Financial Inclusion and Energy Poverty in Ghana: Evidence from the Informal Sector

Financial Inclusion and Energy Poverty in Ghana: Evidence from the Informal Sector | 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 Financial Inclusion and Energy Poverty in Ghana: Evidence from the Informal Sector Matlida Kabutey-Ongor, Michel Adurayi Amenah, Frederick Richmond Yorke This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6513556/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Aug, 2025 Read the published version in Discover Sustainability → Version 1 posted 12 You are reading this latest preprint version Abstract This study investigates the relationship between financial inclusion and energy poverty in Ghana’s informal sector using data from the Ghana Living Standards Survey [GLSS7]. Applying multidimensional energy poverty measures and a range of econometric models, including logit regression, fixed effects, and instrumental variable [IV] estimation, the study identifies financial inclusion as a significant factor in reducing energy poverty. Households with access to financial services, such as bank accounts, insurance, credit, and remittances, are less likely to experience energy deprivation. The instrumental variable analysis, which uses distance to the nearest bank as an instrument for financial inclusion, confirms the robustness of this relationship. The results reveal that rural households, larger households, and those headed by older individuals are more vulnerable to energy poverty, while female-headed and more educated households are less likely to be energy-poor. Policy recommendations include expanding financial services in rural areas through mobile banking and microfinance, promoting energy-specific financial products, and enhancing financial literacy programs. Tailored interventions for women and large households, as well as targeted energy access initiatives for rural areas, are crucial for mitigating energy poverty. This study highlights the importance of integrating financial inclusion into energy poverty reduction strategies to promote sustainable development and improve the well-being of vulnerable populations in Ghana. Energy poverty Financial inclusion Informal sector Sustainable development 1. Introduction Energy poverty is a persistent challenge in Ghana, with profound implications for economic development, health, and environmental sustainability [ 1 , 2 ]. Despite Ghana's progress in expanding electricity access, energy poverty remains a critical issue, particularly for low-income households [ 3 , 4 ]. According to the Ghana Statistical Service [2018], less than 1% of households have access to electricity, while 67% rely on traditional biomass fuels like wood and charcoal for cooking. This reliance on inefficient and polluting fuels contributes to indoor air pollution, linked to respiratory illnesses and other health problems [ 5 , 6 ]. The limited use of modern energy sources is further compounded by the high costs of cleaner alternatives and the low purchasing power of many households [ 7 ]. The informal sector, which constitutes a substantial part of Ghana’s economy, faces distinct challenges in energy access [ 8 ]. This sector, often neglected in policymaking, is characterized by limited access to formal financial services, modern energy infrastructure, and energy-efficient technologies [ 9 ]. As a result, many informal sector households remain trapped in a cycle of energy poverty, impacting their economic productivity and quality of life. Addressing these disparities requires a comprehensive understanding of the interplay between energy poverty and financial inclusion, particularly within marginalized populations, to promote sustainable development and inclusive economic growth [ 3 ]. Energy poverty refers to the inability of households to access or afford the energy services needed for essential daily activities, such as cooking, lighting, and heating [ 3 , 10 ]. It encompasses a range of challenges related to the availability, affordability, and reliability of energy sources. In Ghana, energy poverty is a significant barrier to sustainable development, with many low-income households relying on traditional fuels like wood and charcoal, which contribute to indoor air pollution and adverse health outcomes [ 6 ]. Conversely, fuel poverty is a narrower concept focused specifically on the inability to afford adequate heating, often used interchangeably with energy poverty but with a distinct emphasis [ 11 ]. Financial inclusion, as defined by Demirguc-Kunt et al. [ 12 ], involves access to and effective use of a wide range of financial services, including banking, credit, insurance, and mobile money platforms. It aims to integrate underserved populations into formal financial systems, enhancing their ability to save, borrow, and invest. In the context of energy poverty, financial inclusion is particularly critical. Access to financial services enables households to invest in cleaner, more efficient energy sources, reducing their reliance on polluting fuels and fostering better economic and health outcomes [ 13 ]. In Ghana’s informal sector, the relationship between energy poverty and financial inclusion is especially pronounced. Informal workers often lack formal collateral and credit history, limiting their access to traditional financial services and, consequently, their ability to adopt modern energy solutions [ 14 , 15 ]. Financial inclusion strategies, such as microfinance and mobile banking, can address these barriers, facilitating access to clean energy technologies and supporting energy-saving behaviours [ 16 ]. This restriction impedes their capacity to generate revenue, as access to modern energy resources is essential for business productivity and household well-being. Informal business owners also frequently lack the financial capacity, expertise, and understanding to adopt energy-efficient technologies, leading to high energy costs and associated health problems [ 17 ]. Several studies have examined the positive impact of financial inclusion on household welfare[ 18 , 19 ], specifically in terms of energy consumption [ 20 ], energy poverty[ 3 ] and health[ 21 ], and economic well-being. Twumasi et al.[ 22 ] highlighted that financial inclusion significantly enhances residential energy expenditure in Ghana, particularly benefiting urban households, the poorest populations, and female-headed families. This aligns with findings from Abokyi et al. [ 23 ], who demonstrated that financial inclusion increases the consumption of clean cooking fuels while decreasing the use of traditional and polluting energy sources, particularly among female-headed and rural households. These studies emphasize the crucial role of financial inclusion in supporting energy transitions and improving health outcomes. In a broader West African context, Nwolisa et al. [ 24 ] found that financial inclusion—measured by indicators such as ATMs and bank accounts—has a positive but limited impact on energy access, suggesting that financial infrastructure plays a role in energy poverty reduction. Meanwhile, Kar and Swain [ 25 ] extended this observation to 27 energy-poor countries in Sub-Saharan Africa, demonstrating that financial inclusion significantly reduces energy poverty, reinforcing its relevance in low-income settings. The intersection between financial inclusion and health outcomes has also been explored, with studies like Churchill and Marisetty [ 26 ] showing that improved financial access can reduce energy poverty and its associated health risks, such as respiratory illnesses. This finding is corroborated by Kose [ 5 ], who linked energy poverty to higher rates of chronic illnesses. These health implications underscore the importance of inclusive financial policies that promote access to clean energy. Beyond energy access, financial inclusion's broader socio-economic impacts have been noted. Koomson et al.[ 10 ] revealed that financial inclusion reduces household vulnerability to poverty. The literature suggests that financial inclusion can foster economic resilience by reducing exposure to future poverty, especially among marginalized populations[ 17 ]. However, there are notable gaps in the existing literature. While many studies confirm the positive impact of financial inclusion on energy transitions and socio-economic outcomes, they often focus on urban areas, formal sectors, or national aggregates. There is limited exploration of how financial inclusion specifically impacts energy poverty in Ghana's informal sector—a sector that constitutes a significant portion of the Ghanaian economy but is frequently underserved by traditional financial institutions [ 20 ]. The informal sector's unique characteristics, such as limited access to formal credit and financial services, present distinct challenges and opportunities for energy access that remain underexplored. This study seeks to address these gaps by investigating the relationship between financial inclusion and energy poverty within Ghana’s informal sector. By examining the dynamics of microfinance, mobile banking, and other financial services in low-income and informal settings, the research aims to provide targeted insights that can inform policy interventions. The study will explore how financial inclusion can drive energy transitions in marginalized communities, with implications for health, economic growth, and sustainable development. This paper is presented in six sections. Section 2 presents a discussion on data and variables employed in the study, while the empirical methodology is presented in Section 3. Section 4 presents the empirical results, and discussion of the empirical results fall under Section 5. Section 6 presents the conclusion and policy recommendations based on the outcomes of the study. 2. Data and Variables 2.1 Data The data for this study were derived from the 2017 Ghana Living Standards Survey [GLSS7], which represents the seventh and most recent round of the survey. The data was collected over 12 months, spanning from 22nd October 2016 to 17th October 2017 [ 27 ]. The survey was designed to include approximately 15,000 households across 1,000 Enumeration Areas [EAs], with 561 [56.1%] rural EAs and 439 [43.9%] urban EAs. A two-stage stratified sampling approach was employed for the survey. In the first stage, 1,000 enumeration areas [EAs] were selected to form the Primary Sampling Units [PSUs]. In the second stage, 15 households were systematically selected from each PSU, resulting in a total of 15,000 households [ 28 ]. The GLSS7 provided detailed information on the demographic characteristics of the population, education, health, employment, migration, housing conditions, household income and expenditure, agriculture, and data projection, among other variables [ 28 ]. It included comprehensive data on the socio-economic characteristics of individuals and households, such as age, gender, family size, educational attainment, and basic utility services like cooking fuel, sewage disposal, electricity, and water. We utilized these variables for our study. 2.2 Variables Multidimensional Index for Energy Poverty The Multidimensional Energy Poverty Index [MEPI] framework includes five dimensions and six indicators, capturing various aspects of a household’s energy access, including cooking, lighting, household appliances, entertainment/education, and communication technologies. Inspired by Amartya Sen's theories on capabilities and deprivations and rooted in the multidimensional poverty framework developed by the Oxford Poverty and Human Development Initiative [OPHI], the MEPI provides a comprehensive tool for measuring energy poverty [Alkire & Foster, 2011]. Table 1 Indicators and Weights for Measuring Energy Deprivation Dimension Indicator [Weight] Variable Deprivation Cut-off [Poor if…] Cooking Modern cooking fuel [0.25] Type of cooking fuel Use any fuel besides electricity, LPG, kerosene, natural gas, or biogas Lighting Electricity access [0.25] Has access to electricity False Services provided using household appliances Household appliance ownership [0.25] Has a fridge OR freezer False Entertainment/Education Entertainment/education appliance ownership [0.25] Has a radio OR television False We applied the MEPI framework with weights carefully assigned to each dimension to reflect their relative importance. The cooking and lighting dimensions received higher weights due to their fundamental role in household energy needs. Specifically, cooking indicators were weighted slightly higher [0.205 each] to emphasize their critical importance in the context of developing countries like Ghana, while lighting was assigned a weight of 0.20. The remaining dimensions, including access to household appliances, entertainment/education, and communication, were each assigned weights of 0.13, ensuring an equitable representation across diverse energy needs. The MEPI calculates a household's energy deprivation score as a weighted sum of deprivations, ranging from 0 [no deprivation] to 1 [maximum deprivation]. We used a threshold of 0.33 to classify households as energy poor, indicating a significant level of energy deprivation. The calculation is defined as: $$\:{d}_{i}=\:{w}_{1}{I}_{1}+\:{w}_{2}{I}_{2}+\dots\:+\:{w}_{n}{I}_{n}$$ Where: \(\:{d}_{i}\:\) = household energy deprivation score \(\:{I}_{1}\) = 1 if a household is deprived in indicator \(\:I,\) and \(\:{I}_{i}\) = 0 otherwise \(\:{w}_{1}\) = weight assigned to indicator \(\:I,\:\) with \(\:\sum\:_{i=1}^{d}{w}_{i}=1\) We applied the MEPI to the GLSS7 dataset, which provided comprehensive data on household demographics, economic indicators, and energy use patterns. By employing the MEPI, we were able to identify energy-poor households and provide targeted insights into their challenges, contributing to a nuanced understanding of energy poverty in Ghana’s informal sectors. Financial Inclusion Status For this study, we generated a binary index to assess the financial inclusion status of households using a financial deprivation score. Households were classified as either financially disadvantaged [deprived] or financially included [non-deprived] based on the financial status of the household head. A household was considered financially disadvantaged if its financial deprivation score was less than 0.5, indicating a lack of access to at least two key aspects of financial inclusivity. The assessment evaluated ownership of bank or mobile money accounts, availability of credit or loans, ownership of insurance, and receipt of financial remittances. Each factor was equally weighted at 0.25, providing a balanced measure of financial inclusion. Table 2 Elements, Index, and Description of Each Element. Elements Description Bank account [0.25] A household head who does not possess a bank account Insurance [0.25] A household head who does not possess an insurance package Loan/Credit [0.25] A household head who does not have access to a loan Remittance [0.25] A household head who does not receive remittances The financial deprivation score was calculated as a weighted sum of these four components, with a threshold of 0.5 used to determine the household's financial inclusion status. Households scoring below 0.5 were classified as financially deprived, while those scoring 0.5 or higher were considered financially included. This binary classification facilitated a systematic analysis of the financial inclusion landscape, enabling us to explore its connection with other critical variables, such as energy poverty. By utilizing this approach, we ensured that the classification criteria were both transparent and aligned with the study’s objectives. Control Variables We included several control variables to account for household characteristics that may influence energy poverty, as detailed in Table A1 in the appendix. The age of the household head, expressed in years, was included as a continuous variable to capture the household head’s experience and resources. Older household heads are expected to have more experience, potentially enabling better financial management and resource allocation. To account for non-linear effects, the square of age was also incorporated. The gender of the household head was considered to explore how gender roles and decision-making dynamics affect energy poverty [see Table A1 ]. Additionally, household size, measured as the total number of individuals in the household, was included as a proxy for the dependency ratio. Larger households are expected to face greater energy demands, potentially increasing the likelihood of energy poverty. We also included the employment status of the household head, as employment often provides financial stability and resources for energy expenditures. The location of the household, whether urban or rural, was incorporated to account for disparities in energy infrastructure and access to services. Finally, the educational attainment of the household head was included, reflecting its critical role in financial literacy and the adoption of energy-efficient behaviors [refer to Table A1 ]. These variables provided a comprehensive framework for analyzing the socio-economic determinants of energy poverty. Table 3 presents the summary statistics of key variables for the informal sector. Approximately 42.36% of households are classified as energy-deprived based on the Multidimensional Energy Poverty Index [MEPI]. Male-headed households constitute 66.31% of the sample, while 63.15% of households are located in urban areas. Regarding poverty status, 91.72% of the sample is classified as not poor. Additionally, 41.08% of household heads are married, and only 4.43% are employed. Table 3 Summary Statistics for Informal Sector Variable Frequency Percent MPI [Multidimensional Poverty] Not Deprived [No] 585 57.64% Deprived [Yes] 430 42.36% Gender Male 673 66.31% Female 342 33.69% Location Urban 641 63.15% Rural 374 36.85% Poverty Status Not Poor 931 91.72% Poor 84 8.28% Marital Status Not Married 598 58.92% Married 417 41.08% Employment Status Unemployed 970 95.57% Employed 45 4.43% Source: Author’s compilation from GLSS 7 data Table 4 shows the descriptive statistics for continuous variables. The average age of household heads is 38.03 years, with a standard deviation of 15.97 years. The mean education level is 7.14 years of schooling, while the average household size is 4.34 members. Table 4 Summary Statistics for Informal Sector Variable Obs Mean Std. Dev. Min Max Age 12969 38.031 15.973 5 99 Age2 12969 1701.497 1397.357 25 9801 Education 9663 7.142 4.709 0 19 Hhsize 12969 4.341 2.887 1 28 Source: Author’s compilation from GLSS 7 data 3. Empirical Methodology To analyze the relationship between financial inclusion and energy poverty, we estimated four different models. Each model provided insight into the causal and correlative dynamics. Additionally, we examined household heads in the informal sector, defined as those who lacked access to formal employment benefits, as detailed in Table A2 in the appendix. The study's methodology is influenced by earlier research by Churchill et al. [2020b], Zhang & Posso [2017], and Koomson & Danquah [2021]. Model 1: Logit Margins [Baseline Model] The first model uses a standard logit regression to estimate the probability of a household being energy non-poor \(\:[\text{Y}=1]\) based on financial inclusion \(\:\left[FI\right]\) and control variables \(\:{\:[Z}_{ki}].\) This model accounts for the binary nature of the dependent variable and ensures predictions fall within the range of 0 and 1. It is a baseline model for understanding the relationship between financial inclusion and energy poverty without accounting for unobserved heterogeneity. $$\:{p}_{i}=\:{\Lambda\:}\left(\text{Y}=1\right|{X}_{i}]={\:\beta\:}_{0}+\:{\:\beta\:}_{1}\:{FI}_{i}+\:\sum\:{\:\beta\:}_{k}{\:Z}_{ki}\:+\:{\epsilon\:}_{i}$$ \(\:\varLambda\:\) is the logistic distribution, Y = 1 if the household is energy poor, FI is the financial inclusivity status of the household head, and \(\:{\:Z}_{ki}\) is a matrix of the control variables used in the study [age, sex, household size, employment status] Model 2: Fixed Effect Logit Margins The second model introduces regional fixed effects [ \(\:{\mu\:}_{region}]\) to account for unobserved regional heterogeneity that might confound the relationship between financial inclusion and energy poverty. By including fixed effects, we control for factors specific to each region, such as infrastructure availability, local policies, or cultural differences. Fixed effects also address the correlation between predictors and unobserved factors by controlling for any time-invariant regional characteristics that may influence both financial inclusion and energy poverty. For example, a region's overall level of economic development may affect access to financial services and modern energy infrastructure. By including fixed effects, we ensure these regional-level influences are accounted for, isolating the within-region variation for more accurate estimation. $$\:{p}_{i}=\:{\Lambda\:}\left(\text{Y}=1\right|{X}_{i}]={\:\beta\:}_{0}+\:{\:\beta\:}_{1}\:{FI}_{i}+\:\sum\:{\:\beta\:}_{k}{\:Z}_{ki}\:+{\mu\:}_{region}+{\epsilon\:}_{i}$$ While fixed effects address regional heterogeneity, endogeneity remains a concern, as financial inclusion is influenced by economic well-being and could still correlate with the error term through factors like unobserved regional differences, proximity to basic amenities, or self-selection into remote areas. Model 3: Fixed Effect IV Estimate In the third model, we address the potential endogeneity in financial inclusion using an instrumental variable [IV], "distance to the nearest bank" as previous research has shown that proximity to a bank can improve financial inclusion which impacts energy poverty [Churchill et al., 2020b; Churchill & Marisetty, 2019; Koomson et al., 2020a]. The first-stage regression predicts financial inclusion [ \(\:{FI}_{i,\:}]\) based on the IV, the second-stage regression estimates the causal effect of \(\:\widehat{{FI}_{i}}\) on energy poverty. Regional fixed effects are included to control for unobserved differences across regions. $$\:{FI}_{i}=\:{\alpha\:}_{0}+\:{\alpha\:}_{1}{Distance\:to\:Bank}_{i}+\:\sum\:{\alpha\:}_{k}{Z}_{ki}+\:{\mu\:}_{region}+\:{\upsilon\:}_{i}$$ $$\:{p}_{i}=\:{\Lambda\:}\left(\text{Y}=1\right|{FI}_{i,\:}{Region}_{i}]={\:\beta\:}_{0}+\:{\:\beta\:}_{1}\:\widehat{{FI}_{i}}+\:\sum\:{\:\beta\:}_{k}{\:Z}_{ki}\:+{\mu\:}_{region}+{\epsilon\:}_{i}$$ However, the use of distance to the nearest bank as an instrument for financial inclusion may still be problematic, as it could be correlated with the error term through socioeconomic and geographical factors. For instance, areas with higher levels of economic development may have more accessible financial services and better access to modern energy sources, which may confound the relationship between financial inclusion and energy poverty. Model 4: IV Estimate without Fixed Effects The fourth model uses an instrumental variable approach without regional fixed effects. This provides a causal estimate of the impact of financial inclusion on energy poverty, accounting for endogeneity financial inclusion and energy poverty, while we do not control for unobserved regional differences. $$\:{FI}_{i}=\:{\alpha\:}_{0}+\:{\alpha\:}_{1}{Distance\:to\:Bank}_{i}+\:\sum\:{\alpha\:}_{k}{Z}_{ki}+\:{\mu\:}_{region}+\:{\upsilon\:}_{i}\dots\:\dots\:Stage\:1$$ \(\:\:\:\:\:\:\:\:\:\:\:{p}_{i}=\:{\Lambda\:}\left(\text{Y}=1\right|{FI}_{i,\:}]={\:\beta\:}_{0}+\:{\:\beta\:}_{1}\:\widehat{{FI}_{i}}+\:\sum\:{\:\beta\:}_{k}{\:Z}_{ki}\:+{\epsilon\:}_{i}\) ……………………..Stage 2 Endogeneity can arise due to potential reverse causality between financial inclusion and energy poverty [that is, energy poverty can influence financial inclusion], or omitted variables that correlate with both financial inclusion and energy poverty. This model uses the distance to the nearest bank as an instrument, as proximity to financial services is strongly correlated with financial inclusion, but not directly with energy poverty. The first stage predicts financial inclusion status \(\:{[FI}_{i\:}]\) using the instrument and other variables, while the second stage estimates the causal impact of predicted financial inclusion \(\:\:\widehat{{FI}_{i}}\) on energy poverty. 4. Empirical Results The regression results, which show the impact of financial inclusion on energy poverty, are summarized in Table 5 . Across all models, financial inclusion consistently exhibits a significant and negative relationship with energy poverty, affirming the critical role of financial inclusion in reducing household energy deprivation. In the logit margins [Model 1], financial inclusion significantly reduces the likelihood of energy poverty [coefficient = -0.0473, p < 0.01]. Other significant predictors include rural location, which increases the probability of energy poverty by 0.169 [p < 0.01], and household size, with a positive coefficient of 0.0308 [p < 0.01], indicating that larger households face higher risks of energy deprivation. Education shows a protective effect, reducing the likelihood of energy poverty [-0.0167, p < 0.01]. Interestingly, female-headed households are less likely to experience energy poverty compared to male-headed households [-0.0878, p < 0.01]. Age demonstrates a non-linear relationship, with the square of age yielding a positive coefficient [0.000323, p < 0.01], suggesting that while age initially reduces energy poverty, this effect diminishes over time. In the fixed effect logit margins [Model 2], the introduction of regional fixed effects does not alter the overall direction and significance of the relationship between financial inclusion and energy poverty. Financial inclusion remains a significant negative predictor [-0.0339, p < 0.01]. Similar patterns persist for rural location [0.0894, p < 0.01], age, education, and household size. The protective effect of female-headed households also remains statistically significant [-0.0456, p < 0.01], reinforcing the robustness of these findings across different specifications. The fixed effect IV estimate [Model 3] addresses potential endogeneity in financial inclusion by using distance to the nearest bank as an instrumental variable. The results confirm the negative and statistically significant impact of financial inclusion on energy poverty [-0.0619, p < 0.01]. Rural location and household size continue to be positively associated with energy poverty, while higher levels of education and being married are negatively associated. Notably, distance to the nearest bank is a significant predictor of financial inclusion [-0.00606, p < 0.01], validating its suitability as an instrument. This model provides more causal evidence supporting the link between financial inclusion and reduced energy poverty. In the IV estimate without fixed effects [Model 4], financial inclusion remains a significant negative predictor of energy poverty [-3.587, p < 0.10]. Unlike previous models, however, some control variables, such as age and education, lose their statistical significance. Household size remains positively associated with energy poverty [0.0922, p < 0.05], suggesting that larger households face greater energy deprivation risks even after accounting for financial inclusion. The results from this model, while less robust due to the exclusion of regional fixed effects, still highlight the consistent negative relationship between financial inclusion and energy poverty. Table 5 Marginal Effect Results of Logit Model and Fixed Effect Logit Model of the Impact of financial inclusion on energy poverty in the informal sector for Ghana Variable Logit Margins [ 1 ] Fixed Effect Logit Margins [ 2 ] Fixed Effect IV Estimate [ 3 ] IV Estimate without Fixed Effects [ 4 ] Financial Inclusion -0.0473*** [0.0146] -0.0339*** [0.0124] -0.0619*** [0.0217] -3.587* [2.165] Female -0.0878*** [0.0152] -0.0456*** [0.0140] -0.0840*** [0.0256] 0.0981 [0.130] Rural 0.169*** [0.0149] 0.0894*** [0.0238] 0.144*** [0.0258] 0.105 [0.0775] Age -0.0244*** [0.00372] -0.0121*** [0.000990] -0.0183*** [0.00394] -0.00520 [0.0164] Age² 0.000323*** [0.0000447] 0.000166*** [0.0000145] 0.000259*** [0.0000475] 0.000124 [0.000189] Poor 0.292*** [0.0207] 0.140*** [0.0427] 0.133*** [0.0225] -0.360 [0.333] Education -0.0167*** [0.00137] -0.0102*** [0.00200] -0.0188*** [0.00169] 0.0191 [0.0237] Married -0.117*** [0.0161] -0.0840*** [0.0235] -0.151*** [0.0262] -0.237** [0.0986] Household Size 0.0308*** [0.00299] 0.0155*** [0.00431] 0.0246*** [0.00734] 0.0922** [0.0408] Employed 0.0588* [0.0332] 0.0205 [0.0282] 0.0402 [0.0477] 0.0608 [0.136] Distance to Nearest Bank -0.00606*** [0.000605] Constant 1.029*** [0.0935] 1.623*** [0.483] Note : Standard errors are in parentheses. * p < 0.10, ** p < 0.05, *** p < 0.01 5. Discussion Our findings from the estimation models show that financial inclusion is negatively associated with energy poverty. The estimation models also support the causal effect of financial inclusion on energy poverty. These findings affirm that of the earlier study by Koomson & Danquah [ 3 ]. Even though their study did not specifically focus on the informal sector, the outcome of their study shows that financial inclusion contributes immensely to the alleviation of energy poverty, especially in the rural areas of Ghana, where most employment opportunities fall under the informal sector. Similarly, in Bangladesh, Sen et al. [ 31 ] examined the causal effect of financial inclusion on energy poverty and concluded that financial inclusion is a key policy for mitigating energy poverty. In a study that examines the informal sector in 42 African countries, Kelikume [ 29 ] finds that enhancing financial inclusion through increased mobile and internet usage is associated with declining poverty levels, of which energy poverty is a major component. In another study that examines 426 impoverished counties in China, Liu et al. [ 30 ] report that financial inclusion contributes immensely to the alleviation of multidimensional poverty through its positive impact on enhancing economic activities and production. Financial inclusion not only helps in addressing the problem of credit exclusion among the poor but also provides the means for the poor to improve their economic capacities. In India, Sharma et al. [ 32 ] highlight energy poverty as a major challenge for the poor and workers in the informal sector. The empirical analysis shows that the poor and workers in the informal sector have limited access to electricity and other modern energy sources. The study shows that financial inclusion enhances access to financial or credit facilities for the poor and workers in the informal sector, which plays a major role in eradicating inequality, energy poverty, and other dimensions of poverty. Our results also support the findings by Twumasi et al. [ 22 ], which show that financial inclusion reduces residential energy poverty. The study highlights the role of financial inclusion in improving household net per capita income. Improvement in household income enables households to improve their energy expenditure, especially expenditure on Liquefied Petroleum Gas [LPG] and electricity. Abokyi et al. [ 23 ] also report comparable results, suggesting that financial inclusion reduces energy poverty by improving the usage of clean and improved energy sources. These studies affirm the role of financial inclusion as a key enabler of access to modern energy services and highlight the importance of financial services in promoting clean energy adoption and improving household welfare. By facilitating access to savings, credit, and insurance, financial inclusion empowers households to invest in modern energy solutions such as electricity and clean cooking fuels, reducing their dependence on traditional biomass and mitigating the associated health risks. Financial inclusion, therefore, enhances household income levels, which enables households to improve their energy, thereby reducing energy poverty, especially in the rural areas and informal sector of Ghana. A critical takeaway from this study is therefore the importance of addressing financial exclusion to combat energy poverty, particularly in rural areas [ 3 , 23 ]. Our results show that rural households are more likely to be energy-deprived compared to their urban counterparts. This disparity reflects the uneven distribution of financial services and energy infrastructure in Ghana. Rural areas often lack access to formal financial institutions and modern energy infrastructure, perpetuating a cycle of deprivation and vulnerability [ 23 ]. This finding supports the argument for targeted financial inclusion strategies that prioritize rural communities, such as expanding mobile money services and microfinance institutions to the underserved and underprivileged areas. These measures could help bridge the financial gap and enable rural households to afford clean energy technologies, thereby reducing their exposure to health risks associated with traditional fuels. Household size emerged as another significant predictor of energy poverty. Larger households were more likely to experience energy deprivation, which is unsurprising given the higher energy demands associated with larger households [ 3 , 23 , 33 ]. Our result thus aligns with the broader literature on household resource allocation, which suggests that larger households may prioritize immediate needs like food and shelter over energy investments, increasing their vulnerability to energy poverty. Interestingly, female-headed households were less likely to experience energy poverty compared to male-headed households. This finding aligns with those of Twumasi et al. [ 22 ] and Abokyi et al. [ 23 ]. However, it contradicts other studies such as Koomson & Danquah [ 3 ]. We attribute the difference in outcome to the objectives of most previous studies. While our analysis focuses on the informal sector, most studies in Ghana consider all sectors. The outcome of our study suggests that women may allocate household resources more effectively, particularly in areas related to health and energy use. Female-headed households may also have greater awareness of the health impacts of traditional cooking fuels, leading them to prioritize cleaner alternatives when they have access to financial resources. This result highlights the potential benefits of gender-targeted financial inclusion programs that empower women to make energy-related decisions. The study also underscores the critical role of education in reducing energy poverty. Higher levels of educational attainment among household heads were associated with a lower likelihood of energy deprivation. Education likely improves financial literacy and awareness of clean energy options, enabling households to make informed decisions and adopt modern energy technologies. [ 22 ]. Our finding aligns with those of previous studies [ 3 , 33 ], which emphasize the importance of education in promoting sustainable energy transitions. Policymakers should consider integrating financial and energy literacy programs into formal education systems to enhance awareness and adoption of clean energy solutions. One of the unique contributions of this study is its focus on the informal sector, which is often overlooked in discussions on energy access and financial inclusion. Informal sector workers face distinct challenges, such as limited access to formal credit and insurance, which hinder their ability to invest in modern energy solutions [ 32 ]. Addressing these barriers requires tailored interventions that recognize the unique characteristics of the informal economy. Expanding access to mobile banking services and creating flexible microfinance products for informal sector workers could significantly reduce energy poverty in this group. The instrumental variable analysis strengthened the causal interpretation of the relationship between financial inclusion and energy poverty [ 3 ]. Using distance to the nearest bank as an instrument for financial inclusion, the results confirm that financial inclusion has a robust negative impact on energy poverty. This finding mitigates concerns about endogeneity and reverse causality, providing more credible evidence for policymakers. However, the limitations of the instrumental variable approach should be acknowledged. While distance to the nearest bank is a reasonable instrument as used in previous studies [ 3,22,23], it may not fully capture all unobserved factors that influence both financial inclusion and energy access. We also estimated an IV approach, which does not account for unobserved regional differences. This approach could result in biased IV estimates if regions with better financial access also have superior energy infrastructure. While excluding regional fixed effects simplified the estimation, the IV results are sensitive to the validity of the instrument, requiring robust checks to ensure that the instrument is relevant and exogenous. Despite these limitations, the IV approach provides a more causal interpretation of the relationship between financial inclusion and energy poverty by isolating exogenous variation in financial inclusion due to proximity to financial institutions. The cross-sectional nature of the data also limited our ability to examine the long-term dynamics between financial inclusion and energy poverty. Future research could address this gap by using panel data to track changes over time. While the study focuses on the informal sector, it does not differentiate between various types of informal activities, which may have different energy needs and access constraints. Further research could explore sector-specific differences to provide more targeted insights. 6. Conclusion and Policy Recommendations This study highlights the critical role of financial inclusion in reducing energy poverty in Ghana’s informal sector. Households with access to financial services are less likely to experience energy poverty, as they can invest in clean energy solutions, reducing dependence on polluting fuels. Vulnerable groups, such as rural households and larger families, face higher risks, while female-headed households and those with more education are less likely to experience energy deprivation. These findings underscore the need for targeted financial inclusion and energy access policies. To address these challenges, policymakers should prioritize expanding financial services in rural areas through mobile banking and microfinance. Promoting energy-specific financial products, such as microcredit for solar systems and clean cooking technologies, can facilitate clean energy adoption. Public education on energy efficiency and financial literacy will empower households to make informed decisions. Tailored financial programs for women and investments in rural energy infrastructure, including decentralized solar grids, are crucial for reducing energy poverty. Continuous data collection and monitoring are also essential for tracking progress and refining interventions. By integrating financial inclusion with energy access initiatives, Ghana can promote sustainable development and improve the well-being of its most vulnerable populations. Declarations Funding Statement: Not Applicable. Ethical approval: Not Applicable. Full name of committee: Not Applicable. Confirmation: Not Applicable. Reason: This article does not contain any studies with human participants or animals performed by any of the authors. Informed consent: This article does not contain any studies with human participants performed by any of the authors. Consent to Publish declaration : Not Applicable. Clinical trial number : Not Applicable. Author’s contribution: All authors contributed to the writing of the manuscript. Matilda Kabutey-Ongor was the study lead and contributed to the introduction, literature review, data analysis, discussion of results, and references sections. Michael Adurayi Amenah contributed to the introduction, literature review, data analysis, discussion of results, and references sections. Frederick Richmond Yorke contributed to the discussion of results and reference sections. All authors read and approved the final manuscript. Conflict of interest: All authors declare that they have no known competing financial interests or personal relationships that could directly or indirectly influence the work submitted for publication. Matilda Kabutey-Ongor declares that she has no conflict of interest. Michael Adurayi Amenah declares that he has no conflict of interest. Frederick Richmond Yorke declares that he has no conflict of interest. Data availability statement: The authors declare that there is perfect data transparency, and that the data will always be available at any time upon request. The authors also declare that Stata codes for performing the estimations for this paper will be available upon request. Acknowledgement: Not Applicable. References Nsenkyire E, Nunoo J, Sebu J, Iledare O. Household Multidimensional Energy Poverty: Impact on Health, Education, and Cognitive Skills of Children in Ghana. Child Indic Res. 2023;161:293–315. Adusah-Poku F, Adjei‐Mantey K, Kwakwa PA. Are energy‐poor households also poor? Evidence from Ghana. Poverty Public Policy. 2021;131:32–58. Koomson I, Danquah M. Financial inclusion and energy poverty: Empirical evidence from Ghana. Energy Econ. 2021;94:105085. Abdul-Wakeel Karakara A, Dasmani I. An econometric analysis of domestic fuel consumption in Ghana: Implications for poverty reduction. Cogent Soc Sci. 2019;5[1]. Kose T. Energy poverty and health: the Turkish case. Energy Sources Part B: Econ Plann Policy. 2019;145:201–13. Awaworyi Churchill S, Smyth R. Ethnic diversity, energy poverty and the mediating role of trust: Evidence from household panel data for Australia. Energy Econ. 2020;86:104663. Demirguc-Kunt A, Klapper L, Singer D, Ansar S, Hess J. The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. Washington, DC: World Bank; 2018. Dramani JB, Ofori-Mensah KA, Oppong Otchere NK, Frimpong PB, Adu-Poku A, Kemausuor F et al. Estimating and Forecasting Suppressed Electricity Demand in Ghana Under Climate Change, the Informal Economy and Sector Inefficiencies. 2024. Jacolin L, Massil Joseph K, Noah A. Informal Sector and Mobile Financial Services in Developing Countries: Does Financial Innovation Matter? SSRN Electronic Journal. 2019. Koomson I, Villano R, Hadley D. Effect of Financial Inclusion on Poverty and Vulnerability to Poverty: Evidence Using a Multi-Dimensional Measure of Financial Inclusion. SSRN Electron J. 2020. Churchill SA, Marisetty VB. Financial inclusion and poverty: a tale of forty-five thousand households. Appl Econ. 2020;5216:1777–88. Demirgüç-Kunt A, Klapper L. Measuring Financial Inclusion: Explaining Variation in Use of Financial Services across and within Countries. Brookings Pap Econ Act. 2013;20131:279–340. Bukari C, Broermann S, Okai D. Energy poverty and health expenditure: Evidence from Ghana. Energy Econ. 2021;103:105565. Ofori-Acquah C, Avortri C, Preko A, Ansong D. Analysis of Ghana’s National Financial Inclusion and Development Strategy: Lessons Learned. Global Social Welf. 2022;101:19–27. Asongu S, Odhiambo NM. The role of financial inclusion in moderating the incidence of entrepreneurship on energy poverty in Ghana. J Entrepreneurship Emerg Economies. 2023. Koomson I, Danquah M. Financial inclusion and energy poverty: Empirical evidence from Ghana. Energy Econ. 2021;94:105085. Immurana M, Kisseih KG, Yusif HM, Yakubu ZM. The effect of financial inclusion on open defecation and sharing of toilet facilities among households in Ghana. PLoS ONE. 2022;17[3]:e0264187. Amendola A. Financial Access and Household Welfare Evidence from Mauritania. 2016 [cited 2024 Jan 4]; Available from: http://econ.worldbank.org Addury MM. Impact of Financial Inclusion for Welfare: Analyze to Household Level. J Finance Islamic Bank. 2019;1[2]:90. Dramani JB, Frimpong PB, Ofori-Mensah KA. Modelling the informal sector and energy consumption in Ghana. Social Sci Humanit Open. 2022;6[1]:100354. Gyasi RM, Adam AM, Phillips DR, Financial Inclusion. Health-Seeking Behavior, and Health Outcomes Among Older Adults in Ghana. Res Aging. 2019;418:794–820. Twumasi MA, Adusah-Poku F, Acheampong AO, Evans Osei Opoku E. Residential energy expenditures and the relevance of financial inclusion across location, wealth quintiles and household structures. Heliyon. 2024;10[5]:e26710. Abokyi E, Appiah-Konadu P, Oteng-Abayie EF, Tangato KF. Consumption of clean and dirty cooking fuels in Ghanaian households: The role of financial inclusion. Clean Responsible Consum. 2024;13:100187. Nwolisa CU, Babatope J, Oloyede B, Abiodun T, Ugoji C. Review of Financial Inclusion and Access to Energy in West Africa. Appl J Econ Manage Social Sci. 2022;32:17–27. Kar AK, Bali Swain R. Does financial inclusion improve energy accessibility in Sub-Saharan Africa? Appl Econ. 2024;5649:5789–807. Churchill SA, Marisetty VB. Financial inclusion and poverty: a tale of forty-five thousand households. Appl Econ. 2020;5216:1777–88. Ghana Statistical Service. Ghana Living Standards Survey 7 [Internet]. 2017 [cited 2024 Dec 22]. Available from: https://microdata.statsghana.gov.gh/index.php/catalog/97 Ghana Statistical Service. Ghana Living Standard Survey[GLSS7]. Main Report [Internet]. 2019 [cited 2024 Dec 22]. Available from: https://www2.statsghana.gov.gh/nada/index.php/catalog/97/study-description Kelikume I. Digital financial inclusion, informal economy, and poverty reduction in Africa. J Enterprising Communities: People Places Global Econ. 2021;15[4]:626 – 40. Liu G, Gao L, Wang F. The impact and realization mechanism of financial inclusion on multidimensional poverty: Evidence from 426 national-level impoverished counties in China. Manag Decis Econ. 2022;438:3973–86. Sen KK, Karmaker SC, Hosan S, Chapman AJ, Uddin MK, Saha BB. Energy poverty alleviation through financial inclusion: Role of gender in Bangladesh. Energy. 2023;282:128452. Sharma S, Bose A, Shekhar H, Pathania R. Strategy for financial inclusion of informal economy workers. Working Paper; 2019. Twumasi MA, Jiang Y, Ameyaw B, Danquah FO, Acheampong MO. The impact of credit accessibility on rural households clean cooking energy consumption: The case of Ghana. Energy Rep. 2020;6:974–83. Additional Declarations No competing interests reported. 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Introduction","content":"\u003cp\u003eEnergy poverty is a persistent challenge in Ghana, with profound implications for economic development, health, and environmental sustainability [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite Ghana's progress in expanding electricity access, energy poverty remains a critical issue, particularly for low-income households [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. According to the Ghana Statistical Service [2018], less than 1% of households have access to electricity, while 67% rely on traditional biomass fuels like wood and charcoal for cooking. This reliance on inefficient and polluting fuels contributes to indoor air pollution, linked to respiratory illnesses and other health problems [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The limited use of modern energy sources is further compounded by the high costs of cleaner alternatives and the low purchasing power of many households [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe informal sector, which constitutes a substantial part of Ghana\u0026rsquo;s economy, faces distinct challenges in energy access [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This sector, often neglected in policymaking, is characterized by limited access to formal financial services, modern energy infrastructure, and energy-efficient technologies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. As a result, many informal sector households remain trapped in a cycle of energy poverty, impacting their economic productivity and quality of life. Addressing these disparities requires a comprehensive understanding of the interplay between energy poverty and financial inclusion, particularly within marginalized populations, to promote sustainable development and inclusive economic growth [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEnergy poverty refers to the inability of households to access or afford the energy services needed for essential daily activities, such as cooking, lighting, and heating [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. It encompasses a range of challenges related to the availability, affordability, and reliability of energy sources. In Ghana, energy poverty is a significant barrier to sustainable development, with many low-income households relying on traditional fuels like wood and charcoal, which contribute to indoor air pollution and adverse health outcomes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Conversely, fuel poverty is a narrower concept focused specifically on the inability to afford adequate heating, often used interchangeably with energy poverty but with a distinct emphasis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinancial inclusion, as defined by Demirguc-Kunt et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], involves access to and effective use of a wide range of financial services, including banking, credit, insurance, and mobile money platforms. It aims to integrate underserved populations into formal financial systems, enhancing their ability to save, borrow, and invest. In the context of energy poverty, financial inclusion is particularly critical. Access to financial services enables households to invest in cleaner, more efficient energy sources, reducing their reliance on polluting fuels and fostering better economic and health outcomes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Ghana\u0026rsquo;s informal sector, the relationship between energy poverty and financial inclusion is especially pronounced. Informal workers often lack formal collateral and credit history, limiting their access to traditional financial services and, consequently, their ability to adopt modern energy solutions [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Financial inclusion strategies, such as microfinance and mobile banking, can address these barriers, facilitating access to clean energy technologies and supporting energy-saving behaviours [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This restriction impedes their capacity to generate revenue, as access to modern energy resources is essential for business productivity and household well-being. Informal business owners also frequently lack the financial capacity, expertise, and understanding to adopt energy-efficient technologies, leading to high energy costs and associated health problems [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral studies have examined the positive impact of financial inclusion on household welfare[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], specifically in terms of energy consumption [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], energy poverty[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and health[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and economic well-being. Twumasi et al.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] highlighted that financial inclusion significantly enhances residential energy expenditure in Ghana, particularly benefiting urban households, the poorest populations, and female-headed families. This aligns with findings from Abokyi et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], who demonstrated that financial inclusion increases the consumption of clean cooking fuels while decreasing the use of traditional and polluting energy sources, particularly among female-headed and rural households. These studies emphasize the crucial role of financial inclusion in supporting energy transitions and improving health outcomes.\u003c/p\u003e \u003cp\u003eIn a broader West African context, Nwolisa et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] found that financial inclusion\u0026mdash;measured by indicators such as ATMs and bank accounts\u0026mdash;has a positive but limited impact on energy access, suggesting that financial infrastructure plays a role in energy poverty reduction. Meanwhile, Kar and Swain [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] extended this observation to 27 energy-poor countries in Sub-Saharan Africa, demonstrating that financial inclusion significantly reduces energy poverty, reinforcing its relevance in low-income settings.\u003c/p\u003e \u003cp\u003eThe intersection between financial inclusion and health outcomes has also been explored, with studies like Churchill and Marisetty [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] showing that improved financial access can reduce energy poverty and its associated health risks, such as respiratory illnesses. This finding is corroborated by Kose [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], who linked energy poverty to higher rates of chronic illnesses. These health implications underscore the importance of inclusive financial policies that promote access to clean energy.\u003c/p\u003e \u003cp\u003eBeyond energy access, financial inclusion's broader socio-economic impacts have been noted. Koomson et al.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] revealed that financial inclusion reduces household vulnerability to poverty. The literature suggests that financial inclusion can foster economic resilience by reducing exposure to future poverty, especially among marginalized populations[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, there are notable gaps in the existing literature. While many studies confirm the positive impact of financial inclusion on energy transitions and socio-economic outcomes, they often focus on urban areas, formal sectors, or national aggregates. There is limited exploration of how financial inclusion specifically impacts energy poverty in Ghana's informal sector\u0026mdash;a sector that constitutes a significant portion of the Ghanaian economy but is frequently underserved by traditional financial institutions [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The informal sector's unique characteristics, such as limited access to formal credit and financial services, present distinct challenges and opportunities for energy access that remain underexplored.\u003c/p\u003e \u003cp\u003eThis study seeks to address these gaps by investigating the relationship between financial inclusion and energy poverty within Ghana\u0026rsquo;s informal sector. By examining the dynamics of microfinance, mobile banking, and other financial services in low-income and informal settings, the research aims to provide targeted insights that can inform policy interventions. The study will explore how financial inclusion can drive energy transitions in marginalized communities, with implications for health, economic growth, and sustainable development.\u003c/p\u003e \u003cp\u003eThis paper is presented in six sections. Section 2 presents a discussion on data and variables employed in the study, while the empirical methodology is presented in Section 3. Section 4 presents the empirical results, and discussion of the empirical results fall under Section 5. Section 6 presents the conclusion and policy recommendations based on the outcomes of the study.\u003c/p\u003e"},{"header":"2. Data and Variables","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data\u003c/h2\u003e \u003cp\u003eThe data for this study were derived from the 2017 Ghana Living Standards Survey [GLSS7], which represents the seventh and most recent round of the survey. The data was collected over 12 months, spanning from 22nd October 2016 to 17th October 2017 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The survey was designed to include approximately 15,000 households across 1,000 Enumeration Areas [EAs], with 561 [56.1%] rural EAs and 439 [43.9%] urban EAs. A two-stage stratified sampling approach was employed for the survey. In the first stage, 1,000 enumeration areas [EAs] were selected to form the Primary Sampling Units [PSUs]. In the second stage, 15 households were systematically selected from each PSU, resulting in a total of 15,000 households [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe GLSS7 provided detailed information on the demographic characteristics of the population, education, health, employment, migration, housing conditions, household income and expenditure, agriculture, and data projection, among other variables [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. It included comprehensive data on the socio-economic characteristics of individuals and households, such as age, gender, family size, educational attainment, and basic utility services like cooking fuel, sewage disposal, electricity, and water. We utilized these variables for our study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Variables\u003c/h2\u003e \u003cp\u003e \u003cb\u003eMultidimensional Index for Energy Poverty\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe Multidimensional Energy Poverty Index [MEPI] framework includes five dimensions and six indicators, capturing various aspects of a household\u0026rsquo;s energy access, including cooking, lighting, household appliances, entertainment/education, and communication technologies. Inspired by Amartya Sen's theories on capabilities and deprivations and rooted in the multidimensional poverty framework developed by the Oxford Poverty and Human Development Initiative [OPHI], the MEPI provides a comprehensive tool for measuring energy poverty [Alkire \u0026amp; Foster, 2011].\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\u003e\u003cb\u003eIndicators and Weights for Measuring Energy Deprivation\u003c/b\u003e\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\u003eDimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndicator [Weight]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDeprivation Cut-off [Poor if\u0026hellip;]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCooking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModern cooking fuel [0.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eType of cooking fuel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUse any fuel besides electricity, LPG, kerosene, natural gas, or biogas\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLighting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElectricity access [0.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHas access to electricity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFalse\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eServices provided using household appliances\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousehold appliance ownership [0.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHas a fridge OR freezer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFalse\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEntertainment/Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEntertainment/education appliance ownership [0.25]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHas a radio OR television\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFalse\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe applied the MEPI framework with weights carefully assigned to each dimension to reflect their relative importance. The cooking and lighting dimensions received higher weights due to their fundamental role in household energy needs. Specifically, cooking indicators were weighted slightly higher [0.205 each] to emphasize their critical importance in the context of developing countries like Ghana, while lighting was assigned a weight of 0.20. The remaining dimensions, including access to household appliances, entertainment/education, and communication, were each assigned weights of 0.13, ensuring an equitable representation across diverse energy needs.\u003c/p\u003e \u003cp\u003eThe MEPI calculates a household's energy deprivation score as a weighted sum of deprivations, ranging from 0 [no deprivation] to 1 [maximum deprivation]. We used a threshold of 0.33 to classify households as energy poor, indicating a significant level of energy deprivation. The calculation is defined as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{d}_{i}=\\:{w}_{1}{I}_{1}+\\:{w}_{2}{I}_{2}+\\dots\\:+\\:{w}_{n}{I}_{n}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{d}_{i}\\:\\)\u003c/span\u003e \u003c/span\u003e= household energy deprivation score\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{I}_{1}\\)\u003c/span\u003e \u003c/span\u003e = 1 if a household is deprived in indicator \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:I,\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{I}_{i}\\)\u003c/span\u003e\u003c/span\u003e = 0 otherwise\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:{w}_{1}\\)\u003c/span\u003e \u003c/span\u003e = weight assigned to indicator \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:I,\\:\\)\u003c/span\u003e\u003c/span\u003ewith \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:_{i=1}^{d}{w}_{i}=1\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003cp\u003eWe applied the MEPI to the GLSS7 dataset, which provided comprehensive data on household demographics, economic indicators, and energy use patterns. By employing the MEPI, we were able to identify energy-poor households and provide targeted insights into their challenges, contributing to a nuanced understanding of energy poverty in Ghana\u0026rsquo;s informal sectors.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFinancial Inclusion Status\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFor this study, we generated a binary index to assess the financial inclusion status of households using a financial deprivation score. Households were classified as either financially disadvantaged [deprived] or financially included [non-deprived] based on the financial status of the household head. A household was considered financially disadvantaged if its financial deprivation score was less than 0.5, indicating a lack of access to at least two key aspects of financial inclusivity. The assessment evaluated ownership of bank or mobile money accounts, availability of credit or loans, ownership of insurance, and receipt of financial remittances. Each factor was equally weighted at 0.25, providing a balanced measure of financial inclusion.\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\u003e\u003cb\u003eElements, Index, and Description of Each Element.\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElements\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBank account [0.25]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA household head who does not possess a bank account\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInsurance [0.25]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA household head who does not possess an insurance package\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLoan/Credit [0.25]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA household head who does not have access to a loan\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRemittance [0.25]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA household head who does not receive remittances\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe financial deprivation score was calculated as a weighted sum of these four components, with a threshold of 0.5 used to determine the household's financial inclusion status. Households scoring below 0.5 were classified as financially deprived, while those scoring 0.5 or higher were considered financially included. This binary classification facilitated a systematic analysis of the financial inclusion landscape, enabling us to explore its connection with other critical variables, such as energy poverty. By utilizing this approach, we ensured that the classification criteria were both transparent and aligned with the study\u0026rsquo;s objectives.\u003c/p\u003e \u003cp\u003e \u003cb\u003eControl Variables\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe included several control variables to account for household characteristics that may influence energy poverty, as detailed in Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003eA1\u003c/span\u003e in the appendix. The age of the household head, expressed in years, was included as a continuous variable to capture the household head\u0026rsquo;s experience and resources. Older household heads are expected to have more experience, potentially enabling better financial management and resource allocation. To account for non-linear effects, the square of age was also incorporated.\u003c/p\u003e \u003cp\u003eThe gender of the household head was considered to explore how gender roles and decision-making dynamics affect energy poverty [see Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003eA1\u003c/span\u003e]. Additionally, household size, measured as the total number of individuals in the household, was included as a proxy for the dependency ratio. Larger households are expected to face greater energy demands, potentially increasing the likelihood of energy poverty.\u003c/p\u003e \u003cp\u003eWe also included the employment status of the household head, as employment often provides financial stability and resources for energy expenditures. The location of the household, whether urban or rural, was incorporated to account for disparities in energy infrastructure and access to services. Finally, the educational attainment of the household head was included, reflecting its critical role in financial literacy and the adoption of energy-efficient behaviors [refer to Table \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003eA1\u003c/span\u003e]. These variables provided a comprehensive framework for analyzing the socio-economic determinants of energy poverty.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the summary statistics of key variables for the informal sector. Approximately 42.36% of households are classified as energy-deprived based on the Multidimensional Energy Poverty Index [MEPI]. Male-headed households constitute 66.31% of the sample, while 63.15% of households are located in urban areas. Regarding poverty status, 91.72% of the sample is classified as not poor. Additionally, 41.08% of household heads are married, and only 4.43% are employed.\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\u003eSummary Statistics for Informal Sector\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \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\u003eFrequency\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPI [Multidimensional Poverty]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot Deprived [No]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.64%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeprived [Yes]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.36%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.31%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.69%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLocation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.15%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.85%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePoverty Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot Poor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91.72%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.28%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot Married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58.92%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.08%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmployment Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95.57%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.43%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cb\u003eSource: Author\u0026rsquo;s compilation from GLSS 7 data\u003c/b\u003e\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 descriptive statistics for continuous variables. The average age of household heads is 38.03 years, with a standard deviation of 15.97 years. The mean education level is 7.14 years of schooling, while the average household size is 4.34 members.\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\u003eSummary Statistics for Informal Sector\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=\"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 \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\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1701.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1397.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9801\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.709\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\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHhsize\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSource: Author\u0026rsquo;s compilation from GLSS 7 data\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Empirical Methodology","content":"\u003cp\u003eTo analyze the relationship between financial inclusion and energy poverty, we estimated four different models. Each model provided insight into the causal and correlative dynamics. Additionally, we examined household heads in the informal sector, defined as those who lacked access to formal employment benefits, as detailed in Table \u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003eA2\u003c/span\u003e in the appendix. The study's methodology is influenced by earlier research by Churchill et al. [2020b], Zhang \u0026amp; Posso [2017], and Koomson \u0026amp; Danquah [2021].\u003c/p\u003e \u003cp\u003e \u003cb\u003eModel 1: Logit Margins [Baseline Model]\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe first model uses a standard logit regression to estimate the probability of a household being energy non-poor \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:[\\text{Y}=1]\\)\u003c/span\u003e\u003c/span\u003e based on financial inclusion \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left[FI\\right]\\)\u003c/span\u003e\u003c/span\u003e and control variables \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:[Z}_{ki}].\\)\u003c/span\u003e\u003c/span\u003e This model accounts for the binary nature of the dependent variable and ensures predictions fall within the range of 0 and 1. It is a baseline model for understanding the relationship between financial inclusion and energy poverty without accounting for unobserved heterogeneity.\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{p}_{i}=\\:{\\Lambda\\:}\\left(\\text{Y}=1\\right|{X}_{i}]={\\:\\beta\\:}_{0}+\\:{\\:\\beta\\:}_{1}\\:{FI}_{i}+\\:\\sum\\:{\\:\\beta\\:}_{k}{\\:Z}_{ki}\\:+\\:{\\epsilon\\:}_{i}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\varLambda\\:\\)\u003c/span\u003e \u003c/span\u003e is the logistic distribution, Y\u0026thinsp;=\u0026thinsp;1 if the household is energy poor, FI is the financial inclusivity status of the household head, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:Z}_{ki}\\)\u003c/span\u003e\u003c/span\u003eis a matrix of the control variables used in the study [age, sex, household size, employment status]\u003c/p\u003e \u003cp\u003e \u003cb\u003eModel 2: Fixed Effect Logit Margins\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe second model introduces regional fixed effects [\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{region}]\\)\u003c/span\u003e\u003c/span\u003eto account for unobserved regional heterogeneity that might confound the relationship between financial inclusion and energy poverty. By including fixed effects, we control for factors specific to each region, such as infrastructure availability, local policies, or cultural differences. Fixed effects also address the correlation between predictors and unobserved factors by controlling for any time-invariant regional characteristics that may influence both financial inclusion and energy poverty. For example, a region's overall level of economic development may affect access to financial services and modern energy infrastructure. By including fixed effects, we ensure these regional-level influences are accounted for, isolating the within-region variation for more accurate estimation.\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:{p}_{i}=\\:{\\Lambda\\:}\\left(\\text{Y}=1\\right|{X}_{i}]={\\:\\beta\\:}_{0}+\\:{\\:\\beta\\:}_{1}\\:{FI}_{i}+\\:\\sum\\:{\\:\\beta\\:}_{k}{\\:Z}_{ki}\\:+{\\mu\\:}_{region}+{\\epsilon\\:}_{i}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhile fixed effects address regional heterogeneity, endogeneity remains a concern, as financial inclusion is influenced by economic well-being and could still correlate with the error term through factors like unobserved regional differences, proximity to basic amenities, or self-selection into remote areas.\u003c/p\u003e \u003cp\u003e \u003cb\u003eModel 3: Fixed Effect IV Estimate\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn the third model, we address the potential endogeneity in financial inclusion using an instrumental variable [IV], \"distance to the nearest bank\" as previous research has shown that proximity to a bank can improve financial inclusion which impacts energy poverty [Churchill et al., 2020b; Churchill \u0026amp; Marisetty, 2019; Koomson et al., 2020a]. The first-stage regression predicts financial inclusion [\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{FI}_{i,\\:}]\\)\u003c/span\u003e\u003c/span\u003e based on the IV, the second-stage regression estimates the causal effect of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\widehat{{FI}_{i}}\\)\u003c/span\u003e\u003c/span\u003e on energy poverty. Regional fixed effects are included to control for unobserved differences across regions.\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:{FI}_{i}=\\:{\\alpha\\:}_{0}+\\:{\\alpha\\:}_{1}{Distance\\:to\\:Bank}_{i}+\\:\\sum\\:{\\alpha\\:}_{k}{Z}_{ki}+\\:{\\mu\\:}_{region}+\\:{\\upsilon\\:}_{i}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:{p}_{i}=\\:{\\Lambda\\:}\\left(\\text{Y}=1\\right|{FI}_{i,\\:}{Region}_{i}]={\\:\\beta\\:}_{0}+\\:{\\:\\beta\\:}_{1}\\:\\widehat{{FI}_{i}}+\\:\\sum\\:{\\:\\beta\\:}_{k}{\\:Z}_{ki}\\:+{\\mu\\:}_{region}+{\\epsilon\\:}_{i}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eHowever, the use of distance to the nearest bank as an instrument for financial inclusion may still be problematic, as it could be correlated with the error term through socioeconomic and geographical factors. For instance, areas with higher levels of economic development may have more accessible financial services and better access to modern energy sources, which may confound the relationship between financial inclusion and energy poverty.\u003c/p\u003e \u003cp\u003e \u003cb\u003eModel 4: IV Estimate without Fixed Effects\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe fourth model uses an instrumental variable approach without regional fixed effects. This provides a causal estimate of the impact of financial inclusion on energy poverty, accounting for endogeneity financial inclusion and energy poverty, while we do not control for unobserved regional differences.\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:{FI}_{i}=\\:{\\alpha\\:}_{0}+\\:{\\alpha\\:}_{1}{Distance\\:to\\:Bank}_{i}+\\:\\sum\\:{\\alpha\\:}_{k}{Z}_{ki}+\\:{\\mu\\:}_{region}+\\:{\\upsilon\\:}_{i}\\dots\\:\\dots\\:Stage\\:1$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:{p}_{i}=\\:{\\Lambda\\:}\\left(\\text{Y}=1\\right|{FI}_{i,\\:}]={\\:\\beta\\:}_{0}+\\:{\\:\\beta\\:}_{1}\\:\\widehat{{FI}_{i}}+\\:\\sum\\:{\\:\\beta\\:}_{k}{\\:Z}_{ki}\\:+{\\epsilon\\:}_{i}\\)\u003c/span\u003e \u003c/span\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;..Stage 2\u003c/p\u003e \u003cp\u003eEndogeneity can arise due to potential reverse causality between financial inclusion and energy poverty [that is, energy poverty can influence financial inclusion], or omitted variables that correlate with both financial inclusion and energy poverty. This model uses the distance to the nearest bank as an instrument, as proximity to financial services is strongly correlated with financial inclusion, but not directly with energy poverty. The first stage predicts financial inclusion status \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{[FI}_{i\\:}]\\)\u003c/span\u003e\u003c/span\u003e using the instrument and other variables, while the second stage estimates the causal impact of predicted financial inclusion \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\widehat{{FI}_{i}}\\)\u003c/span\u003e\u003c/span\u003e on energy poverty.\u003c/p\u003e"},{"header":"4. Empirical Results","content":"\u003cp\u003eThe regression results, which show the impact of financial inclusion on energy poverty, are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Across all models, financial inclusion consistently exhibits a significant and negative relationship with energy poverty, affirming the critical role of financial inclusion in reducing household energy deprivation. In the logit margins [Model 1], financial inclusion significantly reduces the likelihood of energy poverty [coefficient = -0.0473, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01]. Other significant predictors include rural location, which increases the probability of energy poverty by 0.169 [p\u0026thinsp;\u0026lt;\u0026thinsp;0.01], and household size, with a positive coefficient of 0.0308 [p\u0026thinsp;\u0026lt;\u0026thinsp;0.01], indicating that larger households face higher risks of energy deprivation. Education shows a protective effect, reducing the likelihood of energy poverty [-0.0167, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01]. Interestingly, female-headed households are less likely to experience energy poverty compared to male-headed households [-0.0878, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01]. Age demonstrates a non-linear relationship, with the square of age yielding a positive coefficient [0.000323, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01], suggesting that while age initially reduces energy poverty, this effect diminishes over time.\u003c/p\u003e \u003cp\u003eIn the fixed effect logit margins [Model 2], the introduction of regional fixed effects does not alter the overall direction and significance of the relationship between financial inclusion and energy poverty. Financial inclusion remains a significant negative predictor [-0.0339, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01]. Similar patterns persist for rural location [0.0894, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01], age, education, and household size. The protective effect of female-headed households also remains statistically significant [-0.0456, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01], reinforcing the robustness of these findings across different specifications.\u003c/p\u003e \u003cp\u003eThe fixed effect IV estimate [Model 3] addresses potential endogeneity in financial inclusion by using distance to the nearest bank as an instrumental variable. The results confirm the negative and statistically significant impact of financial inclusion on energy poverty [-0.0619, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01]. Rural location and household size continue to be positively associated with energy poverty, while higher levels of education and being married are negatively associated. Notably, distance to the nearest bank is a significant predictor of financial inclusion [-0.00606, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01], validating its suitability as an instrument. This model provides more causal evidence supporting the link between financial inclusion and reduced energy poverty.\u003c/p\u003e \u003cp\u003eIn the IV estimate without fixed effects [Model 4], financial inclusion remains a significant negative predictor of energy poverty [-3.587, p\u0026thinsp;\u0026lt;\u0026thinsp;0.10]. Unlike previous models, however, some control variables, such as age and education, lose their statistical significance. Household size remains positively associated with energy poverty [0.0922, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05], suggesting that larger households face greater energy deprivation risks even after accounting for financial inclusion. The results from this model, while less robust due to the exclusion of regional fixed effects, still highlight the consistent negative relationship between financial inclusion and energy poverty.\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\u003e\u003cb\u003eMarginal Effect Results of Logit Model and Fixed Effect Logit Model of the Impact of financial inclusion on energy poverty in the informal sector for Ghana\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \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\u003eLogit Margins [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFixed Effect Logit Margins [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFixed Effect IV Estimate [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIV Estimate without Fixed Effects [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFinancial Inclusion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0473*** [0.0146]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0339*** [0.0124]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0619*** [0.0217]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.587* [2.165]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0878*** [0.0152]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0456*** [0.0140]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0840*** [0.0256]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0981 [0.130]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRural\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.169*** [0.0149]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0894*** [0.0238]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.144*** [0.0258]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.105 [0.0775]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0244*** [0.00372]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0121*** [0.000990]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0183*** [0.00394]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.00520 [0.0164]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u0026sup2;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000323*** [0.0000447]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000166*** [0.0000145]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000259*** [0.0000475]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000124 [0.000189]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePoor\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.292*** [0.0207]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.140*** [0.0427]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.133*** [0.0225]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.360 [0.333]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.0167*** [0.00137]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0102*** [0.00200]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0188*** [0.00169]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0191 [0.0237]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarried\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.117*** [0.0161]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.0840*** [0.0235]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.151*** [0.0262]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.237** [0.0986]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold Size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0308*** [0.00299]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0155*** [0.00431]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0246*** [0.00734]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0922** [0.0408]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEmployed\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.0588* [0.0332]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.0205 [0.0282]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.0402 [0.0477]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0608 [0.136]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDistance to Nearest Bank\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.00606*** [0.000605]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eConstant\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.029*** [0.0935]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.623*** [0.483]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cem\u003eNote\u003c/em\u003e: Standard errors are in parentheses. \u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.10, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e "},{"header":"5. Discussion","content":"\u003cp\u003eOur findings from the estimation models show that financial inclusion is negatively associated with energy poverty. The estimation models also support the causal effect of financial inclusion on energy poverty. These findings affirm that of the earlier study by Koomson \u0026amp; Danquah [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Even though their study did not specifically focus on the informal sector, the outcome of their study shows that financial inclusion contributes immensely to the alleviation of energy poverty, especially in the rural areas of Ghana, where most employment opportunities fall under the informal sector. Similarly, in Bangladesh, Sen et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] examined the causal effect of financial inclusion on energy poverty and concluded that financial inclusion is a key policy for mitigating energy poverty.\u003c/p\u003e \u003cp\u003eIn a study that examines the informal sector in 42 African countries, Kelikume [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] finds that enhancing financial inclusion through increased mobile and internet usage is associated with declining poverty levels, of which energy poverty is a major component. In another study that examines 426 impoverished counties in China, Liu et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] report that financial inclusion contributes immensely to the alleviation of multidimensional poverty through its positive impact on enhancing economic activities and production. Financial inclusion not only helps in addressing the problem of credit exclusion among the poor but also provides the means for the poor to improve their economic capacities. In India, Sharma et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] highlight energy poverty as a major challenge for the poor and workers in the informal sector. The empirical analysis shows that the poor and workers in the informal sector have limited access to electricity and other modern energy sources. The study shows that financial inclusion enhances access to financial or credit facilities for the poor and workers in the informal sector, which plays a major role in eradicating inequality, energy poverty, and other dimensions of poverty.\u003c/p\u003e \u003cp\u003eOur results also support the findings by Twumasi et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], which show that financial inclusion reduces residential energy poverty. The study highlights the role of financial inclusion in improving household net per capita income. Improvement in household income enables households to improve their energy expenditure, especially expenditure on Liquefied Petroleum Gas [LPG] and electricity. Abokyi et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] also report comparable results, suggesting that financial inclusion reduces energy poverty by improving the usage of clean and improved energy sources.\u003c/p\u003e \u003cp\u003eThese studies affirm the role of financial inclusion as a key enabler of access to modern energy services and highlight the importance of financial services in promoting clean energy adoption and improving household welfare. By facilitating access to savings, credit, and insurance, financial inclusion empowers households to invest in modern energy solutions such as electricity and clean cooking fuels, reducing their dependence on traditional biomass and mitigating the associated health risks. Financial inclusion, therefore, enhances household income levels, which enables households to improve their energy, thereby reducing energy poverty, especially in the rural areas and informal sector of Ghana.\u003c/p\u003e \u003cp\u003eA critical takeaway from this study is therefore the importance of addressing financial exclusion to combat energy poverty, particularly in rural areas [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Our results show that rural households are more likely to be energy-deprived compared to their urban counterparts. This disparity reflects the uneven distribution of financial services and energy infrastructure in Ghana. Rural areas often lack access to formal financial institutions and modern energy infrastructure, perpetuating a cycle of deprivation and vulnerability [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This finding supports the argument for targeted financial inclusion strategies that prioritize rural communities, such as expanding mobile money services and microfinance institutions to the underserved and underprivileged areas. These measures could help bridge the financial gap and enable rural households to afford clean energy technologies, thereby reducing their exposure to health risks associated with traditional fuels.\u003c/p\u003e \u003cp\u003eHousehold size emerged as another significant predictor of energy poverty. Larger households were more likely to experience energy deprivation, which is unsurprising given the higher energy demands associated with larger households [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Our result thus aligns with the broader literature on household resource allocation, which suggests that larger households may prioritize immediate needs like food and shelter over energy investments, increasing their vulnerability to energy poverty.\u003c/p\u003e \u003cp\u003eInterestingly, female-headed households were less likely to experience energy poverty compared to male-headed households. This finding aligns with those of Twumasi et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and Abokyi et al. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, it contradicts other studies such as Koomson \u0026amp; Danquah [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. We attribute the difference in outcome to the objectives of most previous studies. While our analysis focuses on the informal sector, most studies in Ghana consider all sectors. The outcome of our study suggests that women may allocate household resources more effectively, particularly in areas related to health and energy use. Female-headed households may also have greater awareness of the health impacts of traditional cooking fuels, leading them to prioritize cleaner alternatives when they have access to financial resources. This result highlights the potential benefits of gender-targeted financial inclusion programs that empower women to make energy-related decisions.\u003c/p\u003e \u003cp\u003eThe study also underscores the critical role of education in reducing energy poverty. Higher levels of educational attainment among household heads were associated with a lower likelihood of energy deprivation. Education likely improves financial literacy and awareness of clean energy options, enabling households to make informed decisions and adopt modern energy technologies. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Our finding aligns with those of previous studies [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], which emphasize the importance of education in promoting sustainable energy transitions. Policymakers should consider integrating financial and energy literacy programs into formal education systems to enhance awareness and adoption of clean energy solutions.\u003c/p\u003e \u003cp\u003eOne of the unique contributions of this study is its focus on the informal sector, which is often overlooked in discussions on energy access and financial inclusion. Informal sector workers face distinct challenges, such as limited access to formal credit and insurance, which hinder their ability to invest in modern energy solutions [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Addressing these barriers requires tailored interventions that recognize the unique characteristics of the informal economy. Expanding access to mobile banking services and creating flexible microfinance products for informal sector workers could significantly reduce energy poverty in this group.\u003c/p\u003e \u003cp\u003eThe instrumental variable analysis strengthened the causal interpretation of the relationship between financial inclusion and energy poverty [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Using distance to the nearest bank as an instrument for financial inclusion, the results confirm that financial inclusion has a robust negative impact on energy poverty. This finding mitigates concerns about endogeneity and reverse causality, providing more credible evidence for policymakers. However, the limitations of the instrumental variable approach should be acknowledged. While distance to the nearest bank is a reasonable instrument as used in previous studies [ 3,22,23], it may not fully capture all unobserved factors that influence both financial inclusion and energy access. We also estimated an IV approach, which does not account for unobserved regional differences. This approach could result in biased IV estimates if regions with better financial access also have superior energy infrastructure. While excluding regional fixed effects simplified the estimation, the IV results are sensitive to the validity of the instrument, requiring robust checks to ensure that the instrument is relevant and exogenous. Despite these limitations, the IV approach provides a more causal interpretation of the relationship between financial inclusion and energy poverty by isolating exogenous variation in financial inclusion due to proximity to financial institutions.\u003c/p\u003e \u003cp\u003eThe cross-sectional nature of the data also limited our ability to examine the long-term dynamics between financial inclusion and energy poverty. Future research could address this gap by using panel data to track changes over time. While the study focuses on the informal sector, it does not differentiate between various types of informal activities, which may have different energy needs and access constraints. Further research could explore sector-specific differences to provide more targeted insights.\u003c/p\u003e"},{"header":"6. Conclusion and Policy Recommendations","content":"\u003cp\u003eThis study highlights the critical role of financial inclusion in reducing energy poverty in Ghana\u0026rsquo;s informal sector. Households with access to financial services are less likely to experience energy poverty, as they can invest in clean energy solutions, reducing dependence on polluting fuels. Vulnerable groups, such as rural households and larger families, face higher risks, while female-headed households and those with more education are less likely to experience energy deprivation. These findings underscore the need for targeted financial inclusion and energy access policies.\u003c/p\u003e \u003cp\u003eTo address these challenges, policymakers should prioritize expanding financial services in rural areas through mobile banking and microfinance. Promoting energy-specific financial products, such as microcredit for solar systems and clean cooking technologies, can facilitate clean energy adoption. Public education on energy efficiency and financial literacy will empower households to make informed decisions. Tailored financial programs for women and investments in rural energy infrastructure, including decentralized solar grids, are crucial for reducing energy poverty. Continuous data collection and monitoring are also essential for tracking progress and refining interventions. By integrating financial inclusion with energy access initiatives, Ghana can promote sustainable development and improve the well-being of its most vulnerable populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Statement:\u003c/strong\u003e Not Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u003c/strong\u003e Not Applicable.\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eFull name of committee: Not Applicable.\u003c/li\u003e\n \u003cli\u003e\u0026nbsp;Confirmation: Not Applicable.\u003c/li\u003e\n \u003cli\u003eReason: This article does not contain any studies with human participants or animals performed by any of the authors.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent:\u0026nbsp;\u003c/strong\u003eThis article does not contain any studies with human participants performed by any of the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration\u003c/strong\u003e: Not Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e: Not Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor’s contribution:\u0026nbsp;\u003c/strong\u003eAll authors contributed to the writing of the manuscript. Matilda Kabutey-Ongor was the study lead and contributed to the introduction, literature review, data analysis, discussion of results, and references sections. Michael Adurayi Amenah contributed to the introduction, literature review, data analysis, discussion of results, and references sections. Frederick Richmond Yorke contributed to the discussion of results and reference sections. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e All authors declare that they have no known competing financial interests or personal relationships that could directly or indirectly influence the work submitted for publication. Matilda Kabutey-Ongor declares that she has no conflict of interest. Michael Adurayi Amenah declares that he has no conflict of interest. Frederick Richmond Yorke declares that he has no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement:\u003c/strong\u003e The authors declare that there is perfect data transparency, and that the data will always be available at any time upon request. The authors also declare that Stata codes for performing the estimations for this paper will be available upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u003c/strong\u003e Not Applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNsenkyire E, Nunoo J, Sebu J, Iledare O. Household Multidimensional Energy Poverty: Impact on Health, Education, and Cognitive Skills of Children in Ghana. Child Indic Res. 2023;161:293\u0026ndash;315.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdusah-Poku F, Adjei‐Mantey K, Kwakwa PA. Are energy‐poor households also poor? Evidence from Ghana. 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Energy Rep. 2020;6:974\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Energy poverty, Financial inclusion, Informal sector, Sustainable development","lastPublishedDoi":"10.21203/rs.3.rs-6513556/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6513556/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the relationship between financial inclusion and energy poverty in Ghana\u0026rsquo;s informal sector using data from the Ghana Living Standards Survey [GLSS7]. Applying multidimensional energy poverty measures and a range of econometric models, including logit regression, fixed effects, and instrumental variable [IV] estimation, the study identifies financial inclusion as a significant factor in reducing energy poverty. Households with access to financial services, such as bank accounts, insurance, credit, and remittances, are less likely to experience energy deprivation. The instrumental variable analysis, which uses distance to the nearest bank as an instrument for financial inclusion, confirms the robustness of this relationship. The results reveal that rural households, larger households, and those headed by older individuals are more vulnerable to energy poverty, while female-headed and more educated households are less likely to be energy-poor.\u003c/p\u003e \u003cp\u003ePolicy recommendations include expanding financial services in rural areas through mobile banking and microfinance, promoting energy-specific financial products, and enhancing financial literacy programs. Tailored interventions for women and large households, as well as targeted energy access initiatives for rural areas, are crucial for mitigating energy poverty. This study highlights the importance of integrating financial inclusion into energy poverty reduction strategies to promote sustainable development and improve the well-being of vulnerable populations in Ghana.\u003c/p\u003e","manuscriptTitle":"Financial Inclusion and Energy Poverty in Ghana: Evidence from the Informal Sector","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-13 11:23:23","doi":"10.21203/rs.3.rs-6513556/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-11T06:18:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-07T22:56:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-06T17:44:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-23T14:08:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"318492468204790013238716354782593337984","date":"2025-05-23T13:51:38+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"141975548197282617875818085218637103276","date":"2025-05-21T16:59:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10485876385532914580155009650724460111","date":"2025-05-13T10:18:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"287952419437440768169351117270593838680","date":"2025-05-08T10:02:51+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-08T09:24:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-29T03:29:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-29T03:27:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2025-04-23T14:20:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0d0c3071-7ce0-4a5c-8315-7509d1e20bb9","owner":[],"postedDate":"May 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-08-18T16:05:35+00:00","versionOfRecord":{"articleIdentity":"rs-6513556","link":"https://doi.org/10.1007/s43621-025-01689-6","journal":{"identity":"discover-sustainability","isVorOnly":false,"title":"Discover Sustainability"},"publishedOn":"2025-08-11 15:57:49","publishedOnDateReadable":"August 11th, 2025"},"versionCreatedAt":"2025-05-13 11:23:23","video":"","vorDoi":"10.1007/s43621-025-01689-6","vorDoiUrl":"https://doi.org/10.1007/s43621-025-01689-6","workflowStages":[]},"version":"v1","identity":"rs-6513556","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6513556","identity":"rs-6513556","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}