Market Integration in the US Beef supply Chain: A Threshold
Cointegration approach
Kedir Nesha Turi
Department of Agricultural and Consumer Economics, University of
Illinois at Champaign-Urbana
1. Introduction
Beef industry is the largest single industry within U.S.
agriculture, generating between $34 and $37 billion per year in
2006-2008 and accounting for 20% of the annual total market value
of agricultural products sold in the U.S. (USDA, 2009). The
industry is part of a vertically-related meat marketing and
distribution channel. Beef packers purchase live cattle from
cattle feedlots (upstream) and sell processed beef to wholesalers
and retailers (downstream). The upstream can be classified into
three distinct production tiers. According to Boyabath et al.
(2010) the three production tiers are cow/calf operations,
stocker and backgrounding operations, and feedlot operations.
1
The industry has under gone significant structural change in the
last decades. The upstream of the supply chain, the majority of
cattle operations are relatively small in scale. In 2007, many of
the cattle operations are small; some 77% of beef cow operations
have fewer than 50 head of cattle (USDA, 2009). Feedlots (where
beef cattle are fed an energy-intense grain diet before
slaughter) can reach much larger sizes; 40% of inventory is
contained in operations with more than 32,000 head (USDA, 2009).
In the same tier, the majority of feedlot operations have fewer
than 1,000 head of cattle. In general, beef cattle and feedlot
operations all have grown larger over the last 10 years, with
sharp declines most evident in the number of dairy operators.
Four meat packers slaughter and process more than 80% of thefed
cattle marketed in the United States (USDA, 2009).
The aforementioned market concentration and implied presence of
market power can potentially influence the degree and dynamics of
price transmission, leading to differential price effects on
different stages of the chain. Additionally, differing market
operation dynamics under competitive and noncompetitive market
2
conditions can have welfare implications with respect to the
efficiency and equity of the marketing system.
On the downstream side, there is a concern about the impacts of
increasing retail grocery store concentration on marketing
margins and farm-level prices (Marsh, 2004). Live cattle
producers argue that retail consolidation translates into market
power or collusive behavior and results in wider red meat
marketing margins. The margin for packers-retail is increasing
while the margin for live cattle-packers is declining. The wider
margins may result in lower live cattle and hog prices because of
a lack of offsetting market power at those levels (Cotterill,
1999; Schrimper, 2001).
The purpose of this paper is to investigate market efficiency and
coordination of vertically integrated US beef industry at
different stage. Specifically, this research analyzes price
cointegration using live cattle, packers and retail prices.
Cointegration is used as a tool to assess market efficiency and
can analyze both perfect and imperfect market conditions.
3
Cointegration of prices in distinct markets is an indication of
price transmission and market integration. Previous research on
efficiency of beef industry employed similar methodology. Except
Goodwin and Holt((1999), however, they assume symmetric short
term adjustment among the prices. Goodwin and Holt (1999) use
weekly data from 1981 to 1997 to test for nonlinearity of
cointegration relationship. They found no linear relationship and
estimated threshold cointegration. We extend Goodwin and Holt
(1999) works in two ways: first, we use monthly data from January
1980 to December 2010 and second, we employing various recent
developments in threshold cointegration methodology. We estimate
linear cointegration where nonlinearty is rejected and threshold
cointegration where nonlinearity is the case.
2. Conceptual Framework
2.1. Price transmission and market power in vertically
integrated market
Degree to which market shocks are transmitted through the
marketing chain is an important indicator of the market
4
efficiency. Vertical price linkages are often considered to be
reflection of the underlying structure, conduct, and performance
of the industry. In competitive markets, increases or decreases
in input prices are likely to be transmitted as proportionate
changes in marginal costs and aconsequently prices change. Price
transmission is widely explored. For example:Brorsen et al, 1985;
Wohlgenant 2001, 1985; Kinnucan and Forker, 1987; Shroeter and
Azzam, 1991; Goodwin and Holt, 1999; von Cramon-Taubadel, 1998;
Goodwin and Harper, 2000; Ben-Kaabia, et al., 2002. The
literature regarding price transmission has not converged yet.
The studies are vary in choice among possible analysis methods,
the assumptions made, the goods analyzed, time frequencies, time
periods and model specification (Frey and Manera, 2007).
Consequently, it is difficult to draw conclusions on which to
base policy decisions (Vavra and Goodwin, 2005).
Price transmission, which is shocks at one level of the market
are realized at other market levels, is considered as a crucial
indicator of the exercise of market power by participating agents
at different level. The link between price transmission and
5
market power has been explored by many prominent authors. For
example: Gardner, 1975; Cowling and Waterson, 1979; McCorriston
et al., 2001; Sexton et al., 2003; Lloyd et al., 2006. However,
Goodwin (2006) finds gap in his review of the topic. In most
articles there is a gap between the theoretical model and the
empirical application, as some of the marketing margin
determinants are unobservable. In meat and livestock markets,
this issue has been of particular interest in light of the
considerable consolidation and concentration of the industry by
packers and processors. Koontz and Garcia (1997) found evidence
of market power among beef meat packers though differs by
geographic regions and the extent is small.
2.2. Asymmetric Price Transmission
Peltzman (2000) argues that asymmetric price transmission is
prevalent in the majority of producer and consumer markets.
Economic theory offers a limited number of justifications for
price asymmetries. Peltzman (2000) articulate that if standard
economic theory that does not account for this situation must be
6
incorrect. Focusing on vertical price transmission, very few
justifications offered by economic theory market. Among these
justifications market power (non-competitive market) is the most
convincing argument.
Under the competitive markets and firm’s profit maximizing
behavior firms forced to adjust their prices to new cost
conditions immediately and presumably symmetrically (Manera and
Frey, 2007). This hold when frictions and imperfections are
absent. In the presence of this friction and imperfections,
output prices respond more quickly to input price increases than
they do to decreases. Manera and Frey (2007) outline the scenario
when tacit collusion in oligopolistic markets is a concern.
Accordingly, when prices rise, each firm is quick to increase its
selling price in order to signal its competitors that it is
adhering to the tacit agreement; when wholesale prices fall, it
is slow in adjusting its price because it does not want to run
the risk of sending a signal that it is cutting its margins and
breaking away from the agreement. Blinder (1994) and Blinder et
7
al. (1998) are among empirical literature that fall to this line
of assertion.
However, in most empirical work this conclusion is presented
without rigorous theoretical underpinning (Jochen Meyer and
Stephan von Cramon-Taubadel, 2004). The direction of impact of
market power on asymmetric price transmission is not determined
ex-ante in most cases. For instance, Ward (1982)argue that
oligopolistic market power can lead to negative asymmetric price
transmission if oligopolists are reluctant to risk losing market
share by increasing output prices.Similarly in the case of firms
facing a kinked demand curve oligopolistic market power can
result in negative asymmetry (Bailey &Brorsen, 1989). That is, if
a firm believes that no competitor will match a price increase
but all will match a price cut, negative asymmetry will result.
Otherwise if the firm presumed that all firms will match an
increase but none will match a price cut, positive asymmetry will
result.
8
Another explanation is the case where asymmetric price
transmission arises due to adjustment costs. In the case of price
changes, adjustment costs are also called menu costs which
originally developed by originally proposed by Barro (1972). Here
a change in nominal prices induces costs(for example, the
reprinting of price lists or catalogues and the costs of
informing market partners). Levy et al. (1997) and Dutta et al.
(1999) provide recent quantifications of menu costs in US retail
markets. For the US beef market, Bailey &Brorsen (1989) show that
packers, unlike feedlots, face significant fixed costs. In the
short run, margins may thus be reduced in an attempt to keep a
plant operating at or near capacity. Therefore, as a result of
competition between different packers, farm prices may be bid up
more quickly than they are bid down (negative adjustment). In
contrast to Bailey &Brorsen, Peltzman (2000) makes a case for
positive adjustment, arguing that it is easier for a firm to
reduce inputs in the case of an output reduction than it is to
recruit new inputs to increase output. This recruitment of inputs
will lead to search costs and price premia in increasing phases.
9
Ward (1982) suggests that retailers of perishable products might
hesitate to raise prices for fear of reduced sales leading to
spoilage. This would lead to negative APT. Many empirical
studies have found evidence that supports asymmetric farm-retail
price transmission for various fresh agricultural products
including dairy (Kinnucan and Forker ,1987; Frigon, Doyon and
Romain,1999; and Carman and Sexton, 2005), peanuts (Zhang,
Fletcher, and Carley, 1995), and citrus (Pick, Karrenbrock, and
Carman, 1999). Powers and Powers (2001) found that the price
transmission from farm to retail for California-Arizona lettuce
is not asymmetric in the magnitude or frequency of price changes.
Furthermore, less plausible economic justificationsgiven for
asymmetry pricetransmission includes: inventory management
strategies (Reagan and Weitzman, 1982), accounting methods such
as FIFO (Balke et al., 1998),and market agents expectation of
government intervention in case of price movements (Kinnucan and
Forker ,1987). Form of government intervention, often in the form
of floor prices, is quite common, especially in agriculture.
10
The often suggested, the main shortcoming of empirical asymmetric
price transmission studies based only on price data is the lack
of empirical tests to explain any imperfections found (Goodwin,
2006). However, since in most empirical analysis different
structural stories may be consistent with the results obtained,
an understanding of the fundamental structure of the markets
under consideration is essential for a proper interpretation of
the results (Goodwin, 2006). Analyzing beef market, Goodwin and
Holt (1999) argued that the adjustments to exogenous shocks at
various levels of the market may be nonlinear as well as
asymmetric. They estimated a full vector error correction (VEC)
model of monthly beef price relationships at the farm, wholesale,
and retail levels. Their results were consistent with causality
running from farm to wholesale to retail levels. They also found
evidence of statistically significant thresholds and asymmetries
in price adjustments.
3. Threshold Vector Error Correction Model (TVECM)
The standard cointegration relationship, specifically vector
error correction model (VECM),assumes linear relationship.11
Following Ben-Kaabaiet. al (2005) specification for two price
series with I(1):
∆pt=μ+α[ (ωt−1 )β]+∑k=1
k−1❑i∆pt−i+ϵt
(1)
Where:(ωt−1 )β is the cointegration vector evaluated at the value
β'=(1,−β )'. (ωt−1 )β=β'pt−1=[1 (ωt−1)β∆pt−1∆pt−2…∆pt−k]'. αis a (nx1)
vector of adjustment coefficients. βis the cointegrating vector
that is commonly interpreted as the long-run equilibrium relation
between the two prices in pt, while α gives the weights of the
cointegration relationship in the VECM equations.❑ii=1,2,…are
(nxn) matrixes of short-run parameters; and ϵt is a iidGuassian
random errors with zero mean and a positive definite covariance
matrix .∑
12
However, the standard error correction model has drawn criticism
for two drawbacks. The first criticism is that it assumes
symmetric adjustment to long-run equilibrium regardless of the
sign of the shock. Granger and Lee (1989) tried to solve the
issue by introducing a non-symmetric error-correction model. The
design is simply to segment the ECM term to positive and negative
deviation. The non-symmetric error correction model was further
developed by Enders and Granger (1998), who argued that standard
unit root tests are misspecified if adjustment is asymmetric.
Consequently,Enders and Granger (1998)tabulated new critical
values for unit root tests.The second criticism against the
conventional error correction model is that after a shock it
assumes instant adjustment towards the equilibrium and hence does
not consider, for example, possible transaction costs.Balke and
Fomby (1997)pointed that the movement towards thelong-run
equilibrium is not linear.That is the adjustment need not to
occur instantaneously but only once the deviations exceed some
critical threshold, allowing thus the presence of an inaction, or
no-arbitrage band (Stigler, 2011). Rather adjustments towards the
long-runequilibrium occur only when deviations from the long-run
13
equilibrium exceed acertain threshold.Small deviation from
equilibrium is characterized by a random walk (i.e. a lack of
cointegration).Balke and Fomby (1997) introduced threshold
cointegration method as solution to combine non-linearity and
cointegration.
Lo and Zivot (2001) extendedtheBalke and Fomby
(1997)’sunivariatecointegrationproperty in bivariate settingwith
a known cointegratingvector. Hansen and Seo (2002) improvedBalke
and Fomby(1997) model by estimating the cointegratingvectorand
the threshold parametersimultaneously. In this paper, a three-
regime threshold vectorerror correction model (TVECM3) with one
cointegrating vector and twothreshold parameters is
considered.The general TVECM model specification following Lo and
Zivot (2001) isas follows:
∆pt=μr+αr (ωt−d(β))+∑
k=1
k−1φtr∆pt−i+εt
r,ifcj−1≤ωt−d≤γj
(2)
14
Where:r=1,2,3 is regime number;i=1,…,k; k is lag length , d is
delay variable and d<k; (ωt−d ) is error correction term (residual
of the cointegration); γis the parameter of the threshold that
separates the regimes( j=1,2 in our case).
Equation (2) can be written our in full:
∆pt={μ1+α1 (ωt−d )β+∑
k=1
k−1φt1∆pt−i+εt1,if−∞≤ωt−dβ≤γ1
μ2+α2 (ωt−d)β+∑k=1
k−1
φt2∆pt−i+εt
2,ifγ1≤ωt−dβ≤γ2
μ3+α3 (ωt−d)β+∑k=1
k−1φt3∆pt−i+εt
3,ifγ2≤ωt−dβ<∞
(3)
A special case of the TVECM given in Eq (2) occurs if price
changes are smaller thantransaction costs. In this case, prices
will not adjust in the second regime (in the middleone) implying
that prices are not cointegreted. That is, μ2=0∧α2=0. The
resultingmodel is the so-called Band-TVECM. If (ωt−d )β=β'pt−1is
within the band, then pricesare not cointegratedandpt follows a
15
VAR (k) without a drift. However, in the outer bandseconomic
forces push prices moving together implying cointegration with
different adjustmentcoefficients.If(ωt−d )β<γ1∨(ωt−d)β>γ2, then the
cointegrating vector reverts tothe regime-specific mean with an
adjustment coefficient. ρ1∨ρ3.While∆pt adjusts to thelong-run
equilibrium with a speed of adjustment vectorα1∨α3. Where the the
adjustment coefficients,ρr, calculated as:
ρj=1+β'αj=1+[1−β2 ][α1
j
α2j]=1+α1
j−β2α2j
In testing for linearity, Balke and Fomby (1997) advocate two
step approach, first testing for cointegration and then for
threshold effect if there is evidence of cointegtaion. However,
this method may suffer of low power when true model contain
threshold effect and first step is tested based on linear
specification(Stigler, 2011). An alternative methodology by
Hansen and Seo (2002) assumes both threshold parameterγ and
cointegrating vector βare unknown and estimated from the data.
Hansen and Seo (2002) proposed a supLM test statistic of linear
16
VECM against a threshold VECM with two regimes when the true
cointegrating vector is unknown.The test is TVECM against the
null hypothesis of linear VECM. The distribution from SupLM test
cannot be tabulated due to presence of nuisance parameters and
hence the authors suggest two bootstrap approaches, with either
fixed regressor or a residual bootstrap(Stigler, 2011).Further,
Seo (2006) developed Sup-Wald test to test for null hypothesis of
no cointegration against Threshold cointegration based on TVECM
model. The test is based on three regimes where the middle
regime not taken in to account, since it does not adjust. The
Sup-Wald test does not depend on nuisance parameter and critical
values can be calculated via bootstrap method.
4. Data
The data used in this analysis is obtained at three level of beef
industry. The price received by live cattle, price received by
feed lot, price received by slaughters and packers (whole sale
price) andpiece received by retail stores (retail price) are used
for analysis. The national price for live cattle is cash price
which was obtained from CME database. This is nominal price. For
17
wholesale price, however, USDA (ERS) calculated equivalent value
starting with retail price and reassembling the parts. The
pricesseries span years from January 1980 to December 2010. This
period is where the consumption of meat declined because of
health concerns. The time-series data is monthly, not seasonally
adjusted, and all price series were converted into their natural
logarithms before analysis. We have total of 492 data point for
each series. Summary of the data is also provided as appnedex.
Visual inspection of graphs shows thatthe three price series are
non-stationary (Figure 1 to 3).. The ACF and PCF of this prices
also confirms the same (Figure 4 to 6). For beef across price
stages and changes in co-movement over time (Figure 1 to 3). We
will formally test for stationarity in the next section.
Table 1.Description of the variables
Variable name Label
lnLC Log of monthly average live cattle price
lnFC Log of monthly average feeder cattle
18
price
lnwhsale Log of monthly average meat packers
(whole sellers) price value
lnretail Log of monthly meat retailers price
value
5. Empirical Estimation and Result
5.1. Testing for unit root
Three beef channel price series were test for unit root. We
start with standard Augmented Dickey fuller test first. However,
literature argues that in the presence of a structural break, the
standard ADF tests are biased towards the non-rejection of the
null hypothesis. Thus, we employ Zivot and Andrews (1992) test
which account for one unknown structural break. This endogenous
structural break test is a sequential test which utilizes the
full sample and uses a different dummy variable for each possible
break date. A break date is chosen where the evidence is least
favorable for null hypothesis that says the series has the unit
root.
19
The unit root test result from Dickey Fuller shows that all
prices series are non-stationary on level. The test included
trend and constant in the specification. We included trend in the
test based on visual inspection of graph for trend and regression
estimation result where trend is significant. However, none of
the price series are trend stationary. Model selectionfor the
test is based on the AKaki Information Criteria of lag selection.
This criteria favored shorter lags models for all series. The
number of lagged used for both Dickey Fuller and Zivot and Andrew
tests are given in table 2.
Table 2.Lags selection and stationarity test (Dickey Fuller
andZivot and Andrew)
Prices Number of
lag (AIC
selected)
Dickey
fuller test
(with trend
and lag)
Zivot and Andrew
test
(with one
unknown break
dates)
Dickey fuller
test on 1st
difference
(with trend
and lag)
20
lnLC 4 -1.983 -4.794 -10.986
lnwhsale 3 -1.924 -5.275 -6.726
lnretail 2 -1.792 -4.016 -10.648
Critical value: Dickey fuller -3.980, -3.420, and -3.130 at 1%, 5% and 10%; Critical
value for Zivot and Andrew test: -5.57, -5.08, and -4.82 at 1%, 5% and 10%
Unit root test based on one time unknown structural break date
shows that all variables are non- stationary on levels. The break
dates are also provided for each series. Naturally, the predicted
break dates are around the same time for live cattle and
wholesale prices, April 1994. As expected structural break for
live cattle price occurs one month earlier than wholesale prices.
Retail price structural break occurs October 2002. The 1994
structural break for live cattle and wholesale price could be
explained by trade liberalization with Japan, Doha round world
trade negotiation, and NAFTA which opens up opportunities for US
beef industry. The 2002 structural change for retail price could
come from improved public attitude on the healthy diet in favor
of beef meat. In addition, two supply factors contributed for the
increased beef prices: first, decreased supply of cattle in the
21
United States due to bad weather and second, the U.S.
government’s ban on Canadian beef and cattle after the BSE
outbreak in Canada (USDA/ERS).
5.2. Cointegration Analysis
The unit root test above, both with and without structural break
consideration, establishes that all price series have unit root.
On the other hand the differenced series are stationary, that is,
the series are cointegrated of order one I(1). Therefore, we
proceed with analysis cointegration relationship between the
series at different stage of the beef supply chain. First we
analyze linear cointegration relationships, with and without
unknown structural break consideration. Then we will test for
linearity (threshold cointegration) both for cointegrated and non
cointegrated relationships. The analysis is conducted both on
bivariate (pair) relationship and multivariate relationship in
the chain.
22
5.2.1. Linear cointegrationwithout structural break adjustment
The linear cointegration analysis was conduct using Johansen
(1988)cointegration rank estimation method which is maximum
likelihood error correction estimation procedure. The model for
each pair prices and multivariate (all series together) were
estimated. In all cases, the specifications included constant in
the coinintegration equations (which means trend in the
underlying VAR). The lag in the accompanying VAR structure was
selected based on the Bayesian Information criterias(BIC). This
information criteria is conservative and prefers shorter lag
numbers. Accordingly two lags were deemed to be appropriate for
this analysis. Table 3 depicts theresult ofcointegration rank
estimation based on these models. According to linear
cointegrationanalysis all pairsprices as well the multivariate
arecointegrated. The bivariate analysis shows that the pairs are
cointegratedof rank one and the multivariate analysis shows
cointegration rank of three. These cointegration analyses are
without consideration of the structural break in underlying
series. Note that we have identified one structural break earlier
during unit root testing procedures. 23
Table 3.Johansen Cointegration Rank test
Prices Tracestatistics
Maximum statistics
Rank Teststatistics
Criticalvalue at5%
Teststatistics
Criticalvalue at5%
lnLC/ lnwhsale 0 17.98 15.41 15.82 14.071 2.56*
**3.76 2.16*** 3.76
lnwhsale/ lnretail 0 18.54 15.41 18.44 14.071 0.09*
**3.76 0.09*** 3.76
lnLC/lnretail 0 13.95 15.41 13.88 14.071 0.08*
**3.76 0.08*** 3.76
lnLC/lnwhsale/lnretail
0 35.41 29.68 19.15 20.971 15.51 15.41 15.41 14.072 0.09*
**3.76 0.09*** 3.76
5.2.2. Linear cointegration with one unknown structural change
adjustment
The above linear cointegration result could be biased since we
have series with structural break in the equations and we did not
account for it in our estimation. We could be falsely rejecting
24
the null hypothesis that no cointegration between the series
where some or all of the underlying series behave like an AR(1)
process with a structural break(Pfaff, 2008). Thus, if the
presence of changes distorts the inference in the univariate
frameworkwe may expect similar effects in the multivariate
framework when testing for the numberofcointegration relations.
This expectation is further supported by evidence from
smallsample studies conducted by Gregory & Hansen (1996) and
Gregory, Nason& Watt(1996) who have found that breaks in the
cointegration relation reduces the power ofsingle-equation
cointegration tests. We follow procedure developed by Lutkepohl,
Saikkonen, and Trenkler (2004) for estimating a VECM in which the
structural shift is a simple shift in level of the process. In
this procedure the structural break date is estimated
endogenously first and the VECM estimation is adjusted
accordingly.
According to this estimation most of the bivariate cointegration
do reject the null hypothesis that no cointegration between the
pair of series (table 4). The cointegration rank betwwen live
cattle and wholesale prices is marginally significant, that is,25
it is significant at 10% significance level. Compared to
estimation without structural break adjustment, the cointegration
relationship between live cattle price and whole sale price
becomes week. It reverts to full rank at 1 and 5% significance
level where both series are stationary by themselves. However,
since the initial stationarity test (using both Dickey Fuller and
Zivot and Andrew test)shows that both series are non-stationary
we trust the 10% significance level result. The cointegration
rank between live cattle and retail and wholesale and retail are
both significant at 5% significance level. The multivariate
equations arecointegratedof rank two at 1% significance level.
The multivariate case is similar result with estimation without
structural break adjustment.
Looking at bivariate relationship may exposes the hidden price
transmission friction which may not be observed when multivariate
model is estimated. Price transmission between sequential stages
of the channel which is hindered either by oligopoly or monopsony
power of one of the agentscannot be observed in multivariate
model. This hindrance is eased depending on the direction of
impact of the two forces on the price transmission in the channel26
(Weldegebriel, 2004). This looks the case for wholesale and live
cattle prices where packers have market power.
Table 4.Linear cointegration rank estimation with endogenous
structural change adjustment
Rank Trace
statistic
s
Critical
value at 5%
lnLC/ lnwhsale 0 43.32 15.83
1 9.32* 6.79
lnLC/lnretail 0 38.72 15.83
1 6.49** 6.79
lnwhsale/ lnretail 0 50.87 15.83
1 6.50** 6.79
lnLC/llnwhsale/lnretail 0 79.84 28.45
1 35.04 15.83
2 3.72*** 6.79
27
5.3. Testing for Linear vs nonlinear cointegration
Once the cointegrationvs no cointegrationrelationshipestablished,
we need test whether the equilibrium price adjustment follow
symmetric (linear) or asymmetric (nonlinear) process for those
cointegrated price relationships. We follow Hansen &Seo (2002)
supLM test where the cointegrating value is estimated from the
linear VECM and then, conditional on this value, the LM test is
run for a range of different threshold values. The maximum of
those LM test values is reported.The null hypothesis for this
test is that the adjustment to equilibrium is linear.
Unfortunately, the current methodology limits this test to be
performed only under bivariate relationship. However, based on
Weldegebriel (2004) theoretical work, breaking down the
relationship into bivariate can help us to clearly see where the
inefficiencies in the market lie. According to this test live
28
bivariate cointegration relationship between cattle price and
whole sale (packers) price and live cattle price and retail price
rejects the null hypothesis at 5% significance level(table 5)..
On the other hand, the cointegration between packer’s price and
retail price follows linear process. The later (the linear)
relationship corroborates the fact that both packers and
retailers in beef market are highly concentrated such that any
price change is pre negotiated. Packers have less output price
uncertainty. The nonlinear cointegration relationship between
live cattle price and packer’s price and live cattle and retailer
price emanates from the production input adjustment cost for
cattle producers. That is, deviation from long term equilibrium
adjusted only when it exceeds certain threshold because adjusting
price within that band is costly.
Table 5.Test for Linearity against Threshold: sup(LR) test
Prices Test Linear Vs. nonlinearity
lnLC/lnwhsale 33.876(0.034)**
29
lnLC/ lnretail 33.618(0.044)**
lnwhsale/lnretial 11.004(0.356)
Numbers in parenthesis are p-values
5.4. Vector error correction
In this section we estimated the error correction model based on
the models selected in the previous sections. First we estimate
VEC for pair of prices and multivariate prices. Then, we estimate
threshold Vector error correction model for those prices that
found to have nonlinear relationship.
5.4.1. Linear Vector Error correction model Estimation
Based on the cointegration rank test in previous section, we now
proceed toward estimation of the vector error correction model.
The lag selection for the underlying VAR model is based more
conservative on Information Criteria (BIC). As the prices series
displays upward trend we included constant in the error
30
correction term (ECT) which means trend in the cointegrating
equation. For comparison reason we present linear cointegration
estimation result for those bivariate prices that exhibits
nonlinear cointegration.
Live cattle and packers price vector error correction estimation
shows that the error correction term is significant. That is
there is long run cointegration between live cattle prices and
wholesale prices. However, the adjustment coefficient is
significant (at 5%) only for live cattle price. That is, in the
short run only the live cattle prices adjust to the equilibrium.
The speed of adjustment is also larger for live cattle price.
Positive deviation from long run equilibrium is adjusted down by
live cattle price.
[LCt−1,−0.8891∗¿∗¿ (0.053 )whsalet−1 ][ −0.123∗¿∗(0.041 )∆LCt−1
0.009 (0.043)∆whhsalet−1]+0.338
31
Wholesale and retail price vector error correction estimation
result below depicts that the error correction term is
significant at 1% significance level. The adjustment coefficient
for both wholesale price and retail price is significant at 1%
significance level. When the cointeration relationship deviates
up (positive) from lung run equilibrium it is corrected down by
whole sale price. On other side, when it deviates down (negative)
it is adjusted up by retail price. That is, whole sale price
corrects for positive deviation while retail price corrects for
negative deviation. The speed of adjustment is higher for
positive deviation. This shows that, the two agents in the
channel work together. This should be the case since both packers
and retailer are concentrated. The packer can exercise oligopoly
power and the retailer can exercise monopsony power which
balances each other. Thus, agents forced towork together than
exercising their power.
[whsalet−1−0.617∗¿∗¿ (0.042 )retailt−1 ] [−0.147∗¿∗(0.029 )∆whhsalet−1
0.029∗¿∗(0.010 )∆retailT−1 ]−1.66932
Cutting out whole sale price and estimating live cattle and
retail price cointegration gives different result. The adjustment
for positive deviation is highly significant (at 1%) while
adjustment for negative deviation is only significant at 10%
significance level. The adjustment speed is also better when live
cattle adjust for positive deviation from equilibrium.
[LCt−1−0.560∗¿∗¿ (0.085 )retailt−1 ] [ −0.069∗¿∗(0.024 )∆LCt−1
0.015∗(0.015 )∆retailT−1]−1.112
Finally, the estimation for all agents together (Live cattle,
Whole sale and retail price) based on two rank shows that all
adjustments are insignificant at 5% significance level.
Adjustment to positive deviation by live cattle is faster and
significant at 10% significance level. Though the statistical
significance level is weak, this result confirms the results we
obtained via bivariate estimation.
33
Eq1=[LCt−1−0.558∗¿∗¿ (0.091 )retailt−1 ] [ −0.085∗(0.043 )∆LCt−1
0.0004(0.015)∆retailT−1]−1.136
Eq2=
[whsalet−1−0.620∗¿∗¿ (0.071 )retailt−1 ] [ −0.084∗(0.053 )∆LCt−1
0.018(0.018)∆retailT−1]−0.690
In summary, the both bivariate and multivariate linear error
correction model estimation shows that live cattle price adjusts
the positive deviation from equilibrium. However, as discussed
under conceptual framework in this model prices react to all sort
of deviations. That is, prices adjust to equilibrium even if the
deviation is too small. It ignores the presence of cost of
adjusting price, cost of input adjustment for production (FIFO
input inventory), cost of price menu change for retail, etc.
5.3.2. Threshold Vector Error correction model Estimation
34
Threshold cointegration extends the usual linear cointegration to
cases where the adjustment towards long-run equilibrium does not
occur after each small deviation but more realistically only when
the deviations exceed some critical thresholds.Thus it account
for transaction costs, other institutional market interventions
and asymmetries in price transmission.In previous section, we
have already tested for possibility of threshold cointegration
instead of linear cointegration. We have found two of the
bivariate relationship to be threshold cointegrated. Here, we
estimate and present the threshold vector error correction model
(TVECM) result for those two bivariate price series.
We implement Hansen and Seo (2002) three regime TVEM estimation
technique. The three regime model makes much more economic sense
that there should be a buffer zone before the adjustment to
equilibrium is triggered. The lag selection for VAR was based on
AIC and BIC. Both AIC and BIC favored two lags. As the case with
linear VECM, two lag and trend was included in the regression
which common for all regimes. Trims of 2% (minimum number of
35
observation to be included in each regime) were set for
regression. The number of bootstrap replication used to estimate
the p values is 500 and the number of grid (threshold points to
be estimated) is also 300. The number of bootstrap replication is
higher than what the authors used in their original paper. We
just wanted to assure robustness of our estimate.
Table 6 depicts the result of the TVECM estimation. For
estimation of cointegration between live cattle and whole sale
price, the two thresholds value are 4.9% and 1.5% of the
equilibrium price. Decrease in live cattle price 4.9% below lung
run equilibrium moves the equilibrium process to regime one,
which calls for adjustment. If live cattle price increase 1.5%
above the long run equilibrium, then, the system moves to regime
there which calls for different adjustment pattern. Prices
adjustment process won’t be triggered within band (between 4.9%
decrease and 1.5% increases from equilibrium prices). The
adjustment process is significant in regime one. The speed of
adjustment is also faster. Note that even though its adjustment
36
coefficients are not statistically significant, regime three
contains majority (50.9%) of the observation.
For live cattle and retail price threshold cointegration the
observation is almost equally divided between regime one and
regime three. The middle regime has extremely low observation.
The two thresholds that drive the system between regime one and
three are 4.4 % and 3.6% decreases (increase) in price of live
cattle from equilibrium. The adjustment coefficients are
significant (at 10%) for negative adjustment and positive
adjustment in regime one and three respectively. Positive
deviation is adjusted by live cattle price in regime one while
negative deviation in regime three is adjusted by retail price.
The speed of adjustment is faster for positive deviation than
negative deviation.
Table 6. TVECM model estimates
Live cattle ~ whole sale
37
Regime I Regime II Regime III
parameter [Lct−1−0.826Wholesalet−1]←0.049−0.057<[Lct−1−0.826Wholesalet−1 ]←0.015[Lct−1−0.826Wholesalet−1]>−0.015
% obs 19.8% 29.3% 50.9%
αLiveCattle -0.369(0.0009)*** 0.104(0.3880) -0.001(0.9906)
αWholesale -0.321(0.0052)** 0.120(0.3328) 0.113(0.2466)
Livecattle ~ Retail
Regime I Regime II Regime III
parameter [Lct−1−0.756retialt−1]←0.044−0.044<[Lct−1−0.756retialt−1 ]←0.036[Lct−1−0.756retialt−1 ]>−0.036
% obs 45% 3.3% 45.3%
αLiveCattle -0.126(0.0130)* 0.279(0.7237) -0.007(0.7399)
αretail 0.030(0.1013) 0.205(0.4793) 0.033(0.0179)*
6. Conclusion
38
The purpose of this research is to assess the integration, and
thus the efficiency of the beef industry. Towards this goal, we
tested the presence of long run cointegration between the price
series. The price series considered in this analysis are live
cattle price, wholesale price and retail price. We tested and
estimated both linear and nonlinear (where linearity is rejected)
relationships between these price series. Accordingly, the both
bivariate and multivariate linear error correction model
estimation shows that live cattle price adjusts the positive
deviation from equilibrium. This result reflects the structure of
the industry where meat slaughters and packers are concentrated.
On the other hand, slaughters and packers and retailers prices
adjust to each other. Since both Packers and retailers have
market power, it is not surprising that this giants work together
in collaboration.
However, as discussed under conceptual framework in this model
prices react to all sort of deviations. That is, prices adjust to
equilibrium even if the deviation is too small. It ignores the
39
presence of cost of adjusting price, cost of input adjustment for
production (FIFO input inventory), cost of price menu change for
retail, etc. Therefore, we estimated TVECM model for live cattle
and wholesale prices and for live cattle retail prices.
Accordingly we find that live cattle and whole sale prices adjust
when the deviation crosses two thresholds value, 4.9% and 1.5%
below and above the equilibrium price, respectively. The both
adjustment coefficients are significant at the lower regime but
level of significant and the speed of adjustment is greater for
live cattle price. Though asymmetric, the basic result is
similar to the linear model. For live cattle and retail prices,
the two thresholds value that drive the system between regime one
and three are 4.4 % and 3.6% (decreases and increase in price of
live cattle from equilibrium respectively). The adjustment
coefficients are significant (at 10%) for live cattle and retail
prices in regime one and three, respectively.
Generally, the price transmission is unidirectional especially at
the upper tier of the market channel. The price the price
40
fluctuation transmitted from live cattle to wholesale but not
vice versa. This result is similar to finding by Goodwin and Holt
(1999). Whole sale and packers work together, that they negotiate
the price beforehand. The slaughter/ packer have price certainty
when it buys fed cattle as input. This is not the case at the
upper level of the channel. This has an implication for the
margin of the upper level agents in the beef industry.
41
References
Abdulai, A. (2002): Using threshold cointegration to estimate
asymmetric price transmission
in the Swiss pork market, Applied Economics, Vol. 34, pp. 679-687.
Azzam, A. M. (1997). Measuring Market Power and Cost-Efficiency
Effects of Industrial Concentration, Journal of Industrial Economics,
45(4), 377–86.
Bailey, D. V. and B. W. Brorsen (1989). Price Asymmetry in
Spatial Fed Cattle Markets, Western Journal of Agricultural Economics
14:246-52.
Balke, N.S. and Fomby, T.S. (1997).Threshold
Cointegration.International Economic Review 38: 627-645.
Balke, N.S., Brown, S.P.A. and Yücel, M.K. (1998). Crude oil and
gasoline prices: An asymmetric relationship? Federal Reserve Bank of
Dallas, Economic Review, First Quarter: 2-11.
42
Barro, R. J. (1972). A theory of monopolistic price adjustment,
Review of Economic Studies 39: 17-26.
Ben-Kaabai, M., Gill, JM., Boshnjaku, L. (2002): Price
transmission asymmetries in the Spanish lamb sector. Paper presented
at the X. Congress of European Association of Agricultural Economists, 28-31 August,
Zaragoza, Spain.
Bernhard Pfaff, Analysis of Integrated and Cointegrated Time
Series with R, Springer New York, 2008.
Blinder, A. S. (1994), “On sticky prices: Academic theories meet
the real world”, in Mankiw,
N.G. (ed.), Monetary Policy, Chicago and London, The University of
Chicago Press.
Blinder, Alan S., E. R. D. Canetti, D. E. Lebow, and J. B. Rudd
(1998). Asking About Prices: A New Approach to Understanding
Price Stickiness , Russell Sag e Foundation, New York.43
Brorsen, B. Wade, Jean-Paul Chavas, and Warren R. Grant (1985).A
Dynamic Analysis of Prices in the U.S. Rice Marketing Channel" J.
Bus.& Econ. Stat., 4:362-369.
Carman, H.F. (1998). California Milk Marketing Margins, Journal
of Food Distribution Research November, 1-6.
Carman, H.F. and R.J. Sexton (2005). A Supermarket Fluid Milk
Pricing Practices in the Western United States, Agribusiness 21:
509-530.
Cotterill, R. W. "Continuing Concentration in the U.S.: Strategic
Challenges to an Unstable Status Quo, Res. Rep. No. 48, Food
Marketing Policy Center, University of Connecticut, August 1999.
Cowling, K and Waterson, M. (1979).Price-Cost Margins and Market
Structure.Economica43: 267-274.
Dawson, P.J. and Tiffin, R. (2000): Structural breaks,
cointegration and the farm-retail price spread for lamb, Applied
Economics 32:1281-1286.
44
Dimitri, C., A. Tegene, and P.R. Kaufman (2003). U.S. Fresh
Produce Markets: Marketing Channels, Trade Practices, and Retail
Pricing Behavior, U.S. Department of Agriculture, Economic
Research Service, Agricultural Economic Report No. 825,
September.
Frey, G. and M. Manera (2007). Econometric models of asymmetric
price transmission. Journal of Economic Surveys 21(2):349-415.
Frigon, M., Doyon, M. and Romain, R. (1999). Asymmetry in Farm-
Retail Price Transmission in the Northeastern Fluid Milk Market,
University of Connecticut, Food Marketing Policy Center, Research
Report No. 45.
Gardner, B.L. (1975). The farm-retail price spread in a
competitive food industry, American Journal of Agricultural Economics
57:399-409.
Goodwin, B.K. (2006). Spatial and Vertical Price Transmission in
Meat markets. Paper presented at the Workshop on Market
45
Integration and Vertical and Spatial Price Transmission in
Agricultural Markets. University of Kentucky, USA, April.
Goodwin, Barry, and Matthew Holt, (1999).Price Transmission and
Asymmetric Adjustment in the U.S. Beef Sector.American Journal of
Agricultural Economics 81.
Gregory, A.W., Hansen B.E. (1996). Residual based tests for
cointegration in models with regime shifts, Journal of Econometrics
70:99-126.
Gregory, A.W., Nason, J.M., Watt, D. (1996). Testing for
structural breaks in cointegrated relationship, Journal of
Econometrics 71:321-341.
Hansen, B. and Seo, B. (2002). Testing for two-regime threshold
cointegration in vector error-correction models, Journal of
Econometrics, 110, pages 293 – 318.
46
Kinnucan, H.W. and O.D. Forker (1987). Asymmetry in Farm-Retail
Price Transmission for Major Dairy Products, American Journal of
Agricultural Economics 69: 285-292.
Koontz, S., & Garcia, P. (1997). Meat-packer conduct in fed
cattle pricing: Multiple-market oligopsony power, Journal of
Agricultural and Resource Economics 22: 78–86.
Levy, Daniel, M. Bergen, S. Dutta, and R. Venable (1997). The
Magnitude of Menu Costs: Direct Evidence from Large U.S.
Supermarket Chains, Quarterly Journal of Economics 112: 791–825.
Lloyd, T. A., McCorriston, S., Morgan, C. W., and Rayner, A. J.,
(2006). Food Scares, Market Power and Price Transmission: The UK
BSE Crisis. European Review of Agricultural Economics 33(2): 119-147.
Lo, C. and Zivot, E. (2001). Threshold Cointegration and
Nonlinear Adjustments to the Law
of One Price. Macroeconomic Dynamics 5: 533-576.
47
Lutkepohl, H., Saikkonen, P. and Trenkler, C. (2004). Testing for
the Cointegrating Rank of a VAR Process with Level Shift at
Unknown Time, Econometrica, Vol. 72, No. 2, 647–662.
McCorriston, S., Morgan, C.W. and A.J. Rayner (2001): Price
transmission: The interaction between market power and returns to
scale, European Review of Agricultural Economics 28:143-159.
Marsh, J., and G. Brester (2004). Wholesale -Retail Margin
behavior in Pork and Beef, Journal of Agricultural and Resource
Economics, 29, 45–64.
Meyer J, von Cramon-Taubadel S. (2004). Asymmetric Price
Transmission: A survey. J. Agric. Econ. 55 (3): 581-611.
Peltzman S. (2000). Prices rise faster than they fall. The Journal
of Political Economy, 108, pp. 466–502.
Pick, D.H., J. Karrenbrock, and H.F. Carman (1990). Price
Asymmetry and Marketing Margin Behavior: an Example for
California B Arizona Citrus, Agribusiness 6: 75-84.
48
Powers, E.T. and N.J. Powers (2001) “The Size and Frequency of
Price Changes: Evidence from Grocery Stores,” Review of Industrial
Organization 18: 397-416.
Reagan, Patricia B., and Martin L. Weitzman (1982). Asymmetries
in Price and Quantity Adjustments by the Competitive Firm, Journal
of Economic Theory 27: 410-420.
Schrimper, R. A. Economics ofAgricultura1 Markets. Englewood
Cliffs, NJ: Prentice-Hall, Inc., 2001.
Schroeder, Ted C. and Barry K. Goodwin (1990). Regional Fed
Cattle Price Dynamics, West. Ournalof Agricultural Economics 15:111-122.
Schroeter, John and A. Azzam (1991). Marketing Margins, Market
Power, and Price Uncertainiy, Amer. .lAgr. Econ.73 : 990-999.
Seo, M. (2006), Bootstrap testing for the null of no
cointegration in a threshold vector error correction model,
Journal of Econometrics 134:129–150.
49
Von Cramon-Taubadel S. (1998). Estimating asymmetric price
transmission with the error correction representation: an
application to the German pork market.European Review of Agricultural
Economics, 25, pp. 1–18.
Ward, R.W. (1982). Asymmetry in Retail, Wholesale and Shipping
Point Pricing for Fresh Vegetable.American Journal of Agricultural
Economics 64: 205-212.
Weldegebriel, H. T., (2004). Imperfect Price Transmission: Is
Market Power Really to Blame? Journal of Agricultural Economics 55: 101-
114.
Wohlgenant,M., and J. Mullen (1987). Modeling the Farm-Retail
Price Spread for Beef, Western Journal of Agricultural Economics 12(2),
115–125.
50
Vara, P. and B.K. Goodwin (2005).Analysis price transmission
along the food chain, OECD Foord Agriculture and Fisheries
Working Papers, No. 3.
Zhang, P., S.M. Fletcher, and D.H. Carley (1995). Peanut Price
Transmission Asymmetry in Peanut Butter, Agribusiness 11: 13-20.
Zivot, E. and D. Andrews, (1992). Further evidence of great
crash, the oil price shock and unit root hypothesis, Journal of
Business and Economic Statistics 10: 251-270.
Appendix 51
Summary of the Data
Variable Obs Mean Std. Dev. Min Max
lnfcma 492 4.289774 0.37709 3.231792 4.845226lnlcma 492 4.147217 0.292086 3.315095 4.62746lnwhsale 492 5.073199 0.286764 4.269698 5.603962lnretail 492 5.518179 0.390141 4.584968 6.115009
6070
8090
100
livec
attle
pric
e
1980m 1 1985m 1 1990m 1 1995m 1 2000m 1M onths
Figure 1.live cattle price series
150
200
250
300
whole
sale
value
1980m 1 1985m 1 1990m 1 1995m 1 2000m 1M onths
52
Figure 2.whole sale price (value) series
250
300
350
400
450
reta
il valu
e
1980m 1 1985m 1 1990m 1 1995m 1 2000m 1M onths
Figure 3. Retail price (value) series
44.5
55.5
6
1980m 1 1985m 1 1990m 1 1995m 1 2000m 1m onth
lnlcm a lnwhsalelnretail
Figure 4.logged Pricesseries for live cattle, wholesale and
retail prices
53
-.15
-.1-.0
50
.05
.1
1980m 1 1985m 1 1990m 1 1995m 1 2000m 1m onth
D.lnlcm a D.lnwhsaleD.lnretail
Figure 5.logged and differenced Prices series for live cattle,
wholesale and retail prices
-1.00
-0.50
0.00
0.50
1.00
Autocorrelations of lnlcm
a
0 10 20 30 40LagBartlett's form ula for M A(q) 95% confidence bands
-0.50
0.00
0.50
1.00
Partial autocorrelations of lnlcm
a
0 10 20 30 40Lag95% Confidence bands [se = 1/sqrt(n)]
Figure 6. ACF and PACF of live cattle priceseries
54
-1.00
-0.50
0.00
0.50
1.00
Autocorrelation
s of ln
whsale
0 10 20 30 40LagBartlett's form ula for M A(q) 95% confidence bands
-0.50
0.00
0.50
1.00
Partial autocorrelation
s of ln
whsale
0 10 20 30 40Lag95% Confidence bands [se = 1/sqrt(n)]
Figure 7. ACF and PACF wholesale price series
-1.00
-0.50
0.00
0.50
1.00
Autoco
rrelat
ions o
f lnretail
0 10 20 30 40LagBartlett's form ula for M A(q) 95% confidence bands
-0.50
0.00
0.50
1.00
Partial
autoco
rrelat
ions o
f lnretail
0 10 20 30 40Lag95% Confidence bands [se = 1/sqrt(n)]
Figure 8. ACF and PACF retail price series
55
-0.40
-0.20
0.00
0.20
0.40
Autocorrelations of D.lnlcm
a
0 10 20 30 40LagBartlett's form ula for M A(q) 95% confidence bands
-0.40
-0.20
0.00
0.20
0.40
Partial autocorrelations of D.lnlcm
a
0 10 20 30 40Lag95% Confidence bands [se = 1/sqrt(n)]
Figure 9. ACF and PACF differenced live cattle price series
-0.30
-0.20
-0.10
0.00
0.10
0.20
Autocorrelation
s of D
.lnwh
sale
0 10 20 30 40LagBartlett's form ula for M A(q) 95% confidence bands
-0.20
-0.10
0.00
0.10
0.20
Partial autocorre
lation
s of D
.lnwh
sale
0 10 20 30 40Lag95% Confidence bands [se = 1/sqrt(n)]
Figure 10. ACF and PACF differenced wholesale price series
56
-0.20
-0.10
0.00
0.10
0.20
Autocorrelations of D.lnretail
0 10 20 30 40LagBartlett's form ula for M A(q) 95% confidence bands
-0.20
-0.10
0.00
0.10
0.20
Partial autocorrelations of D.lnretail
0 10 20 30 40Lag95% Confidence bands [se = 1/sqrt(n)]
Figure 11. ACF and PACF differenced retial price series
-.5
0
.5
1
1.5
-.5
0
.5
1
1.5
-.5
0
.5
1
1.5
0 10 20 30 0 10 20 30 0 10 20 30
lnlc, lnlc lnlc, lnretail lnlc, lnwhsale
lnretail, lnlc lnretail, lnretail lnretail, lnwhsale
lnwhsale, lnlc lnwhsale, lnretail lnwhsale, lnwhsale
M onthsG raphs: im pulse variable and response variable
Figure 12.Impulse response functionbased on VEC estimation
57
Top Related