english | español | acesso restrito | t (55 11) 3813.1444 | f (55 11) 3032.8334 | nereus@usp.br

Regional Policy-Making and Analysis

Regional Policy-Making and Analysis

November, 2020 – January, 2021 – Faculté de Gouvernance, Sciences Économiques et Sociales, Université Mohammed VI Polytechnique, Lot 660 – Hay Moulay Rachid 43150 Ben Guerir – Morocco

Lecturer: Prof. Eduardo Amaral Haddad – University of Sao Paulo (Brazil)

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


Learning objectives

The course goals are:

  • To introduce students to some of the commonly-used tool kits in regional economics that help understanding and interpreting the complexity of the spatial structure of sub-national economies
  • To equip students to start using different methods in regional and inter-regional analysis
  • To develop skills that help analyzing regional development policies



Barro, R. and Sala-i-Martín, X. (1995). Economic Growth. McGraw-Hill.

Capello and Nijkamp (2019). Handbook of Regional Growth and Development Theories. Edward Elgar.

Fischer, M. M. and Wang, J. (2011). Spatial Data Analysis: Models, Methods and Techniques. Springer.

Hoover, E. M. and Giarrantani, F. (1999). An Introduction to Regional Economics. Web Book of Regional Science, http://www.rri.wvu.edu/WebBook/Giarratani/main.htm

Isard, W. (1960). Methods of Regional Analysis. The MIT Press.

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

Johnson, R. A. and Wichern, D. W. (2007). Applied Multivariate Statistical Analysis. Prentice Hall.

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

Schaffer, W. A. (2010). Regional Impact Models. Web Book of Regional Science, http://www.rri.wvu.edu/WebBook/Schaffer/index.html

Complementary readings: To be indicated by the instructor


Examination and grading

The final mark composition:

  • Mid-Term exam: 30%
  • Final exam: 30%
  • Continuous controls: 30% (10% per homework for the 2 mandatory homework)
  • 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 which will be part of the grading.


Late assignments

The Mid-Term Exam will consist of a presentation on “Territorial Development in [Country/Region X]: A Spatial Diagnostic”, to be developed as remotely supervised activities and delivered in the first day of the second part of the course. The Final Exam will consist of a short paper based on the application of an inter-regional input-output model.


Course detailed outline

Class# 1

Topic: “Measures of Concentration, Specialization, and Localization”

Key learning outcome:

  • Descriptive analysis of systems of industrial location
  • Summary indicators for spatial patterns of economic activity

Manual chapters and Readings: Isard (1960): chapter 7

Class# 2

Topic: “Exploratory Spatial Data Analysis (ESDA) – Geovisualization”

Key learning outcome:

  • Introduction to GeoDA
  • Mapping of socioeconomic data
  • Combine map and scientific visualization methods

Manual chapters and Readings: Fischer and Wang (2011): chapters 1 and 2

Web resources:

Class# 3

Topic: “Exploratory Spatial Data Analysis (ESDA) – Global Spatial Autocorrelation”

Key learning outcome:

  • Spatial weights matrix
  • Spatially lagged variable
  • Global measures and tests for spatial autocorrelation

Manual chapters and Readings: Fischer and Wang (2011): chapters 1 and 2

Web resources:

Class# 4

Topic: “Exploratory Spatial Data Analysis (ESDA) – Local Spatial Autocorrelation”

Key learning outcome:

  • Local measures and tests for spatial autocorrelation

Manual chapters and Readings: Fischer and Wang (2011): chapters 1 and 2

Web resources:

Assignment: Homework # 1: ESDA

Class# 5

Topic: “Factor Analysis”

Key learning outcome:

  • Create variables that, by construction, are uncorrelated
  • Build an indicator that allows classifying regions and/or cities

Manual chapters and Readings: Johnson and Wichern (2007): chapter 9

Web resources:

Class# 6

Topic: “Shift-Share Analysis”

Key learning outcome:

  • Identify the sources of regional economic changes

Manual chapters and Readings: Hoover and Giarrantani (1999): chapter 12

Class# 7

Topic: “Introduction to Spatial Econometrics”

Key learning outcome:

  • Estimate spatial regression models in a simple cross-sectional setting

Manual chapters and Readings: Fischer and Wang (2011): chapter 3

Web resources:

Class# 8

Topic: “Regional Convergence”

Key learning outcome:

  • Test the convergence predictions of the  neoclassical growth model by looking at the behavior of regions within countries

Manual chapters and Readings: Barro and Sala-i-Martín (1995): chapter 11

Assignment: Homework # 2: Regional income convergence in [Country/Region X]

Class# 9

Topic: “Economic Base Models”

Key learning outcome:

  • Review the simple Keynesian model and the elementary economic-base models
  • Methods of estimating the values of multipliers

Manual chapters and Readings: Schaffer (2010): chapter 3

Class# 10

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): chapters 1 and 2

Class# 11

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: Miller and Blair (2009): chapter 6

Class# 12

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: Miller and Blair (2009): chapter 3

Class# 13

Topic: “Applications of Input-Output Models I”

Key learning outcome:

  • Linkage measures
  • Key sector analysis
  • Extraction method

Manual chapters and Readings: Miller and Blair (2009): chapter 12

Class# 14

Topic: “Applications of Input-Output Models II”

Key learning outcome:

  • Structural decomposition analysis

Manual chapters and Readings: Miller and Blair (2009): chapter 13

Class# 15

Topic: “Applications of Input-Output Models III”

Key learning outcome:

  • Spatial patterns of household consumption

Manual chapters and Readings: Haddad et al. (2013)

Class# 16

Topic: “Applications of Input-Output Models IV”

Key learning outcome:

  • Trade in natural resources

Manual chapters and Readings: Haddad et al. (2013)


Complementary activities

We will use the tutorial meetings in the afternoon to discuss and carry-on hands-on applications of the concepts and techniques discussed in the morning sessions. Such activities may be complemented by the introduction of extensions of the basic models.


Examples Measures (Class# 1): Excel Data Morocco_1 Morocco_2 Egypt

Examples GeoDa (Class# 2-4):  GeoDa GeoDa Tutorial 2005 PDF GeoDa Tutorial HTML ZIP file Morocco_3

Examples Angola: Data Metadata

Examples Colombia:

  • GeoDA: Colombia MSA Grid100
  • Data by municipality: Excel
  • Índice de Potencial de Desarollo Municipal para la Región Pacífico de Colombia (RPC): Excel Stata

Assignment ESDA: Atividade Dados_Mun_Excel Dados_SP Dados_MG Dados_RS Valor_Adicionado_UF

Examples Morocco:

Assignment Convergence:


© Nereus – Todos os direitos reservados. | Home | Nereus | Equipe | Linhas de Pesquisa | Projetos | Publicações | Dados e Programas | Eventos | Notícias | Contatos | RSS | Mapa do Site