Johansen’s contribution to CGE modelling: originator and guiding light for 50 years

1

CoPSJohansen’s contribution to CGE modelling: Johansen’s contribution to CGE modelling: originator and guiding light for 50 yearsoriginator and guiding light for 50 years

by

Peter Dixon and Maureen Rimmer

paper presented at the 2013 National CGE Workshop

Melbourne, October 7, 2013

Previously presented at the Symposium in memory of Professor Leif Johansen

and to celebrate the fiftieth anniversary of the publication of his“A Multi-Sectoral Study of Economic Growth”

(North Holland 1960)The Norwegian Academy of Science and Letters

May 20-21, 2010

http://www.monash.edu.au/policy/ftp/workpapr/g-203.pdf

2

CoPSPaper has six partsPaper has six parts

I. Introduction - Johansen originator of CGE: individual agents

II. Johansen’s approach to CGE modelling

III. Other starting points

- Scarf, Jorgenson, Adelman & Robinson, Taylor

- But Johansen’s style remains distinctive

IV. Extending Johansen-style CGE modelling in Australia

V. Taking Johansen from Australia to the rest of the world

VI. Validation

3

CoPS

II. Johansen’s approach to CGE modelling

4

CoPSDefining features of Johansen Defining features of Johansen style style

Presentation of model as a rectangular system of linear equations in change and percentage-change variables

Solution by matrix inversion

Use of linear representation and the linear solution:

to clarify properties of the model;

to elucidate real world issues; and

to validate of the model.

5

CoPSLinear representation and Linear representation and solutionsolution

B* L*

T* where 1T B * L

j j j*tj j j jX A * N * K *exp

j jj j j jx * n * k

in linear form

86 endogenous variables 46 exogenous variables

(86,46)

{aggregate capital (1), aggregate employment (1), population (1), exog. demands (22), tech. change (20), price non-comp imports (1)}

{employment (20), capital (20), outputs (22), prices (22), rate of return (1), consumption (1) }

6

CoPSJohansen’s fascination with the T Johansen’s fascination with the T matrix:matrix:

clarifying properties of the modelclarifying properties of the model

0 1 jT x ,k

Johansen uses BOTE model to guide analysis of his T matrix

1. He inspected individual columns

0 1 jT x ,n

Exception: 0equipT x ,n

BOTE predicts

86 x 46 = 3956 results

7

CoPS

Leontief: X=(I-A)-1*C Johansen: v1 = T*v2

1(I A)

submatrix of T

all 1

all 0 but mainly <1mainly < 0

all 0mainly > 0

all 0mainly > 0

mainly < 0

2. He looked at T(x,z), 22 by 22 matrix showing elasticities of outputs with respect to exogenous demands

complementary 1930s

Competition 1950s

Johansen’s fascination with T : clarifying properties of the modelJohansen’s fascination with T : clarifying properties of the model

8


elucidating real world issueselucidating real world issues3. He decomposed growth around 1950

1 2 6 j T(j)* (j), j , , ...,

Six sets of exogenous variables: capital, employment, population, exogenous demands (22), technology (20), price of non-competing imports

• determinants of agricultural employment• capital growth as source of wage growth

9


validating the modelvalidating the model

4. He compared computed growth rates 6

1

jj with

actual growth rates from 1948 to 1953

• computed agricultural employment too high• computed forestry outputs and inputs too high• computed communication and transport outputs and inputs too high

Sets up agenda for model improvements

10

CoPS

IV. Extending Johansen-style CGE modelling in Australia

11

CoPSExtending Johansen-style CGE modelling in Australia

Little development of Johansen’s approach until the 1970s.Why the pause ?IMPACT Project 1975 (ORANI model, DPRS 1977, DPSV1982)

1. Introduction of Armington specification into CGE

2. Large dimensions allowing policy-relevant detail

3. Flexible closures

4. Complex functional forms

5. Multi-step solutions, free from linearization errors

12

CoPSExtending Johansen in AustraliaExtending Johansen in Australia

1. The Armington specification

The Armington elasticities in ORANI were econometrically estimated for about 50 commodities by Alaouze, Marsden & Zeitsch (1977)

With its Armington specification, the ORANI model avoided

flip-flop on the import side

On the export side, ORANI avoided flip-flop by the introduction of downward-sloping export demand curves

13


2. Coping with large dimensions, facilitates policy-relevant detail100+ industries, margins, technical change, sales taxes, regions

Initial specification: 600,000 simple equations, 1.2 million variablesx(i,s,j,k,m) = x(i,s,j,k) + a(i,s,j,k,m)

(100x2x100x2x10)

Dimensions reduced by substitution (condensation)

14


Johansen : fixed allocation of variables between and in giving a single T matrix

B* L*

3. Closure flexibility: reallocation of variables between and In ORANI, short-run versus long-run neo-classical versus neo-Keynesian pricing employment exogenous versus wages exogenous

In MONASH, the 4 closure approach to policy analysis historical (Update, deduce unobservable variables) decomposition (Explains history, effects of policy in historical context)

forecast (Incorporates detailed trends & specialist info. Motivation: meets a demand, matters for policy, adjustment costs, validation)

policy (Deviations from baseline)

15


4. Coping with complex functional forms, e.g. CRESH demand functions

i i ix z * p p

1

n #k k

kp S *p

1

# k kk n

i ii

S *S

S *

i= 1, 2, …n

16

CoPSExtending Johansen in AustraliaExtending Johansen in Australia5. Computing multi-step solutions: eliminating linearization errors while retaining Johansen’s simplicity & interpretability

VI1

+(dV )1 (1,2)

VI1

+(dV )1 (.,2)

VI1

VI1

+(dV )1 true

VI1

+(dV )1 (.,1)

V2

+(dV )2

IIV2

+ (dV )2

I 12

VI2

a

b

c

V1=G(V )

2

Slope = T (2,2)

ISlope = T(V ) = T (1,2)

V2

V1

Error in one-step computation

Error in two-step computation

17

CoPSJohansen-style ORANI model achieves Johansen-style ORANI model achieves acceptance in Australiaacceptance in Australia

(200 published applications 1977-86; only 25% by ORANI-group)

5 reasons (2 to 5 made possible by Johansen’s modelling strategy) :

1. favourable policy and institutional environment sharp issue – protection Industries Assistance Commission – Rattigan

IMPACT Project – Powell

2. credibility-enhancing detail

3. flexibility in application - closures, sectors

4. transferability - documentation, training courses from 1978

5. interpretability - overcoming sceptics

18

CoPSIMPACT ProjectIMPACT Project

Alan A. Powell Peter B. Dixon Brian R. Parmenter

IMPACT was set up in 1975 in the Industries Assistance Commission

Alan A. Powell was the Director

Other principals were Peter B. Dixon and Brian R. Parmenter

19




1. favourable policy and institutional environment sharp issue – protection Industries Assistance Commission – Rattigan

IMPACT Project – Powell



4. transferability - documentation, training courses from 1978

5. interpretability - overcoming sceptics

20




1. favourable policy and institutional environment sharp issue – protection Industries Assistance Commission – Rattigan, Powell IMPACT Project –Powell



4. transferability - training courses starting late 1970s

5. interpretability

21

CoPS

InterpretabilityInterpretability

(a)Qualitative explanations: Johnson (1985), Adams and Parmenter (1993)

(b)Quantitative explanations:

BOTE models: diagrammatic, algebraic, statistical

22

CoPS

Interpretability: diagrammaticInterpretability: diagrammatic

Demand for the right to emit CO2

CO -equivalent tons2

6 billion5 billion

Price

$20

..

23

CoPSInterpretability: algebraicInterpretability: algebraicIntroduction of the GSTIntroduction of the GST

-1.4

-1.2

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

2000 2001 2002 2003 2004 2005 2006 2007 2008

Employment with sticky after-tax wages

Employment with sticky before-tax wages

Employment with sticky after-tax wages

Employment with sticky before-tax wages Powers of taxes

Production gT : 1.036 1.022

Wages wT : 1.250 1.215

Consumption cT : 1.070 1.105

l realA w g cM W *(T *T *T )

1.0%

l realB g cM W *(T *T )

1.9%

24

CoPSInterpretability: statistical Interpretability: statistical

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

12 Idah

o33 N

orthC

arolin34 N

orthD

akota

40 Sou

thC

arolin39 R

hod

eIsland

23 Min

nesota

1 Alab

ama

49 Wiscon

sin50 W

yomin

g29 18 L

ouisian

a42 T

enn

essee41 S

outh

Dak

ota10 G

eorgia26 M

ontan

a8 D

elaware

17 Ken

tuck

y19 M

aine

45 Verm

ont

21 Massach

usett

27 Neb

raska

2 Alask

a6 C

olorado

46 Virgin

ia38 P

enn

sylvania

32 New

York

30 New

Jersey25 M

issouri

24 Mississip

pi

15 Iowa

44 Utah

13 Illinois

51 DistC

olum

bia

48 WestV

irgini

7 Con

necticu

t43 T

exas36 O

klah

oma

31 New

Mexico

4 Ark

ansas

11 Haw

aii35 O

hio

20 Marylan

d9 F

lorida

5 Californ

ia22 M

ichigan

37 Oregon

3 Arizon

a16 K

ansas

14 Ind

iana

28 Nevad

a47 W

ashin

gton

States employment effects of removing import restraints (per cent)

25

CoPSState employment effects explained by State employment effects explained by

1-variable regression1-variable regression

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

12 Idah

o33 N

orthC

arolin34 N

orthD

akota

40 Sou

thC

arolin39 R

hod

eIsland

23 Min

nesota

1 Alab

ama

49 Wiscon

sin50 W

yomin

g29 18 L

ouisian

a42 T

enn

essee41 S

outh

Dak

ota10 G

eorgia26 M

ontan

a8 D

elaware

17 Ken

tuck

y19 M

aine

45 Verm

ont

21 Massach

usett

27 Neb

raska

2 Alask

a6 C

olorado

46 Virgin

ia38 P

enn

sylvania

32 New

York

30 New

Jersey25 M

issouri

24 Mississip

pi

15 Iowa

44 Utah

13 Illinois

51 DistC

olum

bia

48 WestV

irgini

7 Con

necticu

t43 T

exas36 O

klah

oma

31 New

Mexico

4 Ark

ansas

11 Haw

aii35 O

hio

20 Marylan

d9 F

lorida

5 Californ

ia22 M

ichigan

37 Oregon

3 Arizon

a16 K

ansas

14 Ind

iana

28 Nevad

a47 W

ashin

gton

Emp(r) = -0.023 + 2.755*NationalIndex(r)

R-squared = 0.73

26


2-variable regression 2-variable regression

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

12 Idaho

33 NorthC

arolin

34 NorthD

akota

40 SouthCarolin

39 RhodeIsland

23 Minnesota

1 Alabam

a

49 Wisconsin

50 Wyom

ing

29 New

Ham

pshire

18 Louisiana

42 Tennessee

41 SouthDakota

10 Georgia

26 Montana

8 Delaw

are

17 Kentucky

19 Maine

45 Verm

ont

21 Massachusett

27 Nebraska

2 Alaska

6 Colorado

46 Virginia

38 Pennsylvania

32 New

York

30 New

Jersey

25 Missouri

24 Mississippi

15 Iowa

44 Utah

13 Illinois

51 DistC

olumbia

48 WestV

irgini

7 Connecticut

43 Texas

36 Oklahom

a

31 New

Mexico

4 Arkansas

11 Haw

aii

35 Ohio

20 Maryland

9 Florida

5 California

22 Michigan

37 Oregon

3 Arizona

16 Kansas

14 Indiana

28 Nevada

47 Washington

Emp(r) = -0.050 + 3.164*NationalIndex(r) + 0.056*PortIndex(r)

R-squared = 0.88

27


3-variable regression 3-variable regressionEmp(r) = -0.063 + 3.121*NationalIndex(r) + 0.056*PortIndex(r) + 0.011*HolidayIndex(r)

R-squared = 0.90

-0.6

-0.5

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

12 Idah

o33 N

orthC

arolin34 N

orthD

akota

40 Sou

thC

arolin39 R

hod

eIsland

23 Min

nesota

1 Alab

ama

49 Wiscon

sin50 W

yomin

g29 N

ewH

amp

18 Lou

isiana

42 Ten

nessee

41 Sou

thD

akota

10 Georgia

26 Mon

tana

8 Delaw

are17 K

entu

cky

19 Main

e45 V

ermon

t21 M

assach27 N

ebrask

a2 A

laska

6 Colorad

o46 V

irginia

38 Pen

nsylvan

ia32 N

ewY

ork30 N

ewJersey

25 Missou

ri24 M

ississipp

i15 Iow

a44 U

tah13 Illin

ois51 D

istColu

mb

ia48 W

estVirgin

i7 C

onn

ecticut

43 Texas

36 Ok

lahom

a31 N

ewM

exico4 A

rkan

sas11 H

awaii

35 Oh

io20 M

aryland

9 Florid

a5 C

alifornia

22 Mich

igan37 O

regon3 A

rizona

16 Kan

sas14 In

dian

a28 N

evada

47 Wash

ington

28

CoPS

V. Taking Johansen from Australia to rest of world

29

CoPSTaking Johansen from AustraliaTaking Johansen from Australiato rest of the worldto rest of the world

Starting in early 1980s• foreign appointments of ORANI modellers• teaching of foreign students in Australia • international model building projects from Australia: 20 countries including South Africa, Thailand, Brazil, Indonesia, Vietnam, Finland, Netherlands, Malaysia, China and the U.S.A. (USAGE model) • GTAP network: Hertel’s visit to Australia in 1990-1

- 7500 Johansen-style modellers in 150 countriesfacilitated by Ken Pearson’s GEMPACK

30

CoPSGEMPACK dominates GAMSGEMPACK dominates GAMS

Solution time versus no. of sectors: Log-log scale

1

10

100

1000

10000

100 1000sectors

seco

nds

GEMPACK exe

GEMSIM

GAMS MCP

GAMS NLP

MPSGE

MCP-PATH

500

NLP-CONOPT

MPSGE

GEMPACK EXE

GEMSIM

31

CoPSConclusionsConclusions

Since Johansen (1960), CGE modellers have combined data and theory to project implications for macro, industry, regional, occupational, environmental and distributional variables of a wide range of policy and other shocks.

Johansen used a linear representation and solution method. The objection was that the solutions were approximations. This objection was overcome in Australia by 1980 through a multi-step Johansen procedure that eliminated linearization errors.

By adopting the Johansen-style, Australian CGE modellers made rapid progress.

In the 1970s they created CGE models with: price-sensitive treatments of international trade; policy-relevant levels of detail; flexible closures; andthe ability to handle complex functional forms for production and consumption

32

CoPSConclusionsConclusions

By adopting the Johansen-style, Australian CGE modellers made rapid progress.

In the 1980s they: developed world-wide transferable software - GEMPACK; andexpanded the range of CGE application to encompass industry and occupational forecasting, income distribution, micro policy (e.g. ORANI-milk) and the environment (e.g. greenhouse and water modelling)

In the 1990s they developed the 4-closure approach to policy analysis:historical; decomposition; forecast; andpolicy

In the 2000s they focused on validation – checking forecasts against reality; technological realism – combining CGE with engineering models in energy, transport, water ;bottom-up regional modelling – MMRF, TERM

Johansen’s contribution to CGE modelling: originator and guiding light for 50 years

Documents

Transcript of Johansen’s contribution to CGE modelling: originator and guiding light for 50 years