The Global Hunger Index reports a concerning rise in undernourishment, particularly in Sub-Saharan Africa (SSA), where malnutrition rates are more than double the global average. This study focuses on the Western sub-region of SSA, which bears the highest burden of child and maternal malnutrition. Child and maternal malnutrition presents a triple burden of undernutrition, micronutrient deficiencies, and obesity, significantly impacting public health and economic growth. The high prevalence of malnutrition contributes to increased disease incidence and mortality, and is linked to reduced productivity and high healthcare costs. Agriculture forms the backbone of many SSA economies, yet low agricultural productivity contributes to food insecurity and malnutrition. Energy poverty further exacerbates the problem, impacting housing, health, and overall well-being. Human capital, including education and nutrition knowledge, and institutional quality, particularly corruption, are also significant factors. This study aims to fill gaps in the existing literature by analyzing the combined impact of agricultural financing (domestic and foreign aid), energy poverty, human capital, and corruption on child and maternal malnutrition in the Western SSA sub-region using sophisticated econometric methods to account for cross-sectional dependence and non-linear relationships.

Existing literature shows mixed findings on the effect of agricultural financing on malnutrition. Some studies show that agricultural credit and foreign aid improve food security and dietary diversity, while others find mixed or negative effects, depending on the type of credit or aid and its distribution. Regarding agricultural research spending, some research suggests a positive association with improved child nutrition, while others find the impact to be inconclusive. The literature consistently highlights the negative impact of energy poverty and the positive impact of human capital on nutrition. Studies also show a negative relationship between good governance (including corruption control) and food insecurity/malnutrition. However, there's a scarcity of studies integrating these factors simultaneously to analyze their joint effect on both child and maternal malnutrition in the Western SSA region.

The study employs a panel data analysis for nine West Sub-Saharan African countries from 1990 to 2019. The dependent variable is disability-adjusted life years (DALYs) due to child and maternal malnutrition, obtained from the Institute for Health Metrics and Evaluation (IHME). Independent variables include agricultural credit, agricultural research spending, foreign aid to the agricultural sector, energy poverty (access to electricity), human capital index, and a Bayesian corruption index. Data for agricultural interventions are from FAOSTAT, research spending from Agricultural Science and Technology Indicators, human capital from the Penn World Table, and energy poverty and corruption from the World Bank and Standaert (2015) respectively. The study first tests for cross-sectional dependence using several tests (Breusch-Pagan LM, Pesaran scaled LM, bias-corrected scaled LM, and Pesaran CD). It then employs the CIPS unit root test to determine the stationarity of the variables. A Westerlund cointegration test assesses long-run relationships, followed by the method of moments quantile regression (MMQR) to estimate the effects across different quantiles, addressing endogeneity and heterogeneity. Bootstrap quantile regression is used for robustness. Finally, a Dumitrescu and Hurlin panel Granger causality test explores the causal relationships between variables.

The MMQR results reveal that agricultural credit and foreign aid to agriculture significantly and negatively affect both child and maternal malnutrition across most quantiles. Conversely, research spending in agriculture demonstrates a significant positive effect across most quantiles, suggesting that its allocation or implementation needs improvement. Energy poverty has a significant negative impact on malnutrition, indicating that improved access to electricity is crucial. Human capital, as measured by the human capital index, has a significant negative effect on malnutrition across all quantiles. Corruption has a significant positive effect on malnutrition, highlighting its detrimental role. Bootstrap quantile regression largely corroborates these results. The Dumitrescu-Hurlin causality test shows a one-way causality from human capital to malnutrition, malnutrition to corruption, and several other significant relationships, including bidirectional causality between certain variable pairs such as human capital and corruption.

The findings underscore the importance of integrating various factors to address malnutrition effectively. While investments in agricultural credit and foreign aid to the agricultural sector are beneficial, the positive impact of research spending suggests a need for redirecting research efforts toward nutrition-sensitive agriculture. Tackling energy poverty through investments in sustainable energy sources is also crucial. Improving human capital through education and nutrition awareness programs is paramount, and strong governance and anti-corruption measures are essential for effective resource allocation and program implementation. The bidirectional causality between several factors further emphasizes the interconnectedness and the need for holistic interventions.

This study highlights the significant interplay between agricultural financing, energy poverty, human capital, and corruption in influencing child and maternal malnutrition in West Sub-Saharan Africa. Policy recommendations emphasize the need for integrated and multi-sectoral approaches focusing on sustainable agricultural investments, improved access to energy, human capital development, and strong governance. Future research could explore the role of other factors (e.g., climate change) and extend the analysis to other SSA regions for broader comparative analysis.

The study’s limitations include data availability; some relevant variables, such as climate change impacts, were not incorporated due to data constraints. The focus on the Western SSA sub-region limits the generalizability of the findings to other regions. Future studies could address these limitations to provide more comprehensive insights.