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

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.

Disaggregation of Green Space Access, Walkability, and Behavioral Risk Factor Data for Precise Estimation of Local Population Characteristics

“…Model diagnostics: Two kinds of diagnostic measures are generally used in small area estimation, viz., the model diagnostics and the small area diagnostics [ 35 , 36 ]. The model diagnostics are useful to validate the underlying model assumptions while the small area diagnostics specify the validity and reliability of the estimates.…”

Students’ Learning on Sustainable Development Goals through Interactive Lectures and Fieldwork in Rural Communities: Grounded Theory Approach

“…The SDGs refer to the sustainability goals for 2016-2030 as described in the 2030 Agenda for Sustainable Development adopted at the United Nations Summit in September 2015 as the successor to the 2001 United Nations Millennium Development [16][17][18]. These SDGs consist of annual international goals, including 17 goals and 169 targets to realize a sustainable world, focused on the "no one left behind" on the earth concept [19,20]. While the global community works hard to achieve these SDGs, various organizations continue to focus on their respective goals [19][20][21].…”

“…These SDGs consist of annual international goals, including 17 goals and 169 targets to realize a sustainable world, focused on the "no one left behind" on the earth concept [19,20]. While the global community works hard to achieve these SDGs, various organizations continue to focus on their respective goals [19][20][21]. In the future, continuous SDG-focused activities may greatly contribute to rural community continuity via the inflow of various generations into communities and revitalize various social resources for sustainable use [2,22].…”