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Summary

This study uses multivariate small area estimation (SAE) to map earning inequality at the district level in Uttar Pradesh, India. The findings provide crucial data for targeted policies to reduce economic disparities and support sustainable development goals.

Keywords:
CensusEarning inequalityMultivariate small area estimationNSOPeriodic labour force survey

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Area of Science:

  • Econometrics and Applied Statistics
  • Regional Economics and Development Studies
  • Socioeconomic Inequality and Policy Analysis

Background:

  • India's economic growth masks significant earning disparities at the micro-level, hindering access to basic needs like health and education for underprivileged populations.
  • Existing national and state-level data from the Periodic Labour Force Survey (PLFS) lack the granularity for effective district-level policy planning due to small sample sizes and high variability.
  • Accurate, disaggregated data are essential for identifying and addressing earning inequality at local levels, crucial for targeted interventions and monitoring progress on Sustainable Development Goal 10.

Purpose of the Study:

  • To generate precise and representative district-wise estimates of earning distribution inequality in rural and urban Uttar Pradesh using multivariate small area estimation (SAE).
  • To spatially map earning inequality at the district level to visually identify areas with significant disparities.
  • To provide evidence-based insights for policymakers to design targeted interventions, monitor schemes, and reduce socioeconomic inequalities.

Main Methods:

  • Integration of the latest Periodic Labour Force Survey (PLFS) 2018-2019 data with the 2011 Indian Population Census data.
  • Application of a multivariate small area estimation (SAE) model to produce reliable district-level estimates of earning inequality.
  • Diagnostic measures were employed to validate the reliability and representativeness of the generated district-wise estimates.

Main Results:

  • The multivariate SAE method successfully generated reliable and representative district-wise estimates of earning distribution for Uttar Pradesh.
  • Spatial maps revealed significant district-level variations in earning inequality across rural and urban areas within the state.
  • The disaggregate estimates and spatial mapping provide a clear picture of earning disparities, highlighting areas requiring focused policy attention.

Conclusions:

  • The study demonstrates the efficacy of multivariate SAE in producing granular estimates of earning inequality, overcoming limitations of traditional surveys.
  • The district-level insights are vital for effective implementation and monitoring of policies aimed at reducing inequality, aligning with Sustainable Development Goal 10.
  • These findings offer critical evidence for policymakers to direct resources and interventions towards the most disadvantaged areas, fostering inclusive economic development.