggest
that one "true" structural model may not exist.
Importance:
(2) Mean: 2.89;
(3) Median: 3;
(4) Standard deviation: 1.22;
(5) Least important or feasible (%): 16%;
(6) Somewhat important or feasible (%): 22%;
(7) Moderately important or feasible (%): 27%;
(8) Important or feasible (%): 27%;
(9) Most important or feasible (%): 8%;
(10) Number of experts: 37;
Feasibility:
(2) Mean: 2.60;
(3) Median: 2;
(4) Standard deviation: 1.26;
(5) Least important or feasible (%): 23%;
(6) Somewhat important or feasible (%): 29%;
(7) Moderately important or feasible (%): 23%;
(8) Important or feasible (%): 17%;
(9) Most important or feasible (%): 9%;
(10) Number of experts: 35.
Rank: 30;
The literature on demand shifts has emphasized that functional form may
matter to income and price elasticities.
Importance:
(2) Mean: 2.86;
(3) Median: 3;
(4) Standard deviation: 1.12;
(5) Least important or feasible (%): 14%;
(6) Somewhat important or feasible (%): 23%;
(7) Moderately important or feasible (%): 29%;
(8) Important or feasible (%): 31%;
(9) Most important or feasible (%): 3%;
(10) Number of experts: 35;
Feasibility:
(2) Mean: 3.42;
(3) Median: 4;
(4) Standard deviation: 1.02;
(5) Least important or feasible (%): 8%;
(6) Somewhat important or feasible (%): 6%;
(7) Moderately important or feasible (%): 31%;
(8) Important or feasible (%): 47%;
(9) Most important or feasible (%): 8%;
(10) Number of experts: 36.
Rank: 31;
Data reliability has become an issue for the less tangible issues that
affect market sentiment, such as food scares and promotional activity.
Importance:
(2) Mean: 2.81;
(3) Median: 3;
(4) Standard deviation: 1.15;
(5) Least important or feasible (%): 14%;
(6) Somewhat important or feasible (%): 30%;
(7) Moderately important or feasible (%): 24%;
(8) Important or feasible (%): 27%;
(9) Most important or feasible (%): 5%;
(10) Number of experts: 37;
Feasibility:
(2) Mean: 2.48;
(3) Median: 2;
(4) Standard deviation: 1.18;
(5) Least important or feasible (%): 26%;
(6) Somewhat important or feasible (%): 29%;
(7) Moderately important or feasible (%): 16%;
(8) Important or feasible (%): 29%;
(9) Most important or feasible (%): 0%;
(10) Number of experts: 31.
Rank: 32;
A challenge is identifying and modeling weather and drought as they
affect the beef industry.
Importance:
(2) Mean: 2.76;
(3) Median: 3;
(4) Standard deviation: 0.97;
(5) Least important or feasible (%): 11%;
(6) Somewhat important or feasible (%): 29%;
(7) Moderately important or feasible (%): 34%;
(8) Important or feasible (%): 26%;
(9) Most important or feasible (%): 0%;
(10) Number of experts: 38;
Feasibility:
(2) Mean: 3.24;
(3) Median: 4;
(4) Standard deviation: 1.16;
(5) Least important or feasible (%): 11%;
(6) Somewhat important or feasible (%): 16%;
(7) Moderately important or feasible (%): 19%;
(8) Important or feasible (%): 46%;
(9) Most important or feasible (%): 8%;
(10) Number of experts: 37.
Rank: 33;
Good, standardized cost series are lacking at the cow-calf level.
Importance:
(2) Mean: 2.74;
(3) Median: 3;
(4) Standard deviation: 1.16;
(5) Least important or feasible (%): 15%;
(6) Somewhat important or feasible (%): 32%;
(7) Moderately important or feasible (%): 24%;
(8) Important or feasible (%): 24%;
(9) Most important or feasible (%): 6%;
(10) Number of experts: 34;
Feasibility:
(2) Mean: 2.97;
(3) Median: 3;
(4) Standard deviation: 1.10;
(5) Least important or feasible (%): 9%;
(6) Somewhat important or feasible (%): 27%;
(7) Moderately important or feasible (%): 27%;
(8) Important or feasible (%): 30%;
(9) Most important or feasible (%): 6%;
(10) Number of experts: 33.
Rank: 34;
Data to quantify the impact of nutrition on beef demand are lacking.
Importance:
(2) Mean: 2.73;
(3) Median: 3;
(4) Standard deviation: 1.19;
(5) Least important or feasible (%): 19%;
(6) Somewhat important or feasible (%): 29%;
(7) Moderately important or feasible (%): 19%;
(8) Important or feasible (%): 32%;
(9) Most important or feasible (%): 3%;
(10) Number of experts: 37;
Feasibility:
(2) Mean: 2.89;
(3) Median: 3;
(4) Standard deviation: 1.12;
(5) Least important or feasible (%): 8%;
(6) Somewhat important or feasible (%): 33%;
(7) Moderately important or feasible (%): 28%;
(8) Important or feasible (%): 22%;
(9) Most important or feasible (%): 8%;
(10) Number of experts: 36.
Rank: 35;
USDA’s estimates of cattle inventories by class are subject to error.
Importance:
(2) Mean: 2.67;
(3) Median: 3;
(4) Standard deviation: 1.12;
(5) Least important or feasible (%): 20%;
(6) Somewhat important or feasible (%): 23%;
(7) Moderately important or feasible (%): 27%;
(8) Important or feasible (%): 30%;
(9) Most important or feasible (%): 0%;
(10) Number of experts: 30;
Feasibility:
(2) Mean: 3.00;
(3) Median: 3;
(4) Standard deviation: 0.85;
(5) Least important or feasible (%): 3%;
(6) Somewhat important or feasible (%): 24%;
(7) Moderately important or feasible (%): 41%;
(8) Important or feasible (%): 31%;
(9) Most important or feasible (%): 0%;
(10) Number of experts: 29.
Rank: 36;
Data to quantify purchasing power in importing countries are lacking.
Importance:
(2) Mean: 2.51;
(3) Median: 2;
(4) Standard deviation: 1.07;
(5) Least important or feasible (%): 15%;
(6) Somewhat important or feasible (%): 44%;
(7) Moderately important or feasible (%): 18%;
(8) Important or feasible (%): 21%;
(9) Most important or feasible (%): 3%;
(10) Number of experts: 39;
Feasibility:
(2) Mean: 3.22;
(3) Median: 3;
(4) Standard deviation: 1.13;
(5) Least important or feasible (%): 5%;
(6) Somewhat important or feasible (%): 19%;
(7) Moderately important or feasible (%): 43%;
(8) Important or feasible (%): 14%;
(9) Most important or feasible (%): 19%;
(10) Number of experts: 37.
Rank: 37;
Concentration among processors, although likely to be relevant at
levels in the cattle industry, has become more or less a constant and
has not changed substantially in the past few years. It is unlikely to
be statistically significant unless studied over a longer period than
has been done in the recent few years.
Importance:
(2) Mean: 2.49;
(3) Median: 2;
(4) Standard deviation: 1.22;
(5) Least important or feasible (%): 27%;
(6) Somewhat important or feasible (%): 27%;
(7) Moderately important or feasible (%): 19%;
(8) Important or feasible (%): 24%;
(9) Most important or feasible (%): 3%;
(10) Number of experts: 37;
Feasibility:
(2) Mean: 2.94;
(3) Median: 3;
(4) Standard deviation: 1.26;
(5) Least important or feasible (%): 14%;
(6) Somewhat important or feasible (%): 25%;
(7) Moderately important or feasible (%): 28%;
(8) Important or feasible (%): 19%;
(9) Most important or feasible (%): 14%;
(10) Number of experts: 36.
Rank: 38;
Cash price and marketing in any particular time period do not
necessarily determine actual producer incomes, because some producers
participate in the futures market.
Importance:
(2) Mean: 2.46;
(3) Median: 2;
(4) Standard deviation: 1.14;
(5) Least important or feasible (%): 23%;
(6) Somewhat important or feasible (%): 33%;
(7) Moderately important or feasible (%): 21%;
(8) Important or feasible (%): 23%;
(9) Most important or feasible (%): 0%;
(10) Number of experts: 39;
Feasibility:
(2) Mean: 2.78;
(3) Median: 3;
(4) Standard deviation: 1.17;
(5) Least important or feasible (%): 17%;
(6) Somewhat important or feasible (%): 28%;
(7) Moderately important or feasible (%): 19%;
(8) Important or feasible (%): 33%;
(9) Most important or feasible (%): 3%;
(10) Number of experts: 36.
Rank: 39;
Data to quantify exchange rate influences on export prices and
quantities are lacking.
Importance:
(2) Mean: 2.44;
(3) Median: 2;
(4) Standard deviation: 1.10;
(5) Least important or feasible (%): 23%;
(6) Somewhat important or feasible (%): 33%;
(7) Moderately important or feasible (%): 21%;
(8) Important or feasible (%): 23%;
(9) Most important or feasible (%): 0%;
(10) Number of experts: 39;
Feasibility:
(2) Mean: 3.73;
(3) Median: 4;
(4) Standard deviation: 1.04;
(5) Least important or feasible (%): 3%;
(6) Somewhat important or feasible (%): 11%;
(7) Moderately important or feasible (%): 22%;
(8) Important or feasible (%): 41%;
(9) Most important or feasible (%): 24%;
(10) Number of experts: 37.
Rank: 40;
An inability to separate beef imports from total U.S. beef production
may result in overestimating or underestimating how imports affect meat
and cattle prices.
Importance:
(2) Mean: 2.36;
(3) Median: 2;
(4) Standard deviation: 1.13;
(5) Least important or feasible (%): 25%;
(6) Somewhat important or feasible (%): 36%;
(7) Moderately important or feasible (%): 19%;
(8) Important or feasible (%): 17%;
(9) Most important or feasible (%): 3%;
(10) Number of experts: 36;
Feasibility:
(2) Mean: 3.13;
(3) Median: 3;
(4) Standard deviation: 1.13;
(5) Least important or feasible (%): 3%;
(6) Somewhat important or feasible (%): 34%;
(7) Moderately important or feasible (%): 22%;
(8) Important or feasible (%): 28%;
(9) Most important or feasible (%): 13%;
(10) Number of experts: 32.
Rank: 41;
It is a challenge to create an aggregate income index that accounts for
not only aggregate income but also the risk level to achieve that level
of income.
Importance:
(2) Mean: 1.81;
(3) Median: 2;
(4) Standard deviation: 0.97;
(5) Least important or feasible (%): 47%;
(6) Somewhat important or feasible (%): 34%;
(7) Moderately important or feasible (%): 9%;
(8) Important or feasible (%): 9%;
(9) Most important or feasible (%): 0%;
(10) Number of experts: 32;
Feasibility:
(2) Mean: 1.94;
(3) Median: 2;
(4) Standard deviation: 0.96;
(5) Least important or feasible (%): 39%;
(6) Somewhat important or feasible (%): 39%;
(7) Moderately important or feasible (%): 13%;
(8) Important or feasible (%): 10%;
(9) Most important or feasible (%): 0%;
(10) Number of experts: 31.
[A] Experts mentioned these items in response to the following question
in phase I: “What problems or issues would you face in developing a
comprehensive and reliable analysis to estimate domestic cattle prices
and producers’ incomes?” Percentages may not add to 100 because of
rounding.
[End of table]
These ratings in the table were obtained from the experts’ responses to
the following question on the phase II questionnaire:
“In the first phase of this study, we asked you to identify, ‘problems
or issues you would face in developing a comprehensive and reliable
analysis to estimate domestic cattle prices and producers’ incomes.’
“The responses have been organized under two broad categories:
1. Data Issues;
2. Modeling Issues.
“In this section, we present those responses and ask you to rate both
the importance and feasibility of each response on a scale of 1 to 5,
where 1 is least important or least feasible and 5 is most important or
most feasible. In your ratings, consider the following concepts of
importance and feasibility.
1. How important is it to address this problem or issue for purposes of
modeling cattle prices and/or producers’ incomes?
2. How feasible is it to overcome or implement the solution for this
problem or issue for purposes of modeling cattle prices and/or
producers’ incomes?”
The experts then rated each item on a five-point scale from “least
important” or “least feasible” to “most important” or “most feasible,”
as shown in columns 5–9. We gave the experts the option of responding
“don’t know/no opinion”; the default response on the Web-based
questionnaire was “no response.” When rating a factor, experts had to
actively de-select the “no response” option.
[End of section]
Appendix V: Summary of Phase III of Our Survey:
On the questionnaire in phase III of our Web-based survey, we asked the
experts to review the summary and results from the preceding
questionnaire. The summary explained that there was relatively more
variation of responses for the categories of factors relating to
international trade and structural change, while opinions of the
importance of domestic demand for cattle and domestic supply of cattle
factors were more cohesive. We asked the panel,
“(1) in your opinion, why is there greater variation among panel
members over the importance of structural change as a factor
affecting cattle prices and producers’ incomes, and (2) in your
opinion, why is there greater variation among panel members
over the importance of international trade as a factor affecting
cattle prices and producers’ incomes?”
This appendix consists of excerpts from the respondents’ answers (set as
full text within quotation marks).
Panelists’ Responses on Structural Change{
“I think the difference depends on the source of the change, whether in
supply or demand.”
“Again, it’s a less-studied issue, as well as being more amorphous in
its definition. Structural change is not well defined. One aspect of
structural change is differences in markets, which for most industries
have experienced increasing concentration and consolidation. This is
certainly true in the beef industry but appears to have strong supply
and demand drivers, due to cost effects (scale and scope economies) and
demand changes (quality, diversification/processing). These might be
considered structural changes, but I would say they are more basic
supply and demand changes. I think the importance of costs and prices
has increased, as has the potential for scale economies in our ‘new
economy,’ even though this is not exactly a ‘new economy’ industry,
which might be called structural change. These types of
structural/market changes are also likely to expand further in the
near future, I expect.”
“There is disagreement over how important structural change has really
been and will be on the level of prices. Also, some may be thinking of
year-to-year changes in prices (where structure is not important) while
others, like me, are thinking of where average prices are likely to go.
I think structural change will result in continuing downward pressure
on prices, and this will be a big problem for traditional small-scale
cattle feeders.”
“Many economists believe that regardless of structural changes (e.g.,
rising concentration among meat packers), it is the supply of
cattle/beef that determines cattle prices and consequently farmers’
income. In that case, farmers need to control their output through
quality control or to learn to respond to consumer demand better or
explore market expansion, etc. On the other hand, if rising
concentration or vertical integration shuts down or forecloses the
output market for farmers, both cattle prices and farmers’ income will
be adversely affected.”
“The term is not well defined.”
“Some think that structure, in particular large processors, have a
large adverse impact on cattle price. The research says otherwise. It
probably is not a completely resolved issue.”
“I recall some frustration with not being able to identify the
direction of the impacts. Moving to concentrated processing markets was
accompanied by moves to very large packing operations with hourly kills
of up to 400 head of cattle per hour and large feedlots to service
those large-scale processing needs. The packers like IBP, Excel, and
Conagra were first low-cost commodity operators that only recently have
turned to branded products and merchandising. Part of the benefit of
those low packing and fabricating costs were passed back to the fed
cattle owner in the form of higher prices than would have been the case
with smaller plants in the preconcentrated industry. If you adjust the
packer margin as reported by USDA for inflation, it trends down from
the mid-1980s to today, documenting the presence of economies of size
and the passing of at least part of the benefits of low costs to the
producers. I suspect the question was asked under a presumption of
market power imposing lower prices on producers, but the facts simply
do not support that. The market power research that has sometimes
reported a relationship between large firms in concentrated markets is
not valid, in my opinion. An American Journal of Agricultural Economics
article shows the assumptions of the widely used market power tests to
be invalid. It may be that the structure of the industry that has
become very concentrated has prompted a less progressive sector than
there would be if 20 firms, not 3, controlled the roughly 20 large
plants, but I have no research to support that notion.”
“Packer concentration in beef took place between 1986 (after the
Supreme Court ruling on Monfort vs. Cargill) and 1990. Price movements
in cattle since 1990 have not been due to structural change because
concentration levels changed less than 3 percentage points during that
time. In addition, new entrants have come or are coming into beef
packing during 2000–01.”
“I think there’s more true ambiguity of how important this is. That is,
international trade clearly affects levels of prices and quantities.
The implications of structural change are less clear from an
increase/decrease/unchanged perspective of its impacts on prices and
incomes. For example, in considering the swine industry, those that
participated heavily in structural change by rapidly adopting
technology, forming integrated production systems, and branding
products saw their incomes increase dramatically. Those on the other
end saw their incomes decline. So while international trade is more
likely a phenomenon of a ‘rising tide raises all ships,’ structural
change has greater implications for micro-level impacts that depend on
particular circumstances. I’m sure this accounts for more ambiguity:
Maybe net structural change simply leads to the ‘zero profit’ condition
of technical change in markets in the long run?”
“Structural change is difficult to measure, and there has been little
research on the impact of structural change in the beef industry. Some
research on structural change has been done in demand for meat, but the
basic conclusions have been somewhat mixed or have favored no
structural change. That is, relative prices are the important drivers.
I think there are those who believe that structural change has been
substantial and important. There are those who believe little real
structural change has occurred. There are those who believe that
substantial structural change has occurred but it didn’t impact
prices.”
“In my view, the greater variation may be due to differing opinions
about the cause and consequences of structural change. For example in
my opinion, on the topic of value-based marketing and pricing, I could
make the following argument. Livestock producers want to be paid for
the quality of livestock they produce. They want to be paid premium for
producing the kind of the cattle that produces the kind of beef the
consumer demands. Value-based agreements between producers and packers
allow the price signal to be transmitted from the consumer all the way
back to the cow-calf producer, who can make the management decisions
necessary to earn the premiums and avoid the discounts, thus improving
the bottom line and income. Others may argue the following. Large
packing companies have put in place contracts that force discounts on
the producers so that the packer can buy the product cheaper and sell
the product for higher prices to retailers. You can sell product to the
packers only if you agree to their terms and sell them the kind of
cattle they want to buy. Producers who don’t comply lose a market for
their cattle and subsequently don’t have a place to sell their
livestock. Change is occurring in the beef industry, no doubt. The key
is to understand what is driving the change and to fully understand
cause and effect. There is plenty of research describing the changes
taking place. One of the most interesting studies done at Virginia
Tech, I believe, showed that producers have benefited to a great degree
because the efficiencies created in the packing industry have kept
inflation-driven costs, such as wage increases, from being paid by the
producers in terms of lower cattle prices.”
“There may be some confusion about the meaning of the term—I took it to
mean changes in supply/demand balance.”
“From a modeling standpoint, it is hard to incorporate the effects of
structural change. That makes it difficult to decide how important a
factor it has been.”
“Structural change is taking place, but it is difficult to measure
and evaluate.”
“Until recently, the conventional wisdom has been that higher
concentration leads to higher beef prices and lower cattle prices. The
thought in modern industrial organization does not put so much weight
on concentration as on other items such as elasticities of demand and
supply, conduct, quantity, or price as decision variables, dynamics,
etc.”
“‘Structural change,’ like ‘international trade,’ is an imprecise term.
Each person will interpret in his/her own way. Some see structural
change as increasing the competitiveness of industry, therefore a good
thing. Others see it as limiting competitiveness, therefore a bad
thing.”
“I think the research literature is pretty clear on this issue.
Structural change has been important—i.e., significant—but the impact
is relatively small.”
“The impact of structural change is much harder to assess than the old
standbys of supply and demand. The trade suggests that concentration is
having an impact—but if you believe the research, it suggests
differently. The captured cattle question and its effect on price
discovery is truly an important factor. It is important enough that the
government has new discovery rules. But if the industry is moving more
toward ‘alliances’ and away from the ‘auction’ market, the importance
of price discovery becomes paramount to the producer side. I don’t
think that structure is a short-term price/income question; it is a
longer-term question. The industry is likely to work on this question
over time.”
“Concentration in the industry has changed little in the past few
years; thus, much of the impact is long term. Some may be thinking on a
shorter-term or longer-term basis. In addition, structure changed prior
to changes in industry practice. These practices (marketing agreements)
have greater impact on packer market behavior because they are
concentrated and gain market knowledge they would not have with only
one plant using these practices. So as structure impacts practice,
practice impacts prices. Some may see that as structure, others not.”
“Important issues are involved in what one means by ‘structural
change.’ Some might think this applied only to demand (the old health
concerns argument) while others (myself included) think that changing
structure applies to all structure— such as market consolidation,
changing technologies (economies of scale), feeding practices, and
demand.”
“Some judge that there is more opportunity for market power with the
increased concentration of the packer industry than others.”
“Structural change is a less well defined term and can relate to
different levels of the industry with differing degrees of impact.
There is also a time element to structural change that means that
importance from one year to the next is small, but over a long period
of change, the impact shows up as being more significant.”
“Reflects the vigorous debate about the impact of increased packer
concentration on cattle prices.”
“Structural change remains controversial in spite of the large volume
of research completed in this area. In my opinion, we have discovered
in all our research that the effect of structural change, at least on
prices, is significant but not large. Hence, the argument is that there
is no need to regulate the industry. At the same time, large
concentration levels are difficult to rationalize from the point of
view of economics, since they appear to have the potential of having
market power. We need two things: (1) We need more information on the
actual costs of operating packing plants if definitive studies are to
be done and (2) we need to concentrate more on transaction costs to
determine why relationships in these markets are so rigid.”
“It is difficult to define what is meant by structural change. It
includes changes in consumers’ tastes and preferences and technological
change in production and processing, as well as changes in packer
concentration. People may be using different definitions. I think
packer concentration is least important. But the other two do matter.
Further, structural changes are gradual. Therefore, structural change
has little effect on price changes in the short run. Structural change
would need to be considered when estimating an econometric model.”
“Two reasons. First, economists have differing definitions and views of
the meaning of structural change. One extreme is that no such thing as
structural change exists, if one has taken proper account of all the
factors affecting prices. Second, and related, some economists would
likely have relatively broad categories of factors, one of which would
be structural change. In other words, after considering prices and
incomes, everything else would be a change in structure.”
Panelists’ Responses on International Trade:
“International trade has not played as significant a role in the
determination of cattle prices and producer incomes as have the other
factors. International trade in beef is a relatively new function, and
its dollar size compared to the domestic market makes it less
important.”
“The empirical evidence is unclear, especially given the complexity of
the cattle market.”
“We are a huge market, and except in niche products, domestic supply
and demand drive market prices.”
“International trade, while important, is still a relatively small part
of the total demand/supply of beef/cattle. International trade has been
controversial as to its effects. International trade is always less
predictable than domestic trade. Bottom line: More uncertainty exists
about the effects and importance of international trade in cattle/beef
markets.”
“Substantially less research has been conducted on the impact of trade
on prices and income to validate the impact. What work has been
conducted has mixed results. On the other hand, there is substantial
research validating the importance of demand and supply effects on
prices.”
“I believe the discrepancy is due to something like the difference
between interpreting a t statistic and an elasticity. International
trade is significant in impacting domestic livestock and meat prices,
but its elasticity is going to be smaller that those associated with
domestic supply or total domestic demand.”
“It is harder to model and analyze, since there are both import and
export flows to deal with. The difficulty is compounded by a lack of
detailed price data on imports or exports, and there is no detail on
what is in a shipment regarding quality, consistency, etc. The imports
add to the domestic supply of largely processing beef and, taken alone,
would tend to lower beef prices in the United States. But they are not
taken alone, since there are exports of high-quality (nonprocessing)
beef that add to the demand for U.S. beef. The net impact is likely to
be positive by a substantial amount, but this is hard to estimate
empirically, and it still is not as important as domestic supply
variations and then domestic demand variations as a factor in prices
and incomes.”
“It is a small part of total tonnage and value, but it is also the
marginal market and generally the only area for growth.”
“‘International trade’ and ‘structural change’ are specific factors
that may have demand-side and/or supply-side effects of undetermined
magnitude. I think there is much greater scope for differing opinions
about the importance of these factors.”
“Export demand is more volatile than domestic demand. I, however, did
not rate international trade as highly important because trade in
cattle and beef is a small portion of total demand.”
“Because the share of imports and exports is so small, international
trade’s relative importance can change dramatically from one year to
the next.”
“Some people focus on the relatively small volume of U.S. production
that moves through trade channels, but others focus on the volatility,
policy sensitivity, and future possible importance of that volume.”
“International trade is not as ‘free’ when it comes to importing cattle
or beef for various reasons—e.g., importing countries may restrict U.S.
livestock or beef import if our cattle/beef is bioengineered or has
quality problems (perceived or real). For this and similar reasons,
many of us believe that international trade is not as big a factor as,
say, domestic demand.”
“How trade impacts the cattle market in particular, it may affect beef
more than cattle.”
“The direct effect of international trade in meat is probably small.
However, the indirect effect of international trade on cattle prices
and producers’ income may be more important. In particular, the effect
of trade on feed prices can be quite considerable, and feed prices can
have an important effect on cattle prices and ranchers’ income.”
“It is a small percentage of total production. Some might contend that
it is small enough to ignore, and it may be. It is not the major
determinant, but it is important and relevant.”
“If one interpreted the question in a historical sense, then trade is
not important, since it is not a large component of total production.
If the question were interpreted as whether the trade is important in a
general sense, then the answer is important. Indeed, should trade
expand, then it will be important.”
“There is always likely to be more variation in opinions for an issue
that has received less attention and therefore has less information and
consensus. Trade in this industry may have a marginal effect, but
simply the quantity of trade compared to other industries for which
there has been more study suggests that this aspect of the industry is
not going to have an important effect. This is still a more domestic
industry than most.”
“International trade has historically not been extremely important.
However, it has been growing in importance and will likely continue to
become more important.”
“Perhaps because some may be responding to this question from a
theoretical perspective, others may be responding from an empirical
perspective. If one thinks about international trade from a theoretical
perspective, it should be an important variable. I don’t think the
empirical evidence is quite so strong. We found that trade was not a
particularly strong mover of prices-—not unimportant but not a strong
mover. Of course, all our work (mine included) is tentative and subject
to reinterpretation, given new evidence.”
“First, most producers never see their international customers. Second,
trade deals take a long time to establish, negotiate, and implement,
and often the final deal may not seem significant in the eyes of the
producers. During trade negotiations, there is a give and take. Third,
it is easy to discount the importance of trade in order to make
statements about something else—for example, some beef producers knock
NAFTA because of low cattle prices. However, the only thing NAFTA did
for the beef industry was allow the United States to sell boxed beef to
Mexico, and it is now one of our biggest customers. In this case, low
cattle prices brought about anti-NAFTA sentiment. Interestingly enough,
cattle prices would have been low, with or without NAFTA, due to the
cattle cycle, supply, and corn at $5 a bushel. In this case, NAFTA was
actually a benefit to the beef industry or prices would have been
lower, but NAFTA became, in the eyes of many, the cause. Lastly, trade
is often hard to quantify because each opportunity may seem minuscule
when compared to the entire beef market. For example, some may wonder,
how can such a small percentage of product play such a factor in
overall income. The answer is that trade benefits are additive and
building, and growing markets take time. Benefits to trade usually
accrue in the future, so producers don’t see the impact on their bottom
line immediately.”
“A broad range of factors could result in trade’s affecting cattle
prices—i.e., exchange rates as well as imports.”
“On the one hand, trade matters. On the other hand, both transportation
costs and trade barriers contribute to reducing the importance of trade
in the beef sector.”
“Trade does impact the market, but it is around 10 to 12 percent of the
total, and consequently the magnitude of change in percentage terms
required to have the same impact as domestic demand will obviously be
much greater. Also, imports and exports are pretty well balanced,
although the type of product differs between the two. There is an
argument that the availability of lean imported product actually helps
the price of fatter U.S. trimmings as it increases their use in ground
beef etc. Consequently, I do not consider trade to be nearly as
important as domestic demand, but I do believe it to have a reasonably
significant impact on the market, probably more on the export than on
the import side.”
“Trade may be overemphasized as a determinant of total market demand
for cattle. Exports represent only a small share of U.S. cattle
production. Imports also may be overemphasized as a determinant of
total market supply—only a small share of total cattle use is
represented by imports, and for live cattle, the impacts of imports is
fairly localized or regionalized, not a major determinant of prices
nationally.”
Issues Facing Comprehensive Analysis:
In the phase III questionnaire, we presented the panel with the list of
issues facing comprehensive analysis to predict or explain domestic
cattle prices and producers’ incomes. The list of issues derived from
the panel’s responses to the phase I questionnaire were presented in
the order of the importance of each issue. The importance of each issue
was determined by calculating the average importance rating from the
phase II responses.
We first asked the experts whether or not they believed that the federal
government should take action to help overcome these issues. Eighty-five
percent (34) responded “yes,” 2.5 percent (1) responded “no,” and 12.5
percent (5) responded “don’t know.”
We asked those who responded affirmatively to select up to five issues
that they would recommend for federal action. We tabulated the
selections and ordered the list of issues according to the number of
selections on each issue. This produced a prioritized list of issues
recommended for federal action (at least by the 34 panelists who shared
the opinion that federal action is warranted). The responses and
ranking of these issues are presented in table 13.
Table 13: Issues the Panel Recommended the Federal Government Act On:
Rank: 1;
Issue: Data on farmers, processors, and retailers are confidential and
not accessible.
Number: 19.
Rank: 2;
Issue: Reported market prices are not likely to indicate true prices
received due to extensive contracting and pricing quality grid
differences.
Number: 16.
Rank: 3;
Issue: Disaggregated cost and revenue data linking ranchers, feeders,
packers, and retailers are unavailable.
Number: 14
Rank: 4;
Issue: Retail and consumption data are very poor.
Number: 13.
Rank: 5;
Issue: If cattle prices NASS reports no longer represent prices
actually paid to producers for cattle, it is difficult to use these
series for meaningful analysis.
Number: 10.
Rank: 6;
Issue: Many key long-term variables—technical change, policy changes
(e.g., in feed crops), trends in health concerns—are hard to quantify
conceptually, much less get good data for.
Number: 7.
Rank: 7;
Issue: The relationships between the different levels of the food chain
are changing and it is difficult to establish driving factors and
results.
Number: 6.
Rank: 8;
Issue: Publicly available government data do not contain information
over a given period at the transaction or micro level.
Number: 6.
Rank: 9;
Issue: Cattle price data are questionable because they are not weighted
for volume, grade, etc.
Number: 6.
Rank: 10;
Issue: GIPSA has not made available existing data to calculate Lerner
ratios to quantify the impact of packer concentration on live cattle
prices.
Number: 6.
Rank: 11;
Issue: A challenge is appropriate modeling of dynamics in prices due to
the cattle cycle.
Number: 5.
Rank: 12;
Issue: A better understanding of the cattle cycle is needed because
prices and producers' incomes vary significantly at different stages of
the cycle. This is especially important if the cattle cycle is changing
significantly with restructuring of the industry. With increased
reliance on contracts, it has become more difficult to assess how
economic incentives and incomes vary over time and space. It is not
clear who benefits the most from the newly evolving structure and how
the benefits are distributed (if at all) among producers, processors,
retailers, and consumers.
Number: 5.
Rank: 13;
Issue: An inability to separate imports of beef from total U.S. beef
production may result in overestimating or underestimating how imports
affect meat and cattle prices.
Number: 5.
Rank: 14;
Issue: Current supply is a function of profits that producers expected
to receive when they started production. Analysts must use a proxy for
expectations, which measures the underlying concept with error.
Number: 4.
Rank: 15;
Issue: Data to quantify the impact of convenience on beef demand are
lacking.
Number: 4.
Rank: 16;
Issue: There are data constraints on the types of nonprice market power
that may be exercised, such as controlling the flow of supplies to
particular plants or the effects of requirements retailers place on the
industry.
Number: 4.
Rank: 17;
Issue: One very important question to answer to develop a model, keep
misspecification as small as reasonable, and provide some usefulness is
the purpose of the cattle price model. If the purpose of the model is
short-term forecasting, the answer will differ markedly from the answer
for policy modeling or some other reason for designing a model.
Number: 3.
Rank: 18;
Issue: Data to quantify the liberalization of trade barriers are
lacking.
Number: 3.
Rank: 19;
Issue: With consumers setting value at the retail level, there are some
problems with lack of quantity-weighted retail prices.
Number: 3.
Rank: 20;
Issue: Many factors, such as consumer tastes and preferences, needed to
incorporate in a model are difficult to quantify.
Number: 3.
Rank: 21;
Issue: A challenge is identifying and modeling weather and drought as
they impact the beef industry.
Number: 3.
Rank: 22;
Issue: USDA’s estimates of cattle inventories by class are subject to
error.
Number: 3.
Rank: 23;
Issue: Any attempt to come up with one all-encompassing model may be
problematic because problems may differ in different states and
regions. Separate and perhaps more than one type of modeling and
analysis may be needed.
Number: 2.
Rank: 24;
Issue: Most models focus on one piece of the puzzle in isolation or try
to do a more general equilibrium type of analysis with assumptions far
too simplistic to capture what is actually happening. Detailed models of
the cost and demand structure at each level as well as their
connections are important for understanding these patterns.
Number: 2.
Rank: 25;
Issue: One needs to integrate international effects such as from
Australia, Canada, Mexico, New Zealand, and the Pacific Rim countries.
Number: 2.
Rank: 26;
Issue: Properly accounting for changes in market structure makes it more
difficult to estimate prices.
Number: 2.
Rank: 27;
Issue: A system analysis should be included that examines the marketing
channel from cow-calf producer to retail.
Number: 2.
Rank: 28;
Issue: Reliability of data becomes more an issue for the less tangible
issues that impact market sentiment, such as food scares and
promotional activity.
Number: 2.
Rank: 29;
Issue: Good, standardized cost series at the cow-calf level are
lacking.
Number: 2.
Rank: 30;
Issue: Data to quantify the impact of nutrition on beef demand are
lacking.
Number: 2.
Rank: 31;
Issue: Data to quantify purchasing power in importing countries are
lacking.
Number: 2.
Rank: 32;
Issue: Concentration among processors, though likely to be relevant at
levels in the cattle industry, has become more or less a constant and
has not changed substantially in the past few years. It is unlikely to
be statistically significant unless a study is done over a longer period
than the recent few years.
Number: 2.
Rank: 33;
Issue: The theory to model structural change is not very strong and is
especially difficult to model since it is not something typically
measured.
Number: 1.
Rank: 34;
Issue: Prices are made up of a very large number of determinants whose
importance changes over time, suggesting that model misspecification is
always present.
Number: 1.
Rank: 35;
Issue: Complicated dynamic feedback relationships in the cattle sector
suggest that one "true" structural model may not exist.
Number: 1.
Rank: 36;
Issue: Cash prices and marketings in any particular time period do not
necessarily determine actual producer incomes because some producers
participate in the futures market.
Number: 1.
Rank: 37;
Issue: Data to quantify exchange rate influences on export prices and
quantities are lacking.
Number: 1.
Rank: 38;
Issue: Specifying cost functions is notoriously difficult because data
and knowledge about response functions by types of operations are
lacking.
Number: 0.
Rank: 39;
Issue: Although the demand for beef and meats has been analyzed
extensively, there is little consensus as to the fundamental own-price
and cross-price elasticities of demand.
Number: 0.
Rank: 40;
Issue: The literature on demand shifts has emphasized that functional
form may matter to income and price elasticities.
Number: 0.
Rank: 41;
Issue: It is a challenge to create an aggregate income index that
accounts for not only aggregate income but also the risk level to
achieve that level of income.
Number: 0.
[End of table]
Specific Actions the Federal Government Should Take:
After the panel had selected up to five items for recommendation, we
asked it, “What specific actions should the federal government take to
address the issues you recommended for action in question 12? (Answer
only if you made selections from the list in question 12).” The
members’ excerpts from this question follow.
“Establish competitive grants for primary data collection.”
“The government has an important role in making high-quality data
available so that market participants can better evaluate market
conditions. The provision of reliable data provides a public good by
allowing market participants to make informed (economically efficient)
decisions.”
“Improve data transparency while acting to protect the confidentiality
of producers, processors, wholesalers, and retailers.”
“The primary underlying issue in addressing the overall research
question is the availability of reliable and consistent data at the
level of firms and markets. The federal government’s impact from
collecting and disseminating these data would be greater than specific
modeling efforts, because if you build the databases, researchers will
follow, and you will gain multiplier effects for research.”
“The manner in which the Bureau of Labor Statistics (BLS) samples
retail beef prices does not lend itself to an accurate picture of the
price that beef is actually selling at. I would modify this practice to
make it more than a statistical sampling, and retail prices collected
should reflect ‘featuring’ and ‘club-card’ discounts. This could be
accomplished by using commercially available retail scanning data. BLS
and Department of Commerce data can tremendously overstate the retail
price of beef and exaggerate the often maligned retail-to-farm-gate
spread.”
“Significantly improve the quality and quantity of data for the entire
supply chain, starting at the farm/farmer level and ending at the
retail level. Conduct cooperative well-funded research, using a panel
of experts and dividing the work among them according to their
expertise.”
“Fund more data collection efforts and research to answer the questions
noted.”
“The government’s key role should be providing timely and accurate
data. The government currently does a good job. But I do think that the
government’s resources should be devoted more to data collection than
to data analysis.”
“There should be a continued focus on collecting retail price and
quantity, better than is done today. Perhaps USDA should have the lead
in collecting retail price data instead of BLS. There needs to be a
research focus that addresses the many issues like structural change,
the cattle cycle, etc., that would include researchers from both the
government and academic circles.”
“Undertake additional surveys.”
“Most of these issues regard not barriers to modeling but simply
aspects that must be included or taken into account. A perfect model is
impossible, but an adequate job seems within reach according to
feasibility and importance ratings. As actions for government, they
provide guidance about the information that should be collected. For
the future, price reporting must certainly not be diminished (reporting
only when transactions reach a certain number of firms or sales is bad
for the industry and for analysis).”
“Improve data collection on prices/quantities in the beef sector.”
“The primary issue, in my view, after carefully defining the questions
for which answers are sought (this is an important issue, since no
model can answer a wide variety of questions), is data availability and
quality. The importance of supply factors implies that detailed cost
analyses are necessary to determine the impact of cost economies on
observed technological and market structure. This requires plant-level
data, and data over time, which are currently limited. The importance
of consumer demand also suggests that quality variations, as they become
increasingly important price drivers, will be important to track. If
answers are sought for these questions, data availability will be
important to enhance, and studies should be encouraged, or even
commissioned, for particular questions.”
“Quantify impacts from government actions (impacts on demand from
recalls specific to only one species, or changing nutritional
guidelines, for example), education about cattle cycle and
supply/demand impacts on prices, information about impacts of
government feed grain policy, changes on prices for calves. Improved
data regarding changes in consumer tastes and preferences, convenience,
nutrition, and safety, for example.”
“The government sponsors research and collects basic data. Those roles
continue to be important.”
“Only the federal government can provide access to the needed data,
since most are proprietary.”
“I am sympathetic to the ‘data are public goods’ argument. Or, stated a
bit more properly, ‘data have elements of nonexclusivity,’ which is a
necessary but not a sufficient condition for the government to be
involved in data collection and dissemination. I suppose I selected
those data sets where I thought collection and dissemination could be
accomplished at reasonable cost. But understand that I have no real
idea of how costly it would be to collect such data. Perhaps if we rely
on the private market to provide these data, we may increase welfare,
relative to forcing governmental collection and dissemination. My only
problem here is that initial wealth or income levels of parties may be
unequal, giving especially large benefits to those with larger wealth
endowments. When dealing with private contracts between parties, we
have required reporting such prices in other areas (I’m thinking about
rail rates). The cost of such programs may be in parties’ giving up the
right to trade in private (a nonpecuniary cost). This gets us into very
difficult issues of rights of individuals versus rights of the group.
As we evolve to more concentrated or controlled markets (fewer open
outcry sales and more contract sales), these issues of individual rights
versus group rights become central. Why should company X be forced to
divulge the price it paid feeder Y for cattle? But again, I’m not well
versed in the area. My casual observation of the rail rate reporting
case of the 1980s suggests that reporting did have an effect on
industry performance.”
“Improved and broadened data collection.”
“Collect the best data and try to collect data that represent all
quality levels of cattle.”
“Better retail price and volume data would be helpful. The work on
getting and using scanner data is a good start.”
“More involvement in obtaining needed data and processing it for
able/quantitative/qualitative purposes.”
“The federal government’s role should be in data collection—getting
better (i.e., realistic) data that reflect the true actions of the
market. This may require reporting information and monitoring the
reports. The government needs also to review its existing reports and
determine if they need to change.”
“Put together a team of leading academic and government economic
experts to design the modeling and implementation process and have a
team of government economists do it with review by the team members.”
“Collect and provide more data to researchers.”
“Develop an index system to score pasture availability.”
“Put in place more stringent and required reporting of price data at
all levels of the marketing system for cattle and meats. It will be
important to have data on substitute meats, as well.”
“Improve data. Mandatory price reporting legislation is prompting new
efforts, but it is not clear that ERS will provide detail on the prices
of cuts of meats to allow better demand analysis or that it will
release retail meat prices more often than monthly and then with a 6-
week to 7-week time lag. The detail on live prices has been improved by
this legislation, but there are no price data or detail on the grade
and quality of the export shipments. The price-based system will
totally disappear unless data are better, and that is the primary role
the government can and should play in this industry. We do not need, in
my assessment, to impose strict regulations on how buyers and
sellers do business in the meats industry.”
“To take advantage of existing but not-available data, grant
researchers access to data in-house, to use it without taking it home,
under a confidentiality agreement, pretty much the way the Census
Bureau operates. Stimulate research on key priorities identified in
this survey by engaging in a mini-grant competition and bilateral
agreements between USDA and other institutions, as well as within
USDA.”
“Revise price reporting to include contracting. Go after true
transactions: prices, quantities, qualities, other factors. This
requires access to private market transactions data. If politically
infeasible, then report only percentage sold under contract and don’t
report any ‘market’ price information at all. This will force the issue
and prevent further thinning of the market information by those who
formula price off the reported prices. Provide more public data on
market structure. Lerner indexes would be great, but just local market
Herfindahls would be a start. Provide data on imports and exports in
the same format as domestic data are provided.”
“Presumably the government’s direct role at this point should be
limited to considering improvements in the way it generates data and
the types of data that it makes available to researchers. GIPSA has
very good data on packers in many cases, but they are not readily
available to outside researchers. Data at other levels of the market
channel are much poorer, however.”
“Two key weaknesses of industrial organization analysis of the effects
of packer concentration have been that (1) models have been inherently
static and do not do a good job of analyzing structural change in a
dynamic setting. So better modeling of the dynamics of structural
change is critical. (2) The results of the models are only as good as
the data used to estimate them. Often the data are too aggregate in
terms of industry and products and are nonspatial. In addition, it is a
lot easier to measure Lerner indexes directly than via econometric
methods if the data are available. So better data is a key to better
analysis.”
“Many of the issues I checked were related to data issues. The federal
government can make processor data available to researchers with a
protective order agreement that prohibits the researchers from making
data on firms public. The other issues relate to setting an agenda to
have a set of policy models related to cattle that account for market
structure across the various levels of the marketing system.”
“The federal government needs to provide long-term funding for research
on all the issues that motivated this survey. None of these issues are
new. However, many of them will not be researched in an ongoing fashion
if new research dollars involve a competitive grants process. For
example, there was little research on structural change and competition
during the late 1980s because it was not politically popular. It is
interesting to note what a huge issue this topic became in the mid-
1990s. The federal government needs to support the research
infrastructure at land grant universities. Further, the federal
government needs to learn a lesson from the institution of mandatory
price reporting legislation. This legislation had good intentions and
has absolutely harmed the quality of data available on livestock and
meat product prices. The federal government needs to go back to the old
system and needs to be extremely careful before attempting to do
anything in the future. It needs to know what the final product will be
before it acts. If it does not, then it should not act.”
“Retail price reporting needs to be changed. Volume-weighted,
representative price data are needed. Better ways of summarizing
quality-adjusted fed cattle prices are needed. This could be done; it
has not been done adequately in mandatory price reporting.”
[End of section]
Appendix VI: Our Panel of Experts:
Azzeddine Azzam, Professor and Director, Center for Agri-Food Industrial
Organization and Policy, Department of Agricultural Economics,
University of Nebraska–Lincoln.
DeeVon Bailey, Professor, Department of Economics, Utah State University
David A. Bessler, Professor, Department of Agricultural Economics, Texas
A&M University.
Sanjib Bhuyan, Assistant Professor, Department of Agricultural, Food,
and Resource Economics, Rutgers University.
Michael D. Boehlje, Professor, Department of Agricultural Economics,
Purdue University.
Gary W. Brester, Professor, Department of Agricultural Economics and
Economics, Montana State University.
B. Wade Brorsen, Regents Professor and Jean and Patsy Neustadt Chair,
Department of Agricultural Economics, Oklahoma State University.
D. Scott Brown, Assistant Professor, Department of Agricultural
Economics F.A.P.R.I., University of Missouri.
Laurie Bryant, Executive Director, Meat Importers Council of America
Brian Buhr, Associate Professor, Applied Economics, University of
Minnesota.
Jean-Paul Chavas, Professor, Agricultural and Applied Economics,
University of Wisconsin.
Leonard W. Condon, Vice President, International Trade, American Meat
Institute.
Bryan Dierlam, Director, Legislative Affairs, National Cattlemen’s Beef
Association.
Catherine A. Durham, Assistant Professor, Department of Agricultural and
Resource Economics, Food Innovation Center, Oregon State University.
Kenneth Foster, Professor, Department of Agricultural Economics, Purdue
University.
Bruce L. Gardner, Professor, Department of Agricultural and Resource
Economics, University of Maryland.
Barry K. Goodwin, Andersons Professor, Department of Agricultural,
Environmental, and Development Economics, Ohio State University.
Jerry Hausman, Professor, Department of Economics, Massachusetts
Institute of Technology.
Marvin L. Hayenga, Professor, Department of Economics, Iowa State
University.
Stephen R. Koontz, Associate Professor, Department of Agricultural and
Resource Economics, Colorado State University.
Chuck Lambert, Chief Economist, National Cattlemen’s Beef Association
John Lawrence, Associate Professor, Department of Economics, Iowa State
University.
Rigoberto A. Lopez, Professor, Agricultural and Resource Economics,
University of Connecticut.
H. Alan Love, Professor, Department of Agricultural Economics, Texas
A&M University.
John M. Marsh, Professor, Department of Agricultural Economics and
Economics, Montana State University.
Catherine J. Morrison Paul, Professor, Department of Agricultural and
Resource Economics, University of California at Davis.
Jeff Perloff, Professor, Department of Agricultural and Resource
Economics, University of California at Berkeley.
Ronald L. Plain, Professor, Department of Agricultural Economics,
University of Missouri.
Wayne D. Purcell, Alumni Distinguished Professor, Department of
Agricultural and Applied Economics, Virginia Polytechnic Institute and
State University.
P. James Rathwell, Professor, Department of Agricultural and Applied
Economics, Clemson University.
Richard T. Rogers, Professor, Department of Resource Economics,
University of Massachusetts.
C. Parr Rosson III, Professor, Department of Agricultural Economics,
Texas A&M University.
Ted C. Schroeder, Professor, Department of Agricultural Economics,
Kansas State University.
John R. Schroeter, Associate Professor, Department of Economics, Iowa
State University.
Richard J. Sexton, Professor, Department of Agricultural and Resource
Economics, University of California at Davis.
Ian M. Sheldon, Professor, Department of Agricultural, Environmental,
and Development Economics, Ohio State University.
Daniel A. Sumner, Professor, Department of Agricultural and Resource
Economics, University of California at Davis.
William G. Tomek, Professor Emeritus, Department of Applied Economics
and Management, Cornell University.
John J. VanSickle, Professor and Director, International Agricultural
Trade and Policy Center, Food and Resource Economics Department,
University of Florida.
Michael Wohlgenant, William Neal Reynolds Distinguished Professor,
Department of Agricultural and Resource Economics, North Carolina State
University.
[End of section]
Appendix VII: Comments from the U.S. Department of Agriculture:
Note: GAO comments supplementing those in the report text appear at the
end of this appendix.
USDA:
United States Department of Agriculture:
Economic Research Service:
1800 M Street, NW,
Washington, DC 20036-5831:
[hyperlink, http://www.ers.usda.gov]:
March 4, 2002:
Informational Memorandum:
T0: Lawrence J. Dyckman:
Director, Food and Agriculture Issues:
General Accounting Office:
From: [Signed by] Susan E. Offutt:
Administrator:
Economic Research Service:
Subject: GAO Report 02-246, "Economic Models of Cattle Prices"
We have reviewed for the draft report to "Economic Models of Cattle
Prices: How USDA Can Act To Improve Models to Explain Cattle Prices."
Our subject matter experts have identified some changes and points of
clarification that we feel need to be included in the final report. We
would like to have the corrections we have identified included in the
final published report. Our comments are attached.
Attachment:
Comments on Draft Report GAO-02-246:
"Economic Models of Cattle Prices: How USDA Can Act to Improve Models
to Explain Cattle Prices"
March 4, 2002:
The draft report by the General Accounting Office (GAO) addresses
modeling and data issues needed to improve economic models of cattle
prices. ERS commented on both the modeling and data issues, and the
characterization of the industry structure. The other agencies (AMS,
GIPSA, and NASS) focused on recommendations by GAO and GAO's expert
panel to improve the quantity and quality of data available to
researchers.
Summary:
ERS believes that the GAO report mischaracterizes the process used to
develop and document its livestock model. It agrees with GAO that the
model, as developed, was not designed to examine issues of market
structure. Whether or not structural detail could or should be added to
a model designed primarily to forecast supply, demand and trade remains
an open question, as the report itself indicates. However, ERS agrees
that re-estimating the model using more current data could be valuable,
that any new model developed should be appropriately documented and
that applications should provide appropriate measures to evaluate model
performance. [See comment 1]
When the model was initially developed, it was appropriately documented
in Weimer, Mark R. and Richard P. Stillman, "A Long Term Forecasting
Model of the Livestock and Poultry Sectors." Proceedings for the NCR
Conference on Applied Commodity Price Analysis, Forecasting, and Market
Risk Management., June 1990. That documentation, in line with standard
professional practice, included the specification of the model, as well
as evaluation of its goodness of fit and ex post model performance
statistics. Measures provided included t statistics, R-squared and
Thiel's U, mean absolute error and mean square error statistics. [See
comment 2]
The original data set used to initially estimate the model is not
available. However, as ERS analysts attempted to explain, even if these
data were still available, they would not be a useful basis for
additional work with the model. Data series are regularly updated, and
updates are incorporated into improved historical series. Any attempt
to use the model today would begin from an historical series that
included the best available data. In addition, developments in database
technology are making it increasingly possible to provide the public
with direct access to databases, allowing them to select the data they
need for specialized applications. ERS has made substantial progress in
developing such databases, some of which are currently available on its
website. In this environment documenting the selection procedures for
assembling data will be more relevant than producing a static
"snapshot" of data selected at a particular moment in time. [See
comment 3]
ERS recognizes the importance of emerging issues, such as structural
change, retail price monitoring, animal diseases and biosecurity. Four
additional staff have recently been added to the animal products branch
in the Market and Trade Economics Division to increase our capacity in
this important area. ERS does not, however, believe that the best
approach to addressing these issues is to mandate specific tasks, such
as updating the existing model (page 6) but instead to encourage a
broader effort to develop a strong program to address new issues. The
latter approach is consistent with the final recommendation on page 9.
[See comment 4]
Detailed comments:
1. Comments on pages 4, 6, 39, 45 the report seem to suggest that data
needed to validate the initial estimation of the model are "lost." This
is not the case. While the data drawn from databases that existed in
1990 are not available, accurate historical data for the period in
question exist in the public domain and are readily available from
USDA's website. These data are the most accurate series available,
incorporating updates from NASS and other data generating agencies. The
operational definition of the data--the SAS code used to compile it--
also exists. Therefore any researcher with a basic knowledge of SAS
could reconstruct the dataset. The code used for estimation of the
models is the same as code used in simulation model and that code is
thoroughly documented by SAS. PROC MODEL is an estimation procedure as
well as a simulation package and provides the requisite statistical
reliability measures. To re-estimate the model, the research would have
to collect the data from the public sources and make a few minor
changes in the SAS command statements of the programs. [See comment 5]
2. Page 4. The report indicates that standard measures of statistical
goodness of fit and other diagnostics of model performance are critical
to model evaluation. SAS, the statistical program used to produce the
estimates for the ERS cattle model, typically provides these measures
of goodness of fit in the course of producing model results. And,
FAPSIM, the simulation model, provides several statistical evaluation
measures in its output. [See comment 6]
3. Page 5, paragraph 3 under the Principal Findings heading. "The model
was not designed to address these kinds of questions," refers to
concentration and market structure. Several studies have found that
market structure and rising concentration may have led to more market
power and cost-efficiency, but does not have a significant effect on
meat prices. Thus, incorporating these variables might not increase the
explanatory power of the model. See for example: Lopez, R., A. Azzam,
and C. Liron-Spana, Market Power and/or Efficiency: A Structural
Approach," Review of Industrial Organization 20(2):115-126, 2002. Other
ERS-based work also shows despite growth of packer concentration, no
negative effects of packer concentration on cattle prices have been
demonstrated. See the report: U.S. Beef Industry: Cattle Cycles, Price
Spreads, and Packer Concentration, Kenneth H. Mathews, Jr., William F.
Hahn, Kenneth E. Nelson, Lawrence A. Duewer, and Ronald A. Gustafson.
Technical Bulletin No. 1874, April 1999. Access from the ERS website
at: [hyperlink, http://www.ers.usda.gov/publications/tbl874/]. A GIPSA
report indicates that studies have not shown that increase in the use
of captive supply cause spot market prices to fall, or that packers'
use of captive supply causes spot market prices to change. See: Captive
Supply of Cattle and GIPSA's Reporting of Captive Supply. USDA, Grain
Inspection, Packers and Stockyards Administration, January 11, 2000.
Web access from GIPSA site at: [hyperlink,
http://www.usda.gov/gipsa/pubs/captive_supply/captive.htm]. Another
GIPSA report indicates that the analysis did not supply any conclusion
about the exercise of market power by beef packers. See: Concentration
in the Red Meats Packing Industry, Packers and
Stockyards Programs, USDA-GIPSA, February 1996. Web access at:
[hyperlink, http://www.usda.gov/gipsa/pubs/packers/conc-rpt.htm]. [See
comment 7]
4. Page 8, and the chapter detailing the focus group results, referring
to the discussion of relevant variables to include in a model of cattle
prices. We recognize that improved models are needed to more fully
incorporate relevant determinants of variation in cattle and meat
prices. The description fails to capture the modeling problem which
is... there are more possible variables to be explained (endogenous
variables), than there is reliable time-matching data available to
explain them (exogenous variables). Any inclusion of explanatory
variables in a model meant for forecasting requires that those
explanatory variables must also be forecasted. The econometric modeler
must create a model that addresses the relevant question and can
actually be estimated. Even if variables describing market structure
are determined to be relevant, it is difficult to ascertain how future
market structure could be determined for modeling purposes without
proposing a series of assumptions for scenario-driven explorations.
Less formal expert opinion may be better able to incorporate current
information on rapidly changing situations and an understanding that
historic statistical relationships may not currently hold because of
structural and technological changes. [See comment 8]
5. Page 6, text indicates the cattle pricing models need "better
documentation." Admittedly, documentation of the modeling efforts is
sparse, but it is adequate for researchers with a basic knowledge of
SAS. The model has been documented in ERS publications, and the SAS
code provided to GAO contained programming comments to describe
statistical process used in the models. In addition, SAS manuals
document modeling procedures that are routinely used to make the
necessary estimates. [See comment 9]
6. Page 12 Par. 2. Implies that corn is the only concentrate feed.
While corn comprises the majority of US feedlot concentrate rations
other grains e.g. sorghum, barley, and even wheat at times are
economically significant. [See comment 10]
7. Page 12 Footnote 3. Incorrect (or at least awkward) to say "by-
products are important in producing hides". Hides are the most valuable
of the many by-products of animal slaughter. Leather is the product
made from hides and skins. [See comment 11]
8. Page 13 Paragraph 1. It would be more common to say "..graze on
grass and other forages." While the usage is not universal, many use
roughage to mean any high cellulose feed including hay bales brought to
the animal, while forage is used to mean roughage that animals graze
for themselves. [See comment 12]
9. Page 13, Paragraph 2. The phrase "...relying on seedstock
producers.." may suggest more reliance on specialized breeding
operations than is probably the case. According to the NAHMS survey--
most replacement heifers (88%) are retained from their own herd and
only about 1/4 of all operations purchased, borrowed, or leased a bull
in their last breeding season and about 1/2 over the past 12 months
(1992-93) [See comment 13]
10. Page 14, Figure 4. First Column. The gestation period for a beef
cow is 9 months (not 9 to 11 months); 2/3 of cows birth their calves
between 278 and 288 days. (Ensminger) [See comment 14]
11. Page 14, Figure 4. First Column. Average weaning weight is now much
higher than 400 lbs. Average per operation was over 480 lbs. in 1992
and the weighted average (total pounds/number of calves) was over 500
lbs. (NAHMS). And since 1992 mature size of beef cattle has increased.
According to NASS's January 25 Livestock Slaughter report, average live
weight of slaughter cattle was 1255 pounds and dressed weight was about
738 pounds. [See comment 15]
12. Page 14, Figure 4. Second Column. The phrase "Becomes steer or
heifer when weighed, at 750 lbs." is technically inaccurate. Heifer is
another name for female, so a heifer is a heifer calf when born. A bull
calf becomes a steer when castrated, usually well before entering the
feedlot. [See comment 16]
13. Page 14, Figure 4. Third column. Need to add "...supplies fed
cattle to beef packing plant." [See comment 17]
14. Page 14, Figure 4. Third column. Again suggests corn as only
concentrate. Not so serious in the chart where brevity is important.
[See comment 18]
15. Page 14, Figure 4. Fourth column. "buys feed cattle" (presumably)
should be "buys fed steers and heifers and culled cows and bulls." [See
comment 19]
16. Page 14, Figure 4, Fifth column. Add Institutions to the list
beginning with Grocery chains. (e.g. schools, prisons, hospitals) [See
comment 20]
17. Page 15, Figure 5. Change "stockers" to "stocker operations". [See
comment 21]
18. Page 16, 1st paragraph under Figure 6: The statement, "Feed is the
largest direct cots component in cow-calf production," is misleading.
Cow-calf operations depend mainly on forage and pasture to sustain
their cattle and use little feed grains. Thus, land ownership (or
leasing costs) is the largest direct cost for cow-calf operations. Even
in the fed cattle sector, the top direct cost is the price of the
cattle, with feed grains coming in as the second largest direct cost
component. [See comment 22]
19. Paragraph beginning on Page 16 and continuing to Page 17 and
detailing the cattle procurement process. Producers have never relied
"solely" on the spot market to sell their cattle. While the cattle
sector is moving to include more alliances and arrangements that
include contracting and vertical integration, the sector still relies
on the spot market to move animals. The procurement processes of
marketing agreements, forward contracts, and packer fed beef are not
"new" processes as they have been in existence in one form or another
since the middle of last century. [See comment 23]
20. Page 17 Paragraph 4. Reorganization of this paragraph with its
precedents may make it easier to follow; though, the material is
basically correct. Try beginning the discussion on procurement with
definitions or discussions of cash market, to vertical coordination, to
vertical integration. Then, the reader could follow the progression
from pure open cash live-weight auctions with no direct communication
between buyer and seller until the point of sale, to coordinated market
exchange where some transaction attributes are prespecified, to full
ownership of all assets for all stages of production by one entity.
After introducing the concepts and describing coordinated exchange,
Paragraph 4 could be rewritten: Vertically integrated entities
formalize the coordination of production through ownership of some or
all of ranches, feedlots, cattle, feed mills, meat packing and
processing, distribution and retail operations that contribute to the
finished product. The most common ownership arrangements combine meat
packing and cattle feeding in varying ways, but vertical integration is
far less prominent in cattle than in broilers or hogs. About 2 million
of the more than 27 million fed cattle marketed were owned or fed in
packer owned lots in 1999. (GIPSA, NASS) [See comment 24]
21. Page 17, Paragraph 5. The terms value-based pricing and grid
pricing are more interchangeable than suggested in this paragraph, but
not a big issue. [See comment 25]
22. Page 19, Paragraph 4. Again, what is "new"? Most of these
techniques and products have been around for decades, but rates of
adoption may be accelerating. [See comment 26]
23. Page 20, Figure 8 shows and text discusses per capita consumption.
These statements about meat consumption need clarification on the basis
that consumption is reported--carcass, retail, or boneless. Chicken
consumption passed pork in the mid 1980s, if it is measured on a retail-
weight basis. This is what the graph seems to show, so we assume that
the data as for retail-weight per capita consumption. Chicken
consumption passed beef consumption in 1993 when measured on a retail-
weight basis. See: [hyperlink,
http://www.ers.usda.gov/Data/FoodConsumption/Spreadsheets/mtpcc.xls].
[See comment 27]
24. USDA recommends that wording be changed on the bottom of page 36
and top of page 37 to read: "The committee's chairperson sees his role
as helping committee members reach consensus; however the chair has
overall responsibility for approving projections and will impose a
decision if consensus can't be reached." [See comment 28]
25. Page 69, GAO recommends that GIPSA, AMS, ERS, and NASS work
together, "in consultation with other government departments or
agencies" to prepare a plan for improving the amount and quality of
data that are available for research and analysis of prices and related
issues. This issue is summarized on page 8 and again on page 9 of the
Executive Summary where it is recommended that the Secretary of
Agriculture direct ERS to periodically re-estimate and document the
models. While frequent re-estimation is desirable, it requires a
commitment of funding, staff, administrative resources, and computer
resources. ERS is a user of secondary data and has no resources to
conduct data collection necessary to more fully implement model
development. [See comment 29]
GAO's expert panel identified several areas for improvement in cattle
price data. The primary role of the Agricultural Marketing Service
(AMS) with regard to these issues is the collection and dissemination
of market information by AMS Market News. Current Market News reports
released under the livestock mandatory reporting (LMR) program already
address a number of issues identified by the expert panel. For example,
Market News reports on slaughter cattle purchases include information
on volume and weighted average prices categorized by quality grades,
problems identified by the panel. Market News reports for slaughter
cattle include information on negotiated purchases, formulated
purchases, forward contract purchases, and packer-owned cattle. In
terms of spatial and temporal dimensions identified by the panel, daily
and weekly regional reports are available. Information on premiums and
discounts for slaughter cattle are also reported on a weekly basis,
further addressing the need for information on cattle quality as noted
by the panel. Information on imported slaughter cattle currently is
reported, and information on export sales of beef will be included in a
new report to be released in the near future. This new LMR data is
available since April 2001 and will provide a wealth of information
about the pricing of slaughter cattle. [See comment 30]
The expert panel provided several recommendations for providing greater
access to data, including proprietary information such as data
submitted to AMS under LMR. There are two sources of proprietary data
reported to AMS. The first source is proprietary data reported
voluntarily to AMS for price reporting. For example, sales of pork by
packers are not subject to LMR and are reported voluntarily to AMS.
Under voluntary reporting, the information reported to AMS is
aggregated for publication and proprietary data are not maintained
following release of the aggregated information by Market News. The
proprietary data are not maintained because voluntary reporting depends
on the assurance of confidentiality of the information reported to AMS.
Without such assurance, some firms that now report information
voluntarily would cease to participate, which would reduce the quality
and comprehensiveness of reported information substantially. Thus,
providing access to voluntarily reported information is not feasible.
[See comment 31]
The second source of proprietary data reported to AMS is the
information reported by packers on purchases of cattle, hogs, and sheep
and on sales of beef and lamb. GAO's expert panel suggested granting
researchers access to such data in a manner similar to the operation of
the Census Bureau, which provides researchers access to certain
confidential census data in-house. There are differences between the
operation of the Census Bureau and AMS Market News. The time sensitive
nature of current market information reported under LMR is more
restrictive in terms of access and potential impacts on businesses and
the marketplace compared to information available from Census Bureau
data. In addition, the Livestock Mandatory Reporting Act of 1999
requires that confidentiality be preserved of information submitted to
or obtained by AMS regarding "the identity of persons, including
parties to a contract" and "proprietary business information." There
are limited exceptions to this confidentiality provision, but there is
no explicit mechanism or exception for making these confidential data
available to researchers.
Even if LMR data could be made available to researchers, substantial
resources would have to be committed and the infrastructure developed.
For example, the Census Bureau makes data available to researchers
under its American Statistical Association/National Science Foundation/
Census Bureau Research Fellowship Program. This is a substantive
program involving commitment of funding, staff, administrative
resources, computing resources, and physical space. AMS currently has
no such program in place and does not have the funding or resources to
commit to such a program. [See comment 32]
GAO also reports that GIPSA has some of the best data available on
livestock procurement transactions. GIPSA collects these data as a
result of its statutory authority for enforcing the Packers and
Stockyards Act of 1921, as amended. Data are often collected as part of
an investigation, and most of the data are proprietary. While GIPSA
enters into cooperative agreements and makes data available for
research, it requires that recipients sign a strict non-disclosure
agreement that forbids public release of proprietary information. GIPSA
is willing to work with other agencies to address important data
issues, consistent with the Agency's restrictions on release of
confidential data. [See comment 33]
The following are GAO’s comments on the U.S. Department of Agriculture’s
(USDA) letter dated March 4, 2002.
GAO Comments:
1. We are pleased that ERS agrees with our recommendation that
reestimating the livestock model with more current data could be
valuable. In addition, we agree that any new model should be
appropriately documented. We disagree that the GAO report
mischaracterizes the process used to develop and document the
livestock model. Our characterization of this process was based on
interviews of ERS officials and documents that they provided.
2. We agree that when originally developed the livestock model was
appropriately documented. The problems with documentation arose as this
model was subsequently revised. The same kind of documentation was not
continued. In addition, even for the original model, data sets were
lost, thereby making replication or verification very difficult.
3. The principal reason for wanting to have the original data set is
for replication or verification purposes. In addition, some of the
original data would presumably be used along with newer data in
subsequent reestimates.
4. The livestock sector is important and steps taken by ERS to increase
staff devoted to this area recognizes that fact. ERS agrees that
reestimating the livestock model using more current data could be
valuable. Updating this model would include reestimation but could also
involve respecifying its structure, which could come about as a result
of a broader effort to develop a stronger program to address new
issues. Our recommendation to periodically reestimate and validate the
livestock model is intended to ensure credible and accurate results
regardless of what form any future modeling might take. Because data
are readily available, this should not pose an undue burden.
5. Our point is that USDA needs to have better documentation of their
models and there seems to be agreement on that point. Specifically, in
reviewing USDA's livestock model, we noticed that parts of the model
are different from what was originally estimated. As a result, we asked
for complete documentation of the model. In response to our request for
this data, we were told repeatedly that the data was lost during an
office move. Knowing what data was actually used in estimating the
model would allow an outside reviewer to replicate the estimation
results, which would include validation statistics. While historical
data may be available in the public domain, it is not possible to
determine which of these data was actually used in estimating the model
without further documentation. After examining SAS code for the
livestock model, we asked USDA officials for the data sets actually
used to estimate the model and were told that these data were lost.
6. We agree that SAS provides measures of goodness of fit. We were told
that these measures of goodness of fit as they applied to the latest
version of the model were also lost during the move or not documented.
7. We agree that the effect of these structural changes remains unclear.
On pages 5 and 43 of the draft (pages 7, 49, and 50 of the final
report), we point out that according to current USDA research the
effect of these structural changes on cattle prices is inconclusive.
Our panel told us that these factors will be more important in the
future. In addition, re-estimating the model with more current data
would be an indirect way of incorporating any affects that these
structural changes may have had on cattle prices. This is one reason
why we believe reestimating the model with more current data makes
sense.
8. We agree that the econometric modeler must create a model that not
only addresses the relevant questions but also can be estimated. Our
expert panel identified the need for better data to do such modeling.
We agree that expert opinion is valuable in trying to sort out what
makes sense, and we have recommended that USDA review the findings of
our expert panel in this regard.
9. See our response in comment #5.
10. We agree and clarified text.
11. We agree and clarified footnote.
12. We agree and clarified text.
13. We agree and clarified text.
14. We agree and clarified text.
15. We agree and clarified text.
16. We agree and clarified text.
17. We agree and clarified text.
18. We agree and made change in text.
19. We agree and clarified text.
20. We agree and clarified text.
21. Stockers and stocker operations are synonymous.
22. We agree and clarified text.
23. We agree and clarified text.
24. We do not believe any changes are needed.
25. We agree and clarified text.
26. We agree and clarified text.
27. We agree and clarified text.
28. We agree and clarified text.
29. GAO is recommending that AMS, ERS, GIPSA, and NASS review the
findings of our expert panel regarding important data and modeling
issues in preparing a plan for improving data, considering the costs and
benefits of such data improvements, including tradeoffs in departmental
priorities and reporting burdens. As such, this recommendation is not
directly linked to periodic reestimation of the livestock model. Since
ERS is a major user of such data, it makes sense for it to be included
in this planning process.
30. On pages 63 and 64 of the draft, (pages 71 and 72 of the final
report) we recognize AMS's role in collecting data on cattle prices,
including data on cattle weight and quality as well as data on cattle
purchased under marketing agreements and forward contracts. As a
result, AMS is in a good position to offer valuable insight in
developing a plan for further data enhancements.
31. In preparing a plan for addressing the most important data issues
that the expert panel recommended for government action, USDA should
explore creative ways to deal with the issue of confidentiality while
satisfying the needs of researchers.
32. As noted above, we recommend that the costs and benefits of
procuring better data be considered.
33. We are pleased that GIPSA is willing to work with other agencies to
address important data issues, and our recommendation is designed to
harness this cooperative spirit among all relevant agencies and
departments, including those outside USDA. We can appreciate
restrictions on the use of certain data. However, our panel of experts
told us that better data is needed. Perhaps further communication with
the user community can alleviate some of the concerns that the expert
panel had about data. Other data concerns may entail more creative
thinking.
[End of section]
Appendix VIII: GAO Contacts and Staff Acknowledgments:
GAO Contacts:
Nancy R. Kingsbury (202) 512-2700:
Charles W. Bausell, Jr. (202) 512-5265:
Staff Acknowledgments:
Avrum I. Ashery, Carol E. Bray, Brandon Haller, Janeyu H. Li, Theresa A.
Mechem, Lynn M. Musser, Robert P. Parker, Penny Pickett, and Michael S.
Sagalow made key contributions to this report.
[End of section]
Glossary:
Beef Cow:
A sexually mature female bovine used in the production of beef.
Bull:
A bovine male of breeding age.
Bullock:
A young bull younger than 20 months old—that is, not of breeding age.
Cow:
A sexually mature female bovine that has usually produced a calf.
Cow-Calf:
Operation A management unit that maintains a breeding herd and produces
weaned calves.
Economies of Agglomeration:
Average cost reductions resulting from the clustering of activities.
Economies of Scale:
A decrease in the average cost of a product or service as the output of
the commodity rises.
Economies of Scope:
Factors that make it cheaper to produce a range of related products
than to produce any of the individual products on their own.
Fed Cattle:
Steers and heifers that have been fed concentrates, usually for 90 to
120 days in a feed lot.
Feeder Cattle:
Cattle that have been fed on forage but need further feeding on high-
energy rations before slaughter.
Feedlot:
An enterprise in which cattle are fed grain and other concentrates,
usually for 90 to 120 days.
Finished Cattle:
Fed cattle whose time in the feed lot has been completed so that they
are now ready for slaughter.
Forage:
Herbaceous plants, such as grass, used to feed cattle.
Forward Contract:
A transaction that involves a contract to buy or sell a commodity at a
fixed future date and at a price agreed on in the contract.
General Equilibrium Model:
A study of the behavior of economic variables that takes full account
of the interaction between those variables and the rest of the
economy—for example, the effect of a single change such as a change in
the price of milk on the entire economy.
Goodness of Fit:
Refers in statistics to how well the predicted values of a variable
match its observed values.
Heifer:
A young female bovine cow before she produces her first calf.
Partial Equilibrium Model:
A study of the behavior of variables that ignores the indirect effects
that the variable has on the rest of the economy.
Spot Market:
A market for buying and selling commodities for immediate, rather than
future, delivery or for cash payment. The price for such commodities is
called the spot or cash price.
Spot Price:
The price of commodities sold in the spot market.
Steer:
A bovine male castrated before puberty.
Stocker:
Weaned cattle that are fed high roughage diets (including grazing)
before going into feedlots.
Thin Market:
A market in which trading is light and price fluctuations relative to
volume tend to be much greater than in a market where trading is very
active.
Vertical Integration:
The extent to which successive stages in production and distribution are
placed under the control of a single enterprise.
[End of section]
Footnotes:
[1] “Spot market” and other technical terms here and throughout the
report are defined in the report’s glossary.
[2] Statistical goodness of fit is a measure of how well the predicted
values of the model’s variables match its observed values (see the
glossary).
[3] Beef by-products include hides used to make leather and also are
used in a number of industrial applications in food manufacturing and
pharmaceuticals.
[4] James M. MacDonald and others, Consolidation in U.S. Meatpacking,
Agricultural Economic Report 785 (Washington, D.C.: USDA, ERS, 2000).
[5] MacDonald, Consolidation.
[6] Notwithstanding the decline in per capita beef consumption, total
U.S. beef consumption was 15 percent higher in 1999 than in 1970, as
the population increased 33 percent.
[7] Harold A. Linstone and Murray Turoff, eds., The Delphi Method:
Techniques and Applications (Reading, Mass.: Addison-Wesley, 1975).
[8] U.S. Department of Agriculture. USDA Agricultural Baseline
Projections to 2010, WAOB-2001-1 (Washington, D.C. 2001).
[9] For example, the livestock model is designed to project average
outcomes, so it does not project anomalous conditions such as an
increase in the number of cattle brought to market because of drought
conditions.
[10] The four USDA agencies on the meat animals committee are the
Agricultural Marketing Service, Economic Research Service, Farm Service
Agency, and Foreign Agricultural Service.
[11] A number of variables measuring consumer expenditures for various
goods and services are also included in the equations explaining retail
prices for beef, pork, and poultry. Values for these variables are
determined outside the livestock model.
[12] FAPSIM is calibrated to USDA’s national baseline and includes 22
crops and livestock commodities.
[13] The link system models the world market. It consists of 46 country
or sector models. FAPSIM is the U.S. model used in the link system. The
link system is sometimes referred to as the country sector models.
[14] Mark R. Weimar and Richard P. Stillman, A Long Term Forecasting
Model of the Livestock and Poultry Sectors (presented at NCR Conference
on Applied Commodity Price Analysis, Forecasting, and Market Risk
Management, Chicago, Illinois, April 23–24, 1990), 219.
[15] ITC is authorized under section 332 of the Tariff Act of 1930 to
conduct broad economic studies.
[16] A partial equilibrium model typically solves for prices and
quantities for one sector while treating economic variables of other
sectors as predetermined and unchanged.
[17] Dumping occurs when a foreign producer sells a product in the
United States at a price that is lower than that producer’s sales price
in the country of origin (“home market”) or lower than the average cost
of production.
[18] This assumption is relatively standard in applied trade models.
[19] In connection with proceedings to determine whether additional
customs duties must be imposed on imported merchandise, ITC is required
under the Tariff Act of 1930 to investigate claims of material injury
due to subsidized imports or imports selling at less than fair value,
which the Department of Commerce accepts for investigations. Commerce
investigates the allegations of subsidization or less than fair value
sales.
[20] ITC issued a preliminary and final report on this investigation.
International Trade Commission, Live Cattle From Canada and Mexico,
Pub. 3155, (Washington, D.C., 1990) and Live Cattle From Canada, Pub.
3255, (Washington, D.C., 1999). Some of the reasons the ITC
commissioners offered for this determination are related to a small
(less than 4 percent) share of total U.S. cattle supplied by imports
from Canada. The dumping margin determined by Commerce averaged about 6
percent.
[21] The dumping margin is the percentage difference between price (or
cost) in the foreign market and price sold in the U.S. market. The
elasticities measure the sensitivity of quantities demanded (or
supplied) to price changes.
[22] ITC staff said that this linkage could be implicitly considered by
adjusting elasticities.
[23] The influence of these factors could be reflected indirectly in
the estimated values of elasticities used in COMPAS, depending, among
other things, on when these elasticity estimates were made.
[24] International Trade Commission, Cattle and Beef: Impact of the
NAFTA and Uruguay Round Agreements on U.S. Trade, Pub. 3048,
investigation 332-371, (Washington, D.C., 1997).
[25] An econometric analysis tests relationships among economic
variables, using statistical methods. A CGE model is a simplified
representation of the economy that simultaneously determines prices and
quantities in all sectors without employing econometric analysis. Using
a CGE model involves selecting a base year for analysis and assigning
values for parameters representing demand elasticities and production
technologies, among other things. Economic effects of policy changes
are estimated by comparing simulated conditions before and after policy
changes. ITC uses two CGE models. One, representing the U.S. economy,
has 487 production sectors and combines all meat animals into one
sector and all meatpacking plants into another sector. Another,
representing the global economy, has 50 commodities and combines bovine
cattle, sheep and goats, and horses into one sector.
[26] Under the Packers and Stockyards Act, GIPSA is responsible for
helping to guard against unfair and anti-competitive practices, among
other things. GIPSA addresses these concerns by investigating
complaints about anti-competitive activities and by analyzing data on
the structure and operations of the livestock, poultry, and meatpacking
industries.
[27] Grain Inspection Packers and Stockyards Administration, Packers and
Stockyards Programs, Concentration in the Red Meat Packing Industry
(Washington, D.C.: USDA, 1996).
[28] MacDonald, Consolidation.
[29] Grain Inspection Packers and Stockyards Administration, Packers and
Stockyards Programs, Effects of Concentration on Prices Paid for Cattle
(Washington, D.C.: USDA, 1996), 36.
[30] Economic Research Service, “Consolidation in Meatpacking: Causes &
Concerns,” Agricultural Outlook, June–July 2000.
[31] Catherine J. Morrison Paul, Cost Economies and Market Power in
U.S. Beef Packing, Giannini Foundation Monograph 44 (Davis, Calif.:
University of California–Davis, 2000).
[32] John R. Schroeter and Azzeddine Azzam, “Econometric Analysis of
Fed Cattle Procurement in the Texas Panhandle,” Iowa State University,
Ames, Iowa, and University of Nebraska–Lincoln, Lincoln, Nebraska,
1999.
[33] GIPSA compiled extensive data on cattle procurements at four
plants in the Texas panhandle from February 1995 through May 1996.
Grains Inspection Packers and Stockyards Administration, Investigation
of Fed Cattle Procurement in the Texas Panhandle (Washington, D.C.:
USDA, 1999).
[34] Other by-products are fat and bone, blood, and meat meal. Beef by-
products are used in the pharmaceutical industry and in formulating
high-energy and high-protein animal feed. Fat can be classified as
industrial and edible tallow, lard, yellow grease, and feed grade fat. A
relatively high percentage of beef tallow is exported.
[35] U.S. General Accounting Office, Pork Industry: USDA’s Reported
Prices Have Not Reflected Actual Sales, [hyperlink,
http://www.gao.gov/cgi-bin/getrpt?GAO/RCED-00-26] (Washington, D.C.:
Dec. 14, 1999).
[36] [hyperlink, http://www.gao.gov/cgi-bin/getrpt?GAO/RCED-00-26].
[37] [hyperlink, http://www.gao.gov/cgi-bin/getrpt?GAO/RCED-00-26].
[38] Information is also being collected on boxed beef, including the
price per hundredweight, the quantity in each lot of boxed beef cuts
sold, information on the characteristics of each lot, such as domestic
and export sales, USDA quality grade, the type of beef cut, and trim
specification.
[39] The new confidentiality guideline requires three conditions for
publication. First, at least three reporting entities need to provide
data at least 50 percent of the time over the most recent 60-day time
period. Second, no single reporting entity may provide more than 70
percent of the data for a report over the most recent 60-day period.
Third, no single reporting entity may be the sole reporting entity for
an individual report more than 20 percent of the time over the most
recent 60-day period.
[40] Linstone and Turnoff, The Delphi Method.
[41] James P. Wright, “Delphi—Systematic Opinion-Gathering,” The GAO
Review (spring 1972): 20–27.
[End of section]
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