Measuring and Mapping Disaggregate Level Disparities in Food Consumption and Nutritional Status via Multivariate Small Area Modelling

Guha, Saurav; Chandra, Hukum

doi:10.1007/s11205-020-02573-8

Cited by 12 publications

(18 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the study, 41 auxiliary variables were taken into account. However, the suitable variables are chosen using principal component analysis (PCA), which reduces the number of variables to a few key components with minor information loss [10].…”

Section: Auxiliary Variablesmentioning

confidence: 99%

“…There are few publications in the literature on small area estimates that use multivariate small area estimations under the FH model. Studies by [7][8][9][10] evaluated the precision of small area estimators produced from univariate models to those generated from multivariate models for each target variable.…”

Section: Introductionmentioning

confidence: 99%

“…MSE estimators have been studied using variance component estimators under the MFH model [11,12]. The performance of small area estimates under the MFH model was assessed by the coefficient of variation (CV %) and root MSE [10,13].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multivariate small area estimation of undernutrition for children under five using official statistics

Yilema

Shiferaw

Zewotir

et al. 2022

SJI

View full text Add to dashboard Cite

Surveys are mainly used to obtain reliable estimates for planned domains at national and regional levels. However, the unplanned domains (lower administrative layers) with small sample sizes must be estimated. The direct survey estimates of the non-planned domains with small sample sizes lead to large sampling variability. Thus, small area estimations dealt with managing this variability by borrowing the strength of neighboring areas. The target variables of the study were obtained from the 2016 Ethiopian demographic and health survey (EDHS) and the auxiliary variables taken from the 2007 population and housing census data. Multivariate Fay Herriot (MFH) model was used by incorporating the correlations among the target variables. The model diagnostic measures assured the normality assumption, and the consistency of multivariate small area estimates are valid. Multivariate EBLUPs of the target variables produced the lowest percent coefficient of variation (CV) and root mean square error (MSE). Therefore, multivariate EBLUP has improved the direct survey estimates of undernutrition (stunting, wasting, and underweight) for small sample sizes (even zero sample sizes). It also provided better estimates compared to the univariate EBLUPs. Generally, multivariate EBLUPs of undernutrition produced the best reliable, efficient, and precise estimates for small sample sizes in all zones. Zones are essential domains for planning and monitoring purposes in the country, and therefore these results provide valuable estimates for policymakers, planners, and legislative organs of the government. One of the novelties of this paper is estimating the non-sampled zones, and therefore the policymakers will give equal attention similar to the sampled zones.

show abstract

Section: Auxiliary Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multivariate small area estimation of undernutrition for children under five using official statistics

Yilema

Shiferaw

Zewotir

et al. 2022

SJI

View full text Add to dashboard Cite

show abstract

“…The FH model has been extended in various works of literature. The multivariate FH models are investigated by multiple researchers [ 3 , 6 , 32 – 36 ]. The temporal FH model, which borrows strength from historical data, past time instants, and correlations, is studied by [ 27 , 37 ] to produce reliable area-level estimates.…”

Section: Methodsmentioning

confidence: 99%

Small Area Estimation of Undernutrition under Age Five Children Based on Spatial-Temporal Models

Yilema

Shiferaw

Zewotir

et al. 2022

The Scientific World Journal

View full text Add to dashboard Cite

The mean flow of direct survey estimates is mainly concerning the sample adequacy fulfillment unless it has been produced large variance estimates, and therefore, the small area estimations are developed to manage this flaw of the path. Small area estimation improved the direct survey estimates by borrowing strength from the census data and at the same time by using historical data from consecutive surveys. In this paper, we applied the spatiotemporal Fay–Herriot (STFH) model for producing fairly reliable disaggregate-level estimates of undernutrition indicators across all zones. The STFH model is an appropriately fitted model to the undernutrition data since it has the lowest information criteria (IC) value. The spatiotemporal estimates improved both the direct and spatial estimates of undernutrition under the FH model and have brought efficiency gain in the percent coefficient of variation (CV). These results may provide useful information to the government’s planners, policymakers, and legislative organs for effective policy formulation and budget allocation in all zones.

show abstract

“…The emphasis on disaggregate level SDG indicators by various national and international agencies has further lauded the inevitable need of such local level estimates. These local level areas or domains, better known as small areas or small domains are formed by cross-classification of several demographic and topographic variables that includes small topographic areas (e.g., districts) or small demographic groups (e.g., land category, social groups, religion, age-sex groups) or cross classifying both (Guha & Chandra, 2021a ). Besides, in the existing PLFS data of NSO, the small areas or districts may have very small or even zero sample sizes which may lead to large sampling error in case of direct estimation.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Small Area Modelling for Measuring Micro Level Earning Inequality: Evidence from Periodic Labour Force Survey of India

Guha

Chandra

2022

Soc Indic Res

Self Cite

View full text Add to dashboard Cite

The economy of India is growing continuously with its gross domestic product increasing rapidly than most of the developing countries. Nonetheless an increase in national gross domestic product is not revealing the earning parity at micro level in the country. The earning inequality in a country like India has adversely obstructed under privileged in accessing basic needs such as health and education. The Periodic labour force survey (PLFS) conducted by the National Statistical Office of India generates estimates on earning status at state and national level for both rural and urban sectors separately. However, due to a small sample size problem that leads to high sampling variability, these surveys cannot be used directly to produce reliable estimates at micro level such as district or further disaggregate levels. As earnings are often unevenly distributed among the subgroups of comparatively small areas, disaggregate level statistics are inevitably needed in the country for target specific policy planning and monitoring to reduce the earning disparity. Nonetheless, owing to unavailability of estimates at district level, the analysis and spatial mapping related to earning inequality are limited to the national and state level. As a result, the existing variability in disaggregate level earning distribution are often unavailable. This article describes multivariate small area estimation (SAE) to generate precise and representative district-wise model-based estimates of inequality in earning distribution in rural and urban areas of Uttar Pradesh state in India by linking the latest round of PLFS 2018–2019 data and the 2011 Indian Population Census data. The diagnostic measures demonstrate that the district-wise estimates of earning generated by multivariate SAE method are reliable and representative. The spatial maps produced in this analysis reveal district level inequality in earning distribution in the state of Uttar Pradesh. These disaggregate level estimates and spatial mapping of earning distribution are directly pertinent to measuring and monitoring the sustainable development goal 10 of inequality reduction within countries. These expected to offer evidence to executive policy-makers and experts for recognizing the areas demanding additional consideration. This study will definitely provide added advantage to the newly launched schemes of Government of India for fund distribution along with the better monitoring of these schemes.

show abstract

Measuring and Mapping Disaggregate Level Disparities in Food Consumption and Nutritional Status via Multivariate Small Area Modelling

Cited by 12 publications

References 13 publications

Multivariate small area estimation of undernutrition for children under five using official statistics

Multivariate small area estimation of undernutrition for children under five using official statistics

Small Area Estimation of Undernutrition under Age Five Children Based on Spatial-Temporal Models

Multivariate Small Area Modelling for Measuring Micro Level Earning Inequality: Evidence from Periodic Labour Force Survey of India

Contact Info

Product

Resources

About