prioritize nutritional issues is crucial for initiating efforts to address the problem, especially when resources are limited.

# 3. Literature Review

Prior studies have utilized SAE techniques to map food insecurity and nutritional trends at the subnational level. Within this context, we identify two noteworthy trends in employing SAE to better understand nutritional outcomes in developing countries. One prevalent approach combines census data with household expenditure information, utilizing caloric intake thresholds as indicators of malnutrition. For instance, Hossain et al. (2020) applied the SAE methodology to estimate food insecurity at the district level in Bangladesh. They integrated data from the Household Income and Expenditure Survey 2010 with the Bangladesh Population and Housing Census 2011. Their approach gauged food insecurity prevalence based on a per capita calorie intake threshold of 2,122 kcal per day, derived from the survey.

Guha and Chandra (2021) also combined household expenditure data with census data to obtain estimate of food insecurity indicators at the district level for the rural area of the state of Uttar Pradesh in India. Their empirical evidence indicate that the estimates generated by SAE approach are reliable and representative. Spatial maps showing district level inequality in distribution of food insecurity in Uttar Pradesh is also produced. The disaggregate level estimates and spatial maps of food insecurity are found to be directly relevant to help the monitor the sustainable development goal indicator 2.1.2 - severity of food insecurity.

Another notable approach combines census data with standardized health surveys like the Demographic and Health Surveys (DHS) to estimate stunting rates at the subregional level. For instance, Sohnesen et al. (2017) employed this method in Ethiopia to estimate undernutrition within different woreda (sub-regions). Their findings revealed substantial variations in the prevalence of undernourished children within each woreda, highlighting location-specific challenges. Notably, they found limited correlation between the percentages of underweight and stunted children across woredas.

Other studies employed SAE to estimate dynamic changes in nutritional outcomes. In fact, to estimate the trends in age-specific chronic childhood undernutrition in all of Bangladesh's 64 districts spanning from 1997 to 2018, Das et al. (2023) employed advanced small area time-series models. These models integrated direct estimates derived from seven Demographic and Health Surveys, combining crosssectional, temporal, and spatial data to make predictions for all districts during both survey and nonsurvey years. At the national level, their findings revealed a significant reduction in stunting prevalence, decreasing from approximately three in five children to one in three. However, the study also exposed substantial disparities in chronic undernutrition across the districts, with certain areas experiencing less pronounced declines. Moreover, these disparities became more apparent when examining stunting prevalence at the district-by-age level, where only districts in more socio-economically advantaged regions consistently reported declines in stunting across all age groups.

In our present study, we present small area estimates both of monetary poverty and stunting, allowing us to uncover a distinct dimension of undernutrition known as "hidden hunger". Our research unveils the prevalence of hidden hunger and employs a novel bivariate mapping approach to rank and categorize hidden hunger across various communes. This approach is valuable for facilitating location5 | P a g e