scholarly journals Geo-additive Models in Small Area Estimation of Poverty

2019 ◽  
Vol 2 (1) ◽  
pp. 59-67
Author(s):  
Novi Hidayat Pusponegoro ◽  
Anik Djuraidah ◽  
Anwar Fitrianto ◽  
I Made Sumertajaya
Keyword(s):  
Spatial Data ◽  
Small Area ◽  
Spatial Information ◽  
Small Area Estimation ◽  
Additive Model ◽  
Additive Models ◽  
Smoothing Function ◽  
Response Distribution ◽  
Area Estimation ◽  
Best Fit

Spatial data contains of observation and region information, it can describe spatial patterns such as disease distribution, reproductive outcome and poverty. The main flaw in direct estimation especially in poverty research is the sample adequacy fulfilment otherwise it will produce large estimate parameter variant. The Small Area Estimation (SAE) developed to handle that flaw. Since, the small area estimation techniques require “borrow strength” across the neighbor areas thus SAE was developed by integrating spatial information into the model, named as Spatial SAE. SAE and spatial SAE model require the fulfilment of covariate linearity assumption as well as the normality of the response distribution that is sometimes violated, and the geo-additive model offers to handle that violation using the smoothing function. Therefore, the purpose of this paper is to compare the SAE, Spatial SAE and Geo-additive model in order to estimate at sub-district level mean of per capita income of each area using the poverty survey data in Bangka Belitung province at 2017 by Polytechnic of Statistics STIS. The findings of the paper are the Geo-additive is the best fit model based on AIC, and spatial information don't influence the estimation in SAE and spatial SAE model since they have the similar estimation performance.

scholarly journals Simulation Study of Robust Geographically Weighted Empirical Best Linear Unbiased Predictor on Small Area Estimation

2021 ◽  
Vol 5 (1) ◽  
pp. 50-60
Author(s):  
Naima Rakhsyanda ◽  
Kusman Sadik ◽  
Indahwati Indahwati
Keyword(s):  
Small Area ◽  
Spatial Information ◽  
Mean Squared Error ◽  
Small Area Estimation ◽  
Small Sample ◽  
Population Parameter ◽  
Spatial Effects ◽  
Best Linear Unbiased Predictor ◽  
Area Estimation ◽  
Best Linear Unbiased

Small area estimation can be used to predict the population parameter with small sample sizes. For some cases, the population units that are close spatially may be more related than units that are further apart. The use of spatial information like geographic coordinates are studied in this research. Outlier contaminations can affect small area estimations. This study was conducted using simulation methods on generated data with six scenarios. The scenarios are the combination of spatial effects (spatial stationary and spatial non-stationary) with outlier contamination (no outlier, symmetric outliers, and non-symmetric outliers). The purpose of this study was to compare the geographically weighted empirical best linear unbiased predictor (GWEBLUP) and robust GWEBLUP (RGWEBLUP) with direct estimator, EBLUP, and REBLUP using simulation data. The performance of the predictors is evaluated using relative root mean squared error (RRMSE). The simulation results showed that geographically weighted predictors have the smallest RRMSE values for scenarios with spatial non-stationary, therefore offer a better prediction. For scenarios with outliers, robust predictors with smaller RRMSE values offer more efficiency than non-robust predictors.

Expenditure Per Capita Model with Spatial Small Area Estimation

2021 ◽  
Vol 1 (2) ◽  
pp. 38-47
Author(s):  
Ahmad Risal
Keyword(s):  
Small Area ◽  
Spatial Information ◽  
Small Area Estimation ◽  
Random Effect ◽  
Development Program ◽  
Data Availability ◽  
Estimation Model ◽  
Policy Makers ◽  
Area Estimation ◽  
Direct Estimation

Indonesia is one of many countries around the world that attempt to suffer from high poverty rates. Since, poverty information in a certain area is a point of interest to researchers and policy makers. One problem faced is for the development program to be carried out more effectively and efficiently, it is necessary to have data availability up to the micro-scale. The technique used to reach the goal is Small Area Estimation (SAE). Fay-herriot (FH) model is one method on Small Area Estimation. Since, the SAE techniques require “borrow strength” across neighbor areas so thus Fay-Herriot model approach was developed by integrating spatial information into the model. This method known as Spatial Fay-Herriot Model (SFH) or Spatial Empirical Best Linear Unbiased Prediction (SEBLUP). This study aims to compare MSE of direct estimation, FH, and SFH Model to see which method gives the best result in estimating expenditure. The MSE value of the estimated SFH is smaller than direct estimation and FH, but it does not significant. It means adding spatial information in the small area estimation model does not give a better prediction than the simple small area estimation which is takes account the area as a specific random effect.

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.”

Sae: A Stata Package For Unit Level Small Area Estimation

2018 ◽  
Author(s):  
Minh Cong Nguyen ◽  
Paul Corral ◽  
Joao Pedro Azevedo ◽  
Qinghua Zhao
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Area Estimation ◽  
Unit Level

scholarly journals Small Area Estimation of Smoke-free Workplace Polices and Home Rules in U.S. Counties

2021 ◽  
Author(s):  
Benmei Liu ◽  
Isaac Dompreh ◽  
Anne M Hartman
Keyword(s):  
Tobacco Control ◽  
Small Area ◽  
Small Sample Size ◽  
Health Hazard ◽  
Small Area Estimation ◽  
Small Sample ◽  
County Level ◽  
Area Estimation ◽  
Model Based ◽  
Workplace Policies

Abstract Background The workplace and home are sources of exposure to secondhand smoke (SHS), a serious health hazard for nonsmoking adults and children. Smoke-free workplace policies and home rules protect nonsmoking individuals from SHS and help individuals who smoke to quit smoking. However, estimated population coverages of smoke-free workplace policies and home rules are not typically available at small geographic levels such as counties. Model-based small area estimation techniques are needed to produce such estimates. Methods Self-reported smoke-free workplace policies and home rules data came from the 2014-2015 Tobacco Use Supplement to the Current Population Survey. County-level design-based estimates of the two measures were computed and linked to county-level relevant covariates obtained from external sources. Hierarchical Bayesian models were then built and implemented through Markov Chain Monte Carlo methods. Results Model-based estimates of smoke-free workplace policies and home rules were produced for 3,134 (out of 3,143) U.S. counties. In 2014-2015, nearly 80% of U.S. adult workers were covered by smoke-free workplace policies, and more than 85% of U.S. adults were covered by smoke-free home rules. We found large variations within and between states in the coverage of smoke-free workplace policies and home rules. Conclusions The small-area modeling approach efficiently reduced the variability that was attributable to small sample size in the direct estimates for counties with data and predicted estimates for counties without data by borrowing strength from covariates and other counties with similar profiles. The county-level modeled estimates can serve as a useful resource for tobacco control research and intervention. Implications Detailed county- and state-level estimates of smoke-free workplace policies and home rules can help identify coverage disparities and differential impact of smoke-free legislation and related social norms. Moreover, this estimation framework can be useful for modeling different tobacco control variables and applied elsewhere, e.g., to other behavioral, policy, or health related topics.

scholarly journals [Small Area Estimation: An Appraisal]: Rejoinder

1994 ◽  
Vol 9 (1) ◽  
pp. 90-93 ◽  
Author(s):  
M. Ghosh ◽  
J. N. K. Rao
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Area Estimation
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