Multivariate Beta-Binomial Models for the Analysis of Food Security Surveys

• Activity Number: 474 - New Advances in Modeling Survey Data
• Type: Contributed
• Date/Time: Thursday, August 6, 2020 : 10:00 AM to 2:00 PM
• Sponsor: Survey Research Methods Section
• Author(s): Michelle Lacey* and Aleksandra Gorzycka-Sikora and Nancy Mock
• Companies: Tulane University and University of Miami and Tulane University
• Keywords: multivariate modeling; beta-binomial; Bayesian methods; food security; consumption surveys

Abstract

Preventing malnutrition is one of the primary objectives of many humanitarian agencies, and household surveys are periodically conducted for monitoring of food insecurity caused by political, economic, or environmental crises. The frequency of consumption of standard food groups is often collected to characterize food insecurity and measure the impact of food assistance programs, producing a vector of bounded, correlated counts for each household. While aggregate indicators are typically used to summarize these results, they can be difficult to interpret and provide little insight about the effectiveness of assistance programs. To address these limitations, we have developed a Bayesian multivariate Beta-binomial model with random effects for consumption frequency data. We introduce methods to update baseline models for the analysis of the smaller and more variable surveys typically collected in emergency situations, and present an application of our approach to national consumption data collected in Yemen in 2014 and 2016 by the World Food Programme.