# 1. Introduction

The issue of food insecurity among households in developing countries, where individuals or families struggle to access or afford nutritious food to meet their basic dietary needs, has garnered additional attention since the launch of the Sustainable Development Goals. This has led donors and philanthropic organizations to increase their effort to combat food insecurity, especially in poor countries where malnutrition is assumed to be high. Surprisingly, this problem persists even among households that are not considered monetarily poor. This phenomenon, known as hidden hunger, affects approximately 2 billion people worldwide, according to a study conducted by the World Food Programme (WFP, 2020). Hidden hunger is characterized by an inconsistency between adequate calorie intake and intake of essential vitamins and minerals, leading to various health problems, primarily manifested as stunted growth.

Identifying sub-regions or localities afflicted by hidden hunger, or micronutrient deficiencies, is crucial for targeted interventions and effective allocation of resources. Public health experts employ methods such as randomly collecting blood samples from representative population groups, analyzing them in laboratories to determine micronutrient levels, and comparing them against established reference values – nutritional assessment. This approach, combined with geographical mapping, helps identify areas in need, enabling experts to focus their interventions and resources accordingly. Additionally, dietary surveys and administrative data from ad hoc clinical examinations when patients happen to visit local health centers provide further insights into the nutritional status of the population.

Conducting expensive surveys, such as nutritional assessments and dietary surveys, to identify areas with a high prevalence of hidden hunger is not financially sustainable. Health experts instead resort to using poverty maps or standardized health surveys like the Demographic and Health Survey (DHS) or the Multiple Indicator Cluster Survey (MICS) to pinpoint malnourished individuals. However, relying solely on poverty maps assumes that malnourished individuals exclusively live in impoverished households. This may not always be accurate, since typical poverty measures do not measure differences in welfare levels among members of the same household. On the other hand, DHS or MICS surveys may miss households with average poor caloric intake as these surveys do not collect detailed food consumption data. To overcome this challenge and enhance precision in targeting, we recommend an alternative approach that combines small area estimates of monetary poverty and stunting.

This study aims to estimate the prevalence of hidden hunger in Madagascar by employing small area estimation techniques. Our approach involves identifying areas where poverty rates are low but stunting, a common indicator of malnutrition, is high; and vice versa. Poverty estimates are derived from the 2021/2022 household survey, which provides detailed food consumption at the household level. Stunting estimates, on the other hand, are derived from the 2021 Demographic and Health Survey (DHS), a nationally representative survey capturing anthropometric data and nutritional indicators. We develop a small area estimation (SAE) statistical model that “borrowed strength” from the 2018 census to allow the two nationally representative sources of survey data to produce precise and accurate estimates at the commune level and investigate the relationship between monetary poverty and nutrition. This approach enables us to estimate the prevalence of hidden hunger at the commune level within Madagascar and create a taxonomy of different types of hidden hunger, providing valuable insights for nutrition policy prioritization.

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