1,721,166 research outputs found
Analysis of Poverty Data by Small Area Estimation
No full textPoverty 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
Spatial Disaggregation and Small-Area Estimation Methods for Agricultural Surveys: Solutions and Perspectives
No full textThis Technical Report on Spatial Disaggregation and Small-Area Estimation Methods for Agricultural Surveys: Solutions and Perspectives was prepared within the framework of the Global Strategy to Improve Agricultural and Rural Statistics. The Global Strategy is an initiative endorsed in 2010 by the United Nations Statistical Commission, to provide a framework and a blueprint to meet current and emerging data requirements and the needs of policymakers and other data users. Its goal is to contribute to greater food security, reduced food price volatility, higher incomes and greater well-being for rural populations, through evidence-based policies. The Global Strategy is centred upon 3 pillars: (1) establishing a minimum set of core data (2) integrating agriculture into National Statistical Systems (NSSs) and (3) fostering the sustainability of the statistical system through governance and statistical capacity building.
The Action Plan to Implement the Global Strategy includes an important research programme, to address methodological issues for improving the quality of agricultural and rural statistics. The outcome of the research programme is to produce scientifically sound and cost-effective methods that will be used as inputs to prepare practical guidelines for use by country statisticians, training institutions, consultants, etc.
To enable countries and partners to benefit at an early stage from research activity results that are already available, it has been decided to establish a Technical Reports Series, to widely disseminate available technical reports and advanced draft guidelines and handbooks. This will also provide an opportunity for countries to give feedback on the papers.
Technical reports and draft guidelines and handbooks published in this Technical Report Series have been prepared by senior consultants and experts and reviewed by the Scientific Advisory Committee (SAC)1 of the Global Strategy, the Research Coordinator at the Global Office and other independent senior experts. For some of the research topics, field tests will be organized before final results are included in guidelines and handbooks.
The aim of this report on Spatial Disaggregation and Small-Area Estimation Methods for Agricultural Surveys: Solutions and Perspectives is to enhance disaggregation methods for adaptation to various agricultural situations and datasets.
Part 1 reviews the literature on this subject under two topics: i) mapping techniques and ii) small-area estimators.
With regard to mapping techniques, the main areal interpolation methods based on regression techniques are presented. SAE methods are classified as: i) model-assisted methods – for example the generalized regression estimator; and ii) model-based methods, which are considered as unit-level and area-level specifications – the empirical best linear unbiased predictors estimator, M-quantile estimator and Fay and Herriot estimator – with spatial specifications where available. Assumptions are explained and the information needed for each method is given, with illustrations from applications to rural and agricultural statistics or to socio-economic statistics.
Part 2 examines the reliability of the methods in non-standard situations that commonly arise in agricultural surveys. The main topics are sensitivity to spatial model specification, the modifiable area unit problem, robustness of predictors, complexity of sample design, missing data in spatial datasets and excess of zeros in survey data. This part analyses the methods presented in Part 1, presents the main contributions to the topics and proposes methodological and operational solutions.
Part 3 summarizes the issues in the review of mapping techniques and small-area estimators. It also offers remarks and recommendations based on the analysis of the reliability of the methods and draft guidelines for applying them in field tests
Estimation of a population size by capture recapture heterogeneity models: an application to a closed population of firms with two dependent captures
No full textRegressione M-quantilica nella stima per piccole aree. Il caso della produzione di olive in Toscana
No full textTwo-step centre sampling for estimating the size, total and mean of elusive population
No full textThe estimation of the size of an elusive population is a frequently addressed problem in many fields of applications. The paper proposes a two step sampling strategy for the estimation of the population size, under the assumption that each unit of the population is present at least in one centre of aggregation. In the first step a sample of centres is selected and in the second step, from the selected centres, a sample of ultimate units is observed. The design extends the traditional network sampling introducing an additional step of selection. The properties of the Horvitz-Thompson type estimator are evaluated in a design-based approach: the estimator is admissible and consistent; the design is measurable. The approach is also used to estimate other descriptive parameters (the total and the mean of a study variable) for the same population. The expressions of the variance of all the proposed estimators and of their unbiased sample estimators are also proposed. The strategy is applied to a simulated population
Small area estimation in the presence of correlated random area effects
No full textThis article is a contribution to the discussion on the utility of spatial models in the context of Small Area Estimation (SAE) (see Cressie 1991; Pfeffermann 2002; Saei and Chambers 2003, 2005; Singh et al. 2005; Pratesi and Salvati 2008). The attention is on the Fay–Herriot model and its Mean Squared Error (MSE) when a common autocorrelation parameter among small areas is included. Firstly, we discuss the extent to which the spatial effects in data used for SAE motivate the introduction of an autocorrelation parameter in the Fay–Herriot model. Secondly, the performance of MSE estimators is discussed through a simulation study where the joint effect of the area level sampling variance and of the parameter estimation is shown. The importance of the strength of spatial autocorrelation among small areas is confirmed. The results are tenable for different sampling variance patterns. A case study with spatial dependence in the data is presented and estimates at small area level are provided
Weighted estimation in multilevel ordinal and binary models in the presence of informative sampling designs
No full textMultilevel models are often fitted to survey data gathered with a complex multistage sampling design. However, if such a design is informative, in the sense that the inclusion probabilities depend on the response variable even after conditioning on the covariates, the standard maximum likelihood estimators are biased. In this paper, following the Pseudo Maximum Likelihood approach of Skinner (1989), we propose a probability-weighted estimation procedure for multilevel ordinal and binary models which eliminates the bias generated by the informativeness of the design. The reciprocals of the inclusion probabilities at each sampling stage are used toweight the log-likelihood function and the weighted estimators obtained in this way are tested by means of a simulation study for the simple case of a binary random intercept model with and without covariates. The variance estimators are obtained by a boostrap procedure. The maximization of the weighted log-likelihood of the model is domne by the NLMIXED procedure of the SA, which is based on adaptive Gaussian quadrature. Also the bootstrap estimation of variances is implemented in the SAS environment
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