Markov Chain Monte Carlo Simulation for Poverty Transition Analysis in Bangladesh

Abstract

This study employs Markov Chain Monte Carlo (MCMC) simulation methods to analyze poverty transitions among households in Bangladesh. By utilizing longitudinal data from the Bangladesh Integrated Household Survey (BIHS) and other relevant sources, we identify key variables influencing poverty dynamics over time. The MCMC approach allows for robust estimation of complex models that account for unobserved heterogeneity and time-dependent processes. Our findings reveal that factors such as education, employment, access to credit, and asset ownership significantly impact poverty transitions. The results underscore the importance of targeted policy interventions to reduce poverty and promote sustainable economic development in Bangladesh.

Keywords: Poverty Analysis, Poverty, Poverty Dynamics, socioeconomic, human development, Markov, Markov Chain, employment