CONTRIBUTION OF BASIC INFRASTRUCTURE AND SOCIO-ECONOMIC DEVELOPMENT TO IMPROVING HUMAN WELL-BEING IN SOME ECOWAS MEMBER COUNTRIES
AHIPO Aimée Samira 20230853; NIKIEMA Ilmoun-Ouda 20250075;NOMBRE Houdou 20210097;
2025-11-16
ABSTRACT
This report examines the role of transport infrastructure in socio-economic development in selected countries of the Economic Community of West African States (ECOWAS). It analyzes the extent to which this infrastructure contributes to economic growth, regional integration, and human well-being in certain member states. The study focuses on the state of infrastructure, its impact on trade, mobility, and regional integration, as well as the constraints hindering balanced development. The findings aim to inform public policies and regional efforts to optimize the effectiveness of infrastructure in promoting inclusive development.
The objective of our study is to identify and analyze existing transport infrastructure in selected ECOWAS member states while assessing its contribution to economic growth and regional integration. This will highlight disparities between countries and the challenges to be addressed for effective integration, in order to propose recommendations for strengthening networks and reducing infrastructural inequalities.
The methodology of this study is based on Principal Component Analysis (PCA), which identifies relationships between different variables such as education, health, and GDP. This approach highlights the importance of transport infrastructure in reducing regional inequalities and fostering economic development among member countries. Finally, recommendations for improving this infrastructure are formulated, including strengthening public-private partnerships and strategic investments to promote sustainable and inclusive development in certain ECOWAS member countries.
KEY WORDS
Transport infrastructure: All the equipment and facilities (roads, railways, ports, airports) that enable the movement of people and goods. Socio-economic development: The process of sustainably improving the economic (growth, employment, trade) and social (access to services, reduction of inequalities) conditions of a population. ECOWAS (Economic Community of West African States): A regional organization comprising 15 West African countries, aiming to promote economic integration and common development. Opening up isolated areas: The act of reducing or eliminating the geographical isolation of a territory, facilitating its access to external markets and services. Human well-being: Human well-being is defined as an overall state of fulfillment, comfort, and satisfaction of needs, whether physical, psychological, emotional, social, or cognitive. It includes physical health, safety, positive social relationships, and a supportive environment.
INTRODUCTION
Transport infrastructure is a cornerstone of national economic and social development. It forms the network through which goods, services, people, and information flow, facilitating trade, market access, employment, and essential services such as healthcare and education. In the Economic Community of West African States (ECOWAS) region, this infrastructure is of particular importance due to the specific geographical, economic, and social realities of some member states. ECOWAS comprises fifteen countries at varying levels of development, facing major challenges related to weak and unevenly distributed transport infrastructure. This situation creates partial isolation, limiting not only internal mobility within countries but also regional integration and the fluidity of trade between them. Consequently, the question of infrastructure efficiency and its contribution to economic growth is central to the concerns of policymakers and economic actors in the region.
The central issue addressed in this report revolves around the following question: To what extent does basic infrastructure and socio-economic development contribute to improving human well-being in selected ECOWAS member states? This inquiry aims to understand the concrete role of this infrastructure in regional socio-economic development, identify the factors hindering optimal integration, and analyze access to basic services.
First, the report analyzes the state of transport infrastructure in the region, highlighting its strengths and weaknesses by describing road, rail, and air networks. Second, it seeks to assess its economic impact in terms of improved mobility, facilitated trade, and contribution to opening up isolated areas. Finally, it analyzes the quality of life of the population before concluding with recommendations to strengthen the coherence and effectiveness of regional infrastructure policies.
PROBLEM STATEMENT
The central question arising from this theme is: “To what extent does transport infrastructure contribute to the economic development and opening up of certain ECOWAS member states?”
OBJECTIVES
The main objective of our study is to analyze the impact of transport infrastructure on socio-economic development and the improvement of human well-being in certain ECOWAS member states. To achieve this, we must:
Analyze the influence of basic infrastructure (roads, railways, access to water) on the level of human well-being in ECOWAS countries.
Evaluate the impact of socio-economic indicators (GDP, GDP per capita, labor force, imports, exports) on the living conditions of the population.
Examine the relationship between economic development, CO₂ emissions, and the sustainable improvement of human well-being in member states.
I. LITERATURE RESEARCH
Transport infrastructure is the essential foundation of economic development, facilitating the mobility of people and goods, thereby reducing costs, increasing productivity, and fostering greater market integration. Through this facilitation, it stimulates economic growth and improves living conditions, creating a virtuous circle between transport and economic progress. Economic development, for its part, refers to the positive and sustainable transformation of economic and social structures, largely dependent on the availability and efficiency of transport infrastructure, which enables the flow of resources, access to markets, and the diversification of productive activities. In short, a high-performing transport infrastructure is a crucial lever for driving and supporting sustainable economic development, ensuring the smooth and efficient movement of goods and people, a key factor in growth and prosperity. [1]
1. Impact of Transport Infrastructure on Economic Development
Transport infrastructure has a major impact on economic development because it facilitates the mobility of people and goods, reduces logistics costs, and promotes trade. Road transport and related infrastructure are key drivers of sustainable and inclusive growth, efficiently connecting places, goods, services, and people. This infrastructure supports economic activity by enabling businesses to better access markets and resources, stimulating employment in construction and maintenance, and improving quality of life through better access to essential services such as health and education. Furthermore, it promotes regional integration by opening up isolated areas and boosting cross-border trade, thus contributing to more balanced growth and social cohesion. [2]
2. Poverty Reduction and Job Creation through Transport Infrastructure
According to a study on the impact of public infrastructure in Niger, increasing transport infrastructure plays an important role in poverty reduction and job creation. The study showed that a 10% increase in the stock of public infrastructure leads to a long-term increase in economic growth of 2.47%. This translates into improved business productivity, reduced operating costs, and enhanced economic performance across various sectors, including construction and communications. Transport infrastructure fosters the creation of direct jobs in road construction and maintenance, as well as indirect jobs by stimulating surrounding economic activity, particularly in agriculture, industry, and trade. Improved market access enables rural populations to sell their produce, increasing incomes and reducing poverty. This process also contributes to a better geographical distribution of economic opportunities, reducing isolation and territorial inequalities.[3]
3. Factors Hindering Optimal Socio-Economic Development Integration
According to the African Union’s “African Integration Report 2023,” several factors hinder optimal socio-economic development integration. These include:
• The slow establishment of African financial institutions and insufficient mobilization of contributions from member states to finance regional projects.
• Persistent non-tariff barriers and difficulties in facilitating trade.
• Lack of enthusiasm and slow ratification of key protocols, particularly those governing the free movement of people and the liberalization of African airspace.
• Overlapping membership in different Regional Economic Communities, leading to confusion, competition, and duplication of efforts.
• Limited political will among member states, which directly impacts the pace and depth of integration.
• The lack of involvement of non-state actors such as national parliaments, civil society, the private sector, and citizens can lead to a perception of integration as a process reserved for a political elite.
These combined factors slow progress toward full regional integration and harmonious socio-economic development.[4]
4. State of Transport Infrastructure in ECOWAS Member States Strengths and Weaknesses
According to the report “ECOWAS Transport System” published by the World Road Association (PIARC), the state of transport infrastructure in ECOWAS member states presents both strengths and weaknesses: Strengths
The region has several key transport networks, including the Lagos-Nouakchott trans-coastal highway (83% completed) and the Dakar-N’Djamena highway (89% completed), as well as interconnecting roads covering 11,500 km (67% completed).
Most countries have at least one international airport, and the region has about twenty seaports, six of which are major transit ports for landlocked countries.
The railway network, although fragmented into several gauges, comprises 12 national networks and is beginning to be integrated at the sub-regional level.
ECOWAS has established a common policy with a commission dedicated to developing programs for improving transport networks.
The railway network suffers from fragmentation and a lack of coherent interconnection between countries.
River transport is underdeveloped due to fluctuating water levels and the presence of natural obstacles, serving only local and seasonal purposes.
Infrastructure requires significant modernization, particularly for navigational aids at airports.
Financing remains limited despite the intervention of the ECOWAS Bank for Investment and Development (EBID), which often has to act as a co-financing partner.
There is an overall deficit in the development of sustainable and reliable infrastructure, hindering regional economic integration. [5]
II. MATERIALS AND METHODS USED
1. Methodology
this section, we present the methods used to collect and analyze data on the impact of transport infrastructure on the socio-economic development and well-being of populations in selected ECOWAS member countries.
Target Population
Rural and urban populations in member countries who depend on transport infrastructure to access markets, social services (health, education), and employment, particularly those living in remote or underserved areas. Also included are local economic actors such as farmers, traders, entrepreneurs, and workers who benefit from the increased mobility and trade facilitated by infrastructure. Furthermore, policymakers and regional and national organizations responsible for planning, financing, and implementing infrastructure projects are also targeted. Finally, vulnerable populations whose socio-economic situation is directly affected by the quality of transport, such as women, youth, and low-income populations, for whom improved access can mean poverty reduction.
This target is central to the challenges of improving connectivity, regional integration, and equitable development.
2. Materials
To conduct our problem analysis, we used several software tools, namely:
2.1 RStudio
RStudio is an open-source Integrated Development Environment (IDE) designed specifically for the R programming language. It offers a user-friendly interface that facilitates writing, running, and managing R code, particularly for data analysis and statistical visualization. RStudio also allows users to organize projects, create dynamic reports, and import and visualize data. This software is used in both academic and professional settings.
2.2 QGIS
QGIS, or Quantum Geographic Information System, is an open-source Geographic Information System (GIS) software. It allows users to visualize, analyze, and interpret geospatial data for various applications such as mapping, natural resource management, urban planning, and environmental studies.
2.3 Zotero
Zotero is a free, open-source bibliographic reference management software. It allows users to collect, organize, cite, and share bibliographic references as well as research documents (articles, books, web pages, PDFs, etc.). It facilitates the creation of bibliographies and citations in academic and scientific work by integrating this management directly into word processing software.
2.4 KoboCollect
KoboCollect is the mobile tool of the KoBoToolbox platform that transforms paper questionnaires into intelligent digital forms for reliable and professional data collection in the field.
3. Data Analysis Method
For the scientific analysis of our data, we initially chose to adopt a chronological approach. Subsequently, we used several statistical methods, including Principal Component Analysis (PCA), Hierarchical Ascending Classification, and regression analysis, to study our data in depth.
The results obtained from these different methods of analysis are presented below, along with their interpretation.
In this context, a questionnaire primarily targeting the general public was designed to gather opinions on the topic. You will find the questionnaire attached in Appendix 1.
Population Selection
Our study will focus on certain ECOWAS member countries, namely:
Benin
Burkina Faso
Cape Verde
Côte d’Ivoire
Ghana
Guinea
Guinea-Bissau
Liberia
Mali
Niger
Nigeria
Senegal
Sierra Leone
Togo
Variable Selection
Our variables were selected from the Our World in Data website and the World Bank, which are valuable sources of diverse information. This step allowed us to select variables such as:
The data obtained from the sites were filtered to provide the necessary data for our analysis. The data are presented in Table 1 below.
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III. RESULTS AND INTERPRETATION
1. RESULTS
To conduct our study, we first verified the feasibility and relevance of PCA. We performed three essential tests: determinant calculation, Bartlett’s cortest, and KMO(Kaiser-Meyer-Olkin).
library(psych)
library(FactoMineR)
library(Factoshiny)## Loading required package: shiny## Loading required package: FactoInvestigate## Loading required package: ggplot2##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
##
## %+%, alphalibrary(factoextra)## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary(ggplot2)
Ma.base <- read.csv (file="C:/Users/Ouda/Desktop/2IE/DONNEE PTOJET_RTI/DONNEES/DONNE_CSV_BON.csv", header = TRUE, sep = ";",
dec = ",",row.names = "DONNEES" )
matrice.corr <- cor(Ma.base[,1:10])
#verification de la faisabilité de l'ACP
det(matrice.corr)## [1] 1.416513e-10cortest.bartlett(matrice.corr,n=14)## $chisq
## [1] 200.3193
##
## $p.value
## [1] 1.747875e-21
##
## $df
## [1] 45KMO(matrice.corr)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = matrice.corr)
## Overall MSA = 0.67
## MSA for each item =
## PIB PIB.HBTS Population.ACTIVE
## 0.67 0.43 0.85
## ACCES.A.L.EAU ESPERANCE.DE.VIE IMPORTATION
## 0.33 0.67 0.67
## EXPORTATION EMISSION.C02 LONG_ROUTES
## 0.63 0.41 0.74
## LONG_.DE.CHEMIN.DE.FER
## 0.92The correlation matrix determinant (1.41 × 10⁻¹⁰), very close to 0 (ideally < 0.00001), confirms the presence of strong correlations necessary for PCA. The Bartlett test is highly significant (χ² = 200.32, p = 1.74 × 10⁻²¹), which is expected to validate PCA (condition: p < 0.05). The overall KMO of 0.67, within the acceptable range (0.60–0.69), indicates average but sufficient sampling quality for PCA. Thus, despite some low MSAs (< 0.50), the overall results show that the necessary conditions are met and that PCA is appropriate for these data.
res.pca1 <- PCA(Ma.base, scale.unit = TRUE, ncp = 3, graph = TRUE)
## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
1.1 Principal Component Analysis (PCA)
- Observation of Atypical Individuals
Figure 1 illustrates the graph of individuals with atypical elements.
fviz_pca_ind(
res.pca1,
col.ind = "contrib",
gradient.cols = c("blue", "green", "red"),
repel = TRUE,
max.overlaps = Inf # plus de limite
)## Warning in (function (mapping = NULL, data = NULL, stat = "identity", position
## = "identity", : Ignoring unknown parameters: `max.overlaps`
Figure 1: Graph of Individuals with Atypical Elements
The PCA graph shows that the majority of the variance is explained by a gradient of Economic Development (Axis 1, at over 61.1%). Nigeria is the most developed country in the sample on this axis, while the majority of countries (Niger, Liberia, etc.) are at the opposite end. Axis 2, at 23.8%, represents Access to Infrastructure, with Cape Verde and Senegal standing out with their best scores, highlighting the success in Senegal.
fviz_pca_var(res.pca1, col.var = "cos2", gradient.cols = c("blue", "green", "red"))## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## ℹ The deprecated feature was likely used in the ggpubr package.
## Please report the issue at <https://github.com/kassambara/ggpubr/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: `aes_string()` was deprecated in ggplot2 3.0.0.
## ℹ Please use tidy evaluation idioms with `aes()`.
## ℹ See also `vignette("ggplot2-in-packages")` for more information.
## ℹ The deprecated feature was likely used in the factoextra package.
## Please report the issue at <https://github.com/kassambara/factoextra/issues>.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
Figure 2: Graph of variables with an outlier
This graph shows that 84.95% of the variance is explained by the two axes. Axis 1, with 61.14%, is clearly a factor of economic development, dominated by GDP per capita and other economic indicators. Axis 2, with 23.81%, is a factor of social and environmental progress, as life expectancy and CO2 emissions are strongly correlated. Finally, the variable “access to water” is independent of the economic factor (Axis 1), suggesting that access to water is not simply a corollary of the country’s overall wealth. The large majority of the variables in this group are clustered on the left, indicating a low level of development. Axis 2 with 16.4% highlights countries like Burkina Faso and Niger at the top, which stand out for their infrastructure, and Senegal, which is isolated at the bottom, with a distinct socio-environmental profile.
Observation of individuals excluding Nigeria and Cape Verde
Ma.basesans <- Ma.base[!(rownames(Ma.base) %in% c("Nigeria", "Cap-Vert")), ]
matrice.corr <- cor(Ma.basesans[,1:10])
res.pca2 <- PCA(Ma.basesans, scale.unit = TRUE, ncp = 3, graph = TRUE)
fviz_pca_ind(res.pca2, col.ind = "contrib", gradient.cols = c("blue", "green", "red"))
Figure 3: Graph of individuals without atypical elements
This graph shows that the structure of the countries is primarily defined by axis 1, with 55.3% representing the economic development factor. Ghana and Côte d’Ivoire are the most developed countries in this group, while a large majority are clustered on the left, indicating a low level of development. Axis 2, with 16.4%, highlights countries like Burkina Faso and Niger at the top, distinguished by their infrastructure, and Senegal, isolated at the bottom, with a distinct socio-environmental profile.
fviz_pca_var(res.pca2, col.var = "cos2", gradient.col = c("blue", "green", "red"), repel = TRUE)
Figure 4: Graph of variables without outliers
This graph shows how the different variables (GDP, Life Expectancy, etc.) are related. It identifies the two main factors that explain the differences between the countries in the sample. The Primary Factor is the economy and wealth (Horizontal Axis 55%) This factor explains more than half of all the observed differences; the further to the right a country is, the wealthier and more industrialized it is. This means that when a country has a high GDP (wealth), it also has strong imports, exports, and a good road network. This means that economic development is the main driver that ranks countries. The Secondary Factor is based on social and environmental factors (Vertical Axis 16%). It ranks countries on a different dimension, from top to bottom: At the bottom are life expectancy and CO2 emissions (pollution). These two variables are closely related. In this sample, the countries with the highest life expectancy are also those that pollute the most (a sign of their level of industrialization and development). At the top, we find access to water, which is independent, meaning that a country’s success in providing good access to water is not directly linked to its overall wealth.
Contribution of Variables to the Axes
Axis 1
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Figure 5: Correlation of Variables on Axis 1
This table presents the values and correlations of the six (6) socio-economic variables. The economic development variables are almost identical in this sample.
The link between the variables is extremely strong, as all variables (Imports, Exports, Labor Force, GDP, Railways, Roads) are positively correlated with each other, with an average strength of 96% (correlations very close to 1).
This means that if a country has a good score in Imports, it is virtually certain that it will also have a good score in GDP and Exports, thus reflecting the country’s level of wealth and economic activity.
Axis 2
knitr::include_graphics("C:/Users/Ouda/Desktop/2IE/DONNEE PTOJET_RTI/PROJET ETI/WhatsApp Image 2025-11-12 à 16.38.23_d95b0020.jpg")
Figure 6: Correlation of Axis 2 Variables
The table presents, with figures, the role of Axis 2, which explains approximately 24% of the total variance as a Social and Environmental factor. It is strongly correlated with life expectancy and CO2 emissions. Axis 2, being the factor of quality of life and well-being, demonstrates that longevity and social development are key dimensions, but are directly associated with pollution (CO2 emissions) in this sample. GDP per capita, also present in this dimension, shows that wealth has a significant link with this secondary factor.
Observation of the Eigenvalue Graph
fviz_eig(res.pca1, addlabels = TRUE)## Warning in geom_bar(stat = "identity", fill = barfill, color = barcolor, :
## Ignoring empty aesthetic: `width`.
Figure 7: Eigenvalue Graph
The scree graph shows three (3) axes with different eigenvalues: 61.1% for axis 1, 23.8% for axis 2, and 9.3% for axis 3. We observe that the first two axes explain 84.9% of the total variance of the data, and the curve becomes significantly flatter after the second axis (axis 2). Therefore, we will focus on the first two axes./*
PCA-Biplot
fviz_pca_biplot(res.pca2, repel = TRUE, col.var = "blue", col.ind = "red")
Figure 8: PCA-Biplot Graph
Factor analysis showed that 71.7% of the variance in the data is captured by the first two axes. The space is dominated by axis 1, which strongly separates countries along a horizontal gradient. Axis 2 provides secondary differentiation, as countries are distributed according to a structure. Some countries are strongly projected positively onto axis 1, others are isolated on axis 2, while a large majority are homogeneously clustered on the negative side of axis 1.
1.2 Hierarchical Ascending Classification (HAC)
#We used this script to obtain the three graphs in the report. res.shiny=HCPCshiny(res.pca2)
knitr::include_graphics("C:/Users/Ouda/Desktop/2IE/DONNEE PTOJET_RTI/PROJET ETI/capture classification1.png")
Figure 9: Hierarchical Class Graph
The classification performed on the individuals reveals 5 classes.
knitr::include_graphics("C:/Users/Ouda/Desktop/2IE/DONNEE PTOJET_RTI/PROJET ETI/capture classification2.png")
Figure 10: Hierarchical Ascending Classification of Individuals
Class 1 is composed of individuals such as Guinea-Bissau and Sierra Leone. This group is characterized by: low values for the variables ACTIVE.POPULATION, IMPORTATION, and NUMBER OF.RAILROADS (from most extreme to least extreme).
Class 2 is composed of individuals such as Burkina Faso and Niger. This group is characterized by: high values for the variable WATER.ACCESS.
Class 3 is composed of individuals sharing: high values for the variable LONG_ROUTES.
Class 4 is composed of individuals such as Senegal. This group is characterized by:
high values for the variables LIFE EXPECTANCY and CO2 EMISSIONS (from most extreme to least extreme).
Class 5 is composed of countries such as Côte d’Ivoire and Ghana. This group is characterized by:
high values for the variables EXPORTS, GDP (HBS), IMPORTS, and LABOR-RATED POPULATION (from most extreme to least extreme).
knitr::include_graphics("C:/Users/Ouda/Desktop/2IE/DONNEE PTOJET_RTI/PROJET ETI/capture classification3.png")
Figure 11: Hierarchical tree on the factorial plane
1.3 Linear Regression
library(ggplot2)
ggplot(Ma.base, aes(x = EXPORTATION, y = PIB)) +
geom_point(color = "red", size = 3) + # Points rouges
geom_smooth(method = "lm", color = "blue", fill = "lightgray") + # Droite + zone grise
labs(
title = "Relation entre les exportations et le PIB",
x = "Exportations",
y = "PIB"
) +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'
modele <- lm(PIB ~ EXPORTATION, data = Ma.base)
summary(modele)##
## Call:
## lm(formula = PIB ~ EXPORTATION, data = Ma.base)
##
## Residuals:
## Min 1Q Median 3Q Max
## -76.673 1.319 6.877 8.706 27.329
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.6978 7.5654 -0.885 0.393
## EXPORTATION 5.2849 0.4923 10.736 1.65e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 24.95 on 12 degrees of freedom
## Multiple R-squared: 0.9057, Adjusted R-squared: 0.8978
## F-statistic: 115.3 on 1 and 12 DF, p-value: 1.654e-07knitr::include_graphics("C:/Users/Ouda/Desktop/2IE/DONNEE PTOJET_RTI/PROJET ETI/Image_regression linéaire siple_exportation-PIB.png")
Figure 11: Linear Regression
The simple linear regression analysis performed between exports and Gross Domestic Product (GDP) reveals a significant positive linear relationship between the two variables. The estimated slope of the model shows that an increase in exports is accompanied by a rise in GDP, reflecting a strong economic dependence between a country’s level of trade openness and its overall economic performance. In other words, countries that export more tend to generate a higher GDP.
1.4 Representative maps of the different variables and classification
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2. Interpretation
Axis 1 is the Economic Development and Industrialization Factor. It is the main indicator used to classify certain ECOWAS countries according to their level of wealth, trade activity, and industrialization. Axis 1 is defined by a cluster of variables that are not only highly correlated with the axis itself, but also with each other. This group represents traditional economic activity, namely: Wealth, defined by GDP and GDP (HBTS), Trade, defined by Imports and Exports, Infrastructure, defined by Roads and Railways, and Demography, defined by the Working-Age Population.
This means that this statistical grouping reflects an economic model where growth is driven by trade and heavy investment. “The development of intra-regional trade and investment appears to be subject to numerous obstacles, despite the existing business opportunities,” making import and export performance crucial for economic growth. (Sangarea) [6]
Infrastructure variables (roads, railways) are very closely correlated with GDP and trade along this axis.
This implies their strong correlation with economic variables, highlighting that infrastructure is a necessary prerequisite for prosperity in some ECOWAS countries. It is the tool that allows economic potential to be translated into real growth. “The positive impact of infrastructure on economic growth and equitable social development is well established… it will also contribute to higher TFP” (Total Factor Productivity). Furthermore, improved infrastructure allows for “increased human capital… for the benefit of greater economic prosperity.” (African Development Bank - AfDB). [7]
Axis 2 illustrates the specific contribution of social and infrastructural factors to improving well-being, independently of the overall level of economic wealth (Axis 1).
It thus represents the social, health, and environmental factor.
Statistically, the variable ACCESS TO WATER is almost orthogonal (independent) to Axis 1 (GDP, Trade). This dissociation highlights that, for ECOWAS countries, success in basic infrastructure is not simply a corollary of economic growth. It is the result of targeted political will and specific investments. “Access to water is a fundamental right and an absolute necessity for survival. Access to water is also a sine qua non for sustainable development and inclusive growth.” (African Development Bank - AfDB). [8]
Improving access to safe water and sanitation “reduces the spread of waterborne diseases, leads to improved public health, and increases the Human Development Index.” [9]
Axis 2 is also defined by the strong link between LIFE EXPECTANCY and CO2 EMISSIONS. This phenomenon is a typical trade-off in rapidly industrializing economies.
While improving health and longevity in these countries is accompanied, according to this model, by a larger environmental footprint due to higher energy consumption and less environmentally friendly production processes, “improving the availability and reliability of infrastructure increases human capital through improved education and health services, leading to greater economic prosperity.” [10]
The axis shows that this gain in human capital (represented here by Life Expectancy) is currently taking place in a context where the increase in CO2 Emissions is a direct consequence, thus highlighting the challenge of sustainable growth for some ECOWAS countries.
IV. SUGGESTIONS
Strengthening Economic Integration and Infrastructure: GDP, trade, and infrastructure form an integrated system. To improve well-being, this system must be strengthened by addressing the bottlenecks that limit growth. This requires strategic investment in network infrastructure, which is inseparable from economic activity (import/export). Investments should be directed towards regional integration projects. Priority should be given to infrastructure projects that directly connect the economic hubs of certain ECOWAS countries and facilitate intra-regional trade, rather than isolated projects. Results show that indicators measuring the development of economic network infrastructure (telecommunications, electricity, transport, and water and sanitation) have a positive impact on economic growth in WAEMU countries. The contribution of transport, electricity, and sanitation infrastructure is greater than that of telecommunications infrastructure. [11]
Reducing Trade Costs (Non-Tariff Barriers): Trade is a key driver of growth, but it is often hampered. Addressing this issue requires simplifying and harmonizing customs and logistics procedures to reduce transaction costs that hinder total factor productivity (TFP).
Political Disengagement from Water Access (Governance): Since the correlation between water access and GDP is virtually zero, improvement will not come solely from economic growth. Therefore, autonomous and decentralized financing mechanisms for water and sanitation projects must be established to ensure the continuity of efforts, even during periods of low growth. “Low access to water and sanitation is primarily due to poor water management, pollution, waste, and inadequate infrastructure.”[12]
Decarbonizing the Energy Sector for Sustainable Progress: Health progress comes at the price of pollution. Addressing this will require investment in low-carbon energy production to support industrial growth (which increases GDP and life expectancy) without worsening the environmental impact. Improving health infrastructure is therefore a lever for well-being: “Access to safe water reduces the spread of waterborne diseases, leads to improved public health, and increases the Human Development Index.”[13]
CONCLUSION
Our research project on several ECOWAS countries, which explores the relationship between economic development and human well-being, was structured around the integration of multiple variables. The literature review established that improving well-being requires synergy between physical (transport) and social (health) investments, confirming ECOWAS’s policy orientation which aims for “accelerating the socio-economic transformation of the region by focusing on men and women” [14]. Finally, the conclusion emphasized the importance of this synergy for sustainable development, while also noting that infrastructure deficits always amount to “a tax on growth” [15].
REFERENCE BIBLIOGRAPHE
[1]Colletis-Wahl, Kristian, et Corinne Meunier. « Infrastructures de transport et développement économique en espace rural Quelles méthodes pour quels «effets»? » Rapport pour le PREDIT 2 (2003). http://temis.documentation.developpement-durable.gouv.fr/docs/Temis/0073/Temis-0073051/RMT03-013.pdf.
[2]Dattani, Saloni, Lucas Rodés-Guirao, Hannah Ritchie, Esteban Ortiz-Ospina, et Max Roser. « Life Expectancy ». Our World in Data, 28 novembre 2023. https://ourworldindata.org/life-expectancy.
[3] « l’accès à une eau saine, réduit la propagation des pathologies hydriques, entraîne une amélioration de la santé publique et augmente l’indice du développement humain. » (Revue FREG) - Recherche Google ». Consulté le 14 novembre 2025. https://www.google.com/search?q=l%27acc%C3%A8s+%C3%A0+une+eau+saine%2C+r%C3%A9duit+la+propagation+des+pathologies+hydriques%2C+entra%C3%AEne+une+am%C3%A9lioration+de+la+sant%C3%A9+publique+et+augmente+l%27indice+du+d%C3%A9veloppement+humain.+%C2%BB+(Revue+FREG)&rlz=1C1CHBF_enAE1150BF1187&oq=l%27acc%C3%A8s+%C3%A0+une+eau+saine%2C+r%C3%A9duit+la+propagation+des+pathologies+hydriques%2C+entra%C3%AEne+une+am%C3%A9lioration+de+la+sant%C3%A9+publique+et+augmente+l%27indice+du+d%C3%A9veloppement+humain.+%C2%BB+(Revue+FREG)&gs_lcrp=EgZjaHJvbWUyBggAEEUYOdIBCTE5OTk3ajBqN6gCALACAA&sourceid=chrome&ie=UTF-8.
[4]MAÏGA, Abdoulaye, N’famoussa BAGAYOKO, et Fatoumata DEMBELE. « Souaïbou Samba Lamine TRAORÉ* lpapus@ yahoo. fr ». Revue Internationale Dônni 3, no 1 (2023). https://revuedonni.wordpress.com/wp-content/uploads/2023/07/008_11_souaibou_rid5_2_tap-2.pdf.
[5]Mitullah, Winnie V., Romaric Samson, Pauline M. Wambua, et Samuel Balongo. Building on progress: Infrastructure development still a major challenge in Africa. 2016. https://afrobarometer.org/wp-content/uploads/migrated/files/publications/Dispatches/ab_r6_dispatchno69_infrastructure_remains_challenge_en.pdf.
[6]Our World in Data. « GDP per Capita ». Consulté le 9 novembre 2025. https://ourworldindata.org/grapher/gdp-per-capita-penn-world-table.
[7]Our World in Data. « Gross Domestic Product (GDP) ». Consulté le 9 novembre 2025. https://ourworldindata.org/grapher/gdp-worldbank-constant-usd.
[8]Our World in Data. « Sources of Our Population Dataset ». Consulté le 9 novembre 2025. https://ourworldindata.org/grapher/sources-population-dataset.
[9] « Publications de PIARC (Association mondiale de la Route) | L’apport du transport routier au développement durable et au développement économique ». Consulté le 10 novembre 2025. https://www.piarc.org/fr/fiche-publication/33885-fr-L%E2%80%99apport%20du%20transport%20routier%20au%20d%C3%A9veloppement%20durable%20et%20au%20d%C3%A9veloppement%20%C3%A9conomique.
[10]Ritchie, Hannah, Lucas Rodés-Guirao, Edouard Mathieu, et al. « Population Growth ». Our World in Data, 11 juillet 2023. https://ourworldindata.org/population-growth.
[11] Ritchie, Hannah, Pablo Rosado, et Max Roser. « CO₂ and Greenhouse Gas Emissions ». Our World in Data, 5 décembre 2023. https://ourworldindata.org/co2-and-greenhouse-gas-emissions.
[12] Ritchie, Hannah, et Max Roser. « CO₂ Emissions ». Our World in Data, 10 juin 2020. https://ourworldindata.org/co2-emissions.
[13]Ritchie, Hannah, Fiona Spooner, et Max Roser. « Clean Water ». Our World in Data, 26 septembre 2019. https://ourworldindata.org/clean-water.
[14]Traoré, Souaïbou Samba Lamine, Abdoulaye Maϊga, N’famoussa Bagayoko, et Fatoumata Dembele. EFFECTS OF PUBLIC SPENDING ON SOCIAL INFRASTRUCTURE ON HUMAN DEVELOPMENT IN MALI. 3 (2023).
[15] « Water Use and Stress - Our World in Data ». Consulté le 9 novembre 2025. https://ourworldindata.org/water-use-stress.
ANNEXE
This questionnaire aims to gather your views on how infrastructure and socio-economic development improve the well-being of populations in ECOWAS.
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