Economic Models of Cattle Prices: How USDA Can Act to Improve Models to Explain Cattle Prices
Published: 2002-03-15T00:00:00

Fast Facts:

Highlights:
section_title: Why GAO Did This Study
paragraphs: Concerns have been raised that the economic models used by the U.S. Department of Agriculture (USDA) and the U.S. International Trade Commission do not account for all the factors that affect cattle prices and producer incomes. GAO reviewed USDA's livestock model to determine whether it incorporates imports, market concentration, marketing agreements, and forward contracts.
section_title: What GAO Found
paragraphs: In reviewing best modeling practices, GAO's expert panel concluded that domestic cattle demand and supply were the fundamental forces driving cattle prices and producer incomes. The panel identified issues necessary to develop a comprehensive modeling system that predicts cattle prices and producer incomes. The panel recommended the collection of better data to quantify several important factors omitted from the model. The panel also wanted 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.

section_title: Letter
paragraphs: 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.
subsections: section_title: Introduction
paragraphs: 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.
subsections: section_title: SUPPL SUPPLY Y SUPPL SUPPLY
paragraphs: 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.
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.
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.
subsections: section_title: Structural and Technological Changes in the Cattle and Beef Industry
paragraphs: 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. 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.
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.
section_title: Beef’s Competition from Other Meats
paragraphs: 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. 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. 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.
section_title: International Trade in Beef and Cattle Is Growing
paragraphs: 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.
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.
section_title: The Cattle Cycle Is an Important Feature of Supply
paragraphs: 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.
section_title: Modeling the Cattle and Beef Industry Can Take Different Forms
paragraphs: 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.
section_title: Objectives, Scope, and Methodology
paragraphs: 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’   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. 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.
section_title: The USDA and ITC Models Were Not Designed to Answer Questions about Structural Change
paragraphs: 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.
subsections: section_title: USDA’s Models Project Cattle Prices under Baseline Conditions
paragraphs: Each year, USDA publishes an agricultural baseline report with projections  for the livestock sector, including cattle and beef. 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.  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.
section_title: Short-Term Projections Rely on Analysts’ Judgments
paragraphs: 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. 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.
section_title: Long-Term Projections Are Based on USDA’s Livestock Model
paragraphs: 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.  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).
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.
section_title: The Livestock Model Has Not Been Reestimated, Documented, or Validated
paragraphs: 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”. 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.
section_title: ITC’s Models Lack Industry Specifics Needed to Predict Prices
paragraphs: 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.  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.  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.  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.  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.  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.
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).  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.  COMPAS  also does not explicitly account for changes in concentration in the  meatpacking industry, marketing agreements, and forward contracts.
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. 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.   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.
section_title: Research Is Inconclusive on How Structural Change Affects Domestic Cattle Prices
paragraphs: 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. 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. 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.  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”.
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. 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. 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. 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.  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.
section_title: Conclusions
paragraphs: 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.
section_title: Recommendations for Executive Action
paragraphs: 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.
section_title: Agency Comments and Our Evaluation
paragraphs: See appendix VII.
section_title: Many Factors Determine Cattle Prices and Producers’ Incomes
paragraphs: 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.
subsections: section_title: Cattle Demand and Supply, International Trade, and Structural Change
paragraphs: 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.
section_title: Consumer Demand for Beef Influences Demand for Cattle
paragraphs: 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.
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.  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.
section_title: Several Considerations Shape Producers’ Decisions to Supply Cattle
paragraphs: 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.
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.
section_title: International Trade Affects Domestic Prices and Producers’ Incomes
paragraphs: 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.
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.
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.
section_title: Structural Change Is Relevant
paragraphs: 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.
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.
section_title: Conclusions
paragraphs: 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.
section_title: Recommendations for Executive Action
paragraphs: 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.
section_title: Agency Comments and Our Evaluation
paragraphs: See appendix VII.
section_title: Building a Comprehensive Model Depends on Resolving Modeling and Data Issues
paragraphs: 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.
subsections: section_title: Analyzing How Demand and Supply Link Producers to Consumers Is Important
paragraphs: 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.
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.
section_title: Obtaining Better Data to Analyze Consumer Demand Is Important
paragraphs: 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.
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.
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.
section_title: Aspects of Cattle Supply and Prices Are Relevant
paragraphs: 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.
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.  These data may help in future modeling  efforts.
section_title: International Trade Issues
paragraphs: 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.
section_title: Overarching Issues Related to Modeling Scope
paragraphs: 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.
section_title: The Panel’s Priority Items for Government Action
paragraphs: 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.
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.
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.
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.
section_title: Conclusions
paragraphs: 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.
section_title: Recommendations for Executive Action
paragraphs: 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.
section_title: Agency Comments and Our Evaluation
paragraphs: See appendix VII.