Doctoral Dissertation
Electrification with Solar PV Technology and its Implication to
Educational Outcome: Empirical Evidences from South Asia

ALAM MOHAMMAD JAHANGIR

Graduate School for International Development and Cooperation
Hiroshima University

September 2019

Electrification with Solar PV Technology and its Implication to Educational
Outcome: Empirical Evidences from South Asia

D165452
ALAM MOHAMMAD JAHANGIRA Dissertation Submitted to
the Graduate School for International Development and Cooperationof Hiroshima University in Partial Fulfillmentof the Requirement for the Degree ofDoctor of Philosophy
September 2019

Dedication

To my respected parents, my wife, and my son who have been my constant sourceof inspiration.

ii

Acknowledgments
The journey of my PhD has had many dimensions and exciting events, and would have been
impossible without the contribution of my respected supervisor, co-supervisor, other professors,friends, and family members. I am grateful to all those who gave me support in this process. I
would like to express my deepest gratitude to those people who have assisted me so muchthroughout writing this thesis.
Firstly, I would like to express my sincere gratitude to my supervisor Professor KANEKO Shinji
for his unremitting support of my PhD study. I am also very grateful for his patience, motivation,and immense knowledge that helped me to finish my thesis. His all-out effort and guidance
helped to make my research fruitful and assisted me in completing the writing of this thesis. It
has been a great opportunity for me to have an excellent supervisor and mentor for my doctoral
study.
Besides my supervisor, I would like to thank the rest of my thesis committee: Professor
ICHIHASHI Masaru, Professor YOSHIDA Yuichiro, and Associate Professor GOTO Daisaku,
for their insightful comments and encouragement, and also for their critical intellectual advice
which inspired me to widen my research from various perspectives. My sincere thanks also go toProfessor ISHII Idaku who provided me with many on-site education opportunities thatbroadened my thinking.
My immense thanks go to the TAOYAKA program for funding me and providing me withvarious training opportunities that added to my multidisciplinary knowledge. This was a great
platform for me providing me with excellent opportunities, such as on-site education, on-sitetraining, a collaboration program, an internship, etc. I am also grateful to all staff members of the
TAOYAKA program office who gave me guidance and information that helped me tosuccessfully complete the program.
I would not be able to finish my journey without the divine and practical support of my family.
I am very much thankful to my wife Shanaz Ripa for her full support and tolerance during thelong tough journey. I am grateful to my adorable son Sopnil for some time spent in Japan and for
his unconditional support that has helped me to ease many painful situations. Special thanks goiii

to parents for their great role and numerous sacrifices to help me in my life. My immense thanks
go to my brother and sisters who always support me mentally when needed. I thank my fellow
labmates for the stimulating discussions in the lab as well our joint lab seminar.

iv

List of Abbreviations
AMCE
ATE
ATET
BBS
BDT
BEV
BPDB
CDF
CEM
CIA
CREDA
DID
ECP
EV
FE
GDP
GPA
HDI
HH
ICP
ICT
IDCOL
IPW
IT
IV
kgoe
LDC
LPG
MICS
MS
NA
NGO
NNM
OLS
PBS

average marginal component effectaverage treatment effectaverage treatment effect on the treatedBangladesh Bureau of Statistics
Bangladeshi taka (currency)
battery electric vehicle
Bangladesh Power Development Board
cumulative distribution function
coarsened exact matching (method)
conditional independence assumption
Centre for Rural Education and Development Action (India)
difference-in-difference
external choice probability
electric vehicle
fixed effectGross Domestic Productgrade point average
Human Development Index
household headinternal choice probability
information and communications technology
Infrastructure Development Company Limited (Bangladesh)
inverse probability weighting
information technology
instrumental variablekilogram oil equivalent
least-developed countryliquefied petroleum gas
Multiple Indicators Cluster Survey (Bangladesh)
Microsoft
non-attendance
non-governmental organization
nearest neighbor matching (method)ordinary least squares
Palli Bidyut Samity
v

PDF
PPP
PSM
PV
RCA
RCT
REB
SATT
SDG
SE
SHS
Si
UN
UNDP
UNICEF
UNO
WB
WHO

probability distribution function
purchasing power parity
propensity score matching (process)photovoltaic
randomized conjoint analysis
randomized control trial
Rural Electrification Board (now Bangladesh Rural Electrification Board)sample average treatment effect on the treatedSustainable Development Goal
standard error
Solar Home System (program)siliconUnited Nations
United Nations Development Programme
United Nations Children’s Fund
Upzilla Nirbahi Officer
World BankWorld Health Organizationvi

Executive Summary
Access to electricity is an essential component of modern life that enhances people’s living
standard. We cannot sustain our comfortable lives without electricity. Increased electricity
supply improves education, recreation, health, comfort, protection, and productivity. However, asignificant portion of people – mainly concentrated in sub-Saharan Africa and South Asia – live
every day without electricity. This scenario is a fundamental obstacle to the progress of asignificant proportion of the world’s population and affects a wide range of development
indicators, including health, education, food security, gender equality, livelihoods, and poverty
alleviation. Firstly, this study examined the impact of grid electrification on school enrollment in
Bangladesh in the short run as well as the long run. Secondly, the study investigated the impactof a solar home system on educational outcomes in rural Bangladesh. Thirdly, the study
examined consumers’ preferences for an organic solar photovoltaic (PV) system based on aconjoint analysis in rural India.
In the first section, this study aimed to show the impact of access to electricity on school
enrollment in Bangladesh. It offered an empirical investigation of the relationship between
access to electricity and school enrollment status, such as grade progression, grade repetition,and non-attendance. The data were taken from Bangladesh’s Multiple Indicators Cluster Survey(MICS) database from 2012–2013, as provided by the Bangladesh Bureau of Statistics (BBS)
and the United Nations Children’s Fund (UNICEF): the data include two years of grading
information for children aged from 5–15. The study applied propensity score matching (PSM)
and the Markov schooling transition model using matched sample data. The results showed thataccess to electricity has a significant positive effect on grade progression and a significant
negative effect on non-attendance in both the short run and the long run. The simulation result
showed that the non-attendance rate is lower and the school enrollment rate for children ingrades 9–11 is higher in electrified areas compared to unelectrified areas. This result suggeststhat access to electricity is an important strategic indicator for increasing school enrollment inboth primary and secondary schools.
In the second section, this study examined the impact of a solar home system (SHS) on a
student’s academic performance and school enrollment in rural Bangladesh. Data came from avii

random cluster sample of 673 children who had access to an SHS and 1023 children who did not,with all children being from Rahumari upzilla, Kurigram district, Bangladesh. Coarsened Exact
Matching (CEM) method was applied to correct for selection bias in observable characteristics.
The study found that the SHS has a significant positive effect on a student’s grade point average(GPA) as well as on receiving a scholarship. It also had a significant positive effect on grade
progression and a significant negative effect on dropping out. No significant effect of the SHS
was found on grade repetition and out-of-school students. The study concluded that promotion of
SHS adoption among un-electrified areas is needed and should be a priority to improvechildren’s academic performance and school enrollment by ensuring universal education for all
children.
In the third section, this study examined consumers’ preferences for an organic solar PV system.
In rural areas, poor people are used to having a silicon or conventional solar PV system. They
keep their existing system and face difficulty in accepting new technology. In the study, theorganic solar PV system was illustrated to rural people, with its function explained to enhancetheir understanding. This type of demonstration plays an important role in decision making whenchoosing an appropriate solar home system (SHS). Some features, such as the solar panel
appearance, size, color, surface pattern, functional performance, and price are considered to be
the most visual elements in the presentation when respondents are making their choice. The
study’s intervention involved showing a picture of the organic solar PV system of a different sizein one group, while in the other group, the features of the organic solar PV system wereexplained verbally. The study found that consumers preferred the flexible solar PV system over
the rigid conventional PV system.

viii

Table of Contents
Dedication .................................................................................................................................................... ii
Acknowledgments ...................................................................................................................................... iii
List of Abbreviations .................................................................................................................................. v
Executive Summary .................................................................................................................................. vii
Table of Contents ....................................................................................................................................... ix
List of Tables .............................................................................................................................................. xi
List of Figures............................................................................................................................................ xii
List of Photos ............................................................................................................................................ xiii
Chapter 1: Introduction ............................................................................................................................. 1
Chapter 2: Effects of Electrification on School Enrollment in Bangladesh: Short- and Long-Run
Perspectives ................................................................................................................................................. 8
2.1 Introduction ......................................................................................................................................... 8
2.2 Estimation Methods .......................................................................................................................... 122.2.1 Matching Procedure ................................................................................................................... 13
2.2.2 Justification of Covariate Selection ........................................................................................... 14
2.2.3 Markov Model of Schooling Transition ..................................................................................... 16
2.2.4 Estimating Short-Run Impacts: 1-Year Impacts ........................................................................ 17
2.2.5 Simulating Long-Run Impact of Access to Electricity .............................................................. 17
2.3 Description of the Data ..................................................................................................................... 18
2.4 Empirical Results and Discussion ..................................................................................................... 19
2.4.1 Estimated Propensity Score of Access to Electricity ................................................................. 19
2.4.2 Estimated Average Treatment Effect on the Treated ................................................................. 20
2.5 Conclusion ........................................................................................................................................ 28
Chapter 3: Impact of a Solar Home System (SHS) on Educational Outcomes in Rural Bangladesh30
3.1 Introduction ....................................................................................................................................... 30
3.2 Energy Scenario and Solar Home System (SHS) Prospects in Rural Bangladesh ............................ 33
3.3 Study Area and Data Collection Methods......................................................................................... 34
3.4 Matching Techniques ........................................................................................................................ 37
3.5 Findings and Discussion ................................................................................................................... 39
3.5.1 Results and Discussion Based on Coarsened Exact Matching (CEM) Method ......................... 42
3.6 Conclusion ........................................................................................................................................ 48
Chapter 4: Conjoint Analysis of Consumers’ Preferences for Future Organic Solar PV System .... 50
4.1 Introduction ....................................................................................................................................... 50
4.2 Goal of this Study ............................................................................................................................. 52
4.3 Product Design with Organic Solar PV System................................................................................ 52ix

4.4 Target Area and Population .............................................................................................................. 55
4.5 Data Collection ................................................................................................................................. 56
4.6 Conjoint Method ............................................................................................................................... 60
4.7 Motivation for Selecting the Attributes for Product Design ............................................................. 62
4.8 Ideas for Treatment (Demonstration) in the Survey ......................................................................... 64
4.9 Findings and Discussion ................................................................................................................... 67
4.9.1 Pilot Survey ................................................................................................................................ 67
4.9.2 Results of Electricity Use ........................................................................................................... 67
4.9.3 Results of Product Design .......................................................................................................... 69
4.9.4 New Scenario for Product Design 1 ........................................................................................... 71
4.9.5 New Scenario for Product Design 2 ........................................................................................... 73
4.10 Main Survey .................................................................................................................................... 75
4.10.1 Treatment Group ...................................................................................................................... 76
4.10.2 Control Group .......................................................................................................................... 79
4.10.3 Interaction Effect...................................................................................................................... 80
4.11 Social Trust of Villagers ................................................................................................................. 85
4.12 Conclusion ...................................................................................................................................... 88
Chapter 5: Conclusion and Policy Recommendations ........................................................................... 89
Appendix .................................................................................................................................................... 92
References ................................................................................................................................................ 108xList of Tables
Table 2.1: Variable definitions.................................................................................................................... 19
Table 2.2: Probit regression: estimated propensity score based on baseline observed characteristics ....... 20
Table 2.3: Average treatment effect on the treated (ATET) ....................................................................... 21
Table 2.4: Transition matrices (age 5 to 6) ................................................................................................. 23
Table 2.5: Transition matrices (age 6 to 7) ................................................................................................. 23
Table 2.6: Transition matrices (age 12 to 13) ............................................................................................. 24
Table 2.7: Grade distribution at age 5 ......................................................................................................... 25
Table 2.8: Long-run impact of access to electricity .................................................................................... 26
Table 2.9: Simulated school enrollment distribution at age 15 (treatment and control) ............................. 27
Table 3.1: List of villages and target respondents....................................................................................... 36
Table 3.2: Status of household and children regarding SHS ...................................................................... 36
Table 3.3: Covariate t-test statistics ............................................................................................................ 40
Table 3.4: Model specification.................................................................................................................... 43
Table 3.5: Coarsened Exact Matching (CEM) Index summary .................................................................. 45
Table 3.6: Results of sample average treatment effect on the treated (SATT) for Models 1, 2, and 3 ....... 45
Table 3.7: Results of sample average treatment effect on the treated (SATT) for Models 4, 5, and 6 ....... 46
Table 3.8: Results of sample average treatment effect on the treated (SATT) for Models 7, 8, and 9 ....... 46
Table 3.9: Results of sample average treatment effect on the treated (SATT) for Models 10, 11, and 12 . 47
Table 3.10: Balance check (Model 9) ......................................................................................................... 48
Table 3.11: Balance check (Model 10) ....................................................................................................... 48
Table 4.1: Positives and negatives of conventional solar PV system and organic solar PV system........... 53
Table 4.2: Comparison of conventional solar home system and study’s proposed system ........................ 54
Table 4.3: Chhattisgarh’s vital indicators at a glance ................................................................................. 56
Table 4.4: Village breakdown by number of respondents (treatment group) ............................................. 59
Table 4.5: Village breakdown by number of respondents (control group) ................................................. 59
Table 4.6: Summary statistics of respondents (main survey) ..................................................................... 60
Table A1: Transition matrices (age 7 to 8) ................................................................................................. 92
Table A2: Transition matrices (age 8 to 9) ................................................................................................. 93
Table A3: Transition matrices (age 9 to 10) ............................................................................................... 94
Table A4: Transition matrices (age 10 to 11) ............................................................................................. 95
Table A5: Transition matrices (age 11 to 12) ............................................................................................. 96
Table A6: Transition matrices (age 13 to 14) ............................................................................................. 97
Table A7: Transition matrices (age 14 to 15) ............................................................................................. 98
Table A8: Grade transition in un-electrified areas (treatment) ................................................................. 102
Table A9: Grade transition in electrified areas (control) .......................................................................... 102
xi

List of Figures
Figure 2.1: Grade transition (age 5 to 15) ................................................................................................... 26
Figure 2.2: Simulated effects of treatment on school enrollment distribution at age 15 ............................ 28
Figure 3.1: Map of Kurigram district .......................................................................................................... 35
Figure 3.2: Village-level cluster sampling .................................................................................................. 36
Figure 4.1: Map of Chhattisgarh state and Balodabazar district ................................................................. 55
Figure 4.2: Sampling frame of village selection for survey ........................................................................ 58
Figure 4.3: Illustrations used in main survey .............................................................................................. 66
Figure 4.4: Average causal effects on internal choice probability .............................................................. 68
Figure 4.5: Average causal effects on external choice probability ............................................................. 69
Figure 4.6: Average causal effects on internal choice probability .............................................................. 70
Figure 4.7: Average causal effects on external choice probability ............................................................. 71
Figure 4.8: Average causal effects on internal choice probability .............................................................. 72
Figure 4.9: Average causal effects on external choice probability ............................................................. 73
Figure 4.10: Average causal effects on internal choice probability ............................................................ 74
Figure 4.11: Average causal effects on external choice probability ........................................................... 75
Figure 4.12: Average causal effects on internal choice probability ............................................................ 77
Figure 4.13: Average causal effects on external choice probability ........................................................... 78
Figure 4.14: Average causal effects on internal choice probability ............................................................ 79
Figure 4.15: Average causal effects on external choice probability ........................................................... 80
Figure 4.16: Average causal effects on internal choice probability ............................................................ 81
Figure 4.17: Average causal effects on external choice probability ........................................................... 82
Figure 4.18: Average causal effects on internal choice probability ............................................................ 83
Figure 4.19: Average causal effects on external choice probability ........................................................... 84
Figure 4.20: Simplest formula for reverse innovation concept ................................................................... 85
Figure 4.21: Result of the questionnaire; do you feel safe to walk alone in your area after dark? ............. 86
Figure 4.22: Result of the questionnaire; most people in your village are willing to help if you need it ... 86
Figure 4.23: Result of the questionnaire; in this village, people generally do not trust each other in mattersof lending and borrowing money ................................................................................................................ 87
Figure 4.24: Result of the questionnaire; a lost envelope with your name, in which there is some money,
is likely to be returned if someone in your village discovers it. ................................................................. 87
Figure A1: Propensity score distribution for purpose of common support ............................................... 103
Figure A2: Covariate balancing test.......................................................................................................... 104
Figure A3: Attributes and levels of electricity use in pilot survey ........................................................... 105
Figure A4: Attributes and levels of product design in pilot survey .......................................................... 105
Figure A5: Attributes and levels of new product design 1 in pilot survey................................................ 106
xii

Figure A6: Attributes and levels of new product design 2 in main survey ............................................... 106
Figure A7: Example of set of choices for electricity use in pilot survey .................................................. 107
Figure A8: Example of set of choices for new product design 2 in main survey ..................................... 107

List of Photos
Photo 4.1: Interpretation from English to Hindi by Mr Rajeev .................................................................. 57
Photo 4.2: Instruction and practice by Mr Rajeev and Professor Kaneko for enumerators ........................ 57
Photo 4.3: Interview with local residents with a laptop-based explanation ................................................ 58

xiii

Chapter 1: Introduction
Modern energy services are critical for ensuring people’s quality of life and promoting economic
development. While also protecting ecosystems, access to energy is at the heart of issues such as
security, climate change, food production, and economic strengthening. Increased electricity
supply improves education, recreation, health, comfort, protection, and productivity. A
significant portion of people – mainly concentrated in sub-Saharan Africa and South Asia – live
every day without electricity. This scenario is a fundamental obstacle to the progress of asignificant proportion of the world’s population and affects a wide range of development
indicators, including health, education, food security, gender equality, livelihoods and poverty
alleviation. One case study showed that electricity consumption is significantly correlated withper capita gross domestic product (GDP) and Human Development Index (HDI) scores in
120 countries and that a high level of per capita electricity consumption is related to a high levelof economic activities [1]. Ensuring access to electricity has promoted all development, although
1.3 billion people in our global society are still unable to access electricity [2]. In addition, mostof these people live in the rural areas of developing countries, which are often isolated, sparsely
populated, and have poor infrastructure and services. In dispersed and remote villages, gridelectrification is expensive, which may challenge the financial viability of electricity utilities. In
this case, people are increasingly considering how to achieve the goal of universal access toenergy, emphasizing that the role of rural electrification and off-grid small-scale power
generation are two of the most appropriate choices. The benefits of electrification programs inmost developing countries are the savings made by households turning to cheap, clean, safe andreliable electric lighting, with electricity also used for entertainment and cooking rather thanusing expensive batteries, candles, and kerosene.
The Human Development Index (HDI) measures enrollment and literacy as indicators ofeducation, life expectancy as an indicator of health care, while human development is measured
by per capita GDP (measured by purchasing power parity [PPP]). The use of locally available
high-quality energy has been found to increase the HDI value by 16–18% from the original
figure [3]. The use of high-quality energy resources has three impacts: reducing pollution
emissions during heat and lighting transitions, improving energy security, and improving
1income-generating activities. Most households in rural areas use kerosene as a source of light.
The potential for solar photovoltaic (PV) power generation is enormous, as it can solve problems
such as power outages, indoor pollution, and carbon emissions. The current study examines thepotential impacts of access to electricity on an educational outcome that is one of the important
HDI indicators. Quality of lighting also enhances productivity that, in turn, contributes to theregional economy. People in off-grid areas always have to face choosing reliable energy for the
purposes of lighting and cooking. Renewable energy is a viable option for meeting the essential
energy needs of rural low-income people. It is crucial to identify the impact of solar PV systems
on educational outcomes in rural areas. Over the decades, the technological innovation of solar
home system (SHS) packages in rural Bangladesh has been observed. The current study seeks toinvestigate the preferences of consumers living in disadvantaged regions for solar home systems(SHSs).
Most studies have shown the impact of rural electrification on income, health, and education.
Electricity-connected households have benefited in terms of income, education, and agricultural
productivity. One study found that the impact of electricity on income can be measured directlyor through the intermediaries of education, health, and agricultural productivity [4]. The ways inwhich electricity contributes to the productivity of rural populations are diverse. Electricity
services improve the provision of health and education services by providing lighting, cooling,heating, and modern communications. A household with an electricity connection is moreengaged in home business activities than a household with no electricity [5]. The United Nations(UN) 2030 Agenda for Sustainable Development, adopted in September 2015, includes the goalof ending global energy poverty through a universal approach that provides affordable, reliable,
sustainable and modern energy for all. In the academic literature, rural electrification (defined as
the percentage of the rural population with access to electricity) is considered an importantcomponent of socio-economic development [6]. Per capita income, national savings, andpopulation density may further promote rural electrification, while a high level of additional aid,
GDP and rural population ratios may pose challenges to equality [7].
The lack of modern energy services is one of the reasons for poverty and low economic
development. Of Bangladesh’s population of 161 million, nearly 75% lives in rural areas. In thepast decade, Bangladesh has made commendable progress in the education sector. More than2

90% of children eventually attend school, with almost no difference now between attendance byboys and girls. Health and nutrition inputs are often included in education sector strategy as
health and malnutrition are known to affect children’s learning ability. The government of
Bangladesh has launched a school feeding program and a female stipend program to reduce thenumber of out-of-school students in Bangladesh. Poor quality lighting is also another barrier tochildren’s education. Even in the near future, some remote areas in Bangladesh cannot obtaingrid electricity. Rural electrification will help achieve social and economic development.
Improving electricity supply can influence rural industries, increase agricultural productivity, andprovide children with more effective study time at night [8]. In 2016, the total number ofconsumers linked to the grid was 21.8 million. These 21.8 million domestic connections(families) accounted for about 50% of all Bangladeshi households. Another 15% of households
could use off-grid electricity. Ensuring access to electricity for all is a significant challenge due
to resource and technology constraints.
As electricity is a suitable form of energy for lighting, its provision is expected to relate to theuse and level of clean lighting in electrification within a country. The lack of electricity accesshinders development. It affects everything from people’s learning ability to the ability to develop
industries and provide public services, such as health care. This was shown in a study that
reviewed the barriers to energy development, the contributing factors and impacts, and theessential welfare impacts of electrification in rural areas [9]. Most developing countries arelagging behind the UN’s goal of achieving universal access to electricity in 2030. The
Sustainable Development Goals (SDGs) agenda has set ambitious goals to accelerate the pace of
establishing foundations for human development. Some of these goals include improving
people’s living standards at home, such as the provision of access to electricity and clean
cooking energy, and improving water and sanitation. However, the millions of households indeveloping countries that still do not have access to clean, reliable, safe and modern energy
services provided at reasonable prices, continue to pay high costs for inferior alternatives. Thissituation exacerbates poverty, undermines health, limits the availability of local services,increases vulnerability to climate change, limits opportunities, and erodes environmental
sustainability at the local, national, and global levels, having a negative impact on education andhealth.
3This study has added to the literature on the impact of rural electrification on developing
countries. In previous studies, researchers have investigated various aspects of the socioeconomic impact of rural electrification. This existing research supports the hopes of multiple
interests, mainly based on evidence from Asia and Latin America. A considerable number ofcase studies have highlighted the link between access to electricity and different socio-economic
variables [10]. The effects most often highlighted are: educational benefits, due to increased
learning time; increased non-agricultural activities, leading to increased income; and reduced
rates of respiratory disease due to reduced use of kerosene. In addition, high levels of poverty,lack of effective development programs, limited policy resources, and weak institutionalarrangements have contributed to low levels of access to energy in rural areas.
One study has sought to reveal the relationship of access to electricity and rural areas indeveloping countries, with regard to access to electricity and its impact on socio-economic
conditions. That study has pointed out that multidimensional aspects of poverty, such as the
economy, education, and health, have received increasing attention, with access to modern
energy sources, such as electricity, being a possible solution [11]. Energy access has traditionallybeen one of the core aspects of economic and social development. It is also closely related to theurgent issue of energy justice, involving everyone’s access to energy services and associatedsocial benefits, regardless of whether they live in developing or developed countries. The current
study has sought to explore the effect of electrification on educational outcomes and school
enrollment in rural areas in a developing country. Although a large body of literature is available
on different aspects of energy in developing countries, a serious lack of research is evident on theimpact of electrification on specific educational outcome. Therefore, this issue is of sufficientimportance to warrant further investigation. In the current case, the purpose of this literature
review is to investigate electrification’s rural prospects, progress, impact, and challenges as
documented in prior research. The review begins with a description of Bangladesh’s specificenergy-related discussions on energy consumption scenarios and then discusses the effects ofelectrification on school enrollment and students’ academic performance.
As shown in previous reviews, many studies have found the two-dimension nexus ofelectrification, that is, its economic and social dimensions. It is argued that electrification andlocal economic development are complementary, with electrification considered as the fuel for4rural economic development. Studies have highlighted that the framework conditions necessary
for the provision of energy services to rural communities must be identified, and that energy
access must be translated into improved rural development outcomes [12]. The impact ofelectricity on the local economy is believed to be a cross-cutting dimension, with household
electrical lighting reducing the use of kerosene, thereby reducing and improving households’
indoor air pollution. The importance of mitigating energy poverty in rural areas and its impact on
the economy cannot be underestimated. Energy poverty is a severe and growing problem
involving indoor air pollution, personal injury during fuelwood collection, and lack of publichealth care. Energy poverty affects the roles of both genders in society, as well as educational
opportunities for children and adults [13]. The problems faced by rural people in obtaining safe,
clean, and reliable energy supply are not minor inconveniences. Instead, these are significant
obstacles to rural economic development and social well-being. A multifaceted approach tosolving rural energy problems is not only justified but also essential.
The direct benefits of electrification come from improving lighting, promoting extended learningand reading time, and assisting with other housework, thus helping to improve educational
achievement. The causal relationships between electrification and educational outcomes as wellschool enrollment are complex. The existing literature has focused on the impact on socioeconomic development of rural electrification and electricity use. A World Bank working paper,
verifying research methods, findings, and robustness, reported the close link between energy,
economic growth and poverty reduction [14]. Rural socio-economic development and feedback
on various social and economic changes via electrification cannot be adequately estimated. Most
energy access impact assessments assume that energy access has linear, one-way effects.
However, predictions are rarely consistent with reality and, at times, indirect benefits could not
be reflected. From a modeling perspective, lack of attention to causality’s dynamic complexity
may mean that previous studies’ estimation results may be misleading. To improve the causaleffect relationships and to find the solution, the current study conducted a comprehensive review
and extensive analysis of the literature.
The Solar Home System (SHS) program has become the primary tool for providing electricity topeople living in rural areas in Bangladesh. The main reason for the successful adoption of the
SHS program is that it is focused on meeting the needs of the family and has the ability to make5

the system as affordable as possible [15]. Electrification with a solar PV system has many direct
and indirect benefits that ensure environmental sustainability. Reducing the use of kerosene is
the main impact of the SHS which reduces pollution, improves light quality, and increases
lighting time at night, thus reducing the workload involved in cleaning kerosene lamps [16].
Those who were using traditional fuels for small business activities can switch to solar lights andincrease their income due to extended working hours at night. Women and children enjoy greater
benefits due to the quality of light facilities in their homes. Women feel comfortable doinghousehold work and children can study in the evening. With the use of kerosene lamps and wicks,
people are increasingly worried about fire hazards and indoor air pollution [17].
The current study sought to examine the impact of lighting from the grid on school enrollment aswell as the impact of lighting from the SHS on educational outcomes and school enrollment in
Bangladesh. Many studies have shown impact analyses of rural electrification on income,education, and health in developing and developed countries. No specific study has been
undertaken on the impact of good quality lighting on school enrollment and educational
outcomes. Renewable energy is a key component of development, and Bangladesh has madesignificant progress in covering most of the country's population, in urban and in rural areas.
Change in technological innovation and in the SHS package is also seen in Bangladesh.
Consumer demand for the SHS changes with changes in price and in some essential features ofthe existing package. Solar photovoltaic (PV) technology brings enormous potential and benefits
to society. At the present time, a variety of materials are emerging in the PV market. In rural
areas, it is often difficult to purchase and transport kerosene fuel. The brighter SHS lamps arealso a massive improvement of the poor light provided by kerosene lamps. With SHS lighting
facilities, students spend more time learning, and women no longer rely solely on the sun forhousework. Lighting is also beneficial for other family activities, such as women sewing, social
gatherings after dark, etc. Solar power also helps local businesses, such as small shops andvillage markets, to operate at night, as well as assisting irrigation facilities. However, improving
efficiency is one of the critical factors in establishing PV technology in the market. The current
study tests consumers’ preferences for organic solar PV systems in Chhattisgarh, India, based ona randomized conjoint analysis. This study identified major potential benefits, such as organic
PV systems, efficiency, the system lifetime, and customer involvement in implementing solar
6lighting systems for the poor. The study aimed to find out the investment, promotion andappropriate business model to use to enhance the quality of life for poor people.
The current study aimed to overcome the fundamental limitations of many existing studies thatfailed to address the problems of endogeneity. Furthermore, the study sought to determine theimpact of access to electricity on school enrollment in Bangladesh and the impact of the SHS on
educational outcomes. The study used rigorous econometric techniques to analyze data from thecross-sectional Multiple Indicators Cluster Survey (MICS) of households in Bangladesh from2012–2013 and primary data of children with and without SHSs, as collected in 2018. The study
also collected data from India in 2017 to examine consumers’ preferences for future organic
solar PV systems. As discussed later in this study, electrification has benefits in alternative ways,
such as children’s education. This study does not analyze the effects of individual factors that
contribute to welfare benefits.
The study applied the Markov school transition model to conduct a disaggregated evaluation of
the impacts of access to electricity on children’s school enrollment in Bangladesh. This modelingapproach allowed for a more complete analysis of the impacts of electrification than a focus onenrollment rates alone, as found in most previous studies. Comparisons of educational transition
matrices estimated for treatments and controls revealed that electrification has a beneficial
impact on the educational accumulation process, with statistical tests rejecting the hypothesis ofzero impact of electrification for most ages. The SHS has improved the quality of life and created
opportunities for new income-generating activities, such as cell phone charging, small businesses,
watching TV, night work, and enjoying the lighting quality. The student with access to electricity
through an SHS is associated with better grade progression, a lower grade repetition rate, and alower rate of non-attendance. The impact of SHSs on educational outcomes is positive, while
their impact on school enrollment is mixed. Consumers in Bangladesh preferred the low wattpeak SHS at a convenient price. Consumers in rural India chose the solar PV system whichworks in cloudy weather conditions.
The structure of this PhD thesis is as follows. Chapter 2 presents the effects of electrification onschool enrollment in Bangladesh. Chapter 3 shows the impact of a solar home system (SHS) on
educational outcomes in rural Bangladesh. Chapter 4 discusses the conjoint analysis ofconsumers’ preferences for a future organic solar PV system, and Chapter 5 concludes the thesis.
7Chapter 2: Effects of Electrification on School Enrollment in
Bangladesh: Short- and Long-Run Perspectives
2.1 Introduction
Lighting is a basic human need and is also considered an important indicator of everyday
lifestyle. Changes in lighting also change people’s performance. Several mechanisms contributeto increasing human performance through improved lighting, such as visual performance, visual
comfort, interpersonal skills, problem solving, and change processes [18]. Lighting also has nonvisual effects. Good quality lighting affects performance, mood, attention, and synchronization[19]. Most households in unelectrified regions use kerosene lamps, candles, and solar lanterns assources of indoor lighting. These types of lighting adversely affect the safety, health, andenvironment of household members. Access to electricity is regarded as access to lighting
sources. The current study extends reflection on the link between access to electricity and school
enrollment. It is motivated by an empirical study based in rural Mexico which showed that aschool subsidy program was associated with higher enrollment rates, less grade repetition, better
grade progression, and lower dropout rates [20]. Moreover, the limited studies conducted inseveral developing countries have shown the effects of access to electricity on children’s
education in terms of study time and school attainment. The current study sought to examine theeffects of electrification on school enrollment (grade progression, grade repetition, and nonattendance) in both the short run and long run.
Several assessments have been undertaken of the impacts of electricity on socio-economic
development. Access to electricity reduces the time spent by children on activities such as
gathering fuelwood and fetching water, promoting home study and enabling the use of
educational media and communications at school [21]. A study by the United Nations
Development Programme/World Health Organization (UNDP/WHO) showed that education
enrollment ratios correlate with access to electricity [22]. One macro level study using panel data
showed that long-run bidirectional causality exists between electricity consumption and fivehuman development indicators: per capita GDP, consumption expenditure, urbanization rate, life
expectancy at birth, and adult literacy rate [1]. Many researchers have shown a positive
relationship between access to modern energy and economic development. Affordable and8accessible modern energy plays an essential role in development and ensures sustainable
development [23][24][25]. However, electricity and income exhibit two-way causality: income
explains the potential to connect to electricity, and connection to electricity has a substantial andsignificant effect on income [26]. Empirical studies on electrification have generally supported
the benefits to health, education, and income; however, these claims are weak [27][28]. Home
electrification helps to improve children’s education [29]. One study claimed that interactions
between energy and development are complex and not causal [30]. The impact of electrification
appears to increase the hours of work for men and women, in particular, increasing women’s
employment outside the home by releasing them from home production [31]. Dinkelman [31]applied two identification strategies, namely, instrumental variables and fixed-effect approaches,to overcome the endogeneity problem of electrification. The confounding trend of electrification
makes it more difficult to identify the treatment effect on the economy. In the current study, it isassumed that no confounder is present in the study’s model, and propensity score matching(PSM) is applied to identify the impact of electrification on school enrollment.
Household access to electricity has a significant positive impact on children’s nutritional status
as a result of the family’s increased wealth. Children’s nutritional outcomes are affected bycausal channels such as wealth, fertility, and television [32]. One empirical study estimated thecausal impact and showed that electrification has a significantly positive impact on householdincome, expenditure, and school enrollment in Bangladesh [33]. Another study based on
country-level data from urban and rural Brazil showed that electrification has a substantial
positive and significant effect on income, literacy, and enrollment rate components of education[34]. Furthermore, a study indicated that electricity from an SHS increases children’s study timein Bangladesh [35]. The electrification of homes, schools, and communities has a significant
effect on educational outcomes [36]; moreover, electrification has a positive effect on female
enrollment in school and on reading capability for both boys and girls [37].
Bangladesh was the first country in the world to implement school incentive programs toincrease school attendance, especially for children from low-income families. These incentives
include free tuition, books, food provided in exchange for school attendance (Food for
Education), and a stipend for female students. Over the past two decades, a significant proportionof education policies has been applied to increasing school enrollment through Food for9Education and stipends for poor students and female students. This type of education policy
suggests that income is the primary barrier to children continuing their studies in school. The
cash incentive program has a direct effect on school enrollment, and low-income families
respond positively by sending their children to school. Evidence from descriptive statistics has
shown that school incentive programs (Food for Education and female student stipends) in
Bangladesh increase children’s school attendance, and decrease the out-of-school rate and childlabor activity [38]. Another study, based on descriptive statistics and a multivariate model, also
showed that the Food for Education program increased school enrollment, promoting school
attendance and preventing dropping out [39]. The motivation for the current study is to assess theimpact of electrification on school enrollment. In Bangladesh, providing continuous electricity isa major problem, and load shedding is a common scenario in rural areas. Access to electricity has
an indirect effect on children’s study due to the quality of lighting. The current study seeks to
examine the impact of access to electricity on school enrollment.
Education is an essential tool for strengthening human resources and maintaining steadydevelopment in any country. In Bangladesh, gross school enrollment has approached theuniversal level, and the primary school completion rate has remained at 60% since 2000 [40].
Grade repetition, non-attendance, and dropping out also remain as major problems. Some
research has shown that dropping out occurs due to either financial problems or a lack of interestin education. Several factors potentially drive these causes such as age, gender, poor physicalcondition, geographical location, household characteristics, and economic hardship [41][42].
Study completion also relates to gender, family income, and the cost of school fees, books,
uniforms, and transportation [43].
Several pathways can be followed to identify the possible causal impacts of electrification onschool enrollment. Firstly, the use of electricity enhances the income opportunities of thehousehold through extended work hours, and a greater income prompts parents to send theirchildren to school to achieve a better future, based on the social and financial returns ofeducation. Secondly, electricity can improve the lighting status of the household and replace
traditional candles and kerosene lamps. Household electrification leads to a reduction in indoor
air pollution [44]. This helps children to study longer and with better concentration. It also
allows parents to take better care of their children by allowing more flexible use of time. Thirdly,
10

access to electricity provides families with more, better quality information through information
technology (IT) such as cell phones and television. It is possible to speculate that the benefits
from access to electricity can be explained by these three causal channels, but the above causal
impacts cannot be estimated due to the absence of data.
Most previous studies have used different ways to show the impact of access to electricity on
education. This type of nexus states the positive benefit of electricity use on education without
explaining the causal relationship. Some studies have reported correlations that appear to show
the positive impact of electricity use, although multiple socio-economic factors might impact on
education. The existing literature has also failed to capture the reverse causalities and thepotential bias of access to electricity on educational outcomes. The potential drawbacks ofstudies in the existing literature are as follows: (1) most studies rely on correlations and (2) moststudies adopt a single indicator, such as the enrollment rate, dropout rate, or grade repetition rate;thus, they cannot assess the long-run effects of electricity on school enrollment.
For the purpose of evaluation, the current study applied a propensity score matching (PSM)
approach that captures different covariates for participation in a single propensity score. This
study has adopted a non-experimental strategy to assess the impact of electricity on school
enrollment in Bangladesh. Thus, by using panel data from the Multiple Indicators Cluster Survey(MICS) database, through PSM, it is possible to isolate the causal effect of access to electricity
on school enrollment. The nearest neighbor matching (NNM) method was applied to estimate theimpact of access to electricity, in which each treatment unit was matched to the comparison unit
with the closest propensity score. The study constructed transition matrices for both groups basedon age and took the difference to estimate the short- and long-run impact of access to electricity
on school enrollment. The data analyzed in this study cover two years of information on gradesof children studying in primary and secondary schools. Thus, it is impossible to assess the longrun impact due to the short time span of the data. However, the long-run impact is the study’s
key interest as access to electricity is an important indicator of socio-economic development.
Therefore, the study proposes a method for simulating the effects through age transition
matrices. The results, based on the study’s simulation, indicate that if children are aged 5 when
they begin attending school and continue to study to age 15, their school enrollment distribution
would change substantially. Moreover, non-attendance would decrease by 2.48%, and transition11

to grade 11 would increase by 0.43% in electrified regions compared to unelectrified regions.
The simulation results also show that a substantial impact of electrification on school enrollment
occurs for grades 9–11.
The rest of the chapter is organized as follows. Section 2.2 provides the estimation methods,
comprising the matching procedure and the Markov schooling transition model. Section 2.3
presents a description of the data. Section 2.4 provides the empirical results and discussion,
while Section 2.5 presents the conclusion.
2.2 Estimation Methods
If access to electricity were randomly assigned to households, it would be an experiment. It
would be possible to evaluate by household the causal effect of access to electricity on children’s
school enrollment as the difference in average school enrollment between those with and those
without access to electricity. However, home electrification is based on self-selection by eachhead of the household instead of random assignment. The government electrification program isalso influenced by political pressure, regional priority, and donors’ attitudes [45]. Rich
households enjoy more opportunity to install electrification compared to poor households. It canbe said that the treatment assignment is not random and that a systematic difference exists
between the group with electricity and the group without electricity. Selection bias could
possibly occur as unobservable factors influence both the treatment and outcome variables.
Hence, the application of the ordinary least squares (OLS) method would result in biased
estimates. In the current study, the difference-in-difference (DID) method could not be applied as
the study had one-shot data. However, the study could control for selection bias by employing an
instrumental variable (IV) approach. Finding an appropriate instrument from the data set proved
to be difficult [46].
Access to electricity could be considered a non-randomized treatment, with the treatment effectbased solely on observable characteristics. The PSM estimator is a popular method among
analysts, especially for social program evaluation. The current study sought to construct atransition matrix that would reveal the grade transition of children between the two groups;
however, matched samples were needed to construct this kind of transition matrix. The PSM
method assisted the study by grasping matched samples. The study then applied the Markov
12schooling transition model based on these matched samples. The short- and long-run impacts on
school enrollment were thereby evaluated. However, the recent empirical literature has identified
some bias in the PSM method [47] which is associated with factors including: selection of theunobservable; failure of the common support condition; failure to control for local differences;and selection of the dependent variable for both control and treatment groups [48].
2.2.1 Matching Procedure

It is possible to estimate the average treatment effect (ATE) in a counterfactual framework, in
accordance with Rosenbaum and Rubin [49], as follows:
𝐴𝑇𝐸 = 𝑌𝑖𝐴𝐸 − 𝑌𝑖𝑁𝐴𝐸(1)

where 𝑌𝑖𝐴𝐸 and 𝑌𝑖𝑁𝐴𝐸 , respectively, denote school enrollment of children in a household that hasaccess to electricity and children in a household that does not have access to electricity. As both𝑌𝑖𝐴𝐸 and 𝑌𝑖𝑁𝐴𝐸 are not normally distributed, the normal distribution equation can be expressed asfollows:
𝑌𝑖 = 𝑇𝑖 𝑌𝑖𝐴𝐸 + (1 − 𝑇𝑖 )𝑌𝑖𝑁𝐴𝐸 𝑇 = 0, 1

(2)

If P is considered as the probability of observing a household with access to electricity, that is,
𝑇 = 1, the average treatment effect can be written as:
𝐴𝑇𝐸 = 𝑃 · [𝐸(𝑌𝐴𝐸 |𝑇 = 1) − 𝐸(𝑌𝑁𝐴𝐸 |𝑇 = 1)] + (1− 𝑃)[𝐸(𝑌𝐴𝐸 |𝑇 = 0) − 𝐸(𝑌𝑁𝐴𝐸 |𝑇 = 0)](3)

Equation (3) indicates the effect of access to electricity on the entire samples. This is measured
by the weighted average of the effect of access to electricity on both the treated sample and thecontrol sample with each weighted by its relative frequency. It is not possible to estimate thecausal inference of the unobserved counterfactuals, (𝐸(𝑌𝐴𝐸 |𝑇 = 0) and 𝐸(𝑌𝑁𝐴𝐸 |𝑇 = 1)) [32].
An important issue in evaluating the impact of access to electricity on children’s school
enrollment is that it might not be possible to obtain counterfactual information from the existingdata sets. The study sought to solve the problem by using the PSM method that enables theconstruction of a single propensity score from the pre-treatment characteristics [49]. It was then13

possible to use the propensity score for matching with scores from similar individuals. Based on
the treatment, the PSM method, given the conditional pre-treatment variables, is calculated asfollows:
𝑝(𝑋) = 𝑃𝑟[𝑇 = 1|𝑋] = 𝐸[𝑇|𝑋]; 𝑝(𝑋) = 𝐹{ℎ(𝑋𝑖 )}(4)

where 𝐹{. } can be normal or probit cumulative distribution, and X is a vector of covariate
characteristics.
Two conditions need to be fulfilled, namely, the conditional independence assumption (CIA) andcommon support between the two groups. The matching method can be meaningfully applied
over regions of common support (see Appendix Figure A1). A strong argument is that a personwith the same propensity score should have the same X values, with a positive probability of
being both in the treated and control groups [50]. The average treatment effect for the treated(ATET), based on propensity scores, can be estimated as follows:
𝐴𝑇𝐸𝑇 = 𝐸{𝑌𝑖𝐴𝐸 − 𝑌𝑖𝑁𝐴𝐸 |𝑇 = 1},
𝐴𝑇𝐸𝑇 = 𝐸[𝐸{𝑌𝑖𝐴𝐸 − 𝑌𝑖𝑁𝐴𝐸 |𝑇𝑖 = 1, 𝑝(𝑋)}],(5)

𝐴𝑇𝐸𝑇 = 𝐸[𝐸{𝑌𝑖𝐴𝐸 |𝑇𝑖 = 1, 𝑝(𝑋)} − 𝐸{(𝑌𝑖𝑁𝐴𝐸 |𝑇𝑖 = 0, 𝑝(𝑋)}|𝑇 = 1].
This indicates the average difference between those who are treated and their matching partners.
A popular way to estimate the treatment effect is the nearest neighbor matching (NNM) method.
In the current study, the treatment effect is estimated based on the propensity score, but not on
the condition of all covariates. The covariate balancing between the treatment and control groups
after matching needs to be checked (see Appendix Figure A2).
2.2.2 Justification of Covariate Selection

The determinants of household access to grid electricity comprise many factors, with the main
one being household income. A study in South Africa showed that household income andelectricity price are the main determinants of electricity demand [51]. Household size anddwelling type are also important determinants of electricity consumption [52]. For gaining accessto electricity, household location is important. Access to electricity for a rural household has amore significant positive effect on education and health attainments than is the case for an urban
14household [53]. It is expected that electricity demand in rural areas is mainly for the purpose oflighting, with lighting also shown to affect children’s education in developing countries. Gridelectrification is not possible in rural areas due to budget constraints. Kanagawa and Nakata [54]reported that access to electricity was linked to infrastructure, supply capacity, government
policy, and international cooperation.
Adoption of electricity at the household level depends on various socio-economic characteristicsof the household, its geographical position, government policy, etc. Identifying the determinantsof access to electricity at the household level is at times difficult due to a mix of individual
characteristics and geographical factors. Khandker et al. [33] showed that, in Bangladesh, theimpact of access to grid electrification on income and educational outcomes is positive andsignificant. In their study, they applied PSM and the instrumental variable (IV) approach toestimate the causal effect of access to electricity on income, expenditure, and educational
outcomes. Their set of covariates included: gender, age, and level of education of head ofhousehold; household landholding, dwelling, and drinking water; village price of kerosene, etc.
to estimate the propensity score of household access to electricity.
In another study, Khandker et al. [55] showed that, in Vietnam, the impact of access to gridelectrification had significant positive effects on a household’s cash income, expenditure, andeducational outcomes. They applied difference-in-difference (DID), DID with fixed effect (FE)regression, and PSM–DID to estimate the causal effect of electrification. In their study, thepropensity score of access to grid electricity was estimated based on: gender, age, and educationlevel of head of household; household landholding and running water; commune price ofkerosene, etc.
Kumar and Rauniyar [56] applied PSM to show the impact of access to electricity on income andeducational outcomes in Bhutan, finding that it had a positive impact on non-farm income andeducational outcomes. To estimate the propensity score, they used: household size; gender andage of head of household; amount of household land; access to tap water; house structure;religion; and distance as covariates.
The current study chose age, gender, and education level of head of household; family size;
number of sleeping rooms; location, and having a water pump; and the wealth score from the15

MICS database as covariates. The data did not include any income or expenditure information,and the wealth score was used as a proxy for the income variable.
2.2.3 Markov Model of Schooling Transition

The Markov schooling transition probability matrix was used to show the impact of access toelectricity on school enrollment, measured by factors such as grade progression, grade repetition,and non-attendance. This transition matrix provides a convenient framework that can be used toassess the impact on various dimensions.
In Bangladesh, three possible schooling states are available for 5 to 6-year-old children: nonattendance, enrolled in grade 1, or enrolled in grade 2. In Bangladesh, most 6-year-old children
are enrolled in grade 1. For 7-year-old children, four possible schooling states exist: enrolled in
grade 3, enrolled in grade 2, enrolled in grade 1, and non-attendance. The most common state for
7-year-old children is enrolled in grade 2.
A transition probability matrix describes the transition using various ages for children by theirschooling state. The distribution of 7-year-old children’s schooling state, given the initial
distribution of 6-year-olds, is obtained in the following way:
𝑓37
𝑓27
𝑓17
7
(𝑓𝑁𝐴 )

4×1

6
𝐴11
𝐴621
=
𝐴631
6
(𝐴41

6
𝐴12
𝐴622
𝐴632
𝐴642

6
𝐴13
𝐴623
𝐴633
𝐴643 )

𝑓26( 𝑓16 )
6
𝑓𝑁𝐴
3×1
4×3

The above matrix can be written in the following equation:
𝑓 7 = 𝐴6 𝑓 6(6)where A6 is the transition matrix for children aged 6 years old, and f 6 is the vector of schoolingstate proportions. The study needed to increase the number of rows in the A matrix with age as
the number of potential grade levels increases.

16

2.2.4 Estimating Short-Run Impacts: 1-Year Impacts

The current study had grading information for more than 30,000 children from electrified regions
and more than 26,000 children from unelectrified regions. The nearest neighbor matching(NNM)-based PSM matched the data between the two types of region. Grade information was
obtained for 26,499 children for both the treatment and control groups. It was considered that the
1-year impact of access to electricity for children of a given age a could be evaluated bycomparing the age-specific transition matrix estimated for the treated (unelectrified) and control(electrified) groups:
𝐴̂𝑎𝑇=1 − 𝐴̂𝑎𝑇=0
Transition matrices were constructed for both groups which compared the short-run effects ofaccess to electricity on grade progression, grade repetition, and non-attendance at each age, andtaking the difference to estimate the impact of access to electricity. Matching ensured that theeffect of access to electricity could be calculated by simply taking the difference between the twogroups.
The study also tested whether the observed treatment and control group differences were
statistically significant based on Pearson’s chi-squared tests. Two types of tests were examined:
an equivalence test between the treatment and control transition matrices, and a test ofequivalence between the individual columns of the matrices.
2.2.5 Simulating Long-Run Impact of Access to Electricity

The long-run impact of access to electricity on school enrollment was of greater interest in thecurrent study for policy purposes. Children in the data set were observed for only two years,
therefore, the long-run impact of access to electricity could not be directly estimated. Therefore,a simulation approach was applied that used the Markov schooling transition model to predict theeffects of access to electricity on school enrollment at age 15. The study made the following two
assumptions about the greater validity of the evaluation process:Assumption 1: The number of children at age 4 is the same as the number expected to go toschool at age 5.
17Assumption 2: The age-specific transition matrices are consistent over time.
Under both assumptions and given an initial vector of the state proportion at each age, thepredicted schooling state could be found by the product of the previous age enrollment status andthe state proportions of the current age. The mathematical expression for the predicted school
enrollment status of 6-year-old children for both treatment and control groups is as follows:𝑓̃𝑔6 = 𝑓̂𝑔5 𝐴̂6where the predicted enrollment status is indicated with a tilde (~) and things directly estimatedfrom the age transition matrices are indicated with a hat (^). More generally, the predicted grade
status at any age a is given by:𝑓̂𝑔𝑎 = 𝑓̂𝑔𝑎−1 𝐴̂𝑎
The study started with children at age 5 and completed the transition at age 15. At the end of thetransition at age 15, various grade levels and non-attendance information were obtained for both
treatment and control groups, with the difference then taken to judge the long-run impacts ofaccess to electricity.
2.3 Description of the Data
The data are from Bangladesh’s MICS database 2012–2013 created by the Bangladesh Bureau of
Statistics (BBS), the Ministry of Planning, Bangladesh, and the United Nations Children’s Fund
(UNICEF). The survey collected comprehensive, detailed information on a wide range of topics,
including: household information; household characteristics; education; water and sanitation;
children under 5; women; salt iodization; and water quality testing. The data provide estimates at
the national level with disaggregated data by division, location, gender, age, education level, andwealth quintiles. Bangladesh’s MICS database 2012–2013 is based on a sample of51,895 interviewed households and offers a comprehensive picture of children’s education andnutrition. These panel data over two years captured information about children’s school
attendance and grades. From the data set, most 5- and 6-year-olds were enrolled in grade 1 or
one of the three possible schooling states as follows:•

Non-attendance18

•

Enrolled in grade 1 or•

Enrolled in grade 2.

For children aged 7 years, four possible schooling states were available: enrolled in grade 3,
enrolled in grade 2, enrolled in grade 1, and non-attendance. The definitions of the variables are
given in Table 2.1.
Table 2.1: Variable definitions
Variable
Grade progression
Grade repetition
Non-attendance
Access to electricity
Gender of the head of household
Age of head of household
Family size
Sleeping rooms
Location
Education level of head of household
Have a water pumpWealth score

Definition
Promotion from existing grade to upper grade
Kept in the same grade due to bad performance
Discontinue study due to various reasonsHousehold has access to electricity or not (yes/no)
Male/female
Age in years
Average size of household by number of residents
Number of sleeping rooms of household
Household location (rural/urban)
Measured by years of schoolingHousehold has a water pump or not (yes/no)
Composite index which ranges from 1 to 5

School enrollment (grade progression, grade repetition, and non-attendance) was considered as
the outcome variable and access to electricity as the treatment. Some demographic and socioeconomic features of the household were also considered as control variables.
2.4 Empirical Results and Discussion
2.4.1 Estimated Propensity Score of Access to Electricity

The current study sought to estimate the probability that a household would have access toelectricity based on the observed values of characteristics (explanatory variables) such as gender,
age, and education level of the head of household; location; family size; number of sleeping
rooms; having a water pump; and the wealth score. As shown in Table 2.2, the likelihood that ahousehold has access to electricity is smaller if the household family size is large, or if thehousehold has a water pump or is headed by a male.

19

Table 2.2: Probit regression: estimated propensity score based on baseline observed characteristics
Dependent Variable: Access to Electricity
Access to electricity = 1
Explanatory variables: Baseline observed
characteristics
Urban = 1
Family_size
Sleeping_rooms (number)
Water_pump
Wscore (wealth)Head of household age
Head of household gender (male = 1)
Head of household’s education level (years)
Constant
Observations

Full Set of Explanatory
Variables

Limited Set of Explanatory
Variables

Coefficient

Coefficient

0.396 ***
(0.0227)−0.0352 ***(0.00388)
0.00729 **(0.00135)−0.399 ***(0.0636)
1.869 ***
(0.0242)0.000665(0.000445)−0.0556 **(0.0221)0.0115 ***(0.00194)
0.559 ***
(0.0255)
56,071−0.212 ***(0.0211)
0.0947 ***(0.00142)−0.0658 ***(0.0209)
56,071

Note: Standard errors (SEs) in parentheses; *** p < 0.01; ** p < 0.05; * p < 0.1.

In contrast, living in an urban area, having more sleeping rooms, having a larger wealth score,and having a more educated head of the household all increase the likelihood that a household
has access to electricity.
2.4.2 Estimated Average Treatment Effect on the Treated

The average treatment effect on the treated (ATET) always produces an identical outcome. The
study applied the ATET to estimate the impact of electrification on school enrollment (grade
progression, grade repetition, and non-attendance) through the NNM method (see Table 2.3).

20

Table 2.3: Average treatment effect on the treated (ATET)
Outcomes
ATET
Access to electricity (1 vs. 0)
Observations(1)
Grade Progression(2)
Grade Repetition(3)
Non-Attendance

0.0276 ***(0.00592)
56,071−0.00190(0.00177)
56,071−0.0257 ***(0.00577)
56,071

Note: Standard errors (SEs) in parentheses: *** p < 0.01; ** p < 0.05; * p < 0.1.

•

Access to electricity increases grade progression by an average of 0.0276 (2.76%) whichis statistically significant.

•Access to electricity has a negative impact on grade repetition and is statisticallyinsignificant.

•Access to electricity decreases non-attendance by an average of 0.0257 (2.57%) and is
statistically significant.

2.4.2.1 Impact Estimates (Short-Run and Long-Run) Based on Markov’s Schooling Transition Model

Through Markov’s schooling transition model, the study shows how access to electricity affects
school enrollment. Firstly, the short-run impact of access to electricity on school enrollment wasestimated by comparing the treatment and control group children. Secondly, the long-run impactof access to electricity was simulated using the method proposed in Section 2.4.
2.4.2.1.1 Comparison of Treatment and Control Groups (Short-Run)

Tables 2.4–2.6 provide the details of grade transition based on age, with other tables located in
the Appendix. These tables show the estimates for the schooling transition matrices for children
aged 5–15 years. Table 2.4 shows the distributed estimated probabilities of transitioning fromthree potential states at age 5 to four potential schooling states at age 6. The letter ‘G’ indicatesthe source state that corresponds either to grade promotion, to the same grade, or to nonattendance. The top panel of the matrices provides the transition matrix for the treated group(unelectrified), the middle panel provides the transition matrix for the control group (electrified),
and the last panel shows the treatment–control group differences. Matching would imply that thedifferences between treatment and control groups due to electrification are largely supported by
the data.
21Impacts on primary school age children: From ages 5–6, the grade repetition rate isapproximately 8% lower for those who have access to electricity compared to those who do not,as shown in Table 2.4. The transition from grade 1 to grade 2 is 8% more likely for those whohave access to electricity compared to those who do not.
From ages 6–7, as shown in Table 2.5, the grade repetition rate is approximately 3.4% lower for
those who have access to electricity than for those who do not. The transition from grade 1 tograde 2 is 3.4% more likely for those who have access to electricity compared to those who donot. In addition, the non-attendance rate is 10.4% lower for those who have access to electricity
compared to those who do not. Thus, access to electricity appears to foster grade progression andreduce grade repetition. Furthermore, it reduces non-attendance among children which is asignificant difference between the treatment and control samples.
Impacts on the transition to secondary school: From ages 12–13, the grade repetition rate isapproximately 1.33% higher for those who have access to electricity compared to those who donot. Transitioning from grade 6 to grade 7 is 1.33% more likely for those who have access toelectricity compared to those who do not. In addition, the non-attendance rate is 1.56% lower for
those who have access to electricity compared to those who do not. These results are shown in
Table 2.6.

22

Table 2.4: Transition matrices (age 5 to 6)
Grade (G)2
1Treatment Transition Matrix
0.875--0.1250.564--0.436
----8
181
0.003
0.068
Control Transition Matrix
0.833--0.167
0.645--0.355----12
211
0.004
0.078
Treatment–Control Differences
0.042--−0.042−0.08--0.08----20
392
0.004
0.073
0.76
0.08

P(3|G)
P(2|G)
P(1|G)
P(NA|NA)
Observation
P(G)P(3|G)
P(2|G)
P(1|G)
P(NA|NA)
Observation
P(G)P(3|G)
P(2|G)
P(1|G)
P(NA|NA)
Observation
P(G)p-valueNA----0.163
0.837
2483
0.929
----0.207
0.793
2495
0.918
----−0.044
0.044
4978
0.924
0.25

Note: NA = non-attendance.

Table 2.5: Transition matrices (age 6 to 7)
Grade (G)2Treatment Transition Matrix
1.000----0.969--0.031--------16
96
0.005
0.032
Control Transition Matrix
1.000----0.960--0.040--------8
100
0.003
0.037
Treatment–Control Differences
0.000----0.009--−0.009--------24
196
0.004
0.035
0.060
1.000
3

P(4|G)
P(3|G)
P(2|G)
P(1|G)
P(NA|NA)
Observation
P(G)P(4|G)
P(3|G)
P(2|G)
P(1|G)
P(NA|NA)
Observation
P(G)P(4|G)
P(3|G)
P(2|G)
P(1|G)
P(NA|NA)
Observation
P(G)p-value

Note: NA = non-attendance.

23

1

NA

----0.797
0.203
--693
0.233

------0.378
0.622
2166
0.729

----0.830
0.170
--690
0.256

------0.482
0.518
1899
0.704

----−0.034
0.034--13830.244
0.050------−0.104
0.104
4065
0.717
0.008

Table 2.6: Transition matrices (age 12 to 13)
98

P(10|G)
P(9|G)
P(8|G)
P(7|G)
P(6|G)
P(5|G)
P(4|G)
P(3|G)
P(2|G)
P(1|G)
P(NA|NA)
Observation
P(G)

1.000
--------------------10
0.0042--1.000------------------51
0.0215

P(10|G)
P(9|G)
P(8|G)
P(7|G)
P(6|G)
P(5|G)
P(4|G)
P(3|G)
P(2|G)
P(1|G)
P(NA|NA)
Observation
P(G)0.90910.0909------------------11
0.0045--0.9911
0.0089----------------112
0.0457

P(10|G)
P(9|G)
P(8|G)
P(7|G)
P(6|G)
P(5|G)
P(4|G)
P(3|G)
P(2|G)
P(1|G)
P(NA|NA)
Observation
P(G)p-value

0.0909−0.0909------------------21
0.00440.85--0.0089−0.0089----------------163
0.03380.72

Grade (G)6
54Treatment Transition Matrix----------------0.995------0.005
0.9822------0.004
0.973------0.027
0.997------0.003--------------------------------208
445
368
312
0.08750.18720.15480.1313Control Transition Matrix----------------0.9906------0.0094
0.996------0.0178
0.9900------0.0100
0.9861------0.0139--------------------------------320
618
399
360
0.13070.25230.16290.1470Treatment–Control Differences----------------0.0046------−0.0046 −0.0133------−0.0133 −0.0171------0.0171
0.0107------−0.0107--------------------------------528
1063
767
672
0.10940.22030.15890.1392
0.31
0.06
0.46
0.25
7

3

2

1

NA

------------0.983
0.017------232
0.0976--------------0.986
0.014----138
0.0581----------------0.922
0.078
--51
0.0215------------------0.011
0.989
562
0.2364------------0.9744
0.0256------195
0.0796--------------0.9804
0.0196----1020.0416----------------0.9259
0.0741--27
0.0110-------------------0.0262
0.9738305
0.1245------------0.0084−0.0084------427
0.08850.40--------------0.0051−0.0051----2400.04970.87----------------−0.0044
0.0044--78
0.01620.13------------------−0.0156
0.0156867
0.17970.04

Note: NA = non-attendance.
2.4.2.1.2 Long-Run Impacts of Access to Electricity and Comparison of Treatment and Control Groups

From the above results, the short-run impacts of access to electricity on school enrollment bothfor primary and secondary school children can be determined. These impacts have been shown
year by year. The study’s next goal was to connect the impact of access to electricity with school
enrollment over a long period. In the short run, access to electricity has a significant positive
impact on grade progression/transition, a significant negative effect on non-attendance, andmixed effects on grade repetition. The study was interested in determining the impact of access24

to electricity on school enrollment up to age 15, with year-by-year impacts expected toaccumulate.
It was assumed that children had continuous access to electricity starting at age 5 and up to
age 15. Transition matrices were then obtained from age 5 to age 15. It was supposed that
10,000 children of age 4 were expected to attend school at age 5 as shown in Table 2.7 (based on
the transition matrix):
Table 2.7: Grade distribution at age 5
Categories
Treatment (Unelectrified)
Control (Electrified)

Grade 1
690
733

Grade 2
43
40

Non-Attendance
9267
9227

When the transition was examined up to age 15, the following results were obtained (seeFigure 2.1):

Treatment (Unelectrified)25

Control (Electrified)Figure 2.1: Grade transition (age 5 to 15)

Consider a 15-year-old who enrolled in grade 1 at age 5 and will potentially reach grade 12 when
he/she is age 15. He/she needs to complete 12 years of school to reach grade 12. This suggeststhat the treatment can be examined over 10 years based on access to electricity. The impact issummarized in Table 2.8 below:
Table 2.8: Long-run impact of access to electricity
Transition
Non-attendance
Grade 11
Grade 10
Grade 9Treatment (Unelectrified)
6.17%
3.14%
14.89%
28.73%

Control (Electrified)
3.69%
3.57%
18.40%
35.15%

To estimate the long-run impact of access to electricity, a simulation was applied. The simulation
assumed that a child is going to school continuously for 10 years, starting at age 5. The study
compared the predicted school enrollment distribution between the unelectrified (treatment) andelectrified (control) groups at age 15 and omitted non-attendance students. Table 2.9 presents thesimulated probability distribution function (PDF) and cumulative distribution function (CDF)
values for the treatment and control groups, with treatment defined as lacking access tohousehold electricity for 10 years for the age range 5–15.
26

Table 2.9: Simulated school enrollment distribution at age 15 (treatment and control)
Grade
1
2
3
4
5
6
7
8
9
10
11
12

Treatment (PDF)
0.01
0.01
0.13
0.59
2.36
4.89
14.44
27.46
30.62
15.87
3.35
0.27

Control (PDF)
0.02
0.03
0.12
0.37
1.12
3.45
10.59
24.78
36.50
19.10
3.71
0.21

Treatment (CDF)
0.01
0.02
0.15
0.74
3.1
7.9
22.34
49.8
80.42
96.29
99.64
100

Control (CDF)
0.02
0.05
0.17
0.54
1.66
5.11
15.7
40.48
76.98
96.08
99.79
100

Figure 2.2 displays the school enrollment scenario for a 15-year-old child who has continuously
attended school for the last 10 years. The school enrollment rate is higher for grades 9–11 in theelectrified group compared to the unelectrified group, revealing substantial differences between
the treatment and control groups. Most of the treatment impact occurs at age 14.
The study predicted the long-run impact of access to electricity on education, demonstrating that
this is a very strategic policy option that justifies the provision of electricity in unelectrified
regions to reduce school non-attendance. It was also noted that grade progression is affected by
the quality of lighting which, especially in rural areas, is an important indicator on which
education policy should focus. The quality of lighting provides an opportunity for students tostudy at night; it is the researcher’s belief that it can increase school enrollment.

27

Simulated Distribution with Treatment (Unelectrified)
35.00

30.62
27.46

30.00

percent

25.00
20.00

15.87

14.44

15.00
10.00
4.89
5.00
0.01

0.01

0.13

0.59

1

2

3

4

3.35

2.36

0.27

0.00
5

6

7

8

9

10

11

12

education level
Simulated Distribution with Control (Electrified)
40.00

36.50

35.00

percent

30.00

24.78

25.00

19.10

20.00
15.00

10.59

10.00
5.00

0.02

0.03

0.12

0.37

1.12

1

2

3

4

5

3.71

3.45

0.21

0.00
6

7

8

9

10

11

12

education level

Figure 2.2: Simulated effects of treatment on school enrollment distribution at age 15

2.5 Conclusion
In theory, the impact of access to electricity on education is unclear, with multiple mechanisms
possibly at work. No consensus was found in the empirical literature on the impact of access toelectricity on education. This thesis documents empirical research that has tested the strategic
policy question of whether access to electricity increases grade progression and reduces nonattendance rates for children aged between 5 and 15. The relationship between access toelectricity and school enrollment is complex. Although access to electricity affects children’s
study, it is not the only factor (factors such as income, location, culture, and government policy
are also considered very important). The occurrence of non-attendance results from the complex
28

interaction of economic, individual, family, and school-related factors [57]. A positive
relationship has been found between access to electricity and the household’s economic
condition [58]. The current study’s focus point was to assess the impact of electrification onschool enrollment. What was particularly notable in the current study was the impact of access toelectricity on grade progression and non-attendance, observed between the treatment and control
groups in both the short run and long run. Firstly, the average treatment effect on the treated(ATET) showed that access to electricity significantly increased grade progression and reduced
non-attendance. Secondly, the Markov schooling transition model showed that access toelectricity had a positive impact on grade progression and a negative impact on non-attendancein the short and long run. For grade repetition, however, access to electricity had a mixed effect.
Although some researchers found positive effects, others found no effect. In some instances,
access to electricity showed some improvement in the number of study hours and level of school
enrollment [59][60]. However, in another case, lighting had an insignificant effect on children’s
study time [61]. To contribute to the existing literature, the current study evaluated the impact ofaccess to electricity on grade progression, grade repetition, and non-attendance. Overall, it wasapparent that a broad scope exists for enhancing school enrollment by ensuring access toelectricity. Education policies are needed to encourage school enrollment and to reduce nonattendance at primary and secondary schools based on strategic factors. These policies could
provide financial support and better quality lighting, potentially increasing study continuation forchildren. It is noteworthy that the government of Bangladesh could take the initiative to reduce
student non-attendance, which is partially caused by non-access to electricity. One empirical
study showed that access to electricity reduces school attendance [62]. The current study’s
findings did not support this finding. However, improving the quality of lighting could be
regarded as a way to reduce school non-attendance in Bangladesh. Many characteristics of rural
areas make it more challenging (unfeasible and impractical) to provide grid electricity [63].
Improved targeting of educational research and resources on access to lighting and education
might be a strong tool for maximizing school enrollment in Bangladesh.

29

Chapter 3: Impact of a Solar Home System (SHS) on Educational
Outcomes in Rural Bangladesh
3.1 Introduction
Lack of adequate lighting severely undermines children’s ability to learn at school and at homein rural areas [64]. Sustainable development is a common agenda for all countries, and renewableenergy is the most crucial factor for achieving sustainable development through poverty
reduction, energy security, and enhancing socio-economic and human development [65]. Solarenergy is being given more importance in most developing countries, with their increasing
populations and depleting fossil fuel resources [66]. More than two billion people do not haveaccess to reliable energy in developing countries [67]. The range of activities for which SHS
lighting has especially been found to be useful includes studying, reading, preparing food, taking
care of children, processing agricultural products, and social interaction [68]. With thedemographic composition of developing countries, the population under the age of 15 accounts
for about 40% of the total population, so the calculated result would reach a total of 800 million
people. As not all children go to school, the actual number of children studying at night would beless than this figure of 800 million. However, more than 500 million children lack adequate
lighting and use dim, smoky and dangerous kerosene lighting while studying at night [69]. As
the SHS is considered to be a reliable and clean source of lighting in rural areas of developing
countries, the current study sought to examine the impact of the SHS on educational outcomes.
Energy poverty is a big problem for developing countries, and is regarded as a multidimensional
problem. Multidimensional policies are therefore needed to solve the problem, thus ensuring
productive uses of energy [70]. Electrification in rural areas in developing countries throughrenewable energy is a good way to expand the implementation of electricity technology.
However, the policy should target the fostering of human capital accumulation by ensuring local
people’s participation in planning and implementation processes [10]. Political and academic
discourses often assume that access to energy will lead to benefits for development. Although
this is true to a certain extent, education, livelihoods, and the health benefits of access to energy
do not directly manifest themselves. The use of electricity is interrelated with multiple aspects ofsocio-economic development through complex causal relationships, such as income-generating30

activities, market production and revenue, the household economy, local health and population,
education, habits, and social networks [71]. In addition, lighting from electricity reduces indoor
air pollution and the health hazards caused by kerosene lamps [72].
An SHS not only fulfills the household’s lighting demand but it also contributes to income
generation for small business enterprises [73]. One study has critically evaluated the successstory of the SHS program based on milestones such as energy access and uses, social impact,economic impact, management and ownership of the program, environmental impact, and impact
on gender [15]. A household with an SHS can work at night to generate additional income [74].
Owners of SHSs reported that their quality of life increased and that more job opportunities werecreated [75]. Moreover, they enjoyed watching television, recharged their mobile phones athome, and reduced their kerosene consumption expenditure [76]. Based on an evaluation of six
case studies, solar electrification was found to create green employment opportunities. It also
reduced kerosene subsidies. That is, the SHS was considered as friendly to the rural environment
and eco-efficient [77]. One field survey assessment in Assam, India, evaluated the technicalperformance of SHSs, service delivery, institutional arrangements, user satisfaction, and benefits
for rural livelihoods [78].
The current study contributes to strengthening the evidence of the impact of solar lighting onchildren’s education in rural areas in low-income countries. Many methods are useful inimproving children’s educational performance. However, many educational economists havequestioned the effectiveness of these methods over the past decade, whether in developing ordeveloped countries. Some recent studies have argued that providing higher incentives is usuallymore effective than providing a better learning environment to improve children’s educational
outcomes. Interventions such as merit scholarships, school health programs, and information
about educational returns can cost-effectively stimulate school participation [79]. A randomized
evaluation has shown that providing textbooks improved the performance of the best students,
but this had little impact on other students [80]. McEwan [81] did not find evidence of improved
educational scores with the provision of midday meals, using the regression discontinuity
method. A school-based deworming program in Kenya increased the average participation intreatment schools by 7.5 percentage points, reducing overall school absenteeism by at least aquarter [82]. Most studies have examined financial support for children’s educational31performance. In a rural setting, access to electricity is the main barrier to socio-economic
development of which electrification is an integral part. Therefore, it was decided in this study to
examine the impact of the SHS on children’s educational outcomes in rural Bangladesh. For the
purpose of the study, access to the SHS was defined as access to light points (lamps).
Until now, the link between access to electricity and education has barely been analyzed. Most
academic research has assumed that access to electricity has a beneficial effect on education.
However, direct studies are lacking on the connection between access to electricity andchildren’s educational outcomes. Some previous studies have focused on the impact ofelectrification on income, expenditure, and educational outcomes. Evidence from these studies
has been used to provide greater justification for the current study. Kanagawa and Nakata [11]highlighted that, in the case of India, a positive correlation exists between per capita domestic
electricity consumption and education level, indicating that households with very low initial
electricity consumption can obtain high educational attainment by increasing electricity
consumption. Electricity can also significantly improve the length of years at school and thelearning time for children in rural households [33]. Home electrification has a positive effect onenrollment and the average years of schooling, but these facts are only statistically significant forgirls [83]. Better lighting allows girls to redistribute their time to enable their schooling, but thisis less so for boys as their job alternatives to school education are more likely to be at homeengaged in income-generating activities. Children with access to electricity can do their
homework in the evening after school, thus prompting their school attendance [29]. A few
studies have been conducted to investigate the impact of solar energy on educational outcomes.
Gustavsson [84] pointed out that children in families with access to solar energy spend moretime doing homework than neighboring children who do not have access to solar energy. An
SHS provides better quality light and reduces indoor air pollution that may also help to extend
children’s study hours [16].
A considerable amount of literature is already available on a different aspect of the SHS’s
influence on education (extended study hours for children). However, the literature on the causal
inference of the SHS on educational outcomes (students’ performance, grade progression,
dropping out, grade repetition, and non-attendance) is seriously lacking. Most previous studies
have examined the impact of the SHS on socio-economic activities by using descriptive statistics
32

and qualitative analysis. Therefore, the current study sought to estimate the causal impact of the
SHS on educational outcomes in rural Bangladesh.
3.2 Energy Scenario and Solar Home System (SHS) Prospects in Rural Bangladesh
Several sources of lighting are used in Bangladesh including grid electricity, kerosene, solar
power, biogas, etc. Access to electricity is increasing due to expansion of the grid area, with
68% of the total population having access to grid electricity (including renewable energy) but,per capita, electricity generation is still low compared to the world average of 348 KWh [85]. In
1977, the Bangladesh government undertook a major initiative to expand electrification in rural
areas under the Total Electrification Program and established the Rural Electrification Board(REB). According to the Bangladesh Rural Electrification Board Act 2013, the name of the
Rural Electrification Board (REB) is now the Bangladesh Rural Electrification Board (also REB).
The primary objective of REB is to extend electricity supply in rural areas to promote social andeconomic development. Its operational functions are organized in each area by the Palli Bidyut
Samity (PBS) (i.e., the rural electric association or cooperative). The PBSs are semi-autonomous
entities that the REB has approved and registered. Each PBS has adequate capacity in the grid
substation and accessibility to the Bangladesh Power Development Board (BPDB)’s 33 kV line.
The typical PBS covers 5-10 subdistricts with an area of 1,500–2,400 square kilometers and a
consumer population of approximately 35,000–270,000 people. In Bangladesh, 78 PBSs areapproved and registered with the REB and distribute electricity to over 69,000 villages through767 substations. However, only about 30% of rural households have access to electricity fromthe grid [86]. Many rural area characteristics make it more challenging to provide these areas
with electricity than is the case in urban areas [87][88]. Some villages still do not have electricity
from the national grid. Due to the remoteness of these areas, electricity production, transmission,and distribution costs are relatively high.
The rural economy of Bangladesh is characterized by its high dependence on agriculture,
inadequate energy, insufficient infrastructure, and widespread poverty. In Bangladesh, the nonexhaustive energy sources of solar, biomass, biogas, hydro, and wind, all of which areenvironmentally sustainable, can potentially be used to produce electricity in off-grid rural areas.
Bangladesh has great potential for renewable energy development and is blessed withconsiderable solar radiation. Renewable energy can be seen as a potential solution for33

Bangladesh’s future energy needs and can provide electricity, especially in rural areas [89]. The
Infrastructure Development Company Limited (IDCOL) is a government-owned financial
institution that promotes private sector financing for infrastructure and renewable energy-based
electricity in rural Bangladesh [90]. Its market size comprises four million SHSs on a fee-forservice basis in the off-grid areas in Bangladesh. About 18 million people, that is, 12% of thetotal population have access to the SHS in off-grid areas, with these people previously having
used kerosene as a source of lighting. The government’s vision is to provide electricity to all
citizens by 2021 at a reasonable and affordable price. However, the fact is that grid power will
not be available in some remote and isolated areas for the next 20 years. As a result, a very large
number of people will live without electricity for a long time. The share of renewable energy tototal electricity production was 3.5% in 2015 and the government’s expectation is that it will be
10% before 2020 [91].
3.3 Study Area and Data Collection Methods
The study area for the current research is Rahumari upzilla, Kurigram district, Bangladesh. This
area was purposively selected, based on the following three indicators: low access to electricity(18.7%), low literacy rate (34.6%), and low school attendance rate. Cross-sectional surveys wereconducted as the primary method of data collection. The five unions in Rahumari upzilla are
Dadbanga, Chor Showlmari, Bondebar, Rahumari, and Jadurchar. The study area chosen, basedon purposive sampling, was the Chor Showlmari union. Information was gathered from theUpzilla Nirbahi Officer (UNO), the Union Chairman, and non-governmental organizations(NGO) for a greater understanding of the study area selected. The survey was conducted in ninevillages between March 8, 2018 and April 15, 2018.

34

Figure 3.1: Map of Kurigram district

For the data collection, village-level cluster random sampling was employed. In total,
912 households and 1,696 children, ranging in age from 6–20 years with different grade
information over three years, were surveyed. The paper-based questionnaire covered variables
such as the household’s socio-economic status, income generation, and children’s education. The
questionnaire was carefully designed and rigorously implemented to minimize questionnaire
investigator bias and to prevent the investigator from leading the respondents. Despite these
efforts, some responses may reflect the investigator’s bias and need a more explicit framework
for the issues raised.

35

Population groups:
villages in union

Randomly
choose groups
from thepopulation
Figure 3.2: Village-level cluster sampling
Table 3.10: List of villages and target respondents
Name of Villages
Balugram
BatkemariChar Showlmari
Chargenderalga
Gugumari
Miarchor
Pakhiura
Shukherbati
Sonapur
Total

Number of Households
99
66
129
76
101
97
196
76
72
912

Number of Children
158
143
220
136
205
211
340
144
139
1696

The aim of the questionnaire was to collect grading information related to the education ofchildren in participating households through the issues of whether their household had, or did not
have, a solar home system (SHS). These issues involved the socio-economic aspects of lifestyleand the impact of lighting on children’s education in the selected villages. Specifically, this
holistic research and the survey questions were designed to understand how indoor SHS lighting
motivated a child to concentrate on their study and its effect on their household’s living
conditions.
Table 3.2: Status of household and children regarding SHSHouseholdChildrenWith SHS
356
673

Without SHS
556
1023

36

Total
912
1696

3.4 Matching Techniques
When using observational data for impact assessment, selection bias is the primary concern. This
happens when a household with favorable characteristics, arising from their knowledge of the
choices, adopts an SHS earlier than other households. Selection bias may exaggerate themeasured value as early adopters may have received better facilities than late adopters, even
without intervention. Selection bias is the main problem when observational data are used for
impact assessment. It makes the estimation biased and exaggerates the measured impact. The
current study could apply various treatment models to address selection bias in its investigationof the association between the SHS and educational outcomes. In this study, access to an SHS is
the intervention. The study area is not electrified and has no possibility of electrification within
the next 10 years. Solar home systems (SHSs) are the only reliable source of modern lighting forpeople in this disadvantaged region.
Selection bias occurs when a household that has information about the SHS, obtained due to theirchoices, adopts an SHS earlier than other households. The difference in the means of potential
outcomes can be computed using both OLS regression and treatment models, depending onwhether the household has an SHS (treatment) or not (control). The potential outcomes of thesecounterfactuals are defined as 𝑌0 if members of the sample do not have SHS and 𝑌1 if everyonein the sample has a solar home system (SHS). In the absence of unmeasurable confounding
factors, the resulting regression model can be modeled as (𝑌|𝑇, 𝑋) where X refers to theobserved variable, with the average of the two treatment estimates being 𝑇 = 1 and the control
being 𝑇 = 0 . Subsequently, the average treatment effect (ATE) in the population can be
estimated as follows:△ 𝐴𝑇𝐸 = 𝐸(𝑌1 ) − 𝐸(𝑌0 )(7)However, in traditional OLS analysis, ATE is approached by controlling a series of observed
factors in a predictive model. The treatment coefficient effect is explained based on the control,
as indicated by the causal effect on the dependent variable. However, problems arise when
individuals with different background characteristics receive the treatment, leading toconfounding and selection bias [92]. Treatment models attempt to overcome the problem ofdifferentially selected treatments by including the predictive factors observed before the37treatment or weighting the ATE level of the subject, including those associated with thetreatment. This consists of analysis in two steps: estimating the “treatment model” (using
observation factors to predict the treatment status of each subject), and then estimating the“predictive model” (using the same observations plus treatment variables to predict the outcome).
Conversely, if the reverse is correct, the ATE can be correctly estimated if the model predicting
the treatment outcome is correctly specified, while the treatment model is incorrect or incomplete[93][94][95]. In other words, if the propensity score model (balance treatment) is correct or theoutcome model (predicted Y) is correct, the estimate of the average treatment for X is correct.
Causal estimation is identifiable if the three conditions of exchangeability, consistency, andpositivity are all met. This means that the causal effect can be estimated as follows:
𝑇̌ =

1
1
∑ 𝑦𝑖 −
∑ 𝑦𝑖
𝑛𝑇
𝑛𝐶
𝑇𝑖 =1

𝑇𝑖 =0

where 𝑛𝑇 and 𝑛𝐶 indicate the sample size in the treatment and control groups. This estimation is
simply the difference between the average treated and control outcomes. In the case ofconfounding, the exchangeability hypothesis is wrong, which means that the above factors are
not unbiased for the true population average causal effect. Methods are available to eliminate
confounding and to estimate causal effects.
In theory, as empirical tests of causal claims have become more critical within the social sciences,
researchers relying on observational data are faced with the lack of data sets to use in estimating
causal effects. Unlike an experimental design, researchers cannot influence the distribution of thetreatment, which can lead to biased results. Statistical matching provides a way to solve thisproblem by finding “statistical twins,” one with and one without the treatment. However, themost common matching technique – propensity score matching (PSM) – is slow and difficult toapply. Coarsened exact matching (CEM) provides an alternative solution that is faster and easierto understand. It temporarily roughens the data based on the researcher’s thoughts and then finds
an exact match. The increased efficiency and lower bias properties of CEM are attributed to an
exact match between the common strata defined by variance variables in the decision toparticipate or not participate in the program. Effectively, CEM allows a more comparable

38

evaluation of the treatment group and comparison group by creating proportionality between thefactors that contribute to the result concerned.
The current study’s treatment and control groups are not necessarily identical as they lackrandom assignment of participants. Thus, the following steps are needed:✓ The study denotes that all covariates are reasonably coarsened as much as possible.
✓ All coarse variables are placed in a single layer with the same units.
✓ Strata without at least one treated and one control unit are weighted zero (0).
The treatment effect for treated (𝑇𝑖 =1) observation i:
𝑇𝑖 = 𝑌𝑖 (𝑇𝑖 = 1) − 𝑌𝑖 (𝑇𝑖 = 0)
= Observed - Unobserved•

Estimate 𝑌𝑖 (𝑇𝑖 = 0) with 𝑌𝑗 from matched (𝑋𝑖 ≈ 𝑋𝑗 )•Prune unmatched units to improve balance•

Sample average treatment effect on the treated (SATT):
𝑆𝐴𝑇𝑇 =

1𝑛𝑇∑ 𝑇𝐸𝑖
𝑖∈{𝑇𝑖 =1}

3.5 Findings and Discussion
Table 3.3 presents the demographic and socio-economic characteristics of the households that
had access to an SHS and those who did not.

39

Table 3.3: Covariate t-test statistics
With SHS

Without SHS

Potential Covariates
N

Mean

N

Mean

Std. error

Diff

HH Gender (dummy) [1=male, 0=female]673

0.92701023

0.94240.01657-0.0155

HH Age673

44.15171023

41.83270.654942.319***

Religion1 (dummy) [1=Islam, 0 other]673

0.99441023

0.99820.00389-0.00382

Religion2 (dummy) [1=Hindu, 0 other]673

0.00561023

0.00180.00389

0.00382

HH Education (years)

673

3.88481023

1.60470.26183

2.280***

HH Occupation1 (dummy) [1=Agriculture, 0 other]673

0.53371023

0.67450.03266-0.141***

HH Occupation2 (dummy) [1=Business, 0 other]673

0.28651023

0.07730.023860.209***

HH Occupation3 (dummy) [1=Service, 0 other]673

0.05901023

0.01440.01183

0.0446***

HH Occupation4 (dummy) [1=Day labour, 0 other]673

0.05621023

0.19960.02335- 0.143***

HH Occupation5 (dummy) [1=Other, 0 other]673

0.06461023

0.03420.014210.0304*

HH Income (BDT per Year)673

186132.0000

1023

79546.7600

22212.89000

106585.3***

Family Size

673

5.22191023

4.61910.10197

0.603***

No. of Immigrant Members

673

0.14611023

0.01440.024470.132***

No. of Sleeping Rooms

673

2.46071023

1.75180.05595

0.709***

Value of Non_Agri Asset (BDT)

673

40352.7200

1023

7541.3670

2578.64300

32811.4***

Distance from PBS (km)673

69.79491023

68.9568

0.466410.838*

Distance from School (km)673

0.95511023

1.0087

0.06123-0.0536

Agri land (decimal)

673

156.9031

1023

44.471217.38146

112.4***

Toilet1 (dummy) [1=Water-sealed slab, 0 other]673

0.18541023

0.04500.019830.140***

Toilet2 (dummy) [1=Not water-sealed slab, 0 other]673

0.51691023

0.26620.031620.251***

Toilet3 (dummy) [1=Katcha, 0 other]673

0.29491023

0.66910.03159- 0.374***

Toilet4 (dummy) [1=Open space, 0 other]673

0.00001023

0.01800.00705- 0.0180*

Drinking Water 1 (dummy) [1=Tube well, 0 other]673

1.00001023

0.98560.006320.0144*

Drinking Water 2 (dummy) [1=Pond, 0 other]673

0.00001023

0.00180.00225-0.0018

Drinking Water 3 (dummy) [1=River, 0 other]673

0.00001023

0.00180.00225-0.0018

Cooking Fuel1 (dummy) [1=wood, 0 other]673

0.87891023

0.85610.02323

0.0228

Cooking Fuel 2 (dummy) [1=Cow dung, 0 other]673

0.10141023

0.14390.02263-0.0425

40

Cooking Fuel 3 (dummy) [1=LPG, 0 other]673

0.01691023

0.00000.00547

0.0169**

Cooking Fuel 4 (dummy) [1=Biogas, 0 other]673

0.00281023

0.00000.00225

0.0028Notes: BDT=Bangladeshi taka (currency); HH=household head; Katcha=dwelling; LPG=liquefied petroleum gas; PBS=Palli Bidyut Samity; *level of significance 10%; **level of significance 5%;***level of significance 1%413.5.1 Results and Discussion Based on Coarsened Exact Matching (CEM) Method

The core motivation of the coarsened exact matching (CEM) method is to temporarily coarsen
each observed variable into a meaningful group. These coarsened data are then matched exactlywith only the original (un-coarsened) values of the matching data retained. The CEM process
first coarsens the data to C(X), then divides the observation into layers that result in crossclassification of the resulting results, and finally performs an exact match within each segment[96]. Therefore, the primary goal of any matching procedure is to maximize the two balances,namely, the similarity between the intervention and the observed multivariate distribution andthe size of the matching data set. Statistical modeling assumptions must handle any remaining
imbalance after matching.
The current study needed to select the model which ensured the covariate balance between thetreatment and control models. Each matching method faces a fundamental problem: matchingalong the similarity measure does not necessarily achieve covariate balancing. Similarity
measures may create a balance, but the results are usually not guaranteed. In practice, mostmatching methods require the researcher to adjust and re-run the matching program multiple
times until a satisfactory balance is achieved. The current study created different models basedon different combinations of covariates or slightly different covariates to ensure that thesimilarity between the treated and un-treated models was achieved. As shown in Table 3.4,
12 models were created based on covariate differences.

42

Table 11: Model specification
Covariates

Model 1 Model 2

Model 3Model 4Model 5

HH Gender (dummy) [1=male, 0=female]

√

√

√

√

Model 6

Model 7

Model 8

HH Age

√

√

√

√

√

√

√

√

HH Education (years)

√

√

√

√

√

√

√

√

HH Occupation1 (dummy) [1=Agriculture, 0 other]√

Model 9Model 10Model 11√√

Model 12

√

√

√

√

Religion1 (dummy) [1=Islam, 0 other]
Religion2 (dummy) [1=Hindu, 0 other]√√√

HH Occupation2 (dummy) [1=Business, 0 other]

√

√

√
√

HH Occupation3 (dummy) [1=Service, 0 other]
HH Occupation4 (dummy) [1=Day labour, 0 other]
HH Occupation5 (dummy) [1=Other, 0 other]
HH Income (BDT per Year)

√

√

√

Family Size

√

√

√

√

√

√

√

√

√

No. of Immigrant Members

√

√

√

√

√

√

√

√

√

No. of Sleeping Rooms√√

Value of Non_Agri Asset

√

√

√

Distance from PBS (km)

√

√

√

√

√
√

√

√

√

√

√

√
√

√

√

Distance from School(km)Agri land (decimal)√

Toilet1 (dummy) [1=Water-sealed slab, 0 other]√

Toilet2 (dummy) [1=Not water-sealed slab, 0 other]
Toilet3 (dummy) [1=Katcha, 0 other]

√
√

√

√
√

√

Toilet4 (dummy) [1=Open space, 0 other]
DrinkingWater1 (dummy) [1=Tube well, 0 other]√

Drinking Water2 (dummy) [1=Pond, 0 other]
Drinking Water3 (dummy) [1=River, 0 other]
Cooking Fuel1 (dummy) [1=wood, 0 other]√

Cooking Fuel 2 (dummy) [1=Cow dung, 0 other]
Cooking Fuel 3 (dummy) [1=LPG, 0 other]
Cooking Fuel 4 (dummy) [1=Biogas, 0 other]Notes: BDT=Bangladeshi taka (currency); HH=household head; Katcha=dwelling; LPG=liquefied petroleum gas; PBS=Palli Bidyut Samity

43

√

√
√

Coarsened exact matching (CEM) “coarsens” the data by binning along each covariate. Withineach interval of the multivariate histogram, the control unit matches the processing unit. Thismethod is shown as a “monotonic imbalanced boundary” as the maximum distance along any
covariate between the treatment and control groups is controlled by a user-controlled (or default)
value.
Table 3.5 presents the CEM Index for various models by the multivariate distance. The results
here are from running 12 models based on different covariate combinations. For comparison
purposes, the study estimated the multivariate distance before and after coarsened exact
matching (CEM). The overall imbalance is given by the ℒ1 statistic, as a comprehensive
indicator of global imbalances [97]. It is based on the same 𝐿1 difference between themultidimensional histogram of all pre-treatment covariates in the treatment group and in thecontrol group. Firstly, the covariate is coarsened into boxes. To use this metric, a list of bin sizes
for numeric variables is needed. The function within the current study automatically calculates
these functions, or they can be set by the user. The discretized variables of the processing groupand the control group are then cross-tabulated to 𝑋1×…..× 𝑋𝑘 and record the k-dimensional relative
frequency processed treated 𝑓𝑙1 … 𝑓𝑙𝑘 and control 𝑔𝑙1 … 𝑔𝑙𝑘 units. The value of the imbalance iscalculated by the following equation:ℒ1 (𝑓, 𝑔) =1
∑ | 𝑓𝑙1 … 𝑓𝑙𝑘 − 𝑔𝑙1 … 𝑔𝑙𝑘 |2
𝑙1...𝑙𝑘

The result indicates that ℒ1 = 0, that is, perfect global balance and ℒ1 = 1, that is, complete
separation. If 𝑓 𝑚 and 𝑔𝑚 are used to indicate the relative frequency of the matching data set,
then a good matching solution will produce a reduction in the ℒ1 statistic; that is, what the study
wants is ℒ1 (𝑓 𝑚 , 𝑔𝑚 ) ≤ ℒ1 (𝑓, 𝑔).

44

Table 3.5: Coarsened Exact Matching (CEM) Index summary
CEM Index

Model 1Model 2 Model 3 Model 4 Model 5 Model 6

Model 7 Model 8

Model 9Model 10Model 11

Model 12Multivariate L1 distance before CEM

0.96030.77730.85350.76134

0.68650.69240.75820.86600.51730.49190.6394

0.6383

Multivariate L1 distance after CEM

0.84820.58190.59280.45951

0.37640.38890.43910.5057

0.23660.20430.30200.3770

Number of Strata

605

311

452

364

299

252

364

471

103

99

223

285

Matched Strata

61

80

77

91

101

76

105

89

38

29

83

81

Matched Treated

139

356

233

342

408

399

360

249

480

474

439

366

Unmatched Treated

516

299

422

313

247

256

295

406

175

181

216

289

Matched Control

250

745

498

724

789

858

568

450

995

970

819

826

Unmatched Control

754

259

506

280

215

146

436

554

9

34

185

178

Total Treated

655

655

655

655

655

655

655

655

655

655

655

655

Total Control

1004

1004

1004

1004

1004

1004

1004

1004

1004

1004

1004

1004

Table 3.6: Results of sample average treatment effect on the treated (SATT) for Models 1, 2, and 3
OUTCOME VARIABLES

Model 1Model 2Model 3

n

Coef.

SE

n

Coef.

SE

n

Coef.

SE

GPA [Grade point average]389

0.592**

0.197041101

0.578***

0.11464731

0.750***

0.14489

Receiving scholarship389

0.05890.045391101

0.126***

0.02543731

0.127***

0.03147

Grade progression

389

0.05040.050761101

0.05940.03135731

0.06550.03988

Grade repetition

389-0.0285

0.031221101-0.03220.02034731-0.0106

0.02466

Non-attendance

389-0.00967

0.039551101

0.00166

0.02404731-0.01450.03104

Note: *Level of significance 10%; **level of significance 5%; ***level of significance 1%; Coef=coefficient; n=number; SE=standard error

45

Table 3.7: Results of sample average treatment effect on the treated (SATT) for Models 4, 5, and 6
OUTCOME VARIABLES

Model 4Model 5Model 6

n

Coef.

SE

n

Coef.

SE

n

Coef.

SE

GPA [Grade point average]1066

0.554***

0.125321197

0.654***

0.11601,257

0.623***

0.1170Receiving scholarship

1066

0.0990***0.025561197

0.0914***0.02451,257

0.105***

0.0284

Grade progression

1066

0.05490.035691197

0.0746*0.03091,257

0.0584*0.0313

Grade repetition

1066-0.0364

0.028321197-0.01320.0201

1,257-0.00703

0.0191Drop-out

1066-0.0152

0.011761197-0.0344**0.0122

1,257-0.0305*

0.0120Non-attendance

1066-0.00334

0.024281197-0.0270.0242

1,257-0.02090.0246

Note: *Level of significance 10%; **level of significance 5%; ***level of significance 1%; Coef=coefficient; n=number; SE=standard error

Table 3.8: Results of sample average treatment effect on the treated (SATT) for Models 7, 8, and 9
OUTCOME VARIABLES

Model 7

Model 8Model 9

n

Coef.

SE

n

Coef.

SE

n

Coef.

SE

GPA [Grade point average]928

0.650***

0.12229699

0.601***

0.15491

1,475

0.588***

0.1044

Receiving scholarship

928

0.0859**0.02970699

0.101**

0.03260

1,475

0.0769**0.0246

Grade progression

928

0.0556

0.03163699

0.02270.03971

1,475

0.0832**0.0308Grade repetition

928-0.01260.02044699-0.01960.02691

1,475-0.03450.0248Drop-out

928-0.0372*

0.01495699-0.009780.012201,475-0.0292

0.0151

Non-attendance

928-0.00591

0.02220699

0.00666

0.030121,475-0.0194

0.0188

Note: *Level of significance 10%; **level of significance 5%; ***level of significance 1%; Coef=coefficient; n=number; SE=standard error

46

Table 12: Results of sample average treatment effect on the treated (SATT) for Models 10, 11, and 12
OUTCOME VARIABLES

Model 10Model 11

Model 12

n

Coef.

SE

n

Coef.

SE

n

Coef.

SE

GPA [Grade point average]

1444

0.550***

0.098591258

0.718***

0.11625

1,192

0.558***

0.1159Receiving scholarship

1444

0.0704*0.028261258

0.0735**0.02587

1,192

0.0662*0.0295

Grade progression

1444

0.0689**0.026191258

0.0778*0.03234

1,192

0.05610.0310

Grade repetition

1444-0.0259

0.017361258-0.01670.02076

1,192-0.0209

0.0218Drop-out

1444-0.0229*0.010151258-0.0413*0.01877

1,192-0.0293*0.0115Non-attendance

1444-0.0201

0.019531258-0.0198

0.02116

1,192-0.0059

0.0226

Note: *Level of significance 10%; **level of significance 5%; ***level of significance 1%; Coef=coefficient; n=number; SE=standard error47

As can be seen in Tables 3.6, 3.7, 3.8, and 3.9, Models 9 and 10 are suitable for impact analysis
based on the value of multivariate distance and the stable condition of covariates.
Table 13: Balance check (Model 9)
Covariates
Household Head (HH) Education (years)
HH Income (BDT per Year)
Family Size
No. of Immigrant Members
Access to Toilet

Before Matching L1
0.195440.356260.14810.07966
0.23645

After Matching L1
0.012080.04063
0.02886
0.0181
8.1 X 𝑒^ (−15)

Before Matching L1
0.03389
0.19595
0.35672
0.07969
0.22557
0.13736

After Matching L1
9.8 X 𝑒^ (−16)
0.006930.05420.031480.05959
7.0 X 𝑒^ (−16)

Table 14: Balance check (Model 10)
Covariates
Household Head (HH) Gender
HH Education (years)
HH Income (BDT per Year)
No. of Immigrant Members
Distance from PBS (km)Access to Toilet

As seen in the results for Models 9 and 10, the study confirmed that access to an SHS increased
children’s GPA by approximately 0.55 points above those without access to an SHS, with thisresult statistically significant. Children who could study with an SHS benefitted more than those
without an SHS with regard to receiving scholarships. The difference was approximately 8%
more and is statistically significant. Grade progression was achieved by approximately 8% morechildren who studied under SHS lighting than those who did not have an SHS, with this result
also statistically significant.
Grade repetition, drop-out, and non-attending children also decreased in the treated group (i.e.,
those with an SHS) compared to the control group (those without an SHS), but this result was notstatistically insignificant.
3.6 Conclusion
This study has focused on the influence of light (i.e., indoor lighting) on students’ academic
performance and school enrollment. Solar technology can possibly improve educational
outcomes in rural areas. Children with access to an SHS spent more time studying and reading as48

a consequence of improved lighting [84]. The most important benefit of SHSs is that children
can do their homework at night [98]. The study investigated the impact analysis using different
models. The first test involved the relationship between solar lighting and students’ “academic
performance,” while the second test was between solar lighting and students’ “school enrollment.”
The results of this matching showed that the impact of solar lighting on children’s education,
academic performance, and school enrollment is positive and significant. Therefore, a significant
relationship was found between good quality lighting and students’ performance.
Children from households without access to electricity for lighting purposes have previously
been found to be more likely than the average child to not attend school [99]. The current study’s
findings did not support this finding, with the effect of access to an SHS for lighting on nonattendance found to be insignificant. The importance of lighting in the home and its impact on
student “performance.” is an interesting issue and the current study also found some significant
relationships. The lighting quality was found to have a direct effect on students’ academic
performance. The quality of lighting in educational spaces at elementary school has previously
been found to enhance students’ learning and academic performance [100]. The current study
examined the effects of home lighting from an SHS on children’s academic performance as wellas on school enrollment. Good lighting in the home was found to better motivate students tolearn. With good lighting at home, students are more relaxed and not sleepy in class, and they
have the motivation to learn better. A good learning environment, including proper lighting
quality, is an invisible motivation and encourages students in their learning. It allows them tofocus on their tasks and thus to perform better in their subjects. Adjustments to home lighting
can also increase students’ level of attention and improve their performance.

49

Chapter 4: Conjoint Analysis of Consumers’ Preferences for Future
Organic Solar PV System
4.1 Introduction
Access to energy is an essential indicator for ensuring a smooth lifestyle for individuals as wellas enhancing socio-economic development. In 2015, 1.1 billion people globally had no access toelectricity and 300 million people of that disadvantaged total lived in India [101]. India is one of
the largest growing economies in the world, with energy consumption also growing, and about
40% of the total energy use being in rural areas [102]. This situation is exacerbated by the highcost of grid expansion, the local market’s inability to achieve a balance, and the limited progressin rural electrification. Increasing consideration of the goal of universal access to energy has
therefore raised concerns about rural electrification and interest in technologies that go beyond
centralized systems. India’s rural areas are diverse, with large socio-economic disparity due tocaste, level of education, and geographical position [103]. The success of any development
policy depends on the views and attitudes of the consumer, and some rural electrification
programs have failed due to not incorporating this aspect. The concept of the “diffusion of
innovation” is essential and has been found in off-grid renewable energy program development[104]. In one study based on two villages from the Indian state of Odisha, it was shown thathouseholds’ adaptation to solar energy depended on a set of socio-economic, demographic, andinstitutional factors including the government’s approach to rural electrification [105].
When observing the development process of countries around the world throughout the twentiethcentury, electricity supply has undoubtedly played an important role in promoting progress in allsocial sectors, thereby improving people’s well-being. In most developing countries, such as
India, households used multiple fuels, such as kerosene, and electricity for lighting. Kerosene is
known to cause indoor air pollution. An aggressive effort is needed to deal with these problems
and for households to switch from kerosene to electricity [106]. Rural electrification is an
essential factor for poverty alleviation and also enhances rural growth. In India, although the
GDP growth rate is increasing at an average of 8%, the contribution of agriculture is negligible.
This is the reason why the government of India wants electrification for all rural villages [102].
When rural electrification is implemented by many people, off-grid and grid-connected options
50

are usually initiated in parallel, although the basis for selection is not clear. Both options areusually based on political interests or donor priorities. Appropriate resource assessments oranalysis of the cost efficiencies of different approaches are often overlooked. Consumers tend tobuy renewable energy components which are able to provide a stable supply of electricity fromcheap plans with low price fluctuations [107]. Considering the major costs of providing
electricity to a large population, the potential impact of this effort requires a more reasonable
comparison of the choices. Today, the opportunity to overcome the development divide depends
to a large extent on the availability of energy; therefore, it is necessary to better explore therelationship between energy and sustainable development in order to understand how energy canhelp reduce poverty [108]. It is also urgent to give more emphasis to consumers’ preferences for
how a suitable energy policy is to be implemented.
In rural areas, poor people are used to silicon or conventional solar PV systems. They always
keep the existing system and face difficulty with accepting new technology. In the current study,
an organic solar PV system is illustrated, with its function explained to enhance theirunderstanding. Demonstration plays an important role when a consumer is choosing anappropriate solar home system. Respondents consider the system’s features in their choice withthe most visual presentation being the appearance of the solar panel, size, color, surface pattern,functional performance, and price. The information barrier is considered a significant obstacle totechnology adoption. One study, based on a randomized control trial (RCT) conducted in
75 villages, Uttar Pradesh, India, showed that only a demonstration or marketing could increase
the sale of solar home systems (SHSs), rather than easy access to credit [109].
The current study reports on a social and technical analysis of Indian rural consumers’
preferences for a future organic solar PV system. A detailed review of the social aspects ofenergy demand was undertaken through questionnaires which sought responses on preferences
for the pattern of electricity use, product design of a solar PV system, the system’s lifetime, andthe system’s cost. Respondents indicated that they preferred a solar PV system that would work
under bad weather conditions and, in helping to identify socially acceptable and technically
viable energy options, they mentioned having a flexible tent with a rainwater harvesting facility.

51

4.2 Goal of this Study
This study sought to examine the preferences of consumers (in this case, rural people in India)
for a future organic solar PV system based on the following pros and cons of the key features andfunctions of this system:
Pros:•

Lightweight and bendable and, thus, portable and de-mountable•

Translucent and therefore usable for windows and shading eaves•

Functioning under soft sunlight in the morning, the evening, and even in cloudy
weatherCons:•

Low conversion rate•

Short system lifetime

Understanding and addressing users’ preferences on the pros and cons of new features of the
future organic solar PV model could be assisted in the following ways:✓

Impact of additional information (demonstration) on their preferences✓

Social capital (trust among villagers) and preferences on portability and demountability✓

Trade-off between the pros and cons.

4.3 Product Design with Organic Solar PV System
From a technical point of view, before commencing demonstrations, the contribution of the
proposed technology to the disadvantaged region needed to be clear. Villagers should know thesystems’ advantages and weaknesses, and what would be possible by using the proposed system.
Before starting the survey, it was necessary to have a practical product design of at least one or
two systems, as their components needed to be established for the survey. However, only a small
number of real organic solar PV systems is available; therefore it is difficult to identify what52

kinds of features are preferred. The way that the study selected the proper design is described
below:I.

Conventional SHS: SHS that they already know or use.

II. Proposed SHS 1: Using a technical database only (Product Ideas A and B)
III. Proposed SHS 2: Modified through the survey
IV. Proposed SHS 3: Final design
Table 15: Positives and negatives of conventional solar PV system and organic solar PV system
Conventional silicon (Si) crystalline solar PV system
Positives-

Negatives

Comparatively high generation efficiency, -Comparatively heavy owing to the top glass(high output voltage and current), ~20%

and aluminum frame-Long generation lifetime, ~15 years-

Cheap (presently)-

High reliability-

Different size with changing the number of the-

Performancedegradation

inincreased

temperature and angular light-

Small cell scaling; single cell is around 4~5 W(153 mm x 153 mm)

cell series.
Organic solar PV system
Positives

Negatives

Large cell scaling with use of roll-to-roll -Weak for high temperature

fabrication-

Short generation life time, ~5 years-

Flexible (bendable)-

Low generation efficiency, (Low output-

Thin and lightweight-

Designability (transparency, colorfulness)-

Performance retention in weak illuminance-voltage and current), ~11%-

conditions (cloudy weather and angular light)

53

Expensive (presently)

Table 16: Comparison of conventional solar home system and study’s proposed system
Conventional SHS
solar

Proposed SHS 1 (Product Idea A)

SystemSiliconpanel(10W~150W), Organic

solar

panel

(10W~100W),

components

15 years

5 years

Glass top and aluminum frame

Flexible and frame-lessControl BOX

Control BOX- Lead acid battery (12 V, 20 Ah), 5 years- Lead acid battery (12 V, 20 Ah),

- Charge controller, 10 years

5 years------------------------------------------ Charge controller, 10 years

LED light-------------------------------------------

Mobile charge (cable)

LED light (Organic EL)

Small fan

Mobile charge (cable)

Contributes to

Small fanA solar home system (SHS) to provide A solar home system (SHS) to provide

advantages andthe necessary light for minimum daily life the necessary light for minimum daily

applications

life with a lower price and additional
Education (study at night)
Improvementfor

healthfunctionsconditions Education (study at night)(× kerosene lamps, candles)

Improvementfor

healthconditions(× kerosene lamps, candles)
High

design(flexible,

transparent,

Weaknesses andcolorful, lightweight, winding type)
Owing to the weight of the frame and Shorter lifetime

difficulties

covering glass, places in which it can be Generation efficiency is lower than the
installed are limited; it requires a silicon system; output voltage is also
moderately tough structure.

lower.

The bypass diode is necessary in cases of Weakness
shady conditions.

in

high

temperature

conditions. Extreme high temperature

Disposal of the solar module: when the might cause chemical denaturation..
module is fully used or broken, it will be Financial problem of the costconsidered industrial waste → landfill Maintenance and aftercare problems
garbage (environmentally harmful)
Financial problem of the cost
Maintenance and after care problems
54

4.4 Target Area and Population
India is presently the world’s seventh largest economy and the fifth largest energy consumer,
recently overtaking China as the fastest growing large economy. However, around 30% of thetotal population have no access to grid electricity, and per capita energy consumption is very low
at 530 kgoe (kilogram oil equivalent) while the world average is approximately 1800 kgoe.
People living in urban areas can easily access electricity; on the other hand, people living inremote or mountainous areas have much less accessibility to the electricity grid. Thisconsiderable gap leads to strange statistics. The Indian government hopes that the expansion ofrenewable energy will fill the supply gap.
Several reasons were considered by the current study in the selection of villages, such as:❑ A disadvantaged region in terms of geographical conditions and income opportunities.
❑ No national grid electrification.
❑ Some villagers already electrified with solar PV systems while some are still unelectrified; that is, to some extent, the villagers know about solar PV systems.
❑ Access to a very large government subsidy that supports the electrification of poor
people’s houses through the mini grid of a solar PV system.
❑ Initiatives and implementation policy undertaken by the Centre for Rural Education andDevelopment Action( CREDA).

Figure 3: Map of Chhattisgarh state and Balodabazar district
55

Table 17: Chhattisgarh’s vital indicators at a glance
Particulars
No. of districts

18

No. of total villages

20,126

Total population

26 million

Poverty rate

40%

Literacy rate

71%

Electrification

87%

Access to drinking water27%

Based on Population Census 2011. Source: World Bank

4.5 Data Collection
The pilot survey was conducted for eight days from March 5–13, 2017, collecting 54 responses
on electricity use and 82 responses on product design from the following four villages: Rawan,
Dhebi, Debhikhar, and Barnawapara. The main survey was conducted from September 8–18,
2017, after a little revision of the product design set of questions. The questionnaire wastherefore modified, based on the pilot survey analysis.
From the pilot survey, it was confirmed that respondents understood the scenario and the choices.
The main survey was conducted based on the product design questionnaire as the study’s main
aim within the on-site team project was to estimate consumers’ preferences for an organic solar
PV system. In the main survey, the intervention was the illustration picture of the proposedorganic solar PV system, with flexibility plus a rainwater harvesting facility. The sample wascategorized into two groups: the treatment group (109 respondents) and the control group(116 respondents). In both the pilot and main survey, the enumerator used the local language(Hindi) for communication and interpretation purposes to develop greater understanding of thequestionnaire by respondents.
The flow of the data collection is shown below.

56

I.

Interpretation from English to Hindi

Photo 4.1: Interpretation from English to Hindi by Mr Rajeev

II. Instruction and practice for enumerators

Photo 4.2: Instruction and practice by Mr Rajeev and Professor Kaneko for enumerators

57

III.

Obtaining respondents’ completed questionnaires

Photo 4.3: Interview with local residents with a laptop-based explanation
Simple Random Sampling

The following criteria were used by the study for selection of households in the sample:❑ Balodabazar district in Chhattisgarh was chosen as it was a disadvantaged region.
❑ Villages were chosen for a purposive study❑ Households were randomly selected from the listed villages.

Figure 4: Sampling frame of village selection for survey
58

Table 18: Village breakdown by number of respondents (treatment group)
Name of Village
Bar
Dond 1
Dond 2
Loritkhar
Mohda
Gudagarh
Total

No. of respondents
12
32
17
16
18
14
109

Table 19: Village breakdown by number of respondents (control group)
Name of Village

No. of respondents

Amgaon
Dheba

10
27

Dhebi khar
Kawabahara

23
16

Dond 2
Rawan

12
23

Others
Total

5
116

The seven attributes and their respective levels provided 648 sets of choices, two of which wererandomly paired, including the status quo for electricity use, while, for product design, 575 setsof choices were provided. Each respondent was required to make a ranking decision six times forthree different choice sets. In both conjoint experiments, a laptop computerized record with theMicrosoft (MS) Excel Macro program was used for data collection.

59

Table 20: Summary statistics of respondents (main survey)
Variable
Age
GenderYears of schoolingElectrified (yes/no)
Years of electrification
Private SHS (yes/no)
Years of SHS use
Price of SHS
LEDs
TV
Hours of TV watching
Mobile phone (yes/no)

Observation
1,284
1,260
1,272
1,296
1,128
1,248
540
540
1,176
1,236
852
1,296

Mean
37.24299
1.085714
8.198113
0.8611111
11.64043
0.4326923
6.377778
6633.333
3.377551
0.631068
2.394366
0.8055556

Std. Dev.
14.71655
0.2800528
3.602831
0.345964
4.065480.4956476
4.813196
2314.911
1.515977
0.4827108
1.497155
0.3959252

Min
14
1
0
0
0
0
1
1000
0
0
0
0

Max
75
2
16
1
25
1
25
13000
8
1
8
1

Number of mobile phones
in family

936

1.474359

0.9165062

06

4.6 Conjoint Method
The conjoint analysis used the data to construct quantitative models to estimate consumers’
preferences for the product attributes. Conjoint analysis is widely used in marketing to measureconsumers’ preferences, forecast demand, and develop products [110] [111]. The two types ofconjoint analysis are: (1) choice-based conjoint analysis (known as a discrete experiment); and(2) rating-based conjoint analysis. The current study applied the choice-based conjoint
experiment to estimate respondents’ preferences on organic SHS electricity use and productdesign. In a choice-based conjoint experiment, respondents are usually asked to choose
combinations of attribute levels, which are referred to as profiles. The profile design is also
random. The current study used only a small number of profiles and a restricted number of
respondents due to time and budgetary constraints.
The randomization of the conjoint experiment was arranged as follows. Respondents wereselected randomly from a homogeneous population. They were comparable regarding age,gender, economic status, electrification status, etc. Due to the similar setting, the typical scenario
was obtained from the rated profiles.
Two types of conjoint analysis were conducted: electricity use and product design of the solar
home system (SHS):
60

❑ Combined two conjoint surveys: (Pilot survey, March 2017)•

Use of electricity services and product design choice•

Incorporation of some key features and functions unique to the organic solar
module❑ Demonstration as treatment: (Main Survey, September 2017)•

With/without demonstration•

Before/after demonstration❑ Level of knowledge and experience of existing solar home system (SHS)•

Controlled by careful selection of the villagers

In one version, the choice set has two alternatives (A vs. B) from which respondents must choose
one over the other. In another version, the choice set includes a third alternative, namely, theexisting situation, referred to as the status quo. If the respondent does not choose from the firsttwo alternatives, then he/she can choose the third alternative. The current study has at least twovariations: one is selecting the best options, and the other is ranking the other options.
Hainmueller et al. [112] applied the randomized experimental design of conjoint analysis fromthe revised conventional conjoint analysis framework that estimates the effect of causal
components on respondents’ stated preferences without bias, thus extending beyond the findings
from previous methodological literature. The average marginal component effect can becalculated as follows:
7

𝐷𝑙𝑦𝑖𝑡𝑗 = 𝛽0 + ∑ ∑ 𝛽𝑙𝑑 × 𝑎𝑖𝑡𝑗𝑙𝑑 + 𝑢𝑖𝑡𝑗
𝑙=1 𝑑=2

where 𝑎𝑖𝑡𝑗𝑙𝑑 =dummy variable(for the l−level of an attribute l of a policy j in task t of respondent i)
𝐷𝑙=number of levels of an attribute
𝛽𝑙𝑑 =coefficient
𝑢𝑖𝑡𝑗 =error terms
𝑦𝑖𝑡𝑗 ϵ {0, 1}=a choice indicator variable.
61

The approach has two advantages: (i) conditional independency must hold as attributes are
purely randomly ordered for each respondent, and (ii) the causal effects can be estimated nonparametrically as all explanatory variables are dummy variables.
Internal Choice Probability: Policy is preferred to the alternative policy. This compares the
ranking between two proposed choice profiles in which ranking 1 is assigned to an alternativewith a higher ranking, and 0 otherwise, irrespective of the ranking of the status quo.
External Choice Probability: Policy is preferred to the status quo. This compares the ranking
between the status quo and two other alternatives. If 1 is assigned to any choice set, then it
reveals a higher ranking than that of the status quo, and 0 otherwise. If the classification of thestatus quo is the highest or lowest, both two choice sets are assigned zero (0) or 1.
4.7 Motivation for Selecting the Attributes for Product Design
Several factors affect consumers’ preferences for a product, such as price, function, performance,
product familiarity, external factors, information, durability of that product, etc. Many studies
have been conducted on the electric vehicle (EV), seeking consumer preference factors.
Consumer willingness to buy an electric vehicle (EV) is based on some of the vehicle’s technicalspecifications. One study has shown that consumer willingness to buy an EV depends on
increasing the battery lifetime, reducing the charging time, and increasing the EV’s range. A
logistics model was conducted based on the perception of price, age, battery lifetime, charge
times, and range. The research covered 1245 respondents from Spain [113]. Electric vehicles(EVs) are regarded as being environmentally friendly with the potential to reduce greenhouse gas
emissions. Electric vehicle (EV) adoption depends on financial incentives, charging
infrastructure, local production facilities, etc. Multiple regression models have shown that,among all factors, charging infrastructure was most strongly related to EV adoption [114].
Electric vehicle (EV) adoption requires three tactics: reducing the vehicle price through a
subsidy, a local public charging network, and, through government fleet purchase, expanding thenumber and perceivability of completely battery electric vehicles (BEVs) on roadways, as well
as a hybrid mix of these three tactics. That research has shown that the hybrid policy option was
the best in encouraging BEV selection [115]. Electric vehicles (EVs) are an alternative solution
for reducing high dependency on fossil fuel, high carbon dioxide emissions, and addressing other62environmental issues. However, consumers do not have good perceptions of electric vehicles(EVs) due to the possible drivers for and, more specifically, the barriers to EV adoption. Thus, it
is essential to understand consumers’ intentions and behavior in order to increase EV adoption[116]. The consumer is very concerned about some EV indicators such as battery range, cost, andcharging infrastructure. The significant barriers to EV adoption are battery technology andsustainability of fuel sources. The commercial success or failure of EVs depends on consumer
attitudes and acceptance [117].
Several types of research have been conducted on the characteristics and benefits of the solar
home system (SHS). One field survey assessment in Assam, India, evaluated the technicalperformance of SHSs, service delivery, institutional arrangements, user satisfaction, and benefits
for rural livelihoods, with cost the primary barrier to SHS adoption [78]. User satisfaction with
the SHS depends on SHS equipment, reduction of energy costs, extending children’s study hours,
etc. A key finding is that the quality of SHS equipment and lifestyle benefits from the SHS playa significant role in improving user satisfaction in rural Bangladesh [35]. Conjoint analysis was
also used for estimating consumers’ preferences for color, shape, pattern, and SHS frame as well
as examining the trade-off between solar panel appearance, functional performance, and price[118]. In the current study, conjoint analysis was used to estimate consumers’ preferences for thesolar panel and included some existing features as well some hypothetical ones, such as theorganic solar PV system. Consumers’ prior knowledge of a product is an essential factor that also
affects the stability of their preferences [119]. The consumer can make a choice quickly if theyknow the primary attributes of the product. With an unfamiliar product, the consumer cannot
make a smooth preference. The consumer’s preference also changes if they see thecharacteristics of the product. They may evaluate or recall some product characteristics during
the process of choosing a product. The success of any development policy depends on the views
and attitudes of consumers; some rural electrification programs have failed for not incorporating
the consumer perspective. The concept of the “diffusion of innovation” is important and is foundin off-grid renewable energy program development [104].
The current study examined consumers’ preferences for the proposed organic solar panel basedon the following seven attributes: system design of panel and battery; panel size and features;translucency; chargeability in cloudy weather; system lifetime; price; and print design on the63panel surface. The attributes were selected from previous studies’ findings on preferenceindicators for electric vehicles (EVs) and SHSs that broadly examined some important factors. A
demonstration of the picture of the panel was undertaken to capture consumers’ preferences forthe proposed organic solar PV system.
4.8 Ideas for Treatment (Demonstration) in the Survey
A demonstration plays an important role when deciding on the choice of an appropriate solar
home system (SHS). The features of such a system include the following:❑ Panel size
❑ Functional performance❑ Surface pattern❑ Color.
Respondents chose better-looking SHSs with better functional performance despite the highprice [118]. The points that the study needed to consider in the treatment was that it must be time
efficient and understandable for villagers.
Some of the ideas for the treatment were as follows:•

Idea A: Bring only pictures or illustrations of the organic solar panel to the village andshow them while providing a brief explanation.
Time efficiency: Very High; Understandability for villagers: HighTechnology participation: Low; Cost: Very Low•

Idea B: Bring some real devices, such as a flexible organic solar panel to the village sovillagers can see and touch them in real life.
Time efficiency: Low; Understandability for villagers: Very HighTechnology participation: Medium; Cost: High•

Idea C: Bring a developed solar panel which has a boost converter circuit (Hiroshi isengaging) and show the circuit’s function.
Time efficiency: Very Low; Understandability for villagers: Very Low
Technology participation: Very High; Cost: Very High64

The current study adopted Idea A as the treatment as it was time efficient and could help
villagers to imagine the idea of the product at a glance. Also, the study needed to collect the datawith some teams simultaneously aiming at an adequate sample size. Idea B was good as itshowed them a real device to gain their interest in the survey, however, it was still difficult to
explain features of the new product, such as tents or shading eaves. Idea C made a good
contribution to the technology, but this was irrelevant as the study was not seeking to collectresponses on the boost circuit function. Plus, Ideas B and C incurred more costs and it would
also take time to prepare the devices.

65

Figure 5: Illustrations used in main survey

66

4.9 Findings and Discussion
4.9.1 Pilot Survey

The following two types of conjoint analysis were conducted during the pilot survey (in March2017):
1. Electricity use
2. Product design
Information about the electricity use pattern was collected from the local people (the villagers).
The study sought to learn the real demand for electricity in their daily lives and to identify theirpriorities for the uses of electricity. In total, 54 heads of households were interviewed to develop
the actual scenario of the electricity use pattern through the randomized conjoint experiment. The
attributes of the survey were: availability, lighting, mobile charge, TV and radio, table fan,service duration, and cost.
The study proposed the future organic solar PV system to existing users and non-users in order toexamine their preferences for the attributes, with these comprising the existing features of theconventional solar PV system as well the features of the organic solar PV system. The study
interviewed 40 heads of households requesting them to choose from the set of choices based onthese attributes, such as detachability, mounting, full charge under sunny weather, dechargeability under cloudy weather, product lifetime, and cost. Some attributes were changed,
while new attributes were included based on the pilot survey and its results. The new attributes
included were panel size and print design on panel surface, with the study then interviewing
another 42 heads of households based the new choice sets.
4.9.2 Results of Electricity Use

Two types of estimators were applied to estimate the choice probabilities, namely, internal andexternal choice probability. Figure plotting was used for the purpose of analysis. The study
interviewed 54 respondents who were either heads of households or eligible family members,
with each respondent asked six times to choose a preference from different choice sets.

67

Figure 6: Average causal effects on internal choice probability

As shown in Figure 4.4, based on internal choice probability, the probability was to choose thepolicy rather than the alternative policy. It was found that the attributes of ‘Availability,’ ‘TV
and Radio,’ and ‘Table fan’ had a statistically significant positive effect, while no significant
effects for the attributes of ‘Lighting,’ and ‘Mobile charge’ were observed. On the other hand,
the level of the higher burden of the ‘Cost’ was less preferable and statistically significant.

68

Figure 7: Average causal effects on external choice probability

As shown in Figure 4.5, for the attributes of ‘TV and radio’, the level of three hours/day was
positive and statistically significant, as well as the level of six hours/day. Using the attribute of‘Table fan’ three hours/day was also positive and significant. The lower ‘Cost’ of the system wasalso preferable. For the other attributes, no significant result was found in the external choiceprobability.
4.9.3 Results of Product Design

In terms of product design, the study interviewed 40 respondents on attributes such asdetachability, mounting, full charge under sunny weather, chargeability under cloudy weather,system lifetime, and cost of the system. The study repeated the request to each respondent tochoose a preference six times using different choice sets.
69

Figure 8: Average causal effects on internal choice probability

As shown in Figure 4.6, the attributes ‘Full charge under sunny weather,’ ‘Chargeability undercloudy weather,’ ‘Lifetime,’ and ‘Cost’ had results that were significant. Less hours for ‘Full
charge under sunny weather’ was preferable to more hours. No significant result was foundregarding the attributes ‘Detachability’ and ‘Mounting’.

70Figure 9: Average causal effects on external choice probability

As shown in Figure 4.7, most attributes were insignificant, except for the level of a long
‘Lifetime’ (10 years) and the level of high ‘Cost’ (40,000 rupees). A longer system lifetime waspreferred over a shorter system lifetime. The high cost of the system was less preferred to lesscost for the system.
4.9.4 New Scenario for Product Design 1

As no significant preferences were indicated by respondents, the current study added new
attributes. These new attributes were ‘Panel and battery,’ ‘Panel size,’ and ‘Translucency.’ These
kinds of attributes include the features of an organic solar PV system. A survey was conductedamong only 10 respondents based on these new attributes.

71

Figure 10: Average causal effects on internal choice probability

72

Figure 11: Average causal effects on external choice probability

No significant result was obtained for preferences in either internal or external choice probability.
Possible reasons were the lack of respondents’ proper understanding of the new attributes and thesmall sample size.
4.9.5 New Scenario for Product Design 2

The study’s attributes were again changed a little based on an analysis of the findings on the new
Product Design 1 scenario. The level of ‘leopard’ was removed from the attribute of ‘Print’ andone important level was included, named ‘flexible tent plus rain harvesting,’ in the ‘Panel size’
attribute for greater validity of the current research. The study interviewed 32 respondents andrequested each respondent to indicate their preference six times using different random choice
sets.

73

Figure 12: Average causal effects on internal choice probability

As shown in Figure 4.10, the 30% level of ‘De-chargeability’ on the sunny day is positive andstatistically significant. Less ‘Cost’ of the system is also positive and significant. The level of the‘Hindi symbol’ was positive and significant. On the other hand, the level of the ‘family picture’
was positive but insignificant.

74

Figure 13: Average causal effects on external choice probability

As shown in Figure 4.11, the level of 30% of sunny day for ‘De-chargeability’ over the ‘Dechargeability under cloudy weather’ attribute was positive and statistically significant. The
system ‘Cost’ of ‘40,000 rupees’ was negative and statistically significant.
4.10 Main Survey
Based on the pilot survey, several factors, such as price, system cost, de-chargeability undercloudy weather, and system lifetime were found to play an important role in choosing
preferences from the hypothetical profile choice sets in the research design. The study’s aim wasalso to learn consumers’ preferences for some features (e.g., panel size and translucency) of theorganic solar PV system. No significant result was found for either attribute. Further research
was undertaken on the organic solar PV system features and consumers’ technology adoption
behavior.
75

The main survey was conducted from September 7–17, 2017, in which more than 225 heads ofhouseholds were interviewed on the following intervention and procedure:
1. Treatment group: Pictures of the panel size and some functioning features were shown to
respondents for their greater understanding. The enumerator also explained the function
and benefit of the panel size, using the local language.
2. Control group: No demonstration was carried out of the picture and the enumerator
undertook the interview based on hypothetical choice sets.
4.10.1 Treatment GroupTo observe the experiment’s effect, a group of objects was deliberately processed. In theexperiment, the treatment was applied by the researcher to the experimental unit. The treatmentof the study was a demonstration of the flexible organic solar PV system. This system iscurrently still undergoing experimentation in the laboratory. The local people had first heard of
the term but were not able to imagine the kind of system it was. They did not know the functions
and advantages of the organic solar PV system, so the current study conducted a demonstration
to them, with this considered to be the study’s intervention.
The panel size picture was demonstrated, with this including features of flexibility, foldability,
and use as a tent and as a rainwater harvesting facility. A brief explanation was also made by theenumerator regarding the advantages and functions of the proposed organic solar PV system. In
total, 110 respondents participated.

76

Figure 14: Average causal effects on internal choice probability

For the ‘Panel size’ attribute, the level 1 ‘flexible shading, 10 square feet’ was estimated toincrease the respondent’s choice probability for supporting the panel’s flexibility by about
15% compared to the baseline position (hard, 1 square foot). The level 2 ‘flexible and foldable
tent, 30 square feet’ and level 3 ‘flexible and foldable tent, 30 square feet plus rainwater
harvesting’ were preferred more than the baseline by 16% and 20% of respondents, respectively.
For the ‘De-chargeability under cloudy weather’ attribute, the level 1 ‘30% of sunny day’ wasestimated by about 38% of respondents compared to the baseline level.
For the ‘Lifetime’ attribute, the level 1 ‘10 years’ has the potential to increase the probability by
about 18% against the baseline ‘5 years’ level and, for level 2 ‘15 years’ by about 35% against
the baseline level.
77For the ‘Cost’ attribute, the level 2 ‘40,000 rupees’ had the potential to decrease the probability
by 20% compared to the baseline level ‘10,000 rupees.’
For the ‘Panel battery’ and ‘Translucent’ attributes, no clear evidence for the choice probability
estimation was found, despite the positive sign of the 50% level cut for the ‘Translucent’attribute.
For the ‘Print’ attribute, the level of the family picture was positive but insignificant.

Figure 15: Average causal effects on external choice probability

As shown in Figure 4.13, the level of ‘flexible and foldable tent plus rainwater harvesting’ fromthe ‘Panel size’ attribute had the potential to increase the choice probability by 10% compared tothe baseline level. The level of ‘30% of sunny day’ under the ‘De-chargeability under cloudy
weather’ has the potential to increase the choice probability by 18% against the baseline level. In
addition, a longer ‘Lifetime’ of the system was preferable to a shorter ‘Lifetime’ of the system.
78

4.10.2 Control GroupThe study interviewed about 115 respondents without showing them the panel size picture. The
discussion about the function of the proposed panel size was not based on the picture, with theinterviews based only on the hypothetical choice sets.

Figure 16: Average causal effects on internal choice probability

As shown in Figure 4.14, the attributes of ‘De-chargeability under cloudy weather’ and ‘Lifetime’
were positive and significant compared to the baseline level. The lower ‘Cost’ of the system waspreferable to the higher ‘Cost’ of that system.
For other attributes, such as ‘Panel and battery,’ ‘Panel size,’ ‘Translucent,’ and ‘Print,’ nosignificant evidence was found for the estimation in the internal choice probability.

79Figure 17: Average causal effects on external choice probability

For the attribute ‘De-chargeability under cloudy weather’, the level of ‘30% of sunny day’ wasestimated to increase the respondent’s choice probability for supporting the organic solar PV
system by about 15% compared to the baseline position of ‘0%’.
For the ‘Cost’ attribute, the level 2 ‘40,000 rupees’ has the potential to decrease the probability
by about 16% against the baseline ‘10,000 rupees’ level.
4.10.3 Interaction Effect

The two types of interaction effects in conjoint analysis are: (i) interactions between theattributes; and (ii) interactions of the attributes with the respondent’s background characteristics.
From the analysis the choice probability, it can be seen that respondents preferred the low cost of
the system rather than the high cost. For that reason, the study set out to check the interaction
effect between cost and other attributes.

80Treatment group
From both the internal and external choice probability, it was seen that the interaction effect of
cost and other attributes was negligible. The study’s randomized designs of choice sets were
therefore unbiased.

Figure 18: Average causal effects on internal choice probability

81

Figure 19: Average causal effects on external choice probability

82

Control group

Figure 20: Average causal effects on internal choice probability

83

Figure 21: Average causal effects on external choice probability

In the control group, most of the interaction effect among cost and other attributes was negligible,
except between ‘De-chargeability,’ ‘30% of sunny day,’ ‘under cloudy weather,’ and ‘40,000
rupees’ in external choice probability. This interaction effect was positive and significant, thus
implying that respondents wanted to pay more if the system worked under cloudy weather
conditions.
The goal of this study was to understand the real situation of non-electrified villages, as well as
understanding users’ preferences on new features of the future organic solar PV module. The
difficulty in the study was that it had adopted organic solar technology which is one of the futuretechnologies. The organic solar PV system is not yet commercialized; therefore, it is challenging
to estimate the price, size, heaviness, etc. However, the study took the risk of using the organic
solar PV system with a focus mainly on new functions, such as flexibility, portability, andchargeability in bad weather.
Through this study, the researcher was taught one formula;Technology Database + Social Study = Reverse Innovation
84

Through this formula, researchers learn that various types of technology are available and that, as
humans, we have the option of which technology to use. Every region, even in both developed
and developing nations, has suitable technology of its own. A social study can prove “what they
really want in the local area”, as well as “what type of technology is preferable for the people
and the region”. The answer to reverse innovation that was found through this project was thatthe real opinions and preferences of the local people need to be reflected in every way possible in
the product design.

Figure 22: Simplest formula for reverse innovation concept

4.11 Social Trust of Villagers
Another questionnaire survey, together with conjoint analysis, was conducted. This questionnaire
focused on social trust. The reason why this questionnaire was enclosed was to observe therelationship between portability (de-mountability) and social confidence. The following fourquestions (shown in Figures 4.21–4.24) were used to estimate how much the people trusted eachother as a group, with this based on work of the World Bank. It was expected that respondents
would have some concerns about trouble from burglary due to the panel’s portability. However,contrary to the researcher’s expectation, their trust in each other was extreme. Many villagers
85even stated that there would be less risk in the village of trouble from burglary even if very niceprivate SHSs were installed. They were willing to help each other when someone wasexperiencing trouble (see Figures 4.21–4.24). In the electrified village, some villagers went to
the house which had already installed an SHS and used the electricity for free. They shared acooperative mindset; hence, troubles in the villages were not of much concern to the current
study.

Do you feel safe to walk alone in your area after dark?

No

Yes

0

20

40

60

80

100

120

140

160

180

Figure 23: Result of the questionnaire; do you feel safe to walk alone in your area after dark?

Most of the people in your village are willing to help if you need
it
300
200
100
0
Agree strongly

Agree somewhat

Disagree strongly

Figure 24: Result of the questionnaire; most people in your village are willing to help if you need it86

In this village, people generally do not trust each other inmatters of lending and borrowing money
120
100
80
60
40
20
0
Agree strongly

Agree somewhat

Neither agree
nor disagree

Disagreesomewhat

Disagree strongly

Figure 25: Result of the questionnaire; in this village, people generally do not trust each other in mattersof lending and borrowing money

A lost envelope with your name, in which there is some money,
is likely to be returned if someone in your village discovers it.

120
100
80
60
40
20
0
Agree strongly

Agreesomewhat

Neither agree
nor disagree

Disagreesomewhat

Disagreestrongly

Figure 26: Result of the questionnaire; a lost envelope with your name, in which there is some money, islikely to be returned if someone in your village discovers it.

87

4.12 Conclusion
The off-grid rural electrification program based on renewable energy is the most effective way toincrease energy supply in remote areas of developing countries. Although many such programs
have been implemented, the success rate for ensuring long-term sustainability plans is low. Manyof these programs fail to adequately address the social and cultural problems of the target
communities, resulting in little or no acceptance by users. Similarly, many rural electrification
policies have failed to incorporate user needs and perspectives, resulting in policy measures thatcannot be addressed.
Rural areas suffer from energy poverty, and lack of human and economic development. In thecurrent study, one of the preferences of choice depends on energy supply, economic viability,

rural economic development, residue management, the nature of end-user applications, andgovernment programs and policies. Most respondents preferred the solar PV system that worked
under cloudy weather conditions. They disagreed with the appropriate level price of the product.
The actual survey was conducted in two different languages (English and Hindi). Meaningful
efforts were made to control translation and interpretation errors. However, given the nature andlevel of the survey, it may be that some questions may not have been managed as expected.
This study has made it possible to follow a flexible policy development model, tailored bydifferent countries to suit different levels of autonomy in dealing with specific alternatives fortheir own country. The study’s results indicate that energy policy development requires the close
collaboration of central and state government authorities. Understanding community attitudes
and their energy needs, and engaging community involvement in planning and project design
have been found to be the cornerstone for achieving long-term sustainability initiatives.

88

Chapter 5: Conclusion and Policy Recommendations
Energy can be said to be one of the main challenges facing the world today, covering all aspectsof our lives. For those living in extreme poverty, the lack of access to modern energy services
greatly affects health, limits opportunities, and widens the gap between the rich and the poor.
Expanding access to modern energy services is a huge challenge for developing countries,
especially in the poorest countries. Improved energy use, especially access to electricity, has asignificant impact on education. For example, it reduces the inconvenience of this learning
opportunity and allows children to increase their school attendance. They can go to school andother educational activities. Also, due to electrification, rural households receive enoughluminescence for study. The family can use television, radio, and information andcommunications technology (ICT) for educational purposes.
This study assessed the effect on educational outcomes of current energy access in developing
countries. It also discussed changes in package size and technological innovation as well as
consumers’ preferences for the future organic solar PV system. The study accessed a range ofcurrently available energy access data from the MICS database, with data also collected from thefield survey in Bangladesh and India. Extensive energy access data were not only crucial forunderstanding the energy use of developing countries, but also invaluable were the policies andplans being used to address energy poverty issues to strengthen the expansion of modern energy
services.
The purpose of this study was to draw attention to energy access beyond traditional electricity
supply, especially in poorer developing countries where access is most restricted. The study also
raised questions related to the availability of energy access statistics. The study has drawn
attention to:■ Electricity access in the least-developed countries (LDCs) and the effect of grid electrification
on school enrollment in the short run as well as the long run.
■ Access to off-grid electricity, specifically the solar home system (SHS) in rural Bangladesh
and its impact on educational outcomes.

89■ Technical innovation as well as changes in package size of solar home systems (SHSs) in
Bangladesh.
■ Evaluating consumers’ preferences for organic solar PV systems in remote areas in
Chhattisgarh, India.
The main outcomes of this study are as follows:✓ The effect of access to electricity on grade progression is positive and significant. It also
has a significant negative effect on grade repetition. The non-attendance rate is higher in
non-electrified areas compared to electrified areas. The simulation result showed thataccess to electricity is positively associated with better grade progression at upper grades.
✓ Currently, grid electricity is unable to be connected in some remote areas in Bangladesh.
Renewable energy is the only option to ensure good quality lighting for poor people. The
study found a positive impact of SHSs on students’ performance and school enrollment.
The student who has access to an SHS receives better scores compared to those who do
not have access to a solar home system (SHS). The impact of SHSs on grade progression
is positive and significant.
✓ The SHS is, to date, becoming more popular in rural areas in Bangladesh. The SHS
program is one of the most successful off-grid programs with the highest installation rates.
Grameen Shakti is the pioneer organization that implemented and maintained SHSs fromthe early 1990s. Today, dramatic changes are being seen in technological innovation and
the SHS package. Consumers indicated their preference for the low watt peak solar PV
system at a convenient price. The source of imports of the solar PV system has alsochanged.
✓ The study examined consumers’ preferences for future organic solar PV systems overconventional solar PV systems in remote areas in India. A randomized conjoint analysis
was conducted based on some attributes and levels. In this regard, some possible features
were included that related to future organic solar PV systems. The study found that
consumers preferred solar PV systems that were active during cloudy or bad weather
conditions.
90Based on the above findings, the study proposes strategic options to strengthen education andenergy policies as follows:•

The Bangladesh government needs to provide special care to students who study in offgrid areas and arrange a soft loan for poor families to enable them to buy renewableenergy lighting tools.

•

The education policy in Bangladesh needs to be redefined to incorporate the “ensure
good quality of lighting for kids study” policy in the National Education Policy. The
government should introduce a separate wing in the Education Ministry and provide
support to the existing education policy based on field-level research.

•

Steps need to be taken to strengthen and simplify the regulatory framework relating tolocal needs. This would ensure transparency in evaluation and provide accurate
information about the facts of electrification in remote areas.

•

Integrated, appropriate and convenient solar home systems (SHSs) need to be developed
for rural areas, ensure easy access to these systems for poor people.

•

Financial incentives and other inducements should be given to entrepreneurs who arewilling to invest in future organic solar PV systems.

Policies and national plans must be significantly strengthened to address the impact of energy
access on educational outcomes. If countries do not have a clear understanding of energy access,including understanding regional and national trends, rural/urban differences, the range of energy
sources, and consumers’ preferences for energy access that is commonly used in poor households,
this cannot be done effectively. However, existing data sets and reports often provide insufficient
information on access to energy and its effect on educational outcomes. It is essential to identify
consumers’ preferences for renewable energy technology for lighting purposes. This preference
can vary from region to region in developing countries. Proper research and field study areneeded regarding the choices of local people when undertaking and implementing appropriate
policies.

91Appendix
Table 21: Transition matrices (age 7 to 8)
4
P(5|G)
P(4|G)
P(3|G)
P(2|G)
P(1|G)
P(NA|NA)
Observation
P(G)

0.909
0.091
--------11
0.004

P(5|G)
P(4|G)
P(3|G)
P(2|G)
P(1|G)
P(NA|NA)
Observation
P(G)

1.000
----------11
0.004

P(5|G)
P(4|G)
P(3|G)
P(2|G)
P(1|G)
P(NA|NA)
Observation
P(G)p-value−0.0910.091--------22
0.004
0.05

Grade (G)3
2
Treatment Transition Matrix----0.986--0.0140.979--0.021--------74
378
0.026
0.132
Control Transition Matrix----0.960--0.0400.974--0.026--------101
499
0.036
0.178
Treatment–Control Difference----0.026--−0.026
0.005--−0.005--------175
877
0.031
0.155
0.50
0.73

92

1

NA

------0.899
0.101--11630.406--------0.499
0.501
1239
0.432

------0.945
0.055--12120.432--------0.571
0.429
983
0.350

------−0.046
0.046--23750.419
0.00--------−0.072
0.072
2222
0.392
0.29

Table 22: Transition matrices (age 8 to 9)
5
P(6|G)
P(5|G)
P(4|G)
P(3|G)
P(2|G)
P(1|G)
P(NA|NA)
Observation
P(G)

1.000
------------10
0.004

P(6|G)
P(5|G)
P(4|G)
P(3|G)
P(2|G)
P(1|G)
P(NA|NA)
Observation
P(G)

0.857
0.143
----------7
0.003

P(6|G)
P(5|G)
P(4|G)
P(3|G)
P(2|G)
P(1|G)
P(NA|NA)
Observation
P(G)p-value

0.143−0.143----------17
0.003
0.09

Grade (G)4
3
Treatment Transition Matrix----0.965--0.035
0.981--0.019------------57
268
0.022
0.101
Control Transition Matrix----0.966--0.034
0.989--0.011------------87
349
0.035
0.141
Treatment–Control Difference----−0.001--0.001−0.007--0.007------------144
617
0.028
0.120
0.96
0.62

93

2

1

NA

------0.976
0.024----706
0.267--------0.944
0.056
--917
0.347

----------0.486
0.514
683
0.259

------0.979
0.021----8050.324--------0.941
0.059
--799
0.322

----------0.544
0.456
434
0.175

------−0.003
0.003----1511
0.295
0.75

--------0.003−0.003--17160.335
0.46----------−0.058
0.058
1117
0.218
0.72

Table 23: Transition matrices (age 9 to 10)
6
P(7|G)
P(6|G)
P(5|G)
P(4|G)
P(3|G)
P(2|G)
P(1|G)
P(NA|NA)
Observation
P(G)

0.769
0.231
------------13
0.004

P(7|G)
P(6|G)
P(5|G)
P(4|G)
P(3|G)
P(2|G)
P(1|G)
P(NA|NA)
Observation
P(G)

0.913
0.087
------------23
0.007

P(7|G)
P(6|G)
P(5|G)
P(4|G)
P(3|G)
P(2|G)
P(1|G)
P(NA|NA)
Observation
P(G)p-value−0.1440.144------------36
0.006
0.65

Grade (G)5
43Treatment Transition Matrix------0.967----0.033
0.990----0.010
0.981----0.019------------------61
302
632
0.019
0.096
0.201
Control Transition Matrix------1.000------0.983----0.017
0.989----0.011------------------74
401
816
0.024
0.131
0.266
Treatment–Control Difference------−0.033----0.033
0.008----−0.008−0.008----0.008------------------135
703
1448
0.022
0.113
0.233
0.02
0.80
0.09

94

2

1

NA

--------0.982
0.018----8760.279----------0.948
0.052
--730
0.232

------------0.305
0.695
531
0.169

--------0.990
0.010----8820.287----------0.944
0.056
--540
0.176

------------0.334
0.666
335
0.109

--------−0.008
0.008----1758
0.283
0.05

----------0.004−0.004--12700.204
0.74------------−0.029
0.029
866
0.139
0.42

Table 24: Transition matrices (age 10 to 11)
7
P(8|G)
P(7|G)
P(6|G)
P(5|G)
P(4|G)
P(3|G)
P(2|G)
P(1|G)
P(NA|NA)
Observation
P(G)

1.000
----------------8
0.003

P(8|G)
P(7|G)
P(6|G)
P(5|G)
P(4|G)
P(3|G)
P(2|G)
P(1|G)
P(NA|NA)
Observation
P(G)

1.000
----------------11
0.005

P(8|G)
P(7|G)
P(6|G)
P(5|G)
P(4|G)
P(3|G)
P(2|G)
P(1|G)
P(NA|NA)
Observation
P(G)p-value0.000----------------19
0.004
0.80

Grade (G)5
43Treatment Transition Matrix--------0.949------0.051
0.981------0.019
0.983------0.017
0.989------0.011------------------------59
160
470
545
0.025
0.069
0.202
0.235
Control Transition Matrix--------0.939------0.061
0.988------0.012
0.986------0.014
0.988------0.012------------------------99
251
625
575
0.043
0.109
0.271
0.250
Treatment–Control Difference--------0.010------−0.010−0.007----−0.003--0.007------0.003
0.001−0.001------------------------------158
411
1095
1120
0.034
0.089
0.237
0.242
0.28
0.90
0.72
0.17
6

95

2

1

NA

----------0.990
0.010----4880.210------------0.953
0.047
--275
0.118

--------------0.215
0.785
317
0.137

----------0.988
0.012----4030.175--0.0180.006------0.941
0.036
--169
0.073

--------------0.158
0.842
171
0.074

----------0.002−0.002----891
0.193
0.58---0.018-0.006------0.012
0.012
--444
0.096
0.13

--------------0.057−0.057
488
0.105
0.04

Table 25: Transition matrices (age 11 to 12)87

P(9|G)
P(8|G)
P(7|G)
P(6|G)
P(5|G)
P(4|G)
P(3|G)
P(2|G)
P(1|G)
P(NA|NA)
Observation
P(G)0.909091
0.090909----------------11
0.003467--1.0000----------------69
0.021746

P(9|G)
P(8|G)
P(7|G)
P(6|G)
P(5|G)
P(4|G)
P(3|G)
P(2|G)
P(1|G)
P(NA|NA)
Observation
P(G)0.923077
0.076923----------------13
0.004261--0.983193
0.016807--------------119
0.039004

P(9|G)
P(8|G)
P(7|G)
P(6|G)
P(5|G)
P(4|G)
P(3|G)
P(2|G)
P(1|G)
P(NA|NA)
Observation
P(G)p-value−0.01399--0.016807−0.01681--------------188
0.030206
0.70

0.013986----------------24
0.003856
0.8

Grade (G)5
4Treatment Transition Matrix------------0.968992----0.031008
0.982544----0.017456
0.981508----0.018492------------------------258
401
703
0.081311
0.126379
0.221557
Control Transition Matrix------------0.978622----0.021378
0.983957----0.016043
0.983957----0.016043------------------------421
561
748
0.137988
0.183874
0.245166
Treatment–Control Difference------------−0.00963----−0.00141
0.00963--−0.00245--0.001414----0.002449------------------------679
962
1451
0.109094
0.154563
0.23313
0.01
0.31
0.79
6

96

3

2

1

NA

----------0.977586
0.022414------580
0.182792------------0.976253
0.023747----379
0.119445--------------0.92
0.08
--200
0.063032----------------0.050699
0.949301
572
0.180271-----------0.973684
0.026316------494
0.161914------------0.977695
0.022305----269
0.088168--------------0.926606
0.073394--109
0.035726----------------0.063091
0.936909
317
0.1039----------0.003902−0.0039------1074
0.172558
0.89------------−0.00144
0.001442----6480.1041130.08--------------−0.00661
0.006606--309
0.049647
0.01----------------−0.01239
0.012392
889
0.142834
0.04

Table A6: Transition matrices (age 13 to 14)
10

9

8

P(11|G)
P(10|G)
P(9|G)
P(8|G)
P(7|G)
P(6|G)
P(5|G)
P(4|G)
P(3|G)
P(2|G)
P(1|G)
P(NA|NA)
Observation
P(G)

1.000
----------------------1
0.0005--1.000--------------------53
0.0243----0.995
0.005----------------190
0.0871

P(11|G)
P(10|G)
P(9|G)
P(8|G)
P(7|G)
P(6|G)
P(5|G)
P(4|G)
P(3|G)
P(2|G)
P(1|G)
P(NA|NA)
Observation
P(G)

0.900
0.100
--------------------10
0.0041--0.9800.020------------------98
0.0404----0.992
0.008----------------368
0.15160.100--0.020−0.020------------------151
0.03280.04----0.003−0.003----------------558
0.1211
0.06

P(11|G)
P(10|G)
P(9|G)
P(8|G)
P(7|G)
P(6|G)
P(5|G)
P(4|G)
P(3|G)
P(2|G)
P(1|G)
P(NA|NA)
Observation
P(G)p-value−0.100--------------------11
0.00240.90

Grade (G)7654Treatment Transition Matrix------------------------0.997------0.003
0.988------0.012
0.976------0.024
0.979------0.021--------------------------------346
344
209
194
0.15860.1577
0.09580.0890Control Transition Matrix------------------------0.991------0.009
0.988------0.012
0.988------0.012
0.959------0.041--------------------------------530
431
243
148
0.21830.17750.1001
0.0610Treatment–Control Difference------------------------0.007------−0.007
0.000----−0.012--0.000------0.012
0.020-------0.020--------------------------------876
775
452
342
0.19010.1681
0.09810.0742
0.25
0.09
0.80
0.41

97

3

2

1

NA

--------------0.971
0.029------105
0.0481----------------1.000------61
0.0280-------------------1.000----210.0096--------------------0.002
0.998
657
0.3012--------------0.972
0.028------71
0.0292----------------0.967
0.033----300.0124------------------0.905
0.095
--21
0.0086--------------------0.002
0.998
478
0.1969--------------0.000
0.000------176
0.03820.99----------------0.033−0.033----910.01970.01------------------0.095−0.095--420.00910.39--------------------−0.001
0.001
1135
0.24630.50

Table 26: Transition matrices (age 14 to 15)
Grade(G)7
65Treatment Transition Matrix------------------------1.000------0.990----0.010
0.955----0.045------------------------------288195
110
0.13530.0916
0.051638
35
92
Control
Transition
Matrix

11

10

9

8

P(12|G)
P(11|G)
P(10|G)
P(9|G)
P(8|G)
P(7|G)
P(6|G)
P(5|G)
P(4|G)
P(3|G)
P(2|G)
P(1|G)
P(NA|NA)Observatio
n
P(G)

1.000
------------------------1
0.00047--1.000----------------------11
0.0051
69----0.974
0.026------------------151
0.070959------0.988
0.012----------------330
0.155075

P(12|G)
P(11|G)
P(10|G)
P(9|G)
P(8|G)
P(7|G)
P(6|G)
P(5|G)
P(4|G)
P(3|G)
P(2|G)
P(1|G)
P(NA|NA)Observatio
n
P(G)

1.000
------------------------10
0.004073--0.9820.018--------------------55
0.022403----0.990
0.010------------------303
0.123422

P(12|G)
0.000
P(11|G)--P(10|G)--P(9|G)--P(8|G)--P(7|G)--P(6|G)--P(5|G)--P(4|G)--P(3|G)--P(2|G)--P(1|G)----P(NA|NA)
10
Observati
onP(G)0.002182
p-value
0.85
NA = Non-attendance--0.018-0.018--------------------66
0.0144010.43-----0.017
0.017------------------454
0.099062
0.03------------------------0.994------0.006
0.995------0.005
0.986------0.014
0.981------0.019----------------------------------------526
409
222
108
0.21420.16650.09040.043957
99
28
92
Treatment-Control
Differences-------------------------0.006------0.006
0.005-------0.005
0.003-------0.003-0.027------0.027----------------------------------------856
697
417
218
0.18670.15200.09090.0475
77
84
88
67
0.24
0.02
0.86
0.91

98

4

3

2

1

NA

--------------0.985
0.015--------65
0.030545----------------0.956
0.044------45
0.021147------------------0.957
0.043----230.010808--------------------0.846
0.154
--13
0.006109----------------------0.001
0.999
896
0.421053--------------0.971
0.029--------70
0.028513----------------1.000--------31
0.012627------------------0.944
0.056----180.007332--------------------1.000----50.002037----------------------0.006
0.994
698
0.284318--------------0.013-0.013--------135
0.029457
0.33-----------------0.044
0.044------76
0.016583
0.13------------------0.0120-77
0.0120--8------------------------0.154
0.154
--18
0.003928
0.03-----------------------0.005
0.005
1594
0.3478070.33

410.008946
0.66

Table 27: Grade transition in un-electrified areas (treatment)
Age 5
Age 6
Age 7
Age 8
Age 9
Age 10Age 11
Age 12
Age 13
Age 14
Age 15

Grade 1
690
2072
3270
2669
1290
435
200
49
11
1
1

Grade 2
43
355
1662
2973
2592
1270
428
195
48
11
1

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8Grade 9Grade 10

Grade 11

Grade 12

40
344
1631
2932
2600
1286
446
199
54
12

40
343
1613
2893
2621
1305
443
203
55

36
331
1608
2874
2622
1372
467
221

36
328
1594
2873
2564
1369
459

28
311
1545
2867
2543
1355

28
314
1537
2867
2577

25
314
1529
2873

25
314
1489

25
314

25

NA
9267
7533
4685
2348
1207
839
659
625
619
618
617

Table 28: Grade transition in electrified areas (control)
Age 5
Age 6
Age 7
Age 8
Age 9
Age 10Age 11
Age 12
Age 13
Age 14
Age 15

Grade 1
733
2368
3851
2330
1003
299
87
32
12
2
2

Grade 2
40
439
1984
3690
2271
970
300
88
31
12
3

Grade 3

Grade 4

Grade 5

Grade 6

Grade 7

Grade 8Grade 9Grade 10

Grade 11

Grade 12

34
422
1949
3634
2288
986
319
94
33
12

34
405
1941
3628
2312
997
325
104
36

34
396
1907
3599
2333
1007
324
108

29
398
1908
3582
2373
1022
332

26
374
1874
3535
2379
1020

26
370
1860
3517
2387

24
369
1852
3515

22
364
1840

20
357

20

102

NA
9227
7159
3710
1592
727
484
407
382
372
371
369

Figure 27: Propensity score distribution for purpose of common support

103

Figure 28: Covariate balancing test

104

Figure 29: Attributes and levels of electricity use in pilot survey

Figure 30: Attributes and Levels for product design in pilot survey

Figure 31: Attributes and levels of product design in pilot survey

105

Figure 32: Attributes and levels of new product design 1 in pilot survey

Figure A6: Attributes and levels of new product design 2 in main survey

106

Figure 33: Example of set of choices for electricity use in pilot survey

The 2nd trial

Choice ID
857

Choice ID
567

Choice A
Combined, panel and light battery(totally 5kg)
50% cut (such as sun glasses)

Choice B
Combined, panel and light battery(totally 5kg)
100% cut (such as black sheet)

Attribute 1

System design of panel and battery

Attribute 2

Translucent

Attribute 3

Panel size and feature

Attribute 4Lifetime

Attribute 5

Print design on the panel surface

Hindi symbol

Family picture

Attribute 6

Chargeability in cloudy weather

Yes (30% of sunny day)

0 % (same as conventional)

Attribute 7

Price (one time payment)

10000 Rupees

40000 Rupees

Flexible panel & foladable frame, good Flexible panel, good for shading eave
for tent (30 feet2) plus rain water
extended from roof (10 feet2)
15 years
15 years

Choice Cनही चाि हये / Do notwantYour Ranking ==>Figure 34: Example of set of choices for new product design 2 in main survey

107

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