Kuiper, W.E.

Meulenberg, M.T.G.

Verhees, F.J.H.M.

Wageningen Yield 

A METHOD TO DETECT BOTTLE-NECK STAGES WITHIN A MAR-
KETING CHANNEL 
Dr.ir. W.E. Kuiper, Prof.dr.ir. M.T.G. Meulenberg, 
Ir.ing. F.J.H.M. Verhees 
Department of Marketing and Marketing Research 
Wageningen Agricultural University, Hollandseweg 1 
6706 KN WAGENINGEN, The Netherlands 
1. Introduction 
One of the central themes in the current National Debate on new strategies for 
Dutch agriculture is the shift from volume to quality, i.e. from producing at 
the lowest cost to producing products that fit the needs and wants of target 
groups of consumers (Van der Ploeg et al.[l]). This market-driven strategy, 
however, is not new. Its necessity has been recognized for a long time. 
However, the implementation of such a consumer orientated system is not 
easy. Many firms are involved in the implementation process. They can be 
categorized into two or more successive stages of the agricultural marketing 
channel, managing the flow of agricultural goods through the entire value-
added chain (Meulenberg and Kool[2]). Consequently, if firms in at least one 
of the stages are not able or willing to participate in the consumer orientation 
strategy, then the whole channel might be hindered in adopting such a strategy. 
For example, in Western economies, like the Dutch one, the primary produc-
tion of agricultural goods is taking place at a large number of small farms such 
that each farm is a price-taker. Hence, to maximize its income the farming 
family has to minimize its costs. For this purpose, farmers continually apply 
new production technology that decreases production cost per unit output by 
increasing productivity. Moreover, farmers accept lower incomes than the 
average wage when employed in other sectors, simply because each farming 
family wants to stay on its own farm (De Hoogh[3]). Consequently, the farmer 
spends all his efforts producing as efficiently as possible; no time seems to be 
left to monitor the changing needs of consumers. Therefore, it could be that 
farmers will not adopt a market-driven strategy. As a consequence, they cause 
a bottle-neck in the marketing channel when other channel members, like retail 
companies, wholesalers and processing industries, introduce a consumer-
oriented policy. 
Bottle-necks may concern different types of problems such as in the field of 
channel efficiency and -effectiveness, and are often difficult to characterize. 
Nevertheless, ultimately all bottle-necks have an impact on the profits per unit 
387 
output and in this paper we will use this fact in developing a method to 
identify bottle-neck stages within a marketing channel. An empirical applicati-
on of this method to the marketing channel of pork in the Netherlands will also 
be provided. 
This paper is organized as follows: In section 2 we outline our definition and 
method of detecting bottle-neck stages. In section 3 the formal test procedure 
is briefly addressed. In section 4 we present our empirical results. In section 5 
conclusions round off the paper. 
2. Method 
Bottle-neck stages within a marketing channel imply inappropriate responses by 
companies at some stage of the marketing channel to policy-changes and -
outcomes at other stages of the marketing channel, and vice versa. Lack of 
responsiveness of companies within the marketing channel may concern total 
marketing policy, say the introduction of a new product at some stage in the 
channel, or a specific element of a marketing policy, e.g. the logistical opera-
tion. Such bottle-necks may arise from insufficient market orientation or from 
poor coordination mechanisms by chain members or from both. It is important 
to trace bottle-necks to manage chains effectively and efficiently. Methods 
testing for bottle-necks differ depending upon the type of bottle-neck one is 
searching for. For instance, testing for bottle-necks in logistical operations 
requires a different testing procedure as compared to testing for bottle-necks in 
communication. Nevertheless, ultimately all bottle-necks affect the profit per 
unit output, margin, say. Hence, the evolvement of a margin through time for 
a channel stage relative to the evolvement of the margins through time for 
other stages may be considered as an indicator of bottle-necks. Moreover, the 
lack of responsiveness of companies at one stage of the channel to changes in 
prices, margins and profits at other stages of the channel is also an important 
set of potential bottle-necks in marketing channels themselves. Therefore, we 
propose a statistical method to trace such bottle-necks and have applied this 
method to the Dutch pork channel. 
Before we give a more specific definition of bottle-neck for our problem, we 
will first discuss the analytical framework. This framework is based on the 
Law of One Price (LOP). According to Goodwin [4] the "LOP asserts that 
efficient trade and arbitrage activities will ensure that prices in spatially 
separated markets, once adjusted for exchange rates and transportation costs, 
will be equalized. Assuming absolute adherence to the LOP allows the defini-
tion of a single "representative price" common to all trade regions." Along the 
lines of the LOP we may expect that within a marketing channel margins will 
not diverge among the stages in the long run. 
Testing the LOP by cointegration techniques (Engle and Granger[5], 
Johansen[6]) has become popular in recent years (e.g. Goodwin[4], Ardeni[7], 
388 
Baffes[8], Lutz et al.[9], Karbuz and Jumah[10], Silvapulle and Jayasuriy-
a[ll]). In addition to testing for price integration, cointegration analysis can 
also be used to test whether one market can be considered as the central 
market which drives the prices of the other markets (e.g. Silvapulle and 
Jayasuriya[ll], Ericsson[12], Johansen[13]). The hypothesis of a central 
market also seems of interest for a marketing channel when testing for the 
existence of a channel leader structure (Meulenberg and Kool[2]). However, 
there is one important difference between a marketing channel and a set of 
spatially dispersed markets that does not allow for the application of 
cointegration analysis to determine whether one or more stages are leaders. 
The difference being that if some markets are not functioning well, then this 
should not necessarily affect the performance of the other markets. In contrast, 
if the profitability of a marketing channel stage leaves much to be desired, then 
the continuation of production in the other stages of the marketing channel will 
be in danger as well. Therefore, within a marketing channel, the margins of all 
stages, and hence, two successive stages, do not diverge in the long run 
because of the mechanism by which, through time, the margin of a stage does 
not only follow the margins of the previous stage and successive stage, but is 
also followed by the margins of these stages. A stage whose margin is not 
driven by the margin of the downstream or upstream adjacent stage is con-
sidered to be a bottle-neck, because it gives insufficient attention to perform-
ance problems in the other stages of the marketing channel. Notice that this 
lack of attention is not in line with e.g. a chain marketing strategy (Meulen-
berg and Kool[2]) which is an important instrument in becoming market-
oriented or customer-oriented. 
In the next section we formalize our framework by means of an error-correcti-
on model. 
3. Model 
Let M, = {m ,„...,rrip, )' be a vector of margins, where mu (i = l,...,p) is the 
margin of stage i and p ^ 2 is the total number of stages in the marketing 
channel in which stage 1 is upstream and stage p is downstream. Starting point 
of Johansen's cointegration procedure (Johansen and Juselius[14][15], 
Johansen[6][16]) is a vector autoregressive model that can be rewritten as 
AM, = TIM,., + E,.yr^MH + M + *A + £, (1) 
where &M, = M, - M,.„ n are the intercepts, D, are centered seasonal dummies 
which sum to zero over a full year, eu...,sn are IINp(0,A) and M^+I,...,M0 
are fixed. Suppose that {A/,} is integrated of order one, then the coefficient 
matrix II contains information about the long-run relationships between the 
variables in M. If rank(II) = r with 0 < r < p, then there are r long-run (i.e. 
cointegration) relationships and II can be expressed as the outer product of two 
(full column rank) p x r matrices a and ß: U = aß1, such that ß'M, is station-
389 
ary in which case (1) is called a vector error-correction model. The columns of 
ß are called the cointegrating vectors. 
If the margins do not diverge, i.e. are cointegrated, then r = p - 1 and ß'M, 
consists ofp - 1 linear combinations of mtt and mj+], (i = 1,..., 
p-1), cointegrating vector /, say. Given that margins do not diverge, three 
situations can be distinguished. First, if cointegrating vector i does not enter 
the &m„ equation in (1) but enters the ^m,+;, equation in (1), then stage / is 
said to be a bottle-neck. Second, if cointegrating vector i enters the &mu 
equation but not the Ami+U equation, then stage / + / is considered to be the 
bottle-neck. Third, if cointegrating vector /' enters both equations, then none of 
the stages is a bottle-neck. Notice that one may consider the absence of 
cointegrating vector i in both equations as the fourth possible situation. 
However, in that situation there is no error-correction at all and hence, mu and 
mi+Il cannot be cointegrated which does not comply with our assumption that 
margins do not diverge. 
In the next section an empirical application of the procedure outlined in this 
section is presented. 
3. Application 
We consider the pork marketing channel in the Netherlands. Three stages are 
distinguished (p = 3): the breeders (stage 1) who produce piglets, the feeders 
(stage 2) who produce pigs for slaughter and the retailers (stage 3) who sell 
pork to the consumer. We apply the Johansen procedure to test whether the 
margins in this channel are cointegrated, i.e. do not diverge in the long run, 
and to determine which stages are bottle-necks. 
An absolute measure of the margins cannot easily be obtained due to a lack of 
data concerning the costs. By way of approximation, we simply include a trend 
term in the cointegrating space in (1) to capture the changes through time in 
the cost per unit output and replace the mt variables by the output prices. Our 
sample consists of monthly data and runs from January 1989 up to and 
including May 1994. The data and their sources are available from the authors 
upon request. 
First, we determine the number of cointegrating vectors using the trace and 
\max test of the Johansen procedure. The results are displayed in Table 1 and 
indicate that the null hypothesis of no cointegrating vectors is rejected but the 
null hypothesis of one cointegrating vector cannot be rejected. As a conse-
quence, according to the Johansen procedure there is only one cointegrating 
vector instead of p - 1 = 2 . However, the acceptance of the second 
cointegrating vector relies on a p-value of approximately 25 percent, which 
usually would be considered too high. But since the power of the tests are 
likely to be low for cointegrating vectors with roots close to but outside the 
390 
unit circle, it seems reasonable in our case to use higher p-values than the 
usual 5 or 10 percent. Testing the null hypothesis of two cointegrating 
Table 1 Trace and A_„ statistics" 
p-r trace trace trace \mm X ^ Xra 
90% 95% 90% 95% 
*max 
1 5.17 10.49 12.25 5.17 10.49 12.25 
2 17.47 22.76 25.32 12.29 16.85 18.96 
3 58.01 39.06 42.44 40.54 23.11 25.54 
1}
 The critical values are taken from Table 2* in Osterwald-Lenum[17] 
vectors leads to a p-value of approximately 55% and hence, we tentatively 
conclude that there are two cointegrating vectors. 
The estimated cointegrating vectors are 
ß/M, = 0.091/>„ - 0.05QP2, - 0.0247; (2) 
ß2'M, = 0.03 lp* - 2.950pi( - 0.0687; (3) 
whereßj (j = 1,2) is they'th column ofß (i.e. j\h cointegrating vector), p, (i = 
1,2,3) is the output price per unit of stage /', and T is a linear time trend. 
Notice that the parameters attached to the price variables in (2) and (3) have 
opposite signs which is in accordance with non-diverging margins. As an extra 
check on the stationarity of ß/M, and ß/M, we apply the augmented Dickey-
Fuller test (Engle and Granger [5]) and find that in both cases the null hypoth-
esis of non-stationarity must already be rejected at the 1 percent significance 
level. This result confirms our conclusion that the margins in the pork market-
ing channel do not diverge in the long run. 
Finally, we consider the estimated speed of adjusment parameters a to deter-
mine which stage is a bottle-neck. The significant estimates are presented in 
Table 2. According to the Ap, column the margin of the breeders increases 
(decreases) if the margin of the retailers is 
391 
Table 2 Speed of adjustment parameters 
error-correction *p, Ap2 4PJ 
terms 
ß/Mf.t : Ö ^Ö3Ö Ö~ 
(normalized to p2) 
ßjMhl -5.78 0 -0.75 
(normalized to p3) (normalized to p3) 
lower (higher) than the margin of the feeders than is allowed for by the long-
run equilibrium between the margins of the retailers and the feeders. As a 
consequence, if feeders increase (decrease) their profits relative to the margin 
of the retailers, then the breeder will also increase (decrease) their margin. The 
4P2 column reveals that if the margin of the feeders is too high (low) relative 
to the margin of the breeders (according to the long-run equilibrium), then the 
margin of the feeders will decrease (increase). Finally, the Ap3 column shows 
that if the margin of the retailers is too high (low) relative to the margin of the 
feeders (according to the long-run equilibrium), then the margin of the retailers 
will decrease (increase). 
Given the fact that we selected k = 1, so that all lagged AM terms in (1) are 
deleted, we can interpret the results in Table 2 as follows: Because the perfor-
mance of the retail stage is driven by the profitability of the feeder stage, the 
retail stage is not a bottle-neck. On the contrary, the margin of the feeders is 
not driven by the performance of the retailers and hence, according to our 
definition the feeder stage is a bottle-neck. Nevertheless, the margins in the 
feeder stage follow the margins achieved by the breeders. This result could be 
explained by the fact that a considerable number of the farms in the pork 
channel vertically integrate piglet and slaughter pig production. In 1991, for 
instance, 30 percent of the piglets were bora and fed to slaughter weight at the 
same farm (Borgstein[18]). Moreover, if we take into account that full vertical 
control may sometimes be achieved in the absence of vertical integration 
through adequate contracts specifying "vertical restraints" (Tirole[19]), this 
percentage might be even higher. On the other hand, vertical integration or full 
vertical control is rare between the feeder and retail stages. 
Finally, the breeder's margin is not driven by the margin of the feeders and 
thus, the breeder stage is a bottle-neck. Even worse if the retailers perform 
better (worse) than the feeders for some time, then the breeders, anticipating 
that the good (bad) results for the retailers will lead to higher (lower) margins 
for the breeder stage in course of time, plan to increase (decrease) their 
production to such an excessive extent, that (according to -SJSß/M,^) the 
392 
market price of piglets falls (rises) and consequently, (according to -0.30/3/M,. 
;) the market price of slaugther pigs falls (rises) as well, which ultimately 
(through -Q.lSß/M,.,) negatively affects the results in the retail sector. So 
starting with the breeder stage, the breeders and feeders do not react the right 
way to performance signals from the retail stage. Since we may expect that the 
retail stage conducts a consumer-oriented strategy, the breeders and feeders 
have difficulties in adopting this strategy. 
4. Conclusions 
Our method to trace bottle-neck stages within a marketing channel relies on 
(possibly interval-scaled) time series data on the profit for a specific marketing 
channel stage per, representative unit of output. The application of this method 
to the pork marketing channel is shown to give interprétable results. 
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A method to detect bottle-neck stages within a marketing channel.

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