Applied General Equilibrium Models I and II

Class meeting

Dates: August 11 – December 10, 2020

Time: Tuesday and Thursday, 8h00 to 9h50

Time zone: GMT-3:00 (Brasília)

Instructor: Eduardo A. Haddad

Prof. Haddad is Full Professor at the Department of Economics at the University of Sao Paulo, Brazil, where he directs the Regional and Urban Economics Lab (NEREUS). He also holds a position as Affiliate Research Professor at the Regional Economics Applications Laboratory – REAL – at the University of Illinois at Urbana-Champaign, USA. He is a Senior Fellow at the Policy Center for the New South, Rabat, Morocco.

Prof. Haddad has published widely in professional journals on regional and interregional input-output analysis, computable general equilibrium modeling, and various aspects of regional economic development in developing countries; he has also contributed with chapters in international books in the fields of regional science and economic development. His research focuses on large-scale modeling of multi-regional economic systems, with special interest in modeling integration applied to transportation, climate change and spatial interaction.

Prof. Haddad received his B.A. in Economics from the Federal University of Minas Gerais, Brazil, in 1993, and his Ph.D. in Economics from the University of Illinois at Urban-Champaign in 1997. In January-December 1998 he held a post-doctoral position at the University of Oxford. He has served as the president of the Brazilian Regional Science Association (2008-2010), and as the first president of the Regional Science Association of the Americas (2008-2010). He is now the Elect-President of the Regional Science Association International (RSAI). He was the Director of Research of the Institute of Economic Research Foundation – FIPE – from 2005 to 2013. He has spent the period January 2014 to June 2015 on sabbatical as a visitor at the Department of Economics (International Economics Section) at Princeton University, and at the Edward J. Bloustein School of Public Policy and Planning at Rutgers University. In 2017-2018, he was the Chairman of the Department of Economics at USP.

Full CV

Guest instructor: Fernando S. Perobelli

Prof. Perobelli has a degree in Economics from Federal University of Juiz de Fora (1992), Master in Economics from Federal University Fluminense (1995), and PhD in Economics from the University of São Paulo (2004). He is currently a Full Professor at Federal University of Juiz de Fora, Researcher Leader at the Sectorial and Territorial Analysis Laboratory (LATES/UFJF), and Associate Researcher at The University of São Paulo Regional and Urban Lab (NEREUS/USP). He has experience in economics, with an emphasis on regional economics, mainly on the following themes: regional development, input-output, computable general equilibrium models, and sector analysis (energy, transport, health, and services). He was the Coordinator of the Post-graduate Program in Economics (MSc/PhD) at UFJF from 2006 to 2011; President of the Brazilian Regional Science Association from 2011 to 2012 and Pro-rector of Post-graduation studies at the Federal University of Juiz de Fora from 2011 to 2012. Council Member at the International Input-Output Association from 2017 to 2020. Member of the CNPq (Brazilian Federal Research Agency) Advisory Committee in Economics (2017-2020).

As a consultant, he has experience in international projects – Inter-American Development Bank (IDB) – as well as national projects – Federation of Industries of Minas Gerais (FIEMG), Energy Research Company (EPE), Amazon Development Superintendence (SUDAM), Minas Gerais Energy Company (CEMIG), Pernambuco Energy Company (CELPE), Institute of Applied Economic Research (IPEA), Government of Minas Gerais State, Government of São Paulo State, and Government of Rio Grande do Sul State.

Full CV

Teaching assistant: Inácio F. Araújo

Dr. Araújo is a Post-Doc researcher at the Department of Economics and the Regional and Urban Economics Lab (NEREUS) at the University of Sao Paulo, Brazil. His major research interests lie in the field of regional analysis. He has experience in the implementation and application of economic models, especially input-output and computable general equilibrium models.

Full CV

Learning objectives

The course goals are:

To introduce students to the ideas and techniques of input-output analysis and computable general equilibrium modelling, and to equip them to start using operational models, adapting such models for their own simulations
To equip students to start using different methods in multisectoral and multirregional analysis
To develop skills that help analyzing development policies
To introduce students to some of the commonly-used tool kits in regional science that help understanding and interpreting the complexity of the spatial structure of sub-national economies

Readings

Textbooks:

Dervis, K., De Melo, J. and Robinson, S. (1982). General Equilibrium Models for Development Policy. Cambridge University Press.

Dixon, P. D., Jerie, M. and Rimmer, M. T. (2018). Trade Theory in Computsble General Equilibrium Models: Armington, Krugman and Melitz. Springer.

Dixon, P. B. and Jorgenson, D. W. (2013). Handbook of Computable General Equilibrium Modeling. Volumes 1A and 1B. North-Holland, Amsterdam.

Dixon, P. B., Parmenter, B. R., Sutton, J. and Vincent, D. P. (1982). ORANI: A Multisectoral Model of the Australian Economy. North-Holland, Amsterdam.

Dixon, P. B., Parmenter, B. R., Powell, A. A. and Wilcoxen, P. J. (1992). Notes and Problems in Applied General Equilibrium Economics. Advanced Textbooks in Economics 32, Eds. C. J. Bliss and M. D. Intriligator, North-Holland, Amsterdam.

Dixon, P. D. and Rimmer, M. T. (2002). Dynamic General Equilibrium Modelling for Forecasting and Policy: A Practical Guide and Documentation of MONASH. Amsterdam, North-Holland.

Ginsburgh V. and Keyzer M. (1997). The Structure of Applied General Equilibrium Models. The MIT Press, Cambridge.

Haddad, E. A. (1999). Regional Inequality and Structural Changes: Lessons from the Brazilian Economy. Ashgate, Aldershot.

Hertel, T. W. (1997). Global Trade Analysis: Modeling and Applications, Cambridge University Press.

Isard, W. et al. (1998). Methods of Interregional and Regional Analysis. Ashgate, Aldershot.

Miller, R.E. and Blair, P.D. (2009). Input-Output Analysis: Foundations and Extensions. Englewood Cliffs: Prentice-Hall.

Shoven, J. B. and Whalley, J. (1992). Applying General Equilibrium. Cambridge Surveys of Economic Literature. Cambridge University Press, Cambridge.

Complementary readings:

To be indicated by the instructor. A list of complementary readings will be available at the website of the course.

Link for class material distant access

Moodle: https://edisciplinas.usp.br/acessar/

NEREUS: http://www.usp.br/nereus/

Toolboxes of Input-Output Analysis

Análise de Insumo-Produto: Teoria e Aplicações no R (NEDUR-UFPR) – Link

ioanalysis: Input-Output Analysis in R (REAL-UIUC) – Link

PyIO: Python Module for Input-Output Analysis (REAL-UIUC) – Link

REAL I-O: Input-Output Operation Software (REAL-UIUC) – Link

Examination and grading

The final mark composition:

Final exam (course project): 60%
Continuous controls (homework): 30%
Assiduity and in-class participation: 10%

Homework and attendance

Some of the techniques used will be included in homework assignments, which should be delivered on previously established dates. While student cooperation is encouraged to resolve exercises, each student will have to submit his/her own answers. There will be quizzes throughout the course that will be part of the grading.

Late assignments

Regular attendance is expected as well as delivery of homework (quizzes, group exercises, presentations and simulation exercises). In addition, there will be an individual project at the end of each part of the course. The Final Exam will consist of a paper based on an application of an inter-regional input-output model (Part I), and an inter-regional CGE model (Part II).

Class attendance

This year’s course will be online, using the Zoom Platform. This makes possible to open the course for a broader audience in Brazil and in the Lusophone world. Students with working knowledge of Portuguese are welcome to register. The course will be conducted in Portuguese. Applicants therefore must be proficient or have a sound understanding of the Portuguese language in order to understand, communicate, and participate actively in the course activities. Despite lectures being taught in Portuguese, the bibliography will be mainly in English.

Complementary activities

We may schedule eventual tutorial meetings, outside regular course hours, to discuss and carry-on hands-on applications of the concepts and techniques discussed in the sessions. Such activities may be complemented by the introduction of extensions of the basic models.

Course project

In previous editions of the course, we have developed with students applied models for different countries: Greece (2017), Chile (2018), and Colombia (2019). The development of such models has relied on collaboration with scholars and institutions from those countries (e.g. Prof. Yannis Psycharis at Panteion University, the team led by Patricio Aroca at UAI, and Dr. Jaime Bonet and his research team at Banco de la República). In essence, they have helped us with the country’s data and we have developed the models. At the end of the course, students engaged in collaborative research projects using one of the models developed during the semester. Many of the projects the students worked on then became the basis for publications.

Given the unique nature of this year’s course, the focus will be on Brazil. We will update the interstate IO table for Brazil, using 2015 data, and calibrate not only a national CGE model but also an inter-regional CGE model using the IO system. Students will engage on research projects supervised by professors with large experience in multisectoral and multirregional modeling. The best projects will become chapters of a volume on “The Brazilian Economy and its Regional Structural Challenges”, edited by Eduardo A. Haddad, Fernando S. Perobelli, and Inácio F. Araújo, and intended to be published electronically together with free access to all databases and models’ codes needed for replication of the exercises.

Course detailed outline

Part I: EAE6061 – Applied General Equilibrium Models I

Class #1 (August 11, 2020)

Topic: “General Equilibrium: Pure Theory, Applied Theory, and Practice”

Key learning outcome:

Intellectual legacy of general equilibrium theory and applied general equilibrium models
“Cournot’s Problem”

Manual chapters and readings:

Review (linear algebra) – Miller and Blair (2009), appendix

Class #2 (August 13, 2020)

Topic: “Economic Base Models I”

Key learning outcome:

Broad perspective of regional policies, with an overview of the theoretical background and rationale which are essential to the definition of priorities and strategies
Review the simple Keynesian model and the elementary economic-base models
Methods of estimating the values of EB multipliers

Manual chapters and readings:

Article: “O PIB e a Renda” (Paulo R. Haddad) – O Tempo, 17/10/2019

Activity 1: Angola Brazil

Solution: Angola Brazil

Class #3 (August 18, 2020)

Topic: “Economic Base Models II”

Key learning outcome:

Review key elements of regional accounts
Introduce students to EB structural models

Manual chapters and readings:

Activity 2: Questions Angola Brazil

Solution: Brazil

Class #4 (August 20, 2020)

Topic: “Theoretical Structure of Input-Output Models”

Key learning outcome:

Investigate the fundamental structure of the input-output model, the assumptions behind it, and some of the simplest kinds of problems to which it is applied

Manual chapters and readings:

Miller and Blair (2009): chapter 1 and chapter 2
Guilhoto (2011): chapters 1-3

Example IO: Excel (ver https://guilhotojjmgen.wordpress.com/)

Activity 3: Data Brazil_1a Data_Angola

Solution: Brazil Angola

Class #5 (August 25, 2020)

Topic: “Impact Analysis and Multipliers”

Key learning outcome:

Assess the effects on an economy of changes in elements that are exogenous to the model of that economy
Develop summary measures derived from the elements of the Leontief inverse matrix

Manual chapters and readings:

Activity 4: Data_Brazil_2a Data_Angola

Solution: Brazil Angola

Solution: Activity 5: Challenge (Man-year equivalent)

Groups List of participants

Class #6 (August 27, 2020)

Topic: “Regional and Inter-regional Input-Output Models”

Key learning outcome:

Incorporate features of a regional economy into an input-output framework

Manual chapters and readings:

Solution: Activity 6 (Key sectors)

Class #7 (September 01, 2020)

Topic: “Applications of Input-Output Models I”

Key learning outcome:

Linkage measures
Key sector analysis
Output decomposition

Manual chapters and readings:

Solution: Activity 7 (Decomposition of regional output)

Class #8 (September 03, 2020)

Topic: “Applications of Input-Output Models II”

Key learning outcome:

Extraction method

Manual chapters and readings:

Example (SAMIR – Marrocos): Excel file

Activity 8: Data_Brasil Data_Angola

Solution: Activity 8

Class #9 (September 08, 2020)

Topic: “Applications of Input-Output Models III” (Prof. Fernando Perobelli)

Key learning outcome:

Structural decomposition analysis

Manual chapters and readings:

Example (SDA): Excel file

Activity 9 (SDA – Employment): Data

Solution: Activity 9

Class #10 (September 10, 2020)

Topic: “Global Input-Output Models” (Prof. Joaquim Guilhoto)

Key learning outcome:

Review the OECD global input-output databases
Present potential applications

Manual chapters and readings:

Class #11 (September 15, 2020)

Topic: “Methods of Regionalization”

Key learning outcome:

Estimate an inter-regional input-output system using the IIOAS method

Manual chapters and readings:

Miller and Blair (2009): chapter 7 and chapter 8
Haddad et al. (2012) – Lebanon
Haddad et al. (2017) – Brazil
Haddad et al. (2017) – Morocco
Haddad et al. (2018) – Colombia
Haddad et al. (2020) – Mexico

Example (IIOAS Method): Excel file

Class #12 (September 17, 2020)

Topic: “Applications of Input-Output Models IV” (Prof. Geoffrey Hewings)

Key learning outcome:

Miyazawa extended input-output framework
Explore the nature and strength of economic interdependence in metropolitan areas
Analyze interactions between different groups and sectors

Manual chapters and readings:

Class #13 (September 22, 2020)

Topic: “Social Accounting Matrix”

Key learning outcome:

Inspect the different dimensions of income flows embedded in a SAM
Explore the relationships between output, factor demand and income, and the decomposition of these relationships into separate effects

Manual chapters and readings:

Example (SAM for Brazil): Data

Exercise: SAM of a Closed Economy

Activity 10: Questions Data

Class #14 (September 24, 2020)

Topic: “The Johansen Approach”

Key learning outcome:

How microeconomic theory – cost-minimizing, utility-maximizing – underlies the equations of a CGE model
The use of nested production and utility functions
How input-output data are used in equations
How model equations are represented in percent change form
How the choice of exogenous variables makes a model more flexible

Manual chapters and readings:

Dixon et al. (1992): chapter 3
Dixon et al. (1980): chapter 2 (2.3) and 4 (4.2 and 4.20)

Class #15 (September 29, 2020)

Topic: “Introduction to GEMPACK”

Key learning outcome:

Use GEMPACK to solve a CGE model

Manual chapters and readings:

Dixon et al. (1992): chapter 3

Class #16 (October 01, 2020)

Topic: “Course Projects”

Key learning outcome:

Develop presentation skills

Manual chapters and readings:

Extra Class (September 24, 2020)

Time: 16h00

Topic: “Applications of Input-Output Models with R” (Prof. Vinicius Vale and Prof. Fernando Perobelli)

Key learning outcome:

Use R to solve input-output models

Manual chapters and readings:

Database and RScript: see here

Part II: EAE6061 – Applied General Equilibrium Models II

Class #1 (October 13, 2020)

Topic: “The ORANI Model I”

Key learning outcome:

Understand the structure of database

Manual chapters and readings:

ORANI-G (Brazil): Files

Example (ORANI for Brazil): Data

Activity 1: Data

Class #2 (October 15, 2020)

Topic: “The ORANI Model II”

Key learning outcome:

Understand the equation system of a national CGE model

Manual chapters and readings:

Activity 2: Questions

Class #3 (October 20, 2020)

Topic: “The ORANI Model III”

Key learning outcome:

Understand the equation system of a national CGE model
Calibrate a CGE model
Use of a national CGE model for policy analysis

Manual chapters and readings:

Activity 3: Questions

Class #4 (October 22, 2020)

Topic: “The ORANI Model IV”

Key learning outcome:

Use of a national CGE model for policy analysis

Manual chapters and readings:

Exercises: (i) Homogeneity test (ii) Reduction in real wages

Class #5 (October 27, 2020)

Topic: “The Miniature MONASH Model I”

Key learning outcome:

Introduce recursive-dynamics in CGE models

Manual chapters and readings:

Dixon and Parmenter (1996)

Class #6 (October 29, 2020)

Topic: “The Miniature MONASH Model II”

Key learning outcome:

Build baseline and policy scenarios using dynamic CGE models

Manual chapters and readings:

Dixon and Parmenter (1996)

Exercise (database): Questions

MM Model: Files

Wage Cut Simulation: Guide

Activity 4: Questions

Class #7 (November 03, 2020)

Topic: “The EFES Model”

Key learning outcome:

Introduce students to forecasting exercises with a CGE model for Brazil
Top-down regional disaggregation of national results
Review non-academic, government and business-related applications of CGE models

Manual chapters and readings:

Class #8 (November 05, 2020)

Topic: “Spatial CGE Models I”

Key learning outcome:

Introduce students to applied general interregional equilibrium models

Manual chapters and readings:

Isard et al. (1998): chapter 8

Class #9 (November 10, 2020)

Topic: “Spatial CGE Models II”

Key learning outcome:

Develop an operational spatial CGE model under conditions of limited data availability

Manual chapters and readings:

Class #10 (November 12, 2020)

Topic: “The B-MARIA Model I”

Key learning outcome:

Develop bottom-up interregional CGE model
Introduce a transportation sector and non-constant returns to scale into CGE models
Understand the role of transportation costs in spatial CGE models
Integrate CGE models to transportation networks
Apply CGE models for assessing the impacts of transportation infrastructure
Integrate spatial/metropolitan CGE models to travel demand models

Manual chapters and readings:

Class #11 (November 17, 2020)

Topic: “The B-MARIA Model II”

Key learning outcome:

Calibrate a bottom-up interregional CGE model
Use of an ICGE model for policy analysis

Manual chapters and readings:

Examples BMMX

BMMX ICGE Model: files

Class #12 (November 19, 2020)

Topic: “The B-MARIA Model III”

Key learning outcome:

Calibrate a bottom-up interregional CGE model

Manual chapters and readings:

Data for calibration exercise: Files

Class #13 (November 24, 2020)

Topic: “The GTAP Model I”

Key learning outcome:

Introduce students to the structure of the standard GTAP model, a multi-country, multi-sector, CGE model, with perfect competition and constant returns to scale

Manual chapters and readings: