The socioeconomic development of West African countries has remained a major challenge for several decades. Despite abundant natural resources, demographic dynamism, and public policies, the region struggles to achieve lasting improvements in the living conditions of its populations. Wealth disparities, infrastructure deficits, weak industrialization, and persistent poverty are evidence of structural difficulties that limit growth. Among these challenges, access to electricity appears to be a recurring factor, often presented as a prerequisite for modern development.

Indeed, electricity now affects almost every aspect of economic, social, and environmental life. Yet despite its fundamental role, West Africa remains one of the least electrified regions in the world. Access to electricity is uneven, marked by sharp disparities between urban and rural areas, and often insufficient to support sustainable development. Some countries have relatively developed energy infrastructures but struggle to transform this resource into concrete progress for their populations. Conversely, some countries with limited capacities sometimes manage to make better use of the electricity available.

Furthermore, access to electricity remains a major challenge despite the region’s abundant energy resources. With low electrification rates, unequal access, and dependence on fossil fuels, the development of a reliable and sustainable electricity system is essential for socioeconomic progress. The report on the state of access to electricity suggests that without electricity, the path out of poverty would be narrow and prolonged. Although access to electricity is a major concern for developing countries, there is not a great deal of empirical research on the subject.

Electricity is therefore a key driver for improving living conditions. In the health sector, a reliable electricity supply is crucial for the proper functioning of hospitals and medical centers. Without a stable electricity supply, many healthcare facilities are forced to limit their services. In education, electricity facilitates better access to digital tools and extends students’ study hours. A reliable energy supply is also essential for industrial development, business competitiveness, and job creation. There are many other examples illustrating the importance of access to electricity, as its impact is felt across a variety of sectors.

Literature review :

The relationship between access to electricity and economic development in sub-Saharan Africa has been the subject of numerous empirical studies in recent years. Adeniran et al. ¹ demonstrated that access to electricity is a significant determinant of economic development in sub-Saharan Africa, establishing a direct link between electrification rates and development indicators. This positive correlation is confirmed by Babatunde et al. ², whose quantitative study of West African countries reveals a statistically significant impact of electricity on GDP growth, suggesting that an increase in electricity capacity translates into a measurable improvement in national economic performance.

At the sectoral level, electricity infrastructure appears to be a determining factor for industrialization. Diallo et al.³ specifically analyzed the link between electricity infrastructure and industrial development in West Africa, showing that the availability and reliability of electricity largely determine countries’ ability to develop their manufacturing sector and diversify their economies. This analysis is enriched by the work of Touré et al.⁴, who examine the role of energy infrastructure in the economic development of ECOWAS countries, emphasizing that inadequate energy infrastructure is a major obstacle to business competitiveness and attracting foreign direct investment. Furthermore, Ibrahim and Bello ⁵ provide a complementary perspective by documenting how access to electricity promotes the growth of the informal sector in Nigeria, a sector that employs the majority of the region’s working population and contributes significantly to GDP.

One of the major characteristics of electrification in West Africa is the significant disparities between urban and rural areas. Mensah et al.⁶ have systematically documented these urban-rural disparities in electrification in West Africa, revealing considerable gaps in access rates: while urban areas generally enjoy electrification rates of over 70%, rural areas remain largely under-electrified, with rates often below 20% in several countries in the region.

This issue is illustrated in concrete terms by national case studies. Traoré et al. ⁷ examined the results of electrification on social development in rural areas of Mali, highlighting that access to electricity in these areas significantly improves health, education, and quality of life indicators, but that these benefits remain limited due to low electricity coverage. Similarly, Diawara et al. ⁸ identified the multiple barriers to rural electrification in Burkina Faso and their consequences for social development, emphasizing that technical, financial, and institutional obstacles prevent a large part of the rural population from benefiting from the advantages of electricity.

Beyond its economic impacts, electricity generates considerable social effects, particularly in the areas of education and gender equality. Kouassi et al.⁹ conducted a rigorous study on the impacts of rural electrification on school attendance in Côte d’Ivoire, demonstrating that access to electricity significantly increases school enrollment rates and improves academic performance, particularly by enabling students to study in the evening and facilitating the use of digital educational tools.

The gender dimension of electrification is also an important area of research. Sankoh and Thompson ¹⁰ analyzed the link between electrification and women’s empowerment in rural areas of West Africa, showing that access to electricity reduces the time spent on domestic tasks, promotes female entrepreneurship, and improves women’s access to information and health services. This perspective is complemented by Fofana and Coulibaly ¹¹ whose study on energy poverty and economic vulnerability in West Africa reveals that the populations most affected by lack of access to electricity are mainly women and young people in rural areas, thus perpetuating a cycle of economic and social vulnerability.

Despite the recognized benefits of electricity, several structural obstacles are hindering the expansion of electrification in the region. N’Guessan ¹² conducted a comprehensive analysis of barriers to access to electricity in West Africa, identifying multidimensional constraints: high connection costs, insufficient production capacity, outdated distribution networks, and low household incomes. These obstacles are all the more restrictive given that they exist within an often unfavorable institutional context.

Regulatory and governance issues are a major obstacle to electrification. Soro et al. ¹³ examined regulatory barriers to electrification in West Africa, highlighting the complexity of legal frameworks, the instability of energy policies, and the difficulties in enforcing existing regulations. This issue is explored in greater depth by Mendy and Fall. ¹⁴ which analyze governance and investment challenges in West African electricity sectors, revealing that institutional weakness, corruption, and lack of transparency discourage private investment and limit the effectiveness of public policies on electrification.

Given the limitations of expanding centralized electricity grids, decentralized solutions based on renewable energy appear to be a promising alternative. Sharp et al. ¹⁵ have studied the link between decentralized renewable energy systems and socioeconomic development, demonstrating that solar mini-grids and individual solar kits can rapidly electrify isolated areas while creating local economic opportunities and reducing environmental footprints.

The impact of these technologies on employment is another argument in their favor. Bako et al.¹⁶ analyzed solar energy adoption and job creation in West Africa, revealing that the solar sector generates significant direct and indirect jobs, from manufacturing to installation and maintenance, thereby contributing to reducing youth unemployment. This dimension is reinforced by Kamara and Sesay.¹⁷, which documented the adoption of renewable energy and job creation in Sierra Leone, showing that the development of the renewable energy sector not only creates skilled technical jobs but also entrepreneurial opportunities for local populations..

The issue of regional integration is a strategic priority for improving access to electricity in West Africa. Ouattara ¹⁸ examined the role of regional integration and access to energy through the case of the West African Power Pool (WAPP), the specialized institution of ECOWAS responsible for coordinating electricity exchanges between member countries. The analysis shows that interconnecting national grids makes it possible to pool resources, improve security of supply, and reduce production costs through economies of scale. The WAPP aims to create a unified regional electricity market, allowing countries with surpluses to export them to countries with deficits, thereby optimizing the use of existing production capacity.

This vision of regional energy integration is part of the broader framework of ECOWAS development policies, as documented by the World Bank.¹⁹ in its report “Powering Up Western and Central Africa.” This report highlights the coordinated efforts of regional and international institutions to mobilize the financing needed to electrify the region, estimated at tens of billions of dollars over the next decade.

Financing electrification is one of the most critical challenges for the region. World Bank (2024)¹⁹ reveals that this regional coordination significantly amplifies the influence of electricity on development by enabling countries in the region to collectively benefit from investments in electrification, thereby maximizing the socio-economic impact of each regional energy project.

Koné et al. ²⁰ analyzed strategies for increasing private investment in the West African energy sector, showing that improving the business environment, reducing perceived risks for investors, and establishing guarantee mechanisms are essential for attracting the private capital needed to finance electricity infrastructure.

This paradox highlights the need to scientifically examine the link between electricity and socioeconomic development in the region. It is essential, based on reliable and comparable data, to move beyond the simplistic assumption that “the more electrified a country is, the more developed it is.” This study therefore aims to answer the following question: does electricity truly influence the socio-economic development of West African countries, and to what extent?

In this context, the central question of this study is: Does electricity truly influence the socioeconomic development of West African countries, and to what extent?

More specifically, this research aims to analyze the relationship between access to electricity and indicators of socioeconomic development in the region, and to identify the mechanisms through which electricity contributes to economic and social progress. This analysis leads to the formulation of several research questions that will guide this study:

To what extent does access to electricity influence economic growth and industrial development in West African countries?

How does electrification impact social indicators such as education, health, and women’s empowerment in the region?

What factors moderate the relationship between electricity and socioeconomic development in West Africa?

Based on these research questions and the literature reviewed, we formulate the following hypotheses, which will be tested through subsequent empirical analyses:

Hypothesis 1: The quality of electrical infrastructure and energy sector governance moderate the relationship between access to electricity and socioeconomic development.

Hypothesis2: Electrification contributes positively to improving social indicators (school enrollment rates, life expectancy, human development indices) in countries in the region.

Hypothesis 3: The quality of electrical infrastructure and energy sector governance moderate the relationship between access to electricity and socioeconomic development.

Hypothesis 4: Countries that invest more in decentralized renewable energy are more effective at reducing urban-rural disparities in development.

1. Materials and methods :

1. Data presentation :

This study is based on the Our World in Data database, which provides reliable and internationally comparable information. The data used concerns West African countries and covers electricity as well as general socioeconomic indicators.

The main objective of this data collection is to assess the link between access to electricity and socioeconomic development, considering electricity as a factor. potential but not exclusive development. The database allows for direct comparison between countries, identification of regional trends, and quantification of the gaps between nations with better or poorer electrification.

Thus, the variables selected were chosen for their relevance in assessing access to electricity and its socio-economic implications.

The table below shows these variables and their meanings:

Variables	Unit	Abbreviation	Explanatory Summary	Sources
Primary school enrollment rate	%	SER	Measures the share of children enrolled in
primary school.	Our World in Data
Employment rate	%	ER	Indicates the proportion of the population that
is economically active.	Our World in Data
GDP per capita	USD/capita	GDP/capita	Reflects the average level of economic wealth
per person.	Our World in Data
Total GDP	billin USD	GDP	Total value of national economic production.	Our World in Data
Installed electric capacity	MW	EC	Quantifies the maximum electricity generation
capacity of a country.	Our World in Data
Access to electricity (total)	% of population	AE	Share of the population with access to
electricity.	Our World in Data
Urban access to electricity	%	UAE	Proportion of urban households with electricity	Our World in Data
Rural access to electricity	%	RAE	Proportion of rural households with electricity.	Our World in Data
Share of renewable energy	%	SOER	Measures the contribution of renewable sources
to total energy.	Our World in Data
Agricultural production	Billion USD	AP	Measures the economic value of the agricultural

MAP 1 : Urban access to electricity in west Africa

Comment 1: This map shows electricity access in urban areas. It generally highlights high access rates in cities, even in low-electrification countries. Ghana, Côte d’Ivoire, and Senegal usually perform well, while countries such as Niger and Guinea-Bissau are lower, though still better off than rural zones. This map demonstrates that urbanization partially protects populations from energy poverty, revealing strong territorial inequalities

MAP 2 : Share of renewable energy in west Africa

Comment 2: This map presents the share of renewables in the energy mix. Sahelian countries (Niger, Mali, Burkina Faso) often show a high renewable share due to heavy reliance on traditional biomass. More industrialized countries (Côte d’Ivoire, Ghana) have a lower share due to greater thermal and hydroelectric use. A high renewable share does not necessarily mean better electricity access; it often reflects limited modern infrastructure.

MAP 3 : School enrollment rate in west Africa

Comment 3: This map illustrates primary school enrollment rates. Countries with better electrification (Ghana, Côte d’Ivoire, Cape Verde) typically show higher enrollment. Conflict-affected or poorer states (Niger, Mali, Burkina Faso) show lower rates. There is a strong correlation between electricity and education, as electricity supports learning environments and digital tools.

MAP 4 : Rural access to electricity

Comment 4: This is often the most striking map. Most countries show extremely low rural electrification (often below 20%). Only a few (Cape Verde, Ghana, Côte d’Ivoire) perform relatively better. This map highlights the central challenge in the region: rural electrification, which is essential for poverty reduction and social development.

MAP 5 : Poverty rate in west Africa

Comment 5: Countries with low electricity access (Niger, Liberia, Guinea-Bissau) also display the highest poverty levels. More developed countries (Côte d’Ivoire, Ghana, Cape Verde) generally show lower poverty. Electricity access and poverty are deeply linked, supporting the study’s findings.

MAP 6 : People without improved water source in west Africa

Comment 6: This map highlights where access to improved water sources remains limited. Countries like Niger, Guinea, and Sierra Leone often stand out negatively. It shows that lack of electricity often goes hand-in-hand with poor access to basic services, including safe water.

MAP 7 : Hospital reliable electricity in west Africa

Comment 7: Hospitals in low-income countries (Guinea-Bissau, Niger, Liberia) typically have low reliability of electricity. This affects surgeries, vaccine refrigeration, and essential medical equipment. Reliable electricity is a critical indicator of healthcare system quality.

MAP 8 : GDP in west Africa

Comment 8: This map highlights the region’s biggest economies: Nigeria (by far), Ghana, and Côte d’Ivoire. Smaller or landlocked countries show much lower GDP. It confirms the strong relationship between economic size and energy infrastructure.

MAP 9 : GDP per capita in west Africa

Comment 9: Here, top performers include Cape Verde, Senegal, Côte d’Ivoire, and Ghana. The poorest (Niger, Guinea-Bissau, Liberia) show extremely low GDP per capita. This map demonstrates that individual wealth is partly linked to the availability of energy infrastructure.

MAP 10 : Food losses due to cold chain

Comment 10: Countries with weak or unreliable electricity (Niger, Guinea, Liberia) have very high food losses due to insufficient refrigeration systems. This map shows the direct impact of electricity on agriculture and food security.

MAP 11 : Employment rate

Comment 11: More industrialized countries (Ghana, Côte d’Ivoire) tend to have higher employment rates. Poorer or unstable countries show weaker or highly informal employment. Electricity supports job creation through industry, commerce, and services.

MAP 12 : Electrical capacity in west Africa

Comment 12: It distinguishes countries with strong electrical infrastructure (Nigeria, Ghana, Côte d’Ivoire) from those with very limited installed capacity. Installed capacity strongly conditions access, reliability, and socio-economic impact.

MAP 13 : CO2 emissions in west Africa

Comment 13: Countries with higher emissions (Nigeria, Côte d’Ivoire, Ghana) rely more on thermal electricity. Poorer countries emit less because they produce less electricity. The map reveals an energy-development dilemma: electricity increases development but also emissions.

MAP 14 : Agricultural production in west Africa

Comment 14: Countries with stronger agricultural output (Nigeria, Ghana, Côte d’Ivoire) are also those with better access to electricity. Poorer countries produce less. Electricity enhances agricultural productivity through irrigation, storage, and processing.

MAP 15 : Accesss to electricity in west Africa

Comment 15: This is the synthesis map. Electrified countries (Ghana, Côte d’Ivoire, Cape Verde) stand out clearly, while the least electrified (Niger, Guinea-Bissau, Sierra Leone, Liberia) appear in darker/critical colors. It confirms the vast heterogeneity of energy access across West Africa, which is the central theme of your study.

2. Data collection tools :

In addition to secondary statistical data, a questionnaire was designed to

to explore the perceptions, practices, and constraints experienced by populations and stakeholders in the electricity sector.

The objective was not to compile a representative sample at the regional level, but rather to develop a qualitative-quantitative tool that would enhance understanding of the realities on the ground.

The questionnaire was created using KoboToolbox software, a platform designed for multi-site studies and African contexts.

Attached is the access link to the Kobo toolbox.

https://kf.kobotoolbox.org/#/forms/aHtpLxWg9oyjpt74qH5CD3

Sampling and extraction plan

The study covers the 16 countries of West Africa, selected as statistical units due to their geographical and political consistency.

Data extraction was carried out according to the following principles:

Priority should be given to the most recent data, with a preference for the years between 2021 and 2023 in order to ensure comparability.

The data was extracted manually in CSV or Excel format to avoid inconsistencies associated with automatic APIs.

Exclusive use of internationally recognized sources

Handling missing data

Missing data were handled using a three-step approach:

· Additional research in alternative databases: When an indicator was not available in the main source, a search was conducted in other databases such as the World Bank, the IEA, or national reports.

· Cross-checking figures
Data was only included if it was confirmed by at least one additional source or if it came from a reputable institution.

· Exclusion of variables with excessive gaps
If a variable had a level of missing data that was considered excessive or undocumented, it was removed for the sake of scientific rigor. No imputation was performed. numérique n’a été utilisée afin de ne pas modifier artificiellement la structure des données.

Target population

To analyze the impact of electricity on the socioeconomic development of West African countries, the target population is defined in such a way as to include the various levels of actors influenced by access to electricity or involved in its management. The approach adopted is multi-level, allowing for the capture of social, economic, and institutional effects.

· Households: to measure actual access to electricity, its daily uses, and its impact on quality of life, taking into account urban and rural differences.

· Businesses: to assess the impact of electricity on productivity, economic activity, and job creation.

· Institutional actors: to understand electricity policies, planning, and distribution in each country.

Data analysis :

The purpose of the data analysis is to understand how access to electricity influences the socioeconomic development of West African countries. It is based on a combination of descriptive and multivariate statistical methods that identify relationships, trends, and patterns within the dataset.

A descriptive analysis was first conducted to explore the distribution of energy and socioeconomic indicators and detect variations between countries. This step made it possible to visualize regional disparities and prepare the data for more complex analyses.

Next, principal component analysis (PCA) was used to reduce the size of the dataset while retaining essential information. PCA highlighted correlations between variables and synthesized the data structure, facilitating understanding factors that influence electrification.

Simple and multiple linear regressions were applied to quantify the impact of electricity on development. Simple models examine the direct link between access to electricity and indicators such as GDP per capita, while multiple models incorporate several explanatory variables in order to identify the most significant factors. These analyses enable us to test our hypotheses and measure the strength of the relationships observed.

Finally, a hierarchical classification was carried out to group countries with similar profiles, highlighting development typologies and paradoxical situations, such as countries with good electrification but modest economies. The results were represented using thematic maps to visualize geographical disparities.

All analyses were performed using RStudio for statistical processing and visualization, and QGIS for mapping. The hypothetical questionnaire designed on KoboToolbox complements the approach by providing a framework for collecting qualitative and quantitative information on the experiences of households and energy sector stakeholders.

.2.1. Principal Component Analysis (PCA) :

L’Analyse en Composantes Principales (ACP) est une méthode statistique de réduction de dimension qui permet de résumer un ensemble de variables corrélées en un nombre réduit d’axes indépendants appelés composantes principales. Elle facilite la visualisation, la comparaison et l’interprétation de données multidimensionnelles en conservant l’essentiel de l’information initiale.

Étapes de l’Analyse en Composantes Principales (ACP) :

· Standardization of variables: each variable is centered and reduced, i.e., transformed to have a mean of zero and a standard deviation of one. This step allows all variables to be placed on the same scale, preventing a variable with a large amplitude from dominating the analysis.

· Calculation of the covariance (or correlation) matrix: this matrix allows us to study the relationships between variables and identify those that vary in a similar way.

· Principal component extraction: principal components are calculated from the covariance matrix. Each component is a linear combination of the original variables and is ordered according to the variance it explains, with the first component explaining the largest part of the variance.

· Selection of the number of principal components: a small number of components are chosen that capture most of the total variance, allowing the data to be represented in a lower-dimensional space while retaining essential information.

· Data projection: The original data is projected onto the selected principal components, producing a transformed dataset that is easier to analyze and visualize.

· Interpretation and visualization: we can analyze which variables most influence each component and represent the data in the new space using graphs.

.2.2. Multiple linear regression

To analyze the factors influencing access to electricity, we applied multiple linear regression. This method allows us to measure the effect of several socioeconomic and energy variables on the rate of access to electricity and to identify those that have a significant impact.

The regression model is written as follows:

Y = β₀ + β₁ X₁ + β₂ X₂ + β₃ X₃ + β₄ X₄ + β₅ X₅ + β₆ X₆ + β₇ X₇ + β₈ X₈ + β₉ X₉ + β₁₀ X₁₀ + β₁₁ X₁₁ + β₁₂ X₁₂ + β₁₃ X₁₃ + β₁₄ X₁₄ + ε

Où :

Y: Total electricity access rate (% of population)

X₁: primary school enrollment rate (%)

X₂: employment rate (%)

X₃: GDP per capita (USD/cap)

X₄: Total GDP (USD billions)

X₅: installed electrical capacity (MW)

X₆: access to urban electricity (%)

X₇: access to rural electricity (%)

X₈: share of renewable energy (%)

X₉: agricultural production (billions of USD)

X₁₀: percentage of hospitals with reliable electricity (%)

X₁₁: poverty rate (%)

X₁₂: Access to improved water sources (millions of people)

X₁₃: CO₂ emission rate (g/kWh)

X₁₄: food losses related to the cold chain (%)

β₀: value of Y when all explanatory variables are zero

β₁toβ a2>₁₄: coefficients measuring the effect of each variable on electricity consumption

ε: residual error, representing the portion of the electricity access rate that is not explained by the model

The model was adjusted using the total electricity access rate as the dependent variable. The quality of the adjustment was assessed using the coefficient of determination R², which indicates the proportion of variance explained by the model. Finally, the model was used to predict access to electricity on new data, allowing its generalizability to be assessed. This approach helps identify the main determinants of electrification and guide energy policies.

Before starting, the data was normalized, i.e., adjusted so that each criterion (such as GDP, access to electricity, or use of renewable energy) had an equal influence on the results. This prevents a criterion with a large scale (e.g., GDP) from carrying too much weight in the rest of the work.

.2.3. CLASSIFICATION :

To analyze differences in access to electricity between countries, we used a classification method that groups countries according to their similarities across several criteria. This approach allows us to form groups of countries with similar energy profiles, which facilitates understanding of electrification gaps in the region.

Next, countries were grouped according to their proximity across all criteria. The closest countries, i.e., those with characteristics communes sur plusieurs aspects, ont été placés dans le même group. This method makes it possible to identify groups of countries that share similar dynamics in terms of energy access.

The results of this classification show which countries are most similar to each other and which countries have significant differences in their energy systems. This helps to better understand where the disparities lie and to define appropriate policies for each group of countries.

2. Results :

2.1 Principal Component Analysis (PCA) :

Methodology

To explore the relationships between the countries studied and identify groups with similar socioeconomic and energy characteristics, we performed a principal component analysis (PCA). This method reduces the dimensionality of the data while retaining most of the information. The variables used cover the following areas: education, employment, economy, energy, agriculture, health infrastructure, and access to basic services.

In order to ensure a relevant interpretation of the results, we adopted a two-step strategy, taking into account the presence of a highly atypical country: Nigeria.

ACP with all countries

An initial PCA was performed including all countries, including Nigeria. This approach allows us to visualize the entire population studied and detect atypical individuals.

Figure 1 : Factorial plan with NIGERIA

Observation of the factor map shows that Nigeria lies well outside the main cluster, confirming its atypical nature. Its demographic and economic weight strongly influences the first PCA axis, making it difficult to interpret the relationships between the other countries.

ACP excluding Nigeria

To obtain a more detailed reading of the similarities and differences between the other countries, a second PCA was performed after excluding Nigeria. This approach makes it possible to better distinguish the internal structures of the group without being dominated by one extreme country.

Figure 2 : Factorial plan without NIGERIA

The projection of countries shows that Ghana and Côte d’Ivoire, located in the upper right corner, are associated with strong economic and energy development, with good access to electricity and well-developed infrastructure. Countries such as Senegal and Guinea are closer to the center, indicating intermediate energy and economic development. At the bottom left, countries such as Niger, Guinea-Bissau, and Liberia are characterized by limited access to electricity and less developed infrastructure, signaling major economic and energy challenges.

Summary

The use of two separate ACPs (with and without Nigeria) provides a comprehensive and rigorous view of the dataset:

The first ACP highlights atypical countries and justifies methodological separation.

The second ACP allows for a detailed analysis of internal structures and groupings between comparable countries.

This methodical approach ensures a clear and accurate interpretation of the relationships between socioeconomic, energy, and environmental variables.

Explained variance and correlation matrix

Figure 3 : Eigen values

The inertia of the factor axes is an important indicator for assessing the quality of the analysis. Basically, it shows the proportion of data variability explained by each axis. In our case, the first two axes explain approximately 63% of the total data variability. This means that these two axes capture a large part of the differences between countries, making their analysis particularly relevant.

This allows us to say that these axes are sufficient to describe a large part of the differences between countries, without having to add additional dimensions, which simplifies the interpretation of the results. In summary, we focus on the first two axes to analyze and understand the main variations between countries in our study.

Interpretation of the main axes

Figure 4 : Correlation circle

Analysis of the principal axes of the PCA reveals that:

The correlation circle allows us to visualize the relationship between the variables and the first two principal dimensions of PCA (Dim1 and Dim2), which together explain 63.3% of the total variability in the data.

· Dimension 1 (Dim1) is mainly associated with economic and energy factors. It includes variables such as GDP, agricultural production (AP), and electricity consumption (EC). These variables are strongly correlated with Dim1, indicating that this dimension captures differences in economic development and energy infrastructure. For example, countries with stronger economies and greater energy capacity (such as Côte d’Ivoire and Ghana) are positioned positively on this axis. This reflects advanced economic development and relatively high energy access.

· Dimension 2 (Dim2) is distinguished by its association with access to electricity, particularly between urban and rural areas. Variables such as RAE (Rural Access to Electricity) and UAE (Urban Access to Electricity) are more strongly correlated with this dimension. This shows that Dim2 differentiates countries based on their distribution of access to electricity, particularly in rural vs. urban areas. Countries with more developed access in urban areas, such as Cape Verde and Mauritania, are strongly associated with this dimension.

· Other key variables: The poverty rate (TDP), which is close to Dim1, shows that poverty is linked to low levels of infrastructure and economic development. Other variables such as HEF (Hospitals with Reliable Electricity) and PACF (Food Losses) are also visible on the circle, highlighting the importance of access to reliable electricity, particularly in critical sectors such as health.

In summary, Dim1 is dominated by global economic and energy factors, while Dim2 highlights access to electricity between urban and rural areas, emphasizing inequalities in energy access.

Analysis of the principal axes of the PCA reveals that :

The factorial map shows where countries stand in relation to the two main dimensions of PCA. This map allows us to visualize how countries are grouped according to their level of energy and economic development.

Figure 5 : Factorial card

· In the upper right corner: Countries such as Ghana and Côte d’Ivoire stand out for their high access to electricity and strong economic development. These countries are well positioned on Dim1, showing that they have high electricity production capacity and a high GDP. These countries have advanced infrastructure, both in terms of energy and socio-economic development.

· In the lower left corner: Countries such as Niger, Guinea-Bissau, and Liberia are clearly positioned with low electricity generation capacity and limited access to electricity, particularly in rural areas. These countries are also characterized by high poverty rates and underdeveloped energy infrastructure, which points to major challenges in terms of socioeconomic and energy development.

· In the middle zone (around the center): Countries such as Cape Verde, Gambia, and Mauritania are positioned to reflect greater access to electricity in urban areas, but significant disparities in rural areas. They have moderate energy development, with notable progress in urban areas, but challenges remain in rural areas, where access to electricity is more limited.

In conclusion, the factorial map allows us to visualize the hierarchy of countries based on their energy and economic development, and shows clear disparities between countries with strong infrastructure (such as Ghana and Côte d’Ivoire) and those facing significant energy and economic challenges (such as Niger and Liberia). It also helps to understand how access to electricity varies between urban and rural areas, and how this affects the overall development of countries.

2.2 Multiple linear regression

Multiple linear regression analysis was conducted to quantify the impact of various socioeconomic and energy factors on access to electricity in West Africa. This statistical method measures the simultaneous effect of several explanatory variables on the overall rate of access to electricity, identifying those that have a significant influence.

Based on a preliminary study of correlations, five variables stood out for their strong association with access to electricity. These are urban access to electricity, rural access, GDP per capita, primary school enrollment rates, and electricity reliability in hospitals. These variables were incorporated into the following model: the total rate of access to electricity is expressed as a linear function of these five factors, each weighted by an estimated coefficient plus a constant and a residual error.

Analysis of the correlation matrix confirmed that urban and rural access are the variables most closely linked to the total access rate, highlighting the dominant importance of the geographical distribution of electricity. In addition, GDP per capita shows a notable positive correlation, demonstrating the close link between economic development and electrification. Primary school enrollment rates are also positively correlated, reflecting the role of electricity in improving educational conditions. Conversely, negative correlations were observed between access to electricity and indicators such as poverty rates and food losses, highlighting the structural challenges associated with electrification. A paradoxical aspect of renewable energy was noted, where its negatively correlated share of access illustrates countries’ dependence on traditional non-electric biomass.

The multiple linear regression model adjustment showed excellent performance, explaining nearly 95% of the variance in electricity access rates, with very high overall significance. Among the estimated coefficients, urban electricity access emerged as the most decisive factor, closely followed by rural access. Per capita GDP has a positive but more modest effect, while social variables such as school enrollment and hospital electricity reliability have less pronounced or marginally significant effects.

These findings have several major policy implications. First, investments in urban infrastructure have an immediate impact on overall electrification, but rural electrification is essential to achieving universal coverage, especially in a context where the majority of the population lives in rural areas. Second, economic growth, while facilitating access to electricity, is not sufficient on its own; Active and targeted energy policies are needed. Finally, the Network quality, through reliability in sensitive infrastructure such as hospitals, is a crucial indicator of the maturity of the energy system.

The robustness of the model was validated by several statistical tests confirming the normality of the residuals, homoscedasticity, the absence of autocorrelation, and a low risk of multicollinearity between explanatory variables. Predictive power was also found to be satisfactory, with average errors of less than 5% when predicting access rates by country.

Some limitations should be noted: the sample size of only 16 countries limits statistical power; the cross-sectional nature of the data prevents the establishment of causal relationships; and some potentially important variables were not included. To gain further insight, longitudinal studies, subnational analyses, and the inclusion of new factors such as governance and investment in modern renewable energy are recommended.

In short, this modeling highlights the central role of geographical disparities in West African electrification and calls for a differentiated approach to public policy, combining urban grid expansion, targeted promotion of rural electrification, and improvement of the quality of electricity service.

Figure 6 : Graphe de régression

Figure 7 : Classification dendrogram

Figure 8 : Ascending Hierarchical Classification of Individuals

Class 1 : Guinea-Bissau, Liberia, and Niger

Countries in this category share major challenges in terms of socioeconomic and energy development. This group is characterized by low levels of development, both in terms of access to electricity and economic development. Here are the highlights:

· High values for the variables PACF (food losses related to the cold chain), TDP (poverty rate), and TE (employment rate): These countries face significant infrastructure and poverty issues. The high proportion of food losses due to inadequate cold chains and high poverty rates (with a large portion of the population living on less than $3 per day) show that these countries are in a situation of economic vulnerability.

· Low values for UEA (Urban Electricity Access), REA (Rural Electricity Access), HEF (Hospitals with Reliable Electricity), and GDP/CAP (GDP per capita): Access to electricity, both in rural and urban areas, is limited in these countries, which seriously undermines their health and economic infrastructure. Low GDP per capita reinforces the perception of limited economic development, with major challenges to overcome in terms of infrastructure and basic services.

Class 2: Cape Verde, Gambia, and Mauritania

Countries in this class have greater access to electricity, particularly in urban areas, but still face socio-economic challenges. This group shows intermediate energy development, with advances in access to electricity, but with poverty and employment issues still present:

· Strong values for UEA (Urban Electricity Access): These countries have succeeded in improving access to electricity in urban areas, demonstrating a relatively advanced urban energy infrastructure. However, the issue of rural access remains a challenge.

· Low values for TDP (poverty rate) and TE (employment rate): Despite progress in urban electrification, these countries continue to struggle with socio-economic problems, including high poverty and unemployment rates, which are slowing their overall development. These countries are at a stage where energy advances are not yet fully accompanied by overall economic improvements.

Class 3 : Ivory Coast and Ghana

Ivory Coast and Ghana form this group, characterized by well-developed energy infrastructure and strong economic performance. These countries are best positioned in terms of energy access and socioeconomic development. Here are the key elements:

· Strong values for GDP (Gross Domestic Product), AP (Agricultural Production), and EC (Installed Electrical Capacity): Côte d’Ivoire and Ghana have high GDP, significant agricultural production, and strong electrical production capacity. This indicates relatively diversified and developed economies with high electrical production capacity. enabling domestic demand to be sustained.

· Less extreme variables for other indicators: Although these countries have good access to electricity and dynamic economies, their electrification levels are already well established, placing them in a favorable position compared to other countries in terms of energy and economic development.

3. Discussion

This study highlights the multiple factors that influence access to electricity in West Africa through a rigorous analysis combining multiple linear regression, principal component analysis, and country classification. The results confirm the crucial importance of differentiated electrification between urban and rural areas, while emphasizing the interdependence with key socioeconomic variables.

The multiple linear regression model applied reveals that access to urban electricity is the most decisive factor in explaining the variance in overall access to electricity. This is consistent with the reality on the ground, where urban areas benefit from more developed energy infrastructure, higher population density, and a concentration of economic activities that encourage investment. However, the significant weight given to rural electrification also confirms its essential role, especially given that 60 to 70% of the West African population lives in rural areas. This suggests that any policy aimed at ensuring universal access cannot ignore targeted programs and solutions tailored to rural areas, which are often more difficult to electrify.

The positive but modest effect of GDP per capita indicates that economic development certainly facilitates access to electricity, but is not an automatic guarantee. This observation reinforces the finding in the literature that economic growth alone does not guarantee improvements in energy infrastructure or expanded electricity coverage, particularly in countries with limited resources or low incomes. Therefore, proactive energy policies remain essential to overcoming these barriers.

Furthermore, social variables such as school enrollment rates and the reliability of electricity in hospitals, although having less pronounced or marginally significant effects in the model, illustrate the complexity of the mechanisms through which electrification impacts social development. Electricity improves learning conditions through access to lighting and digital technologies, and energy reliability in healthcare facilities is vital to the quality of care. These indicators also reveal the maturity and quality of the electrical system, beyond simple availability.

The additional analyses presented in the document highlight the significant territorial and socioeconomic disparities in electrification in the region, with low access rates in certain countries such as Niger, Guinea-Bissau, and Liberia, where energy poverty is still very pronounced. These countries share major structural challenges, including limited infrastructure, significant food losses due to poor energy supply, and high poverty rates. These factors confirm that electrification is both a condition and a result of socioeconomic development, incorporating economic, social, and institutional dimensions.

Regional integration through initiatives such as the West African Power Pool est une piste prometteuse pour mutualiser les ressources énergétiques, améliorer la sécurité d’approvisionnement et réduire costs. However, governance, institutional quality, facilitation of private investment, and improvement of regulatory frameworks remain major challenges to be addressed in order to realize this potential.

Finally, the prospect of expanding decentralized solutions based on renewable energies appears to be a key component of future strategies, especially for improving access in isolated rural areas, while taking into account environmental issues and local job creation.

In conclusion, this study demonstrates that access to electricity in West Africa depends on a delicate balance between geographical, economic, and social factors. To accelerate universal electrification, policies must combine targeted expansion of urban and rural infrastructure, strong economic support, improved service quality and reliability, and strengthened governance. Quantitative and qualitative analyses thus provide a useful decision-making framework to guide interventions toward inclusive and sustainable energy development in the region.

Conclusion

In conclusion, this study identified the main factors influencing access to electricity in West Africa, highlighting the predominant role of urban and rural electrification. The multiple linear regression model confirms that while access in urban areas is the main driver, rural electrification remains an essential lever for achieving universal coverage. Per capita GDP has a positive but limited effect, indicating that economic growth alone is not enough to guarantee access to electricity without targeted and appropriate energy policies. Social variables, although less statistically significant, illustrate the complexity of the interactions between electricity, education, and health.

The significant territorial and socioeconomic disparities observed call for a differentiated approach, combining infrastructure expansion in urban and rural areas, improved service quality, and strengthened institutional and regulatory frameworks. Regional integration and the development of decentralized solutions, particularly those based on renewable energies, are also promising avenues for overcoming the challenges specific to the region.

Ensuring universal access to reliable and sustainable electricity is therefore a key lever for accelerating socio-economic development and sustainably improving the living conditions of West African populations. This conclusion paves the way for further in-depth research, incorporating longitudinal analyses, subnational studies, and the evaluation of impacts des politiques énergétiques innovantes.

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Influence of access to electricity on the socio-economic development of West Africa

groupe 7

2025-11-18

Introduction