capri-market-module

name

capri-market-module

description

Comprehensive reference and workflow skill for the CAPRI market module (global partial equilibrium trade model). Trigger when the user asks about trade modeling, Armington assumption, CES functions, substitution elasticities, welfare analysis, market clearing, bilateral trade flows, import demand, export supply, price linkage equations, biofuel module, TRQ (tariff rate quota), tariff modeling, market model solving, global trade, Armington aggregator, price transmission, feed demand in market model, processing industry equations, oilseed crushing, dairy processing, intervention stocks, subsidised exports, market equilibrium, or debugging infeasibilities in the CAPRI market module.

CAPRI Market Module

The CAPRI market module is a comparative-static, deterministic, partial, spatial, global equilibrium model for about 65 agricultural commodities traded between 44 trade blocks (derived from ~80 countries with behavioural functions). The full system comprises about 70,000 endogenous variables and equations.

For the detailed Armington equations, TRQ structure, welfare methodology, and market clearing conditions, see references/armington.md in this skill directory.

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Key files in the CAPRI source tree

File	Purpose
arm/market_model.gms	Main market model definition (equations + variables)
arm/market1.gms	Top-level market module driver, includes calibration
arm/simu_prestep.gms	Pre-solve sequence: single-commodity then clustered solves
arm/widen_bounds.gms	Automatic bound widening when infeasibilities occur
arm/cal_armington.gms	Calibration of CES share/shift parameters
arm/modArmington.gms	Modified (non-homothetic) Armington calibration
arm/prep_market.gms	Prepare and test market model calibration at trend values
arm/data_cal.gms	Market model data calibration
arm_sets.gms	Sets specific to the market model (trade blocks, products)
biofuel_markets.gms	Biofuel market balances for non-EU regions
biofuel/def_biofuel_params.gms	Biofuel supply/demand calibration
reports/welfare.gms	Welfare calculations (equivalent variation, GVA, profits)

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Armington trade structure (summary)

Two-stage CES system linking regional markets via bilateral trade flows:

1. Top level (stage 1): composition of demand from domestic sales vs. import aggregate. Substitution elasticities are lower (e.g. 2 for cheese, 8 for most products).
2. Lower level (stage 2): allocation of imports across origins. Substitution elasticities are higher (e.g. 4 for cheese, 10 for most products).

Price aggregators (dual representation) are used in the code rather than primal quantity aggregators, as the dual form is numerically more stable.

CES share parameters delta are calibrated to observed trade flows; shift parameter alpha is absorbed into delta in the implementation. Zero flows remain zero unless the modified Armington approach (Witzke et al. 2005) with commitment parameter mu is activated.

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Behavioural functions

• Supply: normalised quadratic profit function; v_prodQuant depends on v_prodPrice normalised by a price index. Slope terms bs from supply elasticities; constants as re-calibrated each iteration to match supply model results.
• Feed demand: same NQ structure but driven by v_arm1Price; includes feed energy balance linking feed quantities to animal production via pv_feedConv.
• Final demand: Generalised Leontief expenditure system (Ryan & Wales 1996) with commitment terms d, symmetric bd matrix. Guarantees adding-up, homogeneity, correct curvature. Consumer prices derived from v_arm1Price plus margins.
• Processing (oilseeds): NQ profit function on processing margins; fixed I/O crushing coefficients with CES-based slight adjustment of oil/cake shares.
• Processing (dairy): fat/protein balancing; NQ function driven by margin between dairy product price and fat/protein content value.

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Solver strategy

1. Single-commodity pre-solves (arm/simu_prestep.gms): fix cross-price variables, solve each commodity independently using GAMS grid-solve (parallel). Repeat several rounds updating cross-prices.
2. Commodity group clustering: solve groups with strong cross-price links together (e.g. all cereals, all oilseeds). Repeat with updated cross-prices.
3. Full system solve: solve all ~70,000 equations with CONOPT.
4. Bound widening (arm/widen_bounds.gms): if infeasible, inspect dual values on bounds, widen those with shadow values, re-solve.

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Welfare analysis

• Consumer welfare: equivalent variation from Generalised Leontief expenditure function.
• Producer welfare: change in normalised quadratic profit function value (non-EU); GVA + premiums for EU regions (from supply model).
• Dairy/processing/feed industry: analogous NQ-based welfare with margins as prices.
• Taxpayer welfare: tariff revenues, TRQ rent allocation, domestic support outlays.
• Land owner rent: from land supply curve specification.

Computed in reports/welfare.gms.

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Debugging market module issues (workflow)

When the market model produces unexpected results, infeasibilities, or solver failures, follow this diagnostic sequence using GAMS IDE tools:

Step 1: Check solver status

Use chrispahm.gams-ide/gamsSolveStatus to inspect model and solver return codes. Key statuses:

• Model status 1 = optimal, 4 = infeasible, 5 = locally infeasible
• Solver status 1 = normal completion

Step 2: Query result GDX for market conditions

Use chrispahm.gdx-viewer/gdx-sql to run analytical queries on the result GDX file.

Market clearing check -- find products/regions with large imbalances:

SELECT regions, products, value
FROM v_arm1Quant
WHERE ABS(value) > 0.01
ORDER BY ABS(value) DESC
LIMIT 20

Inspect bilateral trade flows:

SELECT r_from, r_to, value
FROM v_tradeFlows
WHERE products = 'SWHE'
ORDER BY value DESC
LIMIT 30

Check price levels and transmission:

SELECT regions, value AS market_price
FROM v_marketPrice
WHERE products = 'BEEF'
ORDER BY value

Compare Armington price aggregates:

SELECT regions, value AS arm1_price
FROM v_arm1Price
WHERE products = 'PORK'
ORDER BY value DESC

TRQ fill rates:

SELECT regions, products, value AS fill_rate
FROM v_trqFillRate
WHERE value > 0.8
ORDER BY value DESC

Step 3: Explore affected dimensions

Use chrispahm.gdx-viewer/gdx-domain to discover which regions and products are in the result GDX and understand the set structure (RM vs RMS, trade block membership).

Step 4: Visualise problematic data

Use chrispahm.gdx-viewer/gdx-reveal to open the GDX editor and show the user specific trade flows, prices, or quantities that appear anomalous.

Step 5: Trace equation dependencies

Use chrispahm.gams-ide/gamsReferenceTree to follow how a price variable (e.g. v_marketPrice) feeds into import price equations, Armington aggregators, and back into behavioural functions. Typical chains:

• v_marketPrice -> impp (import price) -> v_arm2Price -> v_arm1Price -> v_consPrice
• v_prodPrice -> supply/feed equations -> v_prodQuant/v_feedQuant -> market balance

Step 6: Examine solver listing

Use chrispahm.gams-ide/gamsReadListing to inspect the listing file for specific equation blocks (e.g. SupBalM_, armBall_, importShares_) and identify which rows have large infeasibilities or marginals.

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Common issues and solutions

Symptom	Likely cause	Action
Infeasible after tariff removal	CES drives trade flows to lower bounds	Check arm/widen_bounds.gms ran; inspect bound duals
Price explosion in one region	Missing or zero supply response	Check bs slope terms and supply elasticities
Iteration non-convergence	Supply model and market model diverge	Check constant term re-calibration; examine price dampening weights
Biofuel prices unrealistic	Sigmoid supply function parameters	Check biofuel/def_biofuel_params.gms calibration
TRQ regime switching noise	Sigmoid approximation too steep/flat	Inspect TRQ fill rate and sigmoid parameters