Scheda programma d'esame
Academic year2019/20
PeriodSemester 1

Programma non disponibile nella lingua selezionata
Learning outcomes

The course aims at the definition and measurement of coherent and comparable multidimensional local indicators of poverty and vulnerabilities that can be useful for local stakeholders for monitoring Sustainable Development Goals. The traditional data collections methods used in EU Surveys (e.g EU-Survey Income Living Conditions, Household Budget Surveys, Labour Force Survey) are introduced, focusing on the sampling design, sample weighting and estimation. The course also offers a general introduction to the usage of administrative data and large datasets as sources of statistical data (Big Data), with an emphasis on multi-frame surveys. The data sources from the big data repositories are listed and examined, highlighting their potentialities and limitations in the study of poverty and living conditions. Students will learn both traditional and new survey techniques focusing on the problems that might arise in the definition and measure of local indicators of poverty and living conditions.

At the end of the module, students should be familiar with the theme of local indicators and should be aware of the main problems/challenges regarding the usage of different data sources on poverty.

Assessment criteria of knowledge

The knowledge will be assessed by

- meetings of the students to discuss the group work of the R Lab with the professor and the teaching staff


The student will be able to

- search and analysse the official data sources (surveys, Censuses) on poverty and living conditions in Europe

- read and apply the R codes to perform estimation of the indicators

- present the results of the application of the estimatorsl to European data

Assessment criteria of skills

- during the session of the R Lab small individual projects will allow to understand how to run the R SAE codes

- there will be small practical sessions to search and consult the data sources (search tools and methods for a given research topic, searching the Web and the main Eurostat databases)

- the student will  present the results of the small projects and of the searching og the data sources



- the student can develop awareness of the problems of local data on poverty and living conditions in EU

- the student can develop the ability to work in group and to manage the responsibilities as a group leader


Assessment criteria of behaviors

- during the activities of R Lab and data searching the students will have to present short reports on the obtained results


- descriptive statistics and inference

- data processing abilities

- regression models

Teaching methods

The course is in English and it provides

- lectures with slides

- group activities in R Lab using personal Laptop of the students

- seminars of experts, web sites

Maria Pia Sorvillo “Multidimensional Indicators for Sustainable Development Goals in Italy and Europe”

Marina Gandolfo “The implementation of the Agenda 2030 in Europe”

Jan van den Brakel “Big data in Official Statistics in Europe”

Mariola Chrzanowska “Surveying the poor and hard to reach populations in Europe”

Nina Drejerska “Tools for evaluating local policies in Europe”

- co-teachers: Gaia Bertarelli - Francesco Schirripa

- downloadable materials from Moodle platform of the Dept of Economics and Management

- interactions with the Professors through meetings, email, elearning site


The contents of the course are:

  • Survey methods and description of the survey design of the main current European Sample Surveys on households (EUSILC, HBS, LFS)
  • Definition of poverty and living conditions indicators (e.g. Laeken Indicators of Poverty and/or multidimensional indicators of poverty)
  • survey methods and estimation strategies (Horvitz -Thompson estimator, Hayek estimator),
  • introduction to economic official data and aggregates to study poverty and the main european surveys on households (EUSilc: European Survey on Income and Living Conditions, Household Budget Survey, Labour Force Survey). Issues on data integration and new data sources as Big data.



Materials on the definition of indicators, surveys and economic data:

  1. Combating poverty and social exclusion. A statistical portrait of the European Union 2010. Eurostat, 2010 edition.
  2. Haughton, S.R. Khandker (2009) Handbook on Poverty and Inequality, see:

    Materials on survey methods and direct estimation of indicators

    1. UNITED NATIONS SECRETARIAT ESA/STAT/AC.93/5, Statistics Division 03 November 2003- Construction and use of sampling weights, by Ibrahim S. Yansaneh
    2. SAMPLE project deliverables: (last access 18-2-17)
    3. Verma, Betti, Natilli, Lemmi (2006), Indicators of Social Exclusion and Poverty in Europe’s Regions, Working Paper n. 59, April 2006
Non-attending students info

No variations of program, assessment methods, bibliography for non-attending students

Assessment methods

The exam is composed by a seminary to be held during the appello plus and individual written test.

The individual/group work will be presented to the Professor(s) and will be discussed with them.

Updated: 03/03/2020 17:16