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

1. Johansen’s contribution to CGE modelling: originator 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

2. Paper has six parts Introduction - Johansen originator of CGE: individual agents Johansen’s approach to CGE modelling Other starting points - Scarf, Jorgenson, Adelman & Robinson, Taylor - But Johansen’s style remains distinctive Extending Johansen-style CGE modelling in Australia Taking Johansen from Australia to the rest of the world Validation

3. Johansen’s approach to CGE modelling

4. Defining features of Johansen style

5. Linear representation and solution 46 exogenous variables 86 endogenous variables {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) } where (86,46) in linear form

6. Johansen’s fascination with the T matrix:clarifying properties of the model 86 x 46 = 3956 results Johansen uses BOTE model to guide analysis of his T matrix 1. He inspected individual columns BOTE predicts Exception:

7. Johansen’s fascination with T : clarifying properties of the model 2. He looked at T(x,z), 22 by 22 matrix showing elasticities of outputs with respect to exogenous demands Leontief: X=(I-A)-1*C Johansen: v1 = T*v2 all  0 mainly > 0 all  0 mainly > 0 complementary 1930s all 1 mainly < 0 mainly < 0 Competition 1950s all 0 but mainly <1

8. Johansen’s fascination with the T matrix:elucidating real world issues 3. He decomposed growth around 1950 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. Johansen’s fascination with the T matrix:validating the model • 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. Extending Johansen-style CGE modelling in Australia

11. Extending Johansen-style CGE modelling in Australia • Little development of Johansen’s approach until the 1970s. • Why the pause ? • IMPACT Project 1975 (ORANI model, DPRS1977, DPSV1982) • Introduction of Armington specification into CGE • Large dimensions allowing policy-relevant detail • Flexible closures • Complex functional forms • Multi-step solutions, free from linearization errors

12. Extending Johansen in Australia • 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. Extending Johansen in Australia • Coping with large dimensions, facilitates policy-relevant detail • 100+ industries, margins, technical change, sales taxes, • regions • Initial specification: 600,000 simple equations, 1.2 million variables • x(i,s,j,k,m) = x(i,s,j,k) + a(i,s,j,k,m) • (100x2x100x2x10) • Dimensions reduced by substitution (condensation)

14. Extending Johansen in Australia Johansen : fixed allocation of variables between  and  in giving a single T matrix 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. Extending Johansen in Australia • Coping with complex functional forms, e.g. CRESH demand functions i= 1, 2, …n

16. Extending Johansen in Australia • Computing multi-step solutions: eliminating linearization • errors while retaining Johansen’s simplicity & interpretability

17. Johansen-style ORANI model achieves acceptance in Australia • (200 published applications 1977-86; only 25% by ORANI-group) • 5 reasons (2 to 5 made possible by Johansen’s modelling strategy) : • favourable policy and institutional environment • sharp issue – protection • Industries Assistance Commission – Rattigan • IMPACT Project – Powell • credibility-enhancing detail • flexibility in application - closures, sectors • transferability - documentation, training courses from 1978 • interpretability - overcoming sceptics

18. IMPACT Project 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 Alan A. Powell Peter B. Dixon Brian R. Parmenter

19. Johansen-style ORANI model achieves acceptance in Australia • (200 published applications 1977-86; only 25% by ORANI-group) • 5 reasons (2 to 5 made possible by Johansen’s modelling strategy) : • favourable policy and institutional environment • sharp issue – protection • Industries Assistance Commission – Rattigan • IMPACT Project – Powell • credibility-enhancing detail • flexibility in application - closures, sectors • transferability - documentation, training courses from 1978 • interpretability - overcoming sceptics

20. Johansen-style ORANI model achieves acceptance in Australia • (200 published applications 1977-86; only 25% by ORANI-group) • 5 reasons (2 to 5 made possible by Johansen’s modelling strategy) : • favourable policy and institutional environment • sharp issue – protection • Industries Assistance Commission – Rattigan, Powell • IMPACT Project –Powell • credibility-enhancing detail • flexibility in application - closures, sectors • transferability - training courses starting late 1970s • interpretability

21. Interpretability • Qualitative explanations: • Johnson (1985), Adams and Parmenter (1993) • Quantitative explanations: • BOTE models: diagrammatic, algebraic, statistical

22. Interpretability: diagrammatic

23. Interpretability: algebraicIntroduction of the GST  1.9% 1.0%

24. Interpretability: statistical States employment effects of removing import restraints (per cent)

25. State employment effects explained by 1-variable regression Emp(r) = -0.023 + 2.755*NationalIndex(r) R-squared = 0.73

26. State employment effects explained by 2-variable regression Emp(r) = -0.050 + 3.164*NationalIndex(r) + 0.056*PortIndex(r) R-squared = 0.88

27. State employment effects explained by 3-variable regression Emp(r) = -0.063 + 3.121*NationalIndex(r) + 0.056*PortIndex(r) + 0.011*HolidayIndex(r) R-squared = 0.90

28. Taking Johansen from Australia to rest of world

29. Taking Johansen from Australiato 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 countries facilitated by Ken Pearson’s GEMPACK

30. GEMPACK dominates GAMS

31. Conclusions • 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; and • the ability to handle complex functional forms for production and consumption

32. Conclusions • By adopting the Johansen-style, Australian CGE modellers made rapid progress. • In the 1980s they: • developed world-wide transferable software - GEMPACK; and • expanded 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; and • policy • 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