In this study, we contribute to the existing literature on energy poverty and food security in Sub-Saharan Africa (SSA). The paper is carried out on a panel of 36 sub-Saharan African countries over the period 2000 to 2020. Adopting several estimation methods, such as fixed effects, Driscoll-Kraay, Lewbel 2SLS and Generalized Method of Moments, we find positive results of energy on food security. In SSA, the energy development index, access to electricity and access to clean energy for cooking positively influence food security. This may encourage policy makers to prioritise investment in off-grid energy for vulnerable households through small-scale power systems, which can promote food security by instantly affecting local food production, preservation and preparation and contribute to people's sustainability and contribute to preserve the environment.
Research Article
Does Energy Poverty increases Starvation? Evidence from Sub-Saharan Africa
https://doi.org/10.21203/rs.3.rs-1989826/v1
This work is licensed under a CC BY 4.0 License
Journal Publication
published 04 Mar, 2023
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Driscoll-Kraay
Energy Poverty
GMM
Lewbel 2SLS
Starvation
Sub-Saharan Africa
Food security is still a major objective for economic decision-makers in developing countries (DCs) and is closely linked to social stability in these regions, where poverty can reach very high levels. According to FAO et al. (2021), 690 millions of people worldwide suffered from malnutrition in 2019, 821 millions of people in 2017 and 784 millions in 2015. This is about 8.9% of the world's population, with 10 millions more people in one year and almost 60 million in five years at risk of hunger. The vast majority live in developing countries, particularly in Sub Saharan Africa. Goal 2 of the Sustainable Development Goals (SDGs) remains one of the priorities of sub-Saharan African countries. In addition, Millions of people are still in a situation of food insecurity. These are mainly in developing countries, which are plagued by monetary and non-monetary poverty. Thus, two thirds of these people reside mainly in two regions of the world, namely sub-Saharan Africa and South Asia, with 237 millions and 277 millions people suffering from hunger respectively (SDG, 2019).
Moreover, the economic and social potential of these countries does not necessarily translate into improved food security. Indeed, it is confronted with a global economic context, which is characterised by changes in growth, commodity prices, climate, trade and the recent corona virus pandemic. Thus, this alarming situation in SSA is undoubtedly linked to poor storage conditions and post-harvest losses, given the continent's agricultural potential.
Access to energy is one way that sub-Saharan African countries can reverse these alarming trends in malnutrition. As one of the major goals, access to clean energy remains central to the achievement of the other Sustainable Development Goals set since 2015. Thus, access to modern energy services is essential to meet basic social needs while promoting economic development. Modern energy services, including electricity and gas, affect agricultural productivity, health, education, drinking water and communication services (International Energy Agency, 2014). Per capita energy availability and electricity consumption are closely correlated with economic development and other indicators of modern life, with the presumption that electricity consumption is related to a better life and well-being (Starr, 1972; Sambodo et Novandra, 2019).
With the proven importance of energy for the population, some have made enormous progress in terms of access to electricity for their populations, yet others are still lagging behind, such as those in South Asia, Sub-Saharan Africa; with rates of 94.39% and 46.74% respectively, while Latin America and the Caribbean, East Asia and the Pacific, and the Middle East and North Africa show figures of 97.23%, 98.12% and 98.42% (WB, 2022; IEA et al., 2020) of access to electricity. It appears that most of the work done so far has focused on the micro level (Kirubi et al., 2009; Ahlborg et Sjöstedt, 2015; Islam et al., 2017; Mushtaq et al., 2009). This study goes beyond previous studies to a macroeconomic perspective (Candelise et al., 2021). Drawing on various theories linking energy and food security, there is a non-consensual debate on this link. First, there is the livelihoods theory highlighted by authors such as Chambers (1987) and Conway (1991). Then there is Sen's (1985; 1993) capability theory, which emphasises the capacity that each individual may have to take responsibility for himself. Finally, there is the theory of the right to food which has been defended since the work of Sen (1981) and continued by other works such as those of De Schutter (2010) and Burchi and De Muro (2012). These theories have underpinned much work on food security.
Subsequently, this study departs from previous work and attempts to fill gaps in the literature in at least five respects: first, it is the first study to analyze the relationship between energy poverty and food security in the context of sub-Saharan Africa. A region that remains paradoxically rich in fertile land and energy resources. Second, in addition to using access to electricity and access to clean energy for cooking, we constructed an energy development index using the principal component analysis (PCA) method. Third, this relationship is tested by constructing a food security index that is able to reflect the food situation in SSA. In contrast to other measures of food security, this index considers all four dimensions of food security as defined by the FAO. Fourth, the most robust estimation techniques were used in order to obtain results that are very close to reality (Lewbel, 2SLS, Driscoll-Kraay and the method of generalized moments). Finally, fifth we studied the causal mediation from energy poverty to food security in order to detect potential transmission channels.
As for the rest of the paper, it is organized as follows: Section 2, review of literature on energy and food security. The model, data and methodologies are presented in Section 3. Section 4 presents the empirical, robustness results and discussions. Sections 5 is devoted to the conclusion and Policy Implications.
The literature dealing with the link between energy and food security directly and indirectly, especially at the macro level, remains very limited. The interconnection between energy and food has been partially studied and explored in the literature. Nevertheless, a few works empirically assessing the link between these variables (Al-Maadid et al., 2017; Taghizadeh-Hesary et al., 2019, Candelise et al., 2021) have reached mixed results. Taghizadeh-Hesary et al. (2019) find, for example, a negative impact of energy price volatility (i.e. world oil and biofuel prices) on food prices. This in turn negatively affects food security, assuming that higher food prices would jeopardise food security and increase the number of undernourished people. In contrast, other studies show that electricity is needed for food production along the entire value chain: in crop production, fisheries, livestock and forestry; in post-harvest processing (e.g. many renewable energy technologies such as solar dryers and refrigerators can increase resource efficiency); in food storage and processing; in food transport and distribution; in food preparation (FAO, 2012; Edward et al., 2020). In particular, improved access to electricity can improve food quality through cooking and refrigeration (WB, 2017).
The Practical Action report (2012) argues that rural electrification can support agricultural development, boosting productivity (e.g. by providing access to water pumping and irrigation), efficiency of crop conversion and storage, and agri-food products. The same report highlights several ways in which access to electricity can improve agricultural productivity. Electricity can provide mechanical power for soil preparation, planting, cultivation, irrigation and harvesting, thereby increasing productivity and minimising the drudgery of farmers' work. Water availability is a key factor in determining irrigation potential, and electricity can facilitate water pumping. In off-grid rural areas, renewable energy technologies such as solar photovoltaic systems and wind pumps are technically and economically viable options for soil irrigation. Electricity also provides access to ICTs, which in turn can help increase agricultural productivity by providing access to new farming methods through improved communications and knowledge sharing.
Therefore, the use of mobile phones could help to structure service providers for land cultivation. In addition to food storage and freezing, electricity facilitates food preservation (including smoking and forced air drying) and processing into various high-quality/value-added forms (Candelise et al., 2021). In addition, access to electricity has an indirect impact on food security through multiple channels. Thus, many studies have shown the role of energy on economic growth. This claim is also supported by theoretical and empirical macroeconomic studies, which consistently find improvements in food security in conjunction with economic growth (Timmer, 2000; Soriano and Garrido, 2016). Incomes rise and inequality falls (Smith and Haddad, 2002; Tiwari and Zaman, 2010) as countries become richer. This contributes to higher household incomes, which in turn increases purchasing power, allowing households to access food.
The literature on income-generating activities also highlights how the use of electricity can help create small businesses, thereby stimulating production and helping existing businesses to be more efficient, and thus generate new jobs and income (Riva et al., 2018). Therefore, based on a study of nearly 4,000 households in Indonesia, Gibson and Olivia (2010) showed that improving the quality of electricity infrastructure has a positive effect on the growth of rural non-farm enterprises. These contribute to job creation and livelihood diversification. As a result, better access to electricity is presented by Riva et al. (2018) as a factor that can promote income-generating activities for the population.
Saing (2018), analyses the effect of electrification on household consumption in Cambodia, using panel data from surveys over the period 2004–2011. He finds that household electrification in these areas has led to improved food security. As a result of rural electrification, household consumption increases by 16.6%, and households in the top quintile gain the most. Based on the non-exhaustive review of the literature, three conclusions can be drawn. Electricity has a direct and indirect effect on agriculture both in rural and urban areas, according to the literature. The second problem is that very few studies have taken a macro-level look at the impact of energy poverty on food security in sub-Saharan Africa. Furthermore, the non-consensual findings warrant further investigation in a context still marked by food crises, exacerbated by Covid-19. A developing region such as sub-Saharan Africa must assess the effects of energy poverty on food security.
3.1- Model and econometric estimation approach
Based on recent research for a range of developing countries, our model is inspired by Candelise et al. (2021). The model is therefore as follows:
$${y}_{i,t}= {\alpha }_{i,t}+{x}_{i,t}\beta + { + \mu }_{i,t} + {v}_{i,t}+ {\epsilon }_{i,t } \left(1\right)$$
Where\({y}_{i,t}\), represents the dependent variables, \({\alpha }_{i}\) denotes country fixed effects, \({x}_{i,t}\)denotes independent variables, \(\beta\)denotes the coefficient estimate \({\epsilon }_{i,t }\) is the error term, i is the cross-sectional units and t is the period. As our sample consists of several countries, there is a presumption of heterogeneity in our panel. Therefore, it is necessary to take into account individual (country) heterogeneity. Due to this, we use the fixed effect model, which takes heterogeneity into account. By using fixed effects, we assume that each country has its own fixed effect. Errors are always homoscedastic, so specific effects are only taken into account at the residual level. Here is the empirical model:
$${FSI}_{i,t}= {\beta }_{0}+ {\beta }_{1}{EP}_{i,t}+ {\beta }_{2}{X}_{i,t}+ {\epsilon }_{i,t} \left(2\right)$$
The Food Security Index (FSI) measures food security, while energy poverty is measured by the Energy Development Index (EDI), access to electricity (Elec) and access to clean energy for cooking (Clean). \({X}_{i,t}\) it is the set of control variables and \({\epsilon }_{i,t}\) is the error term.
Fixed-effect models allow for heterogeneity as a result of the country effect. A cross-sectional dependency is also present in the panel data. For this reason, we use the Driscoll and Kraay estimation method. It may not be true that our variables have a static relationship as assumed by the fixed effect model or Driscoll and Kraay. Even though Driscoll and Kraay's fixed effects method is easy to implement, it does not account for unobserved heterogeneity. To address the question of endogeneity, we must either use the Lewbel 2SLS estimation method or rewrite our model dynamically. With Lewbel's technique, our results are more robust than when we use instrumental variables. Instrumental variable estimators are generally difficult to use in most applications due to the difficulty of finding suitable instruments that simultaneously satisfy these conditions (Baum et al., 2012; Stock et al., 2002). As a solution to this problem, we use Lewbel's two-stage least squares (2SLS) which is applied when sources of identification, like appropriate internal and external instruments, are unavailable. In the absence of traditional identification information, Lewbel's 2SLS approach is essential for identifying structural parameters in regression models with an endogenous or poorly measured regressor. Instruments based on heteroskedasticity are constructed in-house for this method. Lewbel's 2SLS approach has the advantage of not requiring standard exclusion restrictions to be met.
Accordingly, we apply a generalized method of moments (GMM), which was proposed by Arellano et Bond (1991), Arellano et Bover (1995), and Blundell et Bond (1998) so that the model's dynamics can also be verified. In a system, the Generalized Method of Moments (GMM-S) is used for several reasons. Heteroskedasticity, endogeneity, overidentification, and validity are all econometric problems that are frequently solved with the GMM-S method. The GMM-S method is more efficient at dealing with heteroskedasticity in empirical studies, according to Baum et al. (2012). The related literature uses GMM to assess instrument strength, according to Bazzi et Clemens (2013). The dynamic panel GMM, according to Roodman (2009), can lead to too many instrument problems. The rule of thumb is that the number of instruments should be smaller than the number of countries in order to solve this problem. In addition, the GMM has the advantage of treating all explanatory variables as instrumental variables according to their lagged values (in terms of level and first difference). Hence the following model:
$${FSI}_{i,t}= {\beta }_{0} +{FSI}_{i,t-1} + {\beta }_{1} {EP}_{i,t}+{\beta }_{2} {GDP/h}_{i,t}+ {\beta }_{3}{ Fixe/T}_{i,t}+ {\beta }_{4}+{\beta }_{5}+{ \beta }_{6} {CTC}_{i,t}+ {\beta }_{7}{DCPS}_{i,t}+ {\mu }_{i,t} + {v}_{i,t} + {\epsilon }_{i,t} \left(4\right)$$
In this paper, energy poverty and food security are examined using a two-step system of GMM.
3.2- Data source
From 2000 to 2020, we use 36 sub-Saharan African countries. In the empirical estimations, energy access, food security, and other control variables were used. The empirical estimations are based on the availability of our variables of interest and dependent variables, as well as other control variables. Appendix provides definitions and sources of our key variables. Principal Component Analysis (PCA) is used to construct the food security index. To better understand the food situation in Sub-Saharan Africa, sixteen variables were used to construct this index, taking into account the four dimensions of food security. FAOSTAT provides all of these variables. The variables used to measure energy poverty are access to electricity, access to clean energy for cooking, and an energy development index that was constructed based on the World Development Indicator. In terms of our control variables, they are presented in detail in the appendix, which also takes in the World Development Indicators.
On the food security index, the energy development index, access to electricity, and access to clean energy are positively correlated.
3.2.1. Dependent variable
To measure the dependent variable, a composite index is constructed using principal component analysis (PCA) (Slimane et al., 2016). There are four dimensions to consider: availability, accessibility, use and stability. By transforming correlated variables into uncorrelated variables, the PCA1 method aims to reduce the number of indicators.
3.2.2. Variables of interest
Energy poverty is defined by Acharya and Sadath, (2019); Thomson et al. (2016) as a lack of access to and use of modern energy services. Thus, this concept has been understood differently in the literature. Recently, Churchill and Smyth, (2021) refer to energy poverty as the impact of a lack of adequate access to energy on the objective or subjective well-being of a household. The subjective well-being of a household is reduced by energy deprivation and poor cooking and heating conditions. This may capture the utility of energy access for a household, but is often plagued by self-reporting bias and survey inconsistencies (Herrero, 2017).
In the opposite direction, energy poverty can be measured objectively by the proportion of households that spend a higher percentage of their income on energy bills, which makes them more economically vulnerable. This is particularly the specific case in developing countries (Boardman, 1991; Healy and Clinch, 2004; Hills, 2012; Tod and Thomson, 2017) such as sub-Saharan Africa. Nevertheless, these measures may suffer from sample selection bias, leading to over- or underestimation of energy poverty rates (Herrero, 2017). The IEA (2010) and Banerjee et al. (2021) constructed an energy development index (EDI) using geometric and arithmetic means, while we use principal component analysis. This index is based on three indicators covering the renewable energy sector. These three indicators are: 1) access to electricity; 2) consumption of renewable energy; and 3) access to clean energy for cooking.
3.2.3. Control variables
Gross domestic product per capita (GPD/h) can positively influence food security at the level of a country or a larger region such as SSA (Campi et al., 2021; Candelise et al., 2021). Information and communication technologies have positive effects on food security, particularly through the use of fixed-line telephones (Fixed-T) as demonstrated by Anser et al. (2021). Employment (EmplT) also influences the degree of household food security (McCordic et al., 2021). Population density (PopD) affects the ability of a region to better feed its population (Badami et Ramankutty, 2015; Vijay et Armsworth, 2021; Candelise et al., 2021). Governance as measured by Control of Corruption (CTC) influences policy measures taken to curb famine (Anser et al., 2021). Financial development (DCPS), measured by the volume of financial resources provided to the private sector by financial institutions. It increases the availability of credit and enables agricultural entrepreneurs to access financial resources that can enable them to invest more in the agricultural sector (Chisasa et Makina, 2012). Urban population growth (Urbpop) as noted by Candelise et al. (2021) is able to affect the level of food security. Finally, political stability (Spop) contributes to increasing the level of stability and availability of food (Ribeiro et al., 2021). Imports (Imp) also positively regulate the supply of food in a country (Candelise et al, 2021).
|
Variable |
Abrev. |
Obs |
Mean |
Std. Dev. |
Min |
Max |
Data |
|---|---|---|---|---|---|---|---|
|
Food security Index |
FSI |
756 |
-3.97E-05 |
0.5414625 |
-1.47 |
1.64 |
FAOSTAT(2022) |
|
Energy Development Index |
EDI |
756 |
0.0000397 |
0.0292986 |
-0.04 |
0.09 |
WDI(2022) |
|
Access to electricity |
ELEC |
698 |
39.10533 |
24.86226 |
1.27018 |
100 |
WDI(2022) |
|
Access to clean energy for cooking |
clean |
612 |
20.34379 |
24.47314 |
0.15 |
93.34 |
WDI(2022) |
|
GDP per capita |
GDP/h |
739 |
1.739847 |
4.568149 |
-36.55692 |
28.676 |
WDI(2022) |
|
Fixed-telephone |
Fixed-T |
726 |
4.258437 |
9.935925 |
0 |
59.98999 |
WDI(2022) |
|
Employers total |
EmplT |
720 |
1.991056 |
1.57211 |
0.04 |
8.39 |
WDI(2022) |
|
Population density |
PopD |
756 |
86.79749 |
118.8185 |
2.17977 |
623.5172 |
WDI(2022) |
|
Control of corruption |
CTC |
720 |
34.42657 |
21.96376 |
0.5050505 |
84.84849 |
WGI(2022) |
|
Domestic credit to private sector |
DCPS |
723 |
20.11717 |
18.24886 |
0.449183 |
106.2603 |
WDI(2022) |
|
Imports of goods |
Imp |
676 |
42.47359 |
26.23712 |
4.829832 |
236.391 |
WDI(2022) |
|
Urban population |
Urb |
756 |
42.6223 |
17.11767 |
14.61 |
90.092 |
WDI(2022) |
|
Political stability |
PST |
720 |
35.28784 |
22.99568 |
0 |
93.75 |
WGI(2022) |
| Source : Authors | |||||||
The descriptive statistics show little variation, suggesting unbiased results.
|
Variables |
(1) |
(2) |
(3) |
(4) |
(5) |
(6) |
(7) |
(8) |
(9) |
(10) |
|---|---|---|---|---|---|---|---|---|---|---|
|
(1)FSI |
1.000 |
|||||||||
|
(2)EDI |
0.588 |
1.000 |
||||||||
|
(3)ELEC |
0.403 |
0.892 |
1.000 |
|||||||
|
(4)Clean |
0.260 |
0.937 |
0.793 |
1.000 |
||||||
|
(5)GDP/h |
0.011 |
0.010 |
-0.023 |
0.010 |
1.000 |
|||||
|
(6)Fixe-T |
0.213 |
0.218 |
0.061 |
0.224 |
-0.016 |
1.000 |
||||
|
(7)EmplT |
0.392 |
0.527 |
0.419 |
0.509 |
0.042 |
0.210 |
1.000 |
|||
|
(8)PopD |
0.320 |
0.291 |
0.334 |
0.231 |
0.116 |
-0.112 |
-0.066 |
1.000 |
||
|
(9)CTC |
0.503 |
0.541 |
0.362 |
0.482 |
0.089 |
0.078 |
0.292 |
0.340 |
1.000 |
|
|
(10)DCPS |
0.683 |
0.777 |
0.670 |
0.710 |
0.034 |
0.090 |
0.502 |
0.462 |
0.583 |
1.000 |
| Source : Authors | ||||||||||
[1] All the variables used here are set out in the Annex
Here are the results of the base model. For this first estimation, fixed effects were used. For this first estimation, fixed effects were used. Then, we use the Driscoll-Kraay and Lewbel (2012) technique to test our results.
4.1- Results and interpretation
For all our variables of interest, the results of the basic model are positive and significant. Thus, access to electricity, clean energy for cooking, and energy development index all contribute to food security in sub-Saharan Africa. If access to electricity or clean energy is increased by 1 unit, the level of food security is increased by 0.588 unit and 1.68 unit for the fixed effects, 1.37 unit and 1.78 unit for Driscoll-Kraay and finally, 1.53 unit and 1.7 unit for the Lewbel technique (2012). Moreover, the energy development index also remains positive and significant for all three methods used.
They suffer particularly from nutritional deficiencies and post-harvest losses. In sub-Saharan Africa, post-harvest food products suffer a huge loss in quantity and quality. A study conducted by FAO et al. (2018) in Uganda concluded that 3.3 unit of quantitative losses of maize occur at harvest and 10 unit during storage. During storage, qualitative losses were estimated at 50%. In recent years, farmers have experienced significant losses due to lack of access to electricity at harvest time (Ponguane, 2021). Based on our observation, the control variables have a variety of effects on food security. Food security is positively impacted by gross domestic product per capita, ICT (fixed telephone) and employment. Anser et al. (2021); McCordic et al. (2021); Campi et al. (2021); Candelise et al. (2021) have previously found similar results.
Regarding the fixed, Driscoll-Kraay, and Lewbel effects, financial development and population density are generally positive and significant. With this somewhat divergent result, one can observe a certain feverishness regarding financial development, which is still embryonic in order to really affect people's living standards. According to Chisasa et Makina (2012), financial development plays an important role in reducing food insecurity. Many subSaharan African countries have high population densities relative to food availability, which negatively affects food security. It is consistent with the findings of Badami et Ramankuttty (2015), Vijay et Armsworth (2021), and Candelise et al. (2021) that population density reduces food security in developing countries. Governance also plays a major role in a population's ability to access food. Previous studies (Anser et al., 2021) have found that governance is able to influence food security positively or negatively, depending on whether one is in a country with or without high levels of corruption. In our case, the control of corruption, for example, turns out to have a significant negative impact depending on the techniques used. Nevertheless, it seems important to improve the level of governance in order to be able to redistribute wealth, for example to reduce inequalities and thus facilitate the availability and accessibility of food.
Eliminating food losses increases the availability of food, which may reduce the need to additionally supplement the amount available through transfer programmes (at household level) or through commercial imports or food aid donations (at national level). The increase in food quantity, under normal conditions, should also lead to a reduction in prices for buyers, thus improving overall access to food. It is no coincidence that the renewed interest in price reduction has emerged with the global food price peaks of recent years. Maintaining lower quality products, which are currently most likely to be lost, can excessively benefit the poor when price reductions of lower quality foods are combined (Kadjo et al., 2016). With the availability and accessibility of energy, mainly from renewable sources, a large part of sub-Saharan Africa is able to improve their daily lives in terms of nutrition. This can be done through the acquisition of refrigerators, electric dryers and other food preservation equipment. It would therefore seem necessary for states to invest more resources in the various sectors linked to energy, by facilitating the acquisition of solar panels and access to off-grid electricity.
4.2- Robustness check
We test the robustness or stability of our results in three ways. Firstly, we introduce additional control variables into our basic model, secondly, we homogenize our panel by taking into account the area of residence (urban and rural) and thirdly, we check the dynamic character of our model.
4.2.1. Additional control variables
By increasing the additional control variables, the results are not different from the previous ones, especially with regard to the sign of our variables of interest. Certainly, the impact of the energy poverty index, access to electricity and access to clean energy remains positive and significant on food security. However, we find that our additional variables, such as imports, contribute positively and significantly to increasing the level of food security. Thus, SSA countries rely on external food products to satisfy a good part of their internal nutritional demand. This result is similar to that found by Candelise et al. (2021) for developing countries. Consistent with the same study, urbanization is found to have a significant positive impact on food security in sub-Saharan Africa. Political stability ensures a certain degree of tranquility in agricultural activities, which helps to increase the amount of food stocks over time (Ribeiro et al., 2021).
4.2.2. Analysis of the dynamic character of our model
The method of generalized moments allows us to analyze the dynamic character of our model. Moreover, this technique also allows us to correct for endogeneity problems that might be present among our variables as shown in Fig. 3. Thus, this additional robustness allows us to maintain and even improve on previous results between the different measures of energy poverty and food security in Sub-Saharan Africa. In general, our representations are well-specified based on the analytical tests. There is no rejection of the validity of the instruments by Hansen's test, and there is no rejection of the absence of second-order serial correlation by Hansen's test. Hansen tests may be biased due to over identification restrictions if there are a large number of instruments, Roodman, (2009) recommends that the number of instruments be less than the number of countries. The results of the in-system GMM estimations generated a maximum of 25 instruments, which is less than the number of countries, so our results are valid and robust. There is a realization that countries should continue to devote a significant portion of their financing policies to the energy sector in order to reduce the number of people without access to electricity. This could enable large numbers of people to be lifted out of poverty and thus gain the ability to access food on a much higher income.
Table 5 presents the results using the generalized method of moments. The table shows that food security has a memory effect, hence the positive and significant effect of the delay of food security on itself. This also allows us to verify the dynamic nature in the long term between access to electricity and access to clean energy for cooking and food security in SSA. It appears that this method, by solving the endogeneity problems, confirms the results found above.
4.3- Transmission channels of energy poverty on food security in Sub-Saharan Africa.
The above findings are interesting because they show us that there is a direct link between access to energy and nutritional security. Energy is certainly necessary for agri-food production along the value chain, especially for the production of crops, fisheries, livestock and forests in agricultural areas. Indeed, energy is needed for post-harvest processing. The necessary food resources can be conserved by using renewable energy technologies such as solar dryers and refrigerators, in food storage and processing, food transport and distribution, and food preparation (FAO, 2012; Edward et al, 2020). However, the estimates do not indicate the importance and significance of the channels from energy poverty to food security. The objective of this study is to determine how energy poverty affects food security. To do this, we use a causal mediation analysis. The effect of energy poverty on each transmission channel is presented in Table 7 and Fig. 4.
|
Variables |
EDI |
Elec |
Constant |
Observations |
|---|---|---|---|---|
|
GDP/h |
0.0075936 (0.0543498) |
2.133233 *** (0.1673633) |
630 |
|
|
EmplT |
0.2625461 *** (0.0178471) |
2.045592*** (0.0549578) |
644 |
|
|
CTC |
3.899419 *** (0.2455988) |
34.35511*** (0.7562904) |
644 |
|
|
DCPS |
4.51023 *** (0.1564337) |
19.95973 *** (0.4726351) |
644 |
|
|
Urb |
3.137814*** (0.1737327) |
42.00362 *** (0. 5349878) |
644 |
|
|
GDP/h |
-0.0033336 (0.0065832) |
2.216155 (3086564) |
618 |
|
|
EmplT |
0.0263699*** (0023331) |
1.028664*** (1093864) |
618 |
|
|
CTC |
0.3310444*** (0331656) |
22.00895*** (1.554974) |
618 |
|
|
DCPS |
0.4808217*** (0221031) |
1.571233*** (1.036308) |
618 |
|
|
Urb |
0.4415075*** (0.0195152) |
24.77002*** (0.9149715) |
618 |
|
| Note: Bootstrap standard errors in parentheses; ⁎⁎⁎ is statistical significance at 1% level. | ||||
The results show that only the energy development index and the access to electricity have a positive and significant effect on the selected channels. All else being equal, increased energy access significantly boosts total employment, corruption, financial development and urbanization (Mouraviev, 2021; Nasirov et al., 2021; Koengkan et al., 2020). In contrast, the influence of fuel poverty on income is not significant. In fact, a 1% increase in the energy development index leads to a significant increase in total employment, corruption, financial development and urbanization by 0.2625461; 3.899419; 4.51023 and 3.137814 units respectively. Similarly, a 1% increase in the rate of access to electricity significantly increases total employment, corruption, financial development and urbanization by 0.0263699; 0.3310444; 0.4808217; 0.4415075 respectively.
Since energy poverty partly explains the variation in transmission channels, we calculate the direct and indirect effects of certain measures of energy poverty on food security. As well as the total effect of energy development index and electricity access, Sobel (1982) product of coefficients method was used to calculate its indirect effects. Standard errors are corrected using the bootstrap procedure. The results are presented in Table 6.
|
Variables |
EmplT |
Urb |
CTC |
DCPS |
Urb |
|---|---|---|---|---|---|
|
EDI |
-0.877026*** (0.2110797) |
1.404807*** (0.2814511) |
|||
|
ELEC |
0.0014748*** (0.0002691) |
0.0025392*** (0.0005527) |
0.0017295*** (0.0005195) |
||
|
Controls |
Yes |
Yes |
Yes |
Yes |
Yes |
|
Constant |
2.013434*** (0.0546078) |
42.58649*** (0.58649) |
22.00895*** (1.554974) |
1.571233*** (1.036308) |
24.77002*** (0.9149715) |
|
% of mediated effect |
6% |
9% |
10% |
16% |
14% |
| Standard errors in brackets *** p < 0.01, ** p < 0.05, * p < 0.1 | |||||
Table 7 shows that energy poverty had a mediated effect on food security via the channels. Over the study period, energy poverty reduction indirectly improved food security through total employment, corruption, financial development and urbanization. As well as calculating the total impact of energy poverty on food security, we attempt to calculate the contribution of each channel to it. We find that about 6% and 9% of the total positive indirect effects of the energy development index on food security are due to total employment and urbanization respectively. Similarly, we find that 10%, 16% and 14% of the positive indirect effect of access to electricity on food security comes from corruption, financial development and urbanization.
The indirect positive effect of improved access to energy on food security can be explained by the fact that access to energy contributes to improving the financial system by creating favorable conditions for industrialization and improving the profitability of enterprises in general. This improvement in the profitability of enterprises will be beneficial for employment and therefore savings. Moreover, financial institutions are increasingly keen on digital tools because of the strong potential for closer contact with the customer and the facilitation of control. However, these technologies are highly dependent on energy. In addition, access to energy offers opportunities in terms of food preservation and business opportunities for urban residents. Therefore, income-generating activities driven by energy access can also lead to boosting the production and efficiency of existing businesses and thus generate new jobs and income (Riva et al, 2018). For Gibson et Olivia (2010), A survey of 4,000 rural Indonesian households was conducted shows how improving the quality of electricity infrastructure has a positive impact on the growth of rural non-farm enterprises, contributing to job and income creation, Diversification of livelihoods and poverty reduction in developing countries.
Finally, Access to electricity increases income, which in turn increases people's purchasing power, with a positive impact on people's food security. Thus, the resolution of food insecurity in SSA must be driven in large part by increasing energy supply. Our results can't be directly compared to existing research since transmission channels have not been emphasized in previous studies.
Access to energy, as defined in SDG 7, is an important component of decent livelihoods and is therefore strictly linked to the achievement of the overall goal of sustainable development, and thus well-being (SDG 3). Although it has a significant impact on many dimensions of the SDGs, this study focused on its effect on the level of food security (SDG 2) in sub-Saharan Africa. Although the literature on this issue has largely focused on the micro level, this study is one of the first to empirically investigate the effect of energy poverty on food security in SSA. The results revealed a positive and significant relationship between the energy development index, access to electricity and access to clean energy for cooking on food security. This supports many of the policies that African countries have put in place in recent years to reduce the gap in access to electricity and clean energy. There is therefore a need for governments to devote more financial resources to the energy sector in order to influence food security outcomes in SSA. The results also showed that access to energy has mainly direct and indirect impacts on food security. With effects that can be channelled through employment, economic growth and good governance. This finding can inform policy makers on the priority to be given to investments in proximity access to electricity for vulnerable households, especially in rural areas, including through off-grid decentralised electricity systems (such as mini-grids, micro-grids and "stand-alone" systems for domestic and productive use) which are increasingly seen as offering more cost-effective, faster and flexible solutions for rural and even urban electrification. Only in this way can SSA improve its food security.
-Ethics approval and consent to participate. Not applicable.
-Consent for publication. Not applicable.
-Competing interests. The authors declare no competing interests.
-Authors Contributions: Conceptualization, Methodology, Software, Formal analysis, Writing, original draft, Writing - review & editing.
- Funding: Not applicable.
-Availability of data and materials: The data is available on request from the corresponding author
Author’s statement
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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