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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. See appendix VII. 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. 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. 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. 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. 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. 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. 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. 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. See appendix VII. 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. 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. 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. 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. 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 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. 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. 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. 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. See appendix VII.

Summary: 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. 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.