The effect of farmer market power on the degree of farm-retail price
transmission: A simulation model with an application to the Dutch
ware potato supply chain
Tsion Taye Assefa, Erno Kuiper and Miranda Meuwissen
Business Economics Group, Department of Social Sciences, Wageningen University,
Hollandseweg 1, 6706 KN, Wageningen, The Netherlands
Abstract
A classic oligopoly/oligopsony model is developed to assess the degree of price transmission
in a two-stage farmer-retailer supply chain. A simulation experiment based on data of the
Dutch ware potato sector illustrates how price transmission may become asymmetric as a
consequence of retailer oligopsony power in the sense that farm price decreases are only
partially passed on to consumers whereas farm price increases are fully transmitted. Oligopoly
power by farmers to level their bargaining power vis-à-vis the retailers may even make the
degree of price transmission getting worse.
Keywords: asymmetric farm-retail price transmission, supply chain, retailer oligopsony,
farmer oligopoly, Dutch potato sector
1. Introduction
Price transmission refers to the way a price at one level of the supply chain responds to a price
change at another level of the chain (Bunte, 2006). The issue of price transmission has been
extensively studied by agricultural economists due to its policy and welfare implications
(Meyer and von Cramon-Taubadel, 2004; Gil and Ben-Kaabia, 2007). It has policy implications because absence of perfect price transmission may suggest market failures as a
consequence of non-competitive behaviour of chain actors. Similarly, the absence of perfect
price transmission can result in reduced producer and consumer welfare since they will not be
able to benefit from increased sales and reduced prices, respectively (Meyer and von CramonTaubadel, 2004; Gil and Ben-Kabiaa, 2007). Imperfect price transmission takes place when a
price change at one stage is not fully transmitted, when instantaneous transmission is absent
(time lag) and when the degree of transmission depends on the sign of the price change
(nature of transmission is asymmetric) (Bunte, 2006).
Several studies have identified possible causes of imperfect price transmission (Bunte and
Peerlings, 2003; Cutts and Kirsten, 2006; Gil and Ben-Kabiaa, 2007; Falkowski, 2010;
Acharya et al., 2011). Among the causes identified, the most common ones are market power
in the downstream sector of the food supply chain, adjustment costs at the retail level,
perishability of the product creating retailer resistance from raising prices, and the inventory
accounting method employed by retailers (first-in-first-out method) which can delay the
transmission of farm input price changes. All this literature has particularly focused on the
transmission of price shocks from the upstream sector to the downstream sector (from farm to
retail).
Even though the studies on the causes of asymmetric price transmission tend to be inconclusive in nature (Meyer and von Cramon Taubadel, 2004; Vavra and Goodwin, 2005), there is
still room to believe that strong market power in the downstream sector of the supply chain is
the most prevailing reason for imperfect price transmission given the assertions made by a
majority of the studies. However, research lacks on the degree of price transmission in
situations where farmers have equal or stronger market power than actors in the downstream
sector. Filling this literature gap will help to show whether farm price transmission can be
improved if farmers have equal or stronger market power than the downstream sector. Given
that resources such as land are getting scarce, it is not unreasonable to expect farmers to get
more (or at least equal) power than the downstream sector in the future. In the near future,
policies that help increase the market power of farmers or help reduce that of the downstream
sector might also make that happen. Consequently, the aim of our paper is to specify a classic
two-stage (farmer-retailer) vertical chain model of oligopoly/oligopsony that allows retailers
in their capacity as sellers to be price-takers (full competition) or oligopolists in the consumer
market and as purchasers to be price-takers or oligopsonists in the wholesale market, while in
the latter market farmers, in their capacity as producers and hence, suppliers, are allowed to
be price-takers or oligopolists. As such the model can simulate the consequences of positive
or negative supply shocks for several combinations of market power of which the results in
terms of prices, supply and profit margins are to be compared to assess, among others, the
degree and asymmetry of farm-retail price transmission. In particular, we use the model to
determine the impact of farmer market power on farm gate prices and on the degree of price
transmission in case of positive and negative supply shocks.
The remainder of the paper is organised as follows. First, a discussion of the literature is
conducted in Section 2. Then, in Section 3, the simulation model is derived to assess the
consquences of retailer and farmer market power for the price levels and transmission
throughout the chain. Using this model, simulations are run and discussed in Section 4. At
last, Section 5 accomodates the main conclusions that can be drawn from this study.
2. Literature on price transmission
Price is one of the major mechanisms by which actors in a supply chain are vertically linked
or integrated to each other (Goodwin and Holt, 1999; Meyer and von Cramon-Taubadel,
2004). Therefore, understanding the way price is transmitted from one chain actor to the other
helps to understand the relationship that exists between the actors. According to Falkowski
(2010), studies on price transmission are important because they can help us understand the
relative bargaining positions of the chain actors. Price is also a means by which information is
passed vertically along a supply chain. Imperfect price transmission can therefore imply
imperfect information transmission (Falkowski, 2010). If, for instance, prices have decreased
at the farm level while this decrease is not transmitted to the consumers, the latter can
postpone their purchase decisions due to the high price charged by retailers. Simply said, the
consumers will be led to make the wrong purchase decision by being based on the wrong
price information as reflected in the ‘wrong high price’ charged by the retailers. This reflects
the level of the market inefficiency as well.
As noted above, the degree of price transmission reflects the degree of market efficiency. The
expression market efficiency is used here in the sense that prices reflect the true market
conditions. According to Chavas and Mehta (2004), the common issue that retail prices do not
respond quickly to changes in market conditions raises questions on the efficiency of vertical
markets. Similarly, Griffith and Piggott (1994) add that price levelling by retailers and
wholesalers has raised concern about the efficiency of markets. If prices do not reflect the true
market conditions or simply if markets are not efficient, actors in the vertical market will not
be able to make the right production and/or consumption decisions. In sum, as put by Griffith
and Piggott (1994), an understanding of price transmission is important in explaining pricing
inefficiencies in the marketing of commodities.
The issue of price transmission is an important one also because of its welfare and policy
implications (Meyer and von Cramon-Taubadel, 2004; Vavra and Goodwin, 2005; BenKaabia and Gil, 2007). Welfare implications come from the fact that consumers may not
benefit from farm price reductions due to sticky high prices at the downstream stages of the
supply chain. Alternatively, farmers cannot also benefit from high prices at the downstream
sector. On the other hand, the issue of price transmission has policy implications because
policy reforms, such as those designed to reduce farm price supports, are not benefiting the
consumers with a consumer price reduction (Vavra and Goodwin, 2005). This comes from the
fact that price reductions at the farm level, resulting from agricultural reforms, are not being
transmitted to the consumer levels. The policy implication of price transmission is specially
stressed by several authors (Carman et al., 1990; Griffith and Piggott, 1994; Hayenga and
Miller, 2001; McCorriston, 2002; Girapunthong et al., 2003; Falkowski, 2010).
The policy concerns arise, among others, from the fact that imperfect price transmission leads
to unfair distribution of welfare among producers, processors and consumers (Griffith and
Piggott, 1994). To see this, one can imagine the welfare impact of the decrease in farm prices
(as a result of a change in market conditions) resulting in consumer price increases instead of
a corresponding decrease. Studying price transmission also helps to see the potential benefit
of a given policy reform transmitted via prices along the supply chain (Falkowski, 2010). An
example of such policy reforms are those designed to decrease the cost of production of farms
through subsidies. These reforms may not benefit consumers if retail prices do not decrease in
case of decreasing producer prices (Girapunthong et al., 2003). Asymmetric price transmission studies have also implications for price stabilisation policies (Carman et al., 1990).
For example, when farm prices are volatile but retail prices remain sticky, price stabilisation
programs should focus on the farm level rather than on retail level. In addition, asymmetric
price transmission, which is seen as the rule rather than the exception among food commodities (Acharya et al., 2011), is a manifestation of market power or exercise of market power
by monopolistic middlemen (Meyer and von Cramon-Taubadel, 2004). Therefore, any
agricultural policy reforms designed with the assumption of perfect competition will not work
if the imperfect nature of the market is not explicitly recognized (McCorriston, 2002). The
non-competitive nature of the market can easily be manifested in the imperfect transmission
of prices. This indicates that one needs to study price transmission in order to understand the
competitiveness of the concerning market and then to be able to make agricultural policy
recommendations that take into account the non-competitiveness (or competitiveness) of the
market.
Given the importance of the issue of price transmission, a number of empirical studies have
been conducted to date to understand the phenomenon of vertical price transmission. A
majority of the reviewed literature focus on the meat chain, which includes the beef, lamb,
pork and chicken meat chains. Among these studies, the study by Griffith and Piggott (1994)
on the Australian beef, pork and lamb chains, the study by von Cramon-Taubadel (1997) on
the German pork chain, McCorriston et al. (1998) on the U.S. pork market, Boyd and Brorsen
(1988) on the U.S. pork chain, and Bernard and Willet (1996) on the U.S broiler chain can be
mentioned. Other studied products include the U.S. fresh strawberry farm-retail chain
(Acharya et al., 2011), the U.S wholesale-retail butter market (Chavas and Mehta, 2004) and
the U.S. orange and lemon producer-retail chain (Carman et al., 1990). The findings of these
studies on the presence of asymmetric price transmission are mixed in the sense that both
asymmetry and symmetries are detected. Even though we might not conclude that asymmetry
is the rule rather than the exception, we can still say that asymmetry is more present than
symmetric price transmission. Examples of studies that did not find asymmetry include the
study by Goodwin and Holt (1999) on the wholesale-retail U.S. beef chain and the study of
Boyd and Brorsen (1988) which did not find asymmetry in both the farm-wholesale and the
wholesale-retail U.S. pork chains. Of the studies that found asymmetry, we can mention the
paper by Griffith and Piggott (1994) who found asymmetry in the beef and lamb retailwholesale sector, although they did not come accross asymmetry in the pork wholesale-retail
sector. Hayenga and Miller (2001) who studied asymmetry for high and low frequency price
cycles, found asymmetry in the farm-wholesale price linkage at both frequencies and in the
wholesale-retail prices only for low frequency price cycles. But finding symmetry in price
transmission should not be confused with perfect price transmission in terms of magnitude.
This is because even if output prices adjust with the same magnitude and speed for both
increases and decreases in input prices, it can still be the case that both the increases and
decreases in input prices are only partially translated into output price increases and decreases
respectively.
As put by Meyer and von Cramon-Taubadel (2004), there are two classes of literature on
price transmission: the empirical literature that tests the presence of asymmetry and the
theoretical literature that discusses possible causes of asymmetry. The reviewed empirical
literature discussed in the previous paragraphs also attempt to provide causes for asymmetric
price transmission, though their focus is mostly on detecting the presence of asymmetry. The
major cause of asymmetry identified by the reviewed empirical literature is the market power
of actors in the chain (Bernard and Willet, 1996; Doyon et al., 2002; Falkowski, 2010;
Acharya et al., 2011). These empirical studies, however, only make suggestions that market
power might be the reason for asymmetry instead of really testing for the presence of market
power empirically or using a proxy of market power to see the relationship between
asymmetry and market power. Of course, studies by Acharya et al. (2011) are among the
exceptions since they try to measure both market power and asymmetry empirically.
Identification of other causes of asymmetry is rather made through the theoretical literature
than in the empirical literature. Other major causes of asymmetric price transmission are
adjustment costs or, more specifically, menu costs of retailers when making price changes
(Mankiw, 1985; Dutta et al., 1999; Fishman and Simhon, 2005) and the inventory management strategies of firms (Reagan and Wetzman, 1982; Hayenga and Miller, 2001). Inventory
strategies of firms include the use of the first-in-first-out method (which delays price
transmission) and the asymmetric adjustment of inventory to input price increases and
decreases. The latter means that, in the event of a decrease in demand, firms increase their
inventory levels (decrease sales) instead of reducing price. On the other hand, in the event of a
rise in demand, firms raise their prices and do not adjust their inventories (and keep sales and
production at the same level).
As previously stated, market power by retailers and wholesalers is mentioned by a majority of
authors as a cause of asymmetry. But the drawbacks of these studies is that, first of all, they
use time series analysis, and market power of firms might not significantly change during the
time span they have chosen for their studies. In other words, the studies do not vary the
treatment variable ‘market power’ and see what happens to price transmission when the value
of this variable is changed. Simulation models, as the one used in this study, can thus solve
this problem by providing various scenarios of market power. Secondly, even though the
studies suggest that market power of retailers and wholesalers leads to imperfect price
transmission, none of the studies have tried to show whether farmer market power could
improve the degree of price transmission. Therefore, our paper helps to fill this gap in the
price transmission literature.
3. Model
The model on oligopsonies and oligopolies used by Bunte and Peerlings (2003) and the
approach followed by Sexton and Zhang (2001) will serve as basis to achieve the general
objective of this paper. In their paper, Bunte and Peerlings (2003) studied the impact of
retailers’ oligopoly and oligopsony power on the size of price transmission in case of a supply
shock (of Dutch cucumber production). This paper extends their work by including oligopoly
power of farmers, and sees the impact of such oligopoly power and oligopsony/oligopoly
power of retailers on price transmission, also in the case of a supply shock (of Dutch ware
potato production). The extension to the work of Bunte and Peerlings (2003) is based on the
paper by Sexton and Zhang (2001) who developed a model to study the impact of successive
oligopsonies and oligopolies on prices. The work of the latter looks, however, at successive
oligopolies and oligopsonies at the wholesaler and retailer stage and considers farmers as
price takers. In addition, they did not study scenarios in which an oligopsonist buyer deals
with an oligopolist seller. Nevertheless, their theoretical framework is useful to examine the
effect on prices of successive oligopolies. Hence, our paper applies their theoretical framework after adapting it to the situation where farmers can be oligopolists. The work of Sexton
and Zhang (2001) is particularly useful for our paper because it helps to see the effect of
power both at the farmer and retail level on price transmission whereas the work of Bunte and
Peerlings (2003) helps to see the impact of only retailer power. The model used in our paper
is based on the aggregate consumer demand and farm supply equations, and on the profit
maximisation problems of the farmers and retailers. Each of these elements is discussed in the
following paragraphs. Note that intermediaries between the farmers and retailers such as
wholesalers are omitted in the model for simplification purpose.
The same iso-elastic demand and supply equations as in Bunte and Peerlings (2003) are used.
The iso-elastic functional form of demand and supply is more convenient as it enables the use
of a single figure for elasticity of demand and another single figure for elasticity of supply
during calculations. The elasticities will not change with the levels of quantity demanded and
supplied. The demand equation is given by
Qd  Ap
(1)
where Qd is the aggregate consumer demand, p is the consumer price, (∂Qd∂p)(pQd) is
the price elasticity of consumer demand assumed to be constant and smaller than zero, and A
is a parameter to be estimated. For supply the following equation is employed
Qs  Bw
(2)
where Qs is the aggregate farm supply, w is the farm gate price, β (∂Qs∂w)(wQs) is the
ouput price elasticity of farm supply assumed to be constant and positive, and B is a parameter
to be estimated. We assume that foreign trade can be ignored so that the market clearance
condition is simply given by
Qd  Qs
(3)
Equation (2) is derived from the supply decision of the individual farmer. It is assumed that
the farmer chooses the level of output that maximises his/her profit. The profit of the ith
farmer is given as follows
fi  wqi  cfiqim
(4)
where w is the farm price, qi is the supply of farm output by the ith farmer, wqi is the total
revenue, cfiqim is the total cost for values of m  1, and cfi is a cost function parameter. The
total cost function takes this functional form to indicate that marginal cost increases with the
level of output. This condition is necessary due to the law of diminishing marginal return
which asserts that using more of an input (to increase production) by holding other inputs
constant (such as capital) reduces the marginal product of the added input, and thus increases
the marginal cost. The assumption of increasing marginal cost is reasonable in the case of
farm production where use is made of fixed inputs (i.e., capital) and variable inputs (such as
fertilizers and, to a some extent, the land devoted to the crop of concern). Taking the firstorder derivative of (4) with respect to qi and setting it equal to 0 gives
∂fi∂qi ∂w∂Q)(∂Q∂qi)(qiQ)(Qw)w  w  mcfi qi m −1 
 (1 i)w  mcfi qi m −1 
 qi (1 i)mcfi]1/(m −1) w1/(m −1)
(5)
Equation (5) shows the supply function of the ith farmer. In the equation, i is the conjectural
variation elasticity and given by i  (∂Q∂qi)(qiQ) where ∂Q∂qi (denoted onwards as vi) is
the conjectural variation measuring the degree of the ith farmer’s oligopoly power and Q is
the aggregate quantity of farm output demanded by the retailers such that Q  Qd  Qs. A
value of 0 for vi indicates that the farmer operates in a competitive market and a value of 1
indicates imperfect competition in farm output market characterised by Cournot (Bunte and
Peerlings, 2003). Note that competition among farmers (and retailers) is assumed to be
characterised by Cournot. This means that farmers (and retailers) compete on quantity and not
price. In a quantity setting model of oligopoly/oligopsony, a conjectural variation shows a
firm’s belief about the reaction of rivals to its output/input purchase decisions (Weldegebriel,
2004). A value of 1 for the conjectural variation measuring oligopoly/oligopsony power thus
shows that the firm does not expect its rivals to match its output/input purchase decision. In
other words, the firm has the power to strongly impact aggregate quantity supplied and
purchased, and thus the sales price and purchase price of the traded good. The market share of
the ith farmer is qiQ, and is denoted onwards as si. In equation (5),   ∂Q∂w)(wQ) and
represents the price elasticity of derived retail demand faced by the farmer.
Equation (4) can be rewritten using the format of equation (2) as qi  Biw. As a consequence,
the output price elasticity of farm supply β is given by β  1(m  1) and Bi can be expressed
as Bi  (1 i)mcfi]1/(m −1). Aggregate supply by all farmers is thus given by
Qs i qi  [i Bi]w Bw1/(m −1)







cf. (2). For the sake of simplicity, it is assumed that all farmers are identical; this results in all
farmers having equal market share and thus in si being equal to 1/M, where M denotes the
total number of farmers. Also in equilibrium, it is assumed that each farmer’s conjecture
about the output of each rival farmers is equal. Therefore, vi can be simply written as v and
hence, i can be expressed as vM. Since all farmers are assumed to be identical, the cost
function parameter cfi is also assumed to be the same across all farmers and hence, can be
denoted as cf. Therefore, the aggregate supply function parameter B is given by B  M(1
v)mcf]1/(m−1)and so equation (6) can be fully written as

Qs M(1 v)mcf]1/(m −1) w1/(m −1)
(7)
One parameter in (7) that needs further indication is , the price elasticity of the derived
retailer demand faced by the farmer.This elasticity can be derived from the profit maximisation problem of the retailer and from the aggregate consumer demand function. The profit
of the jth retailer is given by

rj  [p w(1  j)]qrj
(8)
where qrj are the sales of retailer j, w(1  j) represents the total per unit cost of retailer j and
includes the per unit cost w of the produce and the other retailer cost (marketing cost)
represented by wj (jis a fixed percentage). Taking the first-order derivative of equation (8)
with respect to qrj, setting it equal to 0 and after doing some manipulations just like in
equation (5) above, we find
p(1  j w(1  j (1  j)
(9)
where the conjectural elasticities j and j are given by j  (∂Qd∂qrj)(qrjQd) and j 
(∂Qs∂qrj)(qrjQs), respectively. The conjectural variation of retailer j in the consumer market
is denoted as ∂Qd∂qrj  aj and measures the retailer’s degree of oligopoly power, and ∂Qs∂qrj
 bj is the conjectural variation of the retailer in the farm output market measuring the
retailer’s degree of oligopsony power. The market share of the retailer in the consumer market
is denoted as qrjQd  dj and the market share of the retailer in the farm output market is
denoted as qrjQs  ej. Assuming that all retailers are identical, and that farm and retail outputs
are measured in the same units, it then follows that each retailer’s conjecture about rivals’
behaviour in the purchase and sale of the product is equal at equilibrium. We can thus write aj
 a and bj  b. A value of a and b equal to 0 indicates perfect competition in the retail output
and farm output markets respectively, and a value of 1 indicates imperfect competition in each
respective market characterised by Cournot. The assumption of identical retailers also follows
in the market share of each retailer being equal in the output and input market. We can thus
write dj  ej  1N, where N is equal to the number of retailers. Equation (9) can thus be
rewritten as
p(1  a Nw(1  b N(1  )
(10)
Coming back to the determination of the value for , we know that retailer demand for farm
output is equal to consumer demand for retail output (fixed proportion technology is additionally assumed for the retail sector helping us to measure farm and retail output in the same
units). Thus, we can rewrite (10) into
p  w(1  b N(1  )(1  a N 






for substitution in (1) from which it can be immediately seen that   .
Notice that (11) can be considered as the retailers price best-response function to a change in
the farm price. Substituting (1) in (3) and then substituting the resulting expression for Qs in
(7) and solving for p gives the farmers best-response price function normalised to p
p M (1 v)mcf]1/(m −1)α w1/(m −1)α
(12)
The equilibrium solution for prices and quantity is found where both graphs (11) and (12)
intersect. We shall run the following sequence of simulations. First, the equilibrium solution
is obtained under perfect competition in all markets in the chain. Let this solution be the base
scenario. Then, we assume favourable weather conditions which increases the productivity of
the farmers as captured by reducing cf to half its original value. The resulting equilibrium
solution is expected to lead to a lower farm price. Given the price inelastic consumer demand
for basic food products, retailers may not be willing to set their new consumer prices as low
as they will be under perfect competition. Consequently, they will exert oligopsony power
according to the Cournot model (i.e., b  1) vis-à-vis the farmers. A new equilibrium solution
is found and expected to show a considerably smaller decrease in the consumer price
compared with the decrease under perfect competition. Given the oligopsony behaviour of the
retailers, farmers may unite into a few marketing cooperatives or producer organisations to
leverage their bargaining power in the transactional relationship with the retailers. This
scenario could be represented by setting the conjectural variation v equal to one fifth of the
number of farmers N. The resulting equilibrium solution is expected to show an increase not
only in the farm price, but also in the retail price. Therefore, it is to be seen which degree of
price transmission finally results if we compare these prices with their values under the base
scenario, i.e. the situation with perfect competition and normal weather conditions. In the next
section the scenarios will be computed for the Dutch chain of ware potatoes.
4. Simulation results
The simulations are based on data for the Dutch ware potato sector. The data are obtained
from Aardappelinfo.nl, LEI Wageningen UR and Statistics Netherlands (CBS). Detailed
information on the structure of the Dutch potato chain can be found in Mcknight and
Rademakers (1998), Driessen et al. (2008) and Bolhuis et al. (2009). In this paper, however,
we focus on the simplified structure as represented by the chain model presented in the
previous section. From the data the following values and estimates are derived. The price
elasticity of consumer demand is set equal to   0.5. The price elasticity of farmer supply is
given by   0.3. The number of retail chains is equal to N  10 and the number of potato
growers is fixed at M  10,000. Next, using an average value for the consumer price of p 
0.70 euro per kg, for the farm price of w  0.20 euro per kg and for the supply of potatoes of
Qs Qd  5 billion kg, we obtain m from m  (1  )(see equation (6)), A from A 
Qdp−(see equation (1)), B from B  Qs p−(see equation (2)), cf from cf MB)m −1(1 
1M)m (with v  1, see equation (7)) and  from   p(1  1Nw(1  1N (with a 
b  1, see equation (10)). The values of , , N, M, m, A and  shall not be changed during the
simulations. For each simulation the value of B is determined by B  M(1 v)mcf]1/(m−1)
and the outcomes for p and w are obtained by solving (11) and (12).
In the base scenario all actors in the chain are price takers. Hence, the conjectural variations
are all equal to zero: a  b v  0. The resulting retail price is p  0.578 and the solution for
the farm price is given by w  0.275. Next, favourable weather conditions reduce cf to one
half of its base value. Now the farm price decreases to w  0.212 and the retail price to p 
0.446. Consequently, the degree of price transmission under perfect competition is given by
(0.578  0.446)(0.275  0.212)  2.1, which implies a perfect price transmission if we
subtract the value found for , namely  1.1. If the retailers are not willing to lower the
retail price, they may exert oligopsony power vis-à-vis the farmers, i.e. b  1. In that case we
find for the retail price a value of p  0.496 and for the farm price a value of w  0.177, which
implies a price transmission rate of (0.578  0.496)(0.275  0.177)  0.8 showing that the
retailers only partially pass on the farm price decrease. This situation becomes even worse if
farmers respond by concentrating their supply in a marketing cooperative giving them
oligopoly power up to, for example, a conjectural variation of v  2000 (i.e., one fifth of the
total number of farmers given by M). Now the retail price rises to a level of p 0.540 while
the farm price recovers to a value of w  0.193, resulting in a price transmission of degree
(0.578  0.540)(0.275  0.193)  0.5. Assuming that a base scenario with a farm price
increase does not give rise to exerting any oligopoly or oligopsony power, this simple exercise
nicely explains the presence of imperfect and asymmetric vertical price transmission in such a
way that farm price decreases are only partially passed on to consumer prices. Farmers’
countervailing power can even deteriorate the degree of price transmission any further.
5. Conclusions
In the literature several studies have shown that price changes occurring at the farm level are
not always fully transmitted to the retail level. More specifically, a majority of them have
shown that farm price increases are more fully transmitted than farm price decreases. This is
commonly attributed to market power at the retail level, and especially to retail oligopoly. But
since retailer oligopsony power also contributes to imperfect price transmission, it leads one
to wonder if farmer countervailing power could improve the degree of transmission. In this
paper, we have modelled the impact of farmers’ oligopoly power on the degree of price
transmission in case of exogenous positive and negative supply shocks. The model was also
used to study the impact of farmer oligopoly power on farm gate prices. Using the model and
empirical data on the Dutch ware potato sector, farm and retail prices were simulated by
varying the market power distribution between farmers and retailers. Conjectural variations
were used as parameters measuring the degree of market power of farmers and retailers. One
of the key insights from our simulation exercise is the way in which asymmetric price
transmission may arise. Moreover, we have shown that farmer oligopoly power does not
necessarily lead to an improvement of the degree of price transmission, but can even make it
become worse.
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