Measuring disaggregate level food insecurity via multivariate small area modelling: evidence from rural districts of Uttar Pradesh, India

Guha, Saurav; Chandra, Hukum

doi:10.1007/s12571-021-01143-1

Cited by 17 publications

(20 citation statements)

References 15 publications

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“…For selection of suitable covariates, data from 2011 Population Census of India are used and we applied stepwise regression technique for choosing significant covariates. “Efficient estimation of correlated measures like food insecurity, nutritional consumption disparities are often required multivariate modelling approach which takes into account for the correlation between the target variables” (Guha & Chandra, 2021b ). In this analysis, both the target variables viz.…”

Section: Discussionmentioning

confidence: 99%

“…However, at district level, this PLFS data cannot directly be used to generate precise and reliable estimates, since sample size within each district is not adequate to offer district-level estimates with acceptable reliability and precision. Although, district is always being a very crucial part of the planning process in the country, there are no surveys conducted to produce district level estimates in India and this leads to limit the policy interventions at the district or even further lower level (Guha & Chandra, 2021b ).…”

Section: Data Descriptionmentioning

confidence: 99%

“…Here, the empirical best linear unbiased estimator (BLUE) of and the EBLUP of are obtained as and respectively. In small area applications, the mean squared error (MSE) estimates are desirable to measure the precision of estimates and also to construct the confidence interval for the estimates (Guha & Chandra, 2021b ). The analytical MSE estimate of EBLUP of MFH in (4) is obtained following Benavent and Morales ( 2016 ).…”

Section: Multivariate Small Area Modellingmentioning

confidence: 99%

“…Multivariate models often allow for the correlation of several variables and usually suitable in these circumstances. Unlike the FH model, more than one variable of interest is modelled via multivariate Fay–Herriot model (MFH) by allowing for different covariance structure between the vector of the variable of interest and the random effects, see Guha and Chandra ( 2021b ). A number of small area applications for estimating socio-economic indicators, poverty have been described in literature based on univariate FH model that ignores the correlation between the target variables, see for example, Chandra et al ( 2011 , 2020 ), and references therein.…”

Section: Introductionmentioning

confidence: 99%

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

Guha

Chandra

2022

Soc Indic Res

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Data Descriptionmentioning

confidence: 99%

Section: Multivariate Small Area Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

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“…Three articles published by Food Security in 2021 address this topic. Guha and Chandra ( 2021 ) derive reliable and representative estimates of food insecurity indicators at district level for the state of Uttar Pradesh in India, using a multivariate small area estimation technique. The approach used by Sassi and Trital ( 2021 ) highlights the temporal and spatial dynamics of food security, and relied on a multi-group piecewise latent growth curve to model food security at household level in Kenya.…”

Section: Food Security In Households In 2021mentioning

confidence: 99%

Revisiting food security in 2021: an overview of the past year

et al. 2022

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Articles published in Food Security in 2021 are reviewed, showing a wide range of topics covered. Many articles are directly linked with "food" and associated terms such as "nutritive", "nutrition", "dietary", and "health". Another important group is linked with (food) "production" and a range of connected terms including: "irrigation", "cultivated", "organic", "varieties", "crop", "vegetable", and "land". A third group of terms refers to the scales at which food security is considered: "household", "farmer", "farm", "smallholder", "community", "nation" and "region". A few themes of Food Security are considered: (1) food supply and demand, food prices, and global trade; (2) food security in households; (3) food production; (4) value chains and food systems; (5) the evolution of the concept of food security; and (6) global nutrition. In a last section, perspectives for Food Security are discussed along four lines of thoughts: the level of inter-disciplinary research published in Food Security; the importance of the Social Sciences for food security as a collective good underpinned by other collective goods within food systems; the balance between the Global South and the Global North in Food Security; and a warning that urgent global challenges that vitally interact with food security may be left unattended as a result of the current public health emergency.

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Spatial small area estimates of undernutrition for under five children in Ethiopia via combining survey and census data

Yilema

Shiferaw

Zewotir

et al. 2022

Spatial and Spatio-temporal Epidemiology

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Measuring disaggregate level food insecurity via multivariate small area modelling: evidence from rural districts of Uttar Pradesh, India

Cited by 17 publications

References 15 publications

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

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

Revisiting food security in 2021: an overview of the past year

Spatial small area estimates of undernutrition for under five children in Ethiopia via combining survey and census data

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