Introduction to Area-Level Models Based on a Bayesian Approach

The course will cover the following topics:
• Introduction to Bayesian thinking
• Limitations of household surveys
• Fay-Herriot models for producing disaggregated estimates
• Models for poverty mapping
• Models for market labor statistics
• Models for multiple deprivation indexes
This course is designed to provide participants with a fundamental understanding of utilizing Bayesian methodology in the context of area-level models. Encompassing essential topics, the course enables attendees to grasp the foundational principles of Bayesian thinking and its application in disaggregating estimates using small area estimation models. Addressing the limitations associated with household surveys, the course aims to equip participants with insights into the challenges and considerations when working with survey data. The course further delves into Fay-Herriot models, offering participants a comprehensive understanding of their utility for generating disaggregated estimates.
Moreover, the course explores the application of Bayesian models for diverse purposes. Participants will receive instruction on employing these models for poverty mapping, enabling them to identify geographic areas with higher vulnerability and allocate resources more effectively. Additionally, the course extends its coverage to the realm of labor statistics. Finally, the course concludes by addressing models for multiple deprivation indexes.