/build-cge — Build CGE

Category: Build · Primary agent: cge-modeling-specialist

Builds a CGE model (static or recursive-dynamic, single- or multi-region) with benchmark replication, closures, elasticity sensitivity and welfare decomposition.

Arguments: $ARGUMENTS

Purpose

Build a computable general equilibrium model that replicates its benchmark data exactly, solves reliably, and traces how a policy reallocates production, factors and income across sectors and households. The output is a documented model whose results can be decomposed and defended, with closures and elasticities shown to matter or not.

Use it when the question involves economy-wide reallocation through prices: tax and tariff reform, carbon pricing, subsidy reform, productivity shocks by sector, trade agreements with sectoral detail, structural change over decades.

Do not use it when fixed-coefficient multipliers suffice (/build-io-sam), when short-run dynamics with nominal rigidities are central (/build-dsge, /build-semi-structural), or when bilateral trade flows and gravity are the main focus (/build-trade-model).

Before you start

1. Check required inputs. For each input below record FOUND or ABSENT (never 'assumed'):
   • Approved model-spec
   • Balanced SAM or global database
   • Elasticity sources If a required input is ABSENT, that part is NOT ASSESSED — NO DATA: do not estimate or invent it; name what is missing and which skill produces it. If all required inputs are absent, stop and say so.
2. Check confidentiality. Identify the data classification level of the inputs (.claude/docs/confidentiality-and-ai-use.md). Do not proceed with restricted or confidential data in a way the policy does not allow.
3. Collaboration. Follow .claude/docs/collaboration-protocol.md: ask questions first, present options, get approval before writing files, and update production/session-state/active.md after each approved section.

Agents

• cge-modeling-specialist — primary agent; coordinates the work and integrates results
• input-output-sam-specialist — Builds input-output, supply-use and social accounting matrix frameworks
• trade-policy-simulation-specialist — Simulates trade policy with quantitative trade models
• tax-benefit-microsimulation-specialist — Builds tax-benefit microsimulation
• carbon-pricing-emissions-specialist — Analyzes carbon pricing and emissions policy

Run these agents as subagents (the Task tool), in parallel where the steps are independent. Subagents only write to the paths named in your instructions.

Key design decisions (present options, let the user choose)

Decision	Typical options	What it changes
Time	static, recursive-dynamic, forward-looking	Adjustment paths; investment behavior
Regions	single country, multi-region (global database)	Terms-of-trade effects; data needs
Production nests	CES value added and intermediates; energy nests; Leontief intermediates	Substitution possibilities
Trade	Armington with CET exports; small or large country	Import and export responses; terms of trade
Households	one or many groups from the SAM; LES or other demand systems	Distributional detail
Closures	savings–investment, government, external, factor markets, numeraire	Results can change sign across closures
GTAP route (multi-region)	GEMPACK with the standard GTAP model, GAMS (for example GTAPinGAMS)	Solution method, decompositions, license needs

Workflow

1. Agree the structure: regions, sectors, factors, production nests, trade treatment and closures
   • Map the policy question to the sectors, factors and households that must be separate
   • Write the closure rules and justify each for the horizon
2. Calibrate to the SAM; implement as a complementarity problem; replicate the benchmark exactly
   • Check SAM balance first (/build-io-sam if the SAM must be built)
   • Source every elasticity with its sector definition and horizon
   • For a GTAP model: keep the aggregation mapping, record the database version and license, keep the database out of git, and follow .claude/rules/gempack-code.md for GEMPACK
3. Test homogeneity, Walras' law and numeraire invariance
4. Run policy scenarios under at least two closures; run systematic sensitivity on key elasticities
   • Use quadrature or Monte Carlo over elasticity ranges; report the spread
5. Report sectoral, welfare (EV/CV) and, where possible, household effects; document the model for handover
   • Decompose welfare into allocative, terms-of-trade and endowment effects
   • Link to microsimulation for household detail (/distributional-analysis)
6. Register the model as a lego set (/lego-map): a baseplate plus bricks with declared studs (variables, units, timing) and stubs, so components can later be attached, detached or swapped

Checkpoints with the user

• After the structure note: confirm sectors, households and closures
• After benchmark replication: confirm the elasticity table and its ranges
• After first scenarios: confirm the results are interpretable before sensitivity runs

Common pitfalls

• Results driven by an unexamined closure
• Elasticities borrowed from different aggregations or horizons
• Reporting GDP as welfare
• Treating static results as an adjustment path
• Committing or sharing a licensed global database (GTAP)

Output

models/cge/<name>/ (+ model.lego.json)

The folder holds the SAM, calibration scripts, model code, the replication test, scenario and sensitivity scripts, the elasticity table with sources, the results pack with welfare decomposition and the lego manifest. Every run saves its results to output/<run-id>/ in the model folder, with a run log (.claude/rules/model-code.md).

Gate / verdict

• Benchmark replicated to tolerance
• Closure and elasticity sensitivity reported
• Lego manifest passes python tools/lego.py check
• Homogeneity and Walras' law tests pass
• Welfare is reported as EV or CV with its decomposition

Suggested next steps

• /trade-policy-analysis
• /carbon-pricing-analysis
• /distributional-analysis
• /validate-model
• /lego-map