Poverty and living conditions are always at the forefront of analyses and discussions carried out by international and national organizations, governments and researchers from all over the world. All of them agree that the intervention policies to fight against poverty and to improve the quality of life should be specifically designed and implemented at a local level, because the phenomena are heterogeneous and have multiple and different characteristics in the different territorial areas. Obviously, local governments play a fundamental role in implementing actions, but, to do that, they need statistical information (data) to understand the situation and to be able to evaluate the impact of their actions. On the other hand, the stakeholders and citizens are interested in and able to judge the economic situation and the quality of life at a local level and are interested in better understanding the effect of policies on their own territory. However, usually, the data on income, poverty and quality of life are not available at a local level. In fact, the main sources of statistical data in these fields are from sample surveys that cannot support reliable estimation at a local level because their sample sizes are too small. The problem could be overcome by increasing the sample sizes, but in many practical situations cost–benefit analysis excludes it as a time-consuming and unaffordable solution. The key solution in order to be able to comply with the information need for measuring poverty at a local level is the use of Small Area Estimation (SAE) methods that researchers and National Statistical Offices of various countries are developing and implementing. This is confirmed by the large amount of literature on these local estimates resulting from many projects, conferences and books in the last decade. This book provides a very comprehensive and detailed source of information to construct such a key solution; it explains clearly the use of SAE methods efficiently adapted to the distinctive features (identification of relative poverty indicators, classification of statistical units, specific sample design of the surveys, characteristics of panel surveys, etc.) of poverty data coming from surveys and administrative archives. All of these complications add up to make the use of SAE methods a difficult and challenging problem that this book ably and comprehensively tackles. The book, after having discussed the definition(s) of the poverty indicators and data collection and data integration methods to obtain reliable estimations of them, describes and reviews the advanced methods and techniques recently developed and applied to SAE of poverty, addressing the distinctive features mentioned before (impact of sampling designs, etc.). Then, the book presents the SAE models as applied to poverty. In the extensive literature, there are many methods developed and they are often specified to solve the particular estimation problems for the case under study. However, their presentation in the book has been able to single out and address the main general issues in the estimation of poverty at a local level, such as the erroneous specification of the models and the robustness of the estimations, the use of spatio-temporal models, the estimation of distribution function of income and inequalities, and so on. Each chapter of the book describes insights, introduces methodology, and outlines the cutting-edge necessary for effective estimation and analysis of poverty indicators at a local level. Very interesting advanced new methodologies and new challenges to be faced are presented. All of this makes this book very timely. One of the particular attractive features of this book is that it is about both theoretical and practical methods and analysis. It does not simply discuss the methodological tools that can be applied in an idealized setting, but also discusses the issues which all applied statisticians and the National Statistical Offices have to face to produce an estimation of poverty indicators at a local level. The practical aspects of the estimation methods are discussed in many of the chapters and, in a specific way, the last three chapters are devoted to the presentation of the procedures used in the EU, USA and Chile, discussing also the quality of the obtained results. Moreover, most of the chapter authors have supported the methods concerning data analysis and models by presenting specific scripts that are also described and written in SAS or R software in an Appendix available on the book's website. Put together, the attractive features of this book make it a genuinely valuable and very useful book for all the researchers from academia and statistical offices, concerned with the measuring of poverty indicators at a local level and with the survey methodology. Surely this book will stimulate further important research in the field.
Analysis of Poverty Data by Small Area Estimation
PRATESI, MONICA
2016-01-01
Abstract
Poverty and living conditions are always at the forefront of analyses and discussions carried out by international and national organizations, governments and researchers from all over the world. All of them agree that the intervention policies to fight against poverty and to improve the quality of life should be specifically designed and implemented at a local level, because the phenomena are heterogeneous and have multiple and different characteristics in the different territorial areas. Obviously, local governments play a fundamental role in implementing actions, but, to do that, they need statistical information (data) to understand the situation and to be able to evaluate the impact of their actions. On the other hand, the stakeholders and citizens are interested in and able to judge the economic situation and the quality of life at a local level and are interested in better understanding the effect of policies on their own territory. However, usually, the data on income, poverty and quality of life are not available at a local level. In fact, the main sources of statistical data in these fields are from sample surveys that cannot support reliable estimation at a local level because their sample sizes are too small. The problem could be overcome by increasing the sample sizes, but in many practical situations cost–benefit analysis excludes it as a time-consuming and unaffordable solution. The key solution in order to be able to comply with the information need for measuring poverty at a local level is the use of Small Area Estimation (SAE) methods that researchers and National Statistical Offices of various countries are developing and implementing. This is confirmed by the large amount of literature on these local estimates resulting from many projects, conferences and books in the last decade. This book provides a very comprehensive and detailed source of information to construct such a key solution; it explains clearly the use of SAE methods efficiently adapted to the distinctive features (identification of relative poverty indicators, classification of statistical units, specific sample design of the surveys, characteristics of panel surveys, etc.) of poverty data coming from surveys and administrative archives. All of these complications add up to make the use of SAE methods a difficult and challenging problem that this book ably and comprehensively tackles. The book, after having discussed the definition(s) of the poverty indicators and data collection and data integration methods to obtain reliable estimations of them, describes and reviews the advanced methods and techniques recently developed and applied to SAE of poverty, addressing the distinctive features mentioned before (impact of sampling designs, etc.). Then, the book presents the SAE models as applied to poverty. In the extensive literature, there are many methods developed and they are often specified to solve the particular estimation problems for the case under study. However, their presentation in the book has been able to single out and address the main general issues in the estimation of poverty at a local level, such as the erroneous specification of the models and the robustness of the estimations, the use of spatio-temporal models, the estimation of distribution function of income and inequalities, and so on. Each chapter of the book describes insights, introduces methodology, and outlines the cutting-edge necessary for effective estimation and analysis of poverty indicators at a local level. Very interesting advanced new methodologies and new challenges to be faced are presented. All of this makes this book very timely. One of the particular attractive features of this book is that it is about both theoretical and practical methods and analysis. It does not simply discuss the methodological tools that can be applied in an idealized setting, but also discusses the issues which all applied statisticians and the National Statistical Offices have to face to produce an estimation of poverty indicators at a local level. The practical aspects of the estimation methods are discussed in many of the chapters and, in a specific way, the last three chapters are devoted to the presentation of the procedures used in the EU, USA and Chile, discussing also the quality of the obtained results. Moreover, most of the chapter authors have supported the methods concerning data analysis and models by presenting specific scripts that are also described and written in SAS or R software in an Appendix available on the book's website. Put together, the attractive features of this book make it a genuinely valuable and very useful book for all the researchers from academia and statistical offices, concerned with the measuring of poverty indicators at a local level and with the survey methodology. Surely this book will stimulate further important research in the field.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
