A vision of achieving food security: does physical infrastructure matter? A Sub-Saharan African perspective

Abstract

Achieving Food Security and ending hunger in Africa have been identified as necessary for attaining sustainable growth and development. On the other hand, physical infrastructural development has been documented to have a strong impact on global food security. This study investigates the nexus between physical Infrastructure, including transport, electricity, information and communication technology, water supply and sanitation and food security in Sub-Saharan Africa. The study uses the Panel Corrected Standard Errors (PCSEs) estimation technique on data from 2000 to 2021 across a sample of 40 selected countries. The finding indicates that transport, electricity, information and telecommunication, water supply and sanitation have positive and positive effects on food security in selected SSA countries. The estimated coefficient indicates that improvement in transport, electricity, information and telecommunication technology (ICT), water supply and sanitation enhance food security by 0.916%, 0.664%, 0.448% and 0.758%, respectively. This is a reflection that improvement in physical infrastructure has the potential to accentuate the attainment of food security in Africa. In addition, GDP per capita growth, merchandise trade and political stability positively affect food security, while inflation and population growth negatively affect food security. In this light, the study recommends that special attention should be tailored towards the quality of physical infrastructures and access to such infrastructure at all times. SSA countries must strengthen their infrastructure systems to enhance sustainable agricultural practices, which will greatly improve food security.

Keywords:

• Physical infrastructure
• transport
• electricity
• ICT
• water supply and sanitation
• food security
• PCSE

Reviewing Editor:

• M. Luisa, Escudero-Gilete Universidad de Sevilla, Spain

Subjects:

• Agricultural Development
• Agricultural Economics
• Agriculture and Food
• African Studies
• Energy
• Development Studies
• Development Policy
• Rural Development
• Sustainable Development
• Cities & Infrastructure

1. Introduction

Even though global food production has kept pace with demand over the five decades, approximately 1 billion people have sufficient food to eat, and an additional 1 billion lack nutrition (Misselhorn et al., 2012; Pinstrup-Andersen, 2009). Therefore, food insecurity, whether in terms of access, availability, usage and stability, is among the most popular pressing issues of the century (Food & Agriculture Organization, 2020; Schmidhuber & Tubiello, 2007; Wheeler & von Braun, 2013) stipulate that in 2020, about 690 million people in the world faced food crises. By 2030, the number of undernourished people could increase to 840 million, up from the current estimate of between 83 and 132 million (Pinstrup-Andersen, 2009). In Sub Sahara Africa (henceforth: SSA), there is high level of food insecurity. In SSA, more than 20% of people experience food insecurity (Schmidhuber & Tubiello, 2007). World Bank (2024) alludes that, African countries like Nigeria, Burkina Faso, Somalia and South Sudan are experiencing catastrophic levels of food insecurity. In support of this, the United Nations World Food Programme (UNWFP, 2024) reported that 24.8 million people are suffering worsening food insecurity and a widespread system of privation in Nigeria. It was further indicated that about 107.5 million people in Western and Central Africa faced acute food insecurity in 2023.

According to Laura and David (2021), Griliches (1963), Mamatzakis (2003), Llanto (2012) and U. N. H. S. P. UN-HABITAT (2011), in developing world, about 1/3 of food produced for consumption is lost, 1.3 billion tonnes yearly due to poor infrastructure development. Besides, uncertainties in economic activities occasioned by COVID-19 pandemic, Russia-Ukraine as well as Israel-Hamas wars have worsened the world’s hunger problem (World Bank, 2024; UNWFP, 2024). This situation has put severe constraints on food security, economic growth and poverty reduction across the region. Food insecurity continues to be a significant challenge in sub-Saharan Africa. The region has the highest prevalence of undernourishment globally, with 239 million people suffering from hunger (Abegaz, 2018; Sasson, 2012).

In recent years, food security concerns have become a national and international debate priority. The rise in the world’s population, climate change, infrastructural development challenges, and increasing food costs have impeded agricultural patterns and triggered individual’s ability to be food secured. Although, African countries, especially SSA have made several efforts towards ameliorating food insecurity challenges towards achieving Sustainable Development Goal 2 (Atukunda et al., 2021; FAO, 2010) of the Agenda 2030. However (I. FAO, 2021), aver that despite the several efforts to boost food security, SSA, still need to be on track to meet the SDGs, for which 33.33% of its population are in severe hunger.

In SSA, the agriculture is the largest sector of the economy and it continues to be one of the most significant activities as it still provides livelihood to around 50% of the population and poverty and growth-enhancing sector (Hilson & Garforth, 2023; 19). The growth of the farming sector and its components (Suryahadi et al., 2023), such as the growth of agricultural output, income, employment and others, depend primarily on the level of investment made in infrastructure (Lokesha & Mahesha, 2017a, 2017b). In developing economies, agricultural infrastructure plays a significant role, where a large number of the poor depend on subsistence farming (Lokesha & Mahesha, 2017a, 2017b). Additionally, development economists have claimed that infrastructure development is essential to agricultural productivity. This implies that productivity capacity of agriculture can be greatly determined by adequacy of various physical and non-physical (Edeme et al., 2020). Physical infrastructure is viewed as basic physical structures, such as transportation, information and telecommunication technology (ICT), sewage and water disposal systems that aid economic performance. According to Clover (2003), physical infrastructure is essential in agricultural productivity and they include transport, ICT, water sanitation and electricity. Calderon and Serven (2010) and Calderon et al. (2018) contend that physical infrastructure is critical if Africa’s food security agenda is to be realised.

One of the major challenges facing Africa is shortage of physical infrastructure and remains a major obstacle towards the attainment of food security. For years now, enormous efforts have been made to improve physical infrastructure. Despite this, the SSA trails behind other regions regarding infrastructure services quality and delivery, with the vast gap growing over time. As aptly stated by UN-HABITAT (U. N. H. S. P. UN-HABITAT, 2011), poor infrastructure has increased the cost of intra-African trade by 30–40%. Corroborating, Edeme et al. (2020) highlight that the poor state of infrastructure in Africa has reduced economic growth by 2% and dampened productivity by 40%.

To accelerate infrastructural development, various initiatives have been implemented. For example, the Program for Infrastructure Development in Africa (PIDA) was designed to develop a strategic framework for the provision of critical infrastructure in the area of energy, transport, ICT and water resources. Apart from PIDA, the African Development Bank also places high premium on the capacity of Africa development triggered by infrastructural development (Rosnes & Shkaratan, 2011). In spite of these efforts to improve infrastructure, SSA is still grappling with food security challenges.

In Africa, the number of hungry people who do not have food security is on the rise (Graff & Bremner, 2014). Infrastructure development can go a long way in improving food security. For instance, transport infrastructure, which constitutes roads, railways, sea and airports is crucial determinant of food security. Road infrastructure can contribute to crop diversification, an increase in both average and production and increased food accessibility and stability in terms of primary or final product transportation. With available, efficient and reliable transport networks, food security could improve.

The connection between infrastructural development and food security continue to attract attention among researchers and policy makers. As SSA continues to enhance food security through infrastructural development, there is need for a study on the influence of physical infrastructure on food security. While existing studies, such as (Mamatzakis, 2003; Lokesha & Mahesha, 2017a, 2017b; Edeme et al., 2020; Candelise et al., 2021; Teruel & Kuroda, 2005) analysed the role of physical infrastructure on food security around the globe, less attention is paid on such relationship in SSA. Besides, studies have examined the effect of physical infrastructure on aggregated food security. This study differs from the existing ones by examining the effect of physical infrastructure on both aggregated and disaggregated food security components in SSA. To achieve the objective, this study used data from 54 selected SSA countries from 2000 to 2021 based on Panel Corrected Standard Errors (PCSEs) estimation strategy. Finding is indicative that physical infrastructure, such as transport, electricity, ICT as well as water supply and sanitation had positive and significant impact on food security. GDP per capita growth, merchandise trade and political stability had positive impact on food security. Meanwhile, population growth and inflation had negative impact on food security in the selected countries in SSA.

Apart from the introduction, the remaining part of the article is structured as follows: Section 2 is literature review; Section 3 is methodology; Section 4 is result and discussion and Section 5 is conclusion with policy suggestion.

2. Literature review

In the infrastructural improvement and agricultural performance discuss, different theories have emerged. Notable among them is the Science-based rural improvement proposition which emphasise the utilisation of high and productive innovation to aid productivity (food security). The hypothesis stays on improving the physical infrastructure to drive ideal food security. Infrastructure development enables the availability of goods at a relatively lower cost (Edeme et al., 2020). This is premised on the scientifically support theory of agricultural development, which emphasises the adoption of advance productive technology to aid productivity. The theory anchors on the need to improve physical infrastructure rather than non-physical infrastructure to achieve optimal agricultural productivity (Pivoto et al., 2018). Edeme et al. (2020) opine that various form of infrastructure lie ICT, electricity and quality road that aid agricultural output will certainly contribute to the attainment of food security. In a similar vein, Fedderke and Bogetić (2009) contend that the direct impact of infrastructure on manufacturing sector, transport and ICT portrays how significant positive and powerful physical infrastructure affect labour productivity compared to non-physical infrastructure. Ezeoha et al. (2020) estimated the impact of various forms of ICT on food security. It was found that mobile phones and the internet have a strong and consistent connection when it comes to food security.

Helena (2019) investigated on food security on warehousing and infrastructure for poverty eradication programme in Ghana. The study found that more 50% of food crops produced do not get to the final consumer due to post-harvest loses. The finding further reveals that efficient warehousing has influence in achieving food security and particular attention is needed for integrated food production, during and after bumper harvest. Adepoju and Salman (2013) carried out a study on infrastructure access and its impact on agricultural productivity in different regions of Nigeria. The finding indicates that labour and farm size were positive and significantly affected farmers’ productivity. Also, improvement in infrastructure elements, extension visits and soil practices positively affected productivity.

The study by Mamatzakis (2003) focused on the effect of public infrastructure on productivity growth over the period, 1960–1995. The findings reveal that public infrastructure decreased the total cost of agriculture and hence viewed as a productive input, which contribute to productivity growth. Consistent with the previous findings, Teruel and Kuroda (2005) found that public infrastructure significantly contributed to productivity growth during the late 1970s, Furthermore, Adeyeye (2017) noted that food security in Africa is being engendered by the food processing and appropriate storage facilities/technologies. However, the continent is being faced with various obstacles in terms of rapid diet transition, nutrition and over-consumptions, population pressure and rising food prices, which endanger food security.

Studies have also shown that various form of transport infrastructure, such has as roads, railway lines, air and sea ports have influence on food security. The role of transportation in actualising food insecurity is being obscured by the contribution to long-term economic growth. As argued by Pirie (1993), limited transportation options have occasionally hampered food production and distribution SSA. Lokesha and Mahesha (2017a, 2017b), studied the economic benefits of road infrastructure in achieving food security, poverty alleviation, reducing the unemployment rate and promoting industrial development through agricultural development programmes of India and Karnataka. Regarding the results, improvement in road infrastructure, especially rural roads, significantly increased productivity and production, increased profit and crop diversification and therefore boost food security in India. Kiprono and Matsumoto (2018) investigated the impact of rural roads improvement on agricultural growth in Kenya. It was found that road infrastructure positively impacted on farmers’ productivity evidenced in the diversification into large scale farming, fertiliser distribution and milk market participation. In a similar study conducted by Hine (1993), it was found that road transport has played significant role in maintaining food security and rural development in Ghana. Hine’s (1993) aver that road transport play an important role in identifying the agricultural development and food security relationship. During COVID-19 pandemic it was established that transport infrastructure play a positive role in driving food security in South Africa (Pillay & Scheepers, 2020). In Nigeria, the study by Olorunfemi (2018) shows that rising transportation and food prices in rural areas has been fuelled by lack of access to rural roads. This is an indication that the region’s deteriorating road transportation infrastructure impedes agricultural output. Abdulraheem et al. (2021) investigated the impact of transportation on rural agricultural output. It was found that poor quality of road affected farmers output negatively.

Electricity as a component of physical infrastructure helps to make whether goods, services, food available cheaper rate. Candelise et al. (2021) examined the determinant of food security in 54 developing countries for the period, 2000–2014. Results had it that access to electricity exerts positive impact on food utilisation and availability. Al-Maadid et al. (2017) argued the relationship and the volatility spill-overs between food and energy prices which have extensively analysed in the literature of the study, a total observation of 3353, over the periods 2003–2015. Two selected energy prices that is oil and ethanol, and six selected food prices which are; soybeans, corn, cocoa, coffee, wheat and sugar. The empirical results were comparatively mixed in that the sense that energy and food commodity prices were heavily linked. Evidence shows that the turbulence in the global economy has significantly affected their interconnectedness (Al-Maadid et al., 2017). Moreover, Alam et al. (2018) examined the essential of access to electricity in enhancing labour productivity in the developing economies. The study found long run equilibrium relationship between access and labour productivity.

Aker and Ksoll (2016) and Kabbiri et al. (2018) found that ICT, such as mobile phone has enhanced agricultural productivity. A striking conclusion from these studies is that ICT has the potential to facilitate agricultural adoption in the developing economies. However, Aker (2011) posits that agricultural productivity in developing countries has long lagged behind because on non-utilisation of advanced agricultural technologies, such as fertilisers, seeds and cropping methods.

Some other studied, such as Effiong and Eze (2010), Graff and Bremner (2014), Kang (2015), Sun and Zhang (2021), Tavershima et al. (2022), Aiyedogbon et al. (2024), Gnedeka and Wonyra (2023), and Obiora et al. (2023) have also shown that food security can be linked to population growth, economic growth, trade and inflation. Tavershima et al. (2022) found that in the case of Nigeria, population growth has execrated negative effect on food security. Sun and Zhang (2021) and Gnedeka and Wonyra (2023) assed the effect of trade openness on food security and found that trade openness and GDP growth have contributed to food security, though to a certain level. Effiong and Eze (2010) aver that the continuous rise in food prices has series negative implication on food security. In a review of various determinants of food security, Obiora et al. (2023) highlighted that inflation increases food prices, reduces the individual purchasing power and endanger food security.

From the literature, it is evident that there is paucity of study on that have adopted aggregated approach on the relationship between infrastructural development and food security. This study contributes to array of existing studies by examining the effect of physical infrastructure in SSA adopting both aggregated and disaggregated approach.

3. Methodology

3.1. Data sources and measurement

Data for this study are time series spanning the period, 2000–2021. For the purpose of this study, 40 countries were selected. The countries were selected based on data availability (see Appendix for the list of countries used in this study). In this study, the dependent variable is food security. The principal component analysis (PCA) method posited by Jolliffe (2002) was also deployed to further construct food security composite index. The food security dimensions along its indicators were employed. The primary independent variables are physical infrastructure measured by aggregated physical infrastructure and its various composite indexes of transport, electricity, ICT and water supply and sanitation. Following literature, inflation, GDP per capita, merchandise trade, political stability and population growth were included as control variables. Table 1 presents the variable description, measurement, theoretical expectation and data sources.

Table 1. Variable description, measurement, theoretical expectation and data sources.

Variable	Description and measurement	Expected sign	Data source
FS	Food security (measured by the aggregate of food availability, accessibility, stability and utilisation index		https://www.fao.org/faostat/en/#data/FS
AGIF	Aggregate infrastructure (measured by African Infrastructure Development index	+	https://infrastructureafrica.opendataforafrica.org/pbuerhd/africa-infrastructure-development-index-aidi-
Transport	Transport infrastructure index	+
Electricity	Electricity infrastructure index
ICT	Internet and Communication Technology Infrastructure Index
WSS	Water supply and sanitation index
Inflation	Inflationary rate, annual %	_	https://unctadstat.unctad.org/EN/
Population growth	Population growth, annual %	±	https://data.worldbank.org/indicator
Merchandise trade	Export–imports, % of GDP	+
Political stability	Perceptions of tendency of political instability and violence, including terrorism	+

Source: Authors’ compilation.

3.2. Model specification

The essence of this study was to analyse the effect of physical infrastructure on food security in SSA. Using a two-variable form, the restrictive form of the relationship between the dependent and explanatory variables can be represented in panel form as: (1) $$Y_{\mathit{\text{it}}}={\phi Y}_{\mathit{\text{it}}-1}+{\beta }^{\prime }{X}_{\mathit{\text{it}}}+\left({\delta }_i+{\epsilon }_{\mathit{\text{it}}}\right)$$(1) where $Y_{\mathit{\text{it}}}$ = dependent variable, $Y_{\mathit{\text{it}}-1}$ = lag explanatory variable, $X_{\mathit{\text{it}}}$ = $k\times 1$ vector of the explanatory variable; ${\beta }^{\prime }$ = $\mathit{\text{k x }}1$ vector of coefficients of the explanatory variables; $\phi$ = estimated coefficient; ${\epsilon }_{\mathit{\text{it}}}$ = error term with its usual features and ${\delta }_i$ = specific-country effect. Following the objective and in line with the variables considered in the study, the model for empirical estimation is specified as: (2) $$\begin{array}{c}{\mathit{\text{FSI}}}_{\mathit{\text{itg}}}={\beta }_0+\delta {\mathit{\text{Infra}}}^{\prime }{}_{\mathit{\text{itg}}}+{\beta }_1\mathit{\text{infl}}{a}_{\mathit{\text{itg}}}+{\beta }_2\mathit{\text{GDPca}}{p}_{\mathit{\text{itg}}}\\ +{\beta }_3\mathit{\text{Trade}}\_M_{\mathit{\text{itg}}\hspace{0.17em}}+{\beta }_4\mathit{\text{Poli}}\_\mathit{\text{st}}{a}_{\mathit{\text{itg}}\hspace{0.17em}}+{\beta }_5{\mathit{\text{Pop}}}_{\mathit{\text{itg}}}\\ +C_{\mathit{\text{itg}}\hspace{0.17em}}+{\epsilon }_{\mathit{\text{itg}}}\end{array}$$(2) where $\mathit{\text{FSI}}$ = food security, ${\mathit{\text{Infra}}}^{\prime }$ = the vector of the various infrastructure employed in the study, $\mathit{\text{infla}}$ = Inflation deflator, $\mathit{\text{GDPcap}}$ = gross domestic product per capita, $\mathit{\text{Trde}}\_M$ = trade merchandise, $\mathit{\text{Poli}}\_\mathit{\text{sta}}$ = political stability and Pop = population growth.

3.3. Estimation technique

This study examines the effect of physical infrastructure on food security in SSA. To achieve this objective, the study utilised the PCSE developed by Beck and Katz (1995) The advantage of using the PCSE over the dynamic panel generalised method of moments (GMM) lies in the ability to solve the cross-sectional dependence or better still cross-panel correlation and autocorrelation, Control for heteroskedasticity (Reed and Webb, 2010). PCSE technique is robust to the possibility of non-spherical errors and consists of for long panels.

4. Result and discussions

This section presents result and discussing which entails the descriptive statistics, correlation analysis, battery of pre-estimated tests conducted and empirical result show casing the effect of physical infrastructure on food security in SSA.

4.1. Descriptive statistics

To determine the initial behaviour of the variables used for empirical estimation, the descriptive summary statistics were conducted. The result is presented in Table 2 describes the mean, standard deviation, minimum and maximum values of the variables under consideration for the period, 2000–2021.

Table 2. Descriptive summary statistics.

Variable	Mean	Std. dev	Min	Max
Food security	0.37	0.19	0	1
Aggregate infrastructure	17.98	15.95	−2.32	106.79
Transport	9.15	9.66	1.09	56.52
Electricity	7.49	15.29	−0.3	85.69
ICT	5.08	11.50	−30.63	74.61
Water supply and sanitation	53.61	20.99	−6.85	100.11
Inflation	9.31	34.25	−72.73	557.21
Population growth	2.44	0.91	−2.63	5.61
GDP per capita growth	1.45	4.49	−36.56	28.68
Merchandise trade	53.62	28.88	7.81	187.66
Political stability	−0.49	0.86	−2.69	1.28

Source: Authors’ computation.

In Table 2, the average value for food security in SSA for the period covered in the study was 0.37 and standard deviation of 0.19. Political stability exhibited negative average value. This is suggestive that political instability has retarded efforts at achieving food security in SSA. Besides, African Infrastructure Development index or aggregate infrastructure has mean average of 17.98 with 15.95 as its standard deviation. The respective maximum and minimum value of 106.79 and −2.32 shows a very huge gap in infrastructural development in SSA. The average value for transport, electricity, ICT and water and sanitation services stood at 9.15, 7.49, 5.08 and 53.61, respectively. The deviation from the mean of the variables is 9.662, 15.293, 11.503 and 20.993, respectively. The infrastructure variables are highly dispersed from their mean, as the standard deviations of the variables are considerably higher than the means except for water supply and sanitation services. This is indicative that countries in SSA are highly heterogeneous in terms of infrastructural development. For the period covered in the study, the average value for inflationary rate was 9.31% while population and GDP per capita grew at the average of 2.44% and 1.45%, respectively. Merchandise trade stood at $53.82 million with standard deviation of 28.88.

4.2. Correlation analysis

To ascertain the degree of association among the variables, correlation analysis was conducted. The result is contained in Table 3. In Table 3, the result depicts that there is positive correlation between food security and aggregate physical infrastructure (0.879), transport (0.737), electricity (0.731), ICT (0.537), water supply and sanitation (0.879), merchandise trade (0.455) and political stability (0.526). The positive association among the variables is indicative that increase an increase in aggregate infrastructure, transport, electricity, ICT and water supply and sanitation, merchandise trade and political stability leads to corresponding increase in food supply. The correlation results further that inflation (–0.062), population growth (–0.579) and GDP per capita (–0.083) are negatively correlated with food security. The implication is that inflation, population growth and economic growth is negatively associated with food security in SSA.

Table 3. Correlation result.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)
Food security	1.000
Aggregate infrastructure	0.879	1.000
Transport	0.735	0.816	1.000
Electricity	0.731	0.851	0.605	1.000
ICT	0.537	0.688	0.333	0.490	1.000
WSS	0.879	0.787	0.659	0.544	0.466	1.000
Inflation	−0.062	−0.048	−0.034	−0.029	−0.026	−0.041	1.000
Population growth	−0.579	−0.527	−0.561	−0.432	−0.220	−0.490	−0.002	1.000
GDP per capita growth	−0.083	−0.064	0.018	−0.039	−0.137	−0.121	−0.123	0.061	1.000
Merchandise trade	0.455	0.317	0.361	0.282	0.126	0.306	0.004	−0.454	−0.007	1.000
Political stability	0.526	0.443	0.508	0.346	0.151	0.387	−0.117	−0.316	0.062	0.393	1.000

Source: Authors’ computation.

4.3. Diagnostic tests

In determining the suitability of the panel standard errors estimation, series of diagnostic tests, such as cross-sectional dependence, heteroskedasticity, serial correlation and endogeneity tests were conducted.

4.3.1. Cross-sectional dependence test

To check whether the residuals in our estimates are cross-sectionally dependent, the Pesaran (2004) including stationary and unit root dynamic heterogeneous panels test was conducted. Table 4 presents the Persaran tests for cross-sectional dependence test.

Table 4. Cross-sectional dependence test.

Variable	CD-test	p Value	Mean ρ	Mean abs (ρ)
Food security	115.184	0.000	0.88	0.97
Aggregate infrastructure	127.954	0.000	0.98	0.98
Transport	4.803	0.000	0.04	0.49
Electricity	50.474	0.000	0.39	0.57
ICT	129.198	0.000	0.99	0.99
Water supply and sanitation	105.374	0.000	0.80	1.00
Inflation	24.714	0.000	0.19	0.30
Population growth	5.368	0.000	0.04	0.47
GDP per capita growth	23.094	0.000	0.18	0.26
Merchandise trade	14.179	0.000	0.11	0.37
Political stability	−.237	0.813	0.00	0.38

Source: Author’s computation.

4.3.2. Panel unit root test

The panel unit root test was conducted using Persaran unit root test (Pesaran, 2007) to check whether time-series variables are non-stationary. The result is presented in Table 5.

Table 5. Im-Persaran-Shin panel unit root test result.

	Level
Variable	Constant	Trend	1st Difference	Decision
Food security	−2.306***	−2.950***	−2.828***	I (0)
Aggregate infrastructure	−2.093***	−2.547*	−3.875***	I (0)
Transport	−0.945	−1.956	−3.083***	I (1)
Electricity	−2.137***	−2.518*	−3.585***	I (0)
ICT	−2.527***	−2.871***	−3.006***	I (0)
Water supply and sanitation	−1.700	−2.022	−2.469***	I (0)
Inflation	−1.569	−2.261	−4.516***	I (1)
Population growth	−1.197	−2.373	−2.024**	I (1)
GDP per capita growth	−2.153***	−2.550*	−4.389***	I (0)
Merchandise trade	−1.497	−2.681***	−3.324***	I (0)
Political stability	−1.912	−2.753***	−3.719***	I (0)

Source: Authors’ computation.

In Table 5, the result shows that the bias-adjusted test statistics are all significantly less than zero. This is a pointer that the null hypothesis of a unit-root is rejected. Consequently, we accept the alternative hypothesis that all the variables are stationary at levels.

4.3.3. Effect of physical infrastructure on food security

Table 6 presents the result of the effect of physical infrastructure on food in SSA for the period covered in the study.

Table 6. Result of the PCSE estimate on the effect of physical infrastructure on food security.

	(1)	(2)	(3)	(4)	(5)
Variable	fs_index	fs_index	fs_index	fs_index	fs_index
Inflation	−2.7705**	−6.7705**	−6.4906	−3.3505	−1.0705
	(1.3500)	(2.8600)	(2.2300)	(2.6100)	(1.3100)
Population growth	−0.0660**	−0.0130***	−0.0914**	−0.0148**	−0.0479*
	(0.0270)	(0.00292)	(0.00428)	(0.0672)	(0.00260)
GDP per capita growth	0.0157	7.94e-05	0.00207	0.0213	0.0160
	(0.0122)	(0.0224)	(0.0208)	(0.0269)	(0.0119)
Merchandise trade	0.0231***	0.0314***	0.0345***	0.0661***	0.0176***
	(6.42e-05)	(0.0101)	(0.000101)	(0.0137)	(5.83e-05)
Political stability	0.0789***	0.0125***	0.0153***	0.0294***	0.0952***
	(0.00245)	(0.00358)	(0.00408)	(0.00584)	(0.00222)
Aggregate infrastructure	0.0826***
	(0.0625)
Transport		0.0916***
		(0.0794)
Electricity			0.0664***
			(0.0558)
ICT				0.0448***
				(0.0107)
Water supply and sanitation					^0.0758***
					(0.0312)
Constant	0.227***	0.306***	0.329***	0.361***	−0.0315
	(0.0157)	(0.0218)	(0.0204)	(0.0207)	(0.0196)
Observations	880	880	880	880	880
R^2	0.690	0.447	0.492	0.391	0.791
No of countries	40	40	40	40	40

Source: Authors’ computation.

Note: Figures in parentheses are standard errors; ***, **, * indicate p < 0.01, p < 0.05 and p < 0.1, respectively.

The result in Table 6 indicates that all the physical infrastructure indexes, including the aggregate infrastructure index, transport, electricity, ICT and water supply and sanitation, has positive and significant effect on food security in SSA. As indicated, improvement in transport infrastructure, electricity, ICT and water supply and sanitation enhances food security in SSA by 0.009%, 0,058%, 0.048% and 0.076%, respectively. The result conforms to the finding of previous studies that infrastructure positively affects food security. The result also confirms that infrastructure, as defined by African Development Bank have the capacity to accentuate the attainment of food security in Africa. Thus, reaffirming the hypothesis that physical infrastructure positively influences food security in SSA. Based on the assumption that improve African infrastructure development index of aggregate infrastructure, transport, electricity, ICT, water supply and sanitation capacities will enhance food security in SSA countries. This implies that lack of physical infrastructure can also retard economic growth and productivity (Solow, 1956). This is tandem with the neoclassical growth theory that physical capital (infrastructure) can enhance agricultural output (productivity). And of endogenous growth theory (Lucas, 1988; Romer, 1990) that technological development triggers economic growth. The finding is counterintuitive and consistent with the prior expectation of the study by Lokesha and Mahesha (2017a, 2017b), Kiprono and Matsumoto (2018), Dorosh et al. (2012) and O. E. Inoni and Omotor (2009). The presence of good and available transport infrastructure networks especially roads facilitate the achievement of food security. Conversely, a mere increase in transport infrastructural development does not necessarily guarantee improvement in food security.

The result further indicates that electricity exerts positive and significant effect on food security in SSA countries. For every percentage increase in access to electricity, the percentage of individuals suffering from food losses decreases, thereby improving food security. The positive effect of access to electricity on food security confirms the findings of previous works, such as (Candelise et al., 2021; Cheng et al., 2021). Additionally, the use of ICT infrastructure in addressing food security in our study confirms that they intertwine. ICT, therefore, can enhance food security because its usage may improve food safety and the value chain, for instance, mobile phones, television, radio and internet tracking for better accuracy and comprehensiveness. ICT promotes the marketing of agricultural products as a medium for searching and exchanging market information, links farmers and consumers (we have learnt immensely about this during the COVID-19 pandemic), and connects rural farmers with supply and demand information on agricultural produce and material and consumer product. E-commerce may foster agricultural entrepreneurship and create flexible, inclusive employment opportunities. ICT goes beyond educating farmers on new available technologies and strategies in search of weather information (weather focast tool), when and how to plant and harvest crops. The latter will as well enable farmers to come out with dry resistant crop that do well in certain region of SSA. This thus will be going along to enhance food availability, accessibility, utilisation and stability. This has been demonstrated by previous studies like (Aker & Ksoll, 2016; Kabbiri et al., 2018; Aker, 2011; Gouvea et al., 2022).

In addition, water supply and sanitation are found to be important in achieving food security in SSA. As embedded in SDG 17, Clean Water and Sanitation SDG 6 is a particular SDG focusing on addressing water scarcity issues and enhancing sanitation for all people. Drinking water, sanitation and hygiene, treating and reusing wastewater, and ecosystem health are just a few of the topics covered by SDG 6. Massive investments in water supply and sanitation and its management are central to sustainable food security and others. The findings also align with the ‘High 5s’ of AfDB (2023), which attest that water security reinforces food security, for which agriculture represents approximately 70% of total water consumption, energy security, industrialisation, regional integration and improving people’s life quality in Africa. Nevertheless, without proper water supply and sanitation services there are high chances of food poisoning, rising CO₂ emissions, diseases among others.

Moreover, population growth has negative effect on food security in SSA. As portrayed, a percentage increase in population growth reduced food security in SSA by about 0.067% for the period covered in the study. Tavershima et al. (2022) also found that growth in population asserts negative pressure on the quantity of food needed to adequately feed the people. Also, inflation had negative effect on food security in SSA. This is tandem with the finding of Effiong and Eze (2010) that continuous rise in food prices has series negative implication on food security. Obiora et al. (2023) contend that inflation increases food prices, reduces the individual purchasing power and endanger food security. Meanwhile, GDP per capita growth, merchandise trade and political stability had positive influence on food security in SSA for the period covered in the study. Sun and Zhang (2021) and Gnedeka and Wonyra (2023) also found trade openness on food security and found that trade openness and GDP growth has contributed to food security. This is suggestive that increase in GDP per capita and merchandise and trade presence of political stability, the vision of achieving food security in SSA could be realised.

5. Conclusion and policy suggestion

African countries desire to achieve food security through improvement in infrastructure. This study examines the effect of physical infrastructure on food security in SSA. To achieve the study objective, the study employed the PCSE estimation strategy based on data from 40 selected SSA countries for the period, 2000–2021. Findings from the study indicate that physical infrastructure indexes, including the aggregate infrastructure index, transport, electricity, ICT and water supply and sanitation, had positive and significant effect on food security in SSA for the period covered in the study. As indicated, improvement in transport infrastructure, electricity, ICT and water supply and sanitation enhances food security in SSA by 0.009%, 0,058%, 0.048% and 0.076%, respectively. This is a reflection that physical infrastructure has the potential to accentuate the attainment of food security in SSA. Moreover, population growth has negative effect on food security in SSA. As portrayed, a percentage increase in population growth reduced food security in SSA by about 0.067 percent for the period covered in the study. This is in line with the assertion that where the growth in population, the quantity of food needed to adequately feed the people decreases. Also, inflation had negative effect on food security. Meanwhile, GDP per capita growth, merchandise trade and political stability had positive influence on food security in SSA for the period covered in the study. This is suggestive that increase in GDP per capita and merchandise and trade presence of political stability, the vision of achieving food security in SSA could be realised. Arising from the findings, the study recommends, among other things, that special attention should be tailored towards improving the quality and access to physical infrastructure in Sub-Saharan Africa. Besides, SSA countries should consider stability in macroeconomic environment when rolling out plans towards achieving food security.

Data sources

Data employed for the study were generated from the following sources: https://www.fao.org/faostat/en/#data/FS; https://data.worldbank.org/indicator; https://infrastructureafrica.opendataforafrica.org/pbuerhd/africa-infrastructure-development-index-aidi-2022; https://unctadstat.unctad.org/EN

Disclosure statement

The authors did not report any conflict of interest.

Additional information

Funding

No funding was received by the authors.

Notes on contributors

Yaya Deome Hamadjoda Lefe

Yaya Deome Hamadjoda Lefe Pan African Scholar with a Ph.D/Doctorate Degree in Governance and Regional Integration from the Pan African University-African Union Commission. His areas of research interest are: Climate change, food security, infrastructural developments, Governance and Regional integration, climate finance; Gender, Tourism, Inequalities, Development Economics, Migration and others.

Aloysius Mom Njong

Yaya Deome Hamadjoda Lefe Pan African Scholar with a Ph.D/Doctorate Degree in Governance and Regional Integration from the Pan African University-African Union Commission. His areas of research interest are: Climate change, food security, infrastructural developments, Governance and Regional integration, climate finance; Gender, Tourism, Inequalities, Development Economics, Migration and others.

Richardson Kojo Edeme

Yaya Deome Hamadjoda Lefe Pan African Scholar with a Ph.D/Doctorate Degree in Governance and Regional Integration from the Pan African University-African Union Commission. His areas of research interest are: Climate change, food security, infrastructural developments, Governance and Regional integration, climate finance; Gender, Tourism, Inequalities, Development Economics, Migration and others.

Appendix

Table

List of countries included in the study

Angola, Burkina Faso, South Sudan, Niger, Somalia, Nigeria, Rwanda, Liberia, Mali, Cameroon, Gabon, Benin, Botswana, Burundi, Ethiopia, Zambia, Togo, Ghana, Malawi, South Africa, Central African Republic, Chad, Congo, Comoros, Côte d’Ivoire, Democratic Republic of the Congo, Eswatini, Gambia, Guinea Bissau, Kenya, Lesotho, Madagascar, Mauritania, Mauritius, Mozambique, Namibia, Sao Tome and Principe, Senegal, Serra Leone, Uganda and Zimbabwe.