Matteo Richiardi is Professor of Economics and Director of EUROMOD at ISER, University of Essex. He is a labour economist specialised in microsimulation and agent-based modelling techniques. His main areas of interests are inequality, worker insecurity, labour force participation, wage dynamics. He is the Chief Editor of the International Journal of Microsimulation.

Miko Tammik is an experienced EUROMOD developer and trainer with previous experience studying the socio-economic effects of various policy measures. Miko’s research interests are mainly focused on income inequality and poverty and the distributional effects of tax-benefit policies.

Katrin Gasior is involved in EUROMOD for many years, first as a country expert for Austria, now as a core developer at the University of Essex. Among other projects, she was responsible for the development of the web-based microsimulation model SORESI for the Austrian Social Ministry. Katrin has longstanding experience in designing, organising and directing national and cross-national studies in the field of comparative research on modern welfare societies and social security systems with a focus on social inclusion and poverty. Her current research focuses on work incentives and the income situation of migrants.

Diego Collado is Research Data and Policy Analyst at EUROMOD, University of Essex, and Socio-economics PhD(c) from the University of Antwerp. He has published on social policy and poverty in peer-reviewed journals and book chapters. His current research focuses on the labour supply effects of tax-benefit reforms on poverty and public finances in Belgium.

Course Content
The course will cover the following topics:
– General introduction to micro-simulation modelling in the social sciences
– Static non-behavioural tax-benefit microsimulation: General structure and an introduction to the EUROMOD simulation platform
– Behavioural static labour supply models using EUROMOD: Specification options, estimation issues, applications to the microsimulation of reforms.
– Dynamic microsimulation models: General structure, estimation issues and an introduction to NETLOGO.
– Agent-based models: General structure and their relationship to dynamic microsimulation, estimation issues.

Course Objectives
The course is aimed at giving participants a comprehensive overview of microsimulation modelling for the social science. Participants will gain knowledge of the different modelling approaches, and learn how to build and estimate their own microsimulation models, using state-of-the-art microsimulation tools.

Course Prerequisites
– Interest in distributional analysis and the role of heterogeneity.
– Basic knowledge of the definition of disposable household income, equivalence scales, at-risk-of-poverty rate and Gini
– Knowledge of regression analysis

Background Reading
Sutherland, H. & Figari, F., 2013. EUROMOD: the European Union tax-benefit microsimulation model. International Journal of Microsimulation, 1(6), pp.4–26.

Richiardi (2013). The missing link: AB models and dynamic microsimulation. In: Leitner S, Wall, F (eds). Artificial Economics and Self Organization. Agent-Based Approaches to Economics and Social Systems. Springer, Lecture Notes in Economics and Mathematical Systems, vol. 669, Berlin.

Miller, Page (2006). Complex Adaptive Systems: An Introduction to Computational Models of Social Life. Princeton University Press.

Required Text
No required texts but see course outline for suggested readings that help participants to prepare for the course.

Background knowledge required
OLS = m
Maximum Likelihood = e

Computer Background
Stata = m

e = elementary, m = moderate, s = strong

More detailed Information to follow shortly.