Tackling within-country spatial inequalities in household energy use towards sustainable development: The case of Ghana.

Tackling within-country spatial inequalities in household energy use towards sustainable development: The case of Ghana. | 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 Tackling within-country spatial inequalities in household energy use towards sustainable development: The case of Ghana. Elizabeth Nsenkyire, Jacob Nunoo, Joshua Sebu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5571019/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Within-country spatial inequalities in accessibility and usage of modern energy and its services have been recognized by several studies globally. Despite this, studies that commit to analyzing and identifying ways to bridge these spatial disparities are scanty. Being a sub-Saharan African country with hyped improvement in energy access, other dimensions of household energy use deteriorate in Ghana, coupled with spatial inequalities within the country. This study, therefore, examined the socioeconomic drivers of the spatial disparities in household energy accessibility, utilization, and affordability between the three ecological zones of Ghana, as well as the rural and urban divide. Cross-sectional data from the latest Ghana Living Standard Survey (GLSS 7) was analyzed using the multidimensional energy poverty measure, the logit regression model, and the Oaxaca-Blinder decomposition for binary dependent models. The study found spatial differences in multidimensional energy poverty between the two geographical divides to be driven by socioeconomic characteristics such as education, location of residence, and income poverty. The study recommends that the socioeconomic characteristics of households be improved through programs and policies to alleviate the spatial inequalities in modern energy use within countries. Microeconomics Spatial analysis Socioeconomic drivers Energy affordability Oaxaca-Blinder decomposition Multidimensional energy poverty Geographical zones. Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Access and use of modern energy services are essential for equitable, sufficient, and good living (Musango, 2014; Day et al., 2016). Hence, unequal access and consumption of modern energy services between different parts of a country fuel inequitable living standards and well-being (Oseni, 2012). To meet sustainable development goals 10 and 3, which seek to reduce within-country inequality and ensure healthy lives, in addition to goal 7, a critical examination of modern energy consumption disparities within countries and how to address them becomes crucial. Despite the plethora of studies (Gouveia et al., 2019; Mendoza Jr. et al., 2019; Scarpellini et al., 2019; Gupta et al., 2020; Lin & Wang, 2020; Kahouli & Okushima, 2021; Bardazzi et al., 2021; Nsenkyire et al., 2023a) revealing the spatial differences in multidimensional energy poverty within countries and their varying impacts on well-being, those that commit to examining and identifying ways to tackle the spatial differences are scanty. This study, therefore, seeks to address this research gap by examining the spatial disparities in multidimensional energy poverty in Ghana and its socioeconomic drivers to recommend ways of tackling the inequalities. Goal 7 of the sustainable development goals seeks universal access to modern, reliable, affordable, and sustainable energy globally by 2030. Nonetheless, inequality in energy consumption between Africa and the rest of global regions remains the lowest and continues to deteriorate. The case is much more alarming for countries in sub-Sahara Africa where three in every four people who lack electricity access reside, with about 83 percent of the sub-region’s population still depending on unclean cooking fuels and technologies (International Energy Agency, United Nations Statistics Division, International Renewable Energy Agency, World Health Organization, & World Bank, 2021, 2022, 2023; International Energy Agency, 2019, 2022). In addition to the between countries inequalities in energy consumption, within-country spatial inequalities persist in sub-Saharan Africa, especially between urban and rural parts (IEA, 2019; IEA, IRENA, UNSD, World Bank, & WHO, 2023). Spatial differences in energy accessibility, usability, and affordability, among others, within countries are also extant in literature. Studies by Gouveia et al. (2019), Mendoza Jr. et al. (2019), Scarpellini et al. (2019), Gupta et al. (2020), Lin and Wang (2020), Kahouli and Okushima (2021), Bardazzi et al. (2021), and Castaño-Rosa and Okushima (2021), and reveal spatial differences in multidimensional energy poverty between different geographical zones in countries such as Japan, Italy, Philippines, France, China, India, Portugal, and Spain. Also, in sub-Saharan Africa, spatial differences in multidimensional energy poverty have been documented by Ashagidigbi et al. (2020), Ssennono et al. (2021), Nsenkyire et al. (2023a), Nsenkyire et al. (2024), and Oyekale and Molelekoa (2023) in Gambia, Ghana, Nigeria, Uganda, Sierra Leone, and South Africa among others. Being a sub-Saharan African country with hyped improvement in energy access and distinct geographical spaces, Ghana’s ecological zones provide a nuanced means of accessing spatial inequalities. The ecological zones - savannah, forest, and coastal– divide the country into northern, middle, and southern parts. The coastal zone spans the four administrative regions sharing boundaries with the Gulf of Guinea – Western, Greater Accra, Central, and Volta regions. The forest zone, on the other hand, covers the three administrative regions in the middle part of Ghana – Eastern, Brong-Ahafo, and Ashanti regions, with the savannah zone consisting of the remaining three in the northern part – Upper East, Northern, and Upper West regions 1 . These three ecological zones are also defined by distinct geographical and socio-economic characteristics (Seidu et al., 2019), making them suitable for exploring within-country inequalities. Therefore, in addition to the rural-urban divide, this study investigated the socioeconomic drivers of the spatial disparities in multidimensional energy poverty between the three ecological zones in Ghana. The remainder of the study is structured as follows: section two presents the methods, and sections three, four, and five present the data, results, and conclusions and recommendations, respectively. [1] The 10 administrative regions of Ghana were increased to 16 in 2018 after a referendum in 4 regions. Northern region was demarcated into Savannah, Northern, and North East regions. Volta region was demarcated into Oti and Volta regions. Western region was demarcated into Western North and Western regions while Brong-Ahafo region was demarcated into Bono-East, Ahafo, and Bono regions. 2. Methods 2.1 Measuring Multidimensional Energy Poverty Table 1 shows the dimensions and indicators used to create multidimensional energy poverty (MEP). Nussbaumer et al. (2012), who pioneered the MEP studies, argue that to drive public policy, energy poverty dimensions should focus on their relative importance to the people and society in question. Accessibility and consumption of essential energy services stimulate household energy demand (Kowsari & Zerriffi, 2011). Nonetheless, the expenditure on energy access and consumption should not constrain spending on other equally needful and beneficial commodities such as education, nutrition, and health. Affordability of modern energy is also stretched by Gafa and Egbendewe (2021), Tait (2017), Njiru and Letema (2018), and Zang et al. (2019) as a crucial energy poverty indicator, especially for countries in Sub-Saharan Africa (IEA, 2019). This study, therefore, adapts the energy poverty dimensions and indicators of Nsenkyire et al. (2023b), which builds on the energy poverty dimensions and indicators of Nussbaumer et al. (2012), to include an affordability dimension. Table 1 Multidimensional energy poverty measurement Dimensions Indicators Cutoff Weight Accessibility Electricity access Does not have electricity access 0.15 Cooking fuel Uses cooking fuel besides electricity and gas 0.15 Indoor pollution Uses traditional cookstoves 0.10 Applicability Education Does not own a desktop, laptop, or tablet computer 0.05 Refrigeration Does not own a freezer or refrigerator 0.05 Communication Does not own a mobile phone or landline telephone 0.05 Space cooling Does not own a fan or air conditioner 0.05 Entertainment Does not own a television, radio, or home theatre 0.05 Washing and ironing Does not own a washing machine or electric iron 0.05 Food processing Does not own a blender, rice cooker, microwave, or toaster 0.05 Affordability Affordability Spends 10% or more of total household income on energy 0.25 Adapted from Nsenkyire et al. (2023b) In creating the household multidimensional energy poverty, this study employs the methodological approach of Alkire and Foster (2011). Thus, given a population of \(\:m\:\) households with \(\:n\) dimensions of achievements, an \(\:m\times\:n\) matrix of those achievements for all households across \(\:j\:\) dimensions are given by $$\:X=\:\left[{x}_{ij}\right]\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(1\right)$$ The row vector \(\:{x}_{i}=({x}_{i1},\:x,\:\dots\:,\:{x}_{in})\) of \(\:X\) represent household \(\:i{\prime\:}s\) achievement in the various dimensions, whereas the column vector \(\:{x}_{j}=\left({x}_{1j},\:{x}_{2j},\:\dots\:.,\:{x}_{mj}\right)\) displays the outcomes of achievement in the j dimensions for the households. Two cutoff procedures: identification and aggregation, are used to identify a multidimensional energy poor household. First, a deprivation cutoff point is set for each dimension \(\:j\:\:\) to identify households deprived in that dimension. Let \(\:{g}_{ij}^{0}\:\) be a matrix of deprivation which equals \(\:{w}_{j}\) (weight of dimension \(\:j\) ) if household \(\:i\:\) is deprived in dimension \(\:j\:\) but equal to zero if the household is not deprived in dimension \(\:j\) . A vector of deprivation counts: $$\:{C}_{i}=\sum\:_{j=1}^{d}{g}_{ij},\:0\le\:{C}_{i}\ge\:1\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(2\right)$$ is created, which sums the weighted deprivations of household \(\:i\) . A cutoff point \(\:\left(k\right)\:\) is then applied to identify multidimensional energy poor households. Household \(\:i\:\) is multidimensional energy poor if the deprivation counts is equal to or greater than the cutoff point ( \(\:{C}_{i}\ge\:k\) ). The cutoff point ( k ) used to identify multidimensional energy poor households was set at 0.33, drawing on the empirical work by Nussbaumer et al. (2012). Thus, a household is multidimensional energy poor if it is deprived in over one of the \(\:j\) dimensions. A dummy variable household multidimensional energy poverty ( \(\:HMEP)\) is generated such that \(\:\:HMEP=1\) if household \(\:i\) is multidimensional energy poor and \(\:HMEP=0,\) if multidimensionally energy non-poor. The incidence of \(\:HMEP\) which estimates the percentage of multidimensionally energy poor households, is then derived as: $$\:H=\frac{p}{m}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(3\right)$$ Where \(\:p\) is the count of all multidimensional energy poor households. Further, the severity of household multidimensional energy poverty, which captures the average of the weighted indicators for all the multidimensional energy poor households is also derived as: $$\:A=\frac{{\sum\:}_{i=1}^{n}{C}_{i}\left(k\right)}{p}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(4\right)$$ Where \(\:{C}_{i}\left(k\right)\) is the deprivation counts of multidimensional energy poor households. 2.2 Examining the determinants of multidimensional energy poverty Previous studies reveal several socioeconomic, environmental, and demographic factors which affect household multidimensional energy poverty. These factors include gender, age, marital status, and educational level of household head (Bersisa, 2016; Ozughali & Ogwumike, 2018; Abbas et al., 2020); household wealth (Crentsil et al., 2019; Hasanujzaman & Omar, 2022), household size (Gafa & Egbendewe, 2021), household access to remittances (Hosan et al., 2023; Qurat-ul-Ann & Mirza, 2021a); and region and location of residence (Bekele et al., 2015; Ashagidigbi et al., 2020). This study draws on these scholarly works to examine the socioeconomic factors influencing multidimensional energy poverty in the three ecological zones and the rural and urban divides. The empirical model, estimated using the logit regression model, was specified as $$\:{HMEP}_{i}={\sum\:}_{i=1}^{n}{X}_{i}\beta\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(5\right)$$ where \(\:HMEP\) measure household multidimensional energy poverty, \(\:X\) captures the socioeconomic, environmental, and demographic factors that influence multidimensional energy poverty; and \(\:\beta\:\) captures the regression coefficients to be estimated. 2.3 Socioeconomic drivers of the spatial differences in multidimensional energy poverty The Blinder-Oaxaca decomposition approach captures and decomposes the variations in an outcome variable between two groups into unexplained and explained components (Rahimi & Hashemi Nazari, 2021). The explained part shows the difference in the outcome variable resulting from variations in observable characteristics of individuals between the groups. On the other hand, the unexplained part shows the difference in the outcome variable resulting from changes in the estimated coefficients, which is attributed to the structure differences between the groups. Earlier studies such as Etezady et al. (2021), Koh et al. (2020), Sen (2014), Sharaf and Rashad (2016), Sun and Lyu (2020), and Zhang, Liu, and Liu (2019) have employed the Blinder-Oaxaca decomposition approach to examine generational, locational, gender, and racial inequalities. The Blinder-Oaxaca decomposition for binary dependent models was therefore employed to analyze the socioeconomic drivers of spatial differences in multidimensional energy poverty between the ecological zones in addition to the rural and urban divides. Given the binary regression model estimated differently for pairs (F, G) of groups (forest and coastal, savannah and coastal, savannah and forest, and rural and urban) as $$\:{HMEP}_{\varvec{i}\varvec{F}}=\:{\varvec{X}}_{\varvec{i}\varvec{F}}{\varvec{\beta\:}}_{\varvec{i}\varvec{F}}+{\varvec{\epsilon\:}}_{\varvec{i}\varvec{F}},\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(6\right)$$ $$\:{HMEP}_{\varvec{i}\varvec{G}}=\:{\varvec{X}}_{\varvec{i}\varvec{G}}{\varvec{\beta\:}}_{\varvec{i}\varvec{G}}+{\varvec{\epsilon\:}}_{\varvec{i}\varvec{G}},\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\left(7\right)$$ The empirical model estimated using the Blinder-Oaxaca decomposition for binary dependent models was specified as: $$\:\stackrel{-}{{HMEP}_{F}}-\:\stackrel{-}{H{MEP}_{G}}=\left(\stackrel{-}{{X}_{F}}-\stackrel{-}{{X}_{G}}\right)\widehat{{\beta\:}_{F}}+\stackrel{-}{{X}_{G}}(\widehat{{\beta\:}_{F}}-\widehat{{\beta\:}_{G}})\:\:\:\:\:\:\:\:\:\:\:\:\left(8\right)$$ Where \(\:\stackrel{-}{{HMEP}_{F}}-\:\stackrel{-}{H{MEP}_{G}}\) shows the difference in household multidimensional energy poverty between the paired zones, \(\:\stackrel{-}{{X}_{F}}-\stackrel{-}{{X}_{G}}\:\) (explained component) measures the variances in household multidimensional energy poverty resulting from the differences in household demographic and socioeconomic characteristics between the paired groups, and \(\:\widehat{{\beta\:}_{F}}-\widehat{{\beta\:}_{G}}\) (unexplained component) measures the variations in household multidimensional energy poverty resulting from the structural differences between the paired groups. Because this section sought to examine how household socioeconomic characteristics contribute to the spatial disparities in multidimensional energy poverty, the explained components of the decompositions are emphasized. 3. Data The study used data from the current series of the Ghana Living Standards Surveys (GLSS 7). The GLSS is a household survey carried out periodically by the Ghana Statistical Service to provide consistent and nationally comparable information for evaluating living conditions and welfare in Ghana. The current series, GLSS 7, was conducted in 2016/2017 and was designed to collect comprehensive household information such as household demographics, employment, health, education, housing and household conditions, and household asset ownership, among others. For every household, the survey collected information on all the variables in Table 1 , such as electricity access, type of cooking fuel and cook stove, expenditure on energy consumption, total household expenditure, and ownership of electric appliances. In addition, variables including the marital status, sex, age, and educational status of the household head; welfare quintile, household size, household remittances income received, and location and region of residence were also collected in the survey. The overall sample of 14,009 households covered in the survey was used in this study since no missing information was recorded in the variables. The summary statistics of the demographic and socioeconomic variables used in this study are shown in Table 2 . The average age in years of heads of households was approximately 46 for coastal and forest, 47 for savannah and rural, and 45 for urban households. The coastal, forest, and urban samples were dominated by married heads with an average household size of 4 while unmarried heads dominated the savannah and rural households with an average household size of 5. In addition, all five geographical divides were dominated by male headed households while only the savannah sample was dominated by uneducated household heads. Also, most households in the coastal, forest, and urban samples were nonpoor while the majority in the savannah and rural spaces were poor. Table 2 Measurement and Summary Statistics of Variables Coastal Forest Savannah Urban Rural All Variables Measurement Mean Mean Mean Mean Mean Mean S. D Min Max Age Age of household head 46.2 45.6 47.0 44.7 47.4 46.2 15.9 15 99 Household size Number of persons in household 3.6 3.6 5.2 3.6 4.7 4.2 2.9 1 28 Married Equals 1 if household head is married 0.539 0.523 0.303 0.505 0.407 0.449 0.497 0 1 Rural Equals 1 if household resides in a rural area 0.330 0.550 0.763 0.570 0.495 0 1 Female Equals 1 if household head is female 0.377 0.346 0.226 0.353 0.281 0.312 0.463 0 1 Poor Equals 1 if household welfare falls outside the richer and richest category 0.201 0.275 0.695 0.17 0.581 0.405 0.491 0 1 Remittance Log of remittance income received by household 2.176 2.202 1.660 2.163 1.884 2.004 3.014 0 11.513 Educated Equals 1 if household head is uneducated 0.153 0.138 0.556 0.146 0.397 0.290 0.454 0 1 Ecological zones Equals 1 for coastal 0.388 0.144 0.249 0.432 0 1 Equals 1 for forest 0.417 0.383 0.398 0.489 0 1 Equals 1 for savannah 0.194 0.472 0.353 0.478 0 1 Observations Number of households 3,484 5,575 4,950 6018 7991 14,009 Source: Authors computations using GLSS 7 4. Results and Discussion 4.1 Multidimensional energy poverty estimates Figure 1 shows the percentage of households deprived in the eleven weighted indicators of multidimensional energy poverty for the three ecological zones and the rural urban divides. The results reveal majority of households in Ghana and in the five geographic categories to be deprived in cooking fuel, education, refrigeration, and food processing. Compared to the countrywide deprivations, the percentages were relatively higher for households in the forest, savannah, and rural parts but relatively lower in the Coastal and urban parts. Adusa-Poku and Takeuchi (2019) and Nsenkyire et al. (2023b) also found high cooking fuel and refrigeration deprivation among households in such areas in Ghana. This suggests that majority of households in Ghana continue to depend on unclean cooking fuels and technologies and do not patronize most energy services. Likewise, utilization of space cooling and washing and ironing services remain minimal among households in the savannah and rural areas. Electricity access, communication, and affordability registered lower percentage deprivations for households in all the five geographic spaces. Nonetheless, compared to the countrywide deprivation percentages, the household deprivation percentages in electricity access and communication were relatively higher in the savannah and rural parts. These results reveal the country’s significant progress in extending electricity access and mobile telecommunication services availability across the country, as well as the improvement in the growth of mobile phone subscriptions. However, the relatively higher percentage of deprivation in electricity access in the savannah and rural parts reecho the inequalities in energy access rates within the country. On the contrary, the household deprivation percentages in affordability were relatively higher in the coastal and urban parts when compared to the rural and savannah areas. However, the lower percentage deprivations in affordability for rural and savannah spaces may not necessarily reflect decent pricing of modern energy but overreliance on free traditional energy sources to meet their energy demand. The spatial distribution of the affordability indicator as shown in Fig. 2 reveals the regional deprivation in energy affordability to confirm this claim. Thus, the highest incidence of households with affordability deprivations (23%) was found in the Upper West region of the savannah zone with the Western and Ashanti regions of the coastal and forest zones respectively having the next highest incidence (15%). Figure 3 shows the percentage contribution of the indicators to household multidimensional energy poverty for the five geographic locations. The cooking fuel and indoor pollution indicators registered as the ultimate contributors to multidimensional energy poverty in all the five categories. Earlier researchers also found these indicators to contribute the highest to multidimensional energy poverty (Qurat-ul-Ann & Mirza, 2021b; Nsenkyire et al., 2023a). For coastal and urban areas, affordability was the next highest contributor to multidimensional energy poverty. However, in the savannah and rural areas, electricity access was the third highest contributor, which indicates that the accessibility dimension (cooking fuel, indoor pollution, and electricity access) is the leading contributor to household multidimensional energy poverty in these two geographic settings. The contributions of communication and entertainment to household multidimensional energy poverty were the least across all the five geographic spaces. Communication indicator has been found in the literature to contribute the least to energy poverty not only in Ghana (Crentsil et al., 2019) but also in other countries such as Senegal, Togo, and Pakistan (Gafa & Egbendewe, 2021; Qurat-ul-Ann & Mirza, 2021b). Figure 4 shows the incidence and severity of multidimensional energy poverty for Ghana and the five geographic zones. Majority of households in the forest (64%), savannah (87.6%), and rural (82.1%) parts of Ghana were multidimensional energy poor with minority being multidimensional energy poor in the coastal (49%) and urban (48.9%) parts. The average intensity of deprivation among the multidimensional energy poor households was also more severe in the forest (50.4%), savannah (57.6%), and rural (54.5%) areas than in the coastal (48.4%) and urban (47.9%) areas. Disparities in the severity and incidence of household multidimensional energy poverty exists not only in Ghana but also in countries such as the Philippines, Argentina, Brazil, Uruguay, Paraguay, and Pakistan as found by Mendoza et al. (2019), Pereira et al. (2021), and Qurat-ul-Ann and Mirza (2021b). Observations from the MEPI estimates show evidence of the spatial inequalities in multidimensional energy poverty in Ghana between the ecological zones as well as the rural and urban divides. 4.2 Determinates of multidimensional energy poverty Table 3 shows the logistic regression estimates of the socioeconomic factors that influence household multidimensional energy poverty in the five geographical zones as well as Ghana as a whole. Regarding household heads characteristics, the results revealed the age of household head to increase the odds of multidimensional energy poverty among households in all five cases. The relationships observed suggest that the adoption of modern energy and consumption of its services remains low among households with older heads. The cost and perceived risks associated with modern energy consumption, especially LPG, may be dissuading such households who rely on traditional energy from switching fuels. Also, the odds of multidimensional energy poverty for households with female heads were higher compared to households with male heads in all the geographical zones. Thus, female headed households were more likely to live in multidimensional energy poverty than male headed households. Gender inequalities between males and females in Ghana, for that matter, the three ecological zones, may account for the disparities in the likelihood of being multidimensional energy poor. Thus, economic exclusions and social restrictions on females in some institutions and localities may thwart female headed households’ capacity to patronize modern energy and its services. Table 3 Determinates of Household Multidimensional Energy Poverty Energy Poverty Coastal Forest Savannah Urban Rural All Age of head 1.015*** 1.024*** 1.007 1.018*** 1.023*** 1.019*** (0.003) (0.003) (0.005) (0.003) (0.003) (0.002) Sex of head (male) Female 1.444*** 2.119*** 2.264*** 1.725*** 2.144*** 1.82*** (0.157) (0.214) (0.473) (0.148) (0.258) (0.129) Educational status of head (educated) Uneducated 3.232*** 2.176*** 2.848*** 2.545*** 3.216*** 2.711*** (0.54) (0.33) (0.501) (0.322) (0.48) (0.267) Marital status of head (unmarried) Married 0.624*** 0.728*** 1.638*** 0.709*** 0.811* 0.74*** (0.068) (0.077) (0.281) (0.064) (0.092) (0.053) Household size 0.961* 1.033 1.144*** 1.01 1.044* 1.02 (0.023) (0.022) (0.043) (0.019) (0.024) (0.015) Remittance income received 1.004 0.945*** 0.972 0.972** 0.967** 0.97*** (0.015) (0.012) (0.023) (0.011) (0.016) (0.009) Poverty status (nonpoor) Poor 6.42*** 5.658*** 4.93*** 5.213*** 6.593*** 5.753*** (1.064) (0.738) (0.806) (0.666) (0.821) (0.517) Residence (urban) Rural 2.499*** 3.392*** 2.384*** 3.027*** (0.268) (0.284) (0.335) (0.185) Ecological zone (coastal) Forest 1.329*** 1.746*** 1.436*** (0.102) (0.194) (0.091) Savannah 3.367*** 2.908*** 3.171*** (0.388) (0.397) (0.286) Constant 0.324*** 0.234*** 0.478*** 0.253*** 0.408*** 0.21*** (0.055) (0.035) (0.119) (0.034) (0.073) (0.023) Pseudo r-squared 0.154 0.194 0.274 0.126 0.205 0.231 Prob > chi2 0.000 0.000 0.000 0.000 0.000 0.000 N 3484 5575 4950 6018 7991 14009 *** p < 0.01, ** p < 0.05, * p < 0.1; Standard errors in parenthesis. Source: Authors computations from GLSS 7 Further, educational status of household heads also influenced multidimensional energy poverty positively in all five areas. The odds of being multidimensional energy poor were higher for households with uneducated heads than those with educated heads. Education comes with knowledge acquisition, improved socioeconomic status, and increased prospects for gainful employment, which could influence educated household heads to patronize modern energy and its services. Again, the odds of multidimensional energy poverty were lower for households with married heads compared to households with unmarried heads in all the geographic areas except in the savannah zone. Thus, the likelihood of a household being multidimensional energy poor was greater for households with married household heads in the savannah zone. A plausible explanation for the varying observations could be attributed to the differences in the personal attributes of spouses such as educational and employment status among others which are known determinants of household energy poverty. Concerning household characteristics, household size was observed to decrease the odds of multidimensional energy poverty in the coastal zone but increase the odds in the other four geographic spaces, though statistically insignificant in the forest and urban parts. A plausible explanation for the observation could be that larger households in the coastal parts of Ghana may comprise several economically active members and in effect, a higher income level to afford modern energy, therefore, enhancing the adoption and consumption of modern energy services. Contrarily to the coastal zone, the positive relationship between energy poverty and household size in the rural and savannah areas could be attributed to the low level of economic activities in such areas suggesting that larger households in those parts may not necessarily be comprised of economically active members. Again, remittance income received by households was found to reduce the odds of multidimensional energy poverty. For households in the forest, urban, and rural areas, receiving remittance income reduced the likelihood of multidimensional energy poverty. However, for the households in the coastal and savannah zones, household remittance income received did not statistically influence the odds of being multidimensional energy poor. The differences in the relationships observed could be indicative of how the use of remittance income received varies among households between the three ecological zones in Ghana. Poverty status was also observed to influence the likelihood of multidimensional energy poverty among households in all five geographical parts. Specifically, the odds of multidimensional energy poverty were higher for poor households than nonpoor households. Modern energy consumption comes at a relatively higher cost compared to traditional energy. As such, the ease with which households consume modern energy depends on the household’s ability to pay. High modern energy prices may, therefore, prevent poor households from switching from unclean energy sources compared to nonpoor households. With regards to environmental attributes, households residing in rural centers in the coastal, forest, and savannah zones were more disposed to being multidimensional energy poor than those in urban centers. Thus, the odds of multidimensional energy poverty were comparatively higher for rural resident households than urban resident households. Also, in the urban and rural areas, the study found the ecological zone of residence to significantly influence multidimensional energy poverty. To be precise, households residing in the forest and savannah zones had increased odds of being multidimensional energy poor compared to households in the coastal zone. These observations may be attributed to the inaccessibility of modern energy and the abundance of biomass in rural centers as well as the inequalities in modern energy access between the coastal, forest, and savannah zones. The findings observed in Table 3 are coherent with the findings of previous studies by Abbas et al. (2020), Ashagidigbi et al. (2020), Bekele et al. (2015), Bersisa (2016), Crentsil et al. (2019), Edoumiekumo et al., (2013), Gafa and Egbendewe (2021), Hasanujzaman and Omar (2022), Hosan et al. (2023), Ozughalu and Ogwumike (2018), and Qurat-ul-Ann and Mirza (2021a) who found household, environmental, and household head characteristics to influence the incidence of multidimensional energy poverty. 4.3 Socioeconomic drivers Socioeconomic drivers of the spatial differences in multidimensional energy poverty between the household population in the savannah, forest, and coastal zones, and between the rural and urban folks are presented in 4. The Blinder-Oaxaca decomposition results revealed significant variances in the average probabilities of household multidimensional energy poverty between paired zones. Specifically, comparing the forest and coastal zones, households in the forest zones had a 0.15 higher probability of living in multidimensional energy poverty than households in the coastal zone. Also, households in the savannah zone had a 0.386 higher probability of living in multidimensional energy poverty than households in the coastal zone. Further, in comparison to households in the forest zones, the probability of being multidimensional energy poor was observed to be 0.236 higher for households in the savannah zone. Rural folks also had a 0.331 higher probability of being multidimensional energy poor than urban folks. The explained component of the decomposition was observed to significantly contribute to the disparities in the probabilities of multidimensional energy poverty. Thus, about 47.3%, 19.4%, 24.6%, and 16.3% of the disparities between the forest/coastal, savannah/coastal, savannah/forest, and rural/urban respectively resulted from variations in household socioeconomic characteristics with the remaining differences resulting from changes in the estimated coefficients (unexplained component). On the specific socioeconomic drivers, between the forest and coastal zones, the results revealed rural-urban residency and poverty status of households to contribute about 64.8% and 38% respectively to the higher probability of households in the forest zone being multidimensional energy poor. Similarly, differences in urban-rural residency and poverty status, as well as educational status of heads also contributed to the higher incidence of multidimensional energy poverty in the savannah zone by 24%, 54.7%, and 29.3% respectively when compared to the coastal zone, and by 17.5%, 56.1%, and 28.1% when compared to the forest zone. The ecological zone of residence, poverty status, and educational status of heads were also the chief socioeconomic drivers of the disparities in multidimensional energy poverty between rural and urban dwellers (20.4%, 52.9%, & 18.5%). These findings reveal the significant role household socioeconomic characteristics play in driving the spatial differences in modern energy consumption between different parts of the country. More importantly, the results reflect and confirm the gap in welfare, educational attainments, and rural-urban and ecological zones developments in Ghana and their implication on household modern energy consumption. For instance, Cooke et al. (2016) revealed poverty rates among households in the savannah zone to be the highest in Ghana, with rural-urban household inequality having the highest share of the country’s total inequality. Table 4 Socioeconomic Drivers of Multidimensional Energy Poverty Inequality Forest(F)/Coastal(G) Savannah(F)/Coastal(G) Savannah(F)/Forest(G) Rural(F)/Urban(G) Group – F 0.640*** 0.876*** 0.876*** 0.821*** (0.008) (0.006) (0.006) (0.006) Group – G 0.490*** 0.490*** 0.640*** 0.489*** (0.01) (0.01) (0.008) (0.008) Difference 0.150*** 0.386*** 0.236*** 0.331*** (0.013) (0.012) (0.011) (0.01) [100] [100] [100] [100] Explained 0.071*** 0.075*** 0.057*** 0.054*** (0.007) (0.011) (0.008) (0.005) [47.3] [19.4] [24.6] [16.3] Unexplained 0.079*** 0.311*** 0.178*** 0.277*** (0.015) (0.02) (0.016) (0.014) [52.7] [80.6] [75.4] [83.7] Explained Age of head -0.004** 0.0004 0.002*** 0.002*** (0.001) (0.0003) (0.001) (0.001) [-5.6] [0.5] [3.5] [3.7] Household size -0.00006 0.0004 0.004*** 0.001 (0.0002) (0.001) (0.002) (0.001) [-0.1] [0.5] [7] [1.9] Remittance income received 0.0001 0.000008 0.001** 0.0003* (0.0004) (0.0001) (0.0002) (0.0001) [0.1] [0.1] [1.8] [0.6] Sex of head -0.0004 -0.003*** -0.005*** -0.002*** (0.001) (0.000756) (0.001) (0.0004) [-0.6] [-4] [-8.8] [-3.7] Educational status of head 0.002 0.022*** 0.016*** 0.01*** (0.001) (0.004) (0.003) (0.001) [2.8] [29.3] [28.1] [18.5] Marital status of head 0.0009 -0.003*** -0.002** -0.001*** (0.0009) (0.001) (0.001) (0.0003) [1.3] [-4] [-3.5] [-1.9] Residence 0.046*** 0.018*** 0.01*** (0.004) (0.003) (0.002) [64.8] [24] [17.5] Ecological zones 0.011*** (0.002) [20.4] Poverty status 0.027*** 0.041*** 0.032*** 0.032*** (0.003) (0.006) (0.005) (0.003) [38] [54.7] [56.1] [52.9] N 9059 8434 10525 14009 *** p < 0.01, ** p < 0.05, * p < 0.1; Percentage contributions in brackets; Robust standard errors in parenthesis. Source: Authors’ computations from GLSS 7 5. Conclusions and Recommendations Within-country inequality in modern energy access and consumption motivated this study to examine the socioeconomic drivers of spatial differences in multidimensional energy poverty between the three major ecological zones - savannah, forest, and coastal, and rural-urban geographical divides in Ghana. The multidimensional energy poverty measure, the logit regression model, and the Oaxaca-Blinder decomposition for binary dependent models were used to analyzed data from the current series of the Ghana Living Standard Survey (GLSS 7). The study contributes to literature as the first to examine the socioeconomic drivers of within-country spatial differences in multidimensional energy poverty. A higher deprivation percentages and intensity and incidence of multidimensional energy poverty was found in the savannah and rural areas than in the forest, coastal and urban parts. These findings showcase the disparities in modern energy accessibility, utilization, and affordability between the geographical spaces considered in this study. Household demographic and socioeconomic characteristics including age, sex, marital status, and educational level of household head, poverty status, household size, remittance income, and residence were found to influence multidimensional energy poverty in the distinct geographical spaces. Disparities in the probability of multidimensional energy poverty were observed between the geographical divides with socioeconomic characteristics such as education, residence, and poverty being the chief contributors. The observed disparities in the prevalence of household multidimensional energy poverty between the five geographic divides reveal the inequalities in modern energy accessibility and consumption of its services. Modern energy is a requisite for equitable living and its inaccessibility therefore restrict some household activities that foster good living standards. The spatial disparities in multidimensional energy poverty between the divides, among other things, if not tackled may slow down the pace with which inequality within the country will be alleviated to achieve SDG 10 by 2030 as well as SDGs 3 and 7. To that effect, addressing the accessibility, utilization, and affordability issues by extending electrification, especially in the savannah and rural zones, and making modern cooking fuels readily available and affordable throughout the country is key. Nonetheless, that must not be done in isolation. The country must, in addition, put in place poverty-alleviating programs and policies with special attention given to households in the savannah and rural areas to tackle the socioeconomic drivers of the inequality. Also, campaigns on formal education must be intensified and coupled with adult education programs, particularly for the rural and savannah zones. Further, the country must commit resources to savannah and rural developmental projects throughout the country, to bridge the structural differences between the five major geographical spaces. Despite within-country spatial differences in energy poverty being documented by numerous studies across different continents with country-specific socioeconomic characteristics, this study only focused on Ghana, a West African country. Hence, the findings of this study may only apply to tackling within-country spatial energy poverty differences in countries in West Africa and sub-Saharan Africa. Also, the differences in the energy poverty measures between countries in the global south and north as well as developed and developing ones may limit the applicability of the study’s findings to tackling within-country spatial differences in energy poverty for the developed and global north countries. Future studies should, therefore, endeavor to examine the country-specific socioeconomic drivers of the spatial disparities in energy poverty for developed and global north countries to help progress towards achieving SDGs 10 and its related SDGs. Declarations Declaration Statement The authors have no known financial or nonfinancial interest to declare. Funding This research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors. References Abbas, K., Li, S., Xu, D., Baz, K., & Rakhmetova, A. (2020). Do socioeconomic factors determine household multidimensional energy poverty? Empirical evidence from South Asia. 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Nsenkyire","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-8757-9662","institution":"Department of Applied Economics, School of Economics, University of Cape Coast, Ghana","correspondingAuthor":true,"prefix":"","firstName":"Elizabeth","middleName":"","lastName":"Nsenkyire","suffix":""},{"id":385604950,"identity":"58d1469c-89f4-493b-8292-e267ce377c55","order_by":1,"name":"Jacob Nunoo","email":"","orcid":"","institution":"Department of Applied Economics, School of Economics, University of Cape Coast, Ghana","correspondingAuthor":false,"prefix":"","firstName":"Jacob","middleName":"","lastName":"Nunoo","suffix":""},{"id":385604951,"identity":"d660bcfd-cf79-4709-88e8-9df615c6a860","order_by":2,"name":"Joshua Sebu","email":"","orcid":"","institution":"Department of Data Science and Economic Policy, School of Economics, University of Cape Coast, Ghana","correspondingAuthor":false,"prefix":"","firstName":"Joshua","middleName":"","lastName":"Sebu","suffix":""}],"badges":[],"createdAt":"2024-12-03 09:41:10","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5571019/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5571019/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71148126,"identity":"76e5d54c-b842-4d74-86ba-adf9082fc0ad","added_by":"auto","created_at":"2024-12-11 14:30:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":37726,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of Deprived Households in Energy Poverty Indicators\u003c/p\u003e\n\u003cp\u003eSource: Authors computations from GLSS 7\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-5571019/v1/3ecc3d2c8003f603a31fc9e9.png"},{"id":71148129,"identity":"303ce148-cbc2-4ccf-b61d-bd81c127195f","added_by":"auto","created_at":"2024-12-11 14:30:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":54266,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial distribution of energy affordability deprivations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSource: Authors computations from GLSS 7\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5571019/v1/906af2d0d681a1f068ba1bd0.png"},{"id":71145819,"identity":"9d349887-917d-4f3a-8e0c-65c389d0df55","added_by":"auto","created_at":"2024-12-11 14:22:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":42669,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eContribution of indicators to MEPI\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSource: Authors computations from GLSS 7\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5571019/v1/835b34e42cae04364971e1f3.png"},{"id":71145818,"identity":"dfe53ba5-b052-4138-bcc5-87cbf22f7a55","added_by":"auto","created_at":"2024-12-11 14:22:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":9987,"visible":true,"origin":"","legend":"\u003cp\u003eIncidence, Severity, and MEPI Estimates\u003c/p\u003e\n\u003cp\u003eSource: Authors computations from GLSS 7\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5571019/v1/b36ad238523e674cbf7806bc.png"},{"id":71151425,"identity":"52dea826-3a4e-40ee-9a1c-265dbaf772ae","added_by":"auto","created_at":"2024-12-11 14:54:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1083199,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5571019/v1/f534cfc6-34bc-4d69-a1ec-39c0d32d11cf.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eTackling within-country spatial inequalities in household energy use towards sustainable development: The case of Ghana.\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAccess and use of modern energy services are essential for equitable, sufficient, and good living (Musango, 2014; Day et al., 2016). Hence, unequal access and consumption of modern energy services between different parts of a country fuel inequitable living standards and well-being (Oseni, 2012). To meet sustainable development goals 10 and 3, which seek to reduce within-country inequality and ensure healthy lives, in addition to goal 7, a critical examination of modern energy consumption disparities within countries and how to address them becomes crucial. Despite the plethora of studies (Gouveia et al., 2019; Mendoza Jr. et al., 2019; Scarpellini et al., 2019; Gupta et al., 2020; Lin \u0026amp; Wang, 2020; Kahouli \u0026amp; Okushima, 2021; Bardazzi et al., 2021; Nsenkyire et al., 2023a) revealing the spatial differences in multidimensional energy poverty within countries and their varying impacts on well-being, those that commit to examining and identifying ways to tackle the spatial differences are scanty. This study, therefore, seeks to address this research gap by examining the spatial disparities in multidimensional energy poverty in Ghana and its socioeconomic drivers to recommend ways of tackling the inequalities.\u003c/p\u003e \u003cp\u003eGoal 7 of the sustainable development goals seeks universal access to modern, reliable, affordable, and sustainable energy globally by 2030. Nonetheless, inequality in energy consumption between Africa and the rest of global regions remains the lowest and continues to deteriorate. The case is much more alarming for countries in sub-Sahara Africa where three in every four people who lack electricity access reside, with about 83 percent of the sub-region\u0026rsquo;s population still depending on unclean cooking fuels and technologies (International Energy Agency, United Nations Statistics Division, International Renewable Energy Agency, World Health Organization, \u0026amp; World Bank, 2021, 2022, 2023; International Energy Agency, 2019, 2022). In addition to the between countries inequalities in energy consumption, within-country spatial inequalities persist in sub-Saharan Africa, especially between urban and rural parts (IEA, 2019; IEA, IRENA, UNSD, World Bank, \u0026amp; WHO, 2023).\u003c/p\u003e \u003cp\u003eSpatial differences in energy accessibility, usability, and affordability, among others, within countries are also extant in literature. Studies by Gouveia et al. (2019), Mendoza Jr. et al. (2019), Scarpellini et al. (2019), Gupta et al. (2020), Lin and Wang (2020), Kahouli and Okushima (2021), Bardazzi et al. (2021), and Casta\u0026ntilde;o-Rosa and Okushima (2021), and reveal spatial differences in multidimensional energy poverty between different geographical zones in countries such as Japan, Italy, Philippines, France, China, India, Portugal, and Spain. Also, in sub-Saharan Africa, spatial differences in multidimensional energy poverty have been documented by Ashagidigbi et al. (2020), Ssennono et al. (2021), Nsenkyire et al. (2023a), Nsenkyire et al. (2024), and Oyekale and Molelekoa (2023) in Gambia, Ghana, Nigeria, Uganda, Sierra Leone, and South Africa among others.\u003c/p\u003e \u003cp\u003eBeing a sub-Saharan African country with hyped improvement in energy access and distinct geographical spaces, Ghana\u0026rsquo;s ecological zones provide a nuanced means of accessing spatial inequalities. The ecological zones - savannah, forest, and coastal\u0026ndash; divide the country into northern, middle, and southern parts. The coastal zone spans the four administrative regions sharing boundaries with the Gulf of Guinea \u0026ndash; Western, Greater Accra, Central, and Volta regions. The forest zone, on the other hand, covers the three administrative regions in the middle part of Ghana \u0026ndash; Eastern, Brong-Ahafo, and Ashanti regions, with the savannah zone consisting of the remaining three in the northern part \u0026ndash; Upper East, Northern, and Upper West regions\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e1\u003c/a\u003e. These three ecological zones are also defined by distinct geographical and socio-economic characteristics (Seidu et al., 2019), making them suitable for exploring within-country inequalities. Therefore, in addition to the rural-urban divide, this study investigated the socioeconomic drivers of the spatial disparities in multidimensional energy poverty between the three ecological zones in Ghana.\u003c/p\u003e \u003cp\u003eThe remainder of the study is structured as follows: section two presents the methods, and sections three, four, and five present the data, results, and conclusions and recommendations, respectively.\u003c/p\u003e\n\u003cp\u003e[1] The 10 administrative regions of Ghana were increased to 16 in 2018 after a referendum in 4 regions. Northern region was demarcated into Savannah, Northern, and North East regions. Volta region was demarcated into Oti and Volta regions. Western region was demarcated into Western North and Western regions while Brong-Ahafo region was demarcated into Bono-East, Ahafo, and Bono regions. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Measuring Multidimensional Energy Poverty\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the dimensions and indicators used to create multidimensional energy poverty (MEP). Nussbaumer et al. (2012), who pioneered the MEP studies, argue that to drive public policy, energy poverty dimensions should focus on their relative importance to the people and society in question. Accessibility and consumption of essential energy services stimulate household energy demand (Kowsari \u0026amp; Zerriffi, 2011). Nonetheless, the expenditure on energy access and consumption should not constrain spending on other equally needful and beneficial commodities such as education, nutrition, and health. Affordability of modern energy is also stretched by Gafa and Egbendewe (2021), Tait (2017), Njiru and Letema (2018), and Zang et al. (2019) as a crucial energy poverty indicator, especially for countries in Sub-Saharan Africa (IEA, 2019). This study, therefore, adapts the energy poverty dimensions and indicators of Nsenkyire et al. (2023b), which builds on the energy poverty dimensions and indicators of Nussbaumer et al. (2012), to include an affordability dimension.\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\u003eMultidimensional energy poverty measurement\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimensions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCutoff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWeight\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eAccessibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElectricity access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDoes not have electricity access\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCooking fuel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUses cooking fuel besides electricity and gas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndoor pollution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUses traditional cookstoves\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eApplicability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDoes not own a desktop, laptop, or tablet computer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRefrigeration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDoes not own a freezer or refrigerator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCommunication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDoes not own a mobile phone or landline telephone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpace cooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDoes not own a fan or air conditioner\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEntertainment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDoes not own a television, radio, or home theatre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWashing and ironing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDoes not own a washing machine or electric iron\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFood processing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDoes not own a blender, rice cooker, microwave, or toaster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAffordability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAffordability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpends 10% or more of total household income on energy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAdapted from Nsenkyire et al. (2023b)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn creating the household multidimensional energy poverty, this study employs the methodological approach of Alkire and Foster (2011). Thus, given a population of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:m\\:\\)\u003c/span\u003e\u003c/span\u003ehouseholds with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:n\\)\u003c/span\u003e\u003c/span\u003e dimensions of achievements, an \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:m\\times\\:n\\)\u003c/span\u003e\u003c/span\u003e matrix of those achievements for all households across \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\:\\)\u003c/span\u003e\u003c/span\u003edimensions are given by\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:X=\\:\\left[{x}_{ij}\\right]\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe row vector \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{i}=({x}_{i1},\\:x,\\:\\dots\\:,\\:{x}_{in})\\)\u003c/span\u003e\u003c/span\u003e of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:X\\)\u003c/span\u003e\u003c/span\u003e represent household \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i{\\prime\\:}s\\)\u003c/span\u003e\u003c/span\u003e achievement in the various dimensions, whereas the column vector \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{x}_{j}=\\left({x}_{1j},\\:{x}_{2j},\\:\\dots\\:.,\\:{x}_{mj}\\right)\\)\u003c/span\u003e\u003c/span\u003e displays the outcomes of achievement in the j dimensions for the households.\u003c/p\u003e \u003cp\u003eTwo cutoff procedures: identification and aggregation, are used to identify a multidimensional energy poor household. First, a deprivation cutoff point is set for each dimension \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\:\\:\\)\u003c/span\u003e\u003c/span\u003eto identify households deprived in that dimension. Let \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{g}_{ij}^{0}\\:\\)\u003c/span\u003e\u003c/span\u003ebe a matrix of deprivation which equals \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{w}_{j}\\)\u003c/span\u003e\u003c/span\u003e (weight of dimension \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e) if household \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\:\\)\u003c/span\u003e\u003c/span\u003eis deprived in dimension \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\:\\)\u003c/span\u003e\u003c/span\u003ebut equal to zero if the household is not deprived in dimension \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e. A vector of deprivation counts:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:{C}_{i}=\\sum\\:_{j=1}^{d}{g}_{ij},\\:0\\le\\:{C}_{i}\\ge\\:1\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eis created, which sums the weighted deprivations of household \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e. A cutoff point \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(k\\right)\\:\\)\u003c/span\u003e\u003c/span\u003eis then applied to identify multidimensional energy poor households. Household \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\:\\)\u003c/span\u003e\u003c/span\u003eis multidimensional energy poor if the deprivation counts is equal to or greater than the cutoff point (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{i}\\ge\\:k\\)\u003c/span\u003e\u003c/span\u003e). The cutoff point (\u003cem\u003ek\u003c/em\u003e) used to identify multidimensional energy poor households was set at 0.33, drawing on the empirical work by Nussbaumer et al. (2012). Thus, a household is multidimensional energy poor if it is deprived in over one of the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:j\\)\u003c/span\u003e\u003c/span\u003e dimensions.\u003c/p\u003e \u003cp\u003eA dummy variable household multidimensional energy poverty (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:HMEP)\\)\u003c/span\u003e\u003c/span\u003e is generated such that \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:HMEP=1\\)\u003c/span\u003e\u003c/span\u003eif household \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e is multidimensional energy poor and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:HMEP=0,\\)\u003c/span\u003e\u003c/span\u003e if multidimensionally energy non-poor. The incidence of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:HMEP\\)\u003c/span\u003e\u003c/span\u003e which estimates the percentage of multidimensionally energy poor households, is then derived as:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:H=\\frac{p}{m}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\\)\u003c/span\u003e\u003c/span\u003e is the count of all multidimensional energy poor households. Further, the severity of household multidimensional energy poverty, which captures the average of the weighted indicators for all the multidimensional energy poor households is also derived as:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\:A=\\frac{{\\sum\\:}_{i=1}^{n}{C}_{i}\\left(k\\right)}{p}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(4\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{C}_{i}\\left(k\\right)\\)\u003c/span\u003e\u003c/span\u003e is the deprivation counts of multidimensional energy poor households.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Examining the determinants of multidimensional energy poverty\u003c/h2\u003e \u003cp\u003ePrevious studies reveal several socioeconomic, environmental, and demographic factors which affect household multidimensional energy poverty. These factors include gender, age, marital status, and educational level of household head (Bersisa, 2016; Ozughali \u0026amp; Ogwumike, 2018; Abbas et al., 2020); household wealth (Crentsil et al., 2019; Hasanujzaman \u0026amp; Omar, 2022), household size (Gafa \u0026amp; Egbendewe, 2021), household access to remittances (Hosan et al., 2023; Qurat-ul-Ann \u0026amp; Mirza, 2021a); and region and location of residence (Bekele et al., 2015; Ashagidigbi et al., 2020). This study draws on these scholarly works to examine the socioeconomic factors influencing multidimensional energy poverty in the three ecological zones and the rural and urban divides. The empirical model, estimated using the logit regression model, was specified as\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$$\\:{HMEP}_{i}={\\sum\\:}_{i=1}^{n}{X}_{i}\\beta\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(5\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:HMEP\\)\u003c/span\u003e\u003c/span\u003e measure household multidimensional energy poverty, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:X\\)\u003c/span\u003e\u003c/span\u003e captures the socioeconomic, environmental, and demographic factors that influence multidimensional energy poverty; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e captures the regression coefficients to be estimated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Socioeconomic drivers of the spatial differences in multidimensional energy poverty\u003c/h2\u003e \u003cp\u003eThe Blinder-Oaxaca decomposition approach captures and decomposes the variations in an outcome variable between two groups into unexplained and explained components (Rahimi \u0026amp; Hashemi Nazari, 2021). The explained part shows the difference in the outcome variable resulting from variations in observable characteristics of individuals between the groups. On the other hand, the unexplained part shows the difference in the outcome variable resulting from changes in the estimated coefficients, which is attributed to the structure differences between the groups. Earlier studies such as Etezady et al. (2021), Koh et al. (2020), Sen (2014), Sharaf and Rashad (2016), Sun and Lyu (2020), and Zhang, Liu, and Liu (2019) have employed the Blinder-Oaxaca decomposition approach to examine generational, locational, gender, and racial inequalities.\u003c/p\u003e \u003cp\u003eThe Blinder-Oaxaca decomposition for binary dependent models was therefore employed to analyze the socioeconomic drivers of spatial differences in multidimensional energy poverty between the ecological zones in addition to the rural and urban divides. Given the binary regression model estimated differently for pairs (F, G) of groups (forest and coastal, savannah and coastal, savannah and forest, and rural and urban) as\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$$\\:{HMEP}_{\\varvec{i}\\varvec{F}}=\\:{\\varvec{X}}_{\\varvec{i}\\varvec{F}}{\\varvec{\\beta\\:}}_{\\varvec{i}\\varvec{F}}+{\\varvec{\\epsilon\\:}}_{\\varvec{i}\\varvec{F}},\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(6\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$$\\:{HMEP}_{\\varvec{i}\\varvec{G}}=\\:{\\varvec{X}}_{\\varvec{i}\\varvec{G}}{\\varvec{\\beta\\:}}_{\\varvec{i}\\varvec{G}}+{\\varvec{\\epsilon\\:}}_{\\varvec{i}\\varvec{G}},\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(7\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe empirical model estimated using the Blinder-Oaxaca decomposition for binary dependent models was specified as:\u003cdiv id=\"Equh\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equh\" name=\"EquationSource\"\u003e\n$$\\:\\stackrel{-}{{HMEP}_{F}}-\\:\\stackrel{-}{H{MEP}_{G}}=\\left(\\stackrel{-}{{X}_{F}}-\\stackrel{-}{{X}_{G}}\\right)\\widehat{{\\beta\\:}_{F}}+\\stackrel{-}{{X}_{G}}(\\widehat{{\\beta\\:}_{F}}-\\widehat{{\\beta\\:}_{G}})\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\left(8\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{{HMEP}_{F}}-\\:\\stackrel{-}{H{MEP}_{G}}\\)\u003c/span\u003e\u003c/span\u003e shows the difference in household multidimensional energy poverty between the paired zones, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{{X}_{F}}-\\stackrel{-}{{X}_{G}}\\:\\)\u003c/span\u003e\u003c/span\u003e (explained component) measures the variances in household multidimensional energy poverty resulting from the differences in household demographic and socioeconomic characteristics between the paired groups, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\widehat{{\\beta\\:}_{F}}-\\widehat{{\\beta\\:}_{G}}\\)\u003c/span\u003e\u003c/span\u003e (unexplained component) measures the variations in household multidimensional energy poverty resulting from the structural differences between the paired groups. Because this section sought to examine how household socioeconomic characteristics contribute to the spatial disparities in multidimensional energy poverty, the explained components of the decompositions are emphasized.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Data","content":"\u003cp\u003eThe study used data from the current series of the Ghana Living Standards Surveys (GLSS 7). The GLSS is a household survey carried out periodically by the Ghana Statistical Service to provide consistent and nationally comparable information for evaluating living conditions and welfare in Ghana. The current series, GLSS 7, was conducted in 2016/2017 and was designed to collect comprehensive household information such as household demographics, employment, health, education, housing and household conditions, and household asset ownership, among others. For every household, the survey collected information on all the variables in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, such as electricity access, type of cooking fuel and cook stove, expenditure on energy consumption, total household expenditure, and ownership of electric appliances. In addition, variables including the marital status, sex, age, and educational status of the household head; welfare quintile, household size, household remittances income received, and location and region of residence were also collected in the survey. The overall sample of 14,009 households covered in the survey was used in this study since no missing information was recorded in the variables. The summary statistics of the demographic and socioeconomic variables used in this study are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The average age in years of heads of households was approximately 46 for coastal and forest, 47 for savannah and rural, and 45 for urban households. The coastal, forest, and urban samples were dominated by married heads with an average household size of 4 while unmarried heads dominated the savannah and rural households with an average household size of 5. In addition, all five geographical divides were dominated by male headed households while only the savannah sample was dominated by uneducated household heads. Also, most households in the coastal, forest, and urban samples were nonpoor while the majority in the savannah and rural spaces were poor.\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\u003eMeasurement and Summary Statistics of Variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCoastal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eForest\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSavannah\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMeasurement\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\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eS. D\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\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\u003eAge of household head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e47.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e44.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e47.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of persons in household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e28\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eEquals 1 if household head is married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eEquals 1 if household resides in a rural area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eEquals 1 if household head is female\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.346\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eEquals 1 if household welfare falls outside the richer and richest category\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemittance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLog of remittance income received by household\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e11.513\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEquals 1 if household head is uneducated\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.290\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEcological zones\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEquals 1 for coastal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEquals 1 for forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEquals 1 for savannah\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eObservations\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNumber of households\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3,484\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e5,575\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e4,950\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e6018\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e7991\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e \u003cp\u003e\u003cb\u003e14,009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003eSource: Authors computations using GLSS 7\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"4. Results and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Multidimensional energy poverty estimates\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e shows the percentage of households deprived in the eleven weighted indicators of multidimensional energy poverty for the three ecological zones and the rural urban divides. The results reveal majority of households in Ghana and in the five geographic categories to be deprived in cooking fuel, education, refrigeration, and food processing. Compared to the countrywide deprivations, the percentages were relatively higher for households in the forest, savannah, and rural parts but relatively lower in the Coastal and urban parts. Adusa-Poku and Takeuchi (2019) and Nsenkyire et al. (2023b) also found high cooking fuel and refrigeration deprivation among households in such areas in Ghana. This suggests that majority of households in Ghana continue to depend on unclean cooking fuels and technologies and do not patronize most energy services. Likewise, utilization of space cooling and washing and ironing services remain minimal among households in the savannah and rural areas.\u003c/p\u003e\n \u003cp\u003eElectricity access, communication, and affordability registered lower percentage deprivations for households in all the five geographic spaces. Nonetheless, compared to the countrywide deprivation percentages, the household deprivation percentages in electricity access and communication were relatively higher in the savannah and rural parts. These results reveal the country\u0026rsquo;s significant progress in extending electricity access and mobile telecommunication services availability across the country, as well as the improvement in the growth of mobile phone subscriptions. However, the relatively higher percentage of deprivation in electricity access in the savannah and rural parts reecho the inequalities in energy access rates within the country.\u003c/p\u003e\n \u003cp\u003eOn the contrary, the household deprivation percentages in affordability were relatively higher in the coastal and urban parts when compared to the rural and savannah areas. However, the lower percentage deprivations in affordability for rural and savannah spaces may not necessarily reflect decent pricing of modern energy but overreliance on free traditional energy sources to meet their energy demand. The spatial distribution of the affordability indicator as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e reveals the regional deprivation in energy affordability to confirm this claim. Thus, the highest incidence of households with affordability deprivations (23%) was found in the Upper West region of the savannah zone with the Western and Ashanti regions of the coastal and forest zones respectively having the next highest incidence (15%).\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the percentage contribution of the indicators to household multidimensional energy poverty for the five geographic locations. The cooking fuel and indoor pollution indicators registered as the ultimate contributors to multidimensional energy poverty in all the five categories. Earlier researchers also found these indicators to contribute the highest to multidimensional energy poverty (Qurat-ul-Ann \u0026amp; Mirza, 2021b; Nsenkyire et al., 2023a). For coastal and urban areas, affordability was the next highest contributor to multidimensional energy poverty. However, in the savannah and rural areas, electricity access was the third highest contributor, which indicates that the accessibility dimension (cooking fuel, indoor pollution, and electricity access) is the leading contributor to household multidimensional energy poverty in these two geographic settings. The contributions of communication and entertainment to household multidimensional energy poverty were the least across all the five geographic spaces. Communication indicator has been found in the literature to contribute the least to energy poverty not only in Ghana (Crentsil et al., 2019) but also in other countries such as Senegal, Togo, and Pakistan (Gafa \u0026amp; Egbendewe, 2021; Qurat-ul-Ann \u0026amp; Mirza, 2021b).\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e shows the incidence and severity of multidimensional energy poverty for Ghana and the five geographic zones. Majority of households in the forest (64%), savannah (87.6%), and rural (82.1%) parts of Ghana were multidimensional energy poor with minority being multidimensional energy poor in the coastal (49%) and urban (48.9%) parts. The average intensity of deprivation among the multidimensional energy poor households was also more severe in the forest (50.4%), savannah (57.6%), and rural (54.5%) areas than in the coastal (48.4%) and urban (47.9%) areas. Disparities in the severity and incidence of household multidimensional energy poverty exists not only in Ghana but also in countries such as the Philippines, Argentina, Brazil, Uruguay, Paraguay, and Pakistan as found by Mendoza et al. (2019), Pereira et al. (2021), and Qurat-ul-Ann and Mirza (2021b). Observations from the MEPI estimates show evidence of the spatial inequalities in multidimensional energy poverty in Ghana between the ecological zones as well as the rural and urban divides.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 Determinates of multidimensional energy poverty\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the logistic regression estimates of the socioeconomic factors that influence household multidimensional energy poverty in the five geographical zones as well as Ghana as a whole. Regarding household heads characteristics, the results revealed the age of household head to increase the odds of multidimensional energy poverty among households in all five cases. The relationships observed suggest that the adoption of modern energy and consumption of its services remains low among households with older heads. The cost and perceived risks associated with modern energy consumption, especially LPG, may be dissuading such households who rely on traditional energy from switching fuels. Also, the odds of multidimensional energy poverty for households with female heads were higher compared to households with male heads in all the geographical zones. Thus, female headed households were more likely to live in multidimensional energy poverty than male headed households. Gender inequalities between males and females in Ghana, for that matter, the three ecological zones, may account for the disparities in the likelihood of being multidimensional energy poor. Thus, economic exclusions and social restrictions on females in some institutions and localities may thwart female headed households\u0026rsquo; capacity to patronize modern energy and its services.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDeterminates of Household Multidimensional Energy Poverty\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEnergy Poverty\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoastal\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eForest\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSavannah\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAge of head\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.015***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.024***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.018***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.023***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.019***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex of head (male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.444***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.119***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.264***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.725***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.144***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.157)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.214)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.473)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.148)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.258)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.129)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducational status of head (educated)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eUneducated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.232***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.176***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.848***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.545***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.216***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.711***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.501)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.322)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.267)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital status of head (unmarried)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.624***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.728***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.638***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.709***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.811*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.068)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.077)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.281)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.064)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.092)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.053)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eHousehold size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.961*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.144***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.044*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.043)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.024)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eRemittance income received\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.945***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.972**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.967**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.009)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoverty status (nonpoor)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.42***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.658***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.93***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.213***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.593***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.753***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1.064)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.738)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.806)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.666)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.821)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.517)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidence (urban)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.499***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.392***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.384***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.027***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.268)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.284)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.335)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.185)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEcological zone (coastal)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eForest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.329***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.746***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.436***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.102)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.194)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.091)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eSavannah\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.367***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.908***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.171***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.388)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.397)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.286)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.324***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.234***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.478***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.253***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.408***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.055)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.035)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.119)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.034)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.073)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePseudo r-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.231\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eProb\u0026thinsp;\u0026gt;\u0026thinsp;chi2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3484\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5575\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4950\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e7991\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e14009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e*** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, *\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1; Standard errors in parenthesis.\u003c/p\u003e\n \u003cp\u003eSource: Authors computations from GLSS 7\u003c/p\u003e\n \u003cp\u003eFurther, educational status of household heads also influenced multidimensional energy poverty positively in all five areas. The odds of being multidimensional energy poor were higher for households with uneducated heads than those with educated heads. Education comes with knowledge acquisition, improved socioeconomic status, and increased prospects for gainful employment, which could influence educated household heads to patronize modern energy and its services. Again, the odds of multidimensional energy poverty were lower for households with married heads compared to households with unmarried heads in all the geographic areas except in the savannah zone. Thus, the likelihood of a household being multidimensional energy poor was greater for households with married household heads in the savannah zone. A plausible explanation for the varying observations could be attributed to the differences in the personal attributes of spouses such as educational and employment status among others which are known determinants of household energy poverty.\u003c/p\u003e\n \u003cp\u003eConcerning household characteristics, household size was observed to decrease the odds of multidimensional energy poverty in the coastal zone but increase the odds in the other four geographic spaces, though statistically insignificant in the forest and urban parts. A plausible explanation for the observation could be that larger households in the coastal parts of Ghana may comprise several economically active members and in effect, a higher income level to afford modern energy, therefore, enhancing the adoption and consumption of modern energy services. Contrarily to the coastal zone, the positive relationship between energy poverty and household size in the rural and savannah areas could be attributed to the low level of economic activities in such areas suggesting that larger households in those parts may not necessarily be comprised of economically active members.\u003c/p\u003e\n \u003cp\u003eAgain, remittance income received by households was found to reduce the odds of multidimensional energy poverty. For households in the forest, urban, and rural areas, receiving remittance income reduced the likelihood of multidimensional energy poverty. However, for the households in the coastal and savannah zones, household remittance income received did not statistically influence the odds of being multidimensional energy poor. The differences in the relationships observed could be indicative of how the use of remittance income received varies among households between the three ecological zones in Ghana. Poverty status was also observed to influence the likelihood of multidimensional energy poverty among households in all five geographical parts. Specifically, the odds of multidimensional energy poverty were higher for poor households than nonpoor households. Modern energy consumption comes at a relatively higher cost compared to traditional energy. As such, the ease with which households consume modern energy depends on the household\u0026rsquo;s ability to pay. High modern energy prices may, therefore, prevent poor households from switching from unclean energy sources compared to nonpoor households.\u003c/p\u003e\n \u003cp\u003eWith regards to environmental attributes, households residing in rural centers in the coastal, forest, and savannah zones were more disposed to being multidimensional energy poor than those in urban centers. Thus, the odds of multidimensional energy poverty were comparatively higher for rural resident households than urban resident households. Also, in the urban and rural areas, the study found the ecological zone of residence to significantly influence multidimensional energy poverty. To be precise, households residing in the forest and savannah zones had increased odds of being multidimensional energy poor compared to households in the coastal zone. These observations may be attributed to the inaccessibility of modern energy and the abundance of biomass in rural centers as well as the inequalities in modern energy access between the coastal, forest, and savannah zones. The findings observed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e are coherent with the findings of previous studies by Abbas et al. (2020), Ashagidigbi et al. (2020), Bekele et al. (2015), Bersisa (2016), Crentsil et al. (2019), Edoumiekumo et al., (2013), Gafa and Egbendewe (2021), Hasanujzaman and Omar (2022), Hosan et al. (2023), Ozughalu and Ogwumike (2018), and Qurat-ul-Ann and Mirza (2021a) who found household, environmental, and household head characteristics to influence the incidence of multidimensional energy poverty.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 Socioeconomic drivers\u003c/h2\u003e\n \u003cp\u003eSocioeconomic drivers of the spatial differences in multidimensional energy poverty between the household population in the savannah, forest, and coastal zones, and between the rural and urban folks are presented in 4. The Blinder-Oaxaca decomposition results revealed significant variances in the average probabilities of household multidimensional energy poverty between paired zones. Specifically, comparing the forest and coastal zones, households in the forest zones had a 0.15 higher probability of living in multidimensional energy poverty than households in the coastal zone. Also, households in the savannah zone had a 0.386 higher probability of living in multidimensional energy poverty than households in the coastal zone. Further, in comparison to households in the forest zones, the probability of being multidimensional energy poor was observed to be 0.236 higher for households in the savannah zone. Rural folks also had a 0.331 higher probability of being multidimensional energy poor than urban folks. The explained component of the decomposition was observed to significantly contribute to the disparities in the probabilities of multidimensional energy poverty. Thus, about 47.3%, 19.4%, 24.6%, and 16.3% of the disparities between the forest/coastal, savannah/coastal, savannah/forest, and rural/urban respectively resulted from variations in household socioeconomic characteristics with the remaining differences resulting from changes in the estimated coefficients (unexplained component).\u003c/p\u003e\n \u003cp\u003eOn the specific socioeconomic drivers, between the forest and coastal zones, the results revealed rural-urban residency and poverty status of households to contribute about 64.8% and 38% respectively to the higher probability of households in the forest zone being multidimensional energy poor. Similarly, differences in urban-rural residency and poverty status, as well as educational status of heads also contributed to the higher incidence of multidimensional energy poverty in the savannah zone by 24%, 54.7%, and 29.3% respectively when compared to the coastal zone, and by 17.5%, 56.1%, and 28.1% when compared to the forest zone. The ecological zone of residence, poverty status, and educational status of heads were also the chief socioeconomic drivers of the disparities in multidimensional energy poverty between rural and urban dwellers (20.4%, 52.9%, \u0026amp; 18.5%).\u003c/p\u003e\n \u003cp\u003eThese findings reveal the significant role household socioeconomic characteristics play in driving the spatial differences in modern energy consumption between different parts of the country. More importantly, the results reflect and confirm the gap in welfare, educational attainments, and rural-urban and ecological zones developments in Ghana and their implication on household modern energy consumption. For instance, Cooke et al. (2016) revealed poverty rates among households in the savannah zone to be the highest in Ghana, with rural-urban household inequality having the highest share of the country\u0026rsquo;s total inequality.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSocioeconomic Drivers of Multidimensional Energy Poverty Inequality\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eForest(F)/Coastal(G)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSavannah(F)/Coastal(G)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSavannah(F)/Forest(G)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRural(F)/Urban(G)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup \u0026ndash; F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.640***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.876***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.876***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.821***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGroup \u0026ndash; G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.490***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.490***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.640***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.489***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDifference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.150***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.386***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.236***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.331***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.013)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.012)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[100]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[100]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[100]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[100]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExplained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.071***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.075***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.057***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.007)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.011)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.008)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[47.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[19.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[24.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[16.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnexplained\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.079***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.311***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.178***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.277***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.015)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.016)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.014)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[52.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[80.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[75.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[83.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eExplained\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eAge of head\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.004**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-5.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[3.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[3.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eHousehold size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.00006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eRemittance income received\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0003*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[0.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eSex of head\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.005***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.000756)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0004)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-0.6]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-8.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-3.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eEducational status of head\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.016***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[2.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[29.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[28.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[18.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eMarital status of head\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.003***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.001***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0009)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.001)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.0003)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[1.3]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-4]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-3.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[-1.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eResidence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.018***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[64.8]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[17.5]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003eEcological zones\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[20.4]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" align=\"left\"\u003e\n \u003cp\u003ePoverty status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.027***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.032***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[38]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[54.7]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[56.1]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[52.9]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e9059\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8434\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e10525\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e14009\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e*** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.1; Percentage contributions in brackets; Robust standard errors in parenthesis.\u003c/p\u003e\n \u003cp\u003eSource: Authors\u0026rsquo; computations from GLSS 7\u003c/p\u003e\n\u003c/div\u003e"},{"header":"5. Conclusions and Recommendations","content":"\u003cp\u003eWithin-country inequality in modern energy access and consumption motivated this study to examine the socioeconomic drivers of spatial differences in multidimensional energy poverty between the three major ecological zones - savannah, forest, and coastal, and rural-urban geographical divides in Ghana. The multidimensional energy poverty measure, the logit regression model, and the Oaxaca-Blinder decomposition for binary dependent models were used to analyzed data from the current series of the Ghana Living Standard Survey (GLSS 7). The study contributes to literature as the first to examine the socioeconomic drivers of within-country spatial differences in multidimensional energy poverty.\u003c/p\u003e \u003cp\u003eA higher deprivation percentages and intensity and incidence of multidimensional energy poverty was found in the savannah and rural areas than in the forest, coastal and urban parts. These findings showcase the disparities in modern energy accessibility, utilization, and affordability between the geographical spaces considered in this study. Household demographic and socioeconomic characteristics including age, sex, marital status, and educational level of household head, poverty status, household size, remittance income, and residence were found to influence multidimensional energy poverty in the distinct geographical spaces. Disparities in the probability of multidimensional energy poverty were observed between the geographical divides with socioeconomic characteristics such as education, residence, and poverty being the chief contributors.\u003c/p\u003e \u003cp\u003eThe observed disparities in the prevalence of household multidimensional energy poverty between the five geographic divides reveal the inequalities in modern energy accessibility and consumption of its services. Modern energy is a requisite for equitable living and its inaccessibility therefore restrict some household activities that foster good living standards. The spatial disparities in multidimensional energy poverty between the divides, among other things, if not tackled may slow down the pace with which inequality within the country will be alleviated to achieve SDG 10 by 2030 as well as SDGs 3 and 7. To that effect, addressing the accessibility, utilization, and affordability issues by extending electrification, especially in the savannah and rural zones, and making modern cooking fuels readily available and affordable throughout the country is key. Nonetheless, that must not be done in isolation. The country must, in addition, put in place poverty-alleviating programs and policies with special attention given to households in the savannah and rural areas to tackle the socioeconomic drivers of the inequality. Also, campaigns on formal education must be intensified and coupled with adult education programs, particularly for the rural and savannah zones. Further, the country must commit resources to savannah and rural developmental projects throughout the country, to bridge the structural differences between the five major geographical spaces.\u003c/p\u003e \u003cp\u003eDespite within-country spatial differences in energy poverty being documented by numerous studies across different continents with country-specific socioeconomic characteristics, this study only focused on Ghana, a West African country. Hence, the findings of this study may only apply to tackling within-country spatial energy poverty differences in countries in West Africa and sub-Saharan Africa. Also, the differences in the energy poverty measures between countries in the global south and north as well as developed and developing ones may limit the applicability of the study\u0026rsquo;s findings to tackling within-country spatial differences in energy poverty for the developed and global north countries. Future studies should, therefore, endeavor to examine the country-specific socioeconomic drivers of the spatial disparities in energy poverty for developed and global north countries to help progress towards achieving SDGs 10 and its related SDGs.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eDeclaration Statement\u003c/strong\u003e\u003c/p\u003e \u003cp\u003eThe authors have no known financial or nonfinancial interest to declare.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbbas, K., Li, S., Xu, D., Baz, K., \u0026amp; Rakhmetova, A. (2020). Do socioeconomic factors determine household multidimensional energy poverty? 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Changes in Perceived Accessibility to Healthcare from the Elderly between 2005 and 2014 in China: An Oaxaca\u0026ndash;Blinder Decomposition Analysis. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e, 16(20), 3824. https://doi.org/10.3390/ijerph16203824\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Spatial analysis, Socioeconomic drivers, Energy affordability, Oaxaca-Blinder decomposition, Multidimensional energy poverty, Geographical zones.","lastPublishedDoi":"10.21203/rs.3.rs-5571019/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5571019/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWithin-country spatial inequalities in accessibility and usage of modern energy and its services have been recognized by several studies globally. Despite this, studies that commit to analyzing and identifying ways to bridge these spatial disparities are scanty. Being a sub-Saharan African country with hyped improvement in energy access, other dimensions of household energy use deteriorate in Ghana, coupled with spatial inequalities within the country. This study, therefore, examined the socioeconomic drivers of the spatial disparities in household energy accessibility, utilization, and affordability between the three ecological zones of Ghana, as well as the rural and urban divide. Cross-sectional data from the latest Ghana Living Standard Survey (GLSS 7) was analyzed using the multidimensional energy poverty measure, the logit regression model, and the Oaxaca-Blinder decomposition for binary dependent models. The study found spatial differences in multidimensional energy poverty between the two geographical divides to be driven by socioeconomic characteristics such as education, location of residence, and income poverty. The study recommends that the socioeconomic characteristics of households be improved through programs and policies to alleviate the spatial inequalities in modern energy use within countries.\u003c/p\u003e","manuscriptTitle":"Tackling within-country spatial inequalities in household energy use towards sustainable development: The case of Ghana.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-11 14:22:36","doi":"10.21203/rs.3.rs-5571019/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"aa718d1c-1b3e-40b7-925f-ea494d94783a","owner":[],"postedDate":"December 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":41078761,"name":"Microeconomics"}],"tags":[],"updatedAt":"2024-12-11T14:22:37+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-11 14:22:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5571019","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5571019","identity":"rs-5571019","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}