market_module_for_agricultural_outputs
Differences
This shows you the differences between two versions of the page.
Both sides previous revisionPrevious revisionNext revision | Previous revision | ||
market_module_for_agricultural_outputs [2020/03/24 08:24] – [Biofuel module] matsz | market_module_for_agricultural_outputs [2022/11/07 10:23] (current) – external edit 127.0.0.1 | ||
---|---|---|---|
Line 168: | Line 168: | ||
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 // | 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 // | ||
+ | |||
+ | FIXME | ||
\begin{align} | \begin{align} | ||
Line 173: | Line 175: | ||
v\_prodMarg_{seed, | v\_prodMarg_{seed, | ||
& +v\_prodPrice_{seed \rightarrow cak,r} v\_procYield_{cak, | & +v\_prodPrice_{seed \rightarrow cak,r} v\_procYield_{cak, | ||
- | & +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, | + | supply_{cake,r} = proc_{seed, |
supply_{oil, | supply_{oil, | ||
\end{split} | \end{split} | ||
Line 233: | Line 236: | ||
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 | + | **Table 28: Substitution elasticities for the Armington CES utility aggregators((A sensitivity analysis on those elasticities is given in section |
^Product (group) ^Substitution elasticity between domestic sales and imports | ^Product (group) ^Substitution elasticity between domestic sales and imports | ||
Line 248: | Line 251: | ||
{{:: | {{:: | ||
- | 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 | + | 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 |
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. | ||
Line 348: | Line 351: | ||
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, | + | \frac{v\_arm2Quant_{i, |
\end{equation} | \end{equation} | ||
Line 357: | Line 360: | ||
\begin{equation} | \begin{equation} | ||
- | \frac{v\_tradeFlows_{i, | + | \frac{v\_tradeFlows_{i, |
\end{equation} | \end{equation} | ||
Line 419: | Line 422: | ||
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** |
{{:: | {{:: | ||
Line 450: | Line 453: | ||
\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:// |
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, | \mu_{r,xb} = \mu_{r, | ||
Line 472: | Line 475: | ||
\begin{equation} | \begin{equation} | ||
- | x_{r, | + | x_{r, |
\end{equation} | \end{equation} | ||
For biofuel supply from first generation technologies (\(x^{1st}\)) a function of the relation of the respective biofuel price and the corresponding average feedstock per unit costs has been specified. A synthetic supply function was chosen that satisfied some plausibility considerations, | For biofuel supply from first generation technologies (\(x^{1st}\)) a function of the relation of the respective biofuel price and the corresponding average feedstock per unit costs has been specified. A synthetic supply function was chosen that satisfied some plausibility considerations, | ||
+ | \begin{align} | ||
+ | \begin{split} | ||
+ | x_{r, | ||
+ | \begin{bmatrix} | ||
+ | \partial_{r, | ||
+ | +exp \left ( \beta_{r, | ||
+ | \end{bmatrix} | ||
+ | \end{split} | ||
+ | \end{align} | ||
+ | |||
+ | This function consists of three parts on the RHS: the first part is linear (a small positive value for δ), the second part is semi-log and the third is sigmoid. The linear term guarantees a minimal slope, where the sigmoid function would return a slope of almost 0. The semi-log term is active at processing margins considerably higher than in the baseline point and the sigmoid function guarantees a steeper slope in a range where processing starts and production is close to zero when feedstock costs exceed output values. The coefficients β and δ are behavioural parameters in these functions. All biofuel supply equations are generally of the style presented below with an example of bioethanol in France. | ||
+ | |||
+ | **Figure 25: Biofuel supply function in France** | ||
+ | |||
+ | {{:: | ||
+ | |||
+ | The supply of by products is directly linked to the first generation biofuel output: | ||
+ | |||
+ | \begin{equation} | ||
+ | x_{r,xbp} = fd_{r,xf} \alpha_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | Total biofuel output is then defined as the sum over first generation, second generation (secg), non agricultural (nagr) and some exogenous production (exo) from products not mapped to the feedstocks in CAPRI (only relevant in extra EU countries): | ||
+ | |||
+ | \begin{equation} | ||
+ | x_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | ===Biofuel demand=== | ||
+ | |||
+ | The representation of biofuel demand was simplified compared to the approach chosen first and applied in Becker (2011). There the Aglink demand system was more or less reproduced using a different functional form but keeping the three types of biofuel demand, the use as additive, as low blends and in flexible fuel vehicles. The actual biofuel demand equations consist of only one sigmoid function instead of stacking three of them. The share of biofuel in total fuel demand (bsh) is hereby defined as: | ||
+ | |||
+ | \begin{equation} | ||
+ | bsh_{r,xb} = bsh_{r, | ||
+ | \end{equation} | ||
+ | |||
+ | Again the coefficients X are used to specify the exact slope of these functions. The first term (bshq) defines the part of the biofuel demand which is enforced by any kind of obligation quota or mandate, while the second part defines an “endogenous” part of the demand. This term has the upper linmit \(bsh_{max}\) which represents the maximum biofuel share on top of the quota obligation that is deemed reachable in a certain country. The endogenous demand component is driven by the price relation of a biofuel ( \(p_{r, | ||
+ | |||
+ | **Figure 25: Biofuel demand share function in France** | ||
+ | |||
+ | {{:: | ||
+ | |||
+ | Total biofuel demand (\(d_{r, | ||
+ | |||
+ | \begin{equation} | ||
+ | d_{r,xb} = bsh_{r,xb} +d_{r,f} | ||
+ | \end{equation} | ||
+ | |||
+ | ===Total fuel demand=== | ||
+ | |||
+ | |||
+ | //Total fuel demand// is exogenous to the CAPRI model. However, an econometric estimation was undertaken to receive a demand reaction on exogenous drivers like the oil price and GDP. This function can then be used in Scenarios to adjust total fuel demand, if these drivers are altered. A response surface estimation on the basis of available PRIMES scenarios from 2008 was undertaken. The PRIMES output files at hand allow for estimating the relation between total fuel demand, GDP and fossil fuel prices. For the estimation an ordinary least square estimator is used. A double log demand function is chosen where the estimation coefficients can directly be interpreted as elasticities. The regression function and thereby the total fuel demand function is defined by: | ||
+ | |||
+ | \begin{align} | ||
+ | \begin{split} | ||
+ | log(y_{i, | ||
+ | & | ||
+ | \end{split} | ||
+ | \end{align} | ||
+ | |||
+ | where, \\ | ||
+ | //i// = Fuel type \\ | ||
+ | //r// = Region \\ | ||
+ | //s// = Scenario \\ | ||
+ | //t// = Year \\ | ||
+ | //y// = Fuel demand \\ | ||
+ | //p// = Fuelprice including tax \\ | ||
+ | //gdp// = Gross Domestic Product \\ | ||
+ | //trend// = Trend variable \\ | ||
+ | \(\epsilon\) = Error term for regression \\ | ||
+ | \(\delta\) = Intercept \\ | ||
+ | \(\alpha\) = price elasticity of demand \\ | ||
+ | \(\beta\) = GDP elasicity of demand \\ | ||
+ | \(\gamma\) = Trend elasicity of demand \\ | ||
+ | |||
+ | The results of the regression analysis (differentiated into biodiesel and ethanol for every EU MS) cover estimates for α, β, γ and the intercept (δ). The significant estimates are used directly in the respective fuel demand function. If no significance was observed for a coefficient in a respective country, the estimated value is replaced by an average value which is derived from the weighted average of significant coefficients over all EU MS. The resulting matrix of regression coefficients (elasticities) in the fossil fuel demand function are displayed in table below. As the PRIMES data only covers values for European countries but also estimates for the non-European CAPRI regions are required it was assumed that the coefficient estimates for the aggregated EU27 are also applicable for those regions. | ||
+ | |||
+ | 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** FIXME | ||
+ | |||
+ | {{:: | ||
+ | |||
+ | **Biofuel Trade** | ||
+ | |||
+ | Behavioural functions for global bilateral trade of biodiesel and ethanol are intrinsically tied to the final biofuel demand functions. The general methodology is that of a two stage demand system relying on the Armington assumption as already applied for other agricultural commodities in the standard CAPRI version. Biofuel demand for fuel use is considered a derived demand of refineries and responsive to the price ratio of biofuels to fossil fuels. The non fuel demand for biofuels (e.g. ethanol demand of the chemical industry) is consequently set on INDM or PROC (industrial use). | ||
+ | |||
+ | ===Calibration of the biofuel system=== | ||
+ | |||
+ | So far, only the general form of the biofuel supply and demand functions where derived, but without any adjustments, | ||
+ | |||
+ | Firstly, the demand system is calibrated. We here assume that only the part of the observed biofuel demand share in total fuel demand that is above the quota obligations is the result of a consumer decision and thus a result of the flexible parts on the demand equations. To calibrate the demand functions to the observed combination of the price ratio bio- to fossil fuel and demand share in total fuel consumption, | ||
+ | |||
+ | * It recovers the baseline combination of price and quantity relations | ||
+ | * It reaches 90% of the max share (\(bsh_{max}\)) at a certain price relation (currently 0.5 for ethanol and 0.3 for biodiesel)((These values were chosen by trial and error to achieve a reasonable demand response in certain scenarios. However a more empirically based representation of the demand response would greatly improve the system. )). | ||
+ | |||
+ | |||
+ | The maximum biofuel demand share of a region is chosen 2% above the observed baseline share. | ||
+ | |||
+ | The parameters \(\beta^2\), | ||
+ | |||
+ | ====Endogenous policy instruments in the market model==== | ||
+ | |||
+ | ===Subsidised exports=== | ||
+ | |||
+ | On the market side, the amount of subsidised exports (exps) are modelled by a sigmoid function, driven by the difference between EU market (//pmrk//) and administrative price (//padm//), see equation below. The sigmoid function used looks like: | ||
+ | |||
+ | \begin{equation} | ||
+ | Sigmoid(x)= exp \frac {min(x, | ||
+ | \end{equation} | ||
+ | |||
+ | where //x// is replaced by the expression shown below in the equations. | ||
+ | |||
+ | The response was chosen as steep as technically possible by setting a high value for \(\alpha\), i.e. intervention prices are undercut solely if WTO commitment (QUTE) and the maximum quantity of stock changes are reached. | ||
+ | |||
+ | \begin{equation} | ||
+ | expsVal_{i, | ||
+ | \end{equation} | ||
+ | |||
+ | The parameters \(\alpha\), \(\beta\) are determined based on observed price and quantities of subsidised exports. The per unit subsidy is defined from non-preferential exports and the value of the subsidies: | ||
+ | |||
+ | \begin{equation} | ||
+ | expSub_{i, | ||
+ | \end{equation} | ||
+ | |||
+ | The relation is shown in the figure below. | ||
+ | |||
+ | **Figure 27: Modelling of subsidised export costs by a logistic function** | ||
+ | |||
+ | {{:: | ||
+ | |||
+ | ===Endogenous administrative stocks=== | ||
+ | |||
+ | For years, the CAP defended administrative prices in key markets such as cereals, beef, butter and skim milk poweder by direct interventions into markets which where out into public stocks. The basic functioning of that mechanism in CAPRI in shown in the figure below. | ||
+ | |||
+ | **Figure 28: Endogenous administrative stocks in CAPRI** | ||
+ | |||
+ | {{:: | ||
+ | |||
+ | **Purchases to intervention stocks** // | ||
+ | |||
+ | \begin{equation} | ||
+ | v\_buyingToIntervStocks_{i, | ||
+ | \end{equation} | ||
+ | |||
+ | A decrease of the administrative price or an increase of the market price will hence decrease purchases to intervention stocks. | ||
+ | |||
+ | **Releases from intervention stocks** // | ||
+ | |||
+ | \begin{equation} | ||
+ | intd_{i,r} = (intk_{i, | ||
+ | \end{equation} | ||
+ | |||
+ | Releases will hence increase if world market price increases or the EU market price drops, and if the size of the intervention stock increases. The parameters \(\gamma\) are determined from ex-post data on prices and intervention stock levels. The change in intervention stocks //ints// entering the market balance is hence the difference between intervention purchases //intp// and intervention stock releases //intd//: | ||
+ | |||
+ | \begin{equation} | ||
+ | ints_{i,r} = intp_{i,r} - intd_{i,r} | ||
+ | \end{equation} | ||
+ | |||
+ | ====Endogenous tariffs under Tariff Rate Quotas, flexible levies and the minimum import price regime for fruits and vegetables of the EU==== | ||
+ | |||
+ | ===Tariff Rate Quotas=== | ||
+ | |||
+ | Tariff Rate Quotas (TRQs) establish a two-tier tariff regime: as long as import quantities do not exceed the import quota, the low in-quota tariff is applied. Quantities above the quota are charged with the higher Most-Favoured-Nation (MFN) tariff. CAPRI distinguishes two types of TRQs: such open to all trading partners, and bi-laterally allocated TRQs. As a rule, bi-lateral allocated quotas are filled first. Equally, as for all tariffs, TRQs may define ad valorem and/or specific tariffs. | ||
+ | |||
+ | A market under a TRQ mechanism may be in one of the following regimes: | ||
+ | |||
+ | **Quota underfill**: | ||
+ | |||
+ | **Figure 29: Quota underfill regime** | ||
+ | |||
+ | {{: | ||
+ | |||
+ | **Quota binding, i.e. exactly filled**: the in-quota tariff is applied. The willingness to pay of consumers and thus the price paid is somewhere between the border plus the in-quota tariff and the border price plus the MFN tariff. The difference between the price in the market and the border price plus the in-quota tariff establishes a quota rent. Depending on property rights on the quota and the allocation mechanism, the quota rent is shared in different portions by the producers, importing agencies, the domestic marketing chain or the administration. Typically, the quota rent can neither be observed nor is their knowledge about distribution of the rent. | ||
+ | |||
+ | **Figure 30: Quota binding regime** | ||
+ | |||
+ | {{: | ||
+ | |||
+ | **Quota overfill**: the higher MFN-tariff is applied. The quota rent is equal to the difference between the MFN and the in-quota tariff. Again, how the quota rent is distributed to agents is typically not known. | ||
+ | |||
+ | **Figure 31: Quota overfill regime** | ||
+ | |||
+ | {{: | ||
+ | |||
+ | The fill rate for global TRQs is defined in the code as follows, adding all imports which are not under no duty/not quota access (p_doubleZero), | ||
+ | |||
+ | {{:: | ||
+ | |||
+ | There are a couple of further complications, | ||
+ | |||
+ | Besides the problem of defining the regime ex-post, the relation between the import quantity and the tariff is not differentiable but kinked. Therefore, again a sigmoid function is applied in the CAPRI market part: | ||
+ | |||
+ | In many cases, the EU features for the very same market so-called bi-lateral quotas and market access quotas from the URA round which must be open to all imports (“erga omnes”). As the allocation shares for the latter are currently not know to the CAPRI team, any importer is allowed to import under these global TRQs. Importers have bi-lateral quotas might import under global TRQs once the bi-lateral TRQs are overshot. | ||
+ | |||
+ | ===Flexible tariffs=== | ||
+ | |||
+ | Geneally, the WTO rules only set upper bounds on the tariffs (so-called Most Favorite Nature or MFN for short rates), but allows its members to reduce the tariffs as long as the same tariff is applied for all WTO members. Exemption from MFN rates which are implemented in CAPRI are preferential rates for Developing countries (Everything But Arms agreement of the EU), Free Trade Agreements and bi-lateral concessions e.g. results from minium market access obligation from the Uruguay rounds under TRQ. The EU generally uses MFN rates, but operates in the cereal markets a specific form of a variable tariff called the “levy” system. The last WTO EU trade policy review described the operation of the CAP import regime for cereals as follows: “In response to fluctuations in world prices, the EU has, within the limits of its bound tariffs, changed its MFN applied tariffs. It reduced tariffs on cereals to zero in January 2008 in response to high world prices, and reintroduced them at the end of October 2008. For wheat, the tariff is based on the difference between world prices and 155% of the intervention price, up to the bound rate of €95 per tonne for high quality wheat and €148 per tonne for high quality durum wheat with similar systems for other cereals.” | ||
+ | |||
+ | **Figure 32: | ||
+ | |||
+ | {{: | ||
+ | |||
+ | In CAPRI, the system is implemented as follows: | ||
+ | |||
+ | \begin{equation} | ||
+ | v\_flexLevy_{i, | ||
+ | \end{equation} | ||
+ | |||
+ | The actual implementation in the code differs somewhat, as the min and max operators are replaced by “fudging function”, | ||
+ | |||
+ | {{:: | ||
+ | |||
+ | The second equation defines the actual tariff applied: | ||
+ | |||
+ | {{:: | ||
+ | |||
+ | ===Entry price system for fruits and vegetables=== | ||
+ | |||
+ | A somewhat similar instrument is the entry price system used in the fruit and vegetable sectors of the EU. The entry price relates the applied tariff to a specified trigger price in a way that encourages imports at a price (CIF plus tariffs) that is between 92% and 98% of the trigger price. | ||
+ | |||
+ | **Figure 33: EU entry price system for fruits and vegetables** | ||
+ | |||
+ | {{: | ||
+ | |||
+ | In order to implement the system, first the difference beween 96% of the entry price and the cif in relation to the triggerprice is defined, times a possible factor to ease solution. | ||
+ | |||
+ | {{:: | ||
+ | |||
+ | That factor is the fed into a modified sigmoid function which as a result approximates the relations in the graphic shown above: | ||
+ | |||
+ | {{:: | ||
+ | |||
+ | ===Tariff computation in the model=== | ||
+ | |||
+ | The figure below depicts the interaction of the various elements of the tariff calculation discussed above. The blue boxes are policy instruments depicted in the model; the purple ones describe endogenous switches and the green ones intermediate model variables which can be interpreted as intermediate results for the applied tariffs. The red boxes show the rate applied to derive the import price. Arrows upwards from a decision box mean yes, to the left no. | ||
+ | |||
+ | The simplest decision is in the left lower corner: a check if the importer benefits from duty and quota free accesss. Examples are intro-EU trade or import from LDCs into the EU under the “Everything But Arms”-Agreement. If that is the case, the applied tariff is zero. | ||
+ | |||
+ | Next we check for a bi-lateral TRQ. If we find one, we check if it is underfilled in which case we apply the in-quota rate. Next we check if the quota is just binding, in which case the applied rate represents the sum of the in-quota rate and an endogenous per unit quota rent. The remaining case is that of a quota overfill where we are left with the MFN rate. | ||
+ | |||
+ | Next we check for a multi-lateral TRQ, also in case we have an overfilled bi-lateral TRQ. If we find one, we check if it is underfilled in which case we apply the in-quota rate. Next we check if the quota is just binding, in which case the applied rate represents the sum of the in-quota rate and an endogenous per unit quota rent. The remaining case is that of a quota overfill in which case the MFN rate is applied. | ||
+ | |||
+ | In all cases for specific tariffs, the results applied rates are checked against the existence of a minimum border price system. In that case, the import price resulting from applying the tariff to the cif price is compared to the minimum price. If it is higher than the minimum price, the tariff is cut such that the import price becomes equal to the minimum border price as long as the resulting tariff does not become negative. | ||
+ | |||
+ | **Figure 34: Tariff computation in the model** | ||
+ | |||
+ | {{:: | ||
+ | |||
+ | ====Welfare-consistent tariff aggregation module ==== | ||
+ | |||
+ | The heterogeneity of trade policies across different traded goods generates a serious index number problem for large-scale applied equilibrium modelling. Trade policies must be described with aggregate indices in order to incorporate them in the aggregate commodity structure of applied equilibrium models. | ||
+ | |||
+ | **Review of current tariff aggregation approach in CAPRI** | ||
+ | |||
+ | The tariff aggregation in CAPRI is based on the weighted average method, but applies a combination of different weights in order to (1) overcome the endogeneity bias and to (2) correct for outliers that are frequently created by statistical errors in the trade data. Technically, | ||
+ | |||
+ | Although Tariff Rate Quotas (TRQ) are typically defined in the legal texts over tariff lines, the CAPRI database does not contain data on TRQs at that level of product aggregation. In fact, TRQs are defined in CAPRI at a much more aggregated commodity level. As a consequence, | ||
+ | |||
+ | **Advanced tariff aggregation techniques considered** | ||
+ | |||
+ | The two fundamental obstacles to aggregate tariffs and other border protection measures are | ||
+ | |||
+ | * The conversion problem, i.e. different policy instruments need to be expressed in a common metric before they can be aggregated | ||
+ | * The index number problem, i.e. individual trade restrictions must be appropriately aggregated (weighted) | ||
+ | |||
+ | A large number of tariff aggregation techniques are available in the literature, each having its specific objective, drawbacks and merits. Cipollina and Salvatici 2008 provide a typology for the aggregate measures of border protection: | ||
+ | |||
+ | - Incidence measures are based on the intensities of the policy measures, and are derived only from direct observations on policies. They do not consider the distortive effects of the trade policies on the economy. Typical incidence measures are tariff dispersion or the frequency of various types of Non-Tariff Measures. | ||
+ | - Outcome measures incorporate other variables than policy variables in order to take into account the distortive impacts of policies on the economy. Typical outcome measures include trade weighted average tariffs. Outcome measures remain ' | ||
+ | - Equivalence measures provide aggregates that are equivalent to the original data in terms of selected economic variables. Welfare-consistent measures, for example, provide aggregates that are equivalent in their impact on selected indicators of the the economy' | ||
+ | |||
+ | The advanced tariff aggregation module provides three welfare-consistent aggregators: | ||
+ | |||
+ | A traditional outcome measure, called the MacMap-type aggregator, is also available in the tariff aggregation module. The aggregator is named after the conversion rule for TRQs, which is the same as the one underlying the MacMap database: TRQs are converted to an ad-valorem equivalent based on the fill rate of the TRQ. This approach takes into account the quota rent generated by the TRQ, but defines its level arbitrarily (the unit quota rent is set to half of the difference between out-of-quota and in-quota rates). | ||
+ | |||
+ | **Additional data requirement and its integration in CAPRI** | ||
+ | |||
+ | An extraction from the UN-COMTRADE database has been integrated in the global module of CAPRI. The dataset comprises of import values and calculated unit prices for 326 tariff lines (mainly agricultural commodities), | ||
+ | |||
+ | A major difficulty arises when we use raw UN-COMTRADE data for modelling purposes, due to their lack of symmetry. Country A’s import of a given product from country B is not the same as country B’s export of that product to country A. The literature identifies three main causes for this discrepancy (McCleery and DePaolis, 2014): | ||
+ | |||
+ | * An obvious wedge between import and export values is created by the valuation of exports at point of origin (usually f.o.b. prices) and the valuation of imports at destination (mostly c.i.f. prices). | ||
+ | * Border disputes, export bans or prohibitive trade restrictions may lead to only one half of the trade transactions being recorded. | ||
+ | * Border frictions may also lead to distorted trade statistics, e.g. large discrepancies in the US-China trade statistics can be observed due to recording trade with Hong-Kong differently | ||
+ | |||
+ | |||
+ | This problem is currently solved by using UN-COMTRADE data only on imports, applying the assumption that countries tax and regulate imports more thoroughly than exports. | ||
+ | |||
+ | The only source of trade policy data at the tariff line level in the current CAPRI system is the AMAD database. AMAD is not anymore updated by OECD, and in many respect contains outdated policy information. According to the Technical Specification, | ||
+ | |||
+ | The code implementation of the UN-COMTRADE data processing is modular, i.e. it can be switched on and off upon demand. A dedicated option in the GUI activates the data processing algorithms in the global part of CAPRI (see below). Technically, | ||
+ | |||
+ | **Figure 35: Tariff computation in the model** | ||
+ | |||
+ | {{: | ||
+ | |||
+ | The different tasks implemented in the aggreg_tariffs.gms tariff aggregation module includes: | ||
+ | |||
+ | * Defining nomenclatures and sets for the UN-COMTRADE dataset (‘global\comtrade_sets.gms’) | ||
+ | * Processing, filtering and mapping UN-COMTRADE data in order to align it with the CAPRI database | ||
+ | * Aggregate tariffs to the CAPRI regional nomenclature. The aggregation follows the standard CAPRI approach; the only difference is that tariffs are not aggregated over tariff lines. | ||
+ | |||
+ | |||
+ | Unit values in the UN-COMTRADE dataset have been found to be subject to significant statistical errors. An outlier-detection algorithm has been therefore implemented in order to tackle this problem. Using a simple and robust approach, observations outside a given range around the mean are identified as outliers and replaced with the mean. The outlier detection is implemented in R((As a consequence, | ||
+ | |||
+ | **Defining Tariff cut scenarios at the tariff line level** | ||
+ | |||
+ | The tariff aggregation module is loosely linked to the CAPRI modelling system. The feature can be activated by a GUI option (see below), assuming that the intermediate database has been already created by the global module. | ||
+ | |||
+ | **Figure 36: Activation of the tariff aggregation module on the GUI** | ||
+ | |||
+ | {{: | ||
+ | |||
+ | The tariff aggregation module takes over the appropriate tariff cuts from the scenario file and applies them at the tariff line level. The module then feeds back an aggregate tariff equivalent of the resulting (cut) tariffs. | ||
+ | |||
+ | **CES demand structure** | ||
+ | |||
+ | With specific assumptions on the demand side, it is possible to take into account the changes in the consumption bundle, as a response to relative price changes induced by the tariff cut scenario. Loosely speaking, the tariff aggregation module takes into account the substitution between goods within an aggregated CAPRI commodity, under specific assumptions on the demand structure. We assume a nested CES import demand structure on the lines of the usual Armington approach to model bilateral imports with cross-hauling. The current implementation is a “small country” approach: tariffs are aggregated for one importer region after the other, assuming fix border (c.i.f.) prices. | ||
+ | |||
+ | Dropping the small country assumption would require a full partial equilibrium model at the tariff line level for the commodity considered, as done in e.g. Grant et al. (2007). This, however, requires a substantial extension of the CAPRI database by including tariff line-specific trade and trade policy information at the global scale. The complexity of the market model would increase rapidly by adding trade flows at the tariff line level. A simultaneous solve for all commodity markets would be technically impossible. No surprise that similar examples in the literature only focus on selected markets and do not implement a full-fledged market model with many interacting markets at the tariff line level. Grant et al. (2007) focus on the dairy market only, and implements a sequential model linkage in order to reduce the computational requirements of solving the complete system whereas Narayanan et al. (2010) extend the standard GTAP model with a partial equilibrium component that only covers the automobile industry. | ||
+ | |||
+ | **Technical details of implementing tariff aggregation scenarios** | ||
+ | |||
+ | Generally, tariff cuts have to be defined in a specific format in the CAPRI scenario file. A simple example is illustrated in hte following, where ad-valorem and specific tariffs are cut relative to their initial level and TRQ thresholds are increased. | ||
+ | |||
+ | {{:: | ||
+ | |||
+ | {{:: | ||
+ | |||
+ | **Reporting (GUI tables)** | ||
+ | |||
+ | The GUI has been extended with tables under ' | ||
+ | |||
+ | {{: | ||
+ | |||
+ | The MacMap-type aggregators are calculated both with respect to bilateral trade relations and with respect to a total ('from World' | ||
+ | |||
+ | {{: | ||
+ | |||
+ | TRI estimates are also reported in a specific GUI table. By definition the TRI indicies are defined for all trade relations only (not a bilateral index): | ||
+ | |||
+ | {{: | ||
+ | |||
+ | The Anderson tariff combination is presented next. The current implementation is an extension of the original approach, including correction factors for TRQs (Himics and Britz, 2014): | ||
+ | |||
+ | {{: | ||
+ | |||
+ | The Bach and Martin (2001) approach, i.e. a combination of an aggregator for the expenditures and another one for the tariff revenues, is also implemented and reported in a designated GUI table: | ||
+ | |||
+ | {{: | ||
+ | |||
+ | ====Overview on a regional module inside the market model==== | ||
+ | |||
+ | The resulting layout of a market for a country (aggregate) in the market module is shown in the following diagram. Due to the Armington assumption, product markets for different regions are linked by import flows and import prices if observed in the base year. Accordingly, | ||
+ | |||
+ | **Figure 37: Graphical presentation for one region of a spatial market system ** | ||
+ | |||
+ | {{: | ||
+ | |||
+ | ====Basic interaction inside the market module during simulations==== | ||
+ | |||
+ | As with the supply module, the main difficulty in understanding model reactions is based on the simultaneity of changes occurring after a shock to the model. Cross-price effects and trade relations interlink basically all product markets for all regions. Whereas in the supply model, interactions between products are mostly based on explicit representation of technology (land balances, feed restrictions), | ||
+ | |||
+ | Even if the following narrative is simplifying and describing reactions as if they would appear in a kind of natural sequence where they are appear simultaneously in the model, we will nevertheless ‘analyse’ the effect of an increased supply at given prices for one product and one region. Such a shift could e.g. result from the introduction of a subsidy for production of that product. The increased supply will lead to imbalances in the market clearing equation for that product and that region. These imbalances can only be equilibrated again if supply and demand adjust, which requires price changes. In our example, the price in that region will have to drop to reduce supply. That drop will stimulate feed demand, and to a lesser extent, human consumption. The smaller effect on human consumption has two reasons: firstly, price elasticities for feed demand are typically higher, and secondly, consumer prices are linked with rather high margins to farm gate prices. | ||
+ | |||
+ | The resulting lower price at farm gate increases international competitiveness. Due to the Armington mechanism, consumers around the world will now increase the share of that region in their consumption of that product, and lower their demand from other origins. That will put price pressure in all other regional markets. The pressure will be the higher, the higher the import share of the region with the exogenous increase of supply on the demand of that product. The resulting price pressure will in turn reduce supply and stimulate demand and feed everywhere, and, with reduced prices, offset partially the increased competitiveness of the region where the shock was introduced. | ||
+ | |||
+ | Simultaneously, |
market_module_for_agricultural_outputs.txt · Last modified: 2022/11/07 10:23 by 127.0.0.1