Francesca Gagliardi

Cumulation of poverty measures to meet new policy needs

Reliable indicators of poverty and social exclusion are an essential monitoring tool. Policy research and application increasingly require statistics disaggregated to lower levels and smaller subpopulations. This paper addresses some statistical aspects relating to improving the sampling precision of such indicators for subnational regions, in particular through the cumulation of data.

Comparing small area techniques for estimating poverty measures: the case study of Austria and Spain

The Europe 2020 Strategy has formulated key policy objectives or so-called “headline targets” which the EU as a whole and Member States are individually committed to achieving by 2020. One of the five headline targets is directly related to key quality aspects of life, namely social inclusion; within these targets, the EU-SILC headline indicators at-risk-of-poverty or social exclusion and its components will be included in the budgeting of structural funds, one of the main instruments through which policy targets are attained. For this purpose, DG Regional Policy of the European Commission is aiming to use sub-national/regional level data (NUTS 2). Starting from this, the focus of the present paper is on the “regional dimension” of well-being. In fact, we compare two small area techniques, namely the cumulation and the spatial EBLUP (SEBLUP), on the basis of EU-SILC data from Austria and Spain

Multidimensional and Fuzzy measures of poverty at regional level in Mozambique

This study provides a step-by-step account of how fuzzy measures of non-monetary deprivation and also monetary poverty may be constructed at the regional level, based on the Mozambican Household Budget Survey 2008-09 (IOF08). To our knowledge, this is the first attempt to apply Fuzzy Set Theory to poverty measurement in Mozambique. The dataset we used is the most recent budget survey available for Mozambique and it is representative of the national, regional (North, Centre, South), provincial and urban/rural level. In order to construct a Fuzzy Set index of poverty, monetary as well as non-monetary indicators are considered, and two different measures of deprivation are subsequently constructed: the Fuzzy Monetary (FM) and Fuzzy Supplementary (FS).

Latent class Markov models for measuring longitudinal fuzzy poverty

The traditional approach to poverty measurement utilises only monetary variables as indicators of individuals’ intensity of the state of deprivation, causing measurement errors of the phenomenon under investigation. Moreover, when adopted in a longitudinal context, this approach tends to overestimate transition poverty. Since poverty is not directly observable, a latent definition can be adopted: in such a conception is possible to use Markov chain models in their latent acceptation. This chapter proposes to use Latent class Markov models which allow taking into account more observed (manifest) variables. We define those variables via monetary and non-monetary fuzzy indicators.