Improving Employment Survey Estimates in Data-Scarce Regions Using Dynamic Bayesian Hierarchical Models: Addressing Measurement Challenges in Developing Countries

Abstract

Developing countries often struggle with obtaining reliable sub-national employment statistics due to sparse and uneven survey data. This paper proposes a Dynamic Bayesian Hierarchical Model (DBHM) to improve labor force survey estimates in data-scarce regions, using India as a case study. We combine small area estimation techniques with time-series modeling to "borrow strength" across regions and time periods, thereby stabilizing estimates for under-represented populations. The methodology is demonstrated with Indian labour force data (Periodic Labour Force Survey, PLFS) and realistic simulations incorporating public data (e.g. census and survey results). The DBHM yields more precise regional unemployment and labor force participation estimates, significantly reducing estimation error and spurious volatility in areas with limited sample sizes. We present model formulations, estimation procedures, and empirical results showing that the hierarchical Bayesian approach can nearly halve the error of direct survey estimates in small domains. A brief policy discussion highlights how improved granular employment indicators can enhance labor market planning and the targeting of employment programs in developing country contexts. The findings underscore that modern statistical modeling can effectively address measurement challenges, enabling evidence-based policy even when traditional data are limited or noisy.