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Common Agricultural Policy Regionalised Impact –
The Rural Development Dimension
Collaborative project - Small to medium-scale focused research project under
the Seventh Framework Programme
Project No.: 226195
WP3.3 Model development and adaptation –
Improvement of CAPRI
Deliverable: D3.3.1 Literature review in modelling price transmission in PE
models
H.P. Witzke, W. Britz, N. Borkowski
U Bonn
Revised version 28-Jan-2011
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1. INTRODUCTION 3
2. THEORETICAL BACKGROUND OF PRICE TRANSMISSION 3
2.1. SPATIAL ARBITRAGE AND THE TAKAYAMA-JUDGE MODEL 3
2.2. TEMPORAL SCALE 5
2.3. PRODUCT HETEROGENEITY AND THE ARMINGTON ASSUMPTION 6
2.4. MARKET POWER 7
2.5. SPATIAL, GROSS TRADE AND NET TRADE MODELS 8
2.6. POLICY INSTRUMENTS INFLUENCING TRADE AND PRICE TRANSMISSION (TARIFFS, EXPORT SUBSIDIES,
INTERVENTION BUYING) 8
2.7. WELFARE IMPLICATIONS OF DIFFERENT PRICE TRANSMISSION SPECIFICATIONS 10
3. SPECIFIC ECONOMETRIC RESEARCH ON SPATIAL PRICE TRANSMISSION 12
3.1. MAINSTREAM DEVELOPMENT AND FINDINGS 12
3.2. SPECIAL ISSUES 14
4. PRICE TRANSMISSION IN ECONOMIC MODELLING OF AGRICULTURAL MARKETS 16
4.1. TAKAYAMA-JUDGE TYPE MODELS FOR HOMOGENEOUS GOODS 17
4.2. NON-SPATIAL PARTIAL EQUILIBRIUM MODELS FOR HOMOGENEOUS GOODS 18
4.3. ARMINGTON STYLE MODELS FOR HETEROGENEOUS PRODUCTS 21
4.3.1. ECONOMETRIC RESEARCH AND RESULTS ON ARMINGTON PARAMETERS 22
5. CONCLUSIONS FOR CAPRI 24
6. SUMMARY 26
7. REFERENCES 27
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1. Introduction
CAPRI is an economic simulation model for agriculture with a focus on Europe. Developed since 1996
and operational since 1999, its main task is to simulate ex-ante impacts of changes in the Common
Agricultural Policy (CAP) and other shocks on economic, environmental and social indicators related
to agriculture at different spatial scales, from globe to 1x1 km pixel clusters. An important element in
any such analysis are changes in agricultural producer prices which drive to a larger extent allocation
decisions at farm level and farm income. In the enlarged EU, sizeable quality differences and transport
distances between deficit and surplus regions could allow for larger differences in producer prices.
Empirical analysis is necessary to better understand price formation in the enlarged union and thus to
evaluate costs and benefits of common EU safety prices in the EU and the functioning of related
intervention measures.
The CAPRI market model developed from a simple net-trade model capturing EU-15 and the Rest-of-
the-world in its first operational version to a large spatial global trade model for agricultural products.
But the national price formation mechanism has not changed: Member State prices move in parallel
with common EU15 / EU10 / Bulgaria & Romania prices due to fixed percentage margins relative to
these regional aggregate prices. A task in the CAPRI-RD project, drawing on a request from the
project’s call text, refers to improving the price transmission mechanism along with an improved
modelling of market interventions. As a first step, deliverable 3.1.1 presented in here provides a
literature review on the theoretical background of price transmission and how it is modelled in
established tools for agricultural policy impact assessment. Based on that analysis, an improved
methodology for CAPRI will be developed and then later implemented in CAPRI’s code base.
The paper is organized as follows. Section 2 gives a short overview on economic theory relating to
spatial and temporal price transmission. The next section 3 reviews, based on available model
descriptions and paper, how price transmission is modelled in different agricultural market models.
Conclusions for CAPRI are drawn in section 4, followed by a summary.
2. Theoretical background of price transmission
2.1. Spatial arbitrage and the Takayama-Judge model
The Takayama-Judge model (1971) characterises a simultaneous equilibrium in markets for several
commodities, regions and time points. It assumes fixed per unit transport and inter-temporal storage
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costs and unlimited transport and storage facilities. It is based on maximizing a quasi welfare function
defined as the sum of producer and consumer surpluses in each market minus transport and storage
costs, comparing point in times based on a social rate of time discount factor. The constraints define
market clearing conditions for each region and point in time. The first order conditions (FOCs) take the
Kuhn-Tucker form stating that price differences between regions cannot exceed per unit transport costs
– so called spatial arbitrage condition – and price differences between time points cannot exceed per
unit storage costs – so-called temporal arbitrage condition:
Pis ≤ Pir + Tirs
where Pir is the price of product i in region r (or time period r) and Tirs is the unit cost to bring the good
from region (period) r to region (period) s. Trade costs are often assumed symmetric (Tirs = Tisr) but
this is not necessary. The spatial arbitrage condition is also called the weak law of one price whereas it
is called the strong law of one price if it is assumed to hold with equality (Fackler, Goodwin 2001, p.
978). The latter neglects the case of price differences being smaller than transport (and more generally
transaction) costs for the move from region r to region s. Major assumptions underlying most versions
of the model are price taking, fully informed agents and homogenous products.
A model based on the spatial arbitrage condition including a set of potentially trading regions will
show for each pair of regions one of three possible regimes:
(1) no trade at all, because the price difference is smaller than transport costs,
(2) positive trade from region r to region s and Pis = Pir + Tirs,
(3) positive trade from region s to region r and Pis + Tisr = Pir.
An implication of the model is that, in case of a ‘small’ increase in region r demand in regime (2), there
would be a decline in exports from region r to region s and prices would increase in both regions
exactly by the same amount (with Tirs given). In case of a medium increase in demand of region r there
may be a ‘regime switch’ with the exporter r moving from regime (2) to the autarchy case (1) and
P1is < P1
ir + Tirs => P1is - P0
is < P1ir - P0
ir
In the case of switching regimes price changes are not fully passed on from the region where the shock
originates to neighbouring regions. Finally there may be, in case of a strong increase in demand of
region r, a full trade reversal with the exporter r moving from regime (2) to the importer case of regime
(3).
These regime switches may provoke large changes in predicted bilateral trade flows in spite of only
small changes in prices and domestic quantities. This ‘jumpy’ behaviour may be realistic for fairly
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homogeneous goods and highly disaggregated commodities. Already for moderately aggregated
commodities, say all ‘wheat’, we observe some stability of trading patterns. However, if unit transport
cost rise with the volume of trade (Fackler, Goodwin 2001, p. 985) prices will not be fully passed on to
neighbouring regions even if trade regimes do not change and the model will predict a more stable
trade pattern.
The standard version of the model in empirical applications features inverse linear supply and demand
functions. It can be shown that inverse linear demand function cannot represent regularity conditions
for utility maximization under a budget constraint. The resulting error depends on the size of the
income effect of the simulated price changes, so that the model might be defended in applications for
agricultural products in developed countries with their low budget shares.
2.2. Temporal scale
The previous introduction has neglected the fact that, for several reasons, spatial arbitrage takes time:
• delivery lags during transport,
• time for assessment of market opportunities and deciding on optimal actions,
• time to find trading partners, negotiating conditions and setting up contracts.
These delays also create uncertainty if the prices finally received by an exporter are unknown at the
point of decision making and expectations have to be formed therefore. Even without risk aversion this
prevents perfect price transmission in most circumstances and the equilibrium condition in line with
exports from region r to region s becomes (Fackler, Goodwin 2001, p. 989)
δ E[Pist+1] = Pirt + Tirst
if there is a delivery lag of one period and the discount factor is δ. A positive transitory supply shock in
region r would be passed on to region s in the next period (as a part of the ‘excess supply’ if shipped to
the other region) whereas a transitory shock in region s would have only effects on region s, in the case
that agents in region r do not anticipate this shock. Storage creates additional linkages among prices of
different periods (Wright 2001).
Econometric work on price transmission therefore has to consider a wide range of possible dynamics
and the appropriate empirical analysis of such dynamics is the key of a whole literature that will be
reviewed in more detail in section 4.
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2.3. Product heterogeneity and the Armington assumption
Spatial and temporal arbitrage conditions link price differences in space and time to transport and
storage costs under the assumption of homogenous goods. A key outcome of that model is that trade in
a particular good takes always place in one direction only, if at all, such that any pair of regions falls
exactly into one of the three trade regimes. Most statistics however tell otherwise. Why is that the case?
The most frequent explanation is product heterogeneity which seems obvious when looking at
commodities such as cars. Product differentiation by car producers leads to a situation where bi-lateral
exports and imports between car producing countries are the rule rather then the exemption. Some
consumers in most countries will buy foreign brands even if cheaper domestic cars are offered, due to
differences in size, features, design, image etc. If aggregate commodity groups such as “products from
manufacturing” are analysed, the effect becomes even more significant.
One might argue that for clearly defined agricultural products, such as raw sugar, product heterogeneity
does not exist. However, there are two problems with this argument. Firstly, many agricultural products
such as e.g. beef do show sizeable quality differences, as a look at the supermarket counter for meat
proves. And secondly, many multi-commodity models are based on the FAO concept of primary
product equivalents such that consumption and trade of wheat also includes trade in derived products
such a Pasta and Pizza. As the shares of processed products in total trade will vary according to regions,
wheat is not homogeneous anymore even if we neglect quality differences in the raw product, arising
from different protein contents, for example.
When products are heterogeneous, the law of one price need not hold any longer, whether in the weak
or in the strong form. Marginal costs may differ for each regional variant of a product. The same holds
for marginal willingness to pay, giving the starting point for the so-called Armington (1969)
assumption which is by now widely applied in trade modelling. In general, this implies that consumers
consider French and German cars as different products, thus multiplying the number of products with
the number of regions. To avoid specifying very large demand systems with thousands of parameters
the typical application is based on a CES utility function to explain the distribution of total demand for
a commodity (group) to different origins, assuming that each origin represents a different product
quality. That leads in the overall model to a separable, two stage budgeting problem where the top level
determines the budget shares for product groups, and the bottom level distributes these budget shares to
different origins. The Armington assumption is applied in nearly all CGE models in the last years (an
exception: Fischer et. al 1988). The rather simple sub-utility function features solely n+1 parameters:
the so-called substitution elasticity and n so-called share parameters for each origin. With the
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substitution elasticity given, the remaining parameters allow calibration of the model without further
assumptions to any given strictly positive set of trade flows and related prices.
In most CGE models, the Armington assumption on the demand side is complemented by a CET
function on the supply side which assumes that producers differentiate their products by destination.
Coming back to the example of cars, the average price and quality of cars produced in Germany for
export to Japan might be different from those shipped to France or sold domestically. The CET
formulation draws on the same functional form as the CES utility function and can, again, be simply
calibrated against a set of strictly positive export flows and related prices once the transformation
elasticity has been chosen.
2.4. Market power
Product heterogeneity may also facilitate the exertion of market power on regional or national markets
that often are supplied by a few firms only whereas the number of firms is larger on a European or
global scale. A common expectation is that market power causes margin-squeezing price changes (a
drop in output prices or an increase in input prices) being quicker and more completely transmitted
than margin stretching changes (increases of out put prices, decreases of input prices). In this way
market power is seen as one reason for asymmetry in price transmission besides costs of price
adjustment, political and institutional or storage effects, both vertically as well as spatially
(Meyer, Cramon-Taubadel 2004).
However, this assessment is largely based on assumptions of middlemen using their market power to in
crease margins temporarily or permanently in case of price changes. Formal models partly confirm this
intuition but sometimes also give opposing results. A variety of situations may arise that each have
different implications for spatial price transmission: Regional markets may be separated and regional
suppliers, potentially monopolists in ‘their’ region, compete with each other in a wider oligopoly. The
kind of price transmission then depends on the conjectures of the competitors responses to a change in
the own price. Another situation arises if one monopolist or an oligopoly serves several markets at the
same time, perhaps as common on the sugar market. If all suppliers are experiencing the same shocks
prices may by synchronised (Faminow, Benson 1990; Koontz et al. 1993; Koontz and Garcia 1997),
but in case of regional differences in demand elasticities, price setting behaviour will differ as well
(pricing-to-market). Still another situation arises, if a competitive market and an imperfectly
competitive market are linked spatially.
To sum up: Market power involves considerable complications of economic relationships between
regional markets but concluding evidence (see section 4) has not yet emerged and is unlikely be put
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forward quickly. Large scale modelling typically ignores these complications, a simplification that is
probably defendable given the current state of knowledge.
2.5. Spatial, Gross trade and net trade models
Both Takayama-Judge type models and those applying the Armington assumption are so-called spatial
models which comprise endogenous bi-lateral trade flows. Another model class are net trade models
where only the difference between domestic supply and demand can be determined. Conceptually, net
trade models are based on the homogenous good assumption and resemble in that respect the
Takayama-Judge type models. However, they lack the explicit transport cost minimization component
dealing with bi-lateral trade flows. Rather, in net trade models, domestic prices in each region are
linked to a key (world market) price via price transmission functions which might capture trade and
transaction costs as well as policy interventions such as export taxes and subsidies or import tariffs.
Explicit bi-lateral trade flows between regions are absent from the model. One might argue that a linear
price transmission equation in a net trade model summarizes the spatial arbitrage condition from spatial
modelling as long as the set of non-zero trade flows is constant and hence there are no switching
regimes. In that case, the price differences between any one pair of regions do not change even if the
size of flows might, so that one might also relate any regional price to one (averaged or not) key price.
That point will be picked up again in individual model descriptions below: Very often the form of the
price transmission equation suggests that a switch from the net importer to net exporter status cannot be
captured appropriately as the price transmission equation is independent of net trade.
In some cases a non-spatial net trade model is converted to a non-spatial gross trade model by
additional behavioural equations which determine either the level of imports and exports or both. These
equations may include simplified trade policy instruments such as total TRQs (for the rest of the world)
and export subsidies (e.g. in Witzke, Tonini 2008 or Bouamra-Mechemache et al. 2002) but evidently,
they cannot account for bilateral trade preferences. Another problem is theoretical in nature: Separate
behavioural functions permit having different export and import prices, thus acknowledging product
heterogeneity. However, it may be criticized that these prices are unrelated as the exporting regions and
importing regions in the rest of the world could also trade with each other.
2.6. Policy instruments influencing trade and price transmission (tariffs,
export subsidies, intervention buying)
The influence of policy instruments on price transmission has stimulated key contributions in the
agricultural economics literature that rendered the concept of price transmission elasticities popular
(Tyers, Anderson 1992, Mundlak, Larson 1992). For the case of exports from region r to region s the
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simple spatial arbitrage condition for homogeneous goods from section 3.1.1 (regime 2) may be
generalised as follows:
Pis ≤ Pir + Tirs(Pis,Pir) + τirs(Pis,Pir) – σirs(Pis,Pir)
where Tirs(Pis,Pir) now also covers those transaction costs that vary with the value of the goods
(insurance), τirs(Pis,Pir) is a tariff levied by country s that might depend on the prices (for example in the
case of a simple ad valorem tariff), and σirs(Pis,Pir) is an export subsidy that is granted by country r. If
the sum of transactions cost and tariffs net of export subsidies is positive and independent of prices,
absolute price changes from country r would be fully passed on to country s, assuming that the
arbitrage condition holds with equality and no other impediments affect trade. This would imply a price
elasticity εirs = (dPis/dPir) * (Pir/Pis) = 1 * (Pir/Pis) < 1. Conversely, in the case of transactions costs,
tariffs and export subsidies all proportional to Pir we would obtain a price transmission elasticity equal
to one. A relevant case is also that import tariffs decrease, as Pir is increasing, reflecting the desire of
importing country s to stabilise domestic producer prices.
An extreme case of such a stabilisation policy is the system of variable levies of the CAP before
tariffication in the Uruguay round. However, even now we have the special case of cereals where the
applied EU tariffs are cut at 155% of the intervention price. Effectively, this rule has reintroduced the
old flexible levy system for the cereals concerned. Export subsidies were also used in the old CAP as a
stabilisation instrument but they have been largely reduced to zero for most products. Both for tariffs as
for export subsidies a full analysis would have to consider quantitative constraints in border policies as
well, i.e. tariff rate quotas and WTO export subsidy commitments. However, for CAPRI-RD, the most
interesting issues are the implications of CAP stabilisation policies on intra EU price transmission, as
extra EU price transmission is well covered in the CAPRI market module already.
If stabilisation policies take the form of buying into intervention stocks to defend a certain minimum
price Pirmin in an EU Member State r, this may effectively disturb the arbitrage with another Member
State s:
Pis ≤ max(Pir*, Pir
min) + Tirs
where Pir* is the hypothetical equilibrium price without intervention buying. This basically reflects the
situation in potential export region Hungary for maize in 2004-06 relative to potential import region
Spain (where tariffs and export subsidies are irrelevant). Intervention buying had increased market
prices in Hungary to that point where arbitrage to the deficit region Spain became unprofitable. Instead,
it was cheaper to import from the US (region u) in Spain, in spite of tariffs applied. This situation was
due to an exceptional combination of high Tirs , due to poor infrastructure for transport from Hungary
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to Spain, and a low Pir due to good harvests that usually would have triggered intra EU arbitrage (see
Commission 2006, p. 18).
Similar intra EU trade and price effects may also be triggered if exports subsidies tend to support the
prices in particular Member States. This may be part of the EU market management, for example if
export subsidies are granted on purpose to support markets that are hit by a regional loss of consumer
confidence related to animal diseases. However, region specific effects of export subsidies may also be
a consequence of traditional trade linkages (example: Denmark as an established overseas exporter of
pork).
It is unclear whether stabilisation policies (via intervention buying or export subsidies) that affect the
regional market prices need to be considered explicitly in an empirical analysis of intra EU price
transmission. It might be argued that their effect is fully incorporated in the observed market prices
such that their explicit consideration is not necessary. This is different from measures such as the pre
1993 Monetary Compensatory Amounts (MCAs) that amount to intra EU tariffs or export subsidies
τirs(Pis,Pir) – σirs(Pis,Pir) and clearly need to be accounted for (Zanias 1993). Whereas intervention prices
are sometimes incorporated in empirical analyses of extra EU price transmission (Listorti 2009),
studies of intra EU price transmission with explicit acknowledgement of intervention prices or export
subsidies have not been encountered in the literature. It is clear that the updated specification of CAPRI
needs to reflect a regional application of stabilisation instruments. However, rather than becoming part
of the price transmission equation, such policies could also be reflected by the rules to regionally
allocate intervention buying in CAPRI. The solution will be developed as part of the CAPRI-RD
Deliverable 3.3.2.
2.7. Welfare implications of different price transmission specifications
Different specifications of the transaction costs determining intra EU price differences (Tirs(Pis,Pir))
have some implications for appropriate welfare analysis in CAPRI. In a general equilibrium framework,
any income created when transforming agricultural raw materials and distributing them to final
consumers would evidently be accounted for in the sectors concerned and contribute to the total
welfare effects of any policy. In partial equilibrium modelling, effects on the marketing and
distribution sectors are usually neglected. This is consistent with constant margins equal to constant
marginal costs and zero profits but it would lead to an inconsistent welfare analysis if marketing
margins are not constant, for example in the commonly assumed case that marketing services require
costs that are a given percentage of the raw product’s price (or marginal cost). This point is most easily
demonstrated in graphical form.
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Figure 1: Market equilibrium with two specifications for marginal costs in the marketing sector
0
10
20
30
40
50
60
70
0 2 4 6 8 10 12 14 16
Demand
MC(marketing1)
MC(marketing2)
MC(beer)
MC(total1)
MC(total2)
P(equilibrium)
Assume this is a graphical representation of the German market for a Dutch beer variety. The marginal
cost curve for this beer variety is the bottom line starting at 5 Euro per unit, MC(beer). However, to
penetrate the German market also requires increasing transport cost and other cost (promotion,
distribution etc.) such that the total marginal cost curve becomes MC(total1) = MC(beer) +
MC(marketing1). Alternatively, MC(marketing2) represents the case of constant marginal costs in the
food chain per unit of beer delivered in Germany which leads to total marginal cost curve MC(total2)
such that the market equilibrium settles at an equilibrium price of 33 Euros and 6 units of beer.
Now assume that Germany would ban the sales of this beer variety, say for violation of the German
purity law. The welfare loss of such policy would be the triangle bordered by the vertical axis, the
demand curve and either MC(total1) or MC(total2). In the case of constant margins, i.e. with
MC(marketing2), the correct welfare effect could have been calculated as well from the loss of
consumer surplus and producer surplus neglecting the marketing sector altogether, that is integrating
along MC(beer) only. However, this simplified procedure would neglect some welfare effects (the loss
of profits in the marketing sector) if in reality MC is increasing, i.e. with MC(marketing1).
Note that increasing marginal costs in the marketing sector are a plausible assumption for transaction
cost Tirs and more general specifications than assumed in Figure 1 are conceivable, for example a linear
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form but MC(marketing1) constant * MC(beer) or nonlinear forms for MC(marketing1) that are
entirely unrelated to MC(beer).
Note also that Figure 1 may be interpreted more generally as depicting the situation of more aggregate
goods that are consumed and produced in both countries such that the demand curve is an excess
demand curve and the MC(total) curves are excess supply functions of the exporting region. The
conclusion for partial equilibrium modelling remains: All specifications other than constant margins
require additional terms in the welfare accounting. An explicit discussion of this point has not been
encountered in the literature, but for Witzke, Tonini (2008) it motivated a return to constant margins in
the feed industry. The issue also needs to be considered in CAPRI-RD deliverable 3.3.2.
3. Specific econometric research on spatial price transmission
In this section, we will briefly review the vast area of econometric research on spatial price
transmission. While econometric estimations are sometimes also undertaken as a part of the
specification of full partial equilibrium models, the price transmission relationships are only a small
subset of thousands of other equations in such a context. Evidently, specialised econometric research
on price transmission may investigate more thoroughly whether and to what extent spatial distant
prices influence each other.
Complete price transmission is often taken as a sign of market efficiency. Vice versa, lags or missing
market integration hint at welfare losses by suboptimal allocation (Rapsomanikis et al. 2003). Price
transmission elasticities determine the direction, magnitude and distribution of welfare and market
effects of trade policy scenarios in global agricultural partial equilibrium models. Thus there is a
continuous interest in the quality of their estimation (Sharma 2002).
3.1. Mainstream development and findings
Early studies investigating price transmission often used simple measures of correlation and regression
analysis. However, after several authors having demonstrated the shortcomings of these approaches,
most of the recent econometric studies, at the latest since the 1990ies, apply some form of dynamic
vector autoregressive regression technique. This should acknowledge the time series character of the
data, often found non-stationary in the levels, and their usually unclear causal structure
(Rapsomanikis et al. 2003).
One line of pure time series research works by testing for Granger Causality. In his paper Granger
(1969) defined a first time series to Granger-cause a second one if it its lagged values show significant
influences in a regression of the second variable on lags of both the first and the second variable. An
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application in Europe was done by Gordon et al. (1993) for the British and French Lamp Market. They
find the prices integrated but with considerable time-lags which they attribute to political reasons and
quality differences in the regional meat qualities. In an international context there are some studies
confirming Granger causality for spatial distant prices (e.g. Gupta, Mueller 1982; Alexander, Wyeth
1994). Granger causality merely reveals whether two markets are integrated in the sense that shocks are
transferred between regions. It does neither say something about the driving forces behind that nor
about the extent (related to the magnitude of coefficients).
Several authors criticise studies relying on Granger causality as this Vector Auto Regression
framework usually neglects non-stationarity of time series concerned. If time series are non-stationary
the natural econometric pendant to market integration is cointegration analysis (Rapsomanikis et al.
2003). One of the first applications was by Ravaillon (1986). His model regresses the current price of a
peripherical region on lagged prices of this region as well as on current and lagged prices of a central
market. In case of long run integration the regression may be cast into an error correction form. This
approach became one of the standard techniques in price transmission research (Faminow, Benson
1990; Dahlgran, Blank 1992). Whether international commodity markets are spatially integrated based
on such methodology is a contentious issue (Ardeni 1989).
Most recent studies have found price series non-stationary and hence used error correction formulations
in order to test for the LOP in the long run. Zanias (1993) uses a cointegration approach to investigate
the integration of European soft wheat, milk, potato and pork markets. He finds that the highly political
determined milk market is the least integrated, while the wheat market is highly integrated after
allowing for monetary compensatory amounts. Hereby, he confirms Sexton, Kling, and Carman’s
thesis (1991) that non-tariff barriers and imperfect competition are important drawbacks for market
integration. Sanjuán and Gil confirm the integration of pork and lamb markets in the EU (2001).
Another type of analysis has been carried out by Fousekis (2007). Instead of estimating the
transmission coefficients, he uses a clustering algorithm on the error correction adjustment parameters
to test for price clubs in European poultry and pork meat markets. He finds significant clubs where the
weak version of the LOP holds, contradicting the idea of an EU wide common market. Viju, Nolan,
Kerr (2006) approach the question for integrated European markets in another way: They ask whether
the rye, soft wheat, barley, oats and potato markets of Austria, Finland and Sweden were less integrated
with the European Market before their entrance than afterwards. Their study confirms that increasing
integration, however, just for rye and barley between Austria and Germany.
Hockmann and Vönecki (2009) investigate the integration of the Hungarian butter market with the
European one by means of cointegration and impulse response analyses. They find integration between
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European butter prices which is however weaker between old and new Member States than among the
old ones.
3.2. Special issues
The bulk of econometric research on spatial price transmission falls into the ‘level 1’ category (Barrett
1996), where only information on prices is used, basically regressing one set of prices on another set or
investigating their common evolution in vector autoregressive systems. Given that transaction cost data
(on Tirst) is evidently more difficult to compile, ‘level 2’ studies that also make use of those are less
frequent. An exception is Goodwin, Grennes, Wohlgenant (1990) who find support for the (strong) law
of one price in an analysis of US export products if they acknowledge rational expectations and
transport cost data. ‘Level 3’ analyses that consider trade data together with price data are also quite
rare. Typically the trade flow information is only used to identify the appropriate regime and related
arbitrage condition (eg. Barrett, Li 2002).
Baffes (1991) shows in another ‘level 2’ study that one reason for long run failure of the LOP (as in
Ardeni 1989) can be the omission of transaction costs. In his study, he finds a significant relationship
between price differentials and transport costs. Transaction costs create a band in which the prices
move independently of each other as transaction costs are prohibitively high and therefore direct
arbitrage does not happen. In fact, on the background of the theory presented above, this is an evident
point. Other studies acknowledge transactions costs without needing data on them. This applies to
some so-called switching regression models as Sexton, Kling, and Carman (1991) that allow for an
unknown band where prices do not co-move and estimate the relation outside this bandwidth. Baulch
(1997) criticised earlier approaches ignoring transaction costs as suffering from omitted variable biases
and time series drawbacks. He improves on Sexton, Kling, and Carman (1991) by including (some)
data on transaction cost rather than treating them as unobservable and distinguishes three trade regimes.
Apart from transport costs, most components of transaction costs such as search and information costs
are difficult to measure. As long as they are constant per traded unit, stationary and additive they may
become part of the error term and constant. However, if they are large or volatile they may mask
existing price relations. If transaction costs yet reveal to be non-stationary or multiplicative, the applied
models must be either non-linear or include thresholds (Conforti 2004).
Barrett and Li (2002) clearly distinguish between market integration and spatial competitive
equilibrium: Markets might be integrated without trade even if costs of transport are prohibitively high
for example, if both trade with a third region. Conversely, even if there is trade, markets might still not
be in equilibrium if there are potential rents to be earned that are left unexploited. By means of a
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mixture distribution model approach, they assign market relationships to these regimes and a
probability of them being integrated is defined per period. A clear benefit of their approach is that it
does not depend on time series characteristics of the underlying series and considers trade data.
Disadvantages are precisely these data needs, a huge number of coefficients due to the consideration of
bilateral trade and the lack of values for the degree of transmission (Fackler, Tastan 2008).
Apart from transaction costs there are more reasons why even the weak version of the LOP might be
rejected in a price series analysis even if it would hold in reality. The assumption of product
homogeneity is the most prominent example.
An important influence stems from policies: On agricultural markets, intervention prices, export
subsidies or non tariff barriers have a crucial effect on price transmission (Rapsomanikis et al. 2003).
While EU intervention may disturb price integration (as discussed in Section 3.6), it may also create
integration if applied in many regions at the same time (Zanias 1993). Several studies have tested for
structural political influence (Goletti and Babu 1994, Alexander and Wyeth 1994) or split the sample
according to changes in policy regimes (e.g. Brooks, Melyukhina 2005, Viju et al. (2006). Benjamin,
Guéguen, Houée (2003) and Listorti (2009) include the intervention price as an explicit variable in the
price transmission equation for the EU to a selected “world” price.
While transaction costs may be an impediment to measuring existing integration between markets,
market power may cause market integration to be confirmed although there is none (Faminow,
Benson 1990). This mostly applies to vertical price transmission but also holds spatially. Indeed the
first applications of price transmission analysis were in the scope of market definition and antitrust
regulations (Fackler, Goodwin 2001, p. 973).
Empirical studies often suffer from a lack of appropriate proxies for market power. Peltzmann (2000)
tests the number of competitors and the Herfindahl-Hirschmann-Index and finds that they give
contradicting effects. Another problem is that market concentration often stays constant over a long
time meaning that there is hardly any variation in this determinant. McCorriston (2002) proposes
comparing different vertical structures in European Countries but there are a lot of other influences
making inference from such a comparison difficult.
Abdullai (2000) estimates cointegration of three markets in Ghana and finds asymmetries in price
transmission between the central market and both peripheral ones. However, he does not definitely
attribute this to sub-optimal market structures but also to inventory adjustment. Liu (2008) considers
Finnish and German pork prices. The pork market chain in Finland is rather concentrated on processor
level. It is found that German and Finnish pork prices are highly integrated but that large shocks are
transmitted asymmetrically. As expected, it appears that processors in Finland transmit margin
16
squeezing price changes quicker than margins stretching changes in order to increase average profit. In
spite of integration Liu found a very sluggish adjustment to of Finnish prices to a shock of the German
price (3% per week). This evidence is noteworthy because most papers only report various stationarity
or cointegration tests, but hardly ever a price transmission coefficient (or elasticity). Serra and Gil
(2006) also allow for asymmetry in their estimation that focuses on the effect of the size of the trading
partner: They state that price transmission is symmetric between Spain and German, being the leading
pork producers in the EU, while it is asymmetric in favour of Germany concerning Denmark and
France. Market power is mentioned as one possible explanation for this finding but asymmetry in
market infrastructure (tailored toward specific trade directions) and inventory management may also
play a role.
A final issue needs to be mentioned that will be important in an application tailored to CAPRI. A large
part of the empirical research on dynamic relationships of prices from different regions uses sub-
annual data, monthly or even weekly, simply because this is the only way to obtain a sufficient set of
observations needed for a specification of rich dynamics. If these dynamics are only strong over a few
months corresponding models specified with an annual frequency will be much simpler. These effects
of temporal aggregation have been investigated in Cramon-Taubadel, Loy, Musfeldt (1995) at the
example of pig markets where they found that contemporaneous relationships gain in importance over
lagged relationships and that R2 statistics were increasing when moving from weekly to quarterly data.
Other statistical effects are that with a higher frequency the statistical power of tests tends to increase,
whereas a longer span of the data horizon (in years) increases the likelihood of structural breaks (Wang,
Tomek 2007). If sub-annual data have been used for estimation, it is always possible to move to the
annual scale by temporal aggregation, as in Listorti and Esposti (2008). As the first explorations of
intra EU price transmission equations for CAPRI-RD will be based on monthly data, these issues will
be relevant for deliverable 3.3.2.
4. Price transmission in economic modelling of agricultural
markets
The following section reviews how domestic prices are linked to international ones for some widely
used economic simulation models focusing on agriculture. As discussed above, an important distinction
must be a made between approaches based on the homogenous good assumptions and such ones
treating imported and domestic goods as non-perfect substitutes. The review suggests that almost all
established Multi-Commodity Models (MCMs) are based on the homogenous good assumptions,
17
whereas CGEs and CAPRI are based on product heterogeneity. Accordingly, the section first reviews
the standard MCMs and draws some conclusions for the group, before discussing GTAP and CAPRI.
4.1. Takayama-Judge type models for homogeneous goods
An important group of applied spatial equilibrium models of the Takayama-Judge type (see McCarl,
Spreen 1980) are single market models for agricultural commodities. If these commodities are
considered homogeneous goods the Takayama-Judge approach may be preferable to an Armington
style model that better fits the case of differentiated products. Furthermore, behavioural functions of
single commodity models often depend on the own price of the commodity in question only, such that
full demand systems are avoided which simplifies the application of a Takayama –Judge model (no
need to watch for symmetry of demand functions used in the objective function).
An influential study on the pending expiry of the EU milk quota system has been carried out by
Bouamra-Mechemache, Jongeneel and Réquillart 2008 (see also Bouamra-Mechemache and Réquillart
and 2005). While considerable efforts have been made to estimate the key parameters of the “EDIM”
model econometrically it appears that only the own price elasticity has been used in the final market
model (thus avoiding to cope with symmetry), even though the model covers all dairy products (14,
including 6 types of cheese). The spatial model either represents net trade flows only or treats one of
imports and exports as exogenous. Due to the difficulty to handle ad valorem tariffs in a quasi welfare
maximisation (Rutherford 1995) the analysis treats all tariffs as specific tariffs although an explicit
trade policy representation (e.g. regarding some TRQs) is also mentioned as one of the assets of this
approach. The practical problem with an explicit welfare maximisation is that the commodity prices are
determined as shadow values of market balance constraints such that ad valorem tariffs cannot
explicitly be multiplied with those. Calibration problem is solved for behavioural functions with an ad
hoc variation of the PMP methodology while the calibration problem for the trade matrix is not
discussed.
The latter is addressed in the recent analysis of the banana market in Anania (2010) that investigates
different reform options for the EU banana CMO. This relies on a calibration method related to PMP to
specify the matrix of transport costs Tirs in such a way that net trade and prices are exactly calibrated.
However, the observed trade matrix is only one among many optimal solutions such that the
methodology cannot ensure its exact calibration (Anania, Drogué, Paris, 2009).
Another example is Nolte et al. (2010) that presents sugar reform scenarios. Rather than as a standard
welfare maximisation, the model is solved as an MCP problem which may accommodate missing
integrability due to non-symmetric behavioural functions but may also handle ad valorem tariffs.
18
Finally, there are also a few examples of full scale sector models that do not confine themselves to
single markets or a closely related set of markets. In methodological terms EUFASOM (Schneider et al.
2008) and GLOBIOM (Havlík et al. 2010) go back to the US model ASM (recently: Schneider, McCarl,
Schmid, 2007). In contrast to the examples above, these are large scale LP models that are designed to
cover technology choices in detail while the Takayama-Judge approach in trade representation is so far
only used in its standard form (only specific tariffs, no TRQs).
4.2. Non-spatial partial equilibrium models for homogeneous goods
Non spatial models have a so-called “world market price” that is assumed to clear global markets. This
is sometimes also called the “middle Atlantic Ocean price” to show its synthetic nature. However, in
several models such as FAPRI, AGLINK or AGMEMOD, these world market prices are “key prices”
referring to a specific price notation defined by location, product quality and other contract details for
which time series exist, thus resolving the ambiguities on the definition of the world price. Amington
style models do not have such a key price. However, from the data base and scenario results weighted
international averages of different prices (cif, fob, producer etc.) can be calculated and used to report
“world prices” and their changes.
The parameters in price transmission equations can be either estimated from time series / cross
sectional analysis or derived from transport costs and policy parameters such as tariffs or OECD
PSE/CSE calculations, or based on a combination of the two.
The history of MCMs starts with SWOPSIM (Roningen 1986; Roningen, Dixit 1991) in the eighties,
and some SWOPSIM design features are still found in established modelling systems such as
behavioural equations in double log form. SWOPSIM also used iso-elastic functions to link the world
market to domestic prices. The tables with these price-transmission elasticities are documented
(Sullivan 1990), and had been subsequently used also by other studies. The price transmission elasticity
captures in a single parameter the combined impact of transport and transaction costs, policy
interventions (tariffs, export subsidies, domestic measures) and their responsiveness to world market
prices as well as of product heterogeneity. Transport and transaction costs refer to both international (to
border/from border) and domestic transports and transaction (from border to consumer or supplier to
border).
The World Food Model of FAO in an early version comprised double-log price transmission functions
between the world market and domestic prices. A later version used linear ones which included tariffs
and transport costs against the uniform world market price in the model. The transport costs were
added for net importers and subtracted for exporters (Cluff 2003).
19
AGLINK/COSIMO, a combined OECD/FAO effort, is a recursive-dynamic net trade model solved in
yearly steps where in most cases linear price transmission equations are used to link key market to
domestic prices (OECD 2007). The key market prices act as world market prices in the system. The
price transmission equations typically comprise tariffs but also margins. These margins, the so-called
add factors, are time and market specific and set such that domestic prices from country specific
outlooks provided by the different teams line up to the key market prices. However, AGLINK, as a
weak template model, leaves the teams being responsible for the individual country module sizeable
freedom to choose whatever they just appropriate to link quantities and prices from their country
module to the rest of the modelling system. In some cases, instead of using a price transmission
equation, there is domestic market price acting as a clearing variable in the domestic market which is
linked via behavioural equations for imports or exports to the world market. Equally, some world
markets such as for beef are split into a Pacific and Atlantic one.
The FAPRI modelling system consists of individual commodity models or group of commodity
models linked together via cross-price effects. These models can also be used independently by fixing
the cross-prices, and have been estimated independently, typically as single equation regression models,
or specified based on expert knowledge. The documentation for the oilseeds module (FAPRI 2010)
states: “Using price transmission equations driven by estimated or consensus price transmission
elasticities, we link the world price in domestic currency and the domestic price for all products. The
price transmission equations assume that agents in each country are price-takers in the world market.
Countries are either natural importers or exporters if their autarkic price falls above or below the world
price. Abstracting from any spatial consideration and assuming an "ad valorem tariff only" regime, the
domestic price can be expressed as Pd = + · Pw · r · (1+d), where Pd is the domestic price, Pw is
the world price of the commodity including international transportation cost if the country is an
importer (FOB price for exporters), r is the exchange rate, and d summarizes policy interventions
between the world and domestic markets and is expressed in ad valorem form. Parameter captures
the divergence of the domestic and border price that does not depend on the price level but rather
reflects transaction costs arising between the farm gate and the market place, and/or marketing mark-
ups. It is unclear how a regime switch from net importer or exporter is treated as is fixed. The
description is found in similar form also for other modules of FAPRI, but it cannot be excluded that
solutions differing from the standard discussed above are also present for specific country and
commodity combinations. Reviewing FAPRI, Cluff (2003) reports that elasticity numbers in the price
transmission model are a combination of estimated elasticities and elasticities from the literature or
20
from expert opinion. Anchor prices used by the FAPRI are for cereals mostly US Gulf notation, for oils
and cakes mostly notations in European ports, and for dairy northern European prices and livestock
domestic US prices.
AGMEMOD (Bartova, M’Barek 2008) is a Multi-Commodity model for EU agriculture based on
single-regression behavioural equations and structured in country modules. It is linked institutionally to
FAPRI and has adopted many aspects of the FAPRI methodology. Similar to FAPRI’s concept of
international anchor prices, for each European commodity market one specific country’s price is
treated as the key price to which the prices of other countries are linked. The price linkage equations
are usually derived from regression analysis and include self-sufficiency rations in the key and country
market in addition to the prices concerned (Bartova, M’Barek 2008, p. 17). For the key price, the price
transmission equations link to a world market price (from FAPRI) and might comprise besides
exchange rates policy variables such as intervention prices, tariff rate quota levels and subsidised
export limits. Due to the case specific implementation, the variables driving the equations might
however differ between markets.
ESIM is a net trade model where commodities are treated as homogenous and price differences are
hence linked to transport / transaction costs and policy interventions such as border protection
measures. In order to capture the effect of a regime switch from net importer to net exporter, the model
embeds a logistic price transmission function (Banse, Grethe 2006) similar to Lampe (1999) that is
responsive to net trade. Both approaches can be understood as a smooth approximation of the spatial
arbitrage condition. Specifically, the domestic market price smoothly declines from an upper asymptote
reflecting c.i.f. prices plus tariffs to a lower asymptote corresponding to the f.o.b. price when the
country turns from a large net importer to a large net exporter, but with the turning point usually
located at a zero net trade volume.
WEMAC (e.g. Benjamin, Guéguen, Houée 2003, Houée-Bigot 2006) is a partial equilibrium model
that differs from FAPRI and AGMEMOD in its stronger reliance on microeconomic theory and more
rigorous use of econometric techniques (including stationarity tests and use of system methods rather
than OLS). However, in terms of the price linkage it also applies linear price linkage equations for the
intra EU price transmission from the dominant key market to the other Member States represented.
CAPSIM is a partial equilibrium model relying on the same database as CAPRI but on a higher level
of aggregation. It also shares the reliance on calibration techniques, exploiting microeconomic
regularity conditions. The key differences are the simpler specification of the EU supply side
(behavioural functions on the MS level rather than regionalised programming models) as well as
simple gross trade functions for the rest of the world (see section 3.5) rather than a full scale global
21
trade model. However, the last version included an interesting specification of intra EU price
transmission where national producer prices are proportionally linked to EU prices with the scaling
factor being a function of net trade of the MS (Witzke, Tonini 2008, p. 230). This mechanism was
shown to provide a smooth approximation to a switching regime (Witzke, Zintl, Tonini 2009, p. 48-49)
albeit with a quite weak empirical basis.
Trying to summarise the solutions in various modelling systems has to face the problem that many
models use market and region specific specifications, depending on the data. Accordingly, the
empirical content and detail of these individual equations is often impossible to deduct from the general
model description. Bearing this caveat in mind, the following conclusions can be drawn. Overall it
appears that different variants of linear price transmission equations are most frequent. Thus the link
between domestic and world markets is based on some fixed relative and/or additive margin, apart
from trade policy instruments and exchange rates. These margins are either estimated from time series,
calibrated to a base year or chosen to reflect some expert knowledge. They may express transport and
transaction costs under the assumption that the basic trade pattern does not change, but equally they
will capture quality differences between the key price and the domestic one.
4.3. Armington style models for heterogeneous products
As most Computable General Equilibrium models, GTAP treats imported and domestically produced
goods as non-perfect substitutes based on the Armington assumption. Additionally, fob and cif prices
are linked via freight margins, and selling prices of imported goods comprise the effect of tariffs and
taxation. The related equations are linear and based on wedges, and effect of the underlying data base
in values, so that prices are all scaled to unity. Usually price transmission is expressed in terms of the
Armington elasticities that have received particular attention therefore (Section 5.2.1). However, in an
attempt to validate GTAP in terms of the price volatility predicted from the model in stochastic
simulation experiments, Valenzuela et al. (2007) found that a standard short run price transmission
elasticity may also be incorporated on top of the Armington assumption to improve the fit of historical
price volatility in GTAP.
In addition to the Armington assumption on the demand side, each GTAP sector produces outputs
tailored to the domestic market and for exports according to a Constant Elasticity of Transformation
function. In contrast to the MCMs discussed above, no world market or anchor price is present in the
system and all prices for one commodity are indirectly linked by the substitution between import and
domestic sales on the demand side and between domestic sales and exports on the supply side.
22
The market model of CAPRI (Britz & Witzke 2008) differs from all MCMs above as it based on the
Armington assumption. In contrast to GTAP and most CGEs, it does not use a CET function on the
supply side. All prices are expressed in EURO. CIF prices are linked to FOB prices of exporters
(regional market prices net of any export subsidies) with fixed per unit transport costs. Adding ad-
valorem and specific tariffs to CIF prices gives import prices considered by consumers for their
consumption decision making. Ad valorem and specific tariffs can be endogenous variables depending
on TRQs or in case of the EU represent flexible levies ensuring minimal import prices as long as the
bound rate is not reached.
The per unit transport costs in CAPRI had been estimated from time series of per unit export and
import values, and regressed on distances and dummies referring to the exporter and importer country
which can be understood as proxy for port handling costs and similar fixed per unit costs linked to the
origin and destination independent of the transport distance. The raw data are subject to considerable
statistical noise. Trade matrices are available e.g. from FAO which report for each transport flow
importer and exporter notification of values and quantities. Ideally, these notifications should show
identical quantities, so that the derived import and export unit values can be used to estimate average
transport and transaction costs in a given year. However, for a range of reasons such as classification
errors, differences in reporting period and general reporting errors, the exporter and importer
notifications often differ considerably.
In contrast to CGEs, a CAPRI “trade block” featuring the price transmission mechanism described
above can be broken down to several countries or group of countries with their own behavioural
equations. The producer prices of these countries are currently linked with a fixed percentage margin to
the market price of the trade block (for example EU15); a simple specification that will be reconsidered
for CAPRI-RD Deliverable 3.3.2. The percentage margins are determined from the base year producer
prices derived from the Economic Accounts of Agriculture for EU countries. Conceptually, they are
hence unit values, derived from dividing total revenues by production quantities, and do not refer to
clearly defined price notation for a specific quality.
4.3.1. Econometric research and results on Armington parameters
The typical equation to estimate in an Armington model results from the optimality condition of the
consumer to find the optimal mix of imported and domestically produced goods:
r,i
imp,r,i
dom,r,i
dom,r,i
imp,r,i
dom,r,i
imp,r,i
P
P
X
Xσ
δδ
=
23
In log form, this is a linear equation linking the ratio of imported to domestic quantities of good i in
region r to the reciprocal price ratio with the substitution elasticity as a parameter to be estimated
(example: Gallaway, McDaniel, Rivera 2003, surveys: McDaniel, Balistreri 2003; Sarker, Surry 2006).
Due to the key importance of the Armington elasticities for welfare and other results, such estimates
have been undertaken frequently for particular regions and commodities. Short run Armington
elasticities of Gallaway, McDaniel, Rivera (2003) were usually between 0.5 and 2 for food products
with long run values typically between 1 and 3.
Another approach to estimate the Armington elasticity has been introduced by Warr (2008). In
equilibrium, changes in quantities produced for domestic consumption must be the same on the supply
and demand side (if exports are irrelevant) such that the following equilibrium condition holds for the
percentage price changes (small letters, region index dropped):
( )( ) ( )
( )( ) oo,iimp,iimp,io
i,i
o,iimp,i
dom,ii,iimp,iii,i
ii,iimp,idom,i
dom,iimp,iioo,idom,idom,iimp,iimp,ii,i
dom,iimp,iioo,iii,idom,ii,i
ppppshsh
shp
ppppshpsh
ppppp
φφεη
ησεση
σηη
σηηε
+=+−+
+=
⇔−+++=
−++=
where
pi,dom = domestic price of good i
pi,imp = import price of good i
pi = aggregate price of good i
po = price of other goods o
ηi,j = demand elasticity of aggregate consumption of good i with respect to price j
εi,i = supply elasticity of good i with respect to the own price i
σi = substitution elasticity between domestic and imported quantities of good i
shi,imp = value share of imports in total consumption of good i (shi,dom = 1- shi,imp)
φi,imp = price transmission elasticity from imported to domestic prices
The last expression shows the linkage of the price transmission elasticity to the Armington elasticity in
this simple market model. As expected, the transmission elasticity would approach one as σi grows to
infinity. Warr estimated the price transmission elasticity for rice in Indonesia to be about 0.3-0.4 and
inferred that the Armington elasticity would be around 3, using assumed values for the other elasticities
and shares. Even more direct, Bakhshoodeh (2010) applied the equilibrium condition to data from two
24
household surveys to calculate an Armington elasticity around 4 for rice in Iran. Of course, this
approach suffers from several simplifications such as the absence of any policy measures but the
explicit linkage to the price transmission elasticity is illuminating.
More ambitious attempts to quantify Armington elasticities have been undertaken in the GTAP
network. The first is generalisation of the standard practice to calibrate the model against a set of base
year data by Liu, Arndt, Hertel (2004). They used the Armington parameters to fit the past years 1986,
1989, and 1992, starting from a base year 1995 (GTAP 4 database), in a cross entropy setting and a 10
regions x 10 product setting. The Armington elasticity for agriculture was estimated to be 1.05 which
was at the lower bound of the CE approach.
A study of greater relevance to CAPRI is Hertel et al. (2004). This also relied on econometric
estimation of CES demand functions for imports of particular origins like Gallaway, McDaniel, Rivera
(2003). However, due to the availability of a unique, very disaggregated cross section data set on
transport costs and bilateral tariffs (acknowledging preferential tariffs) this study could circumvent a
typical problem of econometric research. Import demand estimation based on import unit values tends
to give downward biased results due to the neglect of quality variations: Demand and unit values will
be high for high qualities such that the negative impact of price on demand will be underestimated.
Armington elasticities for substitution between imports of different origins (i.e. referring to stage 2 in
the CAPRI market model, see Britz, Witzke, p….) were usually between 3 and 10 for food and
agricultural products. Depending on the product, this is sometimes higher or lower than current values
in CAPRI. A detailed comparison seems worthwhile but not in the context of this survey.
Finally, it should be mentioned that accepting product heterogeneity does not imply to accept the
Armington assumption. Indeed, most empirical analyses testing the Armington assumption in a more
general demand specification distinguishing goods by their origin have rejected its separability
assumptions (e.g. Surry, Herrard, Leroux 2002, Hanrahan, Westhof, Young 2001).
5. Conclusions for CAPRI
While it is clear that the spatial arbitrage model provides the key theoretical foundation to price linkage
modelling, its direct applicability is limited as the product list in CAPRI is more aggregate including
processed quantities of agricultural raw products according to the market balances of Eurostat and FAO.
Hence, it appears that Armington type models have greater relevance for CAPRI including intra EU
trade relationships. However, the computational problems would appear to be insurmountable if the
current set of market regions (28) were simply extended by all individual EU (and potentially Western
25
Balkan) Member States. Therefore this review also covered MCMs base on the assumption of
homogeneous good and related empirical research.
Econometric research on spatial price transmission has become quite sophisticated in terms of
methodology but the findings are quite heterogeneous. Depending on the particular database, items,
regions, and periods selected researchers have found examples for almost any relationship between two
regional prices from close short run and long run integration with a price transmission elasticity or
coefficient approaching one, to complete absence of any relationship or even “wrong” signs. It may be
expected that own estimations will have to face a similar heterogeneity that needs some filtering before
being usable in the model. Furthermore, given the inconclusive and diverse literature on market power,
it is suggested to neglect these complications for the time being.
A review of current simulation systems for agricultural markets showed that most MCMs rely on a
simple price linkage based on a constant or proportional margin, like the current CAPRI model in the
case of intra trade block price transmission. Other and therefore more interesting solutions have been
chosen in the AGMEMOD and CAPSIM models that may also be stimulating for future solutions in
CAPRI.
Directly adopting quantitative parameters for price transmission from other models (or existing
econometric studies) is usually inappropriate for many reasons. A first aspect is the temporal scale of
models. AGLINK/COSIMO, FAPRI, AGMEMOD, WEMAC work in yearly steps and comprise
recursive-dynamic features (ESIM as an option) whereas GTAP and CAPRI are comparative static
models giving results after a longer adjustment period. It can e.g. be assumed that on the short run, per
unit transport and transaction costs can be very high if unusual large amounts are needed to be shipped
and market infrastructure is not yet adapted (see the discussion of Hungary in Commission 2006, p. 18).
Accordingly, the comparative static models should show a somewhat higher price transmission, ideally
increasing with the length or run. Similarly, Liu, Arndt, Hertel (2004) have increased their Armington
elasticities depending on the time horizon of their simulation.
Furthermore, there are important differences between models according to acceptance or rejection of
the Armington assumptions. In the latter, price transmission into domestic market is dampened by the
fact that import shares react to changes in import relative to domestic producer prices and that market
prices are a weighted average of the domestic sales and imports. Where imports shares are small, price
transmission can be extremely low. The implied price transmission is hence rather sensible to the
import shares in the calibration point, and, clearly to the substitution elasticities used. Also, the product
aggregation level must be taken into account – which is however comparable at least between most of
the MCMs mentioned above.
26
A further point refers to the details in capturing domestic and border policies. Price transmission
elasticities or the substitution elasticities used in the CES equations can also be used to capture policy
effects e.g. relating to TRQs, specific tariffs, the use of applied tariffs or domestic policy instruments
stabilizing prices. To the extent that these policies are captured explicitly in the model, such as in
CAPRI, parameters referring to price transmission such as the substitution elasticities should be higher.
A further complication relates to the distinction of unit values and true prices. CAPRI uses unit values
to represent FOB and CIF prices. Unit values define also producer and consumer prices for EU
countries covered by the economic accounts of Eurostat and further national data sets. The movement
of prices is clouded in unit values due to changes in their regional, product and quality composition,
creating statistical problems in empirical analysis but also impeding comparisons with models like
AGLINK, FAPRI, or AGMEMOD which often use price notations at specific locations and for specific
qualities.
Solutions obtained elsewhere may thus provide valuable stimulus in terms of methodology, but
reusability of parameters is mostly limited by numerous differences that often become visibly only
after closer scrutiny.
6. Summary
This deliverable has reviewed the theoretical background, specific econometric research and
specifications for price transmission in other modelling systems with a focus on agriculture to draw
conclusions for future developments of CAPRI. While it is clear that the spatial arbitrage model
provides the key theoretical foundation to price linkage modelling, its direct applicability is limited as
the product list in CAPRI is more aggregate. Hence, it appears that Armington type models have
greater relevance for CAPRI, including intra EU trade relationships. However, the computational
problems would appear to be insurmountable if the current set of market regions (28) were simply
extended by all individual EU (and potentially Western Balkan) Member States. Therefore, this review
covered MCMs based on the assumption of homogeneous good as well as the GTAP model that also
relies on the Armington assumption.
The model comparison and the review of econometric research have yielded a wide variety of
approaches and findings. These results may thus provide valuable stimulus in terms of methodology
but reusability of parameters is mostly limited by numerous differences to CAPRI. These may relate to
the level of temporal aggregation, the product definition, treatment of product heterogeneity, policy
coverage or technical issues like the distinction of unit values and true prices. It is necessary therefore
to proceed to an empirical analysis tailored to the definitions and database of CAPRI.
27
7. References
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of Development Economics, 63 (2): 327-349.
Alexander, C.O., Wyeth, J. (1994) Cointegration and Market Integration: an Application to the
Indonesian Rice Market. Journal of Development Studies, 30 (2): 303-308.
Anania, G. (2010) EU Economic Partnership Agreements and WTO Negotiations. A Quantitative
Assessment of Trade Preference Granting and Erosion in the Banana Market. Food Policy,
35 (2): 140-153 (doi:10.1016/j.foodpol.2009.11.001).
Anania, G., Drogué, S., Paris, Q. (2009) Calibrating Mathematical Programming Spatial Models.
AgFoodTrade Working Paper (http://www. agfoodtrade.eu/).
Ardeni, P.G. (1989) Does the Law of One Price really hold for Commodity Prices? American Journal
of Agricultural Economics, 71 (3): 661-669.
Armington, P. (1969) A Theory of Demand for Products Distinguished by Place of Production.
International Monetary Fund Staff Papers, XVI: 159-78..
Banse, M., Grethe, H. (2006) Using the Logistic Functional Form for Modelling International Price
Transmission in Net Trade Simulation Models. Paper presented on the 2006 IAAE Annual Meeting,
August 12-18, Queensland, Australia.
Baffes, J. (1991) Some further Evidence on the Law of One Price: The Law of One Price still holds.
American Journal of Agricultural Economics, 73 (4): 1264-1273.
Bakhshoodeh, M. (2010) Impacts of World Prices Transmission to domestic Rice Markets in rural Iran.
Food Policy, 35 (1): 12-19.
Barrett, C. B. (1996) Market analysis: Are our Enriched Toolkits Well Suited to Enlivened Markets?
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