U.S. Beef Industry: Cattle Cycles, Price Spreads, and Packer Concentration.

By Kenneth H. Mathews, Jr., William F. Hahn, Kenneth E. Nelson, Lawrence A.

Duewer, and Ronald A. Gustafson, Market and Trade Economics Division,

Economic Research Service, U.S. Department of Agriculture. Technical Bulletin

No. 1874.

Abstract

In early 1996, the peak in the current cycle of cattle inventories coincided with a

long list of negative factors--negative returns at the farm and feedlot, record-high

feed grain prices, a severe drought in 1995-96, widening farm-retail price

spreads, a low farmers' share of the consumers' Choice beef dollar, and reports of

high profits for beefpackers. This confluence created an atmosphere in which

some producers and members of Congress questioned whether the cattle industry

was adversely affected by high packer concentration and market power. In this

report, we examine the cattle cycle of the 1990's to determine if there are

differences from previous cattle cycles and, if so, how and why they are different.

We found that values for many variables at the 1996 cyclical peak in cattle

inventories, while bad, were not the worst on record. Further, price levels during

the cattle cycle of the 1990's were better, our models suggest, than they could

have been, given earlier patterns of price adjustment. Finally, despite the growth

of packer concentration, we failed to demonstrate large negative effects of packer

concentration on cattle prices during the 1991-to-present cattle cycle.

Keywords: Cattle cycles, price spreads, packer concentration, cattle slaughter,

steer and heifer slaughter, and cow slaughter.

Acknowledgments

The authors appreciate the helpful comments of Dr. Jim Trapp (Oklahoma State

University), Dr. Wayne Purcell (Virginia Polytechnic Institute and State

University), Dr. Jim Mintert (Kansas State University), as well as reviewers from

the U.S. Department of Agriculture, Grain Inspection, Packers and Stockyards

Administration, Roger Schneider and Larry Haller, and Economic Research

Service, Dr. Mike Ollinger and Dr. John Dyck, and the editor, Sharon Lee.

Wynnice Pointer-Napper prepared the charts.

Washington, DC 20036-5180 April 1999
Contents

Page

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

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

Characteristics of Cattle Cycles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Cattle Inventory Adjustments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 

Weather Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Grain and Beef Trade Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Cropping and Commodity Program Effects . . . . . . . . . . . . . . . . . . . . . . . 6

Beef Industry Structure Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Demand Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Price Spreads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Beefpacker Concentration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Comparing the 1990's With Past Cycles of Cattle Inventories . . . . . . . . . . . 11

Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Statistical Comparisons of Cycles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Means, Variances, and Coefficients of Variation . . . . . . . . . . . . . . . . . . . 13

Relative Changes in Selected Variables from Previous Cattle Cycles . . . . 14

Correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Price Spreads and the Cattle Cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Long-Term Fluctuations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Short-Term Fluctuations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

General Structure of a Price Spread Adjustment Model . . . . . . . . . . . . . . 21

Estimation Results and Interpretations . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Simulations and Forecasts With the Asymmetric Model . . . . . . . . . . . . . . 25

Concentration Measures for the Beef Packing Industry . . . . . . . . . . . . . . . . 30

SCP Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

NEIO Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

GIPSA Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Empirical Measures of Packer Concentration . . . . . . . . . . . . . . . . . . . . . . 31

Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Appendix table--Data Series for Cattle Cycle Analysis . . . . . . . . . . . . . . . . 41

Appendix--Our Asymmetric Model of Price Movements . . . . . . . . . . . . . . . 42

ii U.S. Beef Industry/TB-1874 Economic Research Service/USDA
Summary

Is the cattle cycle that began in 1991 worse than previous cycles? This is the

question that some financially stressed cattle producers asked when they called

on the Federal Government to intervene in cattle markets. This report, a study of

relationships among cattle cycles, price spreads, and market concentration,

concludes that the 1970's cattle cycle was worse for ranchers than the current

cycle, and that farm prices in the current cycle may actually be better than one

might expect.

Cattle producers generally accept cattle cycles. However, in 1996, a combination

of events created an atmosphere in which some producers charged that the cattle

industry was adversely affected by high concentration and market power of beefpacking firms. These events included low cattle prices and widening farm-toretail beef price spreads, a low farmers' share of the consumer spending on

Choice beef, negative returns to cattle producers, and reports of high profits for

beefpackers. In addition to these perceptions, peak cattle inventory numbers for

the cattle cycle of the 1990's coincided with record-high feed grain prices. The

U.S. farm price of corn, which averaged $2.26 per bushel in 1994/95, rose to a

peak of over $5.00 in July 1996. Further, the severe drought in 1995 and 1996 in

some major cattle-raising areas forced many producers to reduce cow herds as

forage supplies declined. Allegations were made that packers were using their

market power to lower bids for cattle, thus lowering prices that producers

received.

Several test results indicated that the link between the cattle cycle and increasing

concentration in the beefpacking sector runs counter to these perceptions. The

study results indicate that the cycle of the 1970's was worse than the cycle of the

1990's, with average estimated losses for cow/calf producers almost twice as bad

per cow at the low point in the 1970's as in 1996. Further, while overall price

spreads for Choice, yield grade 3 steers are growing wider over time, the farm-towholesale portion is only slightly wider than its narrowest levels since at least the

1970's in nominal dollars. In real dollars, the spread is at its narrowest levels

since before the 1970's. Test results showed no evidence to support the assertion

that increasing slaughter concentration results in lower farm prices. The

wholesale-to-retail portion of the price spread is growing because of costs for

additional packing services and new products in the packing industry.

Economic Research Service/USDA U.S. Beef Industry/TB-1874 iii
The U.S. Beef Industry

Cattle Cycles, Price Spreads,

and Packer Concentration

Kenneth H. Mathews, Jr.

William F. Hahn

Kenneth E. Nelson

Lawrence A. Duewer

Ronald A. Gustafson

Introduction

Cattle producers generally acknowledge the existence and importance of the cyclical behavior of cattle inventories,

which have peaked in the United States about once a decade for the last century. Many cattle producers say,

however, that conditions during the cycle that began in 1991 are worse than conditions during previous cycles. In

1996, a combination of conditions created an atmosphere in which some producers charged that the cattle industry

was adversely affected by high concentration and market power of beefpackers (U.S. Department of Agriculture,

1996). These conditions included: (1) low cattle prices combined with widening farm-to-retail beef price spreads,

(2) a low farmer share of consumer spending on Choice beef, (3) negative returns in the feeder and fed cattle

markets, and (4) reports of high profits for beefpackers. In addition to these perceptions, peak cattle inventory

numbers for the cattle cycle of the 1990's coincided with record-high grain prices. The U.S. farm price of corn,

which averaged $2.26 per bushel in 1994/95, rose to a peak of over $5.00 in July 1996. In addition, severe drought

in 1995 and 1996 in some major cattle-raising areas forced many cow-calf producers to reduce cow herds as forage

supplies declined. Packers were accused of using their market power to lower bids for cattle, thus lowering prices

for producers. Some financially stressed livestock producers pressed for government intervention in cattle markets.

This report presents results of a set of related analyses designed to bring perspective to the allegations and the debate

surrounding cattle cycles, price spreads, market concentration, and the interactions of the three. One objective of

this study is to test the hypothesis that conditions at the turning point in this cattle cycle of the 1990's were no more

severe than those of past cattle cycles in terms of inventory, price, price spread, or net income effects. Results from

tests of this hypothesis indicate that the cycle of the 1970's was actually much worse than this cycle of the 1990's,

with average estimated losses almost twice as much per cow at the low point in the 1970's as in 1996 for cow/calf

producers. Further, while overall price spreads for Choice, yield grade 3 steers are growing wider over time, the

farm-to-wholesale portion is only slightly wider than its narrowest levels since at least the 1970's in nominal dollars.

In real dollars, the spread is at its narrowest levels since before the 1970's. We found no evidence to support the

assertion that increasing slaughter concentration results in lower farm prices. The wholesale-to-retail portion of the

price spread is growing because of packing-industry costs of added packing services and new products.

We begin this report with a background section where we describe and characterize cattle cycles and price spreads.

We also present a historical sketch of increasing packer concentration and provide statistical analysis of the current

cattle cycle. Accompanying economic data are examined to see if there are statistically significant differences from

previous cattle cycles. We find that the most recent cycle's turning point is within the bounds associated with

Economic Research Service/USDA U.S. Beef Industry/TB-1874 1
previous cycle turning points, especially with respect to cow/calf profitability measures. Other cycles offer

examples of more extreme values for selected variables like rates of change in cattle inventories, net returns per cow,

and duration of various phases of the cattle cycle.

The next section of the report returns to price spreads and focuses on the relationships between price spreads and

both the cattle cycle and packer concentration. The section begins with some general observations on the long-term

and short-term behavior of price spreads. The model, more fully described in Appendix B, is an asymmetric model

of three equations to characterize net farm, wholesale, and retail Choice beef price movements. We modified the

model in several ways to examine interactions between prices at different levels in the farm-to-retail chain, as well

as effects of the cattle cycle and packer concentration on price spreads. The asymmetric character of our model

comes from a specification incorporating supply and demand variables that allow differences in the magnitude and

speed at which prices adjust either up or down. Economists have estimated many models where the farm price

moves first and drives the wholesale price, which in turn drives the retail price. Rather than pick the farm level (or

any other) as the primary driver of all prices, our asymmetric model allows the retail, wholesale, or farm price to

drive the others, and allows for complex interactions where all prices adjust simultaneously to movements of the

others. Combining the price spread work with cattle cycle phenomena, we find only small cattle cycle-related

effects on price spreads. We also find that, rather than being too low or wide, prices may not have been as low and

price spreads may not have been as wide as model results indicate they could have been.

The last section of this report describes results from previous studies and the measures we developed and used to

measure packer concentration. Then, we assessed these measures through the 1991-to-present cattle cycle. These

measures did not indicate that increased packer concentration adversely affected cattle markets. This section

includes the results from modifying our asymmetric model to test for packer concentration effects on price spreads.

We end the report with a brief section containing our conclusions and some possible implications.

2 U.S. Beef Industry/TB-1874 Economic Research Service/USDA
Characteristics of Cattle Cycles

Many agricultural commodities exhibit cycles, that is, some quantifiable characteristic, like size, prices, or numbers,

that increases to high levels, reaches a peak, declines to a low level, and then repeats the fluctuating pattern. In

nature, cycles occur because the biological lags encounter biological constraints, such as maximum carrying capacity

of a species' habitat, an increase in a predator species, or other limiting natural phenomena. When the environment

is constant, these life cycles are relatively uniform and vary, depending on the time it takes for individuals within

each species to reach each stage in its life cycle. When taken over whole populations, these life cycles generate

longer population cycles that we recognize by various names, like the cattle cycle. When the environment is not

constant due to drought, disease, or other phenomena, large variations in the regularity of these cycles can occur. 

In agricultural commodities, economic phenomena often provide the limits to commodity population inventories.

Cycles in supplies and prices of agricultural commodities occur because of the aggregate effects of interactions

between the limits imposed by nature and the economic environment and biological lags in life histories of

agricultural commodities. At least in this century, and likely for much longer, cycles in agricultural prices and

profits have occurred while cyclical supplies faced a relatively constant or slowly changing demand. 

The environment in which the cattle cycle operates includes variations in economic activities in addition to the

natural and biological factors that influence the length of cattle cycles. Earlier studies attributed cyclical behavior in

cattle numbers to weather, grain exports, government programs, and other factors in addition to biological lags

(Rucker, Burt, and LaFrance, 1984; Crom, 1981; and Arzac and Wilkinson, 1979). Cyclical fluctuations in U.S.

cattle numbers have been observed since at least 1867.

The cattle cycle lasts about 10 to 12 years (Mundlak and Huang, 1996, p. 858; Trapp, 1986, p. 699; Crom, 1981, 

p. 1; and Franzmann and Walker, 1972, p. 507). It consists of about 6 to 7 years of expanding cattle numbers,

usually followed by 1 to 2 years in which cattle numbers are consolidated, then 3 to 4 years of declining numbers

before the next expansion begins (Crom,1981, p. 1). However, economic factors or natural factors can shorten or

lengthen the whole cycle or any of its phases (fig. 1). Cattle inventory numbers began to climb just after the Civil

War and continued to climb for about 23 years before reaching a peak in 1890. Cattle numbers then declined until

1895, when the next expansion began. The expansion that began in 1896 peaked in 1904, a period of 8 years, then

declined until 1912, a cycle of 16 years from inventory low to low. The cycle of the 1980's lasted 10 years from low

to low, but the expansion phase lasted only 3 years. The periods of time between cyclical peaks or lows in cattle

numbers have shortened over time.

Cattle prices also fluctuate during cattle cycles, but have their own patterns, somewhat but not perfectly correlated

with cattle inventories. Mundlak and Huang (1996) observed that, while cow stocks and slaughter showed distinct

10-year cycles, prices showed a different pattern. One peak for slaughter prices appeared at 11 years and another

much more apparent peak at 22 years (their fig. 1, p. 859). These peaks coincided with cattle cycles in the 1950's

and 1970's, two of the more extreme cattle cycles of recent decades.

Economic Research Service/USDA U.S. Beef Industry/TB-1874 3
4 U.S. Beef Industry/TB-1874 Economic Research Service/USDA

40

60

80

100

120

140

-100

0

100

200

300

1910 20 30 40 50 60 70 80 90 98

Note: Costs exclude capital replacement.

Source: USDA, ERS.

Real dollars (1982-84=100)

Figure 1

Total U.S. cattle and calves, real net returns per cow, and per capita beef and veal consumption, 1910-98

Million head/pounds per capita

Total cattle and calves

(million head)

Per capita beef & veal consumption

(pounds per capita) Real net cash returns

($ per cow)
Cattle Inventory Adjustments

Crom (1981) listed seven factors that can influence the severity of biological and economic effects on a cattle cycle:

weather, feed grain exports, feed prices, consumer income and expenditures, inflation, changing consumer

preference, and the structure of cattle feeding. Inflation had a tremendous effect on many aspects of the economy,

including the cattle sector in the late 1970's and early 1980's. These factors combined in the 1970's in a way that

made that cyclical downturn one of the most extreme since the 1950's (Crom, 1981). Some authors attribute the

shortened expansion phase of the 1980's cycle to sharply decreased demand for beef (Purcell, 1996), while others

give more weight to various supply-side events, including drought, conversion of pasture to cropland, and the effects

of high interest rates on production costs. These same factors, or closely related variations, appear to have deepened

the downturn of the 1990's cattle cycle.

Weather Effects

Weather's main effects are on pasture, forage production, and crop production (Rucker, Burt, and LaFrance, 1984;

Crom, 1981; and Arzac and Wilkinson, 1979). The more widespread the weather effects, the more widespread and

severe the cyclical responses. If snow or rainfall is below average in many areas, available pasture is reduced during

the grazing season, which is generally May through September in the midplains, a longer season farther south, and a

shorter season farther north in the United States. Drought can begin to reduce available pasture anytime during the

grazing season, and when that happens, cattle producers have two options: They can sell cattle to bring their grazing

requirements into line with their available pasture and forage resources, or they can feed supplemental harvested

forages. Either of these alternatives has further negative shortrun effects on the beef cattle operation. Rucker, Burt,

and LaFrance (1984, p. 139) observed that hay production was better than hay prices as an explanatory variable in

their dynamic model of cattle inventories, primarily as a measure of winter feed availability, but also as a measure of

the quality of pasture and range conditions--as affected by weather--and a measure of the intermediate-term

commitment of resources to cattle production. It is interesting that Rucker, Burt, and LaFrance failed to get

significant responses from prices of other feedstuffs (p. 141). Other studies have found significant, though not

always large, effects from grain and feedstuff prices (Arzac and Wilkinson, 1979, pp. 299-302).

If operators choose to remove cattle from pastures, they disrupt their normal timing of cattle sales or cash flow,

selling younger cattle at lighter weights. These premature cattle sales combined with planned sales mean more

cattle going to market than normal. With demand fixed or slowly changing, increased sales volumes exert

downward pressure on prices, which, combined with reduced weights per head sold prematurely, translates into

lower receipts for cattle producers. At this point in a cattle cycle, one often observes a narrowing of the differential

between lighter calves and yearlings and heavier calves and yearlings.

The normal price relationship between lighter calves and yearlings and heavier calves and yearlings is for lighter,

younger calves to sell at higher prices per hundredweight (cwt) than heavier, older calves. This happens because

lighter, younger cattle generally gain more on a given amount of feed than heavier, older cattle. When feed is

relatively cheap compared with cattle prices, producers can make more money by buying lighter calves and feeding

them longer. When feed is relatively expensive, producers make more money by buying heavier cattle that will not

require as long a feeding period. On occasion, the differential has been reversed and the lighter cattle actually sold

at a lower price per cwt than the heavier calves and yearlings.

Drought effects are often manifest in late spring or early summer, as in 1996, but can happen at other times,

depending on pasture and range conditions. For instance, during a winter drought, wheat pasture in the southern

Plains may be affected, causing cattle to move to feedlots during the winter or even late fall at lighter weights than

otherwise. If operators choose to feed supplemental forages or other feeds, their operating costs increase. The

increase comes from increased quantities of feed fed to livestock and often concomitant higher prices for hay, other

forage, grain, and other feedstuffs from increased demand and drought-induced lower supplies.

Economic Research Service/USDA U.S. Beef Industry/TB-1874 5
Grain and Beef Trade Effects

Grain exports and livestock exports and imports are increasingly important factors affecting cyclical variations in

cattle numbers and prices. During cyclical peaks in cattle inventories, even ordinary numbers of cattle imports can

appear extremely burdensome. Arzac and Wilkinson (1979, pp. 304-305) observed transitory and permanent effects

of corn exports on beef and cattle prices. Beef imports have also been demonstrated to have an inverse relationship

with domestic cattle prices. Mundlak and Huang (1996) concluded that "relative sizes between price peak and

supply peak agree with how foreign-trade-oriented [a] country's beef industry is" (p. 868). However, as one might

expect, given the manufacturing quality of beef normally imported, only minor effects are reported for fed beef and

high-quality cattle, most of the effects being on nonfed beef and cow prices (Freebairn and Rausser, 1975, pp. 685-

686).

Cropping and Commodity Program Effects

In addition to effects of the international grain trade, U.S. commodity program policies affect the cattle cycle by

motivating substitutions between cropland use as cropland or as improved pasture. Further, Bobst and Davis (1987)

suggest and provide evidence that cattle numbers fluctuate in an inverse relationship to changes in the number of

harvested crop acres--a finding consistent with Rucker, Burt, and LaFrance (1984). Bobst and Davis estimate that

beef cow numbers would decline by 36,600 head for every 1-million-acre increase in productivity-adjusted harvested

cropland. They attribute "the explanation of why beef cow inventories broke away from their usual cyclical pattern

in the 1980's" to substitution of cropland for pasture and rangeland and the rapid expansion of cropland during this

period (p. 774).

Beef Industry Structure Effects

Changes in the characteristics and technologies of not only cattle feeding, but in cattle production and cattle

slaughter as well, have had an effect on more recent cycles. For instance, increased average cattle weights have

coincided with the building of new slaughter plants able to handle larger carcasses (or vice versa). This is especially

noticeable since the 1970's, which were characterized by relatively stable slaughter weights because older packing

plants were somewhat constrained in the size of carcasses they could process. In the early 1980's, an influx of new

and updated packing plants stimulated a rather large increase in average cattle weights. Increased weights have

caused the last two cattle inventory cycles to fluctuate around what appears to be a slight downward trend. This is a

reversal of the uptrend of pre-1970s cycles, even though beef production has generally trended upward for several

decades.

Demand Effects

Finally, consumer income and expenditures and consumer preferences for competing meats, or for no meats at all,

are demand factors having increased effects on beef's market share of the consumers' meat expenditures and cattle

cycles. Per-capita beef consumption (pounds of carcass beef per person) increased slightly after World War II, and

has been relatively constant for most of the last 40 years. However, the large cyclical buildup in cattle inventories

during the 1970's caused a temporary upward jump in per-capita beef consumption measures. So, looking at data for

the last 25 or 30 years leaves one with the impression that per-capita consumption is declining. In addition, since

1986, more fat is trimmed off primal cuts of beef than before, shifting quantity measures slightly lower and

contributing to the impression that beef consumption is declining. Even though per-capita beef consumption has

remained relatively constant, beef's market share has declined since before the 1970's as per-capita poultry

consumption has increased.

6 U.S. Beef Industry/TB-1874 Economic Research Service/USDA
Price Spreads

Farm-to-wholesale and wholesale-to-retail beef price spreads often become an issue when producer prices are low or

falling and retail food prices are high or rapidly rising. Several congressional hearings, task forces, and

commissions have addressed price spreads since the 1970's, alternately concerned with low or falling producer prices

or high or rising consumer prices.

Price spreads for beef have been computed since the early 1920's, when Congress asked the U.S. Department of

Agriculture (USDA) to undertake special studies of marketing margins for livestock. In 1934, at the request of

livestock producers, USDA developed a statistical series to measure changes in marketing costs for a number of

agricultural commodities. A preliminary report was published in 1935 and a report was issued in 1936 on price

spreads for 58 food items, including beef. Price spreads for beef have been published on a regular basis since 1942,

initially in Marketing and Transportation Situation and since 1975 in Agricultural Outlook and in various Situation

and Outlook reports published by USDA's Economic Research Service (ERS).

The Retail Choice Beef price series used in price spreads is a weighted average of prices of Choice beef cuts and

hamburger published by the Bureau of Labor Statistics (BLS). The weights chosen approximate the quantity of each

type of cut that can be taken from a Choice beef carcass. This weighting is designed to maintain a constant quantity

and quality of product at the farm, wholesale, and retail levels. Although the beef from a Choice carcass is assumed

to be sold somewhere, whether in restaurants, grocery stores, or export markets, only meat sold in grocery stores is

represented in the price spread series.

There are several ways to weight different cuts to arrive at a value for the beef from one carcass, depending on the

specific nomenclature of the individual cuts. Some experimental work in ERS using principal component analysis

has shown that over 95 percent of the variation in BLS cut data is captured in the first principal component (Hahn,

internal analysis, 1996).1 This indicates that reweighting cuts would result in a slightly different weighted average

carcass price, but the resulting price series would likely be highly correlated with the current series. In econometric

work, either series would give similar results.

ERS also publishes an All Fresh Beef price series, which includes prices for non-Choice cuts and additional

hamburger from cows and imported sources. This price also incorporates how much of each type of beef is

produced, but not how much of each type is sold from retail counters. No data are available on a frequent and

timely basis that would reveal the distribution of cuts actually sold. Because it includes lower quality beef, the fresh

beef price is lower than the Choice Beef Retail price. However, the Choice price, the All Fresh beef price, and

BLS's Beef and Veal Price Index are all highly correlated (fig. 2).

Economic Research Service/USDA U.S. Beef Industry/TB-1874 7

_________________________________________

1Principal component analysis is a statistical technique that separates linear combinations of factors into groups that may be

considered as variables or proxies for variables in further analyses.
8 U.S. Beef Industry/TB-1874 Economic Research Service/USDA

Price and Price Spread Data Issues

Price and price spread data must be interpreted with care. Misinterpretation of these data and indicators can lead

to unwarranted conclusions about prices and price spreads. Here, we discuss three issues especially relevant to

this report: (1) the interpretation of price spread data, (2) the methods used in calculating the Choice Beef Retail

Price, and (3) the fact that special or sale prices are given no more weight than standard prices by the Bureau of

Labor Statistics (BLS).

Interpreting price spread data. Changes in price spread data include changes in cost efficiency for slaughtering

and processing Choice beef. Over time, the Choice beef price spread measures all the costs and changes in those

costs (and profit or losses) of converting a live animal and transporting Choice beef to the grocery store. Even

so, it does not provide any direct indication of whether observed price changes are cost-justified. Neither does it

separately measure costs or profits for any one type of firm or industry group. 

Calculating the Choice Beef Retail Price. Some observers, especially retailers, have criticized the retail price

used in price spreads because they feel it does not reflect the distribution of beef cuts actually sold out of meat cases

in grocery stores. First, the Choice Beef Retail Price reflects only the price of Choice beef, while many stores also

sell other grades of beef. Many critics also feel that, even within the Choice beef grade, fewer steaks and roasts are

sold in retail foodstores and more lower value hamburger is sold than is reflected in the Choice Beef Retail price.

Weighting of beef retail prices. Special or sale prices are given no more weight than standard prices in

calculating the Choice Beef Retail Price (Tomek, 1996; and Duewer, 1969). BLS collects whatever price is in

effect, regular or special, at the time data are collected. BLS has no data to support and makes no adjustment for

possible increased volume of sale-priced items. No indication is given in the published BLS data series for

frequency or incidence of special prices. This BLS methodology is used for all retail price series collected by

BLS and is not unique to meats.

Some critics argue that a retail price more directly reflective of special prices would not only be lower than the

ERS price but would more closely track farm and wholesale prices. Unfortunately, no data are available to test that

hypothesis. Jan-84 Jan-85

Jan-86Jan-87Jan-88Jan-89Jan-90

Jan-91

Jan-92Jan-93Jan-94Jan-95Jan-96Jan-97

200

220

240

260

280

300

320

340

80

100

120

140

160

Figure 2

Retail beef price measures, January 1984-April 1997

Cents per retail pound Index (1982-84=100)

ERS Choice retail

(left scale)

NCBA-5 cut average

(left scale)

ERS all fresh beef

(left scale)

BLS beef & veal CPI

(right scale)

Note: CPI is Consumer Price Index, NCBA is National Cattlemens Beef Association, and BLS is Bureau of Labor Statistics.

Source: USDA, ERS.
Beefpacker Concentration

Market power in the meatpacking industry has been a concern among livestock producers since at least the 1890's.2

In the early 1900's, the packing industry was dominated (perhaps 50 to 75 percent, depending on market definition)

by five firms: Armour, Cudahy, Morris, Swift, and Wilson (the Big Five). Several species of livestock were

typically slaughtered within a multistory plant, which was generally located at or near a terminal livestock market.

Packer branch houses in population centers provided a local sales outlet to then small retailers.

The Packer Consent Decree of 1920 resulted from a Federal Trade Commission investigation of alleged anticompetitive practices on the part of the Big Five meatpackers. Allegations included the use of collusive devices

such as market pooling. The packers entered into a consent decree wherein they agreed: (1) to divest themselves of

public stockyards, stockyard railroads, market newspapers, and cold storage facilities, (2) not to engage in retailing

of meat and other commodities, and (3) to submit to perpetual jurisdiction of the U.S. District Court. Because of

mergers, reorganizations, and later court actions, the decree is without practical effect today. But it does provide the

background for producers suspicion of packers and for their expectations of government involvement.

Following the decree, the proportion of slaughter accounted for by the largest four firms declined (Morris merged

with Armour), perhaps partly because of the consent decree, but also because of improved transportation, roads, and

communication; increased use of Federal grades for beef; movement of livestock production farther west; and

growth of retail chains. Following World War II, these forces accelerated. New independent single-species, singlestory slaughter plants were built near production areas and away from terminal markets. Along with a decline in the

top four firms' portion of total cattle slaughtered by all firms (the four-firm concentration ratio or CR-4 (Connor,

Rogers, Marion, and Mueller, 1985, p. 70)), the market shares of the largest four firms rapidly declined, to 30

percent by 1956.

In the 1960's, the boxed beef revolution began. Pork packers had for decades cut hog carcasses into smaller cuts for

further processing into bacon, ham, and sausage. Although not the inventor, Iowa Beef (later IBP) was a pioneering

firm in effectively making the change to further processing (cutting beef carcasses into primal or subprimal cuts,

packing cuts in plastic wrap, and shipping boxes of individual cuts). This process fostered incorporating assembly

line processes into meat cutting, reduced shipping weight, improved handling ease and shelf life, and offered a

broader range of alternatives to purchasers. Other packers quickly followed, especially the independents.

In the early 1970's, cattle slaughter increased to record levels, accommodating expansion by the aggressive

independents, and the CR-4 remained below 30 for steer and heifer slaughter. But, in the late 1970's, after the

dramatic downward shift in the previous general uptrend in cattle inventories, the number of cattle slaughtered

dropped dramatically, returning to levels more in line with the general up-trend in total beef production and

precipitating a shake-out among beefpackers. Hog slaughter continued to increase and the old line packers gave up

much of their beef slaughter share to the new breed of packers and concentrated on pork slaughter. By 1980, IBP,

MBPXL (formerly Missouri Beef and later Excel), Dubuque, and Land o'Lakes were the four largest steer and heifer

slaughterers, supplanting the original Big Four, and the CR-4 for steer and heifer slaughter was about 36 percent.

As the industry evolved, steer and heifer slaughter and cow and bull slaughter were largely separate in plants and

firms, partly because culled beef and dairy cows continued to be sold in relatively small lots and remained more

geographically dispersed. As feed grain production increased in the Great Plains with the development of irrigation,

steer and heifer marketings became concentrated among very large feedlots in four or five States after the 1960's.

Economic Research Service/USDA U.S. Beef Industry/TB-1874 9

_________________________________________________________________________________________________

2Meatpacking includes all aspects of slaughter, fabrication, and other delineations of the industry transforming live animals

into meat cuts.
10 U.S. Beef Industry/TB-1874 Economic Research Service/USDA

The 1980's brought the term "merger mania" to the beefpacking industry as many of the large firms combined,

acquired existing plants, and built new plants. Cargill Inc. purchased MBPXL in 1979 and renamed it Excel Corp.

In 1983, Cargill (Excel) purchased a plant from Dugdale and acquired three Spencer Beef plants owned by Land

o'Lakes. The purchase was not challenged by the Department of Justice, but Monfort of Colorado challenged the

acquisition in the District Court in Denver. Ultimately, the sale of Land o'Lakes plants to Cargill went through.

After losing the case regarding the Land o'Lakes plants, Monfort became a part of ConAgra, which also acquired

Swift Independent.

Today IBP, ConAgra (Monfort), and Cargill (Excel) dominate in numbers of steers and heifers slaughtered and

fabricated and pork slaughter and processing, with National Beef a distant fourth (Cattle Buyers Weekly, Sept. 23,

1996. p. 2). ConAgra is also an important poultry processor. Cargill recently sold some integrated poultry

operations to Tyson and purchased some of Tyson's pork slaughter plants. The large firms are also rapidly gaining in

cow slaughter, including instances where large steer and heifer firms have acquired cow slaughter plants.

Throughout recent decades, the number of meatpacking firms and plants has declined, with smaller plants closing

and large plants accounting for more slaughter. In 1974, 1,350 federally inspected plants slaughtered cattle (plus a

large number of plants not federally inspected) in the United States; of that number, 850 plants were large enough to

report to GIPSA (Grain Inspection Packers and Stockyards Administration, formerly the Packers and Stockyards

Administration).3 By 1997, the number of federally inspected plants slaughtering cattle had dropped to 812 and the

number reporting to GIPSA had dropped to 274 (USDA, 1999). 

In 1980, 626 plants in 561 firms reported steer and heifer slaughter to GIPSA. In 1996, 211 plants in 174 firms

reported. The only size group of steer and heifer plants to increase in number after 1985 is the 1,000,000-head-orlarger category, which increased from 5 in 1986 to 14 in 1996. The increase came primarily from existing plant

expansion, not from newly built plants. The 22 plants slaughtering more than 500,000 head per year accounted for

about 22.6 million of the 28.6 million steers and heifers slaughtered in 1996.

________________________________________________________________________________________________

3Plants reporting to GIPSA include federally and nonfederally inspected establishments. Firms purchasing less than 2,000

head of all livestock, or less than 1,000 head of cattle prior to 1977, or less than $500,000 worth of all livestock since 1977 need

not report.
Economic Research Service/USDA U.S. Beef Industry/TB-1874 11

Comparing the 1990's With

Past Cycles of Cattle Inventories

The previous section summarized a great deal of the mechanics and history of cattle cycles, beef production, beef

price spreads, and meatpacker consolidation. This information is helpful in seeing the paths taken by the beef

industry to arrive where it is today. We now turn to the more specific objectives of this report, to characterize cattle

cycles and price movements to determine whether the cycle of the 1990's was atypically worse than the cattle cycles

preceding it in terms of cattle numbers, changes in cattle numbers, prices, price spreads, or changes in prices or price

spreads. Also, we determine whether the cattle cycle of the 1990's, in fact, exhibits signs that various segments of

the industry are taking unfair advantage of other segments. Specifically, is the cycle of the 1990's characterized by

unexpectedly lower returns to cow/calf producers and cattle feeders because of the high level of concentration and

continuing consolidation within the beefpacking sector? And, are price spreads an indicator of this alleged exercise

of market power?

First, we need to demonstrate whether the cycle of the 1990's exhibits more extreme high or low levels of relevant

economic variables than expected, given the behavior of these variables during previous cycles. A review of the

literature does not yield many studies that compare specific cattle cycles with other cycles. Crom's study (1981),

following the severe cattle cycle of the 1970's, is one of the few studies that characterizes cattle cycles in a statistical

sense. Crom identified price, expenditure, and output variables affecting or affected by the amplitude of cattle

inventory cycles and estimated the magnitude of their effects. He observed considerable variation in steer prices,

retail prices, consumer expenditures per person, and the value of the January 1 cattle inventories. Coefficients of

variation ranged from 9 percent (retail prices) to 25 percent (value of January 1 cattle inventory in real dollars), up

to 54 percent (consumer expenditures in nominal dollars) for the period 1950-79. 

We extended Crom's (1981) general methodology by estimating and comparing variances, coefficients of variation,

and means, and by performing other general comparative tests on selected variables. In addition to extending

Crom's methodology, we investigated the behavior of cattle and calf inventories and prices by examining correlation

coefficients between some more important cattle sector variables. 

A question arose during this research about which was more important in affecting cattle inventory behavior, lagged

variables as the basis for producers' expectations or future values of variables as expectations. To investigate this

question about the role of expectations in inventory dynamics, we included both leading and lagged values of

variables in a regression model from which we briefly summarize the results (see box). 

Next, we wanted to know if price spreads exhibited unusual behavior during the cycle of the 1990's. Finally, we

wanted to know if packer concentration allowed packers to significantly influence cattle cycles. To accomplish these

latter objectives, we specified an asymmetric model of farm, wholesale, and retail prices to examine price spreads.

With this asymmetric model, we examined price spreads, the relationship between price spreads and the cattle cycle,

and the relationship between price spreads and packer concentration. We also used the asymmetric model to

simulate expected price movements to see what prices would have looked like over the cattle cycle under a set of

restricted conditions.

Data

Data for this study of cattle inventory cycles are annual data and are available back to 1867 for some variables.

Almost all variables are available back to 1935, and some are available back to about 1913. In some cases,

backcasts were made to extend a data series backward in time, for example, with costs and gross value of production

before 1972 (app. table 1). Variables used in the analysis included cattle and hog inventories, farm level prices,

cow/calf production costs and gross value of production, value per head, corn prices, various productivity indices,

deflators, and other variables.
The prices used in our asymmetric model to explain net-farm, wholesale, and Choice beef retail price movements are

from the series ERS uses to calculate price spreads. The Choice beef price spread is a measure of the total difference between the reported price received by producers for a Choice, yield grade 3 live animal (less noncarcass

byproducts) and the weighted average price paid for the fresh Choice beef cuts from the animal in retail foodstores.

A wholesale boxed-beef value is also calculated such that the total farm-to-retail spread is divided into two component spreads, the farm-to-wholesale spread and the wholesale-to-retail spread. For part of this analysis, the data

were transformed so that farm and wholesale prices are expressed in retail weight equivalents. This transformation

allowed us to calculate a price spread by subtracting the price at one level from the price at another level. 

12 U.S. Beef Industry/TB-1874 Economic Research Service/USDA

The Role of Expectations as 

Drivers of the Cattle Cycle

Producers' expectations about what will happen to them in the context of the cattle industry is one important

source of factors affecting aggregate cattle inventory changes. These expectations combined with the biological

lags inherent in adjusting cattle inventories have implications for the cattle cycle. The literature is divided on the

role that expectations play in the expansion and contraction of beef cattle inventories. There is as yet no clear

consensus in the literature about what decisionmakers consider, that is, what is in their information sets and when

they make their herd adjustment decisions. Much literature regarding cattle cycles prior to the 1980's cycle

rationalized that higher current and past cattle and calf prices motivated heifer retention for herd building (for

example, Jarvis, 1974; Arzac and Wilkinson, 1979, p. 307; and Nelson, 1984). That is, these authors rationalized

that high prices observed in the current period would motivate heifer retention and inventory buildup. However,

other authors, especially since the 1970's cycle, suggest a somewhat reverse mode of expectation formation by

looking into the future (Rucker, Burt, and LaFrance, 1984; Trapp, 1986; Rosen, Murphy, and Schienkman, 1994).

Trapp (1986, p. 702), in his simulation of optimal herd dynamics, emphasized the role of expectations when he

suggested that, in practice, the optimal "[c]ulling and replacement strategies must anticipate the feeder cattle price

cycle by some four to six years." Indeed, Trapp (1986, p. 700) observed that "the most rapid rebuilding phase for

the herd occurs when profit per head is lowest or even negative." He found negative returns for the first 3 years

of his simulated feeder cattle price cycle, during the expansion phase of the simulated cattle cycle, and that "[t]he

optimal herd size exhibits cyclical patterns that peak and bottom three years ahead of the feeder steer price cycle"

(p. 700). 

Using leading values of variables as expectations variables is related to rational expectations and is referred to as

"implicit expectations" (Mills, 1957, and Lovell, 1986). According to Lovell (1986), Mills' (1957) implicit

expectations differ from rational expectations through the assumption that the prediction error term is assumed

uncorrelated with the actual realization, whereas Muth's (1961) rational expectations assumes that the error term

is uncorrelated with the predicted value.

We found no clear evidence in the literature suggesting that leading variables are better at describing inventory

behavior than lagged variables or vice versa. In related work only briefly reported here, we examined directions

of causality, using correlation analysis, as above, and regression techniques. We found that leading variables

have more explanatory power than lagged variables, but lagged variables appear to have larger effects on cattle

and calf inventories. Generally, the leading variables were more significantly different from zero than were the

lagged variables, but the lagged variables had slightly larger parameter esimates. The coefficient for current

cattle prices per hundredweight was significant and negatively related to cattle and calf inventories, indicating

that high prices in the current period may result in sales of cattle and low prices may result in cattle being held

off markets. This result supports the idea that producers are motivated to sell cattle during periods of high prices

rather than retaining them for purposes of herd expansion. Current and expected corn prices had a generally

positive effect on current cattle and calf inventories, while lagged prices had no significant effect in our simple

model. Inventories are often thought to depend on the number of calves born in previous periods, so it was

surprising that the size-of-calf crop variables had no effect on cattle and calf inventories. 
Economic Research Service/USDA U.S. Beef Industry/TB-1874 13

Statistical Comparisons of Cycles

Our first formal hypothesis was that the cycle of the 1990's was no worse than any other previous cycle.4 We set up

a hypothesis of "no worse than" (a null hypothesis) because it is easier to reject something (like "no worse than")

than it is to try and prove the opposite ("is worse than") is true. That is, if we can demonstrate evidence for some

difference, then we can reject our null hypothesis of "no difference" without necessarily knowing exactly what

makes the difference. More specifically, if we can demonstrate some evidence that the 1990's cycle is not the same

as other cycles, then we can reject our null hypothesis. If we can reject our null hypothesis, we can say there is

evidence to support the contention that the cycle of the 1990's is somehow different than previous cycles. In some

cases, we examined the entire data series, while in other instances, we examined each cattle cycle or specific

statistics for each cattle cycle taken across cattle cycles. For instance, we examined the mean of real net returns

above cash costs over the period 1913-90 and the mean of individual cycle means.5

Means, Variances, and Coefficients of Variation

The first question we asked was, how did cow/calf producers fare during the cycle of the 1990's and during previous

cycles? The average (mean) per-cow real net returns above cash costs for the 1913-1990 series was $56.18 in 1982-

84 dollars. Looking at cycle statistics, the mean real net returns above cash costs for the eight cattle cycles before

the 1990 cycle was $52.66 in 1982-84 dollars. The mean for the 1990's cattle cycle, through 1997, was $17.46, well

within a 95-percent confidence interval (-$14.40, $119.70) based on the series variance. Variability, measured by

the coefficient of variation (ratio of the standard deviation to the mean of a variable) of real net returns for the first 6

years of the current cattle cycle is 2.587. The series coefficient of variation for before 1990 is 1.012. The

coefficient of variation for means of real net returns for the last eight cattle cycles before 1990 is 0.64.

As a further test, we wanted to know if there was a difference in how real net receipts above cash costs varied over

cycles before 1968, the last year of the cycles preceding the 1970's, and cycles from 1968 to the present. Net returns

above cash costs and their cyclical averages have trended downward, which is later discussed. However, a simple F

test (ratio of variances) of variances of real net receipts above cash costs between the three consecutive cattle cycles

ending in 1967 (variance of 194.12) and the three consecutive cycles beginning in 1968 (variance of 198.05) did not

show a significant difference in variability between the two periods.

Cattle price volatility is another way the situation could be worse during the cycle of the 1990's. However, real

(1982-84 dollar) cattle prices appear less volatile relative to their means than real net returns. The mean real cattle

price per cwt for the period 1935-96 was $66.55. A 95-percent confidence interval around the mean of $66.55

encompasses an interval from a high of $97.50 to a low of $35.61. No annual real average price per cwt was below 

lower bound of the 95-percent confidence interval, although there were two instances where high prices were higher

than the upper bound of the 95-percent confidence interval. The lowest real price was $37.41 in 1996. Prices in

1950 and 1951 (the year of the highest average real price, $114.19) were higher than the upper bound of a 95-

percent confidence interval. The variance for prices over the same period was 239.4049 (standard deviation the

=15.4727), yielding a coefficient of variation of 0.2325.

All actual values of per-capita pounds of beef and veal consumption on a carcass-weight basis were within the 95-

percent confidence interval bounds. The highest per-capita beef and veal consumption was 131.5 pounds in 1976,

the lowest, 61.5 pounds in 1943.6 Per-capita consumption showed relatively low volatility, with a coefficient of

variation of 0.21.

Relative Changes in Selected Variables

________________________________________________________________________________________________

4Worse in this context means adversely affecting cattle producers, such as returns below some lower bound of a

confidence interval, or inventories above some upper bound.

5We will distinguish between the two sets of statistics by calling the statistic for an entire series the series statistic and the

statistic across cycle subseries statistics the cycle statistic.

6Per capita beef and veal consumption was lower in 1932, at 53.3 pounds, but outside the data range of this specific analysis.
14 U.S. Beef Industry/TB-1874 Economic Research Service/USDA

from Previous Cattle Cycles

Real net receipts above cash costs during 1996 (the second lowest real net receipts above cash costs since 1913)

were -$48, just over half the -$79 loss observed in 1975, but within a 95-percent confidence interval of annual real

net receipts. Real net receipts above cash costs have trended downward since 1913 (simple regression slope of 

-1.17613, t-statistic of -5.3), as has the average per cycle of net receipts above cash costs over the last eight cattle

cycles (slope = -10.7891, t-statistic = -3.16). 

The longest string of consecutive years with negative real net receipts above cash costs occurred from 1981 through

1986, exceeding the series of losses from 1974 through 1977 and 1995 through 1996. April 1997 calf prices were

$91 per cwt, roughly 53 percent higher than April 1996's $59.30 (USDA, Livestock, Dairy and Poultry Situation and

Outlook, April 16, 1997). Cow prices in April 1997 were, however, $39 per cwt, only 13 percent over April 1996's

$34.44. Percentage changes in cattle and calf prices from Apri1 1996 to April 1997 for other weights and classes of

cattle were between these extremes, suggesting that nominal and real net receipts above cash costs for 1997 will be

positive.

Several factors help account for differences in the severity of the last three cattle cycles. President Nixon's beef

price freezes, oil price shocks, drought, and extremely high grain prices compounded the adjustment in cattle

numbers in the 1970's. The peak in cattle inventories during the cattle cycle of the 1970's was also the historical

peak in cattle inventory numbers. Both subsequent cattle cycles peaked at inventories substantially below the 1975

peak (fig. 1). In fact, the inventory peaks for the two cattle cycles since the 1970's have been successively lower.

However, these successive declines in cattle inventory numbers have been at least partially offset by a shift in beef

cattle production technology that increased average pounds of beef produced per cow per year. Beef produced per

cow has increased more than 100 pounds per cow since the first half of the cycle in the 1980's. Total annual beef

production has remained over 25 billion pounds per year since 1994. It peaked at 25.7 billion pounds in 1998, the

same as in 1976. The ability to produce more beef with fewer cows precipitated adjustments in cattle numbers

during the previous and current cattle cycles. This is because fewer cattle are needed to produce the same amount of

beef. Drought also occurred in the 1980's and in 1996. The improved technology combined with the 1996

combination of drought and high grain prices compounded the adjustment in cattle inventories during the mid1990's.

At the same time that cattle cycles seem to be generating years of greater losses in real dollars, annualized rates of

increase in cattle numbers from the beginning of each cattle cycle expansion to the cyclical peak in cattle numbers

appear to be declining. During earlier cycles, cattle numbers increased at an average annual rate of 4 percent or

more. During the last two cycles, expansions occurred at a 1.2-percent annual rate. This decline in the rates of

increase in cattle numbers suggests that cattle producers may be becoming more astute at adjusting herds in the face

of the cattle cycle. Information, including price and demand information, is more widely and readily available and

perhaps is being used in herd management decisions. 

Most of the last eight cattle cycle expansions have taken 6 to 8 years from low to peak. The shortest expansion,

taking only 3 years from low to peak, was during the 1980-90 cycle. In addition, cattle numbers have increased

between 17 and 30 percent from each cycle's low in numbers to its peak, with only two exceptions: the cycle of

1980-90 and the current cycle. The percentage increase from 1980-82 was just under 4 percent, and for the current

cycle that began in 1991 was just under 8 percent. While these rates of buildup look somewhat erratic, a glance at

total beef production shows an increase of almost 14 percent from low to peak for the 1980's cycle and just over 12

percent for the 1990's cycle. Also, beef production peaked at 24.2 billion pounds during the 1980's cycle, in line

with trends in beef production for previous cycles and the 1990's, but excluding the huge jump in beef production

during the cycle of the 1970's.
Many papers have been written suggesting ways to overcome or take advantage of cycles in livestock production,

but the beef cattle cycle persists. There are at least two reasons why beef cattle cycles may persist: One, the very

seasonal production technology may force producers to postpone adjustments in breeding herds. Two, other than

military meat procurement and school lunch program purchases, there are no government programs, support prices,

or other policy interferences with the beef cattle markets. Therefore, there is no way to assess prices in future

periods with any degree of certainty as there was with target prices and other fiat prices that, in the past, provided

price floors for grains. It is interesting to observe that dairy cow numbers are not behaving cyclically, having ceased

robust cyclical behavior during the 1970's.

Correlations

Correlations tell us how closely variables move together. Correlations can suggest causal relationships. In our

study, correlation coefficients between cattle cycle variables yielded some interesting results (table 1), not all of

which were as one would expect given price-quantity relationships suggested by economic theory. The correlations

observed with the highest t-values were between per-capita beef and veal consumption and cattle and calf numbers,

between per-capita red meat consumption and cattle and calf numbers, and between changes in per-capita red meat

consumption and cattle and calf numbers.

All correlations between various measures of final products could be expected to vary from a strict one-to-one

accounting correspondence because of substitution between species meats and because of some slippage due to

imports and exports. At 0.95 (t value = 24.08 (Ezekiel, 1930, p. 256)), the positive relationship between per-capita

beef and veal consumption and cattle and calf numbers was the highest and most statistically significant observed

correlation and was close to the one-to-one relationship one might expect. There is not a lot of leeway in capacity to

store beef once cattle reach slaughter weight or after they have been slaughtered, even when frozen. The second

highest correlation, also related to per-capita consumption was the 0.95 (t value = 23.16) for per-capita beef and veal

consumption and total per-capita red meat consumption. Following closely was the 0.93 (t value = 19.17),

measuring a direct relationship between total cattle and calf inventories and total per-capita red meat consumption. 

Given constant costs or costs that steadily increase or decrease, one could expect close to a one-to-one relationship

between returns and output prices. Real returns per cow and cattle prices in real dollars per hundredweight were

correlated at 0.93 (t value = 18.85). Real receipts per cow were also correlated with real dollars per hundredweight,

0.81 (t value =10.47). This less than perfect correlation is due to fluctuations in feed prices, oil prices, interest rates,

and prices of other inputs. The correlation between the change in net returns above cash costs and the change in

cattle numbers was -0.52 (t value = -4.67). This negative correlation between the change in net returns above cash

costs and the change in cattle numbers would seem to support Trapp's (1986) observation that simulated cow herd

expansion optimally occurs during periods of negative returns.

Economic theory leads one to expect an inverse relationship between quantities and prices, but modified somewhat

by quantities and prices of substitutes and complements. The correlation between changes in cattle and calf numbers

and changes in cattle price per cwt was at -0.45 (t value = -3.89), again the expected inverse relationship, but not as

large as expected, given the changes in farm level income observed in the data and given the causality arguments

encountered in some of the earlier literature and the popular press.

Economic Research Service/USDA U.S. Beef Industry/TB-1874 15
16 U.S. Beef Industry/TB-1874 Economic Research Service/USDA

Table 1--Correlation coefficients and t-statistics for cattle cycle variables, 1935-96

All hogs Per-capita Costs Total cattle Change in Change in Change in

and pigs beef and per cow and calves cattle and net income corn price

and veal calf above cash from

consumption inventory costs from previous

from previous year

previous year

Variable year

Per-capita beef and -0.0342

veal consumption (-.26)

Costs per cow .2615 0.1483

(2.08) (1.15)

Total cattle and .1061 .9527 0.3031

calves (.82) (24.1) (2.44)

Change in cattle and .3623 -.2311 .0096 -0.0460

calf inventory from (2.99) (-1.82) (.07) (-.35)

previous year

Change in net income -.1216 .0047 -.0872 -.0982 -0.5200

above cash costs from (-.94) (.04) (-.67) (-.76) (-4.67)

previous year

Change in corn price .1883 -.0502 -.0911 -.0312 .1879 0.1333

from previous year (1.47) (-.39) (-.70) (-.24) (1.47) (1.03)

Change in per-capita -.0748 .0414 -.0504 .0517 .4229 -.4480 0.1771

beef and veal consump- (-.58) (.32) (-.39) (.40) (3.58) (-3.84) (1.38)

tion from previous year

Change in cattle price -.0222 -.1439 -.0135 -.2168 -.4510 .7878 .0944

per hundredweight from (-.17) (-1.12) (-.10) (-1.71) (-3.89) (9.82) (.73)

previous year

Total red meat per-capita .2187 .9492 .2015 .9283 -.1130 -.0480 -.0002

consumption (1.74) (23.16) (1.58) (19.17) (-.87) (-.37) (-.001)

Net returns above cash .3288 -.5398 .2236 -.5276 -.0160 .3991 .1813

costs per cow (2.67) (-4.93) (1.76) (-4.77) (-.12) (3.34) (1.42)

Corn price .1290 -.5199 .4094 -.4093 .2438 -.0220 .3072

(1.00) (-4.67) (3.45) (-3.45) (1.93) (-.17) (2.48)

Per-capita pork and .7614 -.3413 .1307 -.2536 .3961 -.1570 .1591

lamb consumption (9.02) (-2.79) (1.01) (-2.01) (3.31) (-1.22) (1.24)

Year .0441 .7482 -.0257 .7194 -.2250 -.0650 -.0360

(.34) (8.66) (-.20) (7.95) (-1.78) (-.50) (-.27)

Real gross receipts .3773 -.3957 .5749 -.3256 -.0100 .3013 .1170

per cow (3.13) (-3.31) (5.40) (-2.65) (-.07) (2.43) (.90)

Real price of cattle per .3180 -.1637 .7044 -.0525 .1849 .1457 .0307

hundredweight (2.58) (-1.27) (7.62) (-.40) (1.44) (1.13) (.24)

Continued-- 
Economic Research Service/USDA U.S. Beef Industry/TB-1874 17

Table 1--Correlation coefficients and t-statistics for cattle cycle variables, 1935-96--Continued

Change in Change Total Real net Real Per-capita Year Real

per-capita in cattle per-capita returns price of pork receipts

beef and price red meat per cow corn consumption per cow

veal per cwt consumption

consumption

from

previous

year

-0.6620

(-6.79)

.0637 -0.0540

(.49) (-1.20)

-.3560 .4922 -0.4250

(-2.93) (4.34) (-3.61)

.2411 .1018 -.4380 -0.4628

(1.91) (.79) (-3.75) (4.01)

.0587 -.0020 -.0280 -.4439 0.3420

(.45) (-.02) (-.22) (3.80) (2.80)

-.1570 -.1540 .6788 -.4390 -.7590 -0.3490

(-1.22) (-1.20) (7.10) (-3.75) (-8.95) (-2.86)

-.3190 4080 -.2790 .9261 .5470 .4232 -0.3784

(-2.58) (3.43) (-2.23) (18.85) (5.02) (3.59) (-3.14)

-.2330 .3239 -.0540 .6537 .5231 .3573 -.3675 0.8064

(-1.84) (2.63) (-.42) (6.33) (4.71) (2.94) (-3.04) (10.47)
Price Spreads and the Cattle Cycle

Producers note that the farm value share of the Choice beef retail price declined from nearly 70 percent in the 1970's

to below 50 percent in 1996 (figs. 3 and 4). During the same period, the nominal farm-to-retail price spread

widened from less than 40 cents per pound to over a $1.40 per pound (figs. 3 and 5). Many producers believe these

figures indicate improper pricing behavior by marketing firms, especially beefpackers. However, previous studies

have found conflicting evidence to support such contentions (see next chapter, "Concentration Measures for the Beef

Packing Industry").

Long-Term Fluctuations

Looking at nominal price spread data versus deflated price spread data gives a different perspective on long-term

price spread patterns. On a nominal basis, wholesale-to-retail and farm-to-wholesale price spreads for Choice beef

have increased dramatically since the 1970's (figs. 3 and 5). 

But when adjusted for inflation, the wholesale-to-retail spread has fluctuated within a relatively constant band, while

the farm-to-wholesale spread has declined substantially (figs. 5 and 6). The services provided by beefpacking firms

(processing and packaging) have increased over time, and the cost of those services has risen. 

The index of food marketing costs, a measure of changes in the costs of marketing inputs such as labor, packaging,

transportation, energy, and other inputs, increased four-fold from 1968 to 1994, from 103.5 to 435.0 (Elitzak, 1998).

The farm value share is low, partly because marketing cost changes have paralleled inflation while cattle price

changes have not. Decreasing farm value share is a characteristic of most agricultural commodities and signifies to

some extent improved technological efficiencies. The farm value share for all U.S. domestically produced farm

foods was 40 percent in 1952, 33 percent in 1960, 32 percent in 1970, 31 percent in 1980, 24 percent in 1990, and

21 percent in 1997 (http://www.econ.ag.gov/briefing/foodmark/cost/data/bill/value.htm).

Short-Term Fluctuations

Short-term fluctuations in beef price spreads stem largely from the lag observed between price changes at the farm

and retail levels. Retail prices are more rigid than commodity prices at least partly because retailers set prices for

advertising purposes weeks ahead, and because they believe consumers prefer more stable prices.

ERS research shows that price adjustments at farm and wholesale levels are nearly concurrent using monthly data

(fig. 7). The retail price follows price changes at the farm and wholesale levels with a lag distributed over almost a

year. Retail price adjustments are asymmetric (upward movements in farm prices are followed about 24 percent

more quickly at retail than are downward price movements). Retailers possibly expect that downward movements

are likely to be temporary and wish to avoid marking prices down then back up again. This asymmetry provides one

justification for our asymmetric model of price changes described in a later section and in Appendix B.

For example, from April to May of 1994, the net farm value of Choice beef declined 15 cents, the wholesale value

dropped about 9 cents, and the retail price went up about 1 cent. In May and June, the net farm value dropped

another 12 cents, the wholesale value again fell 9 cents, and the retail price moved down about 5 cents. The retail

price continued to decline through August, even though farm and wholesale prices increased in July and August. As

a result of the lags in price changes at the retail level, the spread tends to widen in the short term when prices are

falling, and narrow, but to a lesser extent, when prices are rising.

18 U.S. Beef Industry/TB-1874 Economic Research Service/USDA
Economic Research Service/USDA U.S. Beef Industry/TB-1874 19 Jan-70 Jan-72 Jan-74 Jan-76 Jan-78 Jan-80 Jan-82 Jan-84 Jan-86 Jan-88 Jan-90 Jan-92 Jan-94 Jan-96 Aug-97

40

50

60

70

80

Note: Average by month, January 1970-August 1997.

Source: USDA, ERS.

Figure 4

Farm share of retail price for beef (Choice, yield grade 3), January 1970-August 1997

Percent Jan-70

Jan-72Jan-74Jan-76Jan-78Jan-80Jan-82Jan-84Jan-86Jan-88Jan-90Jan-92Jan-94Jan-96

Aug-97

0

50

100

150

200

Note: Average by month, January 1970-August 1997.

Source: USDA, ERS.

Figure 3

Farm-to-wholesale, wholesale-to-retail, and total price spreads for beef (Choice, yield grade 3), 

January 1970-August 1997

Cents per retail pound

Wholesale-to-retail spread

Total farm-to-retail spread

Farm-to-wholesale spread
20 U.S. Beef Industry/TB-1874 Economic Research Service/USDA Jan-70 Jan-72 Jan-74 Jan-76 Jan-78 Jan-80 Jan-82 Jan-84 Jan-86 Jan-88 Jan-90 Jan-92 Jan-94 Jan-96 Aug-97

0

20

40

60

80

100

120

140

Farm-to-wholesale spread

Wholesale-to-retail spread

Total farm-to-retail spread

Note: Average by month, January 1970-August 1997. Deflator is Consumer Price Index, United States, all urban, all items, 1982-84=100. 

Source: USDA, ERS.

Figure 6

Farm-to-wholesale, wholesale-to-retail, and total price spreads: Deflated prices for beef 

(Choice, yield grade 3), January 1970-August 1997

Cents per retail pound Jan-70 Jan-72

Jan-74Jan-76Jan-78Jan-80Jan-82Jan-84Jan-86Jan-88Jan-90Jan-92Jan-94Jan-96

Aug-97

0

50

100

150

200

Nominal farm-to-retail spread

Deflated farm-to-retail spread

Figure 5

Farm-to-retail spreads, nominal and deflated, for beef (Choice, yield grade 3), January 1970-August 1997

Cents per retail pound

Note: Average by month, January 1970-August 1997. Deflator is Consumer Price Index, United States, all urban, all items, 1982-84=100. 

Source: USDA, ERS.
Economic Research Service/USDA U.S. Beef Industry/TB-1874 21

General Structure of a Price Spread Adjustment Model

We address several issues concerning price spreads, cattle cycles, and packer concentration, especially for the 1990's

cattle cycle, to respond to the allegations that packers (because of their alleged market power) and the 1990's cycle

had an extremely adverse effect on producers. One issue is whether prices and price spreads change at the same rate

when prices are increasing as when prices are decreasing (asymmetry of price and price spread behavior). A second

issue is whether there was an unexpected shift in prices during the 1990's cattle cycle. A related third issue is the

effect that the cattle cycle had on price spreads. A fourth issue is the effect of packer concentration on price and

price spread behavior. This section addresses the first three issues. The fourth issue is discussed in the next chapter

with the use of the asymmetric model. 

To examine the first three issues, we needed a model of prices and price spreads that would allow us to explain what

happens to farm-, wholesale-, and retail-level prices and price spreads.7 Economists have estimated many models

where the farm price moves first and drives the wholesale price, which in turn drives the retail price. One advantage

of these traditional models is that, if the other prices are following the farm price, economists interested in

explaining price spread movements do not have to worry about why the farm price changes, they have only to track

how the other two prices react to the farm price. We estimated these effects and effects of the cattle cycle on price

spreads by using an asymmetric, endogenous switching model that indirectly explains price spreads. Price spreads

are modeled indirectly by first explaining movements in farm, wholesale, and retail prices of cattle and beef. Once

we have explained the movements of these three prices, we indirectly explain the spreads between these prices. 

The asymmetric model we developed for this report (see Appendix B) was based on previous research on price

interactions in pork and beef markets (Hahn, 1989 and 1990). The model was designed to be flexible and to allow

the past history of price spreads to determine as much of the outcome as possible. The model allowed price

interactions to be either very simple or very complex. Not only can it take time for prices to adjust to changes in

market conditions, but the speed at which they adjust can change, depending on the direction they are going. In our

case, the endogenous variables that we explain are the net-farm, wholesale, and retail beef prices. Jan-70 Jan-72 Jan-74 Jan-76 Jan-78 Jan-80 Jan-82 Jan-84 Jan-86 Jan-88 Jan-90 Jan-92

Jan-94Jan-96

Aug-97

0

100

200

300

400

Retail price Wholesale value

Net farm value

Byproduct allowance

Figure 7

Retail price, wholesale value, net farm value and by-product allowance: Deflated prices for beef 

(Choice, yield grade 3), January 1970-August 1997

Cents per retail pound

Note: Average by month, January 1970-August 1997. Deflator is Consumer Price Index, United States, all urban, all items, 1982-84=100. 

Source: USDA, ERS.

__________________________________________________________________________________________________

7Variables are natural logarithms of underlying variables. A change in prices is a difference in logarithms of two successive

prices. The logarithm of a negative number is undefined.
Our model, like the traditional models, allows for slow price transmission from one level of the market to another.

That is, it allows, though it does not require, retail price adjustments to lag behind wholesale price changes.

However, we made our model more flexible in that we did not prespecify one price as the leader and the others as

followers. Instead, our model allows the retail, wholesale, or farm price to drive the others, letting the data pick the

leading price variable. Our model also allows for the case in which all three prices interact simultaneously. This

extra flexibility requires that our model also have the ability to explain the general level of prices as well as their

spreads. 

The switching part of the name describes the ability of the model's algebraic coefficients to change, depending on

the direction the variables are going. If increasing price coefficients are the same as decreasing price coefficients,

then the relationship is symmetric. If, however, increasing prices can have different coefficients than decreasing

prices, then the relationship is asymmetric, hence, the asymmetric part of the descriptive name for our model.

As we mentioned earlier, one of the notable features of beef prices is that farm and wholesale prices are more

volatile than retail prices. Also, it often appears that retail prices follow farm and wholesale prices, with a lag of

about a month. That is, retail prices tend to go up a month or more after the others go up and drop a month or more

after the others drop. Retail prices will follow wholesale prices if it takes time for retail prices to adjust to changes

in the wholesale price.

The real world is more general than most models would suggest. In addition to determining prices, the beef market

also determines beef production and allocates product to domestic and international markets. Our model focuses

only on prices at three levels of the beef marketing chain. Our model is based on the assumption that we can pull

the pricing parts of the market out of the system as a whole and study their effects. The rest of the marketing

structure is collapsed into the terms that determine the target price spreads and levels.

We assume that the longrun price level is determined by other, more general, supply and demand factors. We model

these effects using information on supplies of beef and pork, consumption of beef, pork, chicken, and turkey, and on

other variables that shift the demands for and supplies of meats. The supply and demand shifters include seasonality

and trends, including technological change. Our explanation of long-term price spreads is relatively simple. We

assume that these grow with inflation along their longrun trend. This assumption is consistent with what we think

we see in the raw data (see the discussion above on long-term fluctuations).

The shortrun effects of supply and demand shifts on prices will vary from the longrun effects unless the model has

complete, not partial, adjustment. In addition to the effects of partial adjustment on prices, shortrun and longrun

effects can vary for other reasons, particularly if there is some shortrun feedback from price changes to quantity

changes. This type of feedback seems particularly likely, especially between prices and demand. 

To help differentiate between the shortrun and longrun effects of the other variables, we divided most of our

explanatory variables into two parts, the change from the previous month (shortrun elasticities) and last month's

value (longrun elasticities). This month's value is the sum of those two parts. Our explanatory variables also

include intercepts, trends, and seasonal factors; for mathematical reasons, these intercept shifters cannot be

meaningfully split into this month's change and last month's value. (Knowing last month's value is the same as

knowing this month's value for these terms. Knowing last month's beef production is helpful in predicting this

month's production, but there is still plenty of room for surprises.) A complete list of explanatory variables and their

parameter estimates can be found in table 2. Except for the intercept, trends, and seasonal factors, the explanatory

variables we used are the natural logarithms of the underlying variables.

We used the monthly consumer price index (CPI) to factor out inflation in current price changes and lagged prices.

The farm, wholesale, and retail prices of beef were also divided by the average value of the deflated retail price. By

doing this, the coefficients of the supply and demand variables could be interpreted as elasticities at the retail price

level.

22 U.S. Beef Industry/TB-1874 Economic Research Service/USDA
Economic Research Service/USDA U.S. Beef Industry/TB-1874 23

Table 2--Asymmetric Model Estimates, equations

Price level Farm-to- Wholesale-toVariables wholesale spread retail spread

Increasing endogenous:

Retail 0 2.6951

Wholesale 2.9796 3.9760 0

Net farm 0 -3.5088

Decreasing endogenous:

Retail .9686 3.5528

Wholesale 3.6695 4.5720 -.0903

Net farm .0381 -3.6091

Lagged endogenous variables:

Retail -.7573 -1.0000

Wholesale 0 -1.0000 1.0000

Net farm -.2427 1.0000

Exogenous variables:

Intercept 1.2745 .1327 .3518

Intercept shift .0790 .0176 .0367

Trend -.0511 -.0047 -.0022

Seasonal effects (these are 

constrained to sum to zero):

January .0122 .0053 .0058

February -.0234 -.0226 -.0057

March -.0180 -.0234 -.0073

April .0130 .0117 .0071

May .0074 .0291 .0173

June -.0276 .0044 .0104

July -.0312 -.0174 -.0031

August .0016 -.0070 -.0106

September .0153 .0059 -.0117

October .0061 .0004 -.0097

November .0143 .0005 -.0010

December .0303 .0130 .0086

Shortrun elasticities (at the 

mean, deflated retail price):

Per-capita beef disappearance .2249 .1387 .0540

Per-capita pork disappearance .5129 .2084 .0235

Per-capita chicken disappearance .3498 .0025 .0650

Per-capita turkey disappearance .0382 .0154 .0140

Commercial beef production -1.0335 -.2152 -.3527

Commercial pork production -.3046 -.1521 .1471

The CPI for food 4.9675 .6917 2.7809

Per-capita consumption expenditures -.8363 -.4459 -.7186

The price of corn -.0383 .0518 .0321

The price of soybean meal .1338 .0801 .0285

The price of hay .1814 .0533 .1016

Longrun elasticities (at the

mean, deflated retail price):1

Per-capita beef disappearance -.5608

Per-capita pork disappearance -.4153

Per-capita chicken disappearance .5003

Per-capita turkey disappearance -.0025

Commercial beef production -.0555

Commercial pork production .3235

The CPI for food .4506

Per-capita consumption expenditures .4133

The price of corn .0076

The price of soybean meal .0153

The price of hay .0518

1The "elasticities" do not correspond to either demand or supply elasticities. They are a combination of both.
Estimation Results and Interpretations

Estimating endogenous switching models is difficult because they are not included in standard econometric software.

They can be estimated using general mathematical optimization software. We set up the equations that we estimated

for the convenience of the software we used, General Algebraic Modeling System (GAMS), and also to make it

easier to impose coherent (sensible) parameter values. We estimated our model using maximum likelihood

estimation and monthly data, previously described, beginning in 1979, and running to the end of 1996. 

One disadvantage of using optimization software is that it does not provide us with measures of the standard errors

of individual coefficients, so we have to use tests that do not depend on parameter standard errors. We used

likelihood ratios to test two hypotheses, one for asymmetry of price behavior, and a second for a recent shift in price

behavior. 

The first test was for the importance of asymmetry. In previous work, Hahn (1989 and 1990) found that pork and

beef prices generally responded asymmetrically. That is, they reacted more quickly when they were going up than

when they were going down. In the current study, our test for asymmetry was significant at the 3-percent level. We

concluded that asymmetry was important, and prices move up faster than they move down.

The second test arose from concern that recent cattle prices have been lower than expected. We added a dummy

variable to each equation to capture shift in pricing in the last 5 years of the sample (1992-96, inclusive). These

three dummy variables, tested as a group, had a huge test statistic. The odds of observing such a number by chance

would be less than 1 in 10,000. We accepted the hypothesis that there has been a shift in cattle pricing (or

technically speaking, we could not reject the null hypothesis of no shift in cattle pricing), but the shift was upward,

not downward as the allegations motivating this study suggested. In other words, prices were actually higher during

the last 5 years than previous behavior suggested they should have been. Our estimates of the asymmetric model

with pricing (intercept) shifts are in table 2.

As noted before, the behavior of the estimated model can switch depending on the direction each price is heading.

With three prices and two directions for each, there are eight possible combinations of price directions. The model

is relatively easy to interpret when the farm and retail prices are increasing. In the two cases where the farm and

retail prices are going up and the wholesale price is going in either direction, the model can be rearranged to get a

wholesale-led model, that is, one where prices are determined in steps.

When the farm and retail prices are increasing, their coefficients in the price-level equation are zero. When the farm

and retail prices are increasing, only the wholesale price can react to move the general price level closer to its target.

Once the wholesale price reacts, the farm and retail prices adjust. The farm price is determined by the farmwholesale-spread equation and the retail price by the wholesale-retail-spread equation. Increasing and decreasing

wholesale prices have positive coefficients in the farm-wholesale equation. This means that current price changes at

wholesale cause current price changes at the farm. The wholesale price tends to move the farm price in the same

direction. With farm-price coefficients smaller (in absolute value) than either wholesale price coefficient, these

estimates (inverses of estimated parameter effects) imply that the farm price tends to overreact to wholesale price

changes. A 1-cent change in the wholesale price tends to cause a greater change in the farm price.

Wholesale-price decreases have a small coefficient in the wholesale-retail spread equation, while wholesale price

increases have a zero coefficient. This means that current wholesale price decreases have little effect on this month's

retail price. Wholesale price increases have no effect on this month's retail price.

When either the farm or retail price is decreasing, the estimates imply more complex behavior. For instance, the

wholesale price is affected by the farm price change in the price-level equation, while the farm price is affected by 

24 U.S. Beef Industry/TB-1874 Economic Research Service/USDA
Economic Research Service/USDA U.S. Beef Industry/TB-1874 25

the wholesale price in the spread equation. When the retail price is decreasing, it also appears in the price level

equation. As a result, none of the three prices can be selected as leading the others. When all three prices are

declining, all three react to move prices closer to the target level, and all three end up being affected by the

difference between actual and target spreads. Additional analysis of the three coefficients indicated that, in the short

run, the wholesale price is the most sensitive to differences between target and actual price levels. More generally,

while the wholesale price has an important role in getting the general price level right, its lagged value is not a factor

in calculating the lagged, general price level. Its estimated coefficient is not statistically different from zero. Also,

the retail price, which has very little direct response (often none) to the price level, has a very high weight.

Simulations and Forecasts With the Asymmetric Model

We used our asymmetric model to simulate and forecast prices and spreads to examine the issues of a shift in the

behavior of recent prices and the effect of cattle inventory cycle behavior on price spreads. 

Beef/Cattle Price Shifts

As noted above, our estimates imply that there was an important shift in the behavior of the system in the past 5

years, but it was actually a shift in favor of cattle producers. We simulated two sets of price forecasts to evaluate the

pattern of beef prices in the last 3 years of our sample. These forecasts can be seen in figures 8, 9, and 10. The first

set of forecasts used coefficients based on all the data, 1979-96. These are the in-sample forecasts. 

The shift in cattle pricing in the last 5 years caused cattle, wholesale, and retail beef prices to be higher than they

would have been given their earlier pattern of reaction to supply and demand shocks. On average, farm and

wholesale prices are 6.6 cents per retail-pound equivalent higher than they would be without the shift, and retail

prices are 3.8 cents per pound higher. The farm-wholesale price spread has been unaffected by the shift, and the

wholesale-retail spread has dropped. The out-of-sample forecasts are based on the coefficients estimated, using only

the 1979-91 data. Note that the out-of-sample forecasts are generally lower than in-sample forecasts, further

evidence that the recent shifts in the system led to higher prices.

We simulated beef prices under a series of alternative scenarios to see the effects of changes in selected scenarios on

beef price spreads. Starting with January 1991, we simulated beef prices given: fixed beef production and

consumption, fixed pork consumption and production, and fixed production and consumption of chicken and turkey.

All other explanatory variables in the model were fixed.

Like all econometric models, ours deals mostly in predicting averages, that is, the average set of price changes one

would expect given a certain set of conditions. Usually, an average set of changes will be different from the actual

set of changes. That is why we built error terms into each of the equations. To make the simulated values as much

like the original values as possible (except for the factors we changed), we added the estimated errors back to each

of the equations. That way, the only difference between the actual and simulated prices would be the different levels

of meat production and consumption.

To no ones surprise, we found beef quantities were much more important in determining beef prices than the

quantities of the other meats. Pork, chicken, and turkey quantities had only minor effects on December 1996 beef

prices. Had beef production and consumption been steady since 1991, beef prices at all levels would have been

noticeably higher, 15 to 20 cents per retail weight equivalent. Price spreads were much less affected by the changes

in beef, pork, or poultry quantities. The largest part of the difference there occurred in the wholesale-retail spread,

which was caused by the slow adjustment of the retail price.
26 U.S. Beef Industry/TB-1874 Economic Research Service/USDA

Jan. Mar. May July Sep. Nov. Jan. Mar. May July Sep. Nov. Jan. Mar. May July Sep. Nov.

100

120

140

160

180

1993 1994 1995

Actual

In sample forecast

Out of sample forecast

Source: USDA, ERS.

Figure 8

Net farm values for beef, January 1993-December 1995

Cents per pound, retail basis

Jan. Mar. May July Sep. Nov. Jan. Mar. May July Sep. Nov. Jan. Mar. May July Sep. Nov.

120

140

160

180

200

1993 1994 1995

Actual

In sample forecast

Out of sample forecast

Source: USDA, ERS.

Figure 9

Wholesale values for beef, January 1993-December 1995

Cents per pound, retail basis
Economic Research Service/USDA U.S. Beef Industry/TB-1874 27

Cattle Cycle Effects

We used model simulations to see how the cattle cycle affected price spreads, in part, to see if there were price

spread behaviors that depended on where in the cattle cycle they were. Econometricians often talk about dividing

data series into trend, cyclical, seasonal, and transient components. We took the actual patterns of beef production

and consumption from the 1980-90 cattle cycle and extracted the trend and cyclical parts (figs. 11 and 12). Since

the cattle cycle implies relatively smooth changes in quantities from one month to the next, most of the month-tomonth changes in beef quantities are due to the seasonal and transient parts, leaving a smooth pattern of prices (figs.

13 and 14). Further, the slow nature of cyclical quantity changes does not put much stress on price adjustment. As a

result, we found almost no cyclical pattern in the price spreads themselves.

We simulated the effects of the cattle cycle by running only the cyclical parts of beef production and consumption

through the model, with every other variable fixed. (We also eliminated the seasonal factors from the model.) We

ran our simulation as a closed loop, that is, as if the cycle ran continuously forever. Our resulting prices represent a

purely cyclical set. Figures 13 and 14 show prices and price spreads over the cattle cycle. Prices (fig. 13) show a

definite cyclical pattern, varying almost 10 percent at the farm level over the cycle. Wholesale prices vary similarly,

but not quite as extremely over the cattle cycle. Retail prices vary the least over the cattle cycle, varying only about

4 percent over the cycle. 

Price spreads also vary over the cattle cycle (fig. 14), but not much (less than 2 percent from peak to trough, and the

three spreads are virtually identical). This is not to say that changes in cattle numbers are irrelevant to price spreads.

The purely cyclical component of quantity changes is just not extreme enough to cause the wide changes in price

spreads actually observed. Other variables, the higher frequency seasonal and transient changes (not shown in figs.

13 and 14), have greater effects on price spreads than do the cyclical components of prices and price spreads.

Jan. Mar. May July Sep. Nov. Jan. Mar. May July Sep. Nov. Jan. Mar. May July Sep. Nov.

260

270

280

290

300

1993 1994 1995

Actual

In sample forecast

Out of sample forecast

Source: USDA, ERS.

Figure 10

Retail values for beef, January 1993-December 1995

Cents per pound, retail basis
28 U.S. Beef Industry/TB-1874 Economic Research Service/USDA

-20

-10

0

10

20

0 36 72 108 144

Smoothed cyclical Detrended actual

Months

Note: The months start from the beginning of the cycle.

Source: USDA, ERS.

Figure 12

Per capita beef disappearance over the cattle cycle

Percent

-20

-10

0

10

20

0 36 72 108 144

Figure 11

Beef production over the cattle cycle

Percent

Smoothed cyclical

Detrended actual

Months

Note: The months start from the beginning of the cycle.

Source: USDA, ERS.
Economic Research Service/USDA U.S. Beef Industry/TB-1874 29

-10

-5

0

5

10

0 36 72 108 144

Months

Note: The months start from the beginning of the cycle.

Source: USDA, ERS.

Figure 13

Deviation from average price over the cattle cycle

Percent

Retail

Wholesale

Net farm value

-0.8

-0.4

0

0.4

0.8

1.2

0 36 72 108 144

Months

Note: The months start from the beginning of the cycle.

Source: USDA, ERS.

Figure 14

Deviation from average over the cattle cycle

Percent

Farm to retail spread

Farm to wholesale spread

Wholesale to retail spread
30 U.S. Beef Industry/TB-1874 Economic Research Service/USDA

Concentration Measures for

the Beef Packing Industry

Azzam and Anderson reviewed the literature on structure, conduct, and performance issues related to the

meatpacking industry (USDA, GIPSA,1996). They reviewed studies under two classifications: Structure, Conduct,

and Performance (SCP) and New Empirical Industrial Organization (NEIO). Results were not consistent across

studies for either classification.

SCP Studies

Azzam and Anderson reported that several studies of beef and pork packing concluded that market power, the ability

to affect prices because of quantities transacted or market share affected by one or a few firms, can arise from

concentration. However, the studies they reviewed, which differed widely in time period and analytical method,

arrived at conflicting results. Some studies were faulted in that their observed correlations could be due to shifts in

supply or demand not properly specified. Specifically, Azzam and Anderson alleged that links between supply

elasticity and prices were not addressed in the regional price-concentration studies they reviewed. They also pointed

out that economies of size rather than noncompetitive behavior, may be the cause of firm growth and increases in

concentration.

NEIO Studies

Azzam and Anderson likewise reported a lack of consistent results across NEIO studies they reviewed. A pattern of

results suggested exercise of market power in live cattle markets, but price effects seemed small and perhaps more

than offset by cost reductions associated with consolidation. Market power did not increase with concentration as

theory would suggest, and evidence indicated that packers were unable to sustain cooperation in restraining prices

paid for cattle. They also concluded that empirical implementation has not equaled theoretical rigor due to lack of

appropriate data and model complexity.

GIPSA Conclusions

GIPSA reported its conclusions from the Azzam and Anderson study as follows:

The researchers' description of historical industry evolution suggests that changes in the

meatpacking industry have resulted from technological change and dynamic rivalry between

firms. The basic question addressed by this project was whether the evidence from

Structure-Conduct-Performance and New Empirical Industrial Organization studies is

persuasive enough to warrant the conclusion that competition in the meatpacking industry is

deficient. Taken as a whole, the literature review led to the conclusion that the answer is no.

Many SCP studies indicated the existence and exercise of market power. However, the

failure to use appropriate theoretical models of conduct in these studies makes industry

generalizations questionable.

The NEIO studies show a persistent gap between the actual price of livestock and the

competitively determined price predicted by theory. However, the studies have not

incorporated sufficient detail to prove noncompetitive behavior.
Empirical Measures of Packer Concentration 

As discussed above, the CR-4 (four-firm concentration ratio) for steer and heifer slaughter as reported by GIPSA

rose to 50 percent in 1985, 72 percent in 1990, and 80 percent in 1996 (preliminary GIPSA, table 3). The CR-4 for

boxed fed beef production for the same years was 62 percent, 79 percent, and 84 percent (1994). Tables 3 to 6

summarize these and additional comparisons.

The Herfindahl-Hirshman Index

Another, generally preferred, measure of concentration is the Herfindahl-Hirshman Index (HHI), also reported by

GIPSA. The HHI has the advantage of taking into account the number of firms and relative distributional shares of

the market held by all firms, not just the largest few. The HHI is calculated by summing each firm's squared

percentage of the market. If 100 firms each have a 1-percent share, the HHI will equal 100. If 1 firm has 100

percent of the market, the HHI equals 10,000. 

Economic Research Service/USDA U.S. Beef Industry/TB-1874 31

Table 3--Slaughter concentration for four largest firms, 1980-96

Four-firm concentration Herfindahl-Hirshman Index

Steer & Cow & Cattle Boxed fed Steer & Cow & Cattle Boxed fed

Year heifer bull beef heifer bull beef

Percent Herfindahl-Hirshman Index

Concentration based on

procurement data reported to GIPSA:

1980 35.7 9.7 28.4 52.9 561 89 361 1,220

1981 39.6 9.6 31.4 57.1 643 96 410 1,359

1982 41.4 9.1 32.0 59.1 683 83 417 1,323

1983 46.6 10.3 36.0 60.2 862 96 522 1,382

1984 49.5 11.0 37.2 61.7 944 98 543 1,439

1985 50.2 17.2 39.0 61.5 999 160 617 1,527

1986 55.1 18.4 42.3 67.4 1,088 173 657 1,691

1987 67.1 20.0 54.2 79.5 1,435 206 946 1,981

1988 69.7 18.4 56.6 79.3 1,589 198 1,055 2,030

1989 70.4 17.5 57.0 79.2 1,602 188 1,055 1,979

1990 71.6 20.4 58.6 79.3 1,661 223 1,118 1,988

1991 73.5 21.1 60.6 78.7 1,766 236 1,204 1,958

1992 77.8 22.0 63.5 81.4 2,005 243 1,336 2,163

1993 79.8 24.0 66.0 82.7 2,052 276 1,393 2,236

1994 80.9 26.3 67.8 85.7 2,096 320 1,460 2,340

1995 79.3 23.4 67.3 84.3 1,982 293 1,437 2,208

Concentration based on

federally inspected slaughter data:

1991 74.5 21.1 NA NA NA NA NA

1992 79.0 23.6 NA NA NA NA NA

1993 81.9 25.1 NA NA NA NA NA

1994 81.8 24.9 NA NA NA NA NA

1995 81.1 26.1 NA NA NA NA NA

1996 80.0 27.2 NA NA NA NA NA

NA = Not applicable.

Source: U.S. Dept. of Agriculture, Packers and Stockyards Statistical Report: 1995 Reporting Year,

GIPSA 97-1, September 1997, Tables 27, 28, and 29.
32 U.S. Beef Industry/TB-1874 Economic Research Service/USDA

Table 4Livestock slaughter plants, by type of inspection, 1972-951

Plants reporting to GIPSA2 Under Federal Non-Federal Total Federal

Under Federal Non-Federal inspection inspection and non-Federal

Year inspection inspection Total January 1 January 1 inspection

Number of plants

1972 735 405 1,140 984 5,172 6,156

1973 753 357 1,110 1,364 4,627 5,991

1974 741 322 1,063 1,437 4,440 5,887

1975 767 274 1,041 1,485 4,602 6,087

1976 761 288 1,049 1,741 4,514 6,255

1977 776 224 1,000 1,682 4,454 6,141

1978 785 213 998 1,701 4,434 6,135

1979 760 207 967 1,687 4,445 6,127

1980 762 209 971 1,627 4,399 6,026

1981 714 187 901 1,542 4,330 5,872

1982 728 156 884 1,688 4,048 5,736

1983 749 144 893 1,652 4,037 5,689

1984 730 137 867 1,666 3,892 5,558

1985 687 117 804 1,608 3,835 5,443

1986 640 99 752 1,544 3,701 5,245

1987 620 102 722 1,483 3,523 5,006

1988 606 99 705 1,387 3,453 4,840

1989 552 87 639 1,364 3,325 4,689

1990 534 89 623 1,268 3,281 4,549

1991 497 90 587 1,186 3,140 4,326

1992 490 79 569 1,125 2,896 4,021

1993 457 77 534 1,090 2,797 3,887

1994 434 66 500 1,030 2,733 3,763

1995 429 58 487 968 2,627 3,595

1Slaughter plants came under Federal inspection in the following 20 States after 1972: AR-6/81; CA-4/76; CO-7/75;

CT-10/75; ID-7/81; KY-1/72; ME-5/80; MA-1/76; MD-3/91; MI-10/81; MO-8/82; NV-7/73; NH-8/78; NJ-7/75; NY-7/75;

OR-7/72; PA-7/72; RI-10/81; TN-10/75 and WA-6/73. Many non-federally inspected plants can only custom slaughter

for others.

2Plants reporting to GIPSA include federally and non-federally inspected establishments. Firms purchasing less

than 2,000 head of all livestock, or less than 1,000 head of cattle prior to 1977, or less than $500,000 of all livestock

beginning in 1977 were not required to report to GIPSA.

Source: U.S. Dept. of Agriculture, Packers and Stockyards Statistical Report: 1995 Reporting Year, GIPSA 97-1,

September 1997, Table 18.
Economic Research Service/USDA U.S. Beef Industry/TB-1874 33 Table 5--Steers and heifers: Slaughter by plant size, packers reporting to GIPSA, by plant size (h

Less than 1,000- 10,000 50,000 100,000 250,000 

1,000 9,000 49,999 99,999 249,999 or larger1 

Year Plants Head Plants Head Plants Head Plants Head Plants Head Plants Head 

No. 1,000 No. 1,000 No. 1,000 No. 1,000 No. 1,000 No. 1,000 

1972 173 75 319 1,209 174 4,132 73 5,257 48 7,682 20 7,778 1973 192 84 302 1,127 166 4,001 75 5,464 37 5,876 23 8,657 1974 178 80 281 1,037 156 3,893 68 4,781 47 7,153 22 8,457 1975 159 77 288 1,127 150 3,685 67 4,617 49 7,530 22 8,536 1976 147 71 300 1,134 144 3,301 71 4,857 52 8,187 17 6,074 1977 130 61 270 1,030 142 3,225 74 5,303 49 7,646 20 7,085 1978 155 73 256 910 141 3,256 56 4,125 49 8,083 17 6,079 1979 182 78 238 843 109 2,795 44 3,117 47 7,420 15 5,103 1980 201 87 212 715 107 2,644 43 3,063 37 5,813 18 6,280 1981 177 79 185 660 80 1,984 33 2,332 32 4,998 22 7,920 1982 181 75 172 590 69 1,771 31 2,293 28 4,497 20 7,119 1983 183 73 172 540 68 1,625 29 2,093 25 3,836 19 6,746 1984 178 71 155 511 64 1,559 24 1,686 27 4,515 16 5,665 1985 157 63 146 445 56 1,439 19 1,366 27 4,276 14 4,999 1986 137 54 133 460 45 1,109 19 1,328 20 3,204 12 4,295 1987 152 53 128 435 34 776 20 1,383 23 4,056 10 3,444 1988 151 50 121 388 37 819 16 1,167 17 2,759 13 4,338 1989 138 49 92 304 32 803 12 891 13 2,141 13 4,426 1990 142 49 86 248 29 690 7 477 13 2,058 15 5,223 1991 130 48 81 235 26 577 6 410 15 2,614 14 5,563 1992 127 51 78 240 17 420 4 281 13 2,006 9 3,133 1993 131 44 66 175 21 465 3 216 12 1,926 8 3,164 1994 98 31 72 207 21 479 4 318 7 1,100 6 2,351 1995 96 36 58 170 19 421 5 369 9 1,533 7 2,692 

1Size limits are 250,000-499,999 beginning in 1976.2Size limits are 500,000-999,999 beginning in 1986.

Source: U.S. Dept. of Agriculture, Packers and Stockyards Statistical Report: 1995 Reporting Year, GIPSA 97-1, Sep
34 U.S. Beef Industry/TB-1874 Economic Research Service/USDA Table 6--Cows and bulls: Slaughter by plant size, packers reporting to GIPSA, by 

Less than 1,000- 10,000 25,000 50,000 

1,000 9,000 24,999 49,999 99,999 

Year Plants Head Plants Head Plants Head Plants Head Plants Head 

No. 1,000 No. 1,000 No. 1,000 No. 1,000 No. 1,000 

1972 279 110 359 1,272 82 1,340 47 1,604 18 1,235 1973 268 107 333 1,138 74 1,208 44 1,563 23 1,702 1974 248 94 316 1,140 68 1,133 39 1,362 31 2,240 1975 206 83 302 1,107 83 1,298 57 2,046 36 2,440 1976 202 85 298 1,149 74 1,183 74 2,558 41 2,740 1977 193 79 299 1,153 76 1,206 61 2,118 35 2,433 1978 206 82 295 1,172 65 1,066 57 1,942 38 2,648 1979 243 98 262 957 52 855 39 1,277 30 1,945 1980 250 93 240 832 56 926 46 1,609 21 1,539 1981 213 86 206 728 55 887 40 1,397 24 1,664 1982 207 86 195 741 44 744 45 1,594 25 1,700 1983 197 77 199 775 48 809 51 1,814 21 1,490 1984 192 73 171 648 48 800 48 1,745 27 1,874 1985 188 71 145 572 48 764 40 1,411 17 1,333 1986 149 55 133 478 43 680 41 1,430 16 1,124 1987 154 51 121 441 48 775 32 1,104 24 1,778 1988 146 48 127 483 36 588 29 934 21 1,541 1989 136 46 110 408 33 579 25 857 17 1,300 1990 140 45 99 330 28 500 17 597 19 1,311 1991 127 42 89 311 24 400 21 766 18 1,344 1992 120 42 77 300 25 435 15 526 15 1,104 1993 114 42 66 244 20 350 12 456 14 1,031 1994 104 38 53 212 19 313 13 474 18 1,372 1995 93 27 58 224 18 314 12 445 12 885 

1Size limits are 100,000-149,999 beginning in 1976.

Source: U.S. Dept. of Agriculture, Packers and Stockyards Statistical Report: 1995 Reporting Year, Table 21.
The U.S. Department of Justice and Federal Trade Commission Horizontal Merger Guidelines (April 1992) provide

insight into the HHI levels considered important in determining whether mergers within a relevant market are likely

to be challenged by the Justice Department:

Post-Merger HHI below 1,000. The Agency regards markets in this region to be

unconcentrated. Mergers resulting in unconcentrated markets are unlikely to have adverse

competitive effects and ordinarily require no further analysis.

Post-Merger HHI between l,000 and l,800. The Agency regards markets in this region to be

moderately concentrated. Mergers producing an increase in the HHI of less than l00 points

in moderately concentrated markets post-merger are unlikely to have adverse competitive

consequences and ordinarily require no further analysis. Mergers producing an increase in

the HHI of more than 100 points in moderately concentrated markets post-merger

potentially raise significant competitive concerns depending on the factors set forth in

Sections 2-5 of the Guidelines.

Post-Merger HHI above l,800. The Agency regards markets in this region to be highly

concentrated. Mergers producing an increase in the HHI of less than 50 points, even in

highly concentrated markets post-merger, are unlikely to have adverse competitive

consequences and ordinarily require no further analysis. Mergers producing an increase in

the HHI of more than 50 points in highly concentrated markets post-merger potentially raise

significant competitive concerns, depending on the factors set forth in Sections 2-5 of the

Guidelines. Where the post-merger HHI exceeds 1,800, it will be presumed that mergers

producing an increase in the HHI of more than 100 points are likely to create or enhance

market power or facilitate its exercise. The presumption may be overcome by a showing

that factors set forth in Sections 2-5 of the Guidelines make it unlikely that the merger will

create or enhance market power or facilitate its exercise, in light of market concentration

and market shares (Section 1.5). 

The HHI for U.S. steer and heifer slaughter was 999 in 1985, 1,661 in 1990, and 1,982 in 1995, above the threshold

1,800 level (fig.15). For all cattle slaughter combined, the HHI's are much lower, reaching only 1,437 in 1995. For

U.S. boxed fed beef, the HHI's are 1,527; 1,988; and 2,208, respectively. If the relevant market were defined as

steer and heifer slaughter or boxed fed beef production by the U.S. Department of Justice, it would seem that future

major mergers would be candidates for challenge. On the other hand, if the relevant market were defined as cattle

slaughter or all livestock purchases, merger challenges would be less likely (tables 3 to 6).

HHI and Our Asymmetric Model 

It is clear that steer and heifer slaughter concentration and fed boxed beef concentration is high by several standards.

It is far less clear what caused industry consolidation or what effects stem from high concentration, and whether any

effects constitute market distortions adverse to producers.

We tested for the effects of concentration by including the HHI in our asymmetric model. We had HHI measures for

only a limited part of the sample. The HHI was statistically significant in the sub-sample, but its effects were

opposite from what one would expect. If packers were using market power to gain an unfair advantage over

producers, one would expect market power to lead to lower farm prices and/or higher farm-to-wholesale spreads.

However, in our results, increasing HHI was associated with higher farm prices and lower farm-to-wholesale

spreads. This type of unexpected result is common in other studies of market concentration's effects on livestock

markets.

Economic Research Service/USDA U.S. Beef Industry/TB-1874 35
While the estimated HHI effect is counter-intuitive, it is also small. As we move from the smallest HHI to the

largest HHI, the farm-wholesale spread drops by less than 1 cent. This drop in the farm-wholesale spread translates

into a 2-cents-per-pound increase in the live price of choice cattle; retail and wholesale prices are basically

unaffected by the HHI. The large amount of data in the asymmetric model make it able to measure relatively small

effects with great accuracy. The small measured effect of the HHI is very statistically significant even though it has

little practical effect. 

There are a number of reasons why the HHI could give this counter-intuitive result. Market concentration may be

driven in part by economies of size. Farm-to-wholesale spreads could be dropping as concentration rises because

some part of the cost-savings from larger slaughter operations are shared with producers. Also, the HHI may not be

the most appropriate measure of market power. There is a difference between the ability to exercise market power,

for example, concentration, and the actual exercise of market power (Jones, Purcell, Driscoll, and Peterson, 1996).

The HHI is meant to measure the ability to exercise market power. It is generally assumed in the literature that the

exercise of market power by an industrial organization is reliably related to the ability to exercise it. The reality may

be more complex.

36 U.S. Beef Industry/TB-1874 Economic Research Service/USDA

1980 82 84 86 88 90 92 94 96

0

500

1,000

1,500

2,000

2,500

Steer & heifer

Cow & bull

Cattle

Boxed beef

Hogs

All livestock

Source: USDA, ERS, from data provided by USDA's, Grain Inspection, Packers and Stockyards Administration.

Figure 15

Herfindahl-Hirshman Index for the meatpacking industry, by category, 1980-96

HHI index
Economic Research Service/USDA U.S. Beef Industry/TB-1874 37

Conclusions

The cattle cycle of the 1990's that began in 1991 reached a peak in cattle numbers of 103,819 thousand head in

1996. A drought in early 1996 resulted in a more severe cattle sell-off than would probably have occurred

otherwise, possibly shortening the consolidation period that often follows an expansion phase of the cattle cycle or

perhaps shortening the entire cycle. The increased sell-off of cattle during 1996 was accompanied by the second

lowest real net receipts above cash costs since 1913. Total beef production has been above 25 billion pounds since

1994 and was 25.7 billion pounds in 1998, the same as the peak in 1976. 

The percentage increase in cattle numbers from the beginning of the 1991-to-present cycle was the second lowest

since the cattle cycle that began in 1913. The smallest percentage increase in cattle numbers and the shortest

expansion phase for a cattle cycle was the previous cycle of the 1980's. Interestingly, total beef production has

trended upward since at least the late 1940's, and the last three cyclical peaks in total beef production have been

within a billion pounds of each other. The large bulge during the late 1970's is the only significant change in this

trend since World War II.

However, nothing in the data suggests that the adverse profit situation of 1996 that prompted the latest round of

packer concentration studies was unexpectedly worse or out of character for where the cattle industry was in 1996 in

terms of the cattle cycle of the 1990s. The research reported here did not find cattle inventory numbers or net

returns to cow/calf producers above cash costs to have fluctuated during the 1990's beyond what would have been

expected at least 95 percent of the time. Further, our estimates imply that, while there was a shift in pricing system

behavior during 1992 to 1996, the shift raised prices for live cattle, wholesale, and retail beef prices higher than they

would have been, given their earlier patterns of reaction to supply and demand shocks. The farm-wholesale price

spread has been unaffected by the shift, and the wholesale-retail spread has declined. The out-of-sample forecasts,

based on coefficients using 1979-91 data, are generally lower than in-sample forecasts, further evidence that the

recent shifts in the system actually led to higher prices.

One of the notable features of beef prices is that farm and wholesale prices are more volatile than retail prices. Also,

it often appears that retail prices are following farm and wholesale prices. That is, retail prices tend to go up a

month or more after the others go up and drop a month or more after the others drop.

Grain Inspection Packers and Stockyards Administration (GIPSA) summarized its work as follows (USDA, 1996):

The findings of the extensive literature reviewed were inconclusive about the effects of

concentration, primarily because of limitations in the methods or data, or both, in the

literature reviewed. While the body of evidence from the literature was insufficient to

support a finding of noncompetitive behavior, it also cannot conclude that the industry is

competitive (p. 55).

Azzam and Anderson's portrayal of the difficulty in assessing the competitiveness from available data held true for

another of the studies contracted by GIPSA titled Effects of Concentration on Prices Paid For Cattle. The GIPSA

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 (p. 36).

The effects of market concentration are widely debated and were included in the analyses here, with interesting

results. Slaughter concentration effects were represented in our model specifications by the HHI. Coefficient

estimates for the HHI indicate that: (1) increasing HHI was significantly associated with higher prices and narrower
farm-to-wholesale spreads, (2) increasing slaughter concentration is associated with higher farm prices, and (3) other

factors not identified in our analysis were associated with size and concentration have been more important than

monopsonistic price effects. Despite these results that suggest that packers do not appear to be exercising market

power, it is also clear that with concentration measures of 80 percent or higher, the potential for exercising market

power in the industry does exist. Continued monitoring of market concentration and additional research into better

measures of the existence and use of market power would be helpful.

Beef producers have expressed concern over the low prices and low producers' share of the retail dollar. Most of

their attention has been directed at beefpackers, even though the largest part of the spread between farm and retail

occurs between the packer and the retailer. 

Figure 16 shows that the producers' share of the wholesale dollar has been more stable than the producers' share of

the retail dollar, which has trended roughly downward since the end of the 1970's. While it is true that the

producers' share of the wholesale dollar hit its lowest value in the 1979-96 period in June 1995, it also hit its highest

value in March 1993. The minimum value in June 1995 is also similar to the lower values in the whole time period.

A notable feature of the producers' share of wholesale dollars in the 1990's has been an apparent increase in

volatility that has led to occasions with unusually high shares. The largest producers' shares occur in the 1990's.

The picture is much different for the producers' share of the retail dollar. Most of the largest shares occur early in

the time period. The lowest 30 shares in this sample occur in the 1990's. 

38 U.S. Beef Industry/TB-1874 Economic Research Service/USDA Jan-79 Jan-80 Jan-81 Jan-82 Jan-83 Jan-84 Jan-85 Jan-86 Jan-87 Jan-88 Jan-89 Jan-90 Jan-91 Jan-92 Jan-93 Jan-94 Jan-95 Jan-96

0

20

40

60

80

100

Retail dollar

Wholesale dollar

Source: USDA, ERS.

Figure 16

Beef producers' shares of wholesale and retail dollars, January 1979-December 1996

Percent
References

Arzac, E.R., and M. Wilkinson. A Quarterly Econometric Model of United States Livestock and Feed Grain

Markets and Some of Its Policy Implications, American Journal of Agricultural Economics. 61:297-308. 1979.

Beale, T., P.R. Hasbargen, J.E. Ikerd, D.E. Murfield, and D.C. Petritiz. Cattle Cycles: How to Profit From Them.

Miscellaneous Publication No. 1430. U.S. Dept. of Agri., Extension Service, in cooperation with the ExtensionIndustry Beef Resource Committee. March 1983. 

Bobst, B.W., and J.T. Davis. Beef Cow Numbers, Crop Acreage, and Crop Policy, American Journal of

Agricultural Economics. 69:771-76. 1987.

Cattle Buyers Weekly. Sept. 23, 1996.

Connor, J.M., R.T. Rogers, B.W. Marion, and W.F. Mueller. The Food Manufacturing Industries. Lexington Books,

D.C. Heath and Co., Lexington, MA. 1985.

Crom, R.J. The Cattle Cycle--Looking to the 80's. ESS Staff Report No. AGESS 810105. U.S. Dept. of Agri.,

Econ. and Stat. Serv. January 1981.

Duewer, L.A. Effects of Specials on Composite Meat Prices, Agricultural Economics Research. 21:70-77. U.S.

Dept. of Agri., Econ. Res. Serv. July 1969.

Elitzak, H. Marketing Bill and Farm Value Components of Consumer Expenditures for Farm Foods, Internet:

Table 15--Marketing bill and farm value components of consumer expenditures for domestically produced farm

foods, (http://www.econ.ag.gov/briefing/foodmark/cost/data/bill/value.htm). October 14, 1998.

Elitzak, H. Food Cost Review, 1997. AER-761. U.S. Dept. of Agri., Econ. Res. Serv. December 1997.

Elitzak, H. Marketing Costs for Food Rose Slightly in 1996, Agricultural Outlook. AO243, U.S. Dept. of Agri.,

Econ. Res. Serv. August 1997.

Elitzak, H. Food Cost Review, 1995. AER-729. U.S. Dept. of Agri., Econ. Res. Serv. April 1996.

Ezekiel, M. Methods of Correlation Analysis. John Wiley and Sons, New York, NY. 1930.

Franzmann, J.R., and R.L. Walker. Trend Models of Feeder, Slaughter, and Wholesale Beef Cattle Prices,

American Journal of Agricultural Economics. 54:507-512. 1972. 

Freebairn, J.W., and G.C. Rausser. Effects of Changes in the Level of U.S. Beef Imports, American Journal of

Agricultural Economics. 57:676-88. 1975.

Gourieroux, J., J. Laffont, and A. Monfort. Coherency Conditions in Simultaneous Switching Regressions,

Econometrica. 48:675-95. 1980.

Hahn, W.F. Unpublished internal analysis, U.S. Dept. of Agri., Econ. Res. Serv. 1996.

Hahn, W.F. Price Transmission Asymmetry in Pork and Beef Markets, Journal of Agricultural Economics

Research. 42:21-30. 1990.

Economic Research Service/USDA U.S. Beef Industry/TB-1874 39
Hahn, W.F. Asymmetry in Price Transmission for Beef and Pork. Technical Bulletin No. 1769, U.S. Dept. of Agri.,

Econ. Res. Serv. December 1989.

Jarvis, L.S. Cattle as Capital Goods and Ranchers as Portfolio Managers: An Application to the Argentine Cattle

Sector, Journal of Political Economy. 82:489-520. 1974.

Jones, R., W.D. Purcell, P.J. Driscoll, and E. Peterson. Issues and Cautions in Employing Behavioral Modeling

Approaches to Test for Market Power, Internet: Research Institute on Livestock Pricing,

(http://www.aaec.vt.edu/rilp/index.html). March 1996.

Lovell, M.C. Tests of the Rational Expectations Hypothesis, The American Economic Review. 76:110-124. 1986.

Mills, E.S. The Theory of Inventory Decisions, Econometrica. 25:222-38. 1957.

Mundlak, Y., and H. Huang. International Comparisons of Cattle Cycles, American Journal of Agricultural

Economics. 78:855-68. 1996.

Muth, J. Rational Expectations and the Theory of Price Movements, Econometrica. 29:315-35. 1961.

Nelson, K.E. The Cattle-Beef Subsector in the United States: A Brief Overview. ERS Staff Report No. AGESS

840106. U.S. Dept. of Agri., Econ. Res. Serv. February 1984.

Purcell, W.D. A Primer on Beef Demand, Internet: Research Institute on Livestock Pricing,

(http://www.aaec.vt.edu/rilp/). March 1996.

Rosen, S., K.M. Murphy, and J.A. Scheinkman. Cattle Cycles, Journal of Political Economy. 102:468-92. 1994.

Rucker, R.R., O.R. Burt, and J.T. LaFrance. An Econometric Model of Cattle Inventories, American Journal of

Agricultural Economics. 66:131-44. 1984.

Schmitz, J.D. Dynamics of Beef Cow Herd Size: An Inventory Approach," American Journal of Agricultural

Economics. 79:532-42. 1997.

Tomek, W.G. Farm-Retail Price Spreads in a Changing Economy. Staff Paper SP 96-07. Dept. of Agricultural,

Resource, and Managerial Economics, Cornell Univ., Ithaca, NY. December 1996.

Trapp, J.N. Investment and Disinvestment Principles with Nonconstant Prices and Varying Firm Size Applied to

Beef-Breeding Herds, American Journal of Agricultural Economics. 68:692-703. 1986.

Schneider, Roger, and Larry Haller. Review comments. U.S. Dept. of Agriculture, Grain Inspection, Packers and

Stockyards Administration. March 1999.

U.S. Dept. of Agriculture, Economic Research Service. Livestock, Dairy, and Poultry Situation and Outlook. LDPM-40, April 16, 1997.

U.S. Dept. of Agriculture, Grain Inspection, Packers and Stockyards Administration. Concentration in the Red Meat

Packing Industry. February 1996.

U.S. Dept. of Justice and Federal Trade Commission Horizontal Merger Guidelines. April 1992.

40 U.S. Beef Industry/TB-1874 Economic Research Service/USDA
Economic Research Service/USDA U.S. Beef Industry/TB-1874 41

Appendix table--Data series for cattle cycle analysis

Length of Period of

Variable time series estimated data Data source

All cattle and calves 1867-1997 NC Agricultural Statistics

Average live weight 1935-1996 NC Agricultural Statistics

Average price/cwt 1935-1996 NC Agricultural Statistics

Dollars per head 1867-1996 NC Agricultural Statistics

Dairy cows 1930-1997 NC Agricultural Statistics

All hogs and pigs 1867-1997 NC Agricultural Statistics

Consumer price index 1913-1995 NC Agricultural Statistics

Costs of and gross 1913-1972, Cost of Production

receipts to production 1972-1995 1996 summaries

Farm output productivity

indexes 1910-1988 NC Agricultural Statistics

Farm input productivity

indexes 1910-1988 NC Agricultural Statistics

Per-capita beef and

veal consumption 1909-1997 NC Agricultural Statistics

Per-capita pork (and

lamb) consumption 1909-1997 NC Agricultural Statistics

Per-capita red meat

consumption 1909-1997 NC Agricultural Statistics

Heifers 1 to 2 years old 1931-1969 1970-1997 Agricultural Statistics

NC = No data were constructed.
42 U.S. Beef Industry/TB-1874 Economic Research Service/USDA

Appendix B--Our Asymmetric Model of Price Movements

Since we have three things to explain (the farm, wholesale, and retail prices of beef), our model has to have three

equations. One equation focuses on the general level of prices and the other two look at the spread between farm

and wholesale prices and between wholesale and retail prices. Our three equations allow adjustment in the general

price level, adjustment to the farm-wholesale spread, and adjustment to the wholesale-retail spread. All the

adjustment equations compare where something is to where it should be and require prices to adjust to get them

closer to where they should be. That is, our adjustment equations are based on the idea that there is a target value

for the price level or some spread. The target level is a longrun value, that is, if current conditions were fixed

forever, prices and spreads would eventually settle down to their targets. In our adjustment models, it is possible for

prices or spreads to go immediately to their targets, or to go partially toward their targets, or even to over-shoot their

targets. If the general price level is too low, having one or all of the prices increase could get the general price level

to where it should be. If the farm-wholesale spread is too low, increasing the wholesale price or decreasing the farm

price could make the spread closer to what it should be. Also, if the wholesale-retail spread needs to increase, either

the retail price goes up or the wholesale price goes down.

Solving endogenous switching models such as our asymmetric model can be more complicated than solving

symmetric models. In fact, there are special coherency conditions on the model's parameters. If these are not met,

the model's equations may not have a solution in some cases or might have more than one solution in other cases.

Gourieroux, Laffont, and Monfort (1980) have a complete explanation of why coherency is necessary and what the

coherency conditions are. In our case, it turns out that we can impose coherency by forcing the parameters to take

sensible values. For example, if the wholesale price follows the farm price, a set of sensible values would make the

wholesale price go up (or at least not down) when the farm price goes up, and vice versa. 

Price Level Equation

Our first equation is the price-level equation. We will explain our price-level equation by starting with a simple

form, then adding complications until we reach the final functional form that we used. 

One common hypothesis in this type of research is that prices are determined at the farm level and wholesale and

retail prices follow the farm price. Suppose that this was the case. Suppose that ft is the farm price in month t, (ft-1

is the previous month's farm price) and that Ft is the target farm price. To hit its target in month t, the farm price has

to move from ft-1 to Ft

. In partial adjustment models, the actual change in the price can be less than that needed to

get the price to its target level. In our simple model, actual movement between months t and t-1, which is ft

-ft-1, is

given by the formula:

α1,f (ft - ft-1) = Ft - ft-1. (1)

Equation 1 is written in a slightly unusual way, but this specification simplifies the task of programing the estimation

routine. The type of adjustment from equation 1 depends on the value of α1,f. If α1,f is 1, there is complete

adjustment. If the term is greater than one, a smaller change on the left-hand side balances a larger change on the

right-hand side, resulting in partial adjustment. A value under 1 leads to overshooting. The sensible values for α1,f

are positive. A negative value means that prices adjust away from their equilibrium values.

To make equation 1 asymmetric, allow the value of α1,f to vary depending on the sign of (ft - ft-1). This will give two

values for the coefficient, α+

1,f when the farm price increases and α1,f when the farm price decreases. We now write

equation 1 as:
Economic Research Service/USDA U.S. Beef Industry/TB-1874 43

α*

1,f (ft - ft-1) = Ft - ft-1 (2)

where α*

1,f is α+1,f or α1,f depending on the direction that farm prices actually change.

When we set up our equations, we did not want to pre-specify that the farm price leads the other two prices. We did

not, in fact, want to specify that any particular price leads the others. So we modified equation 2 to come up with

our final form. Call the general target price level for month t, Gt

, and assume that the general price level in month 

t-1 is some weighted average of the farm price, the wholesale price, wt-1, and the retail price, rt-1. To help ensure that

the adjustment process is stable, we require that the weights on the lagged prices be positive. If prices are in general

too low, we can get closer to the equilibrium price level by raising farm, wholesale, or retail prices. Our general

price-level equation is written:

α*

1,f (ft - ft-1) + α*1,w (wt - wt-1) + α*1,r (rt - rt-1) =

Gt - β1,fft-1 - β1,wwt-1 - β1,rrt-1, 

where 

α*

1,i ≥ 0, i = {f,w,r} and * = {+ , -}

α*

1,i = α+1,i, when it - it-1 > 0,

= α1,i , when it - it-1 < 0,

Σβ1,i = 1, and

β1,i ≥ 0, i = {f,w,r}. (3)

We will leave the specification of Gt until later.

We would like to point out that equation 3 does not actually require that all prices increase when the general target

price, Gt

, is greater than the last month's price. However, if any price decreases, some other price would have to

pick up the slack by increasing enough to offset the decrease in the other price and balance the right-hand side of

equation 3. However, equation 3 does imply that if the last month's price level is too low, at least one price has to

increase this month. 

Farm-to-Wholesale Price Spread Equation

The next equation in our system is the farm-to-wholesale spread equation. Call the target spread in month t, S2,t.

The spread in the previous month can be calculated as (wt-1 - ft-1). If the current target spread is higher than last

month's spread, we can move closer to equilibrium if the farm price goes down or if the wholesale price goes up.

We can write the farm-wholesale spread equation as:

α*

2,w (wt - wt-1) - α*2,f (ft - ft-1) = S2,t - wt-1 + ft-1 (4)

All the αs in equation 4 are required to be positive. (When we estimated the model, it was easier to specify that the

α*

2,f be added on the left-hand side, but that their sign be negative.) If last month's spread was lower than this

month's target, prices can adjust to get the spread closer to its target by dropping the farm price or raising the

wholesale price.
44 U.S. Beef Industry/TB-1874 Economic Research Service/USDA

As in equation 3, equation 4 does not require that farm price go down or that the wholesale price go up when the

target spread is larger than the old spread. One price can move in the "wrong" direction, however. If one goes in

the "wrong" direction, the other has to move even more in the "right" direction to correct. Equation 4 does require

that either the wholesale price rise or the farm price fall when the target spread is larger than last month's spread.

Again, we specified equation 4 so that it could handle a wide variety of cases. Equation 4 is valid if the farm price

leads the wholesale price, if the farm price follows the wholesale price, and cases when their interactions are more

complex. 

Wholesale-Retail Price Spread Equation

The last equation is the wholesale-retail spread. This equation is similar to equation 4: the wholesale price replaces

the farm price and the retail price the wholesale, and there is a different target spread, but otherwise the structure and

interpretations are identical. We use the same type of sign constraints and our model is valid for all general types of

wholesale-retail price interactions.

α*

3,r (rt - rt-1) - α*

3,w (wt - wt-1) = S3,t - rt-1 + wt-1 (5)

The target levels and spreads determine the longrun retail, wholesale, and farm prices, that is, if the target levels

were fixed for a time, eventually all three prices would get to their respective target levels. We have not yet

specified how these longrun targets are determined. As is the case for models of this type, these target levels are

functions of other variables. As these variables change over time, the targets change over time as well. However,

even though the targets change, we will refer to a variable's effect on the target level as its longrun effect. Because

our model allows for partial adjustment, the shortrun effects of variables can vary from their longrun effects. 

Targets

Our model allows changes in supply and demand conditions to affect price spreads in the short run but not in the

long run. To account for this, the price-spread equations contain the change in the supply and demand variables, but

not their lagged values. If the other variables were stabilized for a long period of time, that is, they did not change,

their level would have no effect on the target price spread. Supply and demand variables affect the shortrun and

longrun price level, so the price-level equation has both the changes and the lagged values. Also, no statistical

model is perfect: minor variables could be missing, and even purely random effects could affect price levels and

spreads. These random effects are modeled as error terms, which we designate as ei,t. If we call the current value of

the supply and demand variables, Xt

, the change in these between month t-1 and t, dXt, and the set of intercept,

trend, and seasonal variables, It

, then our equations for the longrun price level target and longrun spread targets can

be written:

Gt = dXt

g1,

dX + Xt-1g1,X + It

g1,I + e1,t, (6)

S2,t = dXt

g2,dX + Itg2,I + e2,t, and (7)

S3,t = dXt

g3,

dX + It

g3,I + e3,t. (8)