The course deals with the theory and methodology aimed at measuring poverty, inequality and well-being. Particular attention is given to the construction of indicators and to present their main applications. The course also provides a short introduction to spatial analisys of statistical indicators.
The textbooks will be announced at the beginning of classes.
Obiettivi Formativi
The course aims at providing knowledge and competences about the indicators construction and application.
Prerequisiti
Knowledge of basic multivariate statistics.
Metodi Didattici
Lectures, seminars and lab sessions.
Use of e-learning platform Moodle where, in particular, will be upload the teaching materials.
Altre Informazioni
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Modalità di verifica apprendimento
Individual oral examination based on a written exam.
A mini-project report (about 6000 words) could be requested.
Programma del corso
Part I
Steps for constructing a composite indicator
Developing a theoretical framework
Selecting variables
Reducing the complexity
The approaches to reduction
Synthesis of indicators: different perspectives
Synthesis of indicators: technical issues (aggregative-compensative approaches and non-compensative approaches)
Representing the complexity (dashboards)
Explaining the complexity: modelling indicators
Part II
Well being and poverty: different approaches in measuring poverty
Key households survey issues: sampling, time period of measurement, comparisons across households (equivalence scales)
Poverty lines
Measures of poverty (1)
Measures of poverty (2): testing and checking for robustness
Poverty dynamics: transition and vulnerability
Multidimensional poverty analysis: poverty identification
Multidimensional poverty analysis: poverty measurement
Part III
Masures of poverty and inequality
Definitions and measurement of economic inequality: size and factorial distributions
Kuznets ratio, Lorenz curve and Gini coefficient
Comparisons across measures
Kuznets’s Inverted-U Hypothesis
Types of poverty and their indicators
Measuring economic poverty: total poverty gap, The Foster-Greer-Thorbecke Index, Multidimensional Poverty Index
Monitoring poverty indicators
Redefining Kuznets’s curve
Within and between country inequality
Globalization and inequality
Indicators of health and education
Human development index: traditional and new approaches
The basics around the OECD framework
Governance statistics in OECD countries and beyond
Developing better well-being metrics
Measuring subjective well-being
Part III
What are the spatial data
Spatial data types
The spatial contiguity
Spatial weighting matrices
The statistical sources of spatial data
Global and local autocorrelation
Global indices of spatial autocorrelation and spatial correlogram
Indices of spatial autocorrelation and local Moran scatterplot Part I
Steps for constructing a composite indicator
Developing a theoretical framework
Selecting variables
Reducing the complexity
The approaches to reduction
Synthesis of indicators: different perspectives
Synthesis of indicators: technical issues (aggregative-compensative approaches and non-compensative approaches)
Representing the complexity (dashboards)
Explaining the complexity: modelling indicators
Part II
Part III
Introduction to the measures of poverty and inequality
Definitions and measurement of economic inequality: size and factoral distributions
Kuznets ratio, Lorenz curve and Gini coefficient
Comparisons across measures
Kuznets’s Inverted-U Hypothesis
Types of poverty and their indicators
Measuring economic poverty: total poverty gap, The Foster-Greer-Thorbecke Index, Multidimensional Poverty Index
Monitoring poverty indicators
Redefining Kuznets’s curve
Within and between country inequality
Globalization and inequality
Indicators of health and education
Human development index: traditional and new approaches
The basics around the OECD framework
Governance statistics in OECD countries and beyond
Developing better well-being metrics
Measuring subjective well-being
Part III
What are the spatial data
Spatial data types
The spatial contiguity
Spatial weighting matrices
The statistical sources of spatial data
Global and local autocorrelation
Global indices of spatial autocorrelation and spatial correlogram
Indices of spatial autocorrelation and local Moran scatterplot.