scholarly journals Spatial Variation of Caste/Ethnic Poverty in Mountain Districts of Nepal: An Approximation through Small Area Estimation Technique

2020 ◽  
Vol 2 (2) ◽  
pp. 158-193
Author(s):  
Bhim Raj Suwal
Keyword(s):  
Spatial Variation ◽  
Ethnic Groups ◽  
Small Area ◽  
Small Area Estimation ◽  
Group Living ◽  
Living Standard ◽  
Population Census ◽  
Area Estimation ◽  
Caste Groups ◽  
East West

Nepal is a multi-ethnic and multi-cultural society where economic condition of various caste/ethnic groups substantially varies. However, adequate attention has not been paid to estimate the level of monetary poverty of various caste/ethnicities and assess how people of the same caste/ethnicity living in different locations (districts) vary in terms of monetary poverty. Focusing only on mountain belt, which is one of the economically poorest areas of the country, this study aims to estimate incidence of monetary poverty for major caste/ethnicities living in the mountain districts with the help of small area estimation (SAE) technique and assess spatial variation in the incidence of monetary poverty of the same caste/ethnic group living along the east-west continuum of the mountain districts. Required data for SAE is derived from Nepal Living Standard Survey (2010/11) and 2011 population census of Nepal. The study shows that, compared to other districts, three eastern mountain districts (Province 1) (Taplejung, Sankhuwasabha, and Solukhumbu) have lowest incidence of poverty for all the caste/ethnicities with much lower incidence in three socioeconomically advanced hill caste groups. Incidence of poverty tends to increase sharply along the east to west continuum of mountain districts with exceptionally high poverty rates for Dalits in the far western mountain districts (Province 7). Some culturally similar caste/ethnic groups follow almost similar pattern of increase in the incidence of poverty along the east-west continuum of the mountain districts and form districts of clusters in each region with similar level of poverty.

scholarly journals Small Area Estimation of Food Insecurity in the Eastern Indo-Gangetic Plain

2021 ◽  
Author(s):  
Hukum Chandra ◽  
Saurav Guha ◽  
Meghana Desai ◽  
Saumyadipta Pyne
Keyword(s):  
Food Insecurity ◽  
Small Area ◽  
Small Area Estimation ◽  
Small Sample ◽  
Sample Survey ◽  
Family Welfare ◽  
Population Census ◽  
Gangetic Plain ◽  
Area Estimation ◽  
Indo Gangetic Plain

Achieving food security for all citizens is an important policy issue in India. While the existing data based on socio-economic surveys provide accurate estimates of food insecurity indicators at state and national level, due to small sample sizes, the surveys cannot be used directly to produce reliable estimates at the district or lower administrative levels. The availability of reliable and representative disaggregated measures of food insecurity is necessary for effective policy planning and monitoring, as food insecurity is often distributed unevenly within relatively small areas. This article explores a small area estimation (SAE) approach to derive reliable and representative estimates of food insecurity prevalence (FIP), gap (FIG), and severity (FIS) among people in different districts of the rural areas of the Eastern Indo-Gangetic Plain (EIGP) region by linking the latest round of available data from the Household Consumer Expenditure Survey collected by the National Sample Survey Office of India as well as the latest available Indian Population Census data. District-specific food insecurity indicators such as FIP, FIG, and FIS were estimated based on a recommended threshold of per capita caloric intake of 2400 kilocalories per day, as defined by the Ministry of Health and Family Welfare, Government of India. Spatial maps showing district-level inequality in the distribution of the indicators of food insecurity among the population in the EIGP region are also produced. Our disaggregated estimates can provide district-specific focused insights into food insecurity to policy analysts and decision-makers, and could thereby prove to be useful and relevant to the U.N. Sustainable Development Goal Indicator 2.1.2.

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).

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

scholarly journals Enhancing the precision of broad-scale forestland removals estimates with small area estimation techniques

2021 ◽  
Author(s):  
John W Coulston ◽  
P Corey Green ◽  
Philip J Radtke ◽  
Stephen P Prisley ◽  
Evan B Brooks ◽  
...  
Keyword(s):  
Small Area ◽  
Small Area Estimation ◽  
Species Group ◽  
Tree Cover ◽  
Estimation Techniques ◽  
Spatial And Temporal Scales ◽  
Area Estimation ◽  
Forest Inventories ◽  
Southeastern Us ◽  
Increasing Demand

Abstract National Forest Inventories (NFI) are designed to produce unbiased estimates of forest parameters at a variety of scales. These parameters include means and totals of current forest area and volume, as well as components of change such as means and totals of growth and harvest removals. Over the last several decades, there has been a steadily increasing demand for estimates for smaller geographic areas and/or for finer temporal resolutions. However, the current sampling intensities of many NFI and the reliance on design-based estimators often leads to inadequate precision of estimates at these scales. This research focuses on improving the precision of forest removal estimates both in terms of spatial and temporal resolution through the use of small area estimation techniques (SAE). In this application, a Landsat-derived tree cover loss product and the information from mill surveys were used as auxiliary data for area-level SAE. Results from the southeastern US suggest improvements in precision can be realized when using NFI data to make estimates at relatively fine spatial and temporal scales. Specifically, the estimated precision of removal volume estimates by species group and size class was improved when SAE methods were employed over post-stratified, design-based estimates alone. The findings of this research have broad implications for NFI analysts or users interested in providing estimates with increased precision at finer scales than those generally supported by post-stratified estimators.

Multinomial-based small area estimation of labour force indicators

2013 ◽  
Vol 13 (2) ◽  
pp. 153-178 ◽  
Author(s):  
Esther López-Vizcaíno ◽  
María José Lombardía ◽  
Domingo Morales
Keyword(s):  
Small Area ◽  
Labour Force ◽  
Small Area Estimation ◽  
Area Estimation
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