CGE model development (1) CGE model development (1) Concept of CGE - - PowerPoint PPT Presentation

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CGE model development (1) CGE model development (1) Concept of CGE - - PowerPoint PPT Presentation

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CGE model development (1) CGE model development (1) Concept of CGE model and Concept of CGE model and simple CGE model based on IO data simple CGE model based on IO data Toshihiko MASUI Toshihiko MASUI National Institute for Environmental

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SLIDE 1

CGE model development (1) CGE model development (1)

Concept of CGE model and Concept of CGE model and simple CGE model based on IO data simple CGE model based on IO data

Toshihiko MASUI Toshihiko MASUI

National Institute for Environmental Studies National Institute for Environmental Studies AIM Training Workshop 2005 AIM Training Workshop 2005 NIES, 7 NIES, 7-

  • 11 November 2005

11 November 2005

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SLIDE 2

AIM, NIES 2

What's "Model"? What's "Model"?

Model represents a specific aspect of real

world.

– When we develop a model, we must understand objectives. – We can simulate the future in advance by using model.

The representation in model is not real

world but ideal world.

– We must take into account difference between actual world and modeled model.

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SLIDE 3

AIM, NIES 3

Model for environmental policies Model for environmental policies

Not only economic activity but also environment

will be taken into account.

What's the relationship between environment and

economy?

– Provision of services and goods – Assimilation of pollutants – Degradation of environmental quality – Maintenance of environment

What is key option to protect the environment?

– Technology: more efficient, renewable energy, ... – Institution: tax, regulation, ... – Management: operation, skill, ...

By using model, effectiveness of environmental

  • ptions can be assessed in advance.
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SLIDE 4

AIM, NIES 4

What's CGE? What's CGE?

"Computable": quantitative "General": treatment of all commodities,

sectors and production factors in the treated society

"Equilibrium": demand and supply of

each commodity and factor are balanced through the price mechanism

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SLIDE 5

AIM, NIES 5

Features of CGE Features of CGE

Multiple interacting agents. Individual behavior based on optimization. Most agent interactions are mediated by market and

prices.

Typically disaggregate, with many agents and markets. Limited data in comparison with the number of

behavioral and technological parameters in the model.

Equilibrium allocations which typically cannot be

characterized as the solution to a single (planner’s )

  • ptimization problem.

Formulation has as implicit or explicit focus on policy

analysis.

By using CGE, detailed impacts of policy on price, activity, income and so on can be simulated in advance.

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SLIDE 6

AIM, NIES 6

Procedure of Procedure of CGE model development CGE model development

1.

Design rough model structure

– Relationship among production sector, final demand, commodity & environment

2.

Define elements

– Classification of production sector, final demand, commodity, ...

3.

Design detailed model structure

– Commodity flow, function, elasticity of substitution, ...

4.

Quantify data

– Parameters setting

5.

Formulate model (programming)

6.

Simulate model

– Replication of benchmark – Quantification of policy simulation

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SLIDE 7

AIM, NIES 7

  • 1. Rough sketch of model
  • 1. Rough sketch of model

Production sector Final demand sector Production factor market Produced goods market

Endowment Intermediate demand Final demand Output Value added

Environment

Pollution, pressure, preservation Input from environment Feedback from environment Input from environment Feedback from environment Pollution, pressure, preservation

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SLIDE 8

AIM, NIES 8

  • 2. Definition of CGE model
  • 2. Definition of CGE model

Simple example:

Based on IO table, model with 2 commodities, 2 sectors & 1 final demand is developed. – Commodity: not only goods & service but also production factor & hypothetical commodity – Sector: production sector & hypothetical sector

  • input (demand) commodities
  • output (supply) commodities

Maximizing profit subject to production function

– Final demand:

  • supply endowments and get income
  • demand commodities

Maximizing utility subject to income.

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SLIDE 9

AIM, NIES 9

Commodity Commodity

In simple example

Produced commodity

– commodity 1 (PY("com1")) – commodity 2 (PY("com2"))

Production factor

– capital (PK) – labor (PL)

Hypothetical commodity

– aggregated final consumption goods (PC) – aggregated investment goods (PI)

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SLIDE 10

AIM, NIES 10

Sector Sector

In simple example

Production sector

– sector producing commodity 1 – sector producing commodity 2

  • Input: com1, com2, CAP & LAB
  • Output: com1 or com2

Hypothetical sector

– aggregation of final consumption goods – aggregation of investment goods

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SLIDE 11

AIM, NIES 11

Final demand Final demand

In simple example

Endowment

– Capital – Labor

Final demand

– Final consumption – Investment (fixed capital formation) = saving

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SLIDE 12
  • 3. Detailed model structure
  • 3. Detailed model structure

AIM, NIES 12

ACT("1") ACT("2") PY("2") PY("1") PY("2") PK PL PY("1") PY("2") PK PL value added value added PY("1") D_C PY("1") PY("2") PC HOUSE D_I PY("1") PY("2") PI PK PL PI PC

Input Output Endowment Final demand Final demand sector Production sector Hypothetical sector

←σ=1 ←σ=1 ←σ=0 ←σ=0 ←σ=1 ←σ=0

In order to check consistency, draw the diagram like this slide!

σ: elasticity of substitution

When the relative price of a commodity increases by 1%, the input of this commodity decreases σ%. ←σ=1

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SLIDE 13
  • 4. Quantify data (IO table)
  • 4. Quantify data (IO table)

AIM, NIES 13 Commodity Value added Commodity Final demand

Total cost = Total sale Total demand = Total supply

com1 com2 con inv total com1 80 20 80 20 200 com2 40 100 40 40 220 cap 30 60 lab 50 40 total 200 220

Total in column = Total in row

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SLIDE 14

AIM, NIES 14

  • 5. Programming
  • 5. Programming

Based on GAMS/MPSGE format. Solution by MCP (Mixed Complementarity

Problem) Pi*fi(Pi)=0, Pi≥0, fi(Pi)≥0 Pi: price fi(Pi): excess supply

When demand equal supply (fi(Pi)=0), price is positive (Pi > 0). When supply exceeds demand (fi(Pi)>0), price is 0 (Pi=0). – Optimization model is converted to simultaneous equations.

See manual for installation of GAMS.

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SLIDE 15

AIM, NIES 15

  • 5. Programming
  • 5. Programming

set com commodity /com1, com2/ v_a value added /cap, lab/ ; alias (com,c_m) ; Table IO(*,*) input output table com1 com2 con inv com1 80 20 80 20 com2 40 100 40 40 cap 30 60 lab 50 40 ; scalar tot_c total consumption tot_i total investment tot_k total capital tot_l total labor ; tot_c = sum(c_m, IO(c_m,"con")) ; tot_i = sum(c_m, IO(c_m,"inv")) ; tot_k = sum(com, IO("cap",com)) ; tot_l = sum(com, IO("lab",com)) ; parameter

  • ut(com) total output

;

  • ut(com)

= sum(c_m, IO(c_m,com)) + sum(v_a, IO(v_a,com)) ;

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SLIDE 16

AIM, NIES 16

  • 5. Programming
  • 5. Programming

set com commodity /com1, com2/ v_a value added /cap, lab/ ; alias (com,c_m) ; Table IO(*,*) input output table com1 com2 con inv com1 80 20 80 20 com2 40 100 40 40 cap 30 60 lab 50 40 ; Definition of commodity type as a set. Copy of set. Dataset by using table format. Here, input-output table is indicated. scalar tot_c total consumption tot_i total investment tot_k total capital tot_l total labor ; tot_c = sum(c_m, IO(c_m,"con")) ; tot_i = sum(c_m, IO(c_m,"inv")) ; tot_k = sum(com, IO("cap",com)) ; tot_l = sum(com, IO("lab",com)) ; parameter

  • ut(com) total output

;

  • ut(com)

= sum(c_m, IO(c_m,com)) + sum(v_a, IO(v_a,com)) ; Definition of parameter. Quantification of defined scalar. Quantification of defined parameter. Definition of scalar.

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SLIDE 17

AIM, NIES 17

  • 5. Programming
  • 5. Programming

$ontext $model:sample $sectors: ACT(com) ! production D_C ! final consumption D_I ! fixed capital formation $commodities: PY(com) ! commodity PK ! capital PL ! labor PC ! final consumption PI ! investment $consumers: HOUSE ! household $prod:ACT(com) t:0 s:0 va:1 O:PY(com) Q:OUT(com) I:PY(c_m) Q:IO(c_m,com) I:PK Q:IO("cap",com) va: I:PL Q:IO("lab",com) va: $prod:D_C s:1 O:PC Q:tot_c I:PY(c_m) Q:IO(c_m,"con") $prod:D_I s:0 O:PI Q:tot_i I:PY(c_m) Q:IO(c_m,"inv") $demand:HOUSE s:1 D:PC Q:tot_c D:PI Q:tot_i E:PL Q:tot_l E:PK Q:tot_k $report: V:ACTPK(com) I:PK prod:ACT(com) V:ACTPL(com) I:PL prod:ACT(com) $offtext $SYSINCLUDE MPSGESET SAMPLE $INCLUDE SAMPLE.GEN SOLVE SAMPLE USING MCP;

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SLIDE 18

AIM, NIES 18

  • 5. Programming
  • 5. Programming

$ontext $model:sample $sectors: ACT(com) ! production D_C ! final consumption D_I ! fixed capital formation $commodities: PY(com) ! commodity PK ! capital PL ! labor PC ! final consumption PI ! investment $consumers: HOUSE ! household $prod:ACT(com) t:0 s:0 va:1 O:PY(com) Q:OUT(com) I:PY(c_m) Q:IO(c_m,com) I:PK Q:IO("cap",com) va: I:PL Q:IO("lab",com) va: Sign of start of formulation. Definition of model name. Definition of sector. Words after "!" show comment Definition of commodity. Definition of final demand. Definition of activity in sector. $prod:D_C s:1 O:PC Q:tot_c I:PY(c_m) Q:IO(c_m,"con") $prod:D_I s:0 O:PI Q:tot_i I:PY(c_m) Q:IO(c_m,"inv") $demand:HOUSE s:1 D:PC Q:tot_c D:PI Q:tot_i E:PL Q:tot_l E:PK Q:tot_k $report: V:ACTPK(com) I:PK prod:ACT(com) V:ACTPL(com) I:PL prod:ACT(com) $offtext $SYSINCLUDE MPSGESET SAMPLE $INCLUDE SAMPLE.GEN SOLVE SAMPLE USING MCP; Definition of activity in final demand. Definition of quantified element. Sign of end of formulation. Sign of preparation of simulation and solution of this model.

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SLIDE 19
  • 5. Programming
  • 5. Programming

AIM, NIES 19

$prod:ACT(com) t:0 s:0 va:1 O:PY(com) Q:OUT(com) P:1 I:PY(c_m) Q:IO(c_m,com) P:1 I:PK Q:IO("cap",com) P:1 va: I:PL Q:IO("lab",com) P:1 va:

elasticity of transformation elasticity of substitution elasticity of substitution among specific inputs name of activity Value represents activity level. Default is one. name of commodity Value represents price. Default is one. O: shows output, and I: shows input. Q: shows reference quantity of input/output when the activity level is one. Default is one. P: shows reference price

  • f commodity.

Default is one. P X Q shows cost of input

  • r sale of output.

Based on these values, share parameters are defined.

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SLIDE 20

AIM, NIES 20

  • 6. Simulation
  • 6. Simulation

1.

Replication of benchmark

2.

Sensitivity analysis to check parameters

3.

Scenario and policy design

4.

Simulation based on scenario

5.

Analysis of results

6.

Assessment of alternative scenarios and policies

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SLIDE 21

AIM, NIES 21

How to apply CGE How to apply CGE

1.

Translate policy into the model instruments.

2.

Guess at the policy results.

3.

Run the simulation and compare results.

4.

Compare the model results with your earlier guess.

5.

Evaluate the outcome and write up your key findings.

6.

Develop sensitivity analyses.

7.

Write up the model.