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market_module_for_agricultural_outputs [2020/03/25 09:00] – [Welfare-consistent tariff aggregation module] matszmarket_module_for_agricultural_outputs [2022/11/07 10:23] (current) – external edit 127.0.0.1
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 The processing margins are replaced by producer prices times -1 for all products besides oilseed. For the latter, the processing margin is defined from the producer prices //v_prodPrice// for the cakes and oils time the respective crushing coefficients minus the buying prices (average of domestically sold and imported quantities) //v_arm1Price//: The processing margins are replaced by producer prices times -1 for all products besides oilseed. For the latter, the processing margin is defined from the producer prices //v_prodPrice// for the cakes and oils time the respective crushing coefficients minus the buying prices (average of domestically sold and imported quantities) //v_arm1Price//:
 +
 +FIXME
  
 \begin{align} \begin{align}
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 v\_prodMarg_{seed,r} = &-v\_arm1Price_{seed,r} \\ v\_prodMarg_{seed,r} = &-v\_arm1Price_{seed,r} \\
 & +v\_prodPrice_{seed \rightarrow cak,r} v\_procYield_{cak,r} \\ & +v\_prodPrice_{seed \rightarrow cak,r} v\_procYield_{cak,r} \\
-& +v\_prodPrice_{seed \rightarrow oil,r} v\_procYield_{oil,r} \\+& +v\_prodPrice_{seed \rightarrow oil,r} v\_procYield_{oild,r} \\
 \end{split} \end{split}
 \end{align} \end{align}
  
 Finally, output of oils and cakes //supply// depends on the processed quantities //proc// of the oilseeds and the crushing coefficients: Finally, output of oils and cakes //supply// depends on the processed quantities //proc// of the oilseeds and the crushing coefficients:
 +FIXME
  
 \begin{align} \begin{align}
 \begin{split} \begin{split}
-supply_{cak,r} = proc_{seed,r} v\_procYield_{cak,r} \\+supply_{cake,r} = proc_{seed,r} v\_procYield_{cak,r} \\
 supply_{oil,r} = proc_{seed,r} v\_procYield_{oil,r} \\ supply_{oil,r} = proc_{seed,r} v\_procYield_{oil,r} \\
 \end{split} \end{split}
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 The following table shows the substitution elasticities used for the different product groups. Compared to most other studies, we opted for a rather elastic substitution between products from different origins, as agricultural products are generally more uniform then aggregated product groups, as they can be found e.g. in CGE models. The following table shows the substitution elasticities used for the different product groups. Compared to most other studies, we opted for a rather elastic substitution between products from different origins, as agricultural products are generally more uniform then aggregated product groups, as they can be found e.g. in CGE models.
  
-**Table 28: Substitution elasticities for the Armington CES utility aggregators((A sensitivity analysis on those elasticities is given in section 5.7 FIXME ))**+**Table 28: Substitution elasticities for the Armington CES utility aggregators((A sensitivity analysis on those elasticities is given in section [[Sensitivity analysis]]))**
  
 ^Product (group) ^Substitution elasticity between domestic sales and imports  ^Substitution elasticity between import flows ^ ^Product (group) ^Substitution elasticity between domestic sales and imports  ^Substitution elasticity between import flows ^
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 {{::figure_19.png?600|}} \\ Soruce: Capri Modelling System {{::figure_19.png?600|}} \\ Soruce: Capri Modelling System
  
-The above “primal” formulation of the Armington approach in terms of quantity aggregators turned out numerically less stable in the implementaiotn than the dual representation in terms of price aggregators. The Armington approach suffers from two important shortcomings. First of all, a calibration to a zero flow is impossible so that only observed import flows react to policy changes while all others are fixed at zero level. For most simulation runs, that shortcoming should not be serious. If it is relevant, it may be overcome using the modified Armington approach as explained in Section 5.4.8 FIXME . +The above “primal” formulation of the Armington approach in terms of quantity aggregators turned out numerically less stable in the implementaiotn than the dual representation in terms of price aggregators. The Armington approach suffers from two important shortcomings. First of all, a calibration to a zero flow is impossible so that only observed import flows react to policy changes while all others are fixed at zero level. For most simulation runs, that shortcoming should not be serious. If it is relevant, it may be overcome using the modified Armington approach as explained in Section [[Market module for agricultural outputs#Price linkages]]. 
  
 Secondly, the Armington aggregator defines a utility aggregate and not a physical quantity. That second problem is healed by re-correcting in the post model part to physical quantities. Little empirical work can be found regarding the estimation of the functional parameters of Armington systems. Hence, substitution elasticities were chosen as to reflect product properties as shown above. Secondly, the Armington aggregator defines a utility aggregate and not a physical quantity. That second problem is healed by re-correcting in the post model part to physical quantities. Little empirical work can be found regarding the estimation of the functional parameters of Armington systems. Hence, substitution elasticities were chosen as to reflect product properties as shown above.
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 The Armington aggregator functions are already shown in the diagram above. The compositions inside of the Armington composite goods can be derived from first order conditions of utility maximisation under budget constraints and lead to the following conditions: The Armington aggregator functions are already shown in the diagram above. The compositions inside of the Armington composite goods can be derived from first order conditions of utility maximisation under budget constraints and lead to the following conditions:
 +FIXME
 \begin{equation} \begin{equation}
-\frac{v\_arm2Quant_{i,r}}{v\_domSales_{i,r}}= \left( \frac{dp_{i,rw,r}}{dp_{i,r,r}} \frac {pmrk_{i,r}}{arm2pricep_{i,r}} \right )^{1/(1+\phi_1)}+\frac{v\_arm2Quant_{i,r}}{v\_domSales_{i,r}}= \left( \frac{dp_{i,rw,r}}{dp_{i,r,r}} \frac {pmrk_{i,r}}{arm2pricep_{i,r}} \right )^{\frac{1}{1+\phi_1}}
 \end{equation} \end{equation}
  
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 \begin{equation} \begin{equation}
-\frac{v\_tradeFlows_{i,r,r1}}{v\_tradeFlows_{i,r,r2}}= \left( \frac{dp_{i,r,r1}}{dp_{i,r,r2}} \frac {impp_{i,r,r2}}{impp_{i,r,r1}} \right )^{1/(1+\phi_2)}+\frac{v\_tradeFlows_{i,r,r1}}{v\_tradeFlows_{i,r,r2}}= \left( \frac{dp_{i,r,r1}}{dp_{i,r,r2}} \frac {impp_{i,r,r2}}{impp_{i,r,r1}} \right )^{\frac{1}{1+\phi_2}}
 \end{equation} \end{equation}
  
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 An achievement of the CAPRI biofuel module is that biofuel supply and feedstock demand react flexibly to the price ratio of biofuel and feedstock prices as well as biofuel demand and bilateral trade flows react flexibly to biofuel prices and further relevant drivers. An achievement of the CAPRI biofuel module is that biofuel supply and feedstock demand react flexibly to the price ratio of biofuel and feedstock prices as well as biofuel demand and bilateral trade flows react flexibly to biofuel prices and further relevant drivers.
  
-**Figure 22: Construction of the ethanol market implemented in CAPRI**+**Figure 22: Construction of the ethanol market implemented in CAPRI** FIXME
  
 {{::figure_22.png?600|}} \\ Soruce: Capri Modelling System {{::figure_22.png?600|}} \\ Soruce: Capri Modelling System
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 \end{equation} \end{equation}
  
-The index r contains all regions in the market module that have biofuel production. All feedstocks that can be used to produce first generation biofuels are stored in the index xf and the by products Glycerine, DDGs and Vinasses in xbp. Prices are denoted by p. One speciality exists in the case of sugar prices in the EU, where a specific ethanol sugar price is assumed in case of the existence of production quotas. This is due to the fact that ethanol beet in the EU purchased at a lower price than beets processed to sugar. These single feedstock costs then form a CES aggregate to give the average cost for the respective biofuel:+The index r contains all regions in the market module that have biofuel production. All feedstocks that can be used to produce first generation biofuels are stored in the index xf and the by products Glycerine, DDGs and Vinasses in xbp. Prices are denoted by p. One speciality exists in the case of sugar prices in the EU, where a specific ethanol sugar price is assumed in case of the existence of production quotas. This is due to the fact that ethanol beet in the EU purchased at a lower price than beets processed to sugar. These single feedstock costs then form a [[https://en.wikipedia.org/wiki/Constant_elasticity_of_substitution|CES]] aggregate to give the average cost for the respective biofuel:
  
 The index r contains all regions in the market module that have biofuel production. All feedstocks that can be used to produce first generation biofuels are stored in the index xf and the by products Glycerine, DDGs and Vinasses in xbp. Prices are denoted by p. One speciality exists in the case of sugar prices in the EU, where a specific ethanol sugar price is assumed in case of the existence of production quotas. This is due to the fact that ethanol beet in the EU purchased at a lower price than beets processed to sugar. These single feedstock costs then form a CES aggregate to give the average cost for the respective biofuel: The index r contains all regions in the market module that have biofuel production. All feedstocks that can be used to produce first generation biofuels are stored in the index xf and the by products Glycerine, DDGs and Vinasses in xbp. Prices are denoted by p. One speciality exists in the case of sugar prices in the EU, where a specific ethanol sugar price is assumed in case of the existence of production quotas. This is due to the fact that ethanol beet in the EU purchased at a lower price than beets processed to sugar. These single feedstock costs then form a CES aggregate to give the average cost for the respective biofuel:
 +FIXME
 \begin{equation} \begin{equation}
 \mu_{r,xb} = \mu_{r,xb}^c-\left [ \sum_{xf} \phi_{r,xf} \left [ \frac{\mu_{r,xf}}{\mu_{r,xb}^c}\right]^{(1-\rho_{r,xb})}\right]^{\frac{1}{(1-\rho_{r,xb})}} \mu_{r,xb} = \mu_{r,xb}^c-\left [ \sum_{xf} \phi_{r,xf} \left [ \frac{\mu_{r,xf}}{\mu_{r,xb}^c}\right]^{(1-\rho_{r,xb})}\right]^{\frac{1}{(1-\rho_{r,xb})}}
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 Assumed elasticities for total fuel demand after filling with average values are demonstrated in the table below. Assumed elasticities for total fuel demand after filling with average values are demonstrated in the table below.
  
-**Table 29: Overview of pillar II measures modelled in CAPRI**+**Table 29: Overview of pillar II measures modelled in CAPRI** FIXME
  
 {{::table_29.png?350|}} \\ Source: Own calculation based on PRIMES 2009 {{::table_29.png?350|}} \\ Source: Own calculation based on PRIMES 2009
market_module_for_agricultural_outputs.1585126840.txt.gz · Last modified: 2022/11/07 10:23 (external edit)
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