Infrastructure and unemployment: gender-differentiated effects in UEMOA

Infrastructure and unemployment: gender-differentiated effects in UEMOA | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Infrastructure and unemployment: gender-differentiated effects in UEMOA Ladioum Bienvenue Drabo, Tibi Didier Zoungrana This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9348425/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract African countries are lagging in terms of infrastructure, all types combined. In addition, on the continent, the problem of employability continues to be acute, often more pronounced according to gender or sex, requiring special attention. The objective of this research is to analyze the effect of a set of infrastructures on unemployment by gender in the WAEMU area. The FGLS PCSE methods were used to determine the effect of the infrastructures. Estimates show that infrastructure has a non-homogeneous effect on unemployment rates. Transport and electricity infrastructure contribute to an increase in overall unemployment, both male and female. On the other hand, only ICT stands out by significantly reducing unemployment rates. As for the WSS water and sanitation infrastructure, it does not affect unemployment rates. These results indicate that the effects of infrastructure on unemployment rates are not different by gender. These effects show that the infrastructure situation in terms of availability, quality, and accessibility persists in the WAEMU countries. In this dynamic, investments in quality, accessible infrastructure directed towards the creation of decent jobs for all social strata, regardless of gender, are important. WAEMU FGLS PCSE unemployment infrastructure gender Introduction The issue of employment is one of the major challenges to economic development in developing countries today. In many African economies, persistently high unemployment, especially among women, represents a significant obstacle to inclusive growth and poverty reduction. In the countries of the West African Economic and Monetary Union, the issue of employment remains of particular concern. Despite economic progress in recent decades, labour markets remain characterised by high levels of unemployment and underemployment, as well as a high prevalence of informal employment. In addition, gender inequalities persist in the labour market, with women often facing more constraints in terms of access to employment, mobility, and economic participation (Alban and Forti, 2022). In 2019, according to McKinsey Global Institute, the gender parity score at work is 0.51 in Africa. WAEMU countries are classified as countries with higher inequality. In this context, investments in public infrastructure are seen as a strategic lever to stimulate economic growth and improve labour market conditions (Beverly et al., 2025 ). Infrastructure, including transport, energy, and information and communication technologies, is seen as key to improving productivity, facilitating market access, and fostering private investment (Barro, 1990 ; World Bank, 2016 ; Ouni and Mraihi, 2025 ). On the other hand, Africa in general and the WAEMU countries in particular are lagging far behind in terms of infrastructure deficits, which requires infrastructure development based on credible policies as well as a rigorous prioritization of projects that promote productive transformation (OECD/AUC, 2025). It is for this reason that, for example, IEA et al. (2023) find that 16 of the 20 countries with electricity deficits are located in Africa. However, World Bank ( 2017 ) believes that economic and spatial inclusion necessarily require the accessibility of quality infrastructure. In the literature, infrastructure can influence unemployment through several channels. By reducing transport and transaction costs, they facilitate the integration of markets and the expansion of productive activities (Barro, 1990 ; Krugman, 1991 ; Leigh & Neill, 2011 ). Energy infrastructure improves the reliability of electricity supply, which promotes the establishment of businesses and the creation of jobs. In addition, digital infrastructure helps to strengthen the flow of information and access to economic opportunities. These effects can result in an increase in the demand for labour and, consequently, in a reduction in unemployment. Water and sanitation infrastructure provides water, improves health and reduces the time it takes to search for water, which in turn increases productivity. This research is in line with SDG 8 on decent work and economic growth. It also contributes to SDG 9 by assessing the role of infrastructure in economic development, as well as SDG 5 by highlighting gender disparities in the labour market. Finally, the results shed light on issues related to the reduction of inequalities (SDG 10). Infrastructure development is at the heart of these SDGs. However, the literature shows that these effects are not automatic and can vary depending on the context. In particular, infrastructure can have differentiated effects on the labour market and lead to inequalities across population groups (Calderón and Servén, 2010 ; World Bank, 2017 ; Alban and Forti, 2022; Adabor and Ayesu, 2025 ). For example, improving transport infrastructure and information technology could help reduce some of the structural barriers that limit women's access to the labour market. The analysis of the differentiated effects of infrastructure on male and female unemployment therefore appears to be an important question for a better understanding of employment dynamics in the region. Despite the importance of these issues, the empirical literature on the relationship between a set of infrastructures and unemployment in African countries remains relatively limited, and even more so when it comes to analyzing gender differences. Most existing work focuses mainly on the effects of infrastructure on economic growth or productivity, while the gender implications for the labour market remain insufficiently explored. In the context of the countries of the West African Economic and Monetary Union, there is no systematic study that examines the impact of infrastructure on total unemployment as well as on unemployment for men and women as far as we know. Faced with these shortcomings, this study aims to analyse the effect of infrastructure on unemployment in the countries of the West African Economic and Monetary Union, distinguishing between total unemployment, male unemployment and female unemployment. Specifically, the aim is to assess the extent to which infrastructure investments contribute to reducing unemployment in the region and whether these effects differ by gender. The study is based on an econometric analysis of panel data covering the member countries of the union, in order to identify the relationships between infrastructure indicators and the different measures of unemployment. By providing an empirical analysis of the relationship between infrastructure and unemployment in the countries of the West African Economic and Monetary Union, this research contributes to the existing literature by highlighting the potentially differentiated effects of infrastructure on the labour market. The expected results can also provide useful lessons for policymakers on infrastructure investment policies, in particular to promote more inclusive growth and reduce inequalities in the labour market. The rest of the research begins with a literature review of the effect of infrastructure on human development indices, this section is followed by the methodology and presentations of the data used in the estimation, the next section presents the results and discussions of the results from the estimation. Finally, a conclusion Literature review Infrastructure can be associated with a reduction in unemployment through several channels, including lower transaction costs, improved business productivity, and stimulated private investment (Aschauer, 1989 ; Barro, 1990 ; Munnell, 1992 ). Similarly, improved transport infrastructure can intensify interregional competition and promote the concentration of economic activities in the most productive areas, to the detriment of less competitive regions, in line with the predictions of the new geographical economy (Krugman, 1991 ; Fujita et al., 1999 ). This process of spatial and sectoral reallocation can thus result in an increase in unemployment in certain areas. However, the literature also highlights a nuanced view, pointing out that infrastructure not only supports economic activity, but also helps to transform the structure of the labour market. For example, certain infrastructures, particularly information and communication technologies (ICTs), are widely recognized as vectors of economic inclusion. They facilitate access to information, improve the matching of labour supply and demand and promote the development of new forms of employment, particularly in the service sectors and the digital economy (Jorgenson, 2001 ). The adoption of capital-intensive technologies can reduce the demand for low-skilled labour and lead to job destruction in the short term (Acemoglu, 2002 ). Guzman and Oviedo ( 2018 ) show that transport subsidies improve accessibility and reduce the cost of transport for poor households. However, their effect on equity remains limited due to targeting issues and spatial inequalities. Hernández et al. (2020) show that better access to jobs via public transport reduces unemployment in Montevideo. Their research highlights that transport infrastructure improves spatial connectivity, facilitates access to employment and reduces labour market frictions, especially for low-income populations. Transport facilitates access to employment without guaranteeing a significant reduction in unemployment. Ndjobo and Abessolo (2023) report that access to roads reduces youth unemployment in 31 African countries using the seemingly unrelated bivariate probit method. Ouni and Mraihi ( 2025 ) show that transport infrastructure plays a crucial role in promoting inclusive development in developing countries. Road and rail infrastructure significantly reduce poverty and inequality, while air transport can exacerbate poverty. In addition, all forms of transport infrastructure contribute to the reduction of unemployment by improving access to economic opportunities. Adabor and Ayesu ( 2025 ) show that transport poverty is positively associated with unemployment, with households with limited or expensive access to transport being less likely to access employment opportunities. These results underline the central role of mobility in the functioning of the labour market. Dawn et al. ( 2025 ) find that ICTs reduce unemployment by creating jobs, improving productivity, and facilitating the functioning of the labour market. They are therefore an important lever for employment in the countries of the Indian Ocean Rim Association (IORA). Lobo et al. ( 2020 ) show that the relationship between broadband speed and unemployment is sensitive to data measurement issues. Although some estimates suggest a negative effect of ICT on unemployment, the results are not systematically robust, highlighting the importance of empirical specifications and data quality. Using the ordinary least squares (OLS) and generalized least squares (GLS) methods, Abbasabadi and Soleimani ( 2021 ) show that the expansion of digital technologies has a negative and significant effect on unemployment in 163 countries. By promoting job creation, improving productivity and the functioning of the labour market, digital technologies contribute to the reduction of unemployment, despite substitution effects on certain jobs. Ünlü and Kabak ( 2024 ) show that ICT reduces unemployment in OECD countries. For these authors, their overall effect is still ambiguous and debated in the literature. They improve productivity, facilitate labour market matching and create new jobs. However, their effects depend on the level of development and skills. Hirschman ( 2021 ) shows that knowledge infrastructures play a central role in the production and dissemination of stylized facts about inequality, particularly with regard to the concentration of income at the top. Goaied and Sassi ( 2019 ) analyse the effect of ICT on employment. They show that ICTs reduce the demand for labour in the short and long term. This effect leads to an increase in structural unemployment. Apergis et al. ( 2023 ) use dynamic panel models to analyze the effect of ICT on unemployment in the United States. They find that ICTs reduce unemployment in the long term, with smaller effects in the short term. Tsaurai ( 2022 ) highlights a negative and significant effect of renewable energies on unemployment. By promoting the creation of green jobs. Afolayan et al. ( 2019 ) find that a 1% increase in electricity consumption leads to a decrease of about 0.22% in Nigeria's unemployment rate. Using panel data and a quasi-experimental identification strategy, Mensah (2024) shows that electricity shortages exert a positive and significant effect on unemployment in Africa. The results indicate that interruptions in energy supply reduce business activity, dampen investment and lead to job losses, highlighting the crucial role of energy infrastructure in the functioning of the labour market. Swain and Karimu ( 2020 ) show that the development of renewable electricity contributes positively to the achievement of the Sustainable Development Goals in the European Union. The results suggest that the energy transition promotes economic growth, human development and environmental sustainability, with potentially positive indirect effects on employment. There is little or no research in the literature on the simultaneous effect of a set of infrastructures on unemployment and their effect by gender. It is this gap that this research intends to fill. Methodology and data The objective of this research is to determine the effect of infrastructure on unemployment rates. To this end, we formulate an equation linking infrastructure and the unemployment rate. Preliminary tests will then be conducted to determine the appropriate estimation method. $$\:{Unemployment}_{it}={\alpha\:}_{0}+\alpha\:{infrastructures}_{it}+\beta\:{control\_variables}_{it}{\:+\:\epsilon\:}_{it}$$ Here \(\:{\alpha\:}_{0}\) represents the constant, et \(\:{\:\epsilon\:}_{it}\) and represents the error term, respectively, \(\:\alpha\:\) represents the vector of infrastructure variables et \(\:\beta\:\) the vector of control variables, \(\:t=1\dots\:18\) the time and \(\:i=1\dots\:8\) the countries. Table 1 Definition of variables Variables Definition Source Expected sign unemployment Unemployment, total (% of total labor force) WDI Male unemployment Unemployment, male (% of total labor force) WDI Female unemployment Unemployment, female (% of total labor force) WDI AIDI Africa Infrastructure Development Index: A composite index measuring the overall level of infrastructure development (transport, energy, ICT, water, and sanitation) African Development Bank (AfDB) + WSS Water and Sanitation Infrastructure Composite Index African Development Bank (AfDB) + Electricity The electricity infrastructure composite index African Development Bank (AfDB) + Transport The transport infrastructure composite index African Development Bank (AfDB) + ICT The information and communication technology infrastructure composite index African Development Bank (AfDB) + Political regime autocracies = 0, electoral autocracies = 1, electoral democracies score 2, and liberal democracies score 3. Our World in data − Political corruption Political Corruption Index WGI − Political stability Indicator of political stability and absence of violence WGI + Inflation Inflation rate based on the Consumer Price Index WDI − Urban population growth Urban population growth rate WDI ± Source: authors The Table 2 of descriptive statistics shows that the average unemployment rate stands at 2.86%, with similar levels between men and women. However, the higher standard deviation for female unemployment, at 1.65 compared to 1.52 for men, indicates greater volatility in unemployment among women. Table 2 Descriptive statistics Variable Obs Mean Std. Dev. Min Max Unemployment 144 2.862 1.547 .316 7.223 Male unemployment 144 2.87 1.52 .39 7.132 Female unemployment 144 2.87 1.652 .216 7.332 infra 144 12.131 5.683 2.227 29.216 WSS 144 49.109 13.335 16.002 77.881 electricity 144 1.822 1.923 .165 7.518 ICT 144 5.675 6.096 0 23.258 transport 144 5.45 2.95 1.212 13.135 Political regime 144 1.597 .546 0 2 Political corruption 144 .619 .181 .183 .915 Resource rente 144 7.738 4.612 1.972 20.994 Urban growth 144 4.047 .811 2.666 7.28 Inflation 144 1.824 2.644 -3.503 11.305 Source: authors Results and discussion The following Table 3 shows the preliminary tests. These tests are the heteroscedasticity test, the autocorrelation test, and the contemporaneously cross-sectionally correlated test. The results show that our data are heteroscedastic, that there is autocorrelation and contemporaneously cross-sectionally correlated. These results lead to the use of appropriate estimation methods. Among the methods we have the PCSE and the FGLS. The FGLS has another particular constraint regarding the number of years and the number of units. If the number of units is smaller than the period, then FGLS can be used, the opposite makes this method inapplicable. In our case we have N = 8 and T = 18 then the FGLS is applicable in our case. The PCSE method is not subject to this constraint. In conclusion, we apply these methods to make our results more robust and convincing. Table 3 : Preliminary test Table 3 Preliminary test AIDI Unemployment heteroskedasticity autocorrelation contemporaneously cross-sectionally correlated 1680.36*** 30.414*** 102.539*** Male unemployment heteroskedasticity autocorrelation contemporaneously cross-sectionally correlated 842.52*** 62.793*** 111.385*** Female unemployment heteroskedasticity autocorrelation contemporaneously cross-sectionally correlated 2438.77*** 27.871*** 91.797*** Disaggregated infrastructure Unemployment heteroskedasticity autocorrelation contemporaneously cross-sectionally correlated 609.42*** 33.254*** 103.020*** Male unemployment heteroskedasticity autocorrelation contemporaneously cross-sectionally correlated 323.50*** 62.279*** 117.423*** Female unemployment heteroskedasticity autocorrelation contemporaneously cross-sectionally correlated 1164.55*** 32.746*** 80.325*** *** p < 0.01, ** p < 0.05, * p < 0.1 Source : authors Table 4 shows the effect of the infrastructure index on the three unemployment rates. The results from the estimates using the PCSE and FGLS methods show a relative robustness of the coefficients, both in terms of signs and statistical significance. This stability reinforces the credibility of the empirical conclusions and suggests that the relationships identified between infrastructure and unemployment are not sensitive to the choice of estimation method. However, differences appear in the magnitude of the effects and the degree of significance of some variables, reflecting the specific properties of each estimator. The results show that the infrastructure index has a positive and significant effect on total unemployment, as well as on unemployment for both men and women. This result can be explained by the modernization effects induced by infrastructure, which favor the substitution of labor by capital and technologies. In addition, infrastructure can lead to structural transformations and skills mismatches, leading to increased unemployment, especially in developing economies. To better understand this result, which is opposed to most empirical results (Afolayan et al., 2019 ; Dawn et al., 2025 ; Ouni and Mraihi, 2025 ) we disaggregate the infrastructure index into its different components. Table 4 AIDI and unemployment Variables Unemployment Male unemployment Female unemployment Unemployment Male unemployment Female unemployment AIDI 0.444** 0.410** 0.497* 0.268** 0.292*** 0.271** (0.215) (0.167) (0.290) (0.117) (0.107) (0.136) Political regime -0.184 -0.192 -0.193 -0.0801 -0.0893 -0.0817 (0.156) (0.126) (0.203) (0.0650) (0.0628) (0.0759) Political corruption -0.115 -0.0743 -0.171 -0.269* -0.201 -0.341** (0.210) (0.186) (0.251) (0.153) (0.147) (0.167) Resource rente 0.170** 0.187** 0.152 0.136** 0.169*** 0.0858 (0.0845) (0.0793) (0.0980) (0.0627) (0.0610) (0.0686) Urban growth -0.628** -0.709*** -0.547* -0.394** -0.553*** -0.245 (0.248) (0.226) (0.287) (0.196) (0.182) (0.222) Inflation -0.000298 0.000627 -0.00136 0.000507 0.00132 -0.000374 (0.00797) (0.00707) (0.00978) (0.00399) (0.00391) (0.00446) Constant 0.490 0.696 0.227 0.621 0.769** 0.450 (0.679) (0.561) (0.875) (0.401) (0.377) (0.455) Observations 144 144 144 144 144 144 R-squared 0.181 0.236 0.125 Number of id 8 8 8 Standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 Source: auteurs Table 5 shows the results of the different types of infrastructure on unemployment rates. The results reveal differentiated effects according to their nature. Access to electricity has a positive and significant effect on unemployment in both the PCSE and FGLS estimates, although the magnitude of this effect is slightly attenuated in the latter case. This result, a priori counterintuitive, can be explained by a capital-labour substitution effect. The positive effect of electricity on unemployment can be explained by the structural inadequacies of the energy system in the WAEMU countries. Indeed, despite an improvement in the electrification rate, the supply of electricity often remains lower than demand, leading to frequent power cuts and energy rationing. These dysfunctions disrupt the activity of companies, increase production costs and slow down job creation, thus contributing to an increase in unemployment. This is what Mensah (2024) pointed out and he distinguishes three possible channels. This author found that electricity shortages inhibit job creation because of their negative effect on entrepreneurship. Second, electricity shortages reduce the production and productivity of existing firms, leading them to reduce their demand for labour. Finally, electricity shortages distort the business climate, reducing the trade competitiveness and export competitiveness of African companies. This perfectly explains the situation of the WAEMU countries that have a low electricity production capacity faced with very high demand, which results in intermittent power cuts. In the context of WAEMU economies, which are characterized by structural dualism, the expansion of modern electrified sectors is not necessarily accompanied by sufficient labour absorption, leading to a form of jobless growth. Moreover, the effect is more pronounced among women, reflecting their increased vulnerability. On the other hand, information and communication technologies (ICTs) are an important lever for reducing unemployment. Their negative and significant effect across the specifications suggests that they facilitate labour market matching, reduce information asymmetries and stimulate entrepreneurship, especially in the informal sector and digital activities. This effect is particularly pronounced among women, indicating that ICTs are helping to reduce structural barriers to women's employment. Although it is often recognized that ICTs can boost growth but increase unemployment by replacing human labour with automation, they are essential for job creation. It is in this sense that research by Abbasabadi and Soleimani ( 2021 ) shows that the expansion of digital technologies has a negative and significant effect on unemployment in 163 countries. Transport infrastructure, on the other hand, has a positive and highly significant effect on unemployment. This result can be interpreted through the dynamics of spatial reallocation of economic activities. Improved transport networks intensify interregional competition and encourage the concentration of activities in the most competitive areas, leading to the loss of jobs in less productive regions. Thus, in the short term, the effects of job destruction can dominate the effects of job creation, especially in vulnerable sectors. Again, the impact is more pronounced among women, who are often over-represented in competitively sensitive informal activities. The positive effect of transport infrastructure on unemployment can be explained by the structural inadequacies, poor quality and inaccessibility of this infrastructure in the WAEMU countries. Indeed, quantitatively limited, degraded or unequally accessible infrastructure can slow down economic activity, increase production costs and limit job creation. Thus, in the absence of adequate and inclusive infrastructure, their development can paradoxically be associated with an increase in unemployment. This is why Adabor and Ayesu ( 2025 ) indicate that precarious transport increases household unemployment. In addition, high transport costs limit access to employment and increase spatial inequalities, which may explain the positive effect of transport infrastructure on unemployment. It is in this sense that Viguié et al. ( 2023 ) show that public transport improves accessibility in the short term. However, in the long run, it benefits the rich more than the poor. Infrastructure can thus increase spatial inequalities. For water and sanitation infrastructure, the coefficients are not statistically significant, suggesting an indirect and long-term effect on the labour market. These infrastructures mainly contribute to the improvement of human capital through health and productivity, without any immediate impact on the level of employment. Institutional variables also play an important role. The improvement of the political regime is associated with a reduction in unemployment, as a result. This result underscores the importance of political stability and the quality of institutions in creating an environment conducive to investment and job creation. The negative effect of the political regime on unemployment can be explained by the role of institutions in creating a favourable economic environment. Stable and well-governed political regimes enhance investor confidence, improve the effectiveness of public policies, and promote private sector development, thereby contributing to job creation and reducing unemployment. More surprisingly, corruption has a negative effect on unemployment in some specifications. This result can be interpreted in the light of the so-called "grease the wheels" hypothesis, according to which corruption can, in constrained institutional contexts, facilitate access to economic opportunities. However, this effect remains short-term and does not call into question the overall harmful effects of corruption on economic development. A similar result was found by Dirie et al. ( 2025 ). Unlike ours, they find that corruption can reduce unemployment in the short term, confirming the "grease wheels" hypothesis. In addition, the negative effect of corruption on unemployment can be explained by its role in reducing administrative rigidities and facilitating access to employment, especially in highly informant economies. However, this effect is significant for women, can be explained in certain weak institutional contexts, corruption can take gendered forms, particularly through sextortion practices, where access to certain economic opportunities is conditioned by abuses of power. These practices can affect women's professional integration in different ways, by accentuating inequalities in the labour market. Moreover, the rent from natural resources has a positive and significant effect on unemployment, confirming the hypothesis of the resource curse. The positive effect of natural resource rents on unemployment can be explained by the low labour intensity of the extractive sectors, as well as by the crowding-out effects they have on other productive sectors. Indeed, dependence on natural resources can lead to deindustrialization and misallocation of resources, thus limiting job creation and contributing to an increase in unemployment. In many African countries in general, natural resource rents do not necessarily translate into significant job creation. This can be explained in particular by the low local processing of resources, the dependence on exports of raw materials and an economic environment that is sometimes not conducive to the development of the private sector. In addition, certain public policy orientations can limit economic diversification and job creation, thus contributing to a positive effect of rents on unemployment. This result is contrary to that found by Akinyele et al. (2022) who estimate that natural resources contribute to long-term job creation in sub-Saharan African countries. Urbanisation appears to be a factor in reducing unemployment, with a negative and significant effect in the majority of specifications. This result reflects the role of urban centres as poles of economic diversification and labour force absorption, particularly through the expansion of the informal sector. However, this effect is more pronounced among men, suggesting the existence of specific constraints on women's employment in urban areas. This is in contrast to the results of Borhan et al. ( 2023 ), who found that urbanization can be a source of unemployment. Our research provides the opposite arguments. The difference in the results may be in the type of data. Finally, inflation does not have a significant effect on unemployment, indicating the absence of a clear Phillips curve-type relationship in the sample studied. Table 5 : Disaggregated infrastructure and unemployment Table 5 Disaggregated infrastructure and unemployment PCSE FGLS VARIABLES unemployment Male unemployment Female unemployment unemployment Male unemployment Female unemployment WSS 0.229 0.331 0.148 0.382 0.353 0.430 (0.474) (0.422) (0.567) (0.294) (0.291) (0.318) Electricity 0.237*** 0.187*** 0.293*** 0.121** 0.102** 0.157*** (0.0703) (0.0658) (0.0801) (0.0497) (0.0506) (0.0533) ICT -0.0449** -0.0470*** -0.0440** -0.0305** -0.0248* -0.0394*** (0.0186) (0.0180) (0.0213) (0.0141) (0.0145) (0.0145) Transport 0.446*** 0.385*** 0.520*** 0.397*** 0.348*** 0.465*** (0.0895) (0.0826) (0.107) (0.0685) (0.0696) (0.0717) Political regime -0.268* -0.254** -0.288 -0.180** -0.167** -0.213** (0.145) (0.121) (0.182) (0.0777) (0.0766) (0.0859) Political corruption -0.333* -0.250 -0.438** -0.265** -0.170 -0.401*** (0.174) (0.162) (0.203) (0.134) (0.137) (0.138) Resource rente 0.294*** 0.307*** 0.270*** 0.194*** 0.212*** 0.165** (0.0877) (0.0832) (0.101) (0.0698) (0.0702) (0.0731) Urban growth -0.487*** -0.621*** -0.318 -0.470*** -0.595*** -0.322** (0.183) (0.179) (0.200) (0.145) (0.152) (0.154) Inflation -0.00594 -0.00488 -0.00729 -0.00152 -0.000580 -0.00313 (0.00758) (0.00640) (0.00964) (0.00465) (0.00451) (0.00516) Constant -0.514 -0.608 -0.571 -0.822 -0.451 -1.323 (1.787) (1.592) (2.153) (1.111) (1.098) (1.206) Observations 144 144 144 144 144 144 R-squared 0.418 0.422 0.385 Number of id 8 8 8 Standard errors in parentheses *** p < 0.01, ** p < 0.05, * p < 0.1 Sources : auteurs Conclusion The results highlight the non-homogeneous nature of the effects of infrastructure on the labour market. While some infrastructures, such as ICTs, appear to be vectors of economic inclusion, others, such as electricity and transport, can generate exclusionary effects due to the structural transformations they induce. These results underline the importance of support policies, particularly in terms of training and human capital development, in order to maximize the positive effects of infrastructure investments on employment. Infrastructure does not automatically guarantee a reduction in unemployment. Therefore, public policies should focus not only on the development of infrastructure, but also on its quality, accessibility and complementarity with employment and training policies. In addition, particular attention should be paid to gender inequalities and territorial disparities in order to promote inclusive growth. Declarations funding: The authors did not receive support from any organization for the submitted work. No funding was received to assist with the preparation of this manuscript. No funding was received for conducting this study. No funds, grants, or other support was received. Disclosure statement: The authors have no relevant financial or non-financial interests to disclose. The authors have no competing interests to declare that are relevant to the content of this article. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. The authors have no financial or proprietary interests in any material discussed in this article. Data Availability Statements: Data and code will be available on request. References Abbasabadi, H. M., & Soleimani, M. (2021). Examining the effects of digital technology expansion on Unemployment: A cross-sectional investigation. Technology in society , 64 , 101495. Acemoglu, D. (2002). 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MIT Press . Goaied, M., & Sassi, S. (2019). The effect of ICT adoption on labour demand: A cross‐region comparison. Papers in Regional Science , 98 (1), 3-17. Guzman, L. A., & Oviedo, D. (2018). Accessibility, affordability and equity: Assessing ‘pro-poor’public transport subsidies in Bogotá. Transport Policy , 68 , 37-51. Hernandez, D., Hansz, M., & Massobrio, R. (2020). Job accessibility through public transport and unemployment in Latin America: The case of Montevideo (Uruguay). Journal of Transport Geography , 85 , 102742. Hirschman, D. (2021). Rediscovering the 1%: Knowledge infrastructures and the stylized facts of inequality. American Journal of Sociology , 127 (3), 739-786. Jorgenson, D. W. (2001). Information technology and the U.S. economy. American Economic Review , 91(1), 1–32. https://doi.org/10.1257/aer.91.1.1 Krugman, P. (1991). Increasing returns and economic geography. Journal of Political Economy , 99(3), 483–499 Leigh, A., & Neill, C. (2011). Can national infrastructure spending reduce local unemployment? Evidence from an Australian roads program. Economics Letters , 113 (2), 150-153. Lobo, B. J., Alam, M. R., & Whitacre, B. E. (2020). Broadband speed and unemployment rates: Data and measurement issues. Telecommunications Policy , 44(1), 101829. Munnell, A. H. (1992). Infrastructure investment and economic growth. Journal of Economic Perspectives , 6(4), 189–198 Nga Ndjobo, P. M., & Abessolo, Y. A. (2023). Access to paved roads, gender, and youth unemployment in rural areas: Evidence from sub‐Saharan Africa. African development review , 35 (2), 165-180. Ouni, M., & Mraihi, R. (2025). Impact of transport infrastructure on poverty, income inequality, and unemployment in developing countries: An assessment of sustainable development goals. African Transport Studies , 3 , 100050. Swain, R. B., & Karimu, A. (2020). Renewable electricity and sustainable development goals in the EU. World Development , 125 , 104693. Tsaurai, K. (2022). Financial development, renewable energy and unemployment in North Africa. Journal of Accounting and Finance in Emerging Economies , 8 (3), 413-424. Ünlü, A., & Kabak, S. (2024). The Effect of Information and Communication Technologies on Unemployment: The Case of Selected OECD Countries. International Econometric Review , 16 (2), 127-147. Viguié, V., Liotta, C., Pfeiffer, B., & Coulombel, N. (2023). Can public transport improve accessibility for the poor over the long term? Empirical evidence in Paris, 1968–2010. Journal of Transport Geography , 106 , 103473. World Bank. (2016). World development report 2016: Digital dividends . World Bank. World Bank. (2017). Promoting Inclusive Growth by Creating Opportunities for the Urban Poor: Philippines Urbanization Review Policy Notes. World Bank. Additional Declarations No competing interests reported. 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In many African economies, persistently high unemployment, especially among women, represents a significant obstacle to inclusive growth and poverty reduction. In the countries of the West African Economic and Monetary Union, the issue of employment remains of particular concern. Despite economic progress in recent decades, labour markets remain characterised by high levels of unemployment and underemployment, as well as a high prevalence of informal employment. In addition, gender inequalities persist in the labour market, with women often facing more constraints in terms of access to employment, mobility, and economic participation (Alban and Forti, 2022). In 2019, according to McKinsey Global Institute, the gender parity score at work is 0.51 in Africa. WAEMU countries are classified as countries with higher inequality.\u003c/p\u003e \u003cp\u003eIn this context, investments in public infrastructure are seen as a strategic lever to stimulate economic growth and improve labour market conditions (Beverly et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Infrastructure, including transport, energy, and information and communication technologies, is seen as key to improving productivity, facilitating market access, and fostering private investment (Barro, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; World Bank, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Ouni and Mraihi, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). On the other hand, Africa in general and the WAEMU countries in particular are lagging far behind in terms of infrastructure deficits, which requires infrastructure development based on credible policies as well as a rigorous prioritization of projects that promote productive transformation (OECD/AUC, 2025). It is for this reason that, for example, IEA et al. (2023) find that 16 of the 20 countries with electricity deficits are located in Africa. However, World Bank (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) believes that economic and spatial inclusion necessarily require the accessibility of quality infrastructure.\u003c/p\u003e \u003cp\u003eIn the literature, infrastructure can influence unemployment through several channels. By reducing transport and transaction costs, they facilitate the integration of markets and the expansion of productive activities (Barro, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Krugman, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Leigh \u0026amp; Neill, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Energy infrastructure improves the reliability of electricity supply, which promotes the establishment of businesses and the creation of jobs. In addition, digital infrastructure helps to strengthen the flow of information and access to economic opportunities. These effects can result in an increase in the demand for labour and, consequently, in a reduction in unemployment. Water and sanitation infrastructure provides water, improves health and reduces the time it takes to search for water, which in turn increases productivity.\u003c/p\u003e \u003cp\u003eThis research is in line with SDG 8 on decent work and economic growth. It also contributes to SDG 9 by assessing the role of infrastructure in economic development, as well as SDG 5 by highlighting gender disparities in the labour market. Finally, the results shed light on issues related to the reduction of inequalities (SDG 10). Infrastructure development is at the heart of these SDGs. However, the literature shows that these effects are not automatic and can vary depending on the context. In particular, infrastructure can have differentiated effects on the labour market and lead to inequalities across population groups (Calder\u0026oacute;n and Serv\u0026eacute;n, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; World Bank, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Alban and Forti, 2022; Adabor and Ayesu, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). For example, improving transport infrastructure and information technology could help reduce some of the structural barriers that limit women's access to the labour market. The analysis of the differentiated effects of infrastructure on male and female unemployment therefore appears to be an important question for a better understanding of employment dynamics in the region.\u003c/p\u003e \u003cp\u003eDespite the importance of these issues, the empirical literature on the relationship between a set of infrastructures and unemployment in African countries remains relatively limited, and even more so when it comes to analyzing gender differences. Most existing work focuses mainly on the effects of infrastructure on economic growth or productivity, while the gender implications for the labour market remain insufficiently explored. In the context of the countries of the West African Economic and Monetary Union, there is no systematic study that examines the impact of infrastructure on total unemployment as well as on unemployment for men and women as far as we know.\u003c/p\u003e \u003cp\u003eFaced with these shortcomings, this study aims to analyse the effect of infrastructure on unemployment in the countries of the West African Economic and Monetary Union, distinguishing between total unemployment, male unemployment and female unemployment. Specifically, the aim is to assess the extent to which infrastructure investments contribute to reducing unemployment in the region and whether these effects differ by gender. The study is based on an econometric analysis of panel data covering the member countries of the union, in order to identify the relationships between infrastructure indicators and the different measures of unemployment.\u003c/p\u003e \u003cp\u003eBy providing an empirical analysis of the relationship between infrastructure and unemployment in the countries of the West African Economic and Monetary Union, this research contributes to the existing literature by highlighting the potentially differentiated effects of infrastructure on the labour market. The expected results can also provide useful lessons for policymakers on infrastructure investment policies, in particular to promote more inclusive growth and reduce inequalities in the labour market.\u003c/p\u003e \u003cp\u003eThe rest of the research begins with a literature review of the effect of infrastructure on human development indices, this section is followed by the methodology and presentations of the data used in the estimation, the next section presents the results and discussions of the results from the estimation. Finally, a conclusion\u003c/p\u003e"},{"header":"Literature review","content":"\u003cp\u003eInfrastructure can be associated with a reduction in unemployment through several channels, including lower transaction costs, improved business productivity, and stimulated private investment (Aschauer, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Barro, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Munnell, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Similarly, improved transport infrastructure can intensify interregional competition and promote the concentration of economic activities in the most productive areas, to the detriment of less competitive regions, in line with the predictions of the new geographical economy (Krugman, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Fujita et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). This process of spatial and sectoral reallocation can thus result in an increase in unemployment in certain areas. However, the literature also highlights a nuanced view, pointing out that infrastructure not only supports economic activity, but also helps to transform the structure of the labour market. For example, certain infrastructures, particularly information and communication technologies (ICTs), are widely recognized as vectors of economic inclusion. They facilitate access to information, improve the matching of labour supply and demand and promote the development of new forms of employment, particularly in the service sectors and the digital economy (Jorgenson, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). The adoption of capital-intensive technologies can reduce the demand for low-skilled labour and lead to job destruction in the short term (Acemoglu, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGuzman and Oviedo (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) show that transport subsidies improve accessibility and reduce the cost of transport for poor households. However, their effect on equity remains limited due to targeting issues and spatial inequalities. Hern\u0026aacute;ndez et al. (2020) show that better access to jobs via public transport reduces unemployment in Montevideo. Their research highlights that transport infrastructure improves spatial connectivity, facilitates access to employment and reduces labour market frictions, especially for low-income populations. Transport facilitates access to employment without guaranteeing a significant reduction in unemployment. Ndjobo and Abessolo (2023) report that access to roads reduces youth unemployment in 31 African countries using the seemingly unrelated bivariate probit method. Ouni and Mraihi (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) show that transport infrastructure plays a crucial role in promoting inclusive development in developing countries. Road and rail infrastructure significantly reduce poverty and inequality, while air transport can exacerbate poverty. In addition, all forms of transport infrastructure contribute to the reduction of unemployment by improving access to economic opportunities. Adabor and Ayesu (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) show that transport poverty is positively associated with unemployment, with households with limited or expensive access to transport being less likely to access employment opportunities. These results underline the central role of mobility in the functioning of the labour market.\u003c/p\u003e \u003cp\u003eDawn et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) find that ICTs reduce unemployment by creating jobs, improving productivity, and facilitating the functioning of the labour market. They are therefore an important lever for employment in the countries of the Indian Ocean Rim Association (IORA). Lobo et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) show that the relationship between broadband speed and unemployment is sensitive to data measurement issues. Although some estimates suggest a negative effect of ICT on unemployment, the results are not systematically robust, highlighting the importance of empirical specifications and data quality. Using the ordinary least squares (OLS) and generalized least squares (GLS) methods, Abbasabadi and Soleimani (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) show that the expansion of digital technologies has a negative and significant effect on unemployment in 163 countries. By promoting job creation, improving productivity and the functioning of the labour market, digital technologies contribute to the reduction of unemployment, despite substitution effects on certain jobs. \u0026Uuml;nl\u0026uuml; and Kabak (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) show that ICT reduces unemployment in OECD countries. For these authors, their overall effect is still ambiguous and debated in the literature. They improve productivity, facilitate labour market matching and create new jobs. However, their effects depend on the level of development and skills. Hirschman (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) shows that knowledge infrastructures play a central role in the production and dissemination of stylized facts about inequality, particularly with regard to the concentration of income at the top. Goaied and Sassi (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) analyse the effect of ICT on employment. They show that ICTs reduce the demand for labour in the short and long term. This effect leads to an increase in structural unemployment. Apergis et al. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) use dynamic panel models to analyze the effect of ICT on unemployment in the United States. They find that ICTs reduce unemployment in the long term, with smaller effects in the short term.\u003c/p\u003e \u003cp\u003eTsaurai (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) highlights a negative and significant effect of renewable energies on unemployment. By promoting the creation of green jobs. Afolayan et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) find that a 1% increase in electricity consumption leads to a decrease of about 0.22% in Nigeria's unemployment rate. Using panel data and a quasi-experimental identification strategy, Mensah (2024) shows that electricity shortages exert a positive and significant effect on unemployment in Africa. The results indicate that interruptions in energy supply reduce business activity, dampen investment and lead to job losses, highlighting the crucial role of energy infrastructure in the functioning of the labour market. Swain and Karimu (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) show that the development of renewable electricity contributes positively to the achievement of the Sustainable Development Goals in the European Union. The results suggest that the energy transition promotes economic growth, human development and environmental sustainability, with potentially positive indirect effects on employment. There is little or no research in the literature on the simultaneous effect of a set of infrastructures on unemployment and their effect by gender. It is this gap that this research intends to fill.\u003c/p\u003e"},{"header":"Methodology and data","content":"\u003cp\u003eThe objective of this research is to determine the effect of infrastructure on unemployment rates. To this end, we formulate an equation linking infrastructure and the unemployment rate. Preliminary tests will then be conducted to determine the appropriate estimation method.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{Unemployment}_{it}={\\alpha\\:}_{0}+\\alpha\\:{infrastructures}_{it}+\\beta\\:{control\\_variables}_{it}{\\:+\\:\\epsilon\\:}_{it}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{0}\\)\u003c/span\u003e\u003c/span\u003e represents the constant, et \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:\\epsilon\\:}_{it}\\)\u003c/span\u003e\u003c/span\u003e and represents the error term, respectively, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\alpha\\:\\)\u003c/span\u003e\u003c/span\u003e represents the vector of infrastructure variables et \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e the vector of control variables, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t=1\\dots\\:18\\)\u003c/span\u003e\u003c/span\u003e the time and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i=1\\dots\\:8\\)\u003c/span\u003e\u003c/span\u003e the countries.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDefinition of variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExpected sign\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eunemployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnemployment, total (% of total labor force)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale unemployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnemployment, male (% of total labor force)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale unemployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnemployment, female (% of total labor force)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAfrica Infrastructure Development Index: A composite index measuring the overall level of infrastructure development (transport, energy, ICT, water, and sanitation)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAfrican Development Bank (AfDB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater and Sanitation Infrastructure Composite Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAfrican Development Bank (AfDB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectricity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe electricity infrastructure composite index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAfrican Development Bank (AfDB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe transport infrastructure composite index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAfrican Development Bank (AfDB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe information and communication technology infrastructure composite index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAfrican Development Bank (AfDB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolitical regime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eautocracies\u0026thinsp;=\u0026thinsp;0, electoral autocracies\u0026thinsp;=\u0026thinsp;1, electoral democracies score 2, and liberal democracies score 3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOur World in data\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolitical corruption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolitical Corruption Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolitical stability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndicator of political stability and absence of violence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWGI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInflation rate based on the Consumer Price Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban population growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban population growth rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026plusmn;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eSource: authors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e of descriptive statistics shows that the average unemployment rate stands at 2.86%, with similar levels between men and women. However, the higher standard deviation for female unemployment, at 1.65 compared to 1.52 for men, indicates greater volatility in unemployment among women.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale unemployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.132\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale unemployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.652\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003einfra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.216\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e77.881\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eelectricity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.518\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e23.258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etransport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.212\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolitical regime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.546\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolitical corruption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.915\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResource rente\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.994\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eSource: authors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Results and discussion","content":"\u003cp\u003eThe following Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the preliminary tests. These tests are the heteroscedasticity test, the autocorrelation test, and the contemporaneously cross-sectionally correlated test. The results show that our data are heteroscedastic, that there is autocorrelation and contemporaneously cross-sectionally correlated. These results lead to the use of appropriate estimation methods. Among the methods we have the PCSE and the FGLS. The FGLS has another particular constraint regarding the number of years and the number of units. If the number of units is smaller than the period, then FGLS can be used, the opposite makes this method inapplicable. In our case we have N\u0026thinsp;=\u0026thinsp;8 and T\u0026thinsp;=\u0026thinsp;18 then the FGLS is applicable in our case. The PCSE method is not subject to this constraint. In conclusion, we apply these methods to make our results more robust and convincing.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: Preliminary test\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePreliminary test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eAIDI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eUnemployment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eheteroskedasticity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eautocorrelation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econtemporaneously cross-sectionally correlated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1680.36***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e30.414***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e102.539***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMale unemployment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eheteroskedasticity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eautocorrelation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econtemporaneously cross-sectionally correlated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e842.52***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e62.793***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e111.385***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eFemale unemployment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eheteroskedasticity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eautocorrelation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econtemporaneously cross-sectionally correlated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2438.77***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e27.871***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91.797***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eDisaggregated infrastructure\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eUnemployment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eheteroskedasticity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eautocorrelation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econtemporaneously cross-sectionally correlated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e609.42***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.254***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103.020***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eMale unemployment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eheteroskedasticity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eautocorrelation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econtemporaneously cross-sectionally correlated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e323.50***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.279***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e117.423***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eFemale unemployment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eheteroskedasticity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eautocorrelation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003econtemporaneously cross-sectionally correlated\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e1164.55***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.746***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e80.325***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eSource : authors\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the effect of the infrastructure index on the three unemployment rates. The results from the estimates using the PCSE and FGLS methods show a relative robustness of the coefficients, both in terms of signs and statistical significance. This stability reinforces the credibility of the empirical conclusions and suggests that the relationships identified between infrastructure and unemployment are not sensitive to the choice of estimation method. However, differences appear in the magnitude of the effects and the degree of significance of some variables, reflecting the specific properties of each estimator. The results show that the infrastructure index has a positive and significant effect on total unemployment, as well as on unemployment for both men and women. This result can be explained by the modernization effects induced by infrastructure, which favor the substitution of labor by capital and technologies. In addition, infrastructure can lead to structural transformations and skills mismatches, leading to increased unemployment, especially in developing economies. To better understand this result, which is opposed to most empirical results (Afolayan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Dawn et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ouni and Mraihi, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) we disaggregate the infrastructure index into its different components.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAIDI and unemployment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnemployment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale unemployment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemale unemployment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUnemployment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMale unemployment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFemale unemployment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAIDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.444**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.410**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.497*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.268**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.292***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.271**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.215)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.167)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.290)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.117)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.136)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolitical regime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0893\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0817\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.156)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.126)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.203)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0650)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0628)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0759)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolitical corruption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.269*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.341**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.210)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.186)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.251)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.153)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.167)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResource rente\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.170**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.187**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.136**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.169***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0858\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0845)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0793)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0980)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0627)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0610)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0686)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.628**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.709***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.547*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.394**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.553***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.248)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.226)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.287)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.196)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.182)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.222)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.000298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.00132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.000374\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.00797)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00707)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.00978)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.00399)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.00391)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.00446)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.490\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.769**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.679)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.561)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.875)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.401)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.377)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.455)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of id\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eStandard errors in parentheses\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eSource: auteurs\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the results of the different types of infrastructure on unemployment rates. The results reveal differentiated effects according to their nature. Access to electricity has a positive and significant effect on unemployment in both the PCSE and FGLS estimates, although the magnitude of this effect is slightly attenuated in the latter case. This result, a priori counterintuitive, can be explained by a capital-labour substitution effect.\u003c/p\u003e \u003cp\u003eThe positive effect of electricity on unemployment can be explained by the structural inadequacies of the energy system in the WAEMU countries. Indeed, despite an improvement in the electrification rate, the supply of electricity often remains lower than demand, leading to frequent power cuts and energy rationing. These dysfunctions disrupt the activity of companies, increase production costs and slow down job creation, thus contributing to an increase in unemployment. This is what Mensah (2024) pointed out and he distinguishes three possible channels. This author found that electricity shortages inhibit job creation because of their negative effect on entrepreneurship. Second, electricity shortages reduce the production and productivity of existing firms, leading them to reduce their demand for labour. Finally, electricity shortages distort the business climate, reducing the trade competitiveness and export competitiveness of African companies. This perfectly explains the situation of the WAEMU countries that have a low electricity production capacity faced with very high demand, which results in intermittent power cuts. In the context of WAEMU economies, which are characterized by structural dualism, the expansion of modern electrified sectors is not necessarily accompanied by sufficient labour absorption, leading to a form of jobless growth. Moreover, the effect is more pronounced among women, reflecting their increased vulnerability.\u003c/p\u003e \u003cp\u003eOn the other hand, information and communication technologies (ICTs) are an important lever for reducing unemployment. Their negative and significant effect across the specifications suggests that they facilitate labour market matching, reduce information asymmetries and stimulate entrepreneurship, especially in the informal sector and digital activities. This effect is particularly pronounced among women, indicating that ICTs are helping to reduce structural barriers to women's employment. Although it is often recognized that ICTs can boost growth but increase unemployment by replacing human labour with automation, they are essential for job creation. It is in this sense that research by Abbasabadi and Soleimani (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) shows that the expansion of digital technologies has a negative and significant effect on unemployment in 163 countries.\u003c/p\u003e \u003cp\u003eTransport infrastructure, on the other hand, has a positive and highly significant effect on unemployment. This result can be interpreted through the dynamics of spatial reallocation of economic activities. Improved transport networks intensify interregional competition and encourage the concentration of activities in the most competitive areas, leading to the loss of jobs in less productive regions. Thus, in the short term, the effects of job destruction can dominate the effects of job creation, especially in vulnerable sectors. Again, the impact is more pronounced among women, who are often over-represented in competitively sensitive informal activities. The positive effect of transport infrastructure on unemployment can be explained by the structural inadequacies, poor quality and inaccessibility of this infrastructure in the WAEMU countries. Indeed, quantitatively limited, degraded or unequally accessible infrastructure can slow down economic activity, increase production costs and limit job creation. Thus, in the absence of adequate and inclusive infrastructure, their development can paradoxically be associated with an increase in unemployment. This is why Adabor and Ayesu (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) indicate that precarious transport increases household unemployment. In addition, high transport costs limit access to employment and increase spatial inequalities, which may explain the positive effect of transport infrastructure on unemployment. It is in this sense that Vigui\u0026eacute; et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) show that public transport improves accessibility in the short term. However, in the long run, it benefits the rich more than the poor. Infrastructure can thus increase spatial inequalities. For water and sanitation infrastructure, the coefficients are not statistically significant, suggesting an indirect and long-term effect on the labour market. These infrastructures mainly contribute to the improvement of human capital through health and productivity, without any immediate impact on the level of employment.\u003c/p\u003e \u003cp\u003eInstitutional variables also play an important role. The improvement of the political regime is associated with a reduction in unemployment, as a result. This result underscores the importance of political stability and the quality of institutions in creating an environment conducive to investment and job creation. The negative effect of the political regime on unemployment can be explained by the role of institutions in creating a favourable economic environment. Stable and well-governed political regimes enhance investor confidence, improve the effectiveness of public policies, and promote private sector development, thereby contributing to job creation and reducing unemployment. More surprisingly, corruption has a negative effect on unemployment in some specifications. This result can be interpreted in the light of the so-called \"grease the wheels\" hypothesis, according to which corruption can, in constrained institutional contexts, facilitate access to economic opportunities. However, this effect remains short-term and does not call into question the overall harmful effects of corruption on economic development. A similar result was found by Dirie et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Unlike ours, they find that corruption can reduce unemployment in the short term, confirming the \"grease wheels\" hypothesis. In addition, the negative effect of corruption on unemployment can be explained by its role in reducing administrative rigidities and facilitating access to employment, especially in highly informant economies. However, this effect is significant for women, can be explained in certain weak institutional contexts, corruption can take gendered forms, particularly through sextortion practices, where access to certain economic opportunities is conditioned by abuses of power. These practices can affect women's professional integration in different ways, by accentuating inequalities in the labour market.\u003c/p\u003e \u003cp\u003eMoreover, the rent from natural resources has a positive and significant effect on unemployment, confirming the hypothesis of the resource curse. The positive effect of natural resource rents on unemployment can be explained by the low labour intensity of the extractive sectors, as well as by the crowding-out effects they have on other productive sectors. Indeed, dependence on natural resources can lead to deindustrialization and misallocation of resources, thus limiting job creation and contributing to an increase in unemployment. In many African countries in general, natural resource rents do not necessarily translate into significant job creation. This can be explained in particular by the low local processing of resources, the dependence on exports of raw materials and an economic environment that is sometimes not conducive to the development of the private sector. In addition, certain public policy orientations can limit economic diversification and job creation, thus contributing to a positive effect of rents on unemployment. This result is contrary to that found by Akinyele et al. (2022) who estimate that natural resources contribute to long-term job creation in sub-Saharan African countries. Urbanisation appears to be a factor in reducing unemployment, with a negative and significant effect in the majority of specifications. This result reflects the role of urban centres as poles of economic diversification and labour force absorption, particularly through the expansion of the informal sector. However, this effect is more pronounced among men, suggesting the existence of specific constraints on women's employment in urban areas. This is in contrast to the results of Borhan et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who found that urbanization can be a source of unemployment. Our research provides the opposite arguments. The difference in the results may be in the type of data. Finally, inflation does not have a significant effect on unemployment, indicating the absence of a clear Phillips curve-type relationship in the sample studied.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e: Disaggregated infrastructure and unemployment\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDisaggregated infrastructure and unemployment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003ePCSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eFGLS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVARIABLES\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eunemployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMale unemployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFemale unemployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eunemployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMale unemployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFemale unemployment\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.474)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.422)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.567)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.294)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.291)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.318)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElectricity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.237***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.187***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.293***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.121**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.102**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.157***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0703)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0658)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0801)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0497)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0506)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0533)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0449**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0470***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0440**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.0305**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.0248*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.0394***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0186)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0180)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.0213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0145)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.446***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.385***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.520***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.397***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.348***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.465***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0895)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0826)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.107)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0685)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0696)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0717)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolitical regime\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.268*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.254**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.180**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.167**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.213**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.121)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.182)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0777)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0766)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0859)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePolitical corruption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.333*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.438**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.265**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.401***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.174)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.162)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.203)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.134)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.137)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.138)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResource rente\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.294***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.307***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.270***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.194***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.212***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.165**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.0877)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.0832)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.101)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.0698)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.0702)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.0731)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.487***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.621***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.470***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.595***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.322**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.183)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.179)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.145)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.152)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.154)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInflation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.00594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.00488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.00152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.000580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.00313\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(0.00758)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(0.00640)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(0.00964)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(0.00465)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(0.00451)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(0.00516)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.323\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(1.787)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(1.592)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(2.153)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(1.111)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(1.098)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e(1.206)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e144\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-squared\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of id\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eStandard errors in parentheses\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.1\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eSources : auteurs\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe results highlight the non-homogeneous nature of the effects of infrastructure on the labour market. While some infrastructures, such as ICTs, appear to be vectors of economic inclusion, others, such as electricity and transport, can generate exclusionary effects due to the structural transformations they induce. These results underline the importance of support policies, particularly in terms of training and human capital development, in order to maximize the positive effects of infrastructure investments on employment. Infrastructure does not automatically guarantee a reduction in unemployment. Therefore, public policies should focus not only on the development of infrastructure, but also on its quality, accessibility and complementarity with employment and training policies. In addition, particular attention should be paid to gender inequalities and territorial disparities in order to promote inclusive growth.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003efunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThe authors did not receive support from any organization for the submitted work.\u003c/li\u003e\n \u003cli\u003eNo funding was received to assist with the preparation of this manuscript.\u003c/li\u003e\n \u003cli\u003eNo funding was received for conducting this study.\u003c/li\u003e\n \u003cli\u003eNo funds, grants, or other support was received.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/li\u003e\n \u003cli\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/li\u003e\n \u003cli\u003eAll authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.\u003c/li\u003e\n \u003cli\u003eThe authors have no financial or proprietary interests in any material discussed in this article.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statements:\u003c/strong\u003e Data and code will be available on request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbbasabadi, H. 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Promoting Inclusive Growth by Creating Opportunities for the Urban Poor: Philippines Urbanization Review Policy Notes. \u003cem\u003eWorld Bank.\u003c/em\u003e\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"WAEMU, FGLS, PCSE, unemployment, infrastructure, gender","lastPublishedDoi":"10.21203/rs.3.rs-9348425/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9348425/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAfrican countries are lagging in terms of infrastructure, all types combined. In addition, on the continent, the problem of employability continues to be acute, often more pronounced according to gender or sex, requiring special attention. The objective of this research is to analyze the effect of a set of infrastructures on unemployment by gender in the WAEMU area. The FGLS PCSE methods were used to determine the effect of the infrastructures. Estimates show that infrastructure has a non-homogeneous effect on unemployment rates. Transport and electricity infrastructure contribute to an increase in overall unemployment, both male and female. On the other hand, only ICT stands out by significantly reducing unemployment rates. As for the WSS water and sanitation infrastructure, it does not affect unemployment rates. These results indicate that the effects of infrastructure on unemployment rates are not different by gender. These effects show that the infrastructure situation in terms of availability, quality, and accessibility persists in the WAEMU countries. In this dynamic, investments in quality, accessible infrastructure directed towards the creation of decent jobs for all social strata, regardless of gender, are important.\u003c/p\u003e","manuscriptTitle":"Infrastructure and unemployment: gender-differentiated effects in UEMOA","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-05 11:28:10","doi":"10.21203/rs.3.rs-9348425/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"abcad1be-eab0-4301-ab1c-f347293c9b4b","owner":[],"postedDate":"May 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-05T11:28:11+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-05 11:28:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9348425","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9348425","identity":"rs-9348425","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}