Gagliardi Francesca
Ученая степень: PhD
Ученое звание: Prof.
Страна: Italy
Город: Siena
Место работы: University of Siena
Должность: Junior researcher at the University of Siena

Область научных интересов



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 European Union as a whole and Member States are individually committed to achieving by 2020. One of the five headline targets is directly related to the key quality aspects of life, namely social inclusion; within these targets, the European Union Statistics on Income and Living Condition (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, Directorate-General 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. We propose to adopt a methodology based on the Empirical Best Linear Unbiased Predictor (EBLUP) with an extension to the spatial dimension (SEBLUP); moreover, we compare this small area technique with the cumulation method. The application is conducted on the basis of EU-SILC data from Austria and Spain. Results report that, in general, estimates computed with the cumulation method show standard errors which are smaller than those computed with EBLUP or SEBLUP. The gain of pooling SILC data over three years is, therefore, relevant, and may allow researchers to prefer this method.

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).

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<Июнь 2017>