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

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  • Saurav Guha

    (ICAR-Indian Agricultural Statistics Research Institute)

  • Hukum Chandra

    (ICAR-Indian Agricultural Statistics Research Institute)

Abstract

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.

Suggested Citation

  • Saurav Guha & Hukum Chandra, 2022. "Multivariate Small Area Modelling for Measuring Micro Level Earning Inequality: Evidence from Periodic Labour Force Survey of India," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 162(2), pages 643-663, July.
    • Handle: RePEc:spr:soinre:v:162:y:2022:i:2:d:10.1007_s11205-021-02857-7
    DOI: 10.1007/s11205-021-02857-7
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    References listed on IDEAS

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    1. Gauri Datta & Tatsuya Kubokawa & Isabel Molina & J. Rao, 2011. "Estimation of mean squared error of model-based small area estimators," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(2), pages 367-388, August.
    2. Saurav Guha & Hukum Chandra, 2021. "Measuring disaggregate level food insecurity via multivariate small area modelling: evidence from rural districts of Uttar Pradesh, India," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 13(3), pages 597-615, June.
    3. Hukum Chandra & Nicola Salvati & U. C. Sud, 2011. "Disaggregate-level estimates of indebtedness in the state of Uttar Pradesh in India: an application of small-area estimation technique," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(11), pages 2413-2432, January.
    4. Benavent, Roberto & Morales, Domingo, 2016. "Multivariate Fay–Herriot models for small area estimation," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 372-390.
    5. Saurav Guha & Hukum Chandra, 2021. "Measuring and Mapping Disaggregate Level Disparities in Food Consumption and Nutritional Status via Multivariate Small Area Modelling," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 154(2), pages 623-646, April.
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