Real-time small area estimation of food security in Zimbabwe: integrating mobile-phone and face-to-face surveys using joint multilevel regression and poststratification

TL;DR

This paper introduces a joint multilevel regression and poststratification method combining mobile phone and face-to-face surveys to produce real-time, fine-scale food security estimates in Zimbabwe, addressing cost and representativeness challenges.

Contribution

The paper presents a novel Bayesian joint modeling approach that integrates diverse survey data sources for high-resolution food security monitoring in resource-limited settings.

Findings

Effective estimation of food security at fine spatial and temporal scales.

Reduces bias from survey modality and socio-economic differences.

Provides a cost-effective, real-time monitoring tool for policymakers.

Abstract

Real-time, fine-grained monitoring of food security is essential for enabling timely and targeted interventions, thereby supporting the global goal of achieving zero hunger - a key objective of the 2030 Agenda for Sustainable Development. Mobile phone surveys provide a scalable and temporally rich data source that can be tailored to different administrative levels. However, due to cost and operational constraints, maintaining high-frequency data collection while ensuring representativeness at lower administrative levels is often infeasible. We propose a joint multilevel regression and poststratification (jMRP) approach that combines high-frequency and up-to-date mobile phone survey data, designed for higher administrative levels, with an annual face-to-face survey representative at lower levels to produce reliable food security estimates at spatially and temporally finer scales than those originally targeted by the surveys. This methodology accounts for systematic differences in survey responses due to modality and socio-economic characteristics, reducing both sampling and modality bias. We implement the approach in a fully Bayesian manner to quantify uncertainty. We demonstrate the effectiveness of our method using data from Zimbabwe, thus offering a cost-effective solution for real-time monitoring and strengthening decision-making in resource-constrained settings.