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Data in Brief 47 (2023) 108948
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Data in Brief
journal homepage: www.elsevier.com/locate/dib
Data Article
Datasets for a multidimensional analysis
connecting clean energy access and social
development in sub-Saharan Africa
Paola Casati
a , b
, Magda Moner-Girona
a , ∗, Ibrahim Khaleel Shehu
c
,
Sandor Szabóa
, Godwell Nhamo
d
a
European Commission, Joint Research Centre
1
, Via Enrico Fermi 2749, 21027 Ispra (VA), Italy
b
University of Bari, Jonian Department of Law, Economics and Environment, Via Duomo 259, 74100 Tar ant o, Italy
c
African Union Commission, African Union Headquarters, P.O. Box 3243, Roosvelt St reet, W21K19,Addis Ababa,
Ethiopia
d
Institute for Corporate Citizenship, University of South Africa, P.O. Box 392. UNISA 0 0 03, Pretoria, South Africa
a r t i c l e i n f o
Article history:
Received 11 January 2023
Revised 25 January 2023
Accepted 30 January 2023
Available online 7 Februar y 2023
Dataset link: Social Clean Energy Access
Index (Original data)
Keywo rds:
Electricity access
Sustainable development goals
Clean energy
Social composite indicator
Multicriteria analysis
Sub-Saharan Africa
a b s t r a c t
In this article we present datasets used for the construction
of a composite indicator, the Social Clean Energy Access (So-
cial CEA) Index, presented in detail in [1] . This article con-
sists of comprehensive social development data related to
electricity access, collected from several sources, and pro-
cessed according to the methodology described in [1] . The
new composite index includs 24 indicators capturing the sta-
tus of the social dimensions related to electricity access for
35 SSA countries. The development of the Social CEA Index
was supported by an extensive review of the literature about
electricity access and social development which led to the
selection of its indicators. The structure was evaluated for
its soundness using correlational assessments and principal
component analyses. The raw data provided allow stakehold-
ers to focus on specific country indicators and to observe
how scores on these indicators contributed to a country over-
all rank. The Social CEA Index also allows to understand the
number of best performing countries (out of a total of 35)
for each indicator. This allows different stakeholders to iden-
∗Corresponding author.
E-mail address:
magda.moner@ext.ec.europa.eu (M. Moner-Girona) .
Social media: @casati_paola (P. Casati), @MMonerGirona (M. Moner-Girona), @of_cheap (G. Nhamo)
1 @EU _ ScienceHub
.
https://doi.org/10.1016/j.dib.2023.108948
2352-3409/© 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license
(
http://creativecommons.org/licenses/by/4.0/ )
2 P. Casati, M. Moner-Girona and I.K. Shehu et al. / Data in Brief 47 (2023) 108948
tify which the weakest dimensions are of social development
and thus help in addressing priorities for action for funding
towards specific electrification projects. The data can be used
to assign weights according to stakeholders’ specific require-
ments. Finally, the dataset can be used for the case of Ghana
to monitor the Social CEA Index progress over time through
a dimension’s breakdown approach.
©2023 The Author(s). Published by Elsevier Inc.
This is an open access article under the CC BY license
( http://creativecommons.org/licenses/by/4.0/ )
Specifications Table
Subject Energy
Specific subject area Renewable Energy, Sustainability and the Environment
Type of data Tabl e
Figure
How the data were acquired Queried from Open Data portals, systematically joined and cleaned. Compiled
based on a comprehensive horizon-scanning of data sources that are processed for
a composite indicator.
Data format Raw:
Formatted data
Processed and analysed data
Description of data collection Raw data was collected by systematic queries. Formatted raw data compilations
are utilized for data processing and analyses in the context of the research work.
Data source location Secondary data in supplementary material.
Primary data
sources:
Moner-Girona, M., Kakoulaki, G., Falchetta , G., Weiss, D.J., and Tayl or, N. (2021).
Achieving universal electrification of rural healthcare facilities in sub-Saharan
Africa with decentralized renewable energy technologies. Joule 5, 2687–2714.
https://doi.org/10.1016/j.joule.2021.09.010
[2] .
USAID (2021). The Demographic and Health Surveys Program STATcompiler.
https://dhsprogram.com/publications/index.cfm
(accessed July 10, 2021) [3] .
WHO (2021). The Global Health Observatory. https://www.who.int/data/gho
(accessed July 10, 2021) [4] .
FAO (2022). Sustainable Development Goals. Indicators under FAO custodianship
database. https://www.fao.org/sustainable- development- goals/indicators
(accessed
July 10, 2021)
[5] .
UNDESA (2014). Electricity and education: The benefits, barriers, and
recommendations for achieving the electrification of primary and secondary
schools. In Energy and Education Journal (Issue 12), pp.1–36
[6] .
The Worl d Bank Group (2021). World Bank Indicators. Datasets.
https://data.worldbank.org/
(accessed July 10, 2021) [7] .
Radboud Unive rsity (2022). Global Data Lab. GDL Area Database (4.0).
https://globaldatalab.org/areadata/
(accessed July 10, 2021) [8] .
UNICEF (2021). Monitoring the situation of children and women.
https://data.unicef.org/topic/education/covid-19/
(accessed July 10, 2021) [9] .
Lardies, C.A., Dryding, D., and Logan, C. (2019). Gains and gaps - Perceptions and
experiences of gender in Africa.
https://afrobarometer.org/sites/default/files/publications/Policy%20papers/
ab _ r7 _ policypaperno61 _ gains _ and _ gaps _ gender _ perceptions _ in _ africa.pdf
[10] .
Rysankova, D., Putti, V. R., Hyseni, B., Kammila, S., & Kappen, J. F. (2014). Clean and
improved cooking in sub-Saharan Africa: A landscape report. In Africa Clean
Cooking Energy Solutions Initiative. https://documents1.worldbank.org/curated/en/
164241468178757464/pdf/98664- REVISED- WP- P146621- PUBLIC- Box393185B.pdf
[11] .
Siebert, S., Henrich, V., Frenken , K., Burke, J. (2013). Update of the Digital Global
Map of Irrigation Areas to Version 5. https://www.fao.org/3/I9261EN/i9261en.pdf
[12] .
( continued on next page )
P. Casati, M. Moner-Girona and I.K. Shehu et al. / Data in Brief 47 (2023) 108948 3
Moner-Girona, M., Bender, A., Becker, W., Bódis, K., Szabó, S., Kararach, A.G.G.,
Anadon, L.D.D., Kararach, G., and Diaz Anadon, L. (2021). A multidimensional
high-resolution assessment approach to boost decentralised energy investments in
Sub-Saharan Africa. Renew. Sustain. Energy Rev. 148 , 111,282.
https://doi.org/10.1016/j.rser.2021.111282
[13] .
Bender, A., Moner-Girona, M., Becker, W., Bódis, K., Szabó, S., Kararach, A.G.,
Anadon, L.D. (2021). Dataset for multidimensional assessment to incentivise
decentralised energy investments in Sub-Saharan Africa. Data Br.
https://doi.org/10.1016/j.dib.2021.107265
[14] .
Szabó, S., Pinedo Pascua, I., Puig, D., Moner-Girona, M., Negre, M., Huld, T.,
Mulugetta, Y. , Kougias, I., Szabó, L., and Kammen, D. (2021). Mapping of
affordability levels for photovoltaic-based electricity generation in the solar belt of
sub-Saharan Africa, East Asia and South Asia. Nat. Sci. reports 11.
https://doi.org/10.1038/s41598 –021 –82638-x [15] .
The Worl d Bank (2010). The Global Consumption Database [database].
http://datatopics.worldbank.org/consumption/
(accessed July 10, 2021) [16] .
Data accessibility https://doi.org/10.2905/938F628A- 5D2C- 408A- B2B9 –6E84871665B0
https://data.jrc.ec.europa.eu/collection/id-0076
Related research article Casati, P., Moner-Girona, M., Shehu, I. K., Szabó, S., and Nhamo, G. (2023). Clean
energy access as an enabler for social development: a multidimensional analysis
for Sub-Saharan Africa. Energy for Sustainable Development. Volume 72, Pages
114–126
[1] .
Value of the Data
• The data is suitable for constructing a composite index encapsulating multiple indicators
related to education, health and wealth that are vital in shaping national and international
policies supporting electricity access.
• The raw data is made publicly available and represent a unique resource which allows dif-
ferent stakeholders to identify most appropriate social ecosystem for decentralized elec-
tricity access financing or/and funding.
• The data can support stakeholders in monitoring the effects of electricity access pro-
grammes on social development by tracking trends over time.
• Stakeholder can tailor the weights assigned to the different dimensions to match their
specific requirements. For example, a philanthropic organization may use the Social CEA
Index to find regions where funding in electricity generation may have the greatest health
benefits. Policy organisation can give weight to factors of specifict policies (poverty alle-
viation, children well-being, women empowerment etc) to see the specific policy’s energy
implications.
1. Objective
In this article we describe the datasets used for the construction of the Social Clean Energy
Access (Social CEA) Index, discussed in detail in P. Casati et al. (2023). The Social CEA is a novel
composite index including 24 indicators capturing the status of specific social dimensions re-
lated to electricity access for 35 SSA countries. It was created to identify the most suitable coun-
tries for funding and implementing decentralised renewable energy systems, shedding light on
the opportunities for improving social conditions through clean electrification. In addition, the
dataset has been extended to monitor the Social CEA Index trend over time through a dimen-
sion’s breakdown approach in Ghana. The development of the Social CEA Index was supported
by an extensive review of the literature focusing on the relationship between social outcomes
and electricity access. A low score of the final indicator implies that financing clean electrifica-
tion programs is likely to improve specific social outcomes in the identified country. The dataset
would allow policy makers, non-for-profit organizations, researchers, and entrepreneurs to po-
tentially re-use the data for tailoring the Index or analyse individual indicators trends, stream-
lining target countries electrification policies.
4 P. Casati, M. Moner-Girona and I.K. Shehu et al. / Data in Brief 47 (2023) 108948
2. Data Description
This article contains the dataset used for the design and development of the Social CEA Index
for Sub-Saharan African countries. The Social CEA Index is built in 5 main dimensions (Health-
care, Education, Gender equality, Quality of life and Economic development), 12 sub-dimensions
and 24 indicators. Thus, this dataset focuses on the salient social dimensions related to clean
electricity access, further emphasizing the increasingly evident interconnections between energy
and the society. The imperative to increase access to clean and affordable energy services is key
in promoting poverty alleviation in SSA, thereby contributing to improve social outcomes (e.g.
healthcare, education, gender equality), quality of life conditions and economic development.
The description of the datasets is presented in this article, while raw data are provided in
the Supplementary Information. The original research article [1] describes the methodology used
to create the Social CEA Index, providing evidence about the status of social factors related to
electricity access in SSA.
Table 1 summarizes the classification, source, year and description of the 24 indicators com-
posing the Social CEA Index.
Table 2 illustrates the correlation between the “Electricity access” indicator and the remaining
23 indicators.
Table 3 illustrates the Social CEA Index variability under three different stakeholder’s per-
spectives.
Table SI.1 shows the description of each indicator and the weights used. Indicators were ag-
gregated according to a weighting system established through a public consultation involving
different stakeholders (private sector, public sector and civil society) [17] and the support of
internal experts.
Tables SI.2-SI.6 show the methodology used to collect the raw data for the composition of the
five dimensions of the Social CEA Index: Healthcare (Table SI.2), Education (Table SI.3), Gender
equality (Table SI.4), Quality of life (Table SI.5) and Economic development (Table SI.6).
Table SI.7 contains the original data used as inputs in the COIN tool [18] , without data treat-
ment.
Table SI.8 contains the data after winzorization.
Table SI.9 includes the results of COIN tool after calculating the correlations between indica-
tors (Pearson coefficients r) taking into account the direction of effects.
Table SI.10 contains the datasets after the FOREST imputation.
Table SI.11 includes the final dataset related to the trend of the Social CEA Index in Ghana
used as inputs in the COIN tool.
Table SI.12 includes the sensitivity analysis, showing the Social CEA Index variability under
three different stakeholders perspectives (private sector, international donors and civil society)
and according to an equal weights approach.
Table SI.13 contains the Principal Component Analysis (PCA).
Fig. 1 shows the structure of the Social CEA Index.
Fig. 2 shows the Social CEA Index scores.
Fig. 3 depicts the Principal Components Analysis (PCA) investigating the underlying struc-
ture of the index data, in particular that all indicators contributed to one key measure of social
development.
Fig. 4 A) displays correlational assessments carried out in the COIN tool on the non-imputed
data sets; Fig. 4 B) displays correlational assessments carried out in the COIN tool on the Miss-
Forest imputed data sets.
Fig. 5 displays the Social CEA Index scores for Ghana over the selected time frame.
Fig. 6 represents the breakdown of the Social CEA for Ghana using the attributed weights.
P. Casati, M. Moner-Girona and I.K. Shehu et al. / Data in Brief 47 (2023) 108948 5
Tabl e 1
Structu re of the social CEA index.
Dimension Sub-dimension Indicator name Data source Year Description Indicator direction
Healthcare Healthcare
facilities
ind.01 Electricity access
in health facilities
Moner, M. &
Kakoulaki, G.
[2]
2021 Percentage of healthcare facilities with
electricity access in selected countries.
Information on electricity access for healthcare
facilities has been collected in the electricity
access health facility database (EHFDB). The
lower the healthcare facilities with access to
electricity the greater the potential for
decentralised renewable energies to improve
electricity access in these facilities, and thus
healthcare outcomes.
Positive
ind.02 Vaccinated
children
USAID [3] 2010–2019 Percentage of children 24–35 months who
received all age appropriate vaccinations. The
lower the number of vaccinated children, the
more beneficial decentralised renewable energy
solutions may be in providing electricity to
store vaccines.
Positive
Households
healthcare
ind.03 Death caused by
HH pollution
WHO [4] 2016 Number of deaths attributable to household air
pollution resulting from
solid fuels for cooking. Evidence from
epidemiological studies have shown that
exposure to smoke from incomplete
combustion of solid fuels is linked with a
range of conditions including acute and chronic
respiratory diseases. Of these, evidence for
three have been assessed on sufficiently strong
basis for inclusion in the burden of disease
estimates: acute lower respiratory infections in
young children (under 5 years); chronic
obstructive pulmonary disease in adults (above
25 years); lung cancer in adults (above 25
years).
Negative
( continued on next page )
6 P. Casati, M. Moner-Girona and I.K. Shehu et al. / Data in Brief 47 (2023) 108948
Tabl e 1 ( continued )
Dimension Sub-dimension Indicator name Data source Year Description Indicator direction
ind.04 Underweight
children
WHO [4] 2010–2019 Prevalence of underweight (weight-for-age
< −2 standard deviation from the median of
the Worl d Health Organization (WHO) Child
Growth Standards) amongst children under 5
years of age. Survey estimates are based on
standardized methodology using the WHO
Child Growth Standards. Global and regional
estimates are based on methodology outlined
in
UNICEF-WHO-The World Bank: Joint child
malnutrition estimates - Levels and trends
(UNICEF/WHO/WB 2012).
Negative
ind.05 Population
undernou-
rishment
FAO [5] 2018 Percentage of undernourished people. The
higher the incidence of undernourished people
the more beneficial decentralised renewable
energy solutions may be, in terms of improving
both cooking facilities within households and
agricultural productivity with positive impacts
on nutrition. Thus electricity can be used to
make agricultural practices more efficient and
refrigerate food produced to store for longer.
Negative
Education Educational
facilities
ind.06 Schools without
electricity
UNDESA [6] 2014 Percentage of schools in Africa reporting to
have no electricity. The lower the education
facilities with access to electricity the greater
the potential for decentralised renewable
energies to improve electricity access in these
facilities and thus educational outcomes.
Negative
ind.07 Pupil-teacher
ratio
World Bank [7] 2010–2019 Average number of pupils per qualified teacher
at primary level education in a given academic
year. Pupil-teacher ratio is calculated by
dividing the number of students at the
specified level of education by the number of
teachers at the same level of education. Data
on education are collected by the UNESCO
Institute for Statistics from official responses to
its annual education survey.
Negative
( continued on next page )
P. Casati, M. Moner-Girona and I.K. Shehu et al. / Data in Brief 47 (2023) 108948 7
Tabl e 1 ( continued )
Dimension Sub-dimension Indicator name Data source Year Description Indicator direction
ind.08 Educational
attendance
Radboud
Universit y
[8]
2010–2019 Percentage of children aged 6–8 that currently
attends, or in the current school year attended,
school. This indicator measures the potential
educational impact of bringing electricity to
schools; therefore, the impact of binging
electricity will be higher where the educational
attendance is low.
Positive
Households
education
ind.09 Adults literacy UNICEF [9] , World
Bank
[7]
2010–2019 Percentage of population aged 15 and older
that can both read and write a short, simple
statement about their everyday life. This
indicator measures the potential educational
impact, in terms of literacy, of bringing
electricity to communities; therefore, the
impact of binging electricity will be higher
where the literacy level is low.
Positive
ind.10 Children with
internet at home
UNICEF [9] 2010–2019 Percentage of children in a school attendance
age (approximately 3–17 years old depending
on the country) that have internet connection
at home. Also in this case the indicator relates
to the potential educational impact of
electrification on children and young people.
Positive
ind.11 Upper secondary
completion rate
UNICEF [9] 2010–2019 Percentage of cohort of young people three to
five years older than the intended age for the
last grade of upper secondary level of
education who have completed that level of
education. This indicator measures the
potential impact of electricity on yout h
education, that represents a crucial pillar for
the development of a country.
Positive
( continued on next page )
8 P. Casati, M. Moner-Girona and I.K. Shehu et al. / Data in Brief 47 (2023) 108948
Tabl e 1 ( continued )
Dimension Sub-dimension Indicator name Data source Year Description Indicator direction
Gender equality Women security,
health and
education
ind.12 Physical/sexual
violence on
women
USAID [3] 2010–2019 Percentage of women who experienced
physical or sexual violence. This indicator
focuses on the importance of bringing
electricity to public infrastructures, in
particular streets. Improving street lighting can
make these infrastructures safer especially for
women and thus reduce the number of women
who experienced any type of violence in public
spaces.
Negative
ind.13 Maternal
mortality
UNICEF [9] 2017 Number of maternal deaths during a given
time period per 100 000 live births during the
same time period. The lower the number of
healthcare facilities with access to electricity
the greater the potential for decentralised
renewable energies to reduce maternal
mortality.
Negative
ind.14 Literate women USAID [3] 2010–2018 Percentage of women who are literate. This
indicator highlights the importance of
electricity for women, who spend the majority
of their time taking care of the households.
Electricity can ease girls and young women
from households duties and allow them to
attend schools. Thus, the lower the number of
households with access to electricity the
greater the potential for decentralised
renewable energies to improve literacy levels
amongst women.
Positive
Wome n
empowerment
ind.15 Employed women USAID [3] 2010–2019) Percentage of women who worke d in the 12
months preceding the survey and are working
currently. A lower score reflects weaker female
emancipation within the labour market, and
thus a higher potential impact of electricity
access for improving wo men empowerment.
Positive
( continued on next page )
P. Casati, M. Moner-Girona and I.K. Shehu et al. / Data in Brief 47 (2023) 108948 9
Tabl e 1 ( continued )
Dimension Sub-dimension Indicator name Data source Year Description Indicator direction
ind.16 Wome n with
internet access
Lardies et al. [10] 2019 Percentage of women with regular access to
the internet. The indicator reflects again the
importance of electricity for the empowerment
of women, allowing them to communicate and
share information. Thus, lower indicator scores
reflect a potential improvement in women
empowerment when bringing electricity.
Positive
Quality of life Energy access
status
ind.17 Electricity access World Bank [7] 2019 Percentage of population with access to
electricity. Data, provided by the World Bank,
have been collected amongst different sources:
mostly data from nationally representative
household surveys (including national
censuses) were used. Access to electricity
captures the portion of the population who
already have access to electricity and therefore
indicates a lower potential social impact of
electricity access.
Positive
ind.18 Access to clean
cooking
World Bank [7] 2016 Proportion of total population primarily using
clean cooking fuels and technologies for
cooking. Under WHO guidelines, kerosene is
excluded from clean cooking fuels. The
indicator measures the potential improvement
in the quality of life of population due to
electricity and lower levels indicates a greater
potential impact of electricity access.
Positive
Time savings ind.19 Wate r accessibility USAID [3] 2010–2020 Percentage of households with water more
than 30 min away round trip. The indicator
reflects the crucial role of the water energy
nexus and indicates that electricity access
could generate greater social impacts when the
portion of households living far from water
sources is high in a given country.
Negative
ind.20 Firewood colle-
ction time
Rysankova et al.
[11]
1998–2012 Average time spent by households on fuel
collection daily. The higher the number of daily
hours that households spend in collecting fuel,
the higher the improvements in the quality of
life of communities due to electricity access.
Negative
( continued on next page )
10 P. Casati, M. Moner-Girona and I.K. Shehu et al. / Data in Brief 47 (2023) 108948
Tabl e 1 ( continued )
Dimension Sub-dimension Indicator name Data source Year Description Indicator direction
Economic
development
Productive use ind.21 Area equipped for
irrigation
Siebert et al. [12] 2005 Area equipped for irrigation with groundwater,
expressed in m
2
multiplied by GWh/m
2
. Lower
values of the indicator indicated that electricity
could improve the number of areas equipped
for irrigation, and consequently both economic
and social outcomes (i.e. healthcare and
nutrition).
Positive
Weal th ind.22 International
Weal th Index
Radboud Unive r-
sity
[8]
2010–2019 The International Wealth Index is an
asset-based wealth index that runs from 0 (no
assets) to 100 (all assets) and is comparable
across place and time. Lower levels of the
Index indicates that electricity access The
lower the level of the Index, the greater the
potential of electricity access to reduce poverty
and foster development.
Positive
Employment ind.23 Job creation Moner-Girona
et al.
[13] , Bender
et al.,
[14]
2019 Estimated number of jobs created directly
related to the deployment of PV mini-grids.
The indicator was calculated using data on the
total MWh of electricity output anticipated if
the total number of potential mini-grids were
established within each country and the
employment factors come from OECD. If the
estimated number of jobs created is high, it
means that PV mini-grids have a large
potential both in terms of deployment and
social development.
Negative
Affordability ind.24 Affordability of PV
electricity
Szabó et al.
[15] ,
World Bank
[16]
2019 Electricity expenditure per day, in US$. Higher
levels of electricity expenditure together with
high affordability may indicate that the
country has a greater proportion of people
with affordable electricity access and thus the
social impact of further electrification in those
areas will be comparatively more limited.
Positive
P. Casati, M. Moner-Girona and I.K. Shehu et al. / Data in Brief 47 (2023) 108948 11
Tabl e 2
Correlation between ind.17 “Electricity access” and the re-
maining 23 Social CEA indicators.
Fig. 1. Structure of the social CEA index. Source: Authors’ own elaboration.
12 P. Casati, M. Moner-Girona and I.K. Shehu et al. / Data in Brief 47 (2023) 108948
Tabl e 3
Social CEA Index variability under three different stakeholders perspectives (private sector, international donors and civil
society) and according to an equal weights approach.
Fig. 2. Final Social CEA Index scores. In order to obtain the final Index score data intensification, outlier treatment,
missing data imputation, data normalization and indicators weighting and aggregation were carefully carried out.
P. Casati, M. Moner-Girona and I.K. Shehu et al. / Data in Brief 47 (2023) 108948 13
Fig. 3. Principal components analysis (PCA). Source: authors’ own elaboration through the software R.
14 P. Casati, M. Moner-Girona and I.K. Shehu et al. / Data in Brief 47 (2023) 108948
Fig. 4. A) Correlational assessments carried out in the COIN tool on the non-imputed data sets. B) Correlational assess-
ments carried out in the COIN tool on the MissForest imputed data sets.
Fig. 5. Social CEA index trends in Ghana, also considering dimensions breakdown.
P. Casati, M. Moner-Girona and I.K. Shehu et al. / Data in Brief 47 (2023) 108948 15
Fig. 6. Breakdown of the Social CEA for Ghana.
3. Experimental Design, Materials and Methods
The Social CEA Index was built in accordance with the “best practice” for composite indi-
cator design outlined by the European Commission’s guidance on composite indicators [18] . Its
structure was empirically tested and, if possible, improved in terms of accuracy and robustness
Fig. 1 [1] illustrates the structure of the Social CEA Index.
The following steps have been completed to ensure raw data were appropriate for use in the
final Social CEA Index:
1. The structure of the Social CEA composite indicator was determined prior to data selec-
tion. This was done through an extensive review of the existing literature on the social
impact of electrification in the context of SSA.
2. Indicator datasets were retrieved from Moner, M. & Kakoulaki, G. (2021) [2] , USAID (2021)
[3] , WHO (2021) [4] , FAO (2022) [5] , UNDESA (2014) [6] , World Bank (2021) [7] , Radboud
University (2022) [8] , UNICEF (2021) [9] , Lardies et al. (2019) [10] , Rysankova et al. (2014)
[11] , Siebert et al. (2013) [12] , Moner-Girona et al. [13] , Bender et al. (2021) [14] , Szabó
et al. (2021) [15] , World Bank (2010) [16] and then grouped according to the identified
framework.
3. The datasets were intensified to ensure their comparability across countries. For example,
by dividing the indicator by country’s population or other metrics.
4. Data processing was then carried according to [18] . To treat outliers, datasets were then
winsorized when skew was greater than 2 and kurtosis was greater than 3.5.
5. Countries and indicators with a coverage lower that 63% were removed and then correla-
tional assessments conducted to investigate the underlying structure of the index.
6. Missing data were imputed (i.e. replaced with some substitute value to retain most of the
1information of the dataset) using the MissForest package in the software R and struc-
tural assessments were re-run to ensure data-imputation had not significantly altered the
underlying structure of the index.
7. In order to bring indicators onto a common scale, rendering them comparable, the dataset
was normalised using the min-max method of normalisation.
16 P. Casati, M. Moner-Girona and I.K. Shehu et al. / Data in Brief 47 (2023) 108948
8. Principal component analysis (PCA) was carried in order to show that all indicators con-
tributed to one key measure of social development.
9. Finally, indicators were aggregated according to the weighting system established through
both the results of a public consultation [17] and the support of internal experts. Fig. 2 il-
lustrates the final Social CEA Index scores.
3.1. Social clean energy access (Social CEA) index methods
3.1.1. Data selection
Data selection was critical in determining the overall quality of the Social CEA Index. There-
fore, to ensure that the datasets used to construct the index were not selected based on conve-
nience, literature review and expert consultations contributed to the development the hierarchi-
cal structure of the index prior to data collection. Indicators were chosen from reliable sources
and where possible these were collected from International Organisations working under statis-
tical regulations or codes of conduct. The quality of the indicator raw data was assessed using a
combination of criteria outlined by the OECD and the European Commission in the “Handbook
on Constructing Composite Indicators” [19] . Each of the main dimensions of the indicator was
carefully constructed to align with the overall Social CEA composite indicator.
3.1.2. Initial processing
Once the indicator raw data had been compiled, we ensured that indicators were compara-
ble across SSA countries that are characterized with diverse population sizes, land areas, and
natural resources. This implied the intensification of appropriate indicators. Data sets were also
winsorized, again following the recommendations of the COIN tool for best practice in compos-
ite indicator design. This removed the negative impacts of potentially spurious outliers within
data sets. Countries missing more then 63% of data across the indicators were removed from
the analysis using the COIN tool.
3.1.3. Structural and correlational assessments
To identify the underlying structure of the social composite indicator, both correlational and
principal component assessments (PCA) were conducted. Initial correlational investigations were
conducted using the COIN tool [18] . These correlational assessments were undertaken to ensure
that indicators within the same sub-dimension were not highly correlated (high positive cor-
relation: + 0.5), rendering the use of one of them redundant. This was repeated to additionally
ensure no indicators were negatively correlated with other indicators in their sub-dimension
(high negative correlation: −0.5), which would have suggested an inconsistency between the
indicators and what was being measured. Indicators that were either positively or negatively
correlated with their neighbors were investigated to determine whether there was a theoretical
grounding for this. In the Social CEA Composite Indicator negative correlations were retained
only within the gender equality dimension, albeit none of these exceeded −0.5. Furthermore,
after the structural assessments, four indicators pertaining to the quality-of-life dimension were
categorized in a new dimension, i.e. economic development, addressing in this way the issue of
negative correlations.
Particular attention was devoted to the evaluation of the correlation between the ind.17
“Electricity access” and the remaining 23 indicators ( Table 2 ). Correlations have been identified
again using the COIN tool [18] but in this case + 0.3 represented the threshold for high positive
correlation and –0.3 for high negative correlation. This analysis was essential in order to further
evaluate synergies between electricity access and social development.
Finally, PCAs ( Fig. 3 ) were conducted using the software R in addition to the correlational
assessments, carried out using the COIN tool, to visualize and better understand the underlying
structure of the social composite indicator. In particular, the PCA was undertaken to show that
all indicators contributed to one key measure of social development, in addition to the qualita-
P. Casati, M. Moner-Girona and I.K. Shehu et al. / Data in Brief 47 (2023) 108948 17
tive stakeholder suggestions and literature review. This resulted in a refined composite indicator
that was valid both qualitatively and quantitatively.
3.1.4. Imputation of missing data
Then challenge of missing data was also addressed. For imputing missing values two different
methods can be adopted:
I. Multiple Imputation via Chained Equations, i.e. MICE)
II. Implementation of a random forest algorithm, i.e. MissForest)
Considering the results obtained from [14] and [20] we decided to implement a random for-
est algorithm (MissForest). In fact, MissForest made fewer assumptions about the shape of each
dataset and did not require a specific regression model to be specified for imputation.
Then, structural assessments were re-run to ensure that data imputation had not significantly
altered the underlying structure of the index. Fig. 4 A and B show the correlational assessments
carried out in the COIN tool on the non-imputed data and on the MissForest imputed datasets.
3.1.5. Normalization
The completed data sets were normalised to ensure comparability between indicators orig-
inally existing at different scales and ranges, and measured in disparate units. Considering the
results provided by [13] and [14] , we selected the rescaling or min-max method of normalisa-
tion because this preserved the shape of the data distribution for each indicator and did not
disproportionately reward or punish exceptional indicator values in contrast to methodologies
using Z-scores.
3.1.6. Aggregation and sensitivity assessments
Indicators were aggregated according to the weighting system developed in [1] . We did not
opt for an equal weights approach, due to the presence of some social indicators having greater
importance in directing financing in decentralised renewable energy systems. Thus, the adopted
weighting system was based on the results of a public consultation carried out through a survey
[17] and the support of internal experts. Then weights were multiplied by the country’s score
for each indicator, and then scores across all the 24 weighted indicators were summed together
to produce a country’s final index score ( Fig. 2 ). A sensitivity analysis was carried out to check
whether the scores (and the associated inferences) were robust with changes in stakeholder
perspectives ( Table 3 ) [1] .
3.2. Social CEA Index in Ghana
Finally, a dataset attempting to analyse the Social CEA Index trend was also developed. This
was done in order to assess the Social CEA Index trends in a chosen time frame, also according
to a dimension breakdown. The lack of complete time series data for several individual indica-
tors limited the possibility of observing the evolution of the Social CEA Index for all countries.
Therefore, only the case of Ghana was analysed. Following the methodology adopted for the
construction of the Social CEA Index, data have been normalized through the min-max method
and the lowest values have been assigned to zero; this explains the low starting scores in 2003
Due to data availability issues, the Index include only 15 out of 24 indicators ( Fig. 5 ).
Fig. 6 illustrates the breakdown of the Social CEA in Ghana. The size of the coloured squares
represents the overall weights of the dimension (Healthcare, Education, Gender equality, Quality
of life and Economic development) and the size of each square the weights of the individual
indicator.
18 P. Casati, M. Moner-Girona and I.K. Shehu et al. / Data in Brief 47 (2023) 108948
Ethic statement
The authors declare that the work meets the ethical requirements for publication in Data in
Brief and does not involve studies with animals and humans.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal rela-
tionships which have or could be perceived to have influenced the work reported in this article.
Disclaimer: The views expressed are purely those of the authors and may not in any circum-
stances be regarded as stating an official position of the European Commission.
Data Availability
Social Clean Energy Access Index (Original data) (Joint Research Centre Data Catalogue).
CRediT Author Statement
Paola Casati: Conceptualization, Methodology, Data curation, Investigation, Visualization,
Writing –review & editing; Magda Moner-Girona: Conceptualization, Methodology, Investiga-
tion, Visualization, Writing –review & editing; Sandor Szabó: Investigation, Writing –review &
editing; Godwell Nhamo: Investigation, Writing –review & editing.
Acknowledgements
The authors would like to thank Abbie Bender (Victoria University of Wellington, New
Zealand), Nigel Taylor (Joint Research Centre - European Commission) and Steve Borchardt for
providing valuable insights. We would like to acknowledge Pere Roca Ristol (Joint Research Cen-
tre - European Commission) for the initiation of the development of the Social CEA web tool
and Fernando Fahl, Georgia Kakoulaki and Marco Pittalis for their insights in its development.
Paola Casati acknowledges the support and feedback from Prof. Alessandro Rubino (University
of Bari). The authors would like to thank Natalia Caldés and Athena Koulouris (DG International
Partnerships F.1- European Commission) for providing valuable insights and supporting the de-
velopment of the interactive tool of this study.
Supplementary Materials
Supplementary material associated with this article can be found, in the online version, at
doi: 10.1016/j.dib.2023.108948 .
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