M-Quantile Small Area Models for Measuring Poverty at a Local Level

2014 ◽  
pp. 19-33 ◽  
Author(s):  
Monica Pratesi
Keyword(s):  
Small Area ◽  
Local Level

Book Reviews

2016 ◽  
Vol 54 (3) ◽  
pp. 946-948
Keyword(s):  
Social Exclusion ◽  
Small Area ◽  
Spatial Information ◽  
Local Level ◽  
Small Area Estimation ◽  
Material Deprivation ◽  
Absolute Poverty ◽  
Time Dimension ◽  
Area Estimation ◽  
New Methods

Enrica Chiappero-Martinetti of the University of Pavia reviews “Poverty and Social Exclusion: New Methods of Analysis,” by Gianni Betti and Achille Lemmi. The Econlit abstract of this book begins: “Fifteen papers explore new methods for estimating poverty at the local level and examine recent multidimensional methods of the dynamics of poverty. Papers discuss measuring multidimensional deprivation with dichotomized and ordinal variables; poverty and the dimensionality of welfare; income, material deprivation, and social exclusion in Israel; multidimensional and fuzzy measures of poverty and inequality at the national and regional level in Mozambique; assessing the time-dimension of poverty; intertemporal material deprivation; measuring chronic poverty; measuring intertemporal poverty—policy options for the poverty analyst; measuring levels and trends in absolute poverty in the world—open questions and possible alternatives; small area methodology in poverty mapping—an introductory overview; small area estimation of poverty using the ELL/PovMap method and its alternatives; estimation of poverty measures in small areas; the use of spatial information for the estimation of poverty indicators at the small area level; outlier robust semiparametric small area methods for poverty estimation; and poverty and social exclusion in 3D—multidimensional, longitudinal, and small area estimation.”

scholarly journals Small Area Procedures for Estimating Income and Poverty in Egypt

2019 ◽  
pp. 1-17
Author(s):  
Mai M. Kamal El Saied ◽  
Amal A. Talat ◽  
Mervat M. El Gohary
Keyword(s):  
Millennium Development Goals ◽  
Small Area ◽  
Local Level ◽  
North East ◽  
Poverty Measures ◽  
Small Area Statistics ◽  
Development Goals ◽  
The Mean ◽  
Poverty Indicators ◽  
North Sinai

In recent years, the demand for small area statistics has greatly increased worldwide. A recent application of small area estimation (SAE) techniques is in estimating local level poverty measures in Third World countries which is necessary to achieve the Millennium Development Goals. The aim of this research is to study SAE procedures for estimating the mean income and poverty indicators for the Egyptian provinces. For this goal the direct estimators of mean income and (FGT) poverty indicators for all the Egyptian provinces are presented. Also this study applies the empirical best/Bayes (EB) and the pseudo empirical best/Bayes (PEB) methods based on the unit level - nested error - model to estimate mean income and (FGT) poverty indicators for the Egyptian border provinces with (2012-2013) income, expenditure and consumption survey (IECS) data. The (MSEs) and coefficient of variations (C.Vs) are calculated for comparative purposes. Finally the conclusions are introduced. The results show that EB estimators for poverty incidence and poverty gap are smaller than PEB for all selected provinces. EB figures indicate that the largest poverty incidence and gap are for the selected municipality at the scope of the border south west of Egypt (New Valley). The PEB figures indicate that the largest poverty incidence and gap are for the selected municipality at the scope of the border north east of Egypt (North Sinai). As expected, estimated C.Vs for EB of poverty incidence and poverty gap estimators are noticeably larger than those of PEB estimators in all selected provinces.

Analysis of the spatial distribution of under-5 mortality rate in local areas of South Africa

2020 ◽  
Vol 36 (4) ◽  
pp. 1161-1173
Author(s):  
Yegnanew A. Shiferaw
Keyword(s):  
South Africa ◽  
Mortality Rate ◽  
Healthcare Service ◽  
Small Area ◽  
Local Level ◽  
Small Area Estimation ◽  
National Level ◽  
Population Census ◽  
Area Estimation ◽  
Local Municipality

Policymakers and healthcare service managers demand reliable, accurate and disaggregated information about child deaths at the sub-national level to plan and monitor healthcare service delivery and health outcomes. In support of this demand, this research aimed at providing reliable local municipality estimates of the under-5 mortality rate (U5MR) in South Africa. The paper used a small area estimation approach to improve the precision of local municipality estimates of U5MR by linking data from the 2016 Community Survey (CS) and the 2011 Population Census (PC). The diagnostic measures and validation of the reliability of the results showed that the local municipality estimates of U5MR produced by small area estimation are more efficient and precise than direct estimates of U5MR based only on the CS data. Further, accurate and cost-effective local municipality estimates of U5MR were produced without the need for more resources through combining the available data sources. This was achievable since the research did not require a separate survey for this purpose. The results can be used to monitor U5MR at the local level in South Africa since they link directly with the Sustainable Development Goals (SDGs).

Poverty at the Local Level: National and Small Area Poverty Estimates by Family Type for Australia in 2006

2010 ◽  
Vol 4 (3) ◽  
pp. 145-171 ◽  
Author(s):  
Riyana Miranti ◽  
Justine McNamara ◽  
Robert Tanton ◽  
Ann Harding
Keyword(s):  
Small Area ◽  
Local Level ◽  
Family Type

scholarly journals Small Area Estimation of Latent Economic Well-being

2019 ◽  
pp. 004912411982616 ◽  
Author(s):  
Angelo Moretti ◽  
Natalie Shlomo ◽  
Joseph W. Sakshaug
Keyword(s):  
Small Area ◽  
Local Level ◽  
Small Area Estimation ◽  
Social Indicators ◽  
Weighted Average ◽  
Real Data ◽  
Well Being ◽  
The European Union ◽  
Area Estimation ◽  
Prediction Approach

Small area estimation (SAE) plays a crucial role in the social sciences due to the growing need for reliable and accurate estimates for small domains. In the study of well-being, for example, policy makers need detailed information about the geographical distribution of a range of social indicators. We investigate data dimensionality reduction using factor analysis models and implement SAE on the factor scores under the empirical best linear unbiased prediction approach. We contrast this approach with the standard approach of providing a dashboard of indicators or a weighted average of indicators at the local level. We demonstrate the approach in a simulation study and a real data application based on the European Union Statistics for Income and Living Conditions for the municipalities of Tuscany.

scholarly journals 817Official small area health statistics using the stratified reweighting method

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Ian Rayson ◽  
Sean Buttsworth
Keyword(s):  
Health Survey ◽  
Small Area ◽  
Census Data ◽  
Local Level ◽  
Health Data ◽  
Local Health ◽  
Health Statistics ◽  
Data Set ◽  
Acceptable Quality ◽  
Generalised Linear Mixed Model

Abstract Background The Australian Bureau of Statistics (ABS) presently produces health data for small population groups using a Generalised Linear Mixed Model (GLMM) method. Although this method is highly effective at producing reliable local level health data, it takes several months to compile data once it’s collected. The Stratified Reweighting Method (SRM) was investigated as an innovative efficient method for producing local level health data. Methods The SRM harnesses information from both health survey and Census data. A cluster analysis of 12 Census data items creates 13 area groups with similar population demographics. A replicated survey data set is then created where each small area is bolstered by the other small areas within its area group. The survey weights from this dataset are adjusted to match Census data of each small area across several demographic variables. A final survey weight adjustment ensures consistency of the small area predictions with national survey estimates. Results Health statistics were produced for over 20 health outcomes in the latest ABS National Health Survey; and the ABS Survey of Disability, Ageing and Carers. It was found that, compared to the GLMM method: the models had lower, but still acceptable quality; the errors of prevalence estimates were similar magnitude; and the data compilation time was reduced to within two weeks. Conclusions The SRM is an efficient approach for producing acceptable quality official local health statistics. Key messages The SRM is an innovative and efficient weight-based method using health survey and population Census data to produce official local health statistics.

Rocking Beam Microarea Diffraction Applied to Metallic thin Films

1976 ◽  
Vol 34 ◽  
pp. 396-397
Author(s):  
R. H. Geiss
Keyword(s):  
Thin Films ◽  
Electron Diffraction ◽  
Small Area ◽  
Objective Lens ◽  
Electron Optics ◽  
Metallic Thin Films ◽  
Transmission Electron Diffraction ◽  
Transmission Electron ◽  
Sample Height ◽  
Scanning Transmission

The theory and practical limitations of micro area scanning transmission electron diffraction (MASTED) will be presented. It has been demonstrated that MASTED patterns of metallic thin films from areas as small as 30 Åin diameter may be obtained with the standard STEM unit available for the Philips 301 TEM. The key to the successful application of MASTED to very small area diffraction is the proper use of the electron optics of the STEM unit. First the objective lens current must be adjusted such that the image of the C2 aperture is quasi-stationary under the action of the rocking beam (obtained with 40-80-160 SEM settings of the P301). Second, the sample must be elevated to coincide with the C2 aperture image and its image also be quasi-stationary. This sample height adjustment must be entirely mechanical after the objective lens current has been fixed in the first step.

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