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Social Statistics

Angelo Moretti

Title

Measuring Poverty and Wellbeing at Local Level

PhD summary

Measuring poverty and wellbeing is a key point of every government. Policy makers need detailed information about the geographical distributions of social indicators at a sub-regional level. However, many social sample surveys (e.g. EUSILC) are not designed to be representative at a sub-regional level. Hence, direct estimates cannot be computed with high accuracy. For that reason, we need indirect estimation methods to produce accurate estimates.
My research project aims to analyse multidimensional poverty and well-being indicators at a local level using small area estimation methods. First, we will apply small area estimation methods in order to estimate social indicators in a dashboard approach. Then, small area estimation methods will be applied to estimate composite indicators calculated as a factor scores using factor analysis modelling to measure latent variables for poverty and well-being. The goal is to develop multivariate small area estimation methods to provide accurate estimates of composite indicators. Simulation studies and applications using real EUSILC data will be carried out.
My research interests are primarily in multivariate small area estimation methods, particularly applied to poverty and wellbeing indicators. I am also interested in survey design and estimation, statistical modelling and mixed effect models.

Dates

September 2014 - September 2017

Supervisors

Biography

I joined the Cathie Marsh Institute for Social Research in September 2014 as a PhD student in Social Statistics funded by an ESRC NWDTC +3 studentship, and enhanced stipend for using Advanced Quantitative Methods.

My background is in quantitative methods for economics and survey methodology. I received my BSc degree in Economics (quantitative methods pathway) in 2012 and MSc in Marketing and Market Research summa cum laude in 2014 from the University of Pisa in Italy. My MSc final dissertation was in small area estimation for poverty and wellbeing indicators. I particularly studied the small area estimation problem of non-linear poverty indicators. I used EUSILC data and the Italian Census for Tuscany region.

Before coming to Manchester, I worked as a statistics and accounting teacher in a high school for continuing education in Italy and as a TA in statistics at the University of Pisa. I worked for many years in Fair Trade projects, and I was a member of the board of directors of an Italian Fair Trade Cooperative for six years. I was in charge of the training of new volunteers as well.

Contact details

Office: G45, Humanities Bridgeford St Building
Email: angelo.moretti@manchester.ac.uk