Search results

Cover -- Title Page -- Copyright -- Contents -- Foreword -- Preface -- Acknowledgements -- About the Editor -- List of Contributors -- Chapter 1 Introduction on Measuring Poverty at Local Level Using Small Area Estimation Methods -- 1.1 Introduction -- 1.2 Target Parameters -- 1.2.1 Definition of the Main Poverty Indicators -- 1.2.2 Direct and Indirect Estimate of Poverty Indicators at Small Area Level -- 1.3 Data-related and Estimation-related Problems for the Estimation of Poverty Indicators -- 1.4 Model-assisted and Model-based Methods Used for the Estimation of Poverty Indicators: a Short Review -- 1.4.1 Model-assisted Methods -- 1.4.2 Model-based Methods -- References -- Part I Definition of Indicators and Data Collection and Integration Methods -- Chapter 2 Regional and Local Poverty Measures -- 2.1 Introduction -- 2.2 Poverty - Dilemmas of Definition -- 2.3 Appropriate Indicators of Poverty and Social Exclusion at Regional and Local Levels -- 2.3.1 Adaptation to the Regional Level -- 2.4 Multidimensional Measures of Poverty -- 2.4.1 Multidimensional Fuzzy Approach to Poverty Measurement -- 2.4.2 Fuzzy Monetary Depth Indicators -- 2.5 Co-incidence of Risks of Monetary Poverty and Material Deprivation -- 2.6 Comparative Analysis of Poverty in EU Regions in 2010 -- 2.6.1 Data Source -- 2.6.2 Object of Interest -- 2.6.3 Scope and Assumptions of the Empirical Analysis -- 2.6.4 Risk of Monetary Poverty -- 2.6.5 Risk of Material Deprivation -- 2.6.6 Risk of Manifest Poverty -- 2.7 Conclusions -- References -- Chapter 3 Administrative and Survey Data Collection and Integration -- 3.1 Introduction -- 3.2 Methods to Integrate Data from Different Data Sources: Objectives and Main Issues -- 3.2.1 Record Linkage -- 3.2.2 Statistical Matching.

There has been rising interest in research on poverty mapping over the last decade, with the European Union proposing a core of statistical indicators on poverty commonly known as Laeken Indicators. They include the incidence and the intensity of poverty for a set of domains (e.g. young people, unemployed people). The EU-SILC (European Union - Statistics on Income and Living Conditions) survey represents the most important source of information to estimate these poverty indicators at national or regional level (NUTS 1-2 level). However, local policy makers also require statistics on poverty and living conditions at lower geographical/domain levels, but estimating poverty indicators directly from EU-SILC for these domains often leads to inaccurate estimates. To overcome this problem there are two main strategies: i. increasing the sample size of EU-SILC so that direct estimates become reliable and ii. resort to small area estimation techniques. In this paper we compare these two alternatives: with the availability of an oversampling of the EU-SILC survey for the province of Pisa, obtained as a side result of the SAMPLE project (Small Area Methods for Poverty and Living Conditions, http://www.sample-project.eu/), we can compute reliable direct estimates that can be compared to small area estimates computed under the M-quantile approach. Results show that the M-quantile small area estimates are comparable in terms of efficiency and precision to direct estimates using oversample data. Moreover, considering the oversample estimates as a benchmark, we show how direct estimates computed without the oversample have larger errors as well as larger estimated mean squared errors than corresponding M-quantile estimates.

"There has been rising interest in research on poverty mapping over the last decade, with the European Union proposing a core of statistical indicators on poverty commonly known as Laeken Indicators. They include the incidence and the intensity of poverty for a set of domains (e.g. young people, unemployed people). The EU-SILC (European Union - Statistics on Income and Living Conditions) survey represents the most important source of information to estimate these poverty indicators at national or regional level (NUTS 1-2 level). However, local policy makers also require statistics on poverty and living conditions at lower geographical/domain levels, but estimating poverty indicators directly from EU-SILC for these domains often leads to inaccurate estimates. To overcome this problem there are two main strategies: i. increasing the sample size of EU-SILC so that direct estimates become reliable and ii. resort to small area estimation techniques. In this paper the authors compare these two alternatives: with the availability of an oversampling of the EU-SILC survey for the province of Pisa, obtained as a side result of the SAMPLE project (Small Area Methods for Poverty and Living Conditions, http://www.sample-project.eu/ ), they can compute reliable direct estimates that can be compared to small area estimates computed under the M-quantile approach. Results show that the M-quantile small area estimates are comparable in terms of efficiency and precision to direct estimates using oversample data. Moreover, considering the oversample estimates as a benchmark, they show how direct estimates computed without the oversample have larger errors as well as larger estimated mean squared errors than corresponding M-quantile estimates." (author's abstract)

The aim of this paper is twofold: first it shows how the identification of seven vulnerable labour market groups in the 2018 European Union Labour Force Survey (EU-LFS) is possible. These groups include age, gender identity, sexual orientation, single parenthood, migration (ethnicity, nationality, and migration status), religion, and disability. Second, it presents a study on how statistically reliable indicators can be obtained for a selection of those identified vulnerable groups. For the first identification part, the exercise showed that the sample size of the vulnerable group is largely dependent on the operationalisation utilised. Several of the vulnerable groups included straightforward definitions that are widely agreed on and correspond well with items in the EU-LFS. For other groups, such as disability and migrant status, the sample size varied widely based on operationalisation used to identify respondents. Age and gender identity all provide for straightforward identification with sizeable sample sizes. Sexual orientation was limited to same-sex couples living in the same household, which faced additional restrictions like anonymisation and limited detailed household data. Identification of single parenthood depended on the age cut off for children in the household, as the EU-LFS defines a dependent child in a way that deviates from research norms. Identifying disability and migrant status also provided difficult as there is not a single operationalisation for either and identification has to be done indirectly. There was no measurement for religion. Additionally, we find issues with removing duplicate responses from the EU-LFS as repeat sampling varies at the country level and there are no consistent identifiers for both household and individuals in all countries In the second part, the use of Small Area Estimation methods was proved to be useful for obtaining reliable estimates of some selected vulnerable groups indicators based on the EU-LFS 2018 data.

This book includes a wide selection of the papers presented at the 48th Scientific Meeting of the Italian Statistical Society (SIS2016), held in Salerno on 8-10 June 2016. Covering a wide variety of topics ranging from modern data sources and survey design issues to measuring sustainable development, it provides a comprehensive overview of the current Italian scientific research in the fields of open data and big data in public administration and official statistics, survey sampling, ordinal and symbolic data, statistical models and methods for network data, time series forecasting, spatial analysis, environmental statistics, economic and financial data analysis, statistics in the education system, and sustainable development. Intended for researchers interested in theoretical and empirical issues, this volume provides interesting starting points for further research.

L'opera si propone di illustrare in modo sintetico e sistematico le tecniche di stima dei parametri di una popolazione finita che fanno uso delle informazioni ausiliarie disponibili, al fine di affrontare i problemi che emergono nelle indagini reali. In queste infatti ci si trova a dover fronteggiare gli effetti delle imperfezioni nelle basi di campionamento, della mancata osservazione di tutte le variabili da rilevare o di una parte di esse nelle unità designate a far parte del campione, degli errori di misura. Il volume si propone di rendere il lettore consciodi tali effetti e capace di farv