Introduction

According to the Global Hunger Index report, globally, it is estimated that 735 million people were undernourished in 2022, which indicates an increase of 122 million when compared with the 2019 statistics (von Grebmer et al., 2023; FAO, IFAD, UNICEF, WFP and WHO, 2023). With the rapid increase in population growth rate, the figures are soaring higher, especially in Sub-Saharan Africa (SSA) region, which has the fastest-growing number of malnourished and accounts for about a third of the malnourished children (Akombi et al., 2017; FAO, IFAD, UNICEF, WFP and WHO, 2023). The rate of malnutrition in the region in 2022 is reported to be 22.5% and it is not only the highest of all regions in the world but also represents more than double the global average of 9.2% (FAO, IFAD, UNICEF, WFP and WHO, 2023). Consequently, SSA is home to the highest number of people who cannot afford a healthy diet, which stood at 83.4% in 2021 (FAO, IFAD, UNICEF, WFP and WHO, 2023). The emergence of COVID-19 at the end of 2019 disrupted the food supply chain in SSA with severe consequences for the production of crops as well as food prices. As a result, the International Food Security Assessment report indicated that food insecure people in SSA region increased by more than 9% between 2019 and 2021 compared to only 4% increase between 2017 and 2019 (Dabou et al., 2024).

The challenge gets further compounded as child and maternal malnutrition embodies a triple liability of undernutrition, deficiency in micronutrients as well as obesity (Giller, 2020). Given the fast-rising population growth rate and stagnant agricultural productivity, a huge threat looms regarding child and maternal health (Benzekri et al., 2015). Even though progress is being made globally on stunting and wasting (key indicators of malnutrition), SSA still houses the highest number of malnourished children in the globe, with about one-third of children in the region estimated to be malnourished (FAO, IFAD, UNICEF, WFP and WHO, 2023; Njatang et al., 2023).

The high prevalence of malnutrition has induced major health causes and challenges. In the realm of public health, malnutrition contributes significantly to the high incidence of diseases and infections since the immune system is lowered as a result of deficiencies in macronutrients such as protein and carbohydrates and micronutrients like vitamins and minerals (Müller and Krawinkel, 2005). In SSA, an increase in malnutrition from 5.5 million in 2009 to 30 million in 2019 contributed to child mortality of over 3.5 million (Drammeh et al., 2019). Christian and Dake (2022) noted that SSA had experienced a significant increase in obesity or overweight, and in approximately all sub-regions, undernutrition has been on the rise. The region is vulnerable to the simultaneous coexistence of all forms of malnutrition including undernutrition, obesity as well as micronutrient deficiency. FAO reported that malnutrition in children in 2020 for SSA consisted of 37% stunting, 6.7% wasting, and 5.7% overweight (Njatang et al., 2023), all of which have a direct association with death occurrence as discovered by Müller and Krawinkel (2005). Furthermore, malnutrition has been linked with several health issues responsible for premature deaths, such as acute respiratory infections, malaria, diarrhea, and perinatal deaths, among others (Müller and Krawinkel, 2005).

Figure 1 depicts the overall scenario of child and maternal malnutrition among different regions of the world. Before the millennium, South Asia was the region with the highest child and maternal malnutrition, but SSA overtook South Asia after 2000.

Fig. 1: Child and Maternal malnutrition.
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Percentage of child and maternal malnutrition in terms of 7 regions between the years 1990, 2000, 2010 and 2019.

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In western and central Africa, stunting in children rose by 6.5 million between 2000 and 2018. This is one of the factors influencing the continued bane of child and maternal deaths in SSA, even though the rates are declining. Child mortality amounted to 6.2 million in 2018, with almost 300,000 maternal deaths in 2017 (Schlein, 2019). When compared to the global statistics, half of the child mortality and two-thirds of maternal mortality occur in SSA. This is detrimental to the much-coveted sustainable development goals (SDGs), particularly SDGs 2, 3, and 8, which are on zero hunger/improving nutrition, ensuring healthy lives, and sustaining economic growth, respectively. Regarding economic growth, it is important to note that adequate nutrition helps to accumulate human capital and increase productivity. Child malnutrition is associated with poor labor market and educational outcomes (Haile et al., 2021). According to Horton and Steckel (2013), ~12% of GDP is lost due to lower productivity and high healthcare costs in poorer countries that face high rates of malnutrition.

Agriculture is the mainstay of livelihoods and food security for a large proportion of the SSA population, contributing to 20–50% of GDP, and is a major employer of labor (Giller, 2020). According to Tesfaye et al. (2021), low agricultural productivity contributes to the region’s high poverty rate, food insecurity, malnutrition, and their ripple effects. Another major cause of malnutrition in the SSA region has been the lack of electricity or energy poverty. Studies such as Kose (2019) and Thomson et al. (2017) show that energy poverty is associated significantly with the different measures of health conditions where lack of energy or inadequate access causes poor health conditions due to poor housing conditions and temperatures. Another potentially influencing factor of malnutrition is human capital since superior knowledge and nutrition education can reduce the prevalence of malnutrition (El Mouzan et al., 2010). Furthermore, institutional quality has also been found to be an important factor in affecting malnutrition (Cassimon et al., 2022).

Owing to the above discussion, this study attempts to identify the potential factors influencing child and maternal malnutrition in the western sub-region of SSA with a specific emphasis on agricultural financing and energy poverty. As such, there are several significant ways this study contributes to the empirical works of literature. First, we focus on the western part of SSA because this sub-region experiences the highest child and maternal malnutrition (SDG 3) burden in the entire SSA. Figure 2 shows that within the SSA region, the burden of child and maternal malnutrition as measured by Disability-adjusted life years (DALY) has consistently rested heavily on the western part of SSA through the years from 1990. This classification is according to the global burden of disease (GBD) classification criteria, which classifies the world regions into seven superregions with 21 sub-regions (Vos et al., 2020). The SSA region, as a super region, contains Eastern, Central, Western and Southern SSA.

Fig. 2: Child and Maternal malnutrition in SSA.
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Number of child and maternal malnutrition in terms of 4 SSA subregions between the years 1990, 2000, 2010 and 2019.

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Second, this study considers agricultural credit and agricultural research spending as the domestic government investment and development aid in the total agricultural sector is considered as foreign aid. Most previous studies have only considered these interventions in isolation. For example, Mcdermott et al. (2015), Iftikhar and Mahmood (2017), and Fleuret and Fleuret (1980) focused on agricultural research, agricultural credit, and agricultural flows, respectively. We firmly believe that charting this usually unexplored method of analyzing the agriculture-malnutrition relationship will allow for a more accurate, effective, and precise policy targeting in combating the double-edged sword of child and maternal malnutrition in SSA. Foreign aid is advocated by SDG 17.2, which aims to enhance development assistance.

Third, most of the studies only focus on child malnutrition (see Debela et al., 2021; Shafiq et al., 2019; Carletto et al., 2017; Gillespie and van den Bold, 2017; Akombi et al., 2017), with only Kabir et al. (2020) examining maternal malnutrition. This study is one of the few attempts that incorporate both child and maternal malnutrition in the same study framework, as they both constitute a nutrition bane in SSA. Fourth, our paper investigates the impacts of energy poverty, corruption and human capital development on malnutrition, which, although critical, have been mainly neglected in the literature for child and maternal malnutrition. To the best of our knowledge, this is the first study to take account of energy poverty, corruption and human capital development simultaneously in affecting child and maternal malnutrition, and therefore the contribution of this study is large.

Lastly, the study applies a novel methodological approach to the analysis of malnutrition by using superior econometric techniques such as the second-generation approach and the method of moments quantile regression (MMQR) (Machado and Silva, 2019). MMQR regression allows us to ascertain the effects of agricultural financing, energy poverty, corruption, and human capital across different quantiles. It is advantageous over other conventional models like NARDL as it produces robust results in nonlinear models with varied conditions and also allows for location-specific asymmetries in the impact analysis (An et al., 2021). This analytical technique is, therefore, suitable for the impending analysis as it handles asymmetry and nonlinearity, thereby circumventing the problem of heterogeneity and endogeneity concurrently. For robustness analysis, we estimate bootstrap quantile regression.

The rest of the study is organized as follows: the second part of the study consists of previous literature summary, the third part contains data and methodology, the fourth part is regarding the result and analysis, and the last part provides a summary of the whole study and recommendations.

Literature review

Agricultural financing and malnutrition

Amao et al. (2023) assessed the role of credit finance and agricultural revenue in their study to investigate the factors that influence food security in Nigerian households using dietary diversity as a proxy for the latter. Data was gathered from the Living Standards Measurement Study—Integrated Surveys on Agriculture (LSMS-ISA). Employing IV-Poisson, they found that agricultural revenue has a negative relationship with nutrition diversity in urban households, while it has a positive relationship for rural households. Overall, agricultural revenue was found to be positively affecting nutrition diversity. On the other hand, credit access has a positive relationship for all households and rural areas, while no significant relationship was discovered between credit access and nutrition for urban households.

Similarly, Kihiu and Amuakwa-mensah (2020) assessed the effect of gendered agricultural market access in terms of market infrastructure, credit facilities, and marketing channels on dietary diversity in Kenyan homes. The study used an inverse probability-weighted treatment-effect estimator and found that improving agricultural market access for both genders exerts a positive influence on a household’s dietary diversity.

Focusing on the effect of foreign capital proxied by foreign direct investment (FDI) and foreign aid food security, Dhahri and Omri (2020) employed four measures of foreign aid, which include agriculture–forestry–fishing-aid (AFFA). Employing Tobit analysis, the findings reveal that both FDI and AFFA positively impact food security, while the effect becomes stronger when FDI and aid work together.

A similar study explored the relationship between credit access, household income, and diet variety in Ghana using the Food Diversity Index and Food Consumption Score. Annim and Frempong (2018) employed household samples from the Ghana Living Standards Survey while employing instrumental variable analysis to analyze the data gathered. Results revealed income and credit have an incremental effect on diet variety. This establishes a negative effect of credit on malnutrition as diet diversification will allow the incorporation of various classes of food for the supply of the required dietary needs.

These findings, however, contradict the studies of Iftikhar and Mahmood (2017) and Islam et al. (2016). Iftikhar and Mahmood (2017) document the effect of agricultural credit to be mixed when they found institutional credit to be instrumental in improving food security, while non-institutional credit was found to have a contrary effect. The work of Haque et al. (2013) supports the former assertion. In addition, Islam et al. (2016) investigated the effect of microcredit on various dimensions of food security—calorie availability, dietary diversity, and anthropometric measures in reproductive women and children under 5—in Bangladesh. The results of the regression-adjusted propensity score matching (PSM) indicated that credit has a positive, negative, and mixed effect on calorie availability, dietary diversity, and anthropometric measures, respectively. Despite the diverse results obtained in the review, the conclusion of the majority of literature tends towards the fact that agricultural credit has a huge potential to curb malnutrition, mostly through boosting agricultural production, enhancing food security, and promoting diet diversification.

Focusing on the second aspect of internal investment, which is agricultural research spending, Adjaye-Gbewonyo et al. (2019) examined the effect of government assistance to agricultural trade on child nutrition in a cross-country analysis. Results from the fixed effect regression analysis indicated positive associations as increases in the 5-year average assistance rate improved children’s nutritional status. However, the conclusions of Mcdermott et al. (2015) differ as they report that agricultural research efforts have not yielded the required and desired nutritional outcomes. A similar view is expressed in a dated study by Harriss (1987), who implied the impact of agricultural research spending on nutrition is inconclusive, majorly due to factors bordering on identification, categorization, and location of the malnourished, among others. In terms of external development investment in agriculture, Fleuret and Fleuret (1980) classified this type of intervention as the indirect approach to tackling malnutrition which has been found to have a positive influence on nutritional status. However, other studies such as Berg and Muscat (1973) and Reutlinger and Selowsky (1976) are of diverse opinions noting that external development assistance may not be influential in combating malnutrition. This is attributed to the uneven, inefficient, and/or unfair distribution of external investments.

Human capital and malnutrition

Osei and Lambon-Quayefio (2022) investigated how malnutrition in children affects educational outcomes using panel data random-effects and Poisson estimations. The findings indicate that although malnutrition hampers educational outcomes, the effect is temporary as it is discovered to disappear in the future. With a focus on adults, Eglseer et al. (2019) examined the effect of nutrition education on malnutrition in medical schools in Europe. Being a cross-sectional study, data was gathered from 31 European countries from an online Web-Survey. The study concluded that 50% of the curricula covered malnutrition as a topic. This agrees with Fadare et al. (2019a), who examined the effect of nutrition-related knowledge of the mother on the child’s nutritional status using DHS data for Nigeria. The authors concluded that a mother’s nutrition knowledge has a significant and positive effect on a child’s nutrition, thereby significantly reducing malnutrition among children. Benson et al. (2018), on the other hand, showed that parent’s educational status does not have any effect on the child being stunted in northern Nigeria.

Fadare et al. (2019b) adopted a cross-sectional survey method to examine factors that influence micronutrient-rich food and investigate the effect of the same on child stunting. Gathering data from 419 children and 413 households in Kwara State, Nigeria, logistic regression and descriptive analysis were applied to the data collected. Results show that higher levels of education among parents and superior knowledge of micronutrients have a high likelihood of improving consumption of micronutrient food. The study, therefore, concludes that human capital reduces the prevalence of malnutrition. The study outcome agrees with Amare et al. (2021), who demonstrated that a mother’s educational attainment, along with that of a spouse, has a negative impact on children’s stunting. Adesugba et al. (2018) also found that uneducated households are more likely to have underweight children.

A similar study on the relationship between maternal education and malnutrition in children was conducted by Hasan et al. (2015) in Bangladesh using data from 1996 to 2011 with log-binomial as the analytical technique. Results show malnutrition was constantly high in children with mothers who have low educational qualifications. Smith and Haddad (2015) found education among the influencing factors of child undernutrition in their study cross-country study using data from 1970 to 2012. El Mouzan et al. (2010) explored a different direction of the education-malnutrition nexus by examining the effect of educational attainment of the household head on children’s malnutrition in Saudi Arabia. Gathering data from a stratified multistage sampling, the prevalence of malnutrition was calculated using weight for age, height for age, and weight for height for children under 5 as indicators. The likelihood of malnutrition was revealed to increase from 7.4% for tertiary education to 15.2% for illiterate heads of house.

Energy poverty and malnutrition

Dake and Christian (2023) examined the effect of energy poverty on malnutrition in 18 SSA countries using the DHS data. Energy poverty was measured in terms of energy used for lighting, cooking, entertainment, and information access, while malnutrition was proxied by indicators such as undernutrition, overnutrition, and anemia among children under 5 years and women aged 15–49 years. Findings reveal energy poverty to be associated with a higher likelihood of undernutrition but a lower probability of overnutrition. This aligns with the study of Kose (2019) which shows that energy poverty is associated significantly with the different measures of health. By studying Turkish household surveys, they provided multi-level model evidence of the negative relation between energy poverty and the health of the persons. Some of the several situations related to energy poverty include poor housing conditions, health as well as indoor temperature (Thomson et al., 2017).

The nutritional status of children was explored from the perspective of rural electrification in rural areas of Bangladesh by Fujii et al. (2018). The authors found that electricity access improves children’s nutritional status specifically through fertility and wealth channels. Lewis (2018) found that electrification in the rural US has contributed to a 15–19% decrease in infant mortality.

Corruption and malnutrition

Investigating the impact of corruption on food security at the macro level, Onder (2021) utilized panel data from 75 countries for the period between 2012 and 2016. With Driscoll and Kraay’s method of analysis, his findings indicated that corruption negatively influences food security. Also, Qingshi et al. (2020) found that corruption, among other factors, worsens food security in 124 countries comprising both developing and developed countries. This aligns with the study of Anik et al. (2013), whose study suggests a reduction in food security is occasioned by farm-level corruption.

The effects of different governance quality indicators on food security were examined by Cassimon et al. (2021). It was revealed that regulatory quality and rule of law have positive effects, while government effectiveness and corruption control have negative effects on food security. On the other hand, the overall governance index had no significant effect on food security from the pooled OLS. The random effect result demonstrated a positive effect of controlling corruption on food security. On the other hand, control of corruption was positively associated with nutrition security from pooled OLS.

In another study, Cassimon et al. (2022) have also suggested that undernourishment is negatively affected by corruption control mechanisms and the stability of the political system. It was also found that child undernutrition is negatively influenced by good governance quality. In another seminal work, Ogunniyi et al. (2020) demonstrated from their system GMM analysis that food and nutrition security in SSA are positively affected by corruption control score, governance effectiveness, rule of law, and stability of the political system. However, controlling corruption had the largest effect. Another work on SSA by Cassimon et al. (2023) revealed that stability of the political system and control of corruption have significant effects on nutrition and food security and thereby reduce undernourishment.

Data and methodology

Model specification

The model used to explore the behavior of child and maternal malnutrition in selected Western Sub-Saharan countries is reported as follows:

$$\begin{array}{l}{{\rm{MALNUT}}}_{{it}}={\beta }_{0}+{\beta }_{1}{{\rm{AGRCREDIT}}}_{{it}}+{\beta }_{2}{{\rm{RAGR}}}_{{it}}\\\qquad\qquad\qquad+\,{\beta }_{3}{{{{FAID}}}}_{{it}}+{\beta }_{i}{{X}}_{{it}}+{\mu }_{{it}}\end{array}$$
(1)

where MALNUT refers to child and maternal malnutrition, AGRCREDIT refers to the credit in the agricultural sector, RAGR is research spending in agriculture, FAID is foreign aid, intercepts \({{X}}_{{it}}\) is a composite term that incorporates the effect of other control variables used in the model, such as energy poverty, corruption, and human capital index. The dependent variable (MALNUT) is disability-adjusted life in years due to child and maternal malnutrition, and the data for this variable is collected from the Institute for Health Metrics and Evaluation (IHME), which coordinates the Global Burden of Disease database. The GBD provides global health metrics data for 204 nations and sub-nations and classifies world regions into 7 super regions and 21 sub-regions, with SSA being one of the super regions and the Western part of SSA being one of these sub-regions (Vos et al., 2020; GBD, 2021).

The data of agricultural interventions such as agricultural credit and foreign aid are collected from FAOSTAT whereas research spending in agriculture by the government comes from Agricultural Science and Technology Indicators. The reason for selecting foreign aid instead of foreign direct investment into agriculture is because of the data limitation associated with the agricultural foreign direct investment in the western part of the SSA region. The data on human capital is derived from the Penn World table. Energy poverty is collected from WDI measured by access to electricity. Finally, Bayesian corruption index data is collected from Standaert (2015). This corruption index is selected instead of other corruption measures because this index can represent the underlying data in a true manner, and it is not biased by the composer’s modeling choices (Teorell et al., 2021). Table 1 reports a detailed description, source, and reference of data used in this study.

Table 1 Description of the variables.
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The analysis is carried out for 9 countries that are located in the western part of Sub-Saharan African countries and the data period is from 1990 to 2019. The list of the countries included in this study is presented in Supplementary Table S1 online. The selected variables with different units are transformed by taking the natural logarithm. The logarithmically transformed version of Eq. (1), along with the control variables, is presented as follows:

$$\begin{array}{l}{{\mathrm{ln}}{\rm{MALNUT}}}_{{it}}={\beta }_{0}+{\beta }_{1}{{\mathrm{ln}}{\rm{AGRCREDIT}}}_{{it}}+{\beta }_{2}{{\mathrm{ln}}{\rm{RAGR}}}_{{it}}\\\qquad\qquad\qquad\quad+\,{\beta }_{3}{{\mathrm{ln}}{\rm{FAID}}}_{{it}}+{\beta }_{3}{{\mathrm{ln}}{\rm{EPOV}}}_{{it}}+{\beta }_{3}{ln}{{\rm{HCI}}}_{{it}}\\\qquad\qquad\qquad\quad+\,{\beta }_{3}{{ln}}{{\rm{CORRUPTION}}}_{{it}}+{\mu }_{{it}}\end{array}$$
(2)

where ln represents the natural logarithm, EPOV is Energy Poverty, HCI is the human capital index and CORRUPTION is the Bayesian corruption index.

Estimation strategy

Cross-sectional dependence

The selected panel of variables is first subject to a cross-sectional dependence test for exploring the effect of shock in one country on others as all countries belong to the same region (Western Sub Sharan Africa). There are various variants of cross-sectional dependence tests to realize this objective, such as the Lagrange multiplier (LM) test proposed by Breusch and Pagan (1980), scaled LM test and CD test introduced by Pesaran (2004), and recently developed bias-corrected scaled Lagrange multiplier test proposed by the Baltagi et al. (2012). The cross-section dependence test proposed by Breusch and Pagan (1980) would be appropriate when N is fixed and \(T\to \infty\). The drawback of this test is that it cannot be applicable in the case that N tends to be infinite. Therefore, Pesaran (2004) has introduced a cross-sectional dependence test that is applicable with finite N and T. But in the case when N > T, Pesaran (2004) has proposed a different test. A more recent test to explore cross-section dependency by addressing potential issues associated with the above-discussed test is that of Baltagi et al. (2012), who proposed a bias-corrected scaled LM test. The test statistic is assumed to be distributed asymptotically under the weak cross-sectional dependence null hypothesis.

CIPS unit root test

The existence of cross-sectional dependence implies that the residuals of panel cross-sections (i.e. countries or industries in the panel) are significantly correlated with each other, which implies that shock to one of the cross-sectional entities has an impact on one or more other cross-sectional entities. Therefore, a traditional first-generation panel unit root test can result in a biased conclusion as they do not allow cross-sections to be dependent. In contrast, the recently advanced second generation of panel unit root tests augments traditional tests to address cross-sectional dependency. Pesaran (2007) augmented the traditional Dickey–Fuller unit root test to address cross-sectional dependency. The null distribution of this test is that of homogenous nonstationary, and the rejection of which proves that the series is stationary.

Second-generation cointegration test

After the CD and stationary test, investigation of the long-run relationships of the studied variables is essential, which will be carried out using panel cointegration techniques. Specifically, in the presence of CD, robust panel cointegration methods developed by Westerlund (2007) are employed. The rationale behind this is that the techniques provide statistical values that ascertain whether the data series have a long-run relationship. There are four test statistics and the null hypothesis of the methods suggested that there is no evidence of cointegration. Hence, if the statistic value of each test is greater than the critical value, the hull hypothesis will be rejected in favor of the alternative hypothesis, which denotes the presence of a long-run association.

Method of moments-quantile regression (MMQR)

After establishing the presence of cointegration, the next stage is to assess the long-run relationship. However, estimation techniques such as FMOLS OR DOLS provide bias estimates in the presence of outliers; therefore, Koenker and Bassett (1978) introduced quantile regression in their seminal paper. Generally, in the presence of outliers, the simple quantile regression provides more robust estimates and also gives more pertinent results when the relationship between the conditional mean values of variables is weak or nonexistent. The drawback of simple quantile regression is that it is unable to address unobserved heterogeneity across cross-sections along with the endogeneity issues. This problem is addressed by Machado and Silva (2019) by introducing the method of moments quantile regression (MMQR) that authorizes the individual effects and addresses the conditional heterogeneous covariance effect. The MMQR approach is the most appropriate panel estimation technique that can incorporate both asymmetric and nonlinear linkages and also deals with both endogeneity and heterogeneity issues simultaneously. This study, therefore, employed the recently advanced MMQR technique to address the behavior of child and maternal malnutrition in selected Western Sub-Saharan Africa. For robustness purposes, bootstrap quantile regression is employed (Koenker, 2005).

Panel Dumitrescu and Hurlin's causality estimates

The estimation of the long-run association between explained and explanatory variables cannot explore the flow of the Granger causality trend, and in the empirical analysis, investigation of the causality flow is crucial owing to its expediency efficient policy designing (Usman and Hammar, 2021). In this regard, Dumitrescu and Hurlin (2012) developed a panel non-causality test named the panel Dumitrescu and Hurlin (D–H) test that illustrated the casual association between variables. The D–H test offers produce more efficient and robust outcomes as compared to other traditional causality tests (Intisar et al., 2020). Moreover, this panel causality test is further suitable for evaluating the balanced and unbalanced time series and cross-correlations across each unit (countries).

Results and discussion

Table 2 provides the descriptive statistics of the variables under study with logarithmic presentation. Before the estimation of the long-run coefficients, cross-sectional dependence (CSD) needs to be tested to avoid erroneous analysis and conclusions from the models. Table 3 shows the results of the four types of CSD tests. For all the variables, the majority of tests demonstrate that there is cross-sectional dependence. Table 4 presents the result of the second-generation CIPS unit root test, which reveals that variables have a mixed integration order with lnMalnut, lnHCI, and lnCORRUPTION being integrated at order 1.

Table 2 Descriptive statistics.
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Table 3 Results for cross-sectional dependence.
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Table 4 CIPS unit root test results.
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For conducting a meaningful long-run analysis, the variables should also be cointegrated. The four test results (Gt, Ga, Pt, Pa) of Westerlund cointegration are presented in Table 5. All the tests have a null hypothesis of no cointegration. Two tests (Pt and Pa) have a robust probability of more than 99% to reject the null hypothesis, which strongly indicates the presence of cointegration or a long-term relationship amongst the variables.

Table 5 Westerlund (2007) cointegration test result.
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With the confirmation of the long-term cointegrating relationship amongst the variables, we can move toward estimating the models for quantifying the long-term relationship.

Our main estimation technique is MMQR, the result of which is presented in Table 6. There are three facets of investment in the agricultural sector for improving the child and maternal nutritional status. The first one is the credit provided in the agricultural sector. The second facet is the investment in research and development in the agricultural sector to improve productivity and thereby enhance nutritional status. The third and last facet is the external assistance or the foreign aid received for the betterment of the state of the agriculture sector, which should lead to better nutritional status. In the results of the MMQR estimation, the credit provided to the agriculture, forestry, and fishing sectors is reducing child and maternal malnutrition across the quantiles. This finding is in line with the literature of Kiresur et al. (2010), who argue that increasing access to agricultural credit in rural areas is a significant way to increase the purchasing power of rural households, which in turn contributes to enhancing the status of the child and maternal nutrition. Amao et al. (2023) also found that credit access increases nutrition security for all households.

Table 6 Method of moments-quantile regression (MMQREG).
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To our surprise, the investment in research and development in the agricultural sector is increasing the malnutrition in the countries selected for the study. When we refer to the literature to find out the reasons for such unexpected results, Kadiyala et al. (2014) argue that agricultural research and development projects may increase the demand for household labor. It increases the women’s time burden, and they are left with less time to take care of their children, which ultimately has a negative influence on the children’s nutrition. Herforth et al. (2012) also blamed the investment in agricultural research and developmental projects for the decline in women’s own and their children’s nutritional status due to the physically demanding nature of such projects. Due to these negative effects, McDermott et al. (2015) have recommended that investment in research and development projects in agriculture must take into account their impact on the women included in such projects. People managing such projects should make sure that in order to improve the nutritional status, these projects should not inadvertently harm the existing nutritional balance of the women and their children.

After the R&D investments in agriculture, we see the impact of external assistance on the nutritional status of mothers and their children. External assistance, as expected, qualifies to mitigate malnutrition and improve maternal and child nutritional status. The values keep on rising from the lowest to the highest quantile, which means that the higher the external assistance, the more the reduction in maternal and child malnutrition. This finding corroborates with Fleuret and Fleuret (1980) where they mention that external assistance acts as a development fund for developing or underdeveloped nations and enables them to meet their needs which improves the child and maternal nutritional status in these countries.

Human capital has a negative and significant impact on malnutrition, and a higher human capital value leads to less malnutrition in mothers and their children. All the values of the quantiles are negative and significant. The result agrees with Victora et al. (2008) who observed that a better human capital value signifies higher economic productivity, which would help in combating the malnutrition problem. This finding is also in line with several studies conducted in countries of SSA, such as Fadare et al. (2019a), Adesugba et al. (2018), and Amare et al. (2021).

The result of energy poverty shows that it has a significant and negative influence on child and maternal malnutrition. The values keep on rising from lowest to highest quantile, again proving that as access to electricity increases, child and maternal malnutrition decreases. In other words, as energy poverty gets reduced, so does malnutrition. This is in line with Churchill and Smyth (2021) who found that if there is a standard deviation rise in energy poverty, it will reduce health by standard deviations of 0.099 and 0.296. Kose (2019) also proved that individuals’ health status is negatively affected by energy poverty.

Finally, the effect of corruption is reported, and it shows that it has a positive and significant effect on child and maternal malnutrition. This indicates that if there is widespread corruption in a SSA country, this will lead to increased malnutrition among the children and mothers. This is confirmed by Cassimon et al. (2021), who found that nutrition security is improved when corruption is controlled. In another study, Cassimon et al. (2022) again confirmed that child undernutrition can be negatively affected by the quality of good governance. This result can be explained by the statement of Bain et al. (2013) where they mentioned that misappropriation of state funds, poor governance, nepotism, tribalism, and corruption of public funds have resulted in income inequalities, and people in the lower segment are facing acute food insecurity and malnutrition.

The result of the bootstrap quantile regression result is presented in Table 7 for robustness analysis. For most of the quantiles, our result from MMQREG agrees with bootstrap quantile regression result.

Table 7 Bootstrap quantile regression result.
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Table 8 shows the findings of the pairwise Dumitrescu–Hurlin panel Granger causality test. There is one-way causality running from human capital to malnutrition, malnutrition to corruption, human capital to agricultural credit, energy poverty to agricultural credit, human capital to foreign aid, human capital to energy poverty, foreign aid to research spending in agriculture. Bidirectional causality can be observed between human capital and corruption, research spending in agriculture and corruption, research spending in agriculture and human capital, human capital and foreign aid, and energy poverty and foreign aid.

Table 8 Pairwise Dumitrescu–Hurlin panel causality tests.
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Conclusion and policy implications

Child and maternal malnutrition creates various challenges for society as well as for the economy as a whole. Women and children are considered to be the most vulnerable in terms of nutrition as they have higher nutrient requirements, but these are not often met (Lartey, 2008). Globally, one of the highest burdens of child malnutrition is in the Sub-Saharan Africa region, and it is a major public health burden that requires urgent actions (Akombi et al., 2017). The prevailing economic and environmental conditions in Sub-Saharan Africa make it more challenging for women and children to meet their nutrition requirements in this region. This study presents the scope and opportunities of agricultural financing to tackle child and maternal malnutrition in the Western part of Sub-Saharan Africa, which bears the highest burden of this type of malnutrition in this region. Additionally, the roles of energy poverty, human capital, and corruption in malnutrition are also analyzed. To achieve the above-mentioned objectives, several econometric techniques, including the second-generation unit root test, Westerlund Cointegration test, and the novel Method of Moments Quantile regression, are implemented.

The results of the study can be summarized as follows: (1) Agricultural credit, external investment in agriculture and human capital have significant negative impact on child and maternal malnutrition; (2) Access to electricity has negative and significant impact on malnutrition, implying that tackling energy poverty tackles malnutrition as well; (3) Research spending in agriculture in the economy induces malnutrition in child and mother; (4) Corruption has positive and significant effect on child and maternal malnutrition; (5) Bidirectional causality can be observed between human capital and corruption, research spending in agriculture and corruption, research spending in agriculture and human capital, human capital and foreign aid and energy poverty and foreign aid; (6) There is one way causality running from human capital to malnutrition, malnutrition to corruption, human capital to agricultural credit, energy poverty to agricultural credit, human capital to foreign aid, human capital to energy poverty, foreign aid to research spending in agriculture.

Based on the findings, several policy implications can be derived for the Western sub-region of Sub-Saharan African countries in order to achieve SDG 3, which mentions health and well-being. Although achieving food and nutrition security is not the burden of the agricultural sector only, it does play a significant role in enhancing nutrition security. Therefore, access to credit and external investment in agriculture (SDG 17.2) should be increased in order to tackle the malnutrition problem among the children and mothers in this sub-region. Especially, sustainable nutrition-focused interventions should be promoted by both private and public sectors and by the domestic government as well as by international donors. Different Incentives to promote nutrition-friendly agricultural development are also crucial for achieving nutrition and food security. Urgent investment and action are needed to prevent and treat malnutrition so that this problem does not become severe. Effective implementation of nutrition programs is also necessary to tackle this phenomenon. The result highlighted that research spending in agriculture has a negative effect on nutritional outcomes for the child and mother. This could be due to the fact that researchers are mostly concerned with the short-term profit generation of the agriculture sector rather than the long-term. Therefore, more research expenditure should be driven towards developing sustainable agricultural policies that will improve the quality of the food and ultimately help reduce malnutrition in this region. Especially, research and development in the smallholder farming system should be promoted, which has very little environmental concerns. Research spending in agriculture also needs to be aligned with nutrition strategies and the commitments of the governments. More coordinated and combined efforts of the agricultural and nutrition sectors can overcome hindrances of nutrition governance. Furthermore, the establishment of indicators to track national commitments and coordinating mechanisms is inevitable to plan for, advocate for and promote better nutrition. With regards to energy poverty, it is necessary to tackle energy poverty which can be achieved by investment in renewable and sustainable energy (SDG 7). It is also important to tackle corruption in these countries and ensure a just governance system that can support the government initiatives taken to minimize child and maternal malnutrition. Governance policies must be well aligned with the agricultural sector such that research and development are utilized properly to increase nutrition security for the mother and the children. However, not just the government initiatives, to tackle malnutrition, it is especially important to increase awareness among the households, especially among the women. In this regard, women’s empowerment through education can help to tackle their own malnutrition as well as that of the child. Therefore, in addition to increasing expenditure in the agricultural sector, it is also important to increase expenditure in women’s education.

Due to the limitations of data, many important variables (e.g. climate change) have not been incorporated in this study. Future studies can incorporate them and provide robust findings. Also, the study can be extended to other SSA regions as well to provide a comparative analysis.