Hierarchical Bayes Measurement Error Small Area Model for Estimation of Disaggregated Level Workers Mobility Pattern in India

Periodic Labour Force Survey (PLFS) is the major source of data on various labour force indicators in India at annual or quarterly basis which is on the field since 2017–18. It has strategically reformed the previous quinquennial Employment and Unemployment Survey of National Statistical Office, India. Mobility pattern of workers, basically in terms of commuting is one of the key information contained therein which essentially entails the workplace characteristics of the workforce. In this article PLFS 2017–18 and 2018–19 data is analysed which depicts state-wise large disparities in the commuting behaviour of workers, whereas most of the workers are out-commuting from rural areas. The potential reason behind is the rapid pace of urbanization and associated improved transportation facilities as well as search for stable non-farm employment opportunities by the rural workforce. Further, the planning of urbanization or creation of employment opportunities at rural places in each state requires within-state regional or disaggregated level information of workplaces, spatial concentration of works and workers. To pursue that, disaggregated level analysis of commuting pattern of workers is done using small area estimation approach. In particular, this article describes hierarchical Bayes (HB) measurement error (ME) small area model for binary variable of interest indicating whether individual in the workforce is commuting or not. The HBME model has been implemented to obtain district level rural commuters proportions in Uttar Pradesh state of India. This state specifically tops amongst the states in the number of rural commuters. A spatial map has been generated for visual inspection of disparity in commuting behaviour of workers, also such map is useful to the policy makers and administration for framing decentralized level plans or strategies eyeing stable mobility behaviour to persuade improvement in employment rate.