<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The earning inequality in India has unfavorably obstructed underprivileged in accessing elementary needs like health and education. Periodic labour force survey conducted by National Statistical Office of India generates estimates on earning status at national and state level for both rural and urban sectors separately. However, due to small sample size problem, these surveys cannot generate reliable estimates at micro-level viz. district or block. Thus, owing to unavailability of district-level estimates, analysis of earning inequality is restricted to the national and the state level. Therefore, the existing variability in disaggregate-level earning distribution often goes unnoticed. This article describes multivariate small area estimation method to generate precise and representative district-wise estimate of earning distribution in rural and urban areas of the Indian State of Bihar by linking Periodic labour force survey data of 2018–2019 and 2011 Population Census data of India. These disaggregate-level estimates and spatial mapping of earning distribution are essential for measuring and monitoring the goal of reduced inequalities related to the sustainable development of 2030 agenda. They expected to offer insightful information to decision-makers and policy experts for identifying the areas demanding more attention.</p>
</abstract>

The earning inequality in India has unfavorably obstructed underprivileged in accessing elementary needs like health and education. Periodic labour force survey conducted by National Statistical Office of India generates estimates on earning status at national and state level for both rural and urban sectors separately. However, due to small sample size problem, these surveys cannot generate reliable estimates at micro-level viz. district or block. Thus, owing to unavailability of district-level estimates, analysis of earning inequality is restricted to the national and the state level. Therefore, the existing variability in disaggregate-level earning distribution often goes unnoticed. This article describes multivariate small area estimation method to generate precise and representative district-wise estimate of earning distribution in rural and urban areas of the Indian State of Bihar by linking Periodic labour force survey data of 2018–2019 and 2011 Population Census data of India. These disaggregate-level estimates and spatial mapping of earning distribution are essential for measuring and monitoring the goal of reduced inequalities related to the sustainable development of 2030 agenda. They expected to offer insightful information to decision-makers and policy experts for identifying the areas demanding more attention.

Measuring and Mapping Micro Level Earning Inequality towards Addressing the Sustainable Development Goals – A Multivariate Small Area Modelling Approach

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The earning inequality in India has unfavorably obstructed underprivileged in accessing elementary needs like health and education. Periodic labour force...

Measuring and Mapping Micro Level Earning Inequality towards Addressing the Sustainable Development Goals – A Multivariate Small Area Modelling Approach

Online veröffentlicht: 12. Sept. 2022

Seitenbereich: 823 - 845

Eingereicht: 01. März 2021

Akzeptiert: 01. Jan. 2022

DOI: https://doi.org/10.2478/jos-2022-0036

Schlüsselwörter
Multivariate small area estimation, earning inequality, SDG, periodic labour force survey, NSO, census

© 2022 Saurav Guha et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Measuring and Mapping Micro Level Earning Inequality towards Addressing the Sustainable Development Goals – A Multivariate Small Area Modelling Approach

Online veröffentlicht: 12. Sept. 2022

Seitenbereich: 823 - 845

Eingereicht: 01. März 2021

Akzeptiert: 01. Jan. 2022

DOI: https://doi.org/10.2478/jos-2022-0036

SchlüsselwörterMultivariate small area estimation, earning inequality, SDG, periodic labour force survey, NSO, census

© 2022 Saurav Guha et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Multivariate small area estimation, earning inequality, SDG, periodic labour force survey, NSO, census