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Analysis of Poverty Data by Small Area Estimation

Analysis of Poverty Data by Small Area Estimation
Author: Monica Pratesi
Publisher: John Wiley & Sons
Total Pages: 485
Release: 2016-02-23
Genre: Mathematics
ISBN: 1118815017

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A comprehensive guide to implementing SAE methods for poverty studies and poverty mapping There is an increasingly urgent demand for poverty and living conditions data, in relation to local areas and/or subpopulations. Policy makers and stakeholders need indicators and maps of poverty and living conditions in order to formulate and implement policies, (re)distribute resources, and measure the effect of local policy actions. Small Area Estimation (SAE) plays a crucial role in producing statistically sound estimates for poverty mapping. This book offers a comprehensive source of information regarding the use of SAE methods adapted to these distinctive features of poverty data derived from surveys and administrative archives. The book covers the definition of poverty indicators, data collection and integration methods, the impact of sampling design, weighting and variance estimation, the issue of SAE modelling and robustness, the spatio-temporal modelling of poverty, and the SAE of the distribution function of income and inequalities. Examples of data analyses and applications are provided, and the book is supported by a website describing scripts written in SAS or R software, which accompany the majority of the presented methods. Key features: Presents a comprehensive review of SAE methods for poverty mapping Demonstrates the applications of SAE methods using real-life case studies Offers guidance on the use of routines and choice of websites from which to download them Analysis of Poverty Data by Small Area Estimation offers an introduction to advanced techniques from both a practical and a methodological perspective, and will prove an invaluable resource for researchers actively engaged in organizing, managing and conducting studies on poverty.


Introduction to Small Area Estimation Techniques

Introduction to Small Area Estimation Techniques
Author: Asian Development Bank
Publisher: Asian Development Bank
Total Pages: 152
Release: 2020-05-01
Genre: Business & Economics
ISBN: 9292622234

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This guide to small area estimation aims to help users compile more reliable granular or disaggregated data in cost-effective ways. It explains small area estimation techniques with examples of how the easily accessible R analytical platform can be used to implement them, particularly to estimate indicators on poverty, employment, and health outcomes. The guide is intended for staff of national statistics offices and for other development practitioners. It aims to help them to develop and implement targeted socioeconomic policies to ensure that the vulnerable segments of societies are not left behind, and to monitor progress toward the Sustainable Development Goals.


Small-Area Estimates of School-Age Children in Poverty

Small-Area Estimates of School-Age Children in Poverty
Author: National Research Council
Publisher: National Academies Press
Total Pages: 271
Release: 2001-01-16
Genre: Social Science
ISBN: 0309171245

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The Panel on Estimates of Poverty for Small Geographic Areas was established by the Committee on National Statistics at the National Research Council in response to the Improving America's Schools Act of 1994. That act charged the U.S. Census Bureau to produce updated estimates of poor school-age children every two years for the nation's more than 3,000 counties and 14,000 school districts. The act also charged the panel with determining the appropriateness and reliability of the Bureau's estimates for use in the allocation of more than $7 billion of Title I funds each year for educationally disadvantaged children. The panel's charge was both a major one and one with immovable deadlines. The panel had to evaluate the Census Bureau's work on a very tight schedule in order to meet legal requirements for allocation of Title I funds. As it turned out, the panel produced three interim reports: the first one evaluated county-level estimates of poor school-age children in 1993, the second one assessed a revised set of 1993 county estimates; and the third one covered both county- and school district-level estimates of poor school-age children in 1995. This volume combines and updates these three reports into a single reference volume.


Administrative Records for Survey Methodology

Administrative Records for Survey Methodology
Author: Asaph Young Chun
Publisher: John Wiley & Sons
Total Pages: 384
Release: 2021-04-06
Genre: Mathematics
ISBN: 1119272041

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ADMINISTRATIVE RECORDS FOR SURVEY METHODOLOGY Addresses the international use of administrative records for large-scale surveys, censuses, and other statistical purposes Administrative Records for Survey Methodology is a comprehensive guide to improving the quality, cost-efficiency, and interpretability of surveys and censuses using administrative data research. Contributions from a team of internationally-recognized experts provide practical approaches for integrating administrative data in statistical surveys, and discuss the methodological issues—including concerns of privacy, confidentiality, and legality—involved in collecting and analyzing administrative records. Numerous real-world examples highlight technological and statistical innovations, helping readers gain a better understanding of both fundamental methods and advanced techniques for controlling data quality reducing total survey error. Divided into four sections, the first describes the basics of administrative records research and addresses disclosure limitation and confidentiality protection in linked data. Section two focuses on data quality and linking methodology, covering topics such as quality evaluation, measuring and controlling for non-consent bias, and cleaning and using administrative lists. The third section examines the use of administrative records in surveys and includes case studies of the Swedish register-based census and the administrative records applications used for the US 2020 Census. The book’s final section discusses combining administrative and survey data to improve income measurement, enhancing health surveys with data linkage, and other uses of administrative data in evidence-based policymaking. This state-of-the-art resource: Discusses important administrative data issues and suggests how administrative data can be integrated with more traditional surveys Describes practical uses of administrative records for evidence-driven decisions in both public and private sectors Emphasizes using interdisciplinary methodology and linking administrative records with other data sources Explores techniques to leverage administrative data to improve the survey frame, reduce nonresponse follow-up, assess coverage error, measure linkage non-consent bias, and perform small area estimation. Administrative Records for Survey Methodology is an indispensable reference and guide for statistical researchers and methodologists in academia, industry, and government, particularly census bureaus and national statistical offices, and an ideal supplemental text for undergraduate and graduate courses in data science, survey methodology, data collection, and data analysis methods.


Using Census and Survey Data to Estimate Poverty and Inequality for Small Areas

Using Census and Survey Data to Estimate Poverty and Inequality for Small Areas
Author: Alessandro Tarozzi
Publisher:
Total Pages: 0
Release: 2008
Genre:
ISBN:

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Household expenditure survey data cannot yield precise estimates of poverty or inequality for small areas for which no or few observations are available. Census data are more plentiful, but typically exclude income and expenditure data. Recent years have seen a widespread use of small-area "poverty maps" based on census data enriched by relationships estimated from household surveys that predict variables not covered by the census. These methods are used to estimate putatively precise estimates of poverty and inequality for areas as small as 20,000 households. In this paper we argue that to usefully match survey and census data in this way requires a degree of spatial homogeneity for which the method provides no basis, and which is unlikely to be satisfied in practice. The relationships that are used to bridge the surveys and censuses are not structural but are projections of missing variables on a subset of those variables that happen to be common to the survey and the census supplemented by local census means appended to the survey. As such, the coefficients of the projections will generally vary from area to area in response to variables that are not included in the analysis. Estimates of poverty and inequality that assume homogeneity will generally be inconsistent in the presence of spatial heterogeneity, and error variances calculated on the assumption of homogeneity will underestimate mean squared errors and overestimate the coverage of calculated confidence intervals. We use data from the 2000 census of Mexico to construct synthetic "household surveys" and to simulate the poverty mapping process using a robust method of estimation; our simulations show that while the poverty maps contain useful information, their nominal confidence intervals give a misleading idea of precision.


Spatial Microsimulation: A Reference Guide for Users

Spatial Microsimulation: A Reference Guide for Users
Author: Robert Tanton
Publisher: Springer Science & Business Media
Total Pages: 272
Release: 2012-11-13
Genre: Social Science
ISBN: 9400746237

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This book is a practical guide on how to design, create and validate a spatial microsimulation model. These models are becoming more popular as academics and policy makers recognise the value of place in research and policy making. Recent spatial microsimulation models have been used to analyse health and social disadvantage for small areas; and to look at the effect of policy change for small areas. This provides a powerful analysis tool for researchers and policy makers. This book covers preparing the data for spatial microsimulation; a number of methods for both static and dynamic spatial microsimulation models; validation of the models to ensure the outputs are reasonable; and the future of spatial microsimulation. The book will be an essential handbook for any researcher or policy maker looking to design and create a spatial microsimulation model. This book will also be useful to those policy makers who are commissioning a spatial microsimulation model, or looking to commission work using a spatial microsimulation model, as it provides information on the different methods in a non-technical way.


Small-Area Income and Poverty Estimates

Small-Area Income and Poverty Estimates
Author: National Research Council
Publisher: National Academies Press
Total Pages: 221
Release: 2000-09-25
Genre: Political Science
ISBN: 0309071461

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Recent trends in federal policies for social and economic programs have increased the demand for timely, accurate estimates of income and poverty for states, counties, and even smaller areas. Every year more than $130 billion in federal funds is allocated to states and localities through formulas that use such estimates. These funds support a wide range of programs that include child care, community development, education, job training, nutrition, and public health. A new program of the U.S. Census Bureau is now providing more timely estimates for these programs than those from the decennial census, which have been used for many years. These new estimates are being used to allocate more than $7 billion annually to school districts, through the Title I program that supports educationally disadvantaged children. But are these estimates as accurate as possible given the available data? How can the statistical models and data that are used to develop the estimates be improved? What should policy makers consider in selecting particular estimates? This new book from the National Research Council provides guidance for improving the Census Bureau's program and for policy makers who use such estimates for allocating funds.


Small Area Estimation

Small Area Estimation
Author: J. N. K. Rao
Publisher: John Wiley & Sons
Total Pages: 340
Release: 2005-02-25
Genre: Mathematics
ISBN: 0471431621

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An accessible introduction to indirect estimation methods, both traditional and model-based. Readers will also find the latest methods for measuring the variability of the estimates as well as the techniques for model validation. Uses a basic area-level linear model to illustrate the methods Presents the various extensions including binary response data through generalized linear models and time series data through linear models that combine cross-sectional and time series features Provides recent applications of SAE including several in U.S. Federal programs Offers a comprehensive discussion of the design issues that impact SAE


Small-Area Estimates of School-Age Children in Poverty

Small-Area Estimates of School-Age Children in Poverty
Author: National Research Council
Publisher: National Academies Press
Total Pages: 100
Release: 1997-07-27
Genre: Social Science
ISBN: 9780309058322

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The Panel on Estimates of Poverty for Small Geographic Areas was established by the Committee on National Statistics at the National Research Council in response to the Improving America's Schools Act of 1994. That act charged the U.S. Census Bureau to produce updated estimates of poor school-age children every two years for the nation's more than 3,000 counties and 14,000 school districts. The act also charged the panel with determining the appropriateness and reliability of the Bureau's estimates for use in the allocation of more than $7 billion of Title I funds each year for educationally disadvantaged children. The panel's charge was both a major one and one with immovable deadlines. The panel had to evaluate the Census Bureau's work on a very tight schedule in order to meet legal requirements for allocation of Title I funds. As it turned out, the panel produced three interim reports: the first one evaluated county-level estimates of poor school-age children in 1993, the second one assessed a revised set of 1993 county estimates; and the third one covered both county- and school district-level estimates of poor school-age children in 1995. This volume combines and updates these three reports into a single reference volume.