  * December 2023

# Social and Economic Impact Analysis of Solar Mini-Grids in Rural Africa: A
Cohort Study from Kenya and Nigeria

A.T. Carabajal1, A. Orsot2, M.P.E. Moudio3, T. Haggai4, C.J. Okonkwo5, G.T.
Jarrard III1, and N.S. Selby2 1Renewvia Energy Corporation, 2951 Flowers Rd.
S., Suite 217, Atlanta, GA 30341  2Renewvia Solar Ethiopia PLC, Addis Ababa,
Ethiopia, Bole sub-city, Woreda 03, House No. 174/175, Saya Building, 5th
Floor  3Department of Industrial Engineering and Operations Research, UC
Berkeley, 2521 Hearst Ave, Berkeley, CA, USA, 94709  4Department of
Anthropology, Gender and African Studies, University of Nairobi, PR98+JC9
University Way, Nairobi  5Planetary Health Management Mission Pathway,
Entrepreneurial Leadership Program, Africa Leadership University Rwanda
Campus, Bumbogo, Kigali, Innovation City, Kigali, Rwanda
<EMAIL_ADDRESS>
###### Abstract

This study presents the first comprehensive analysis of the social and
economic effects of solar mini-grids in rural African settings, specifically
in Kenya and Nigeria. A group of 2,658 household heads and business owners
connected to mini-grids over the last five years were interviewed both before
and one year after their connection. These interviews focused on changes in
gender equality, productivity, health, safety, and economic activity. The
results show notable improvements in all areas. Economic activities and
productivity increased significantly among the connected households and
businesses. The median income of rural Kenyan community members quadrupled.
Gender equality also improved, with women gaining more opportunities in
decision making and business. Health and safety enhancements were linked to
reduced use of hazardous energy sources like kerosene lamps. The introduction
of solar mini-grids not only transformed the energy landscape but also led to
broad socioeconomic benefits in these rural areas. The research highlights the
substantial impact of decentralized renewable energy on the social and
economic development of rural African communities. Its findings are crucial
for policymakers, development agencies, and stakeholders focused on promoting
sustainable energy and development in Africa.

††: Environmental Research: Infrastructure and Sustainability

Keywords: mini-grids, sub-Saharan Africa, rural electrification, gender
equality, health and safety, productivity, economic activity

##  1 Introduction

Figure 1: A solar mini-grid in Bayelsa, Nigeria

Sub-Saharan Africa (SSA) has vast renewable energy resources, including solar,
wind, hydropower, and geothermal [1]. However, these resources remain largely
untapped, and the region is still heavily reliant on fossil fuels due to
factors including a lack of infrastructure, financing, and technical expertise
[2]. Decentralized renewable energy (DRE) solutions can offer a promising
alternative to grid-based electricity in SSA [3, 4]. DRE systems like the one
illustrated in Fig. 1 are typically smaller in scale and can be installed in
rural areas where grid extension is not feasible or cost-effective. DRE
systems can also be operated by local communities, which can help to empower
these communities and promote economic development [5, 6]. Several studies
have investigated the potential of renewable mini-grids (RMGs) as
decentralized renewable energy solutions in SSA [7, 8, 9]. These studies have
found that RMGs can have a significant positive impact on access to
electricity, livelihoods, and economic development [10]. RMGs are small power
grids that typically serve a few hundred to a few thousand households and
businesses. They are often powered by renewable energy sources, such as solar
or wind, and can provide a reliable and affordable source of electricity to
communities that are not connected to the national grid.

Rural electrification is the process of providing fast, reliable, and
affordable access to electricity to residents in rural areas who currently
lack it. Access to electricity is seen as key to reducing poverty and is a
critical component of economic development and poverty alleviation due to its
ability to improve access to and quality of essential services such as
education, healthcare, and clean water [11, 12]. It can also create new
employment opportunities and boost agricultural productivity. SSA has the
lowest electrification rate in the world and the majority of those without
access live in rural areas due to lack of financial ability to extend the
grid, population density, and other social and cultural factors [13]. Data
from a 2019 World Bank report shows that 14 of the 20 countries in the world
with the highest deficit in electricity connections are African countries
[14]. Only $45\%$ of the population in SSA currently have access to
electricity. The electrification coverage is expected to rise to only about
$60\%$ of the SSA population by 2030 given current conditions [15]. Despite
representing approximately $14\%$ of the world’s population, SSA accounts for
only about $4\%$ of global energy consumption [16].

Rural electrification can significantly benefit communities [17]. According to
[16], the introduction of electrification via RMG has had significant positive
impacts on socioeconomic factors such as health care and economic development.
RMG systems promote faster and more flexible energy supply, especially when
integrated with additional components such as electricity storage in a hybrid
system [18]. The slow extension of conventional grid systems has led to
increased interest in RMGs, a decentralized alternative not connected to the
public grid and generating electricity based on various technologies [19]. The
main obstacle that prevented private households from using electricity was the
high price. However, according to [15], the incremental cost of providing
electricity access to households is below the cost of alternatives such as
kerosene lighting.

Various socioeconomic factors affect rural electrification in SSA. According
to [20], income levels are a key driver of rural electrification. As incomes
rise, households are more likely to be able to afford more electricity and
appliances. The rising demand in RMGs due to growing populations, industrial
growth, and government policies can play a significant role in promoting rural
electrification [21]. Technological advancements also play an important role
in rendering the electrification process more feasible and affordable to
provide electricity to rural areas. Moreover, digital and information
technologies have several positive effects on the development and regulation
of efficient energy consumption [22, 23].

While existing research has acknowledged the significance of social impacts
stemming from RMG implementation in SSA, there remains a notable absence in
empirical measurements [24]. The prevailing studies predominantly focus on
technical and economic aspects, neglecting the quantification of social
ramifications [25, 26, 27, 28]. A recent study in the Gbamu Gbamu village in
Nigeria uses a combination of statistical tests to measure the financial
impact of RMGs on local businesses using predominantly economic factors
including gender, marital status, household size, age, education level, years
of business establishment, hours of operation, building tenure, capital
source, number of employees, generator ownership, and the days of operation
[29].

A few studies have attempted to formally quantify social impacts of RMGs on
communities. In [30], researchers apply a mediation model to illustrate how
community engagement results in a $1\%$ increase in the perceived renewable
energy potential, leading to a $0.195\%$ increase in perceived poverty
reduction. These results suggest that community empowerment is indispensable
in creating electricity demand and delivering development impact of renewable
RMGs in the context of deep poverty. In another study carried out in Kyenjojo
District in western Uganda, the authors incorporated social factors in a study
of RMG operation, causes of failure, sources of discomfort to customers, and
customer behavior [31]. Moreover, researchers in [32] conducted a systematic
review of diverse RMG projects aimed at extracting qualitative insights into
the factors driving project success and community benefits. Subsequently,
these findings were empirically validated, enabling the identification of key
factors contributing to the success and cost-effectiveness of RMG projects
[33]. These studies have highlighted the necessity for broader assessments
that go beyond solely examining economic impacts and RMG operational
challenges in rural areas relying on RMGs. We aim to broaden this perspective
by considering both economic and social factors like gender equality and
community residents’ safety. This shift recognizes the interconnection between
socioeconomic elements, addressing a significant gap in understanding the
overall transformational impact of RMG initiatives within rural settings.

Our study aims to bridge the gap in empirical measurement of social impact of
RMGs on rural communities by introducing a comprehensive framework that
delineates five crucial axes encompassing gender equality, productivity,
health, safety, and economic activity. By leveraging a survey designed
specifically around these axes, we endeavor to quantify the multifaceted
impacts of solar RMGs on various social and economic factors within rural
African communities. Unlike previous single-region analyses, our research
embraces a comparative approach, drawing insights from two distinct countries,
Kenya and Nigeria. This deliberate expansion of our dataset promises a richer
understanding of the nuanced effects of RMG interventions across diverse
sociocultural landscapes. The rest of the paper is organized as follows. In
Section 2, we discuss the model, data, and estimation techniques. The results
and findings are presented in Section 3. Further discussion and methodology
limitations are shown in Section 4. We conclude in Section 5.

##  2 Methodology

###  2.1 Project Description and Data Collection

Between 2021 and 2023, we installed solar mini-grids in and conducted a study
across 22 communities in Nigeria and Kenya to assess if there were any
significant changes regarding the quality of life of the populations connected
to their mini-grids. To collect the necessary information, we used surveys
composed of semi-structured questionnaires that captured quantitative and
qualitative data such as demographics, access to education for children,
access to clean drinking water, creation of jobs and businesses, and economic
opportunities for women, as well as many others. Primary data was collected
from a variety of respondents categorized either as households or commercial
and institutional organizations (e.g. businesses, schools, clinics, etc.)
through face-to-face interviews by independent, third-party field enumerators.

This study examined the impact of the mini-grids on key performance indicators
(KPIs) by comparing responses before and one year after installing a solar
mini-grid in the respective communities. Due to the lack of data from
communities without a solar mini-grid, only pre-and post-treatment data have
been used in this study. The pre-treatment survey was conducted between 2021
and 2022, while the post-treatment survey was conducted between 2022 and 2023.

###  2.2 Survey Design

This study aimed to investigate the impact of installing mini-grids in rural
communities in Sub-Saharan Africa, focusing on five KPIs: gender equality,
productivity, safety, health, and economic growth.

A cohort study design was employed, enabling an analysis of the mini-grid’s
effects on individuals over time. This approach was particularly chosen for
its ability to observe changes between pre- and post-mini-grid installation.
The survey targeted both commercial and residential mini-grid users in
communities shortly before connection and one year after connection to new
mini-grids.

The survey was developed based on literature review and global KPIs relevant
to mini-grid stakeholders. No pilot testing was conducted. A diverse set of
question types was utilized, including demographic queries, Likert-scale
items, interval and quantity-specific questions, and open-ended questions.
This mix aimed to capture both quantitative and qualitative aspects of the
mini-grid’s impact. To ensure reliability and validity, the survey employed
clear, concise, and neutrally-worded questions. Professional survey
administrators were engaged to maintain consistency and integrity in data
collection.

Surveys were carried out in person by independent, third-party surveyors who
used electronic forms to minimize transcription bias. Participants were
briefed and consent obtained through signed forms, ensuring ethical
compliance. No incentives were offered.

Data analysis involved various statistical tests, including paired $t$-tests,
Wilcoxon signed-rank tests, and linear regression, among others, using R
software. This comprehensive approach aimed to rigorously assess the mini-
grid’s impact across multiple dimensions.

###  2.3 Analysis Techniques

####  2.3.1 Paired Samples t-Test

The paired samples $t$-test is a parametric statistical method to determine
whether the mean difference of paired measurements is 0 or not. It follows the
assumptions that the observations are independent, the paired differences are
approximately normally distributed, and there are no extreme outliers in the
differences. The paired samples $t$-test includes the following null and
alternative hypotheses:

$\begin{array}[]{c}H_{0}:\mu_{d}=0\\\ H_{1}:\mu_{d}\neq 0\end{array}$ (1)

where $\mu_{d}$ is the mean of differences from the pairs, $H_{0}$ the null
hypothesis stating the mean paired difference is equal to 0, and $H_{1}$ the
alternative hypothesis stating that the mean paired difference does not equal
0.

The paired samples $t$-test utilizes the $t$ statistic

$t=\frac{\bar{d}\sqrt{n}}{\sigma_{d}}$ (2)

where $\bar{d}$ is the mean value of the differences between paired samples,
$\sigma_{d}$ the standard deviation the differences between paired samples,
and $n$ is the sample size.

####  2.3.2 Wilcoxon Signed-Rank Test

The Wilcoxon signed-rank test is a statistical method to compare two dependent
samples from matched or paired data. While it does assume the distribution of
the differences is symmetric, it does not assume any specific distribution of
the samples themselves and serves as a non-parametric equivalent to the paired
samples $t$-test, particularly applicable to categorical variables with
meaningful differences between ranks (i.e. ordinal data). The test is
evaluated taking into account both the sign and magnitudes of observed
differences. The Wilcoxon signed-rank test is implemented using the null and
alternative hypotheses:

$\begin{array}[]{c}H_{0}:\mathrm{M}=0\\\ H_{1}:\mathrm{M}\neq 0\end{array}$
(3)

where M is the median of the paired differences, $H_{0}$ the null hypothesis
stating no difference between paired observations, and $H_{1}$ the alternative
hypothesis stating a significant difference between paired observations.

The Wilcoxon signed-rank test utilizes the W statistic

$W=\mathrm{min}(T_{-},T_{+})$ (4)

where $T_{-}$ is the sum of the negative differences and $T_{+}$ is the sum of
the positive differences.

####  2.3.3 Sign Test

The Sign test is a non-parametric statistical method designed to determine if
two dependent samples, ordered in pairs, are of equal magnitudes. Unlike the
Wilcoxon signed-rank test, it does not assume symmetry and considers only the
direction of change, making it suitable for categorical variables where
arithmetic differences are not meaningful. It is often viewed as a less
powerful test since it does not measure the magnitude of differences between
pairs, but it is still useful for assessing the significance of observed
changes. Although the Wilcoxon signed-rank test and the Sign test operate
similarly, the choice between them depends on the data characteristics. The
Wilcoxon signed-rank test is preferable for differences that are approximately
normally distributed and have meaningful magnitudes, while the Sign test is
more appropriate in other cases. The sign test is implemented using the null
and alternative hypotheses:

$\begin{array}[]{rl}H_{0}:&\mathrm{The\ signs\ of\ +\ and\ -\ of\ differences\
are\ of\ equal\ size}\\\ H_{1}:&\mathrm{The\ signs\ of\ +\ and\ -\ of\
differences\ are\ not\ of\ equal\ size}\end{array}$ (5)

The sign test utilizes the test statistic

$Z=\frac{(2S-n)\sqrt{n}}{n}$ (6)

where $n$ is the total number of signs, ignoring 0s, and $S$ the number of
less frequent signs.

####  2.3.4 McNemar’s Test

McNemar’s test is a non-parametric test used to analyze paired nominal data.
It is a test on a $2\times 2$ contingency table that checks the marginal
homogeneity of two dichotomous variables. The test requires one nominal
variable with two categories and one independent variable with two dependent
groups.

| Post: Yes | Post: No | Total
---|---|---|---
Pre: Yes | a | b | a+b
Pre: No | c | d | c+d
Total | a+c | b+d | n
Table 1: McNemar Contingency Table

We use Table 1 to calculate the $\chi^{2}$ goodness-of-fit statistic with the
following null and alternative hypotheses:

$\begin{array}[]{c}H_{0}:P_{b}=P_{c}\\\ H_{1}:P_{b}\neq P_{c}\end{array}$ (7)

where $H_{0}$ is the null hypothesis stating that the two marginal
probabilities, $P_{b}$ and $P_{c}$, for each outcome are the same, and $H_{1}$
is the alternative stating otherwise.

McNemar’s test utilizes the $\chi^{2}$ statistic:

$\chi^{2}=\frac{(b-c)^{2}}{(b+c)}$ (8)

####  2.3.5 Pearson Correlation Coefficient

The Pearson correlation coefficient is a measure representing the strength of
association between two variables and the direction of the relationship. It
produces a value between -1 and +1, serving as a descriptive statistic. A
value nearing +1 suggests that a change in one variable will lead to a similar
directional change in the other, whereas a value approaching -1 indicates that
altering one variable results in a change in the opposite direction for the
other. We obtain the Pearson correlation coefficient $r$ using the formula:

$r=\frac{\sum((x_{i}-\bar{x})(y_{i}-\bar{y}))}{\sqrt{\sum(x_{i}-\bar{x})^{2}\sum(y_{i}-\bar{y})^{2}}}$
(9)

where $x_{i}$ is the value of the $i^{\mathrm{th}}$ predictor in a sample,
$\bar{x}$ is the mean of the values of the predictor variable, $y_{i}$ is the
value of the $i^{\mathrm{th}}$ response in a sample, and $\bar{y}$ the mean of
the values of the response variable.

####  2.3.6 Linear Regression t-Test

The simple linear regression is a statistical method to evaluate the
relationship between a predictor variable and a response variable by finding
the equation of a “best-fit” line that minimizes the sum of squared residuals
between it and the data. It estimates the nature of the relationship, either
positive or negative, and the expected change in the response based on a
change in the predictor. A one-sample $t$-test is applied to the slope to
determine if said relationship is statistically significant given the
following null and alternative hypotheses:

$\begin{array}[]{c}H_{0}:\beta_{1}=0\\\ H_{1}:\beta_{1}\neq 0\end{array}$ (10)

where $H_{0}$ is the null hypothesis stating that there is no relationship
between outcome and predictor, $H_{1}$ is the alternative stating otherwise,
and $\beta_{1}$ is the slope of the “best-fit” line given by

$\hat{y}=\beta_{0}+\beta_{1}x$ (11)

where $\hat{y}$ is the expected value of response; $\beta_{0}$ is the
intercept, i.e the expected value of response when the predictor is 0;
$\beta_{1}$ is the slope coefficient, i.e the average change in the response
given a unit increase in the predictor; and $x$ is the value of predictor.

The linear regression $t$-test utilizes the $t$-statistic

$t=\frac{r\sqrt{n-2}}{\sqrt{1-r^{2}}}$ (12)

where $r$ is the Pearson correlation coefficient and $n$ is the number of data
points $(x,y)$. Note that this $t$-statistic has a $t$-distribution with $n-2$
degrees of freedom if the null hypothesis is true.

In this study, regression analysis was implemented at two levels:

  * •

At the individual customer level, investigating the relationship between
average monthly electricity consumption for a specific user, the predictor,
and various survey question responses; and

  * •

At the community level, investigating the relationship between predictor
variables of mini-grid PV system capacity, total number of customers in the
community, and total mini-grid capital expenditure (CAPEX), and various
aggregated survey question responses. For the purposes of this paper, “mini-
grid PV system capacity,” or simply “mini-grid capacity” or “PV Size”
represents the maximum amount of power that the solar panels can produce under
ideal conditions (STC), usually given in units of kilowatt-peak (kWp). For
example, a 10-kWp solar mini-grid would be expected to produce up to 10
kilowatts of power during peak sunlight conditions.

For ordinal variables with three levels such as -1, 0, and 1, additional
transformations were carried out to determine a pertinent response variable.
First, the proportion for the level of interest (e.g., “1” to denote an
increase in schooling for girls) was obtained for each site and then
multiplied by the total number of customers for the relevant customer type
(e.g., households, schools, businesses, etc.) depending on the response
variable.

####  2.3.7 Likelihood-Ratio Test

The likelihood-ratio test is a statistical method used to assess the
significance of a predictor variable in the context of logistic regression,
which models the relationship between a binary response variable and a ratio
predictor variable. Logistic regression predicts the log odds of the
occurrence of an event by fitting data to a logistic curve. This study
considers only the case of simple binary logistic regressions in which the
data are fitted in a probabilistic sense to a function of the form:

$p(x)=\frac{1}{1+\mathrm{exp}(-t)}$ (13)

The likelihood-ratio test compares the goodness-of-fit of two models: one
“full model” that includes the predictor variable (i.e.
$t=\beta_{0}+\beta_{1}x$) and one “reduced model” that does not (i.e.
$t=\beta_{0}$). The test evaluates whether the inclusion of the predictor
significantly improves the model. The null hypothesis for this test is that
the predictor variable has no effect, and the reduced model is sufficient. The
alternative hypothesis for this test is that the predictor variable has a
significant effect, and the full model is more appropriate.

In logistic regression, the likelihood of observing the given data is
maximized, and the test statistic is calculated as:

$D=-2\ln\left(\frac{\text{Likelihood of reduced model given the
data}}{\text{Likelihood of full model given the data}}\right)$ (14)

This test statistic follows approximately a $\chi^{2}$ distribution with
degrees of freedom equal to the difference in the number of parameters between
the full and reduced models. The decision about the significance of the
predictor variable is made based on the $p$-value obtained from this
$\chi^{2}$ distribution.

In this study, we utilize the likelihood-ratio test to determine whether or
not the inclusion of the average monthly electricity consumed by a customer
significantly improves a logistic regression model’s ability to predict binary
or dichotomous survey response variables.

##  3 Results

In this section, we highlight the most notable descriptive statistics and
statistically significant findings. A detailed summary of the outcomes for
each quantitative, comparative survey question is provided in the Appendix.

In instances where survey questions covered multiple key performance
indicators (KPIs), the findings are presented across multiple subsections.
This approach ensures that each KPI is thoroughly addressed and the results
are clearly communicated in their respective areas of relevance.

For the paired testing, which compared pre-connection data to post-connection
data in that order, a negative result indicates a decrease over time while a
positive result signifies an increase.

In this section, where applicable, 95% confidence intervals for a result are
denoted using the ”$\pm$” symbol (e.g., $10\pm 1$).

###  3.1 Survey Distribution

Figure 2: Distribution of Survey Questions

There are a total of 86 questions for the pre-connection household survey and
93 and 37 for the post-connection household and organizational surveys,
respectively. The pre- and post-connection household surveys shared 64
questions, thus enabling direct comparison of individual customer responses
before and after connection. The questions were distributed among the five
KPIs according to Fig. 2. Correlation tables detailing the responses to the
survey questions are presented in Figs. 14, LABEL:, 15, LABEL:, and 16 in the
appendix.

###  3.2 Characteristics of the Respondents

Out of the 3,952 total responses from the initial survey, 564 distinct
households (14.3%) were retained for analysis. To isolate the sample from pre-
connection households, the remainder of observations at post-connection and
duplicate responses were excluded. At post-connection, 2,202 responses were
retained from the 2,603 (84.6%) total responses collected after a similar data
validation process. From the pre and post samples, 468 respondents could be
paired. The high turnover rate is mainly due to the nomadic or otherwise
transitory nature of the communities surveyed.

Variables |  | Statistic | Total
---|---|---|---
|  |  | Pre-connection | Post-connection
Sample Households |  | Count | 564 | 2202
Gender | Male | Count | 400 | 1518
| Female | Count | 164 | 678
| Unidentified | Count | - | 6
Age |  | Median | 41 | 38
Household Size |  | Median | 6 | -
Employment | Seasonal | Count | 360 | -
| Regular | Count | 139 | -
| Unemployed | Count | 60 | -
| Unidentified | Count | 5 | -
Table 2: Descriptive Statistics of Households

A demographic overview of the household respondents is presented in Table 2.
Heads of household were responsible for responding to the survey, and the
distribution based on gender shows a predominance of male respondents, with
men making up 71% of the pre-connection sample and 69% of post-connection
sample. The median age of the respondents was 41 and 38 years old for the pre-
and post-survey, respectively. Before connection to the mini-grid, the median
household had six members, and the breakdown of respondents’ employment types
was as follows: 64% were seasonally employed, 25% had regular employment, and
11% were unemployed.

Variables |  | Statistic | Post-Connection
---|---|---|---
Sample Organizations |  | Count | 465
Status | In Operation | Count | 449
| Closed | Count | 7
| Unidentified | Count | 9
Organization Type | Business | Count | 340
| School | Count | 36
| Clinic | Count | 22
| Religious and Institution | Count | 67
Table 3: Descriptive Statistics of Commercial and Institutional Customers

Out of the 470 total commercial and institutional responses from the initial
survey, 465 distinct entities (99%) were retained for further analysis. The
remainder of duplicates and unidentifiable respondents were excluded. An
overview of the socio-economic status of commercial and institutional
customers is presented in Table 3. The distribution based on organization type
was broken down with 340 (73%) businesses, 36 (8%) schools, 22 (5%) clinics,
and 67 (14%) religious institutions.

###  3.3 Gender Equality

(a) An additional $50,000 CAPEX investment correlated with an additional 20
households reporting an increase in the number of girls attending school.

(b) An additional $50,000 CAPEX investment correlated with an additional 21
households reporting an increase in the number of boys attending school.

Figure 3: Investing more money to build a larger mini-grid leads to more girls
and boys attending school.

The full results of the statistical analysis for questions pertaining to
gender equality are available in LABEL:tab:app:gender in the appendix.

In the pre-survey, respondents reported that 793 out of 1,190 boys were
attending school while 719 out of 1,080 girls were attending school, thus
placing the school’s enrollment rate at 67%. After the mini-grid, 16% and 18%
of respondents noted a positive change in the schooling for girls and boys,
respectively. Initially, 144 respondents reported not being able to enroll
girls into school and 141 reported similarly for the boys. Investigating the
remaining barriers to parents who still did not enroll their children in
school even after connection to the mini-grid, 37% of respondents reported
that the reason for keeping their children out of school was insufficient
funds to support tuition and other school fees.

Community-level regression tests on gender equality showed statistical
significance. For example, as illustrated in Fig. 3, an increase of $50,000 in
total capital expenditure (CAPEX) on the mini-grid correlated with an increase
of $20\pm 12$ households reporting an increase in the number of girls
attending school and $21\pm 12$ households reporting an increase in the number
of boys attending school.

(a) Residents of newly electrified communities observed statistically
significant declines in water and cooking fuel collection times.

(b) Increased CAPEX investment in mini-grids is correlated with ever-greater
reductions in time spent collecting water.

Figure 4: The presence of a mini-grid reduces the time residents have to spend
on household chores.

One reason behind the increase in school enrollment after connection to the
mini-grid is that, in the pre-survey, 53.4% of households reported delegating
the responsibility of water collection to school-aged children. After
connection to the mini-grid, only 15% reported a similar answer in the post-
survey. In the case of paired respondents, a comparable trend was noted,
evidenced by a 29 percentage-point decrease in the proportion of households
delegating water-fetching responsibilities to school-aged children. Overall,
there was a $29\%\pm 8\%$ decrease in the likelihood of school-aged children
being tasked with this chore following the mini-grid installation.

As illustrated in Fig. 4, there was a notable reduction of $15\pm 8$ hours
spent collecting water and $36\pm 10$ hours spent collecting cooking fuel per
100 households. An increase of 10 kWp in mini-grid PV capacity would lead to
$25\pm 22$ additional households reporting a decrease in water collection
time, and an extra $50,000 in CAPEX investment would result in an average of
$15\pm 15$ more households noting a similar improvement.

The proportion of households where men were involved in household chores, like
collecting cooking fuel, rose from 10% to 14% between the pre-connection and
post-connection surveys. However, this increase was less significant in the
context of paired households, where the proportion only grew by 0.4 percentage
points.

Figure 5: For every ten additional customers connected to a mini-grid,
approximately one more business would report hiring at least one more female
employee.

The analysis of economic opportunities for women involved two key questions
directed at households and organizations focusing on business creation and job
availability. Initially, 28% of households reported having a woman-owned
business, but this figure declined to 19% post-connection. Among paired
respondents, this represented a decrease of 4 percentage points. On the other
hand, 17% of the organizations surveyed reported employing at least one new
female worker after the introduction of the mini-grid, marking a significant
change in this aspect. As illustrated in Fig. 5, it was observed that
connecting an additional 100 customers to the mini-grid would lead to $7\pm 5$
more organizations employing at least one female worker.

###  3.4 Productivity

Figure 6: The installation of a new mini-grid had profound effects on
community members’ productivity, increasing the number of light hours and
quantity of electrical appliances in the community while decreasing the
distance required for members to charge their phones and collect water.

The full results of the statistical analysis for questions pertaining to
productivity are available in LABEL:tab:app:productivity in the appendix.

As illustrated in Fig. 6, the installation of the mini-grid also had a notable
impact on daily activities, with 58% of respondents able to charge their
phones every day and 94% doing so at home, reducing the need to travel to
neighbors, shops, or other locations for charging by a reported 66%.

Regarding water and cooking fuel collection time, there was a marked
improvement in efficiency post-connection to the mini-grid. In the post-
survey, 66% of respondents reported that it took them less than one hour to
collect water, a significant increase from the 57% who said the same in the
initial survey. This increase was also reflected in the paired sample, where
an 18 percentage-point change in proportions was observed. Similarly, the time
spent collecting cooking fuel decreased notably. In the post-survey, 63% of
respondents indicated that this task took less than one hour, a substantial
improvement from the initial survey, where only 35% reported such efficiency.

(a) Almost half of all additional community members connected to a mini-grid
are statistically likely to reduce or eliminate their usage of unclean power
sources such as diesel, petrol, and kerosene.

(b) Almost nine out of every ten additional community members connected to a
mini-grid are statistically likely to buy new appliances to take full
advantage of their new source of electricity.

Figure 7: Upon connecting to a mini-grid, community members quickly transition
from using unclean power sources like diesel, petrol, and kerosene to
utilizing this cleaner, cheaper energy option for powering their electrical
appliances.

Prior to connecting to the mini-grid, a majority of the households had limited
or no reliable power sources. Specifically, 55% of households depended on
petrol generators. However, one year after the mini-grid connection, this
number dramatically decreased to just 1%, with the mini-grid becoming the
primary power source for 91% of survey respondents. The shift was also evident
in the broader reduction of unclean energy sources, including diesel, petrol,
and kerosene, which decreased from 66% in the pre-survey to 19% in the post-
survey. Among paired households, this represented a 7 percentage-point
reduction. As illustrated in Fig. 7(a), every 100 additional customers
connected onto the mini-grid would result in $47\pm 45$ fewer households using
unclean power sources.

Post-connection, households reported significant improvements in their energy
access: an average of seven hours of lighting per day and the use of an
average of four electronic devices, compared to only four hours of lighting
and two appliances before connection. Moreover, 55% of households acquired new
appliances since connecting to the mini-grid. As illustrated in Fig. 7, for
every 100 additional customers connected to the mini-grid, $87\pm 45$ acquired
new appliances.

(a) Additional mini-grid solar array capacity resulted in more schools
reporting improvements in children’s academics.

(b) Every additional $50,000 in CAPEX investment into the mini-grid led to
approximately 15 more households reporting an improvement in their kids’
academics.

Figure 8: Educators and parents both report that larger mini-grids lead to
more well-educated students.

With the connection to the mini-grid providing a more reliable source of
power, there were extended lighting hours for both schools and households,
directly benefiting school-aged children. In this context, both households and
schools were surveyed about changes in academic performance following the
mini-grid installation. The results, illustrated in Fig. 8, were telling: 46%
of households observed a positive change in the academic performance of their
children, while a significant 92% of schools reported similar improvements.
Connecting an additional 100 customers to the mini-grid would lead to $19\pm
17$ more households observing an improvement in the children’s academics.
Similarly, an investment of an additional $50,000 in CAPEX is associated with
$15\pm 15$ more households reporting a comparable improvement. Furthermore, an
increase of 10 kWp in the mini-grid capacity would result in $2\pm 2$ more
schools noting such an improvement in academic performance.

(a) Additional mini-grid solar array capacity resulted in more product or
service offerings.

(b) Every additional 100 customer connections led to approximately 4 more
establishments hiring at least one more worker.

Figure 9: Establishments reporting an increase in their workforce and
offerings.

Among commercial and institutional customers connected to the mini-grid, a
substantial 87% reported an increase in their operational hours. Among all
establishments, 16% of them have recruited new employees, while 51% have
broadened their range of products and services. As illustrated by Fig. 9, an
increase of 10 kWp in the mini-grid capacity is associated with $9\pm 5$ more
organizations reporting an expansion in their product or service offerings,
and every additional 100 customer connections correlates with $4\pm 3$ more
organizations employing at least one new worker.

###  3.5 Health

The full results of the statistical analysis for questions pertaining to
health are available in LABEL:tab:app:health in the appendix.

Local health clinics in newly electrified communities benefited greatly from
access to power from their mini-grids. The access to electricity in clinics
showed a significant improvement in the post-survey, with 82% of respondents
indicating that the nearest clinic had electricity, a considerable increase
from only 35% in the initial survey. This positive shift was also evident in
the paired sample, where an increase of 53 percentage points was observed. One
way in which clinics utilized electricity was to provide refrigeration,
enabling the safe storage of vaccines, medications, and blood supplies: 86% of
post-survey responses confirmed refrigeration availability, up from 41%
initially, translating to a 48 percentage-point improvement in the paired
sample. Among all the clinics surveyed, 79% reported the addition or
enhancement of at least one service they provide, with a median of three
services being improved or added.

(a) For every four additional customers connected to a mini-grid,
approximately three of which will be households, all three will likely report
an improvement in the quality of healthcare in their community.

(b) For every four additional customers connected to a mini-grid, two
households in the community will likely report an improvement in their overall
quality of life.

Figure 10: Connecting additional customers to a mini-grid significantly
enhances a community’s perceived well-being.

Electrification of clinics significantly influenced community members’ views
on their health. Following the mini-grid installation, 58% of respondents
reported an enhancement in their lives owing to improved healthcare
facilities. The connection of an additional 100 customers to a mini-grid
results in an average of $76\pm 18$ more households reporting access to better
healthcare. Furthermore, it leads to approximately $47\pm 14$ more households
reporting an overall enhancement in their quality of life. These effects are
illustrated in Fig. 10.

(a) The median household used one kerosene lamp before the mini-grid
connection, and none one year after connection.

(b) For every ten new customer connections, of which eight will likely be
households, seven of those households will likely decrease their reliance on
kerosene.

Figure 11: Community kerosene consumption decreases significantly after the
installation of a new mini-grid.

Connecting 100 additional households to the mini-grid is expected to result in
a significant reduction in the use of kerosene lamps: connecting 100
households results in $36\pm 20$ fewer kerosene lamps used. Fig. 11
illustrates the profound effect of mini-grids on reducing kerosene usage. Put
simply, the median household used one kerosene lamp before the mini-grid
connection, and none one year after connection.

The transition to the mini-grid has led to notable improvements in water
quality for the surveyed communities. There was a decrease in the use of dirty
water, with pre-survey respondents reporting a 38% usage rate, which dropped
to just 15% in the post-survey. Concurrently, the majority of households
shifted to using community wells or pumps, accounting for 50% of the water
sources post-connection.

When specifically asked about access to clean drinking water, 61% of post-
survey respondents confirmed having such access, a significant rise from 36%
in the initial survey. In the paired samples, this represented a positive
increase of 12 percentage points. The primary source of this clean drinking
water shifted dramatically, with 60% of post-survey respondents sourcing it
from the community, up from 22% initially. This shift also led to a reduced
reliance on boiling water for purification, which decreased from 41% to 23%.
One year after connection to a mini-grid, households were 1.9 times more
likely to have access to clean drinking water.

###  3.6 Safety

The full results of the statistical analysis for questions pertaining to
safety are available in LABEL:tab:app:safety in the appendix.

Although there was no statistically significant change in the overall number
of respondents who reported feeling safe in their communities, statistically
significant shifts were observed in the reasons cited by those who felt
unsafe. The perceived threat from potential theft saw a significant increase,
rising from 31% pre-connection to 73% one year after connection. Conversely,
concerns related to the lack of community lighting decreased substantially
from 40% to 17%, and the lack of safety due to unsafe travel dropped from 23%
to 7%. This change is partly attributed to the improvement in community
lighting, with 56% of respondents acknowledging its presence post-connection,
a considerable increase from just 22% pre-connection. This represents a 13
percentage-point increase in the paired sample proportions.

Household lighting saw improvement, with exterior lights increasing from 21%
to 58% of households post-connection, and 68% being powered by the mini-grid.

###  3.7 Economic Activity

The full results of the statistical analysis for questions pertaining to
economic activity are available in LABEL:tab:app:econ in the appendix.

Following the mini-grid installation, there was a noticeable shift in the
occupational dynamics within households. Specifically, 20% of households
reported a change in the occupation of the primary income provider during the
first year after connection to the mini-grid. Part of this is due to a
discernible increase in entrepreneurial activity. Post-mini-grid installation,
73% of households confirmed having a business owner in the household, a rise
from 70% observed initially. Notably, 19% of these households reported that
the business was established immediately following the mini-grid installation.
This represents a 10 percentage-point increase in business ownership within
the paired sample.

(a) Every additional 10 kWp of installed solar panels correlated with six
local businesses reporting increased earnings.

(b) For every ten new customer connections in Kenya, of which eight will
likely be households, five of those households will likely experience an
increase in their average monthly income in Kenyan Shillings.

Figure 12: Earnings and Kenyan Monthly Incomes increased significantly after
the installation of a new mini-grid.

Among the commercial and institutional customers surveyed, a substantial 85%
reported an increase in their overall earnings following the mini-grid
installation. As illustrated in Fig. 12, for every additional 10 kWp in mini-
grid capacity, there is an increase of $6\pm 4$ organizations reporting a
boost in their earnings.

In Kenya, the median monthly income in the communities rose from 5,000 to
6,000 Kenyan Shillings (KES) post-connection, while in Nigeria, the median
household income actually decreased from 47,500 to 25,000 Nigerian Naira (NGN)
after the mini-grid connection. Notably, 24% of Kenyan households reported a
positive increase in their incomes, compared to 18% of Nigerian households.
Among the paired respondents, the income change was significant: in Nigeria,
there was a decrease of $24,079\pm 4,496$ NGN, whereas in Kenya, there was an
increase of $13,037\pm 1,662$ KES. Every 10 additional customer connections in
Kenya is correlated with $5\pm 3$ more households reporting an increase in
their average household income.

Figure 13: Installing a mini-grid in a community reduced the cost of phone and
appliance charging, cooking energy, kerosene lamps, and water.

As illustrated in Fig. 13, before the mini-grid installation, 18% of Nigerian
households spent between 0 and 5,000 NGN monthly on water. Post-installation,
90% of respondents indicated zero water costs, instead getting all their water
from community wells, marking a 94 percentage-point increase in those not
paying anything and a substantial decrease of 3,000 NGN per household for
water costs. For every 10 customer connections in Nigeria, $2\pm 2$ more
households would report a decrease in water cost. In Kenya, prior to
connection, 74% of households spent between 0 and 3,000 KES monthly on water,
and 27% paid 5,000 KES or more. After connection, 57% of households reported
no water expenses.

Before connecting to the mini-grid, 84% of Nigerian households were spending
between 0 and 1,000 NGN monthly to charge their phones due to the absence of
electricity. Post-connection, a significant reduction in this expense
occurred, with 96% of post-survey respondents reporting no additional costs
beyond their household power bill. In the paired sample, this equates to a 97
percentage-point increase in those not paying for phone charging and a marked
decrease of $900\pm 100$ NGN per household each month. For every additional 10
customer connections in Nigeria, $7\pm 4$ more households would report a
decrease in phone charging cost. In Kenya, pre-connection, 43% of households
spent between 0 and 750 KES monthly on phone charging, and 55.5% paid 1,000
KES or more. After connection, a considerable number of households saw this
cost alleviated, with 45% of post-connection respondents indicating no extra
expenses beyond their household power bill. In the paired sample, this
translates to a 15 percentage-point increase in those not incurring phone
charging costs and a significant monthly reduction of 100 KES per household.

Initially, 84% of Nigerian households incurred costs between 0 and 6,000 NGN
monthly for appliance charging due to unreliable power sources. However, one
year after connecting to the mini-grid, 93% of respondents no longer faced any
additional charging costs. In the paired sample, there was a 94 percentage-
point increase in households reporting no appliance charging costs and a
significant monthly reduction of 3,000 NGN per household. In Kenya, 99% of
households initially spent between 0 and 4,000 KES monthly to charge
appliances. A year after mini-grid connection, 37% of households reported no
longer having this expense. The paired sample showed a 13 percentage-point
increase in those not paying for appliance charging and a notable decrease of
150 KES per household each month.

In Nigeria, before the mini-grid installation, 84% of respondents spent
between 0 and 3,000 NGN monthly on cooking energy. One year after
installation, 34% reported their cooking costs had fallen to between 0 and
1,000 NGN, a significant increase from 14% pre-connection. In the paired
sample, this shift represented a 26 percentage-point increase in those with a
lower cooking energy bill in the 0 to 1,000 NGN range, alongside a notable
monthly decrease in cooking energy costs of 1,500 NGN per household. In Kenya,
prior to the mini-grid connection, 96% of households were spending between 0
and 2,000 KES monthly on cooking energy. Post-connection, a cost reduction was
observed, with 79% now paying either nothing or between 0 and 1,500 KES. The
paired sample analysis showed a 4 percentage-point increase in households
paying nothing, a 27 percentage-point decrease in those spending between 0 and
1,000 KES, and a 17 percentage-point increase in those now spending between
1,000 and 1,500 KES on cooking energy.

Prior to the mini-grid connection, 57% of Nigerian households were spending
between 0 and 1,400 NGN monthly on expenses related to kerosene lamps. A year
after connection, a significant change was observed: 91% of post-survey
respondents reported no expenditure on kerosene. In the paired sample, this
equated to an 89 percentage-point increase in households no longer incurring
costs for kerosene lamps, with a substantial monthly reduction of $1,000\pm
400$ NGN per household. For every additional 10 customer connections in
Nigeria, $6\pm 2$ more households would report a decrease in the amount spent
on kerosene lamps. In Kenya, initially, 98% of households spent between 0 and
1,000 KES on kerosene lamp-related costs. After a year of mini-grid
connectivity, 72% of respondents reported eliminating these expenses. This
change represented a 47 percentage-point increase in the paired sample for
those not needing to spend on kerosene lamps, and a notable monthly decrease
of 200 KES per household.

##  4 Discussion and Qualitative Insights

This study conducts a thorough analysis of solar mini-grids’ impact in
surveyed communities, examining changes across a wide range of variables in
the first year post-installation. Utilizing both descriptive and inferential
statistics, it highlights key observed changes. Although not exhaustive, the
study captures diverse socio-economic benefits of electrification, shedding
light on its potential positive impact on African rural communities. The
findings are organized into themes: gender equality, productivity, health,
safety, and economic activity, enabling detailed exploration of implications
and study limitations. This approach aims to comprehensively understand the
multifaceted impact of solar mini-grid implementation.

In this context, gender equality was defined as increased equal access to
education for both boys and girls, enhanced economic opportunities for women,
and reduced time spent on household chores typically assigned to young and
adult females in those communities. There were 11 questions pertaining to this
KPI from which several variables were extracted for analysis. The results
indicate a notable and significant improvement ranging from access to
education for boys and girls alike to increased economic inclusion for women.
The associated investment into the installation and capacity of the mini-grid
proved to be positively correlated with higher school enrollment rates across
communities. Responses from open-ended survey questions added depth to these
findings. Post-connectivity, school-aged children, especially girls, reported
higher grades, attributed to extended study hours into the night and the
ability to complete schoolwork more effectively. Additionally, the mini-grid
connection facilitated access to the internet, providing children with
additional learning materials. This connectivity proved particularly
beneficial for girls, who gained increased exposure to and knowledge about
feminine care and hygiene issues. Furthermore, the reduction in time spent on
chores like water and cooking fuel collection offered relief to the primary
caregivers in the household, who were often young or adult women. Furthermore,
the availability of nighttime lighting allowed for the completion of chores
later in the evening, resulting in earlier meal preparations. The broader
economic landscape also saw a transformation, with the mini-grid connection
opening up new job opportunities from which women substantially benefited.

Before the mini-grid connection, many economically productive activities were
often determined by the natural light hours and were limited by little-to-no
access to electricity. For instance, many residents had to travel to charge
electronic appliances such as phones and, so, restrict the time they could
spend on other activities. Respondents involved in the fishing industry noted
that their fishing activities were limited due to the absence of ice for
preserving their catch before reaching shore. Consequently, they restricted
their daily catch to quantities they could sell immediately, as smoking was
the only available method for preservation. These inefficiencies were
curtailed or eliminated by a reliable power source. More broadly, productivity
is related to light hours for households, time spent on and distance traveled
for specific activities like water collection and appliance charging, and
enhanced operational hours and workforce capacity for organizations. The
surveys contained 31 productivity-related questions for households and 7 for
businesses. Analyses of the metrics related to productivity demonstrated gains
and savings in terms of resources allocated to various activities, thus
pointing to greater efficiency at the individual and community levels.
Connecting to the mini-grid gave more light hours, reduced reliance on unclean
or non-renewable power sources, and provided on-demand charging stations at
home for all their electronic devices. The responses from open-ended survey
questions provided additional depth and nuance to these findings. The
availability of electronic information devices such as televisions and radios
has expanded access to information for residents, while the convenience of
charging phones has enhanced communication within families and the community
at large. The ability to access social media platforms has also heightened
residents’ awareness of local, national, and international news. Furthermore,
the acquisition of electric appliances like clothes irons has enabled
residents to wear ironed clothes, contributing to an improved sense of self-
presentation. There was also an improvement in the academic performance of
school-aged children affirmed by both households and schools. Children could
now extend their study hours at home, and schools could provide more services
to support the student body, potentially at later hours. Most organizations
also had a notable increase in their hours of operation—now with a reliable
source of electricity—thus driving more business growth in hiring more workers
and expanding their lines of products and services.

Sixteen health-related questions were posed to households, focusing on the
ease of access to the nearest clinic, use of kerosene lamps, water
purification methods, and the overall quality of healthcare. The results
highlighted a significant positive impact on the health and well-being of
communities connected to mini-grids. Residents gained access to clean drinking
water, primarily through community wells or pumps, reducing health risks
associated with the use of unclean water. Clinics reported shorter wait times,
extended hours of operation, the capacity to treat more patients, and improved
cold storage for vaccines and medicines. Residents directly confirmed an
overall enhancement in healthcare services and living conditions. The adoption
of mini-grids led many households to discontinue the use of hazardous kerosene
lamps, known for their risks of poisoning, fires, and explosions. Here, too,
open-ended survey responses further enriched the findings. The consistent
voltage supply from the mini-grid has enabled a shift away from petrol-powered
generators, significantly reducing noise and pollution in residential areas.
Residents, like clinics, also benefit from cold storage facilities, allowing
access to cold drinking water and other chilled beverages as needed, alongside
a notable reduction in food wastage. This transition has contributed to an
improved quality of life for the residents, underscoring the multifaceted
health and lifestyle benefits brought about by the mini-grid installation.

The safety of residents was assessed based on how safe they felt and any
potential reasons they might feel unsafe, including due to theft, unsafe
travel, and lack of community lighting, with 10 questions allocated from the
surveys. Households were more likely to have exterior lighting, thus
contributing to a feeling of safety in and around the house. On the feeling of
safety, there was no significant increase or decrease, thus indicating that
most residents did not affirm feeling more or less safe. However, there was a
notable and positive change in the reasons related to electrification. Indeed,
there was a drop in respondents denoting community lighting and lack of safety
when traveling as reasons for feeling unsafe. Even without an overall increase
in the feeling of safety, those two reasons indicate that the mini-grid
installation reduced the incidence of electrification-related concerns. On the
other hand, there was a significant increase in the perceived threat of theft
following the mini-grid installation. This observation suggests a need for
further investigation to assert a direct correlation with the mini-grid
installation. Future work should avoid this limitation by expanding the number
of questions dedicated to assessing safety.

Survey questions measuring economic activity assessed changes in household
incomes and spending, earnings for businesses, employment, and business
ownership. This section contained 17 questions. The median household income in
Nigeria decreased between 2021 and 2023, but this can likely be best explained
by macroeconomic trends [34, 35]. On the other hand, Kenyan households
observed an increase in income after mini-grid connection. Among the paired
respondents—those community members who were present both before the mini-grid
installation and at least until the post-survey one year after
connection—there was a significant rise in median monthly income. This group
saw their median income increase from 4,000 KES to 18,000 KES, highlighting a
substantial economic uplift due to the mini-grid installation. Across both
countries, business ownership was higher, and previously established
businesses had increased earnings. The mini-grid installation bolstered the
entrepreneurial economy as electrification brought additional economic
opportunities. Households could also save on the charging cost for phones and
appliances, cooking energy, and water costs, freeing up money for other
investments.

##  5 Conclusion

This paper offers an in-depth evaluation of the tangible socioeconomic impacts
of solar mini-grids in rural communities across sub-Saharan Africa. It
utilizes robust empirical methods to examine five Key Performance Indicators
(KPIs): gender equality, productivity, health, safety, and economic activity.
This constitutes the first comprehensive analysis of the social and economic
effects of solar mini-grids in rural African settings.

Surveys were conducted in Nigeria and Kenya at selected sites before and one
year after solar mini-grid installation, yielding a detailed insight into the
mini-grids’ impacts. Generating both quantitative and qualitative data, the
surveys provided a holistic perspective on various aspects such as household
chores, education access, time use, healthcare, and incomes. Descriptive
statistics and comparative tests, including the paired t-test and McNemar’s
test, were used to evaluate significant differences pre- and post-
installation.

The study showed marked improvements in children’s schooling, including better
academic performance and less involvement in household chores, indicating that
mini-grids enhanced educational opportunities and potentially raised literacy
rates. Additional lighting hours and reduced time for water and cooking fuel
collection from the mini-grid led to increased productivity. Additionally,
replacing hazardous kerosene lamps with the cleaner, reliable power of the
mini-grid improved both community health and productivity.

The mini-grid’s on-demand home electricity significantly reduced household
expenses, notably in services like phone charging. This installation led to
diverse improvements, positively affecting many aspects of community life.

Despite the study’s positive findings, certain limitations were encountered.
The survey’s structure led to an imbalance between discrete and continuous
variables, limiting the pool available for normal distribution in regression
analyses. This restriction affected the robustness of testing electricity
consumption effects at the household level. Site-level analyses provided some
compensation, but they offered only community-wide results, amalgamating
individual household and business impacts. Furthermore, many survey questions
yielded outputs unsuitable for parametric hypothesis testing due to their lack
of meaningful order or magnitude.

This study’s insights on solar mini-grids in Nigeria and Kenya pave the way
for future research. Longitudinal studies tracking participants over years
would deepen understanding of mini-grids’ long-term effects. Comparing these
communities with nearby unelectrified ones could create a natural quasi-
experiment. Broadening the study to other regions would enhance understanding
of mini-grids’ diverse impacts. Assessing how evolving renewable technologies
affect these impacts, and examining the influence of government policies,
subsidies, and international aid, are also crucial. Addressing these aspects
will enrich the knowledge base, aiding the effective use and optimization of
solar mini-grids for greater social and economic advantages.

Solar mini-grids have significantly driven positive transformations in
communities, leading to enhanced opportunities for women and girls, better
healthcare, and economic growth. Their impact on various socioeconomic factors
highlights their role in achieving the Sustainable Development Goals (SDGs) by
2030.

## Conflict of Interest Statement

This study was conducted with the involvement of several authors who have
direct employment ties with the primary funder, Renewvia Energy Corporation.
Specifically, authors A.T. Carabajal, A. Orsot, and N.S. Selby are employed by
Renewvia Energy Corporation. Additionally, G.T. Jarrard III holds the position
of CEO at Renewvia Energy Corporation.

## Funding Statement

This project was primarily funded by Renewvia Energy Corporation. While no
specific grants were allocated for this project, authors employed by Renewvia
Energy Corporation led the study design, analysis, interpretation, and writing
of the report. This collaboration was in partnership with the African
Leadership University, the University of Nairobi, and UC Berkeley. Data
collection was carried out by independent, third-party surveyors.

## Data Availability Statement

In line with our commitment to transparency and scientific reproducibility,
the anonymized responses from the surveys conducted for this study are
released alongside the publication of this paper. We have ensured that all
personally identifiable information has been removed from these responses to
maintain the confidentiality and privacy of the participants. This
anonymization process was undertaken with great care to preserve the integrity
and utility of the data for further analysis and research.

Researchers and interested parties can access these datasets to validate our
findings, conduct further analysis, or for comparative studies in similar
fields. The data is available in CSV format.

By providing open access to our research data, we aim to foster a
collaborative and transparent research environment that encourages knowledge
sharing and collective advancement in our field.

## References

## References

  * [1] Hafner M, Tagliapietra S, de Strasser L, Hafner M, Tagliapietra S and de Strasser L 2018 Energy in Africa: challenges and opportunities 47–75
  * [2] IRENA A 2022 International Renewable Energy Agency and African Development Bank, Abu Dhabi and Abidjan
  * [3] Moro B and Apollinaire A 2018 Ensuring Sustainable Electricity for All in West Africa through Innovative Decentralised Renewable Energy Solutions: An Analysis of Ivorian Context Master’s thesis PAUWES
  * [4] Rasagam G and Zhu D 2018 Current Sustainable/Renewable Energy Reports 5 230–239
  * [5] Deshmukh A 2009 Maharashtra case study, India Assessed from http://lup. lub. lu. se/luur/download
  * [6] Edwards I 2018 Quaker United Nations Office (QUNO), Geneva
  * [7] Rabetanetiarimanana J C I, Radanielina M H, Rakotondramiarana H T et al. 2018 Smart Grid and Renewable Energy 9 171
  * [8] Okunlola A, Evbuomwan O, Zaheer H and Winklmaier J 2018 Assessment of decentralized hybrid mini-grids in sub-saharan africa: Market analysis, least-cost modelling, and job creation analysis Africa-EU Renewable Energy Research and Innovation Symposium 2018 (RERIS 2018) (Springer) pp 21–34
  * [9] Moner-Girona M, Solano-Peralta M, Lazopoulou M, Ackom E, Vallve X and Szabó S 2018 Renewable and Sustainable Energy Reviews 91 1148–1161
  * [10] Dawoud S M, Lin X and Okba M I 2018 Renewable and Sustainable Energy Reviews 82 2039–2052
  * [11] Hussein M A, Leal Filho W et al. 2012 Scientific Research and Essays 7 2656–2666
  * [12] Hanjra M A, Ferede T and Gutta D G 2009 Agricultural Water Management 96 1062–1070
  * [13] Zebra E I C, van der Windt H J, Nhumaio G and Faaij A P 2021 Renewable and Sustainable Energy Reviews 144 111036
  * [14] 2019 Tracking SDG7: The energy progress report 2019 Report World Bank Washington, D.C.
  * [15] Valickova P and Elms N 2021 Energy Policy 148 111935
  * [16] Falk J, Angelmahr M, Schade W and Schenk-Mathes H 2021 Energy, Ecology and Environment 6 513–530
  * [17] Chakravorty U, Emerick K and Ravago M L 2016 Resources for the Future Discussion Paper 16–22
  * [18] Mazzeo D, Matera N, De Luca P, Baglivo C, Congedo P M and Oliveti G 2021 Journal of Cleaner Production 295 126070
  * [19] Korkovelos A, Zerriffi H, Howells M, Bazilian M, Rogner H H and Fuso Nerini F 2020 Sustainability 12 1793
  * [20] Poblete-Cazenave M and Pachauri S 2021 Energy Economics 98 105266
  * [21] Antonanzas-Torres F, Antonanzas J and Blanco-Fernandez J 2021 Energies 14 990
  * [22] Shabalov M Y, Zhukovskiy Y L, Buldysko A, Gil B and Starshaia V 2021 Energy Reports 7 2664–2680
  * [23] Consultant B K, Klooss B and Consultant S 2021 Making energy transition possible: How technology can help URL https://www.opml.co.uk/blog/making-energy-transition-possible-how-technology-can-help
  * [24] Eales A, Walley L, Buckland H, Frame D and Strachan S 2018 Social impacts of mini-grids: Towards an evaluation methodology 2018 IEEE PES/IAS PowerAfrica (IEEE) pp 354–359
  * [25] Pueyo A and DeMartino S 2018 Energy for Sustainable Development 45 28–37
  * [26] Wassie Y T and Adaramola M S 2021 Energy for Sustainable Development 60 52–66
  * [27] UWINEZA M P 2021 Analysis of socio-economic impact of mini-Grids for rural area development in Rwanda. Case of Mukungu mini-hydropower plant Ph.D. thesis University of Rwanda (College of science and Technology)
  * [28] Lee K, Miguel E and Wolfram C 2020 Journal of Political Economy 128 1523–1565
  * [29] Babalola S O, Daramola M O and Iwarere S A 2022 Philosophical Transactions of the Royal Society A 380 20210140
  * [30] Liu Y and Bah Z 2021 Energy Policy 154 112294
  * [31] Cartland R, Sendegeya A M and Hakizimana Khan J d D 2022 Journal of Energy in Southern Africa 33 36–50
  * [32] Duran A S and Sahinyazan F G 2021 Socio-Economic Planning Sciences 77 100999
  * [33] Nyarko K, Whale J and Urmee T 2023 Heliyon
  * [34] 2023 Macro poverty outlook for nigeria : April 2023 - datasheet Brief 181380 World Bank Washington, D.C.
  * [35] 2023 Nigeria development update - turning the corner: From reforms and renewed hope, to results Report 186416 World Bank Washington, D.C.

##  6 Appendix

Figure 14: Correlation Matrix of Household Pre-Survey Responses to 23 Numerical Questions Figure 15: Correlation Matrix of Household Post-Survey Responses to 17 Numerical Questions Figure 16: Correlation Matrix of C&I Post-Survey Responses to 3 Numerical Questions Table 4: Statistical Testing Results for Gender Equality |  |  |  |  |  |  |
---|---|---|---|---|---|---|---
Variable |  Sample |  Test |  Estimate |  Predictor | Result | 95% CI |  $p$-value
Increase in Schooling for Girls |  Post |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | 1.824* | (0.425, 3.223) | 0.0140
|  |  |  |  Customer Count | 0.0680 | (0.1181 0.2541) | 0.448
|  |  |  |  CAPEX (k USD) | 0.4134** | (0.1793, 0.6475) | 0.0020
Increase in Schooling for Boys |  Post |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | 1.764* | (0.273, 3.256) | 0.0235
|  |  |  |  Customer Count | 0.0674 | (-0.1252, 0.2601) | 0.467
|  |  |  |  CAPEX (k USD) | 0.4184** | (0.1722, 0.6645) | 0.0027
Water Collection Time |  Paired |  Paired $t$-test |  Mean Difference |  RMG Presence | 0.1483*** | (0.0708, 0.2259) | 0.0002
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | 2.455* | (0.2576, 4.652) | 0.0320
|  |  |  |  Customer Count | 0.0735 | (-0.2225, 0.3695) | 0.592
|  |  |  |  CAPEX (k USD) | 0.3348** | (0.0279, 0.6418) | 0.0354
Water Collection Responsibility (School-Aged vs. Not) |  Paired |  McNemar |  Odds Ratio |  RMG Presence | 0.2894*** | (0.2104,0.3927) | 2e-16
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | 4.019 | (-0.8265, 8.892) | 0.0942
|  |  |  |  Customer Count | 0.2279 | (-0.3544, 0.8117) | 0.4035
|  |  |  |  CAPEX (k USD) | 0.0333 | (-0.7445, 0.8111) | 0.9259
Cooking Fuel Collection Time |  Paired |  Paired $t$-test |  Mean Difference |  RMG Presence | 0.3610*** | (0.2668, 0.4551) | 4.3e-13
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | 0.0440 | (-4.2914, 4.3795) | 0.9824
|  |  |  |  Customer Count | -0.0134 | (-0.4792, 0.4524) | 0.9503
|  |  |  |  CAPEX (k USD) | 0.2247 | (-0.3544, 0.8037) | 0.4076
Women-led Business Ownership |  Paired |  McNemar |  Odds Ratio |  RMG Presence | 0.7957 | (0.5783, 1.092) | 0.164
Employment Opportunity for Women |  Post |  Likelihood-Ratio Test |  Odds Ratio |  Consumption (kWh) | 0.0036 | (-0.0071, 0.0126) | 0.4618
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | -0.1333 | (-0.8511, 0.5845) | 0.6878
|  |  |  |  Customer Count | 0.0697* | (0.0155, 0.1239) | 0.0168
|  |  |  |  CAPEX (k USD) | 0.0334 | (-0.0686, 0.1353) | 0.4825
***, **, and * represent statistical signiﬁcance at $\textit{p}<0.001$,
$\textit{p}<0.01$, and $\textit{p}<0.05$, respectively.

Table 5: Statistical Testing Results for Productivity |  |  |  |  |  |  |
---|---|---|---|---|---|---|---
Variable |  Sample |  Test |  Estimate |  Predictor | Result | 95% CI |  $p$-value
Light Hours |  Paired |  Paired t-test |  Mean Difference |  RMG Presence | -3.1211*** | (-3.5087,-2.7336) | 2e-16
Power Source (Clean vs Unclean) |  Paired |  McNemar |  Odds Ratio |  RMG Presence | 0.1449*** | (0.0954, 0.2132) | 2e-16
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | -1.398 | (-6.4602, 3.6642) | 0.5521
|  |  |  |  Customer Count | 0.4686* | (0.0235, 0.9137) | 0.0409
|  |  |  |  CAPEX (k USD) | 0.2525 | (-0.4390, 0.9440) | 0.4348
Appliances Count |  Paired |  Paired $t$-test |  Mean Difference |  RMG Presence | -1.5867*** | (-1.7789, -1.3945) | 2e-16
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | 0.2424 | (-6.8040, 7.2888) | 0.9404
|  |  |  |  Customer Count | 0.8710** | (0.4270, 1.3149) | 0.0014
|  |  |  |  CAPEX (k USD) | 0.6005 | (-0.2788, 0.1480) | 0.1591
Phone Charging Travel Distance |  Paired |  Wilcoxon |  Median |  RMG Presence | 0.9999*** | (0.9999, 1.0000) | 2e-16
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | -4.410 | (-10.7694, 1.9484) | 0.1533
|  |  |  |  Customer Count | 0.0901 | (-0.6679, 0.8480) | 0.7965
|  |  |  |  CAPEX (k USD) | -0.1692 | (-0.1142, 0.8034) | 0.7064
Water Collection Travel Distance |  Paired |  Wilcoxon |  Median |  RMG Presence | 6.5e-6* | (-5.2e-5, 6.7e-7) | 0.0429
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | 0.8506 | (-2.0535, 3.7547) | 0.5287
|  |  |  |  Customer Count | 0.0472 | (-0.2697, 0.3641) | 0.7470
|  |  |  |  CAPEX (k USD) | 0.2094 | (-0.1738, 0.5925) | 0.2513
Clinic Travel Distance |  Paired |  Wilcoxon |  Median |  RMG Presence | -3.6e-5 | (-2.8e-5, 6.1e-7) | 0.9765
Academic Performance (Households) |  Post |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | 1.4668 | (-0.1765, 3.1100) | 0.0765
|  |  |  |  Customer Count | 0.1874* | (0.0139, 0.3609) | 0.0361
|  |  |  |  CAPEX (k USD) | 0.3049* | (0.2175, 0.5880) | 0.0367
Academic Performance (Schools) |  Post |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | 0.2344** | (0.0803, 0.3885) | 0.0074
|  |  |  |  Customer Count | -0.0089 | (-0.0355, 0.0178) | 0.4712
|  |  |  |  CAPEX (k USD) | 0.0201 | (-0.017, 0.0572) | 0.2514
Hours of Operations |  Post |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | 1.0059 | (-0.0224, 2.0343) | 0.0545
|  |  |  |  Customer Count | 0.0872 | (-0.0439, 0.2183) | 0.1758
|  |  |  |  CAPEX (k USD) | 0.0568 | (-0.1396, 0.2533) | 0.5449
Employment Opportunity For All |  Post |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | 0.0348 | (-0.3229, 0.3925) | 0.8344
|  |  |  |  Customer Count | 0.0382* | (0.0090, 0.0674) | 0.0149
|  |  |  |  CAPEX (k USD) | 0.0039 | (-0.0511, 0.0589) | 0.8783
Change in Products/Services |  Post |  Likelihood-Ratio Test |  Odds Ratio |  Consumption (kWh) | 0.0037 | (-0.0045, 0.0133) | 0.3808
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | 0.8667** | (0.3663, 1.3671) | 0.0023
|  |  |  |  Customer Count | 0.0376 | (-0.0435, 0.1187) | 0.3374
|  |  |  |  CAPEX (k USD) | 0.0743 | (-0.0369, 0.1856) | 0.1737
***, **, and * represent statistical signiﬁcance at $\textit{p}<0.001$,
$\textit{p}<0.01$, and $\textit{p}<0.05$, respectively.

Table 6: Statistical Testing Results for Health |  |  |  |  |  |  |
---|---|---|---|---|---|---|---
Variable |  Sample |  Test |  Estimate |  Predictor | Result | 95% CI |  $p$-value
Access to Clean Drinking Water |  Paired |  McNemar |  Odds Ratio |  RMG Presence | 1.8689*** | (1.3577, 2.5941) | 0.0001
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | -1.364 | (-6.0004, 3.2721) | 0.5269
|  |  |  |  Customer Count | -0.1371 | (-0.6367, 0.3624) | 0.5545
|  |  |  |  CAPEX (k USD) | -0.1735 | (-0.8175, 0.4706) | 0.5618
Kerosene Lamps |  Paired |  Paired $t$-test |  Mean Difference |  RMG Presence | 0.3627*** | (0.1658, 0.5597) | 0.0004
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | -2.148 | (-6.2876, 1.9919) | 0.2745
|  |  |  |  Customer Count | 0.0951 | (-0.3739, 0.5639) | 0.6612
|  |  |  |  CAPEX (k USD) | -0.3178 | (-0.8855, 0.2499) | 0.2407
Kerosene Lamp Usage |  Paired |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | -3.6820 | (-8.4022, 1.0379) | 0.1171
|  |  |  |  Customer Count | 0.7038** | (0.2907, 1.1170) | 0.0025
|  |  |  |  CAPEX (k USD) | -0.2940 | (-1.1234, 0.5355) | 0.4598
New Health Services |  Paired |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | 0.0262 | (-0.1488, 0.2012) | 0.7335
|  |  |  |  Customer Count | 0.0062 | (-0.0125, 0.0249) | 0.4585
|  |  |  |  CAPEX (k USD) | -0.0016 | (-0.0318, 0.0285) | 0.9007
Clinic Access To Electricity |  Paired |  McNemar |  Odds Ratio |  RMG Presence | 117*** | (32.03, 971.8) | 2e-16
Clinic Access To Refrigeration |  Paired |  McNemar |  Odds Ratio |  RMG Presence | 11.895*** | ( 7.4418, 20.126) | 2e-16
Better Access to Healthcare |  Post |  Likelihood-Ratio Test |  Log Odds |  Consumption (kWh) | -0.0174 | (-0.0513, 0.0316) | 0.4269
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | -0.949 | (-5.0190, 3.1209) | 0.6264
|  |  |  |  Customer Count | 0.7576*** | (0.5821, 0.9331) | 1.48e-7
|  |  |  |  CAPEX (k USD) | 0.1364 | (-0.5194, 0.7922) | 0.6623
Self-Reported Improvement |  Post |  Likelihood-Ratio Test |  Log Odds |  Consumption (kWh) | -0.0140 | (-0.0379, 0.0089) | 0.2299
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | 0.7911 | (-1.8564, 3.4386) | 0.5338
|  |  |  |  Customer Count | 0.4694*** | (0.3253, 0.6135) | 4.72e-6
|  |  |  |  CAPEX (k USD) | 0.2127 | (-0.2632, 0.6886) | 0.3540
***, **, and * represent statistical signiﬁcance at $\textit{p}<0.001$,
$\textit{p}<0.01$, and $\textit{p}<0.05$, respectively.

Table 7: Statistical Testing Results for Safety |  |  |  |  |  |  |
---|---|---|---|---|---|---|---
Variable |  Sample |  Test |  Estimate |  Predictor | Result | 95% CI |  $p$-value
Feeling Unsafe Due to Unsafe Travel |  Paired |  McNemar |  Odds Ratio |  RMG Presence | 0.1759*** | (0.1020, 0.2882) | 2.7e-16
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | 1.0250 | (-1.7876, 3.8377) | 0.4357
|  |  |  |  Customer Count | -0.0609 | (-0.3700, 0.2482) | 0.6699
|  |  |  |  CAPEX (k USD) | 0.1351 | (-0.2555, 0.5257) | 0.4587
Feeling Unsafe Due to No Community Lighting |  Paired |  McNemar |  Odds Ratio |  RMG Presence | 0.4094*** | (0.2988, 0.5550) | 1.1e-9
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | -3.145 | (-7.5491, 1.2582) | 0.1426
|  |  |  |  Customer Count | 0.3992 | (-0.0497, 0.8482) | 0.0757
|  |  |  |  CAPEX (k USD) | -0.1154 | (-0.7931, 0.5623) | 0.7123
Feeling Unsafe Due to Potential Theft |  Paired |  McNemar |  Odds Ratio |  RMG Presence | 5.925*** | (4.2245 8.5047) | 2e-16
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | 0.9278 | (-0.7589, 2.6144) | 0.2484
|  |  |  |  Customer Count | -0.0562 | (-0.2465, 0.1341) | 0.5252
|  |  |  |  CAPEX (k USD) | 0.1628 | (-0.0599, 0.3854) | 0.1344
Presence of Home Exterior Lights |  Paired |  McNemar |  Odds Ratio |  RMG Presence | 4.1395*** | (2.9526, 5.9187) | 2e-16
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | -1.141 | (-5.8565, 3.5753) | 0.6018
|  |  |  |  Customer Count | 0.3410 | (-0.1134, 0.7955) | 0.1255
|  |  |  |  CAPEX (k USD) | 0.1380 | (-0.5171, 0.7932) | 0.6489
***, **, and * represent statistical signiﬁcance at $\textit{p}<0.001$,
$\textit{p}<0.01$, and $\textit{p}<0.05$, respectively.
Table 8: Statistical Testing Results for Economic Activity |  |  |  |  |  |  |
---|---|---|---|---|---|---|---
Variable |  Sample |  Test |  Estimate |  Predictor | Result | 95% CI |  $p$-value
Average Household Income - Nigeria |  Paired |  Paired $t$-test |  Mean Difference |  RMG Presence | 24080*** | (19583, 28574) | 2e-16
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | 0.3541 | (-5.4645, 6.1727) | 0.8179
|  |  |  |  Customer Count | 0.0896 | (-0.3758, 0.5550) | 0.4946
|  |  |  |  CAPEX (k USD) | -0.2976 | (-8.4745, 7.8792) | 0.7242
Average Household Income - Kenya |  Paired |  Paired $t$-test |  Mean Difference |  RMG Presence | -13037*** | (-11375, -14698) | 2e-16
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | 1.0640 | (-4.4101, 6.5380) | 0.6511
|  |  |  |  Customer Count | 0.4714** | (0.1781, 0.7648) | 0.0077
|  |  |  |  CAPEX (k USD) | 0.0867 | (-0.6213, 0.7946) | 0.7747
Household Income Signed Change |  Post |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | 0.7668 | (-0.7463, 2.2798) | 0.2971
|  |  |  |  Customer Count | 0.1416 | (-0.0130, 0.2963) | 0.0698
|  |  |  |  CAPEX (k USD) | 0.1492 | (-0.0065, 0.3050) | 0.0590
Business Earnings Signed Change |  Post |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | 1.1001* | (0.1734, 2.0269) | 0.0233
|  |  |  |  Customer Count | 0.0904 | (-0.0325, 0.2133) | 0.1370
|  |  |  |  CAPEX (k USD) | 0.0894 | (-0.0928, 0.2717) | 0.3105
Business Ownership |  Paired |  McNemar |  Odds Ratio |  RMG Presence | 6.1111*** | (2.9962, 14.0661) | 3.5e-5
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | -0.4382 | (-5.8828, 5.0065) | 0.8613
|  |  |  |  Customer Count | 0.1741 | (-0.3990, 0.7471) | 0.5139
|  |  |  |  CAPEX (k USD) | -0.4673 | (-1.14676, 0.2122) | 0.1564
Phone Charging Cost - Nigeria |  Paired |  Sign |  Median |  RMG Presence | -4*** | (-5, -4) | 9e-52
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | 7.729 | (-0.0621, 15.5200) | 0.0507
|  |  |  |  Customer Count | 0.7266* | (0.2671, 1.1861) | 0.0209
|  |  |  |  CAPEX (k USD) | 0.7277 | (-4.4249, 5.8804) | 0.3236
Phone Charging Cost - Kenya |  Paired |  Sign |  Median |  RMG Presence | -1** | (-1, -1) | 0.0022
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | -4.4173 | (-11.3411, 2.5066) | 0.3741
|  |  |  |  Customer Count | -0.2931 | (-1.0558, 0.4696) | 0.3834
|  |  |  |  CAPEX (k USD) | -0.6168 | (-1.4672, 0.2335) | 0.1263
Appliances Charging Cost - Nigeria |  Paired |  Sign |  Median |  RMG Presence | -3*** | (-3, -3) | 1e-50
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | 7.4115 | (-1.9588, 16.7818) | 0.0765
|  |  |  |  Customer Count | 0.7266 | (0.4684, 0.9848) | 0.0067
|  |  |  |  CAPEX (k USD) | 0.7450 | (-9.764, 11.254) | 0.5332
Appliances Charging Cost - Kenya |  Paired |  Sign |  Median |  RMG Presence | -1*** | (-1,-1) | 2.60e-6
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | -4.0440 | (-11.5460, 3.4581) | 0.2353
|  |  |  |  Customer Count | -0.2322 | (-1.0476, 0.5833) | 0.5121
|  |  |  |  CAPEX (k USD) | -0.6206 | (-1.5169, 0.2757) | 0.1412
Cooking Energy Cost - Nigeria |  Paired |  Wilcoxon |  Median |  RMG Presence | 1.49*** | (0.99, 1.5) | 6e-12
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | 4.560 | (-23.6620, 32.7829) | 0.5588
|  |  |  |  Customer Count | 0.6825 | (-1.293, 2.658) | 0.2755
|  |  |  |  CAPEX (k USD) | -0.3187 | (-43.6965, 43.059) | 0.9407
Cooking Energy Cost - Kenya |  Paired |  Wilcoxon |  Median |  RMG Presence | -4.89e-5** | (-0.99, -1.91e-5) | 0.001442
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | 0.2391 | (-2.2511, 2.7294 ) | 0.8221
|  |  |  |  Customer Count | 0.0074 | (-0.2414, 0.2562) | 0.9442
|  |  |  |  CAPEX (k USD) | 0.0295 | (-0.2891, 0.3481) | 0.8285
Kerosene Lamps Cost - Nigeria |  Paired |  Sign |  Median |  RMG Presence | -3*** | (-4, -3) | 1e-20
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | 6.0134 | (-2.674, 14.701) | 0.0967
|  |  |  |  Customer Count | 0.6042** | (0.4334, 0.7749) | 4e-3
|  |  |  |  CAPEX (k USD) | 0.734 | (-11.1702, 12.6383) | 0.5769
Kerosene Lamps Cost - Kenya |  Paired |  Sign |  Median |  RMG Presence | -1*** | (-1, -1) | 5e-12
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | -3.1656 | (-9.5667, 3.2356) | 0.2718
|  |  |  |  Customer Count | -0.1546 | (-0.8480, 0.5388) | 0.6051
|  |  |  |  CAPEX (k USD) | -0.6074 | (-1.2898, 0.0750) | 0.0723
Water Cost - Nigeria |  Paired |  Sign |  Median |  RMG Presence | -2*** | (-2, -2) | 3e-4
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | 2.2100 | (-0.6804, 5.1004) | 0.0812
|  |  |  |  Customer Count | 0.2098* | (0.0157, 0.4039) | 0.0433
|  |  |  |  CAPEX (k USD) | 0.1136 | (-1.138, 1.365) | 0.4547
Water Cost - Kenya |  Paired |  Sign |  Median |  RMG Presence | -0.5 | (-1.0, -1.7e-5) | 0.0062
|  |  Linear Regression |  Slope of Regression Line |  PV Size (kWp) | 0.3696 | (-3.8569, 4.5962) | 0.8376
|  |  |  |  Customer Count | -0.1981 | (-0.5710, 0.1748) | 0.2414
|  |  |  |  CAPEX (k USD) | 0.1584 | (-0.3607, 0.6774) | 0.4835
***, **, and * represent statistical signiﬁcance at $\textit{p}<0.001$,
$\textit{p}<0.01$, and $\textit{p}<0.05$, respectively.