Shared Agency and Bidder Dominance in Repeated Cattle Auctions
Kalyn T. Coatney
Dept. of Economics & Finance
University of Wyoming Dept. 3985
1000 E. University Ave.
Laramie, WY 82071
307-766-2178
Dale J. Menkhaus
Dept. of Agricultural & Applied Economics
University of Wyoming Dept. 3354
1000 E. University Ave.
Laramie, WY 82071
307-766-5128
Owen R. Phillips
Dept. of Economics & Finance
University of Wyoming Dept. 3985
1000 E. University Ave.
Laramie, WY 82071
307-766-2195
Jennifer L. Scheer
Dept. of Agricultural & Applied Economics
University of Wyoming Dept. 3354
1000 E. University Ave.
Laramie, WY 82071
307-766-2386
March 2005
*Draft, please do not quote without permission from the authors. Research support from the
Paul Lowham Research Fund is gratefully acknowledged. Any opinions, findings,
conclusions, or recommendations expressed in are those of the authors and do not necessarily
reflect the views of the funding source.
Shared Agency and Bidder Dominance in Repeated Cattle Auctions
Introduction
Oftentimes in the production chain firms share the same agents to be upstream buyers or
downstream sellers. This paper empirically identifies the market effects of the same firms
sharing a common buying agent in repeated English auctions. The data came from cattle
auctions in Wisconsin, in which several beef processors have a common buying agent. Shared
agency, as Bernheim and Whinston (1985) observe is a widespread practice. Many products and
services are bought and sold through agents who represent principals that are competitors in
upstream or downstream markets. Downstream products are sold through merchandizing agents
and brokers, often through a franchise. Similarly upstream inputs may be secured through
commissioned agents. As a case in point, approximately $20.78 billion of cattle livestock is
traded in the United States through registered auctions (USDA, GIPSA 2003).1 Of this amount,
88 percent is purchased by commissioned cattle order-buyers and dealers. Commissioned order
buyers typically purchase for multiple principals. This practice has been longstanding.2
Bernheim and Whinston (1985) show that principals have an incentive to engage
common agents in order to reach collusive price levels. These agents not only help with
coordinating the input or output they buy or sell, but aid in the overall cooperation of firms
across all strategic variables. Through the incentive constraints placed on them, shared agents
have the capability to wipe away all competition between principals. In an auction environment,
the practice of multiple principals using a common buying agent increases bidder concentration.
An increase in bidder concentration increases monopsony power and the potential for collusion
among the agents. Phillips, Menkhaus and Coatney (2003) and Menkhaus, Phillips and Coatney
(2003) have demonstrated in experimental English auctions the collusive tendencies of
1
concentrated buyers, and identified factors that facilitate cooperation. The essential properties of
the English auction: open outcry or other methods of signaling, combine with multiple-unit and
repeated auction contact to facilitate collusive behavior and specifically the formation of bidding
rings (Adams, et al (1991) and Klemperer 2002).
In a bidding ring the act of bidding signals ring members of intent to purchase. The bids
are easily monitored. Bids also can be used to punish a defector from a prescribed plan of action,
and they can be used to exclude potential rivals from entering. It is widely known among cattle
buyers that bids are used to raise a rival’s cost. This is referred to as “buying your seat” at cattle
auctions. Large buyers simply drive up the winning bid of the targeted rival. This is a risky
proposition as the target bidder may quit bidding and leave the predator with a higher priced unit.
Unlike the typical predatory pricing model, where the predator is selling or purchasing all units
at the same price, at an auction the agent is purchasing multiple units over the course of the
auction at different prices. This allows the predator to average out any over-priced units that
may have been purchased while preying on the target. The sophisticated predator with enough
purchasing volume could actually raise a rival’s costs with little or no increase in winning bid
payments.
In the sale of cull cows agents are controlled by principals through buy orders.3 These
orders specify a targeted number of units the buying agent should seek, and price guidelines for
different cattle qualities.4 Principals develop their buy-orders prior to an auction. These orders
are then communicated to their agents. Agents are generally paid on a commission basis. Before
the auction sale begins an agent reports to the principal(s) the expected competition and quantity
for sale.5 Orders may be adjusted as a result of this information. The agent carries out the orders
of the principal and generally competes against several other agents and principals representing
2
themselves. The agent will communicate to the principal during the sale to inform the
principal(s) regarding the success of an order. If the agent is unable to purchase items at the buyorder price(s), the principal may then either increase buy-order prices or accept fewer units. The
principal may be simultaneously competing in other auctions via agents. Shared agency reduces
the number of potential bidders over time and across auctions.6
The geographic market for cull cattle is local. Sellers normally haul cows in small
quantities (pickup stock racks) for short distances. It can be infered that the marginal value
related to hauling long distances does not outweigh opportunity costs. These costs include the
marginal cost of transportation, weight loss or loss of the unit, and most importantly, the
opportunity cost of time and effort spent away from the seller’s main business of farming or
ranching. Cull cow packers have substantiated that most sellers are local, and sell within 50
miles of the auction site. Producers who choose not to use auctions may sell their stock direct to
a processor or feedlot. The cattle still must be hauled, and unlike auctions, the seller will not
receive multiple bids; a buyer tends to make a take it or leave it offer.
The purpose of this study is to identify the price impacts of a dominant bidder created by
principals sharing a commissioned agent in a repeated English auction. The focus is on the
bidding behavior of the dominant agent in a series of cattle auctions. We empirically measure
the market power exerted by this dominant agent. Some general results are worth keeping in
mind as the analysis is described: (1) an increase in bidder concentration has a measurable effect
on lowering overall bid prices at an auction (2) the dominant bidder pays less on average than
other bidders, and (3) consecutive purchases by the dominant bidder generate a decreasing trend
line in winning bid prices.
3
Data and Descriptive Statistics
The data used for the analyses are from a Wisconsin auction firm that sold cull cows
during the period October 4, 1999 through January 26, 2000. There were 34 sale dates or
auctions during this time frame and 7,722 individual observations or sale tickets. Each sale
ticket is for a single cow and contains a ticket number. The ticket number identifies the animal’s
owner, date and time of the sale, along with animal breed, weight, and negative physical
attributes (if any). Also on the ticket is the starting bid and final selling price with the principle
buyer’s number. All of this information is publicly observable, except for the seller’s
identification. Verification of common agency was conducted by matching agent signatures on
the principle’s final billing invoices. Principals representing themselves signed their own
invoices.
The auction house supplied the information regarding the type of business engaged in by
the principles and the representation of each buying agent. 7 The configuration of buyers at an
auction went as follows. There were five meat processors or packing plants (VPacker, TPacker,
EPacker, PPacker and GPacker) that were principle buyers; one firm owned 2 plants (VPacker
and Tpacker). All of the regional packers are represented and PPacker is the largest of these
processors. There was one feedlot principle (OS) and one dealer principle (TA). There were two
producer principles (L and F). Another category of buyers is “other,” which includes one-time
or infrequent purchasers such as dairy, feeder, farmer or rancher producers. See Table 1. There
was one shared agent (SHRAG). This commission agent represented these processing plants
(VPacker, TPacker and EPacker), and the one feedlot firm (OS). The other two processors
(PPacker and GPacker) each sent a salaried agent to buy. All other buyers represented
themselves at these auctions (L, F, and Other).
4
Table 1 reports the number of head, prices paid and average live weight purchased by
each principal over the 34 auctions. SHRAG purchased 73.72% of the total available units for
sale and is clearly the dominant bidder.8 All other bidders purchased the remaining 26.28% and
are referred to simply as FRINGE. From the separate principal perspective, no principal
purchased more than 23.98 % of the total units sold and no single principal held a dominant
position in the market. Interestingly, SHRAG managed to purchase almost the same number of
cattle for VPacker, EPacker and OS during the four-month time period and 34 separate auctions.
SHRAG only represented TPacker from October 4 through November 8; no additional cattle
were purchased for that principal’s plant for the remainder of the sales.
The average price of all 7,722 animals sold was $33.72/cwt, and the average live weight
was 1283.18 pounds (Table 1). SHRAG's average winning bid was $1.23/cwt (3.6%) below the
average winning bid, while FRINGE paid $3.41/cwt (10.11%) above the average winning bid.
This is preliminary evidence that the dominant buyer was able to raise rivals’ cost. The average
weight of cows purchased by SHRAG was 1257.74 pounds, while the average weight of animals
purchased by FRINGE was 1354.56 pounds. PPacker purchased the heaviest cull cows (1602.64
pounds) at the highest average price ($40.77/cwt) while VPacker purchased the lightest cull cows
(1095.57 pounds) at an average price of ($26.61). These price-weight relationships are expected
as heavier cull cows tend to have less negative attributes and yield more red meat.
Holsteins constituted the majority (75.33 %) of cull cows sold and on average they sold
for $33.44/cwt (Table 2). Beef breeds made up about 21.39 % of the animals sold, with an
average price of $34.82/cwt. Beef breeds tend to yield more red meat on a percentage basis than
Holstiens and also tend to be more efficient converters of feed stuffs to meat. As a result, beef
breeds command a higher price on average. Table 3 depicts the distribution of the principles'
5
purchases as they relate to breed types. It appears that every principle purchased both dairy and
beef cows. However, the percentages of total purchases by principal for Holsteins were variable,
ranging from 56.95 % to 93.29 %. The lower percentages tended to be associated with firms that
are feeders or speculators, while the higher percentages generally were slaughtering firms. This
may indicate that principals have preferences regarding breed type, because of the expected
marginal yields and marginal value products specific to their business.
The average price of cull cows with no negative attributes was $35.05/cwt, as compared
to the average price of cull cows with negative attributes of $25.66/cwt (Table 2). 9 Table 4
depicts the distribution of the principles' purchases as they relate to negative attributes. All
principals purchased cull cows with and without negative attributes. VPacker was the only
principal to purchase a high percentage of cull cows with noticeable defects relative to its total
purchases (42.41 %). This partially explains the lower average price paid by SHRAG because a
significant percentage of his purchases are for VPacker (25.28 %). All other principals tended to
purchase cull cows with a higher incidence of no negative attributes ranging from 88.38% to
97.63% of their total purchases.
Auction Concentration
Given the descriptive analysis of the data, it appears that every principal listed could
compete for any animal at some point during the auctions. Thus, every principal is a potential
competitor with every other principal, although each principal may prefer, though not
exclusively, different live animal characteristics. These finding support a product market
definition of cull cows. A price dependent hedonic model is used to describe the competition
among the competing principals via their agents and account for the heterogeneous nature of the
item being sold. This analysis further develops a concentration measure that tests bidder
6
concentration impacts on auction prices. The measure relies on the Herindahl-Hirshman Index
(HHI).
Although all potential entrants represented at the auction have sunk costs of attending,
the barrier to entry/exit in bidding on individual units is considered small.10 At an auction, entry
and exit occur during the bidding for a unit and across units sold. Concentration calculations
reflecting the complete history of play must be dynamic to reflect changing market shares as the
auction is conducted. 11 Our data only identify those bidders who successfully win a bid during
the auction and not those who may have bid but did not win.12 So bidding behavior on a single
item cannot be studied.
Market share is measured by the proportion of winning bids during an auction.13
Concentration is updated as the sale of units progresses through an auction. A cumulative
Herfindahl-Hirschman Index (CHHI) is formulated to measure the change in winning bid
concentrations during auction. The CHHI is a continuously updated measure as each unit within
an auction is sold. Generally the CHHI will show high and unstable levels of concentration early
in an auction, and as units are sold it converges to a less concentrated and more stable figure. A
concentration measure is more reliable if multiple auctions, with virtually all the same buyers are
thought of as a single game, then the first periods of the next auction are part of the bidding
strategy from the last auction.
A statistical procedure was devised in order to reduce wide swings in the CHHI between
auctions. A transition period was established by comparing the mean ten-period moving average
standard deviation of the CHHI across all auctions to an auction specific ten-period moving
standard deviation of the CHHI (figure 2). 14 Once the auction’s ten-period moving average
standard deviation of its CHHI equaled the overall mean ten-period moving average standard
7
deviation CHHI, this marked the transition length for that auction. Therefore, as the volatility in
the beginning of the auction increased, the transition period increased. The transition length
averaged 34 observations or about 15 percent of an average auction and 22 observations or about
10 percent of an average auction for winning bid concentration and represented principal
concentration calculations, respectively.
The transition period’s CHHI is calculated using the previous auction’s overall CHHI as
the starting point15 and the current auction’s CHHI. Therefore, the transition period’s CHHI was
calculated by giving progressively greater weights to the current auction’s CHHI and
progressively less weight to the previous auction’s overall CHHI, after which the current
auction’s CHHI is used for the remainder of the auction. Its converged value is then in turn used
toward the calculation of the next auction’s transition period CHHI. Therefore, the CHHI
calculated allows for not only within, but across auction analysis.
Figure 1 depicts the progression of the CHHI for winning bid concentration with shared
agency (CHHIA) across three example auctions. The CHHIA is higher than a CHHI that
presupposed the principles buying independently (CHHIP). This assumes each principle
represents itself and exactly replicates the observed purchases. The CHHIP is on average 2,160;
the CHHIA is on average 5,667. Shared agency in these auctions more than doubles the
Herfindahl measure of concentration.
The number of animals purchased in succession by the shared agent (SHRAGdur), is
another indication of market power. The minimum winning bid duration of the SHRAG is zero
and the maximum is 28, with a mean and standard deviation of 3.13 and 3.97, respectively. A
value of zero indicates successful entry by fringe competitors which occurred 2,029 times during
the 7,722 observations. The antithesis of SHRAG duration is fringe duration (FRINGEdur) or
8
fringe competition duration. The minimum fringe duration is zero and the maximum is 6 with a
mean and standard deviation of 0.37 and 0.74, respectively. A value of zero indicates successful
entry by the SHRAGinant bidder. This occurred, as expected, on 5,693 occasions.
SHRAG is naturally related to SHRAGdur and FRINGEdur, both of which are related to
the CHHIA and CHHIP variables. The focus of this analysis is on how the structural dynamics
of an auction change from the sale of one unit to the next within and across auctions. Therefore,
the presence of the SHRAG bidder contributes not only to direct but also indirect impacts on
price by its impact on duration and overall concentration. Once the dominant bidder’s indirect
impacts on price are adequately accounted for, the direct impacts on price are should be related
solely to the bidding strategy of the dominant bidder.
Model and Estimation Procedure
A system of linear equations is estimated to explain how the selling price for cull cows in
an English auction is impacted by winning bidder concentration, represented principal
concentration, the presence of a dominant bidder, the duration of dominance by the dominant
bidder, the duration of dominance by the fringe competition, lagged selling price and the
observed cull cow characteristics. Simultaneity between the structural variables CHHIA and
CHHIP and price is expected and is reflected in the following set of equations along with all a
priori expectations. CHHIA cannot be a function of CHHIP as it is the agent with the winning
bid who determines which buy-order the animal is best placed. In the present case, SHRAG is
the only agent representing multiple principles. The following system meets the rank and order
conditions for identification.
SP = f(CHHIA, CHHIP, SHRAG, SHRAGDur, FRINGEDur, L1SP, HC, Neg, Wt, Wt2, Wt3, T) (1)
a priori (-)
(-)
(-)
(-)
(+)
(+) (-) (-) (+) (-) (+) (-)
9
CHHIA = f(SP, L1CHHIA, HC, Neg, Wt, Wt2, Wt3, T)
a priori
(-)
(+)
(?) (?) (?) (?) (?) (?)
(2)
CHHIP = f(SP, CHHIA, L1CHHIP, HC, Neg, Wt, Wt2, Wt3, T)
a priori
(-)
(+)
(+)
(?) (?) (?) (?) (?) (?)
(3)
where: SP = Selling Price - $/cwt
CHHIA = Cumulative Winning Bid Concentration for the Agents – 0 – 10,000
CHHIP = Cumulative Representative Principal Concentration – 0 – 10,000
SHRAG = Shared Agent – 0-1, Fringe = Base
SHRAGDur = Shared Agent Duration – Successive Animals Purchased
FRINGEDur = Fringe Bidder Duration – Successive Animals Purchased
L1SP = Selling Price Lagged One Unit Sold - $/cwt
HC = Holstein Cow – 0-1, Non-Holstein = Base
Neg = Negative Attribute – 0-1, Non-Negative = Base
Wt = Animal Weight – Pounds
T = Trend – Observation, 1 – 7,722
L1CHHIA = Cumulative Winning Bid Concentration for the Agents Lagged One Unit
Sold – 0 – 10,000
L1CHHIP = Cumulative Representative Principal Concentration Lagged One Unit Sold –
0 – 10,000
Each of the equations contains cattle characteristics (HC, Neg and cubic Wt) as
regressors, in order to assure that the remaining influences on price and the measures of
concentration (CHHIA and CHHIP) accounts for the private values of principles. These private
values are unknown a priori. If a characteristic is more (less) preferred by principals due to
varying marginal value products related a characteristic, then we would expect the characteristic
10
to positively (negatively) impact the concentration measures. If a fringe buyer prefers one
characteristic over another, then both concentration measures may be impacted. If, however, one
of the SHRAG client’s prefers one characteristic over another, due to the shear volume of
purchases by the dominant agent, we would expect an impact only on the principal concentration
measure. The cattle characteristics variables are limited to the data and are by no means
complete. Frame, flesh condition and muscling would also be important observable
characteristics for buyers to estimate value. However, the cubic weight price relationship is
assumed to be a good proxy as weight is a function of the missing characteristics (citation).
A proxy for trend (T) is included in each equation to capture outside influences not
explained over all auction periods and is simply the unit sold. Lagged endogenous variables also
are included in each equation in order to capture residual price and concentration dynamics.
These account for trend related changes within the sale not captured by other variables.
Inclusion of lagged dependent variables in the model, particularly in the presence of
autocorrelation, results in an inconsistent ordinary least squares estimator, as well as an
inconsistent estimator of the autocorrelation coefficient. An alternative to the use of the lagged
dependent variable (Greene (1993, pp. 435-436)), is based on the method of instrumental
variables, resulting in lagged predicted endogenous variables (L1PSP, L1PCHHIA and
L1PCHHIP). The selling price was predicted using an auxiliary equation with price as a function
of the auctioneer’s asking and first offer prices, beef carcass price from the previous day, the
cattle characteristics and a trend variable for the auction (sale). The asking price is the dollar
value/cwt at which the auctioneer starts the bidding for the animal. The carcass price is the daily
average price ($/cwt) for a carcass of 90 percent lean fresh beef, Omaha. The total R2 for this
Yule-Walker estimated equation was 0.90. Predicted values of the concentration variables were
11
obtained by regressing CHHIA and CHHIP on the same variables used to predict the selling
price. The respective total R2 values for these equations were 0.98 and 0.96. The predicted
values were then lagged and used as instruments in the respective equations.
Due to the nature of the data, serial correlation is expected in all equations. Serial
correlation originates from the nature of the data in that the price of a previous animal likely has
some impact on the price of the current animal, resulting in successive error terms being
correlated. Similarly, under prediction of the concentration variables in one period is expected to
be followed by an under prediction in the next. The lagged dependent variable in each equation
also potentially contributes to serial correlation. The first and second stage equations were
therefore estimated using the Yule-Walker method (SAS/ETS User’s Guide 1990).
Results
The second stage Yule-Walker results for the system of simultaneous equations are
reported in table 5. As expected, cattle characteristics are important determinants of price and
are of the expected signs. Cattle traits also are important determinants of agent and principal
concentrations and exhibit the same signs as in the price equation. In so far as possible, these
variables create a fairly homogenous product from which to evaluate the impacts of market
structure variables on price.
The market structure and dominant bidder variables impacts on price are of primary
interest in this study. An increase in the winning bidder concentration during the course of an
auction negatively impacts price. On the other hand, an increase in the represented principal
concentration positively influences price, a result that is contrary to the a priori expectation.
This result may indicate disciplining within the SHRAG group maintained by the shared agent,
suggesting that higher prices must be paid to deviate from a presubscribed plan of equal market
12
shares. This explanation is consistent with the observation that the fringe bidders pay higher
prices on average than the dominant bidder.
The shared agent (SHRAG) contributes to a significant reduction in the average winning
bid price ($2.65/cwt) for each purchase. The converse of which is also true, in that the fringe
average winning bid was significantly greater than the dominant bidder. Similarly, for each
successive purchase made by the dominant bidder, price decreases by $0.05 providing support
for Proposition 3. However, successive purchases by fringe bidders do not significantly impact
price, suggesting that fringe bidders cannot gain market momentum. The latter result may also
be due the fringe firm’s inability to maintain the consistent higher prices paid, thus giving up its
dominance to the dominant bidder.
As expected, selling price and both concentrations are inversely related – lower (higher)
prices result in higher (lower) concentrations. By definition, higher winning bidder
concentration also resulted in higher principal concentration in the market under investigation.
Finally, according to a priori expectations, all lagged instrumental variables were positive and
significant.
The following represent the total impacts of the COB on the winning bid price, along
with the progressive winning bidder concentration, on price.
SP
SP
 SP 
E




 SHRAG  SHRAG SHRAGdur
(4)
SP
SP
SP CHHIP


CHHIA CHHIA CHHIP CHHIA
(5)
where:
E[∂SP/∂SHRAG] ≡ Total Expected price impact due to the dominant bidder
∂SP/∂SHRAG ≡ Direct price impact due to the dominant bidder
13
∂SP/∂SHRAGdur ≡ Direct price impact of dominant bidder dominance
υ ≡ Mean dominance duration of the dominant bidder
∂SP/∂CHHIA ≡ Total price impact of progressive winning agent bidder concentration
(∂SP/∂CHHIP)(∂CHHIP/∂CHHIA) ≡ Total indirect price impact of progressive winning bidder
concentration on price through the represented principal concentration
The total expected combined effect of the dominant firm in the market reduced price by
$2.81/cwt. A change in winning bidder concentration by 1000 units reduces price by $0.48,
taking into account both the direct impact of bidder concentration on price and the indirect
impact of bidder concentration on price through principal concentration.
Conclusions
The purpose of this study was to identify the impact on prices in a repeated unit repeated
English auction, where a dominant bidder was created in part by due to multiple packerprincipals sharing a common buying agent. A shared agent can negatively impact the winning
bid when he purchases an item, ceterus paribus, in a repeated unit repeated English auction
setting. The impact on price due simply to a dominant buyer’s presence in the market, however,
is not known as all bidding data were not analyzed. Entry by fringe bidders sharply increased the
winning bid demonstrating the significant contribution of small entrants. Business tactics which
inhibit entry by fringe firms will most likely have a negative impact on prices at auctions.
The use of a continuously updating bidder concentration facilitated the investigation of
the resulting impacts of the current and history of winning bidder concentration on winning bid
prices as concentration changes throughout the sale. Due to shared agency, the level of winning
bidder concentration in an auction should be measured at the agent, not principal, level.
Measurement of concentration at the principal level may distort the results of an inaccurately
14
defined bidding structure. No matter which agent contributes to increases in the level of bidder
concentration in the auction, the winning bidder concentration alone may result in depressed
prices received by producers. Therefore, business practices, regardless of intent, which inhibit
the number of potential bidders at auction may result in lower prices paid. In an auction setting,
the dominance duration of a dominant bidder feeds upon itself and is the means for only its
temporary destruction.
The conclusions drawn from this research rely on the fact that a shared agent for multiple
principals decreases the number of potential buyers. Shared agency may facilitate the agent’s
ability to gain a dominant position in the auction and gain market power. Producers potentially
lost about $34.65 on an average weight animal purchased by the dominant buyer or $197,262.45
during the four month period. There appears to be a not so insignificant amount of commerce
affected regarding the bidding behavior of the shared agent.
Whether or not prices would necessarily increase if the principle cartel is disbanded is not
clear since competition among principles takes place in a simultaneous auction framework. If
principles are using some kind of sharing arrangement and the results of increased price paid by
the fringe indicate an across market discipline strategy, then the principles need only readjust the
location of each firm’s auction market dominance. Eliminating shared agency all together may
increase the difficulties of maintaining a cartel not only within an auction, but across auctions.
These results reveal the need for further investigation not only of the current auction, but also
other simultaneous auction markets within the principles relevant geographic market. Additional
work could establish if the results are robust in general to English auctions in other relevant
markets.
15
Endnotes
1
The auctions utilize the English auctioning method.
2
It is common to observe cattle buyers who are participating in the family tradition, where
purchasing agreements or salaried positions are handed down from generation to generation.
3
Cull cows are cows that do not meet the productivity requirements of their current owner, most
generally ranchers, dairymen and feeders. The cows may be suffering from such afflictions as
lameness, cancer, infertility, mastitis, or simply old age. These cows may be primarily dairy
cows or beef cows depending upon the region in the United States.
4
Principles tend to use private rather than public definitions of quality.
5
Buyers typically walk the alleys and catwalks to observe the quality of cattle delivered, and
each other, prior to the sale. The auction house generally provides information to the buyers
regarding the total quality delivered. Buyers at cattle auctions may also gather un a “buyers”
room provided by the auction house which may have one or more phones where conversations
can be readily overheard by the rival buyers.
6
Packer principles are generally simultaneously purchasing cattle at multiple auctions whole
accepting direct deliveries from local purchasers and dealers. Large order-buying firms typically
are simultaneously matching a large number of buy-orders across multiple auction locations for
packers, feeders and other order-buying firms.
7
For reasons of confidentiality, the names of the principles and agents cannot be provided in this
document.
According to the auction house, COB’s COBinance has existed for many years prior to the
analysis period and persists today even though EPacker has changed ownership since the time
frame of this analysis.
8
These attributes are visual appraisals and were recorded by the auctioneer. Examples of
negative attributes would be lameness, mastitis udders, or cancer eyes. These negative attributes
affect the usefulness of the animal for further production (feeding, milking, calving or slaughter),
and are in fact causes for culling from production. Negative attributes such as lameness and
cancer eyes reduce the red meat yield as the affected areas are normally condemned.
9
Cattle auctions do not charge the buyers an entry fee, but instead charge the sellers for
auctioning services. Therefore, the marginal costs of entry entail information gathering costs
from the bidding history. Entry also conveys information to other bidders and may be
considered a cost if a bidder’s private valuation is truly revealed, subjecting the bidder to
strategic behavior by rivals. Exit costs curtail opportunity, although the opportunity related to
winning may in fact be negative.
10
In the presence of turn taking by all agents, this progressive calculation is not meaningful as no
learning, only monitoring, is taking place.
11
16
The data were collected at a typical English auction for cattle that utilize only verbal auctioning
were agents bids are submitted by gesturing. The auctioneer and ring-men mentally keep track
of the bidding. Since only the last bid is of consequence, this is not a formidable feat. It may
take only 10 seconds to sell a cull cow in which time numerous bids may occur. This auctioning
environment provides a formidable task of recording all bids. Electronic auctions that record all
bidding activity would not suffer from this limitation.
12
Cattle auctioneers will call out the buy-order number of the principle when an agent purchases
the animal. This informs auction employees as to which pen to yard the animal. Animals are
kept separate for shipment to the respective principles after the auction.
13
Resulting choices of the transition period are not significantly sensitive to choosing a five, ten,
fifteen period moving average standard deviation.
14
We used the average HHI calculated from the entire data series for the first auction’s starting
HHI measure.
15
17
References (CHECK REFERENCES FOR COMPLETENESS)
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Concentration, and Risk Attitudes: An Experimental Analysis,” Review of Agricultural
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20
Table 1. Total Number of Head, Average Prices and Average Live Weights Purchased by
Principals and Shared Agent
Principal/Agent
Number of Head
Price ($/cwt)
Live Weight (lbs)
VPacker*
1832
26.61
1095.57
OS*
1851
36.29
1388.44
EPacker*
1852
34.52
1294.10
TPacker*
158
32.32
1180.57
PPacker
627
40.77
1602.64
L
610
34.82
1256.38
GPacker
169
35.96
1315.75
F
164
36.83
1220.61
TA
149
36.41
1188.56
Other
310
35.44
1217.81
Total/Average
7722
33.72
1283.18
SHRAG
5693
32.49
1257.74
FRINGE
2029
37.13
1354.56
*Principles Represented by Dominant Bidder
21
Table 2. Breed and Negative Attributes - Frequencies and Average Values
Breed Type
Number of Head
Price ($/cwt)
HC = Holstein (Dairy)
5817
33.43
JE = Jersey (Dairy)
124
29.01
BR = Brown Swiss (Dairy)
75
35.24
BL = Black (Beef)
768
35.05
HE = Hereford (Beef)
330
33.81
XB = Crossbred (Beef)
305
35.00
RE = Red (Beef)
249
35.35
OT = Other (Unknown)
54
37.48
Total/Average
7722
33.72
1 = Negative Attribute
1103
25.66
0 = No Negative Attribute
6619
35.05
22
Table 3. Frequency Distribution of Principle Purchases by Breed Type
HC1
HC
JE1
BR1
BL2
HE2
XB2
RE2
OT
Total
%Total
VPacker*
1439
78.55
95
10
122
70
53
38
5
1832
OS*
1054
56.94
10
19
360
173
134
85
16
1851
EPacker*
1637
88.39
4
24
80
18
30
50
9
1852
TPacker*
124
78.48
1
0
22
2
3
6
0
158
PPacker
476
75.92
1
6
47
22
34
31
10
627
L
466
76.39
3
5
69
18
27
16
6
610
GPacker
155
91.72
0
5
2
1
1
5
0
169
F
125
76.22
3
2
11
9
4
7
3
164
TA
139
93.29
2
0
4
3
0
1
0
149
Other
202
65.16
5
4
51
14
19
10
5
310
Total
5817
75.33
124
75
768
330
305
249
54
7722
COB(Subtotal)
*
Principles Represented by Shared Agent
1
Dairy Breed
2
Beef Breed
23
Table 4. Distribution of Principle Purchases by Negative Attributes: Frequency of Purchases,
Percentage of Total of Type Available for Sale and Percentage of Principles Purchases
No Negative Attribute
Negative Attribute
Hd
%Total
%Purchases
Hd
%Total
%Purchases
VPacker*
1055
15.94
57.59
777
70.44
42.41
OS*
1711
25.85
92.43
140
12.69
7.57
EPacker*
1800
26.19
97.19
52
4.71
2.81
TPacker*
148
2.24
93.67
10
.91
6.33
PPacker
612
9.25
97.61
15
1.36
2.39
L
566
8.55
92.79
44
3.99
7.21
GPacker
165
2.49
97.63
4
.36
2.37
F
150
2.27
91.46
14
1.27
8.54
TA
138
2.08
92.62
11
.10
7.38
Other
274
4.14
88.39
36
3.26
11.61
Total
6619
1103
*Principles Represented by Dominant Bidder
24
Table 5. Two-Stage Least Squares AR(1) Yule-Walker Estimated Coefficients and Standard
Errors (in Parentheses).
Equation/
Regressors
Intercept
Selling Price
CHHIA
CHHIP
6.28
(3.33)
414.49*
(93.26)
-38.79*
(0.97)
172.04*
(56.14)
-10.60*
(0.61)
0.03*
(0.003)
SP
CHHIA
CHHIP
COB
COBdur
FRINGEdur
L1PSP
-0.0005*
(0.0001)
0.0007*
(0.0003)
-2.65*
(0.19)
-0.05*
(0.01)
-0.13
(0.11)
0.09*
(0.008)
0.97*
(0.003)
L1CHHIA
-76.90*
(3.40)
-267.72*
(8.09)
1.75*
(0.23)
-0.001*
(0.0002)
2.23E-7*
(4.43E-8
0.008*
(0.0009)
0.85*
(0.008)
-23.89*
(2.06)
-75.99*
(5.00)
0.54*
(0.13)
-0.0003*
(0.0001)
6.38E-8
(2.65E-8)
0.0005
(0.0006)
0.98
0.94
L1CHHIP
HC
NEG
WT
WT2
WT3
T
-1.86*
(0.10)
-7.21*
(0.13)
0.05*
(0.008)
-0.00003*
(6.14E-6)
5.64E-9*
(1.57E-9)
0.0002*
(0.00002)
Total R2
0.58
* - Significantly different from zero,  = 0.01.
25
Figure 1: Example CHHI for Agents and Principals – October 4, 6 and 11 Sales
Cumulative HHI for Agents and Principles: Example Auctions
10000
October 6
October 4
October 11
9000
8000
7000
6000
5000
4000
3000
2000
1000
Daily HHIA
CumHHIA
26
Daily HHIP
CumHHIP
203
181
159
137
115
93
71
49
5
27
174
152
130
108
86
64
42
20
221
199
177
155
133
89
111
67
45
23
1
0