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MADUKWE CHIOMA EVANGELINE
[PG/MSC/10/54743]
DOMESTIC ENERGY USAGE PATTERN OF HOUSEHOLDS IN SELECTED
URBAN AND RURAL COMMUNITIES OF ENUGU STATE.
INSTITUTE FOR DEVELOPMENT STUDIES
Chukwuma Ugwuoke
Digitally Signed by: Content manager’s Name
DN : CN = Webmaster’s name
O= University of Nigeria, Nsukka
OU = Innovation Centre
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DOMESTIC ENERGY USAGE PATTERN OF
HOUSEHOLDS IN SELECTED URBAN AND RURAL
COMMUNITIES OF ENUGU STATE.
BY
MADUKWE CHIOMA EVANGELINE
[PG/MSC/10/54743]
INSTITUTE FOR DEVELOPMENT STUDIES
UNIVERSITY OF NIGERIA
ENUGU CAMPUS
SUPERVISOR
B. D. UMOH
MARCH, 2014.
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CERTIFICATION
This is to certify that I, Madukwe, Chioma Evangeline, a postgraduate student in the Institute For Development Studies with registration number PG/MSC/10/54743 carried out this study. The study is however adequate in scope, content and quality as read by the Institute For Development Studies in partial fulfillment of the requirement for the award of a Masters of Sciences Degree of Development Studies.
--------------------------------- --------------------------------------
B. D. Umoh Date
Project Supervisor
--------------------------------- --------------------------------------
-
Prof. Osita Ogwu Date
Director
Institute For Development Studies
University Of Nigeria, Enugu Campus
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APPROVAL
This research project has been presented to the panel of the Institute For Development
Studies and certified as the original work of Madukwe, Chioma Evangeline with
registration number PG/MSC/10/54743. The work has been approved as meeting the
requirements of the Institute For Developemt Studies, University of Nigeria for an
award of the degree of Masters (M.Sc) in Development Studies.
--------------------------------- --------------------------------------
B. D. Umoh Date
Project Supervisor
--------------------------------- --------------------------------------
-
Prof. Osita Ogwu Date
Director
Institute For Development Studies
University Of Nigeria, Enugu Campus
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DEDICATION
This study is dedicated to God Almighty who gave me life and strength to undertake it.
I also dedicate the study to my wonderful parents, Elder Chukwuma Thomas and Mrs.
Justina Nwakaego Madukwe for their gracious and endless support and prayers.
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ACKNOWLEDGEMENT
I sincerely praise God for his unconditional love, strength, wisdom and grace he
gave me to carry out this study. I am indebted to my supervisor B. D. Umoh, who gave
me his time, efforts and mentorship. I remember him calling me on phone and pressing
me to move on as though the degree was to his name; he made me to see research in a
new and different dimension. God bless you richly sir.
My heartfelt gratitude goes to Dr. Chukwuma Agu for his guidance, direction
and encouragement in my academic work. I also sincerely thank all my lecturers in the
Institute For Development Studies including the Director, Prof. Osita Ogwu for their
supports. You all wished me well and encouraged me to enroll for my Ph.D
programme in future.
May I also express my thankful heart to Yuni Denis Nfor, who guided me in
methods of analysis, he taught me application of STATA package. I thank my cousin
Egwuji Chineye and my siblings Joy, Chukwuka, Ebele, Chukwuma and
Kosisochukwu for their kind supports.
Very big thanks to Ikpo Kobi who introduced this great Institute to me and
encouraged me to enroll. I will not forget to mention Ezeh Chukwuka Theophilus,
Deputy Director Environmental, Enugu State Ministry of health for his assistance in
reaching out to the local governments under the study and Uche Nnamani who also
assisted the study.
Madukwe, Chioma Evangeline.
08034419334
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TABLE OF CONTENTS
TABLE OF CONTENTS ………………………………………………………… i
ABSTRACT ………………………………………………………………….. ii
CHAPTER ONE ………………………………………………………………… 1
1.1 Background of the Study …………………………………………………. 1
1.2 Statement of problem ………………………………………………………. 2
1.3 Objectives of the study …………………………………………………… 4
1.4 Research questions ………………………………………………………… 4
1.5 Research hypotheses ……………………………………………………… 5
Hypotheses 1 ……………………………………………………………… 5
Hypotheses 2 ……………………………………………………………… 5
Hypotheses 3 ……………………………………………………………… 5
1.6 Significance of the study …………………………………………………… 5
1.7 Scope of the study. ………………………………………………………. 6
1.8 Limitations of the study ……………………………………………………. 6
CHAPTER TWO ……………………………………………………………… 7
LITERATURE REVIEW
2.0 Introduction ………………………………………………………………. 7
2.1 Conceptual Framework ……………………………………………………. 7
2.1.1 Concept and Evolution of Energy. ……………………………………….. 7
2.1.2 Concept of household ……………………………………………………. 8
2.1.3 Household Energy use …………………………………………………… 9
2.1.4 Domestic Energy Use in Rural and Urban settings of Enugu State. ……… 10
2.1.5 Types of Energy Use in Nigeria……………………………………………. 11
2.2 Theoretical Framework ……………………………………………………. 14
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2.2.1 Energy ladder theory ……………………………………………………… 14
The Energy ladder model: ………………………………………………… 16
2.2.2 Energy Stack model ………………………………………………………. 17
Stack Model Theory by Masera. …………………………………………. 18
2.3 Review of Empirical Literature …………………………………………… 18
2.4 Summary of Literature …………………………………………………….. 27
2.5 Gaps in Literature ………………………………………………………… 28
CHAPTER THREE …………………………………………………………… 29
METHODOLOGY
3.0 Introduction ………………………………………………………………. 29
3.1 Area of study ……………………………………………………………… 29
3.1.1 Demography of the Rural and Urban communities selected. …………….. 30
3.2 Study design …………………………………………………………….. 31
3.3 Study population …………………………………………………………. 32
Table 1: Population distribution of households…………………………….. 32
3.4 Sample size ……………………………………………………………….. 32
3.5 Sampling method …………………………………………………………. 32
3.6 Instruments for data collection …………………………………………… 34
3.6.1 Sources of data …………………………………………………………… 34
3.7 Validation of primary instrument ………………………………………… 35
3.8 Data Collection ……………………………………………………………. 35
3.9 Reliability of Measurement ………………………………………………. 35
3.10 Methods of data analysis ………………………………………………….. 36
Hypothesis 1 ……………………………………………………………………… 36
Hypothesis 2 ……………………………………………………………………… 37
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Hypothesis 3 ……………………………………………………………………… 38
CHAPTER 4 ……………………………………………………………………. 39
DATA PRESENTATION
4.0 Introduction ………………………………………………………………. 39
4.1 Questionnaire Distribution and retrieval …………………………………. 39
4.2 Socio- Demographic Characteristics of Respondents ……………………. 39
4.2.1 Relationship of Respondent with the Head of household ………………… 40
4.2.2 Distribution of respondents by Gender …………………………………… 40
4.2.3 Distribution of respondents by marital status ... 41
4.2.4 Distribution of respondents according to Age ………………………. 41
4.2.5 Distribution of respondents according to Religion ……………………….. 42
4.2.6 Distribution of respondents according to Ethnic groups…………………… 42
4.2.7 Distribution of respondents by Educational attainment …………………… 43
4.2.8 Distribution of respondents by Occupation ……………………………… 44
4.2.9 Income distribution of respondents ……………………………………….. 44
4.2.10 Distribution of respondents according to Size of households ……………… 45
4.3.1 The relative household energy uses attributable to different energy sources
between urban and rural areas. …………………………………………….. 45
4.3.2 Test of hypotheses I……………………………………………………….… 51
4.3.3 The Determinants of Energy Types Used in Households. ………………… 59
4.3.4 Test of hypotheses II……………………………………………………… 71
4.3.5 The Preferences of Households on different Energy Types. ……………… 73
4.3.6 Test of Hypothesis III……………………………………………………… 75
CHAPTER FIVE ………………………………………………………………… 79
DISCUSSION OF RESULTS
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5.1 The household energy types attributable to different energy uses in Enugu.. 79
5.2 Factors that influence the types of energy used by households …………… 80
5.3 The Energy preferences of household ……………………………………. 82
CHAPTER SIX …………………………………………………………………… 85
SUMMARY OF FINDINGS, CONCLUSION AND RECOMMENDATION
6.0 Introduction………………………………………………………………….. 85
6.1 Summary……………………………………………………………………. 85
6.2 Development Implication ……………………………………………….. 86
6.3 Conclusion ………………………………………………………………… 86
6.5 Recommendations ………………………………………………………… 87
6.6 Suggestions for further study ……………………………………………… 88
REFERENCES ……………………………………………………………. 89
APPENDIX ………………………………………………………………………. 84
Questionnaire ……………………………………………………………............... 84
Reliability measurement ………………………………………………………….. 101
Objective I ……………………………………………………………………. 102
Hypothesis I ………………………………………………………………….. 114
Objective II …………………………………………………………………… 126
Hypothesis II …………………………………………………………………… 126
Objective III …………………………………………………………................ 158
Hypothesis III ……………………………………………………………............ 161
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BSTRACT
“The Study sought to investigate domestic energy usage pattern of households in selected
urban and rural communities in Enugu State. A total of 300 households were randomly
selected for the study. Variables of interest included: types of energy used for various
purposes, factors that influence such use and preferences for the different types of energy. The
questionnaire was used for data collection. Table summaries, frequencies, charts were used
for data presentation while ANOVA and Regression were used analytically to test for
associations. The findings show that urban households daily rely more on kerosene (18.5%);
while the rural households daily rely more on firewood (21.7%). Economic factors were
found to influence the choice of energy used in homes. Significant positive correlation was
found between the type of energy use and accessibility, educational qualification, energy
price, and monthly income of urban dwellers. Most rural household energy choice was
dictated by cultural beliefs, energy price and accessibility. The rural households preferred
firewood for their cooking (92%) and combination of both traditional and modern energy for
non-cooking (51%), while the urban households preferred the combination of both for
cooking (54%) and only modern energy for other non-cooking (92%) when made available,
affordable and they earned higher income. Based on these findings the study concludes that
households in Enugu urban area tends to climb the energy ladder from low grade energy
types to modern energy when income increases and such energy made available while the
rural still resort to low grade traditional energy especially for cooking, basically because of
their cultural belief and preference. The study recommend that the rural households be
sensitized on the health effects of traditional energy to their environment, reduce poverty both
in rural and urban areas to enable the households use modern energy and also, make such
energy affordable and available since many were willing to switch when made affordable.
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CHAPTER ONE
1.1 Background of the Study
Concerns for energy required for the running of homes, industries and the economy
generally has been of global concern for some decades (Stern, 2007). In terms of utilization,
household energy accounts for about forty percent of the total energy consumption in
developing countries (Obueh, 2008). Households use energy for lighting, heating, cooling,
ironing, food and drinks preservation, powering electronic devices, cooking and vacuum
cleaning. Therefore when energy shortage occurs or prices rise, many things may go wrong.
This in part explains why members of the public show serious concerns when prices of energy
rise.
As with many goods and services, the demand for energy and type of energy used
depend on several factors. According to World Bank (2005) 74% of households in Asia use
solid fuels, mostly in the form of biomass. The situation is not much different in Nigeria
where traditional energy sources accounts for over 70% Household energy supply (World
Bank, 2005). While rural households rely more on biomass fuels than those in urban areas, a
substantial number of urban poor households’ in Nigeria rely on fuel wood, charcoal, or wood
waste to meet their cooking needs. According to International Energy Agency, IEA (2006),
the proportion is likely to increase since it is estimated that 61% of the world’s population
will be living in urban areas by 2025.
Available estimates show that Nigeria consumes over 50 million metric tons of fuel
wood annually, a rate, which exceeds the replenishment rate through afforestation,
(Intergovernmental Panel on Climate Change -IPCC, 2007). Sourcing fuel wood for domestic
and commercial uses is a major cause of desertification in the arid-zone states and erosion in
the southern part of the country (Sambo, 2005). From available statistics, the nation’s 15
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million hectares of forest and woodland reserves could be depleted within the next fifty years
(Energy Commission of Nigeria -ECN, 2003).
Generally, rural communities are mainly characterized by high population density,
vicious circle of poverty and lack of infrastructure which includes lack of energy at household
level (UK Department for International Development -DFID, 2008). This does not exclud the
urban slums which share almost same characteristics with the rural communities which is
different from the high income urban. The poor might not use the term ‘energy’, but they can
spend far more time and effort obtaining energy services than the rich; and do spend a high
proportion of their household income on energy just for basic human survival – cooking,
cooling among other uses (DFID, 2008).
It is against this background that this study investigated the household energy use
patterns across selected urban and rural areas in Enugu State putting into considerations the
household energy uses attributable to different energy sources as well as some factors that
influence the choice of energy consumption and the preferences of households if given an
option in order to ensure a balanced development of both urban and rural centers of the state.
1.2 Statement of problem
As of 2010, available statistics showed that 1.4 billion people around the world lack
access to electricity and 1 billion households lack access to clean cooking facilities. Some
85% of those that lack electricity live in Sub-Saharan Africa. Without additional dedicated
polices by 2030, the number of the people will drop to only 1.2 billion (OECD/IEA, 2010).
More than 60% of Nigerians (about 100 million people) have no access to electricity and half
of Nigerians live in rural communities, where four in five households go without power
(Onyeji, 2009).
Energy is one of the essential inputs for improved well-being of individuals and socio-
economic development of nations. In spite of the importance of energy, most households in
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Enugu State are still faced with the over-consumption of low grade traditional energy sources
(National Bureau of Statistics, 2009). However, the collection and utilization of traditional
energy sources is at some cost which often manifest in forms of in-door air pollution,
flood/erosion, desertification and loss of biodiversity. Several localities in the state are already
energy deficient (ECN, 2003). This is evident in distances over which fire wood may have to
be fetched from and the prices of fuel wood.
The type of energy used in Enugu State in recent times, especially the poor
households, have not been helped by poverty and rising prices of other more efficient energy
types like electricity, kerosene and gas. Gasoline prices in Nigeria, where two-thirds of the
population of about 164 million live on less than $1.25 a day, surged after the President
abolished 1.2 trillion naira ($7.4 billion) of subsidies on Jan. 1, 2012. Fuel price had been
capped at 97 naira a liter, undermining investment in refineries that resulted in the country
importing about 70 percent of its fuel (The Nation, 2012).
For instance, in the year 2000, a litre of kerosene sold for fifteen naira. Today it sells
for one hundred and ten naira (Nigerian National Petroleum Commission, NNPC, 2012). This
represents a percentage increase of 86% percent and has put more pressures on households to
devise coping strategies to cope with rising energy cost. Large amounts of human energy are
spent gathering fuel wood in many parts of the state, and the burden tends to fall more heavily
on women and children. In many communities today, it is not uncommon to see women and
children trek several kilometers in search of fuel wood in both rural and urban slums of Enugu
State.
One of the reasons that traditional energy source is the preferred domestic fuel is that
it does not require a complex and expensive infrastructure to be produced and used as a fuel.
Furthermore, so far it is the cheapest (usually free) available energy resource for the rural
population and urban poor (Onyekuru, 2008). The energy use patterns of urban households
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may differ to that of the rural households since they have different geographical
characteristics. There has been no research known to me carried out to investigate the
significant difference in patterns of energy usage and demands among the urban and rural
households in Enugu state.
It is against these problems that this study sought to investigate the patterns of
domestic energy usage of households between in selected urban and rural communities of
Enugu State and what influences their usage patterns. The study also investigated what would
be the households’ preferences of energy types, if given an option.
1.3 Objectives of the study
The overall aim of this research is to investigate the differences in energy usage
patterns of households across selected rural and urban communities of Enugu state. The
following specific objectives guided the study.
i. To identify the relative household energy uses attributable to different energy sources
between urban and rural areas.
ii. To identify the factors influencing household energy uses of different energy types.
iii. To investigate the preferences of households energy use for different energy types
between urban and rural areas.
1.4 Research questions
The following questions are asked to serve as a guide;
i. What are the relative household energy uses attributable to different energy sources in
the area?
ii. What are the factors influencing the uses of the energy types by households in the area?
iii. What are the preferences of households on different energy types in the area?
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1.5 Research hypotheses
The following null hypotheses were formulated to guide the study:
Hypotheses 1
There are no differences in household energy uses attributable to different energy sources
among the selected rural and urban communities in the state.
Hypotheses 2
There are no factors that influence the uses of different energy types by households in the
areas.
Hypotheses 3
There are no preferences of households for different energy types in the areas.
1.6 Significance of the study
From a development point of view, providing households with modern and efficient
energy services is a critical step towards development since clean and available modern
services in energy sector are indispensable to the escape from poverty (IEA, 2002). This study
will thus serve to provide insight into where we are in terms of meeting the energy needs of
households, and also be helpful for further researches on domestic energy usage of
households. Findings from this research will elucidate the burdens the households face in
accessing clean energy for their domestic consumptions.
Findings from the study will enable both international and donor agencies to partner
with energy regulatory bodies, Rural Electrification Agency (REA) and Power Holding
Company (PHC) to make energy available and accessible to the households in the state. Given
the importance of switching to modern energy to the socio-economic well-being of a state and
the availability of energy to the economic growth and poverty eradication of the nation,
finding out the proportion of Enugu state households that rely on traditional energy for
domestic consumption will be a guide to energy policy makers in the state in knowing how
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much efficient energy that needs to be increased for the environment to be preserved in order
to ensure environmental sustainability which will be favorable to the entire populates in the
state and will also hopefully inform government policy on energy supply and availability
across the country.
1.7 Scope of the study.
The study covers domestic energy use of households in two selected rural and urban
areas of Enugu State. The energy used for cooking food at home for the household is included
while energy used for food processing and preparation before the household purchases the
food was not included. Besides, energy used for household transportation and communication
is not included in the study.
The study was carried out during the non-festive periods of 2012/2013; this may cause
the findings to vary with any other study in Enugu concerning household energy use during
festive periods. The study also was carried out using only 300 households as sample size to
represent the entire households in Enugu state rural and urban areas which may affect
generalization.
1.8 Limitations of the study
This study has some limitations associated with it. For instance, the analytical tools
used in testing the hypotheses; regression and analysis of variance (ANOVA) had stringent
assumptions because the system used in the research was not wholly linear and the
independent variables were not strictly random.
The study was only able to capture the households’ energy use patterns during the
non-festive periods, energy needs and demands of households may vary at periods that
households celebrated festivals.
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CHAPTER TWO
LITERATURE REVIEW
2.0 Introduction
This chapter is concerned with the review of related literature on domestic energy use
and will be done under the following sub-headings: Conceptual Framework; Theoretical
Framework; Review of empirical studies, Summary of Literature Review and gaps in the
literature.
2.1 Conceptual Framework
2.1.1 Concept and Evolution of Energy.
Over time, humans have developed an understanding of energy that has allowed them
to harness it for uses well beyond basic survival (World Wind Energy, 2009). The first major
advance in human understanding of energy was the mastery of fire by James Prescott Joule.
The use of fire to cook food and heat dwellings, using wood as the fuel, dates back at least
400,000 years. The burning of wood and other forms of biomass eventually led to ovens for
making pottery, and the refining of metals from ore. The first evidence of coal being burned
as a fuel dates as far back as approximately 2,400 years (WWE, 2009).
After the advent of fire, human use of energy per capita remained nearly constant until
the Industrial Revolution of the 19th century This is despite the fact that, shortly after
mastering fire, humans learned to use energy from the Sun, wind, water, and animals for
endeavors such as transportation, heating, cooling, cooking and agriculture. The invention of
the steam engine was at the center of the Industrial Revolution. The steam engine converted
the chemical energy stored in wood or coal into motion energy. The steam engine was widely
used to solve the urgent problem of pumping water out of coal mines (WWE, 2009). As
improved by James Watt, Scottish inventor and mechanical engineer, it was soon used to
move coal, drive the manufacturing of machinery, and power locomotives, ships and even the
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first automobiles. It was during this time that coal replaced wood as the major fuel supply for
industrialized society. Coal remained the major fuel supply until the middle of the 20th
century when it was overtaken by oil (Bowman and Balch, 2009).
The next major energy revolution was the ability to generate electricity and transmit it
over large distances. During the first half of the 19th century, British physicist Michael
Faraday demonstrated that electricity would flow in a wire exposed to a changing magnetic
field, now known as Faraday’s Law. Humans then understood how to generate electricity
(WWE, 2009). In the 1880s, Nikola Tesla, a Serbian-born electrical engineer, designed
alternating current (AC) motors and transformers that made long-distance transmission of
electricity possible. Humans could now generate electricity on a large scale, at a single
location, and then transmit that electricity efficiently to many different locations (Oleson,
2009).
2.1.2 Concept of household
A household-dwelling unit consists of the permanent occupants of a dwelling place.
Persons who according to the Population Information System of the Population Register
Centre are institutionalized, or are homeless, or are abroad, or are registered as unknown, do
not form part of a household-dwelling unit (United Nations, 1998). Additionally, persons
living in buildings classified as residential homes do not form household-dwelling units if
their living quarters do not meet the definition of a dwelling, also ‘the household is central to
the development process. Not only is the household a production unit but it is also a
consumption, social and demographic unit’ (Canberra Group, 2001).
The concept of household-dwelling unit was adopted in the 1980 census. According to
UN (1993), a household is based on the arrangements made by persons, individually or in
groups, for providing themselves with food or other essentials for living. A household
consisted of family members and other persons living together who made common provision
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for food. Since 1980 subtenants have been classified in the same household-dwelling units
with other occupants (Eurostat, 1985). A household comprises one person living alone or a
group of people living at the same address, sharing their meals and the household, and having
sole use of at least one room. All persons in a household must receive from the same person at
least one meal a day and spend at least four nights a week (one, if they are married) in the
household. The household includes staff, paying guests and tenants, and also anyone living in
the household during the period in which expenditure is recorded. Persons who normally live
in the household, but who are absent for a period of more than one month, are excluded
(Eurostat, 1985).
2.1.3 Household Energy use
Household energy is energies use in homes mostly for cooking, heating and cooling
processes. They are the major uses of energy. Hot water heating is also a sizable use of
energy, as is the cooking process with electricity, sawdust, charcoal fuelwood, stove and
oven.
According to Khare (2009), almost all the cities of the developing countries are
characterized by slums, squatter settlements and other low-income areas which house the
majority of urban dwellers. The people living in those areas have energy consumption
patterns entirely different from those of the high-income groups. Instead, the energy
consumption patterns of such urban areas are very similar to those in rural areas. The heavy
dependence of rural populations and urban poor on non-commercial energy sources has
several implications. For instance, the exploitation of vegetation cover is leading to serious
problems of ecological balance (Sambo, 2008).
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2.1.4 Domestic Energy Use in Rural and Urban settings of Enugu State.
According to Onyekuru (2008), the energy requirements in the both urban and rural
areas of Enugu state consist of two different and distinct components. Each possesses a
unique characteristic which reflects the economic and social conditions of its inhabitants. One
component follows patterns that are similar to cities (urban areas) in industrialized societies.
The energy demands of those areas are similar to those of urban settlements in developed
countries and reflect energy consumption patterns of the urban well-to-do, who use energy for
commercial buildings, amenities, recreation and transport. Thus, the energy problems in that
component of human settlements in developing countries are similar to those found in many
developed countries (EIA, 2006). The other component (rural areas) involves the slums and
squatter settlements whose energy-related problems bear a close resemblance to those of the
rural population. The energy requirements of the low-income population, whether living in
urban squatter’s settlements or rural areas, can be narrowed down initially to domestic needs.
The rapid growth of concentrated populations in urban centers has led to an extreme
scarcity of housing, deterioration of living conditions and the breakdown of infrastructure and
services, especially transportation, Household and industrial energy supply, water
reticulations and health care (Onyekuru, 2008).
The annual per capita energy consumption of the urban poor in the city does not differ
significantly from that of the rural poor, since the main share of energy consumption in both
cases goes to cooking (Govinda, Gautam and Michael, 2001). However, with rising
incomes, the energy consumption patterns of urban households in urban areas of many
developing countries tend to increase. Cooking and lighting account practically for all the
energy consumed by people in the lowest income group (Sambo, 2008). Appliances and space
and water heating account for up to 60 percent of the energy consumed by the rich in the
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cities. With rising incomes, fuelwood tends to be replaced by kerosene and kerosene replaced
by gas/electricity for cooking and lighting (Onyekuru, 2008).
2.1.5 Types of Energy Use in Nigeria
i. Crude Oil
The Nigeria economy is heavily dependent on its oil sector which accounts for 85% of
government revenues and is the 2nd largest contributor to GDP following agriculture (Energy
Information Agency (EIA, 2003). Nigeria produces over 2.17 million barrels of oil per day
(bbl/d) making it the largest producer of oil in Africa. The large majority of this oil is
exported to other countries. Oil exports are approximately 1.9 million per day (EIA). In 2008,
Nigeria consumed about 286,000 bbl/d of oil. This accounted for nearly 53% of the energy
consumption in the country (EIA, 2003). Although Nigeria has four refineries with a
combined capacity of around 500,000 bbl/d, the country imports 85% of refined products that
only one of these refineries remains operational, but it is running below capacity (EIA, 2003).
The operation problems in these refineries are attributed to corruption, poor
maintenance, theft, and fire (NCE, 2006). Many of these factors are recurring issues around
production in the energy sector as a whole. Plans to privatize these refineries in order to
increase investment and improve performance have been met with stiff opposition from
government parties and workers unions. Current price subsidy schemes put in place by the
government lead oil producers in Nigeria to sell overseas rather than to local refineries. Given
the current reserves and rate of exploitation, the expected lifespan of Nigeria’s crude is about
44 years (Ajoa and Ajimotokan, 2009). Oil consumption in the country is mainly used for
automobiles, but the natural gas is given off during the refining process is very important for
the production of electricity (NCE, 2003).
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ii. Natural Gas
The next largest contributor to total energy consumption in Nigeria is natural gas. It
makes up about 39% of energy consumption in the country. Nigeria has the largest natural gas
reserve in Africa, but the country has limited infrastructure in place to develop the sector
(EIA, 2006). With over 184 trillion cubic feet of proven natural gas, Nigeria is the seventh
largest natural gas holder in the world (EIA, 2006). Most of the natural gas reserves are in the
Nigeria delta. Gas discovery in Nigeria was incidental to oil exploration and production. In
2007 Nigeria produced 1.20 trillion cubic feet of natural gas while consuming 465 billion
cubic feet of natural gas (EIA, 2006).
Approximately 794 billion cubic feet of natural gas was exported (EIA, 2003). Issues
with the production of natural gas center around the flaring of Nigeria’s oil fields. Due to the
lack of adequate infrastructure, refineries are unable to sufficiently capture the natural gas that
is given off during the refining process. This gas instead burns up as flares. These oil fields
often flare because they lack the infrastructure necessary to efficiently produce and market
associated natural gas (EIA, 2003). Over 75% of the natural gas produced in the past has
flared (Ajoa et al., 2009). Laring was recently reduced to an annual rate of 36% as a result of
strident efforts by the Nigerian government. Yet in 2007 Nigeria flared 593 Bef of natural gas
which cost the country US$ 1.46 in lost revenue (Ajoa et al., 2009).
About 80% of the natural gas that Nigeria produces is used domestically for electricity
generation while the remaining is used mostly for other purposes in the industrial sector. A
negligible amount of the natural gas is used for other purposes in households (Ajoa et al.
2009). With the necessary infrastructure for improved plants and pipelines in Nigeria, it is
possible to convert more natural gas to electricity. The country is currently in the process of
establishing infrastructure to better harness this energy. The government has also been
working for several years to end natural gas flaring, but the deadline has been continuously
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pushed back (NCE, 2003). Nigerian policymakers recently planned to implement a Gas
Master Plan that would promote new gas-fired power plants that would help reduce gas
flaring and subsequently provide improved electricity generation (EIA, 2006). Security issues
have also affected the production of natural gas in the Niger Delta (EIA, 2006). Given the
current reserves and rate of exploitation, the expected lifespan of Nigeria’s natural gas is
about 88 years (Ajoa et al., 2009).
iii. Hydro-Electric Power
The history of electricity development in Nigeria can be traced back to the end of the
19th century when the first generating power plant was installed in the city of Lagos in 1898.
From then until 1950, the pattern of electricity development was in the form of individual
electricity power undertaking scattered all over the towns (IEA, 2003). Hydro-electric power
makes up the remaining 7% of total energy consumption in Nigeria (EIA, 2003). It is
estimated that 7,714 GWh of hydro-electric power is produced and consumed in the country
(EIA, 2003). Hydropower systems rely on the potential energy downstream (Ajoa et al.,
2009). There are several problems associated with the production of hydro-electric power in
Nigeria. Infrastructure in this sector is inadequate and in need of rehabilitation as well. The
poor maintenance of the hydro-electric plants results in electricity output far below capacity.
The overall hydropower resources exploitable in Nigeria are over 11,000MW, while current
production is slightly less than 2,000 MW (Ajoa et al., 2009 and IEA, 2006). Outputs from
these plants are further hindered because production is highly oscillatory according to
seasonal droughts. It has been put forward that production has been hindered by climate
change causing a continual loss of water (Darling et al., 2008).
The two rivers that provide the majority of hydropower generation are the Niger and
the Benue. These rivers pass through Niger and Cameron prior to entering Nigeria. (NCE,
2003). Nigeria exports a portion of the energy generated by its hydro-plant to Niger in order
25
to compensate the country for not installing their own dams on the rivers and taking away
from potential production capacity. Electricity production within hydro-electricity is therefore
limited without even considering the inefficiencies of the plants themselves (Ajao et al.,
2009).
iv. Biomass Energy
At present, one of the most widely used types of renewable energy that is available to
Nigeria is biomass (Sambo, 2003). Biomass includes a broad spectrum of energy producing
products, including fuel wood, charcoal, saw dust and agricultural residue and municipal
waste. While biomass agents are currently used throughout Nigeria, fuelwood is the most
prolific (Onyekuru, 2006). Its heavy usage is evidenced by the fact that between the years of
1989 and 2000 fuel wood and charcoal constituted between 32% and 40% of energy
consumption in the country (Kevelaitis, 2008). However, while fuel wood is an attractive
source of renewable energy as it is easily acquired and less hazardous to the environment that
burning coal or processing petroleum, its wide spread use has led to problems of
deforestation. According to current calculations, approximately 350,000 hectares of natural
vegetation and forest are destroyed annually, and the deforestation rate is expected to increase
alongside the increasing demand for energy (Sambo, 2005). Therefore, while fuel wood is
more beneficial to the atmosphere than fossil fuel, its use in fact depletes vital resources.
Thus, it is necessary to examine other sources of renewable energy.
2.2 Theoretical Framework
A lot of theories have been adopted in the past on domestic energy use in households
both in urban and rural communities. For the sake of this particular research work, the
research will adopt two theories namely, ‘Energy ladder theory’ and ‘Energy Stack models’.
26
2.2.1 Energy ladder theory
The energy ladder hypothesis is one of the most common approaches used in studying
the household energy use patterns (Rajmohan and Weerahewa, 2005). The concept of energy
ladder hypothesis states that people with low incomes generally use traditional fuels as their
main energy source and people with higher incomes tend to use modern fuels (Nicolai and
Fiona, 2008) this trend tend to shift from traditional fuels to modern fuels basically when the
income of the households increases. As used by different researchers on household energy;
(Davis, 1998; Masera, Saatkamp and Kammen, 2000; Barnett, 2000; Sheilah and Alison,
2002; Arnold, Kohlin, and Persson, 2006; Nicolai et al., 2008) results of the energy demand
research revealed that the income, fuel prices, government policies, Intra-household income
distribution, Fuel availability, Distribution network proximity, Cultural preferences,
Demographic distribution, Physical environment (rural or urban) and household
characteristics influence energy consumption levels. There is evidence to show in these
researches that people in urban areas use more kerosene, LPG, and electricity. They also
suggest that price-based and quantity-based government policies tend to influence the urban
fuel demand patterns more than does the household income level (Bhatia, 1988).
It is shown that the concept of energy ladder hypothesis is loosely based on economic
theory of consumer behavior (Hosier and Kipondya, 1993). this explains the theory partially,
showing when income increases households not only consumes more of the same good but
they also climb the ladder to more modern goods with higher quality i.e. as a household gains
socioeconomic status, it ascends the ladder to cleaner and more efficient forms of energy
Further it assumes that cleaner fuels are normal economic goods while traditional fuels are
inferior goods (Rajmohan et al, 2005). The lower the household income, the greater the
proportion of income spent on energy, poor families spend between 30 - 50% of their income
on energy, whereas those with higher income spend less than 10%. The use of energy is
27
determined in part by the ‘internal living environment’ of the home. For example, as the rural
poor have no bathrooms they bathe less frequently and tend therefore to use less energy than
the urban poor to heat water (Sheilah et al, 2002).
The Energy ladder model:
fuel
Low Income High
Source: Rajmohan and Weerahewa (2005).
The energy ladder provides a theoretical framework for explaining the transition from
the traditional fuels to modern fuels and devices inside households. From the bottom rung of
inefficient traditional fuels (e.g. sawdust, fire wood and charcoal) through fossil fuels (e.g.
kerosene and gas) to the top rung of efficient modern fuels (e.g. electricity), the ladder sets out
a progressive ladder where users move towards what are considered more efficient and clean
fuels, and away from less efficient and unclean fuels. It proposes that with increasing
affluence, households not only shift to more modern energy fuels for vital services, but
additionally they purchase more advanced technologies, including heating and
communication devices, cooling, and other appliances. Essentially, the energy ladder is a
derived version from the economic theory of the consumer, which states that when income
Sur dust
Charcoal
Firewood
Kerosene
Gas
Electricity
28
rises households consume not only more of the same good, but they also shift to consuming
higher quality goods' (Hosier & Dowd, 1987). Energy ladder provides a theoretical
framework for explaining the transition from lower quality goods to higher quality goods.
2.2.2 Energy Stack model
According to Maserea et al (2000) rural household do not switch fuels entirely, but
more generally follow a multiple fuels or fuels stacking model. Energy Stack Model is ability
of households to combine both traditional and modern fuels to meet their domestic energy
needs. The so called fuel switch is actually a step towards multiple fuels cooking or fuel
stacking; Fuel stacking is also a step towards fuel switch, because by stacking, households
start the process of de-stacking of conventional fuels (Maserea et al., 2000). Fuel stacking and
energy ladder is not contradictory rather complementary to elucidate fuel switch process and
direction.
This model rejects the linear simplification of the energy ladder, suggesting that
households do not wholly abandon inefficient fuels in favour of efficient ones. Rather,
modern fuels are integrated slowly into energy-use patterns, resulting in the contemporaneous
use of different cooking fuels (Nicolai et al., 2008). This model is supported by empirical data
presented by Masera et al. (2000) and has been confirmed by further studies of the dynamics
of fuel switching (Pachauri and Spreng, 2003).
29
Stack Model Theory by Masera.
Socio-economic status
Primitive fuels Transition fuels Advanced fuels
Firewood charcoal LPG
Animal waste kerosene bio-fuels
Agric waste coal electricity
Source: Masera, Saatkamp and Kammen (2000)
While moving up the energy ladder suggests greater fuel efficiency and thus reduction
of total emissions, this multiple fuel use or 'fuel stacking' strategy may instead lead to greater
energy use by the household in the process of moving 'up the energy ladder' (Masera et al.,
2000). Thus, a multiple fuel use pattern challenges the capacity of rural energy development
to alleviate any existing pressure on the environment. This pattern of multiple fuel use has
been documented since the 1980s (Masera et al., 2000). Moreover, it is noted as the rule
rather than the exception in many urban and rural areas of the developing world.
2.3 Review of Empirical Literature
A lot of researchers have carried out research related to this research topic both within
and outside Nigeria and for the sake of this work, these empirical evidence would be reviewed
as follows:
30
A study was conducted by Masekoameng, Simalenga, and Saidiin (2005) on
‘Household energy needs and utilization patterns in the Giyani rural communities of Limpopo
Province, South Africa’ between 2004-August, 2005, with the aim of identifying types of
energy resources used and the patterns of utilization of such energy sources. Surveys were
conducted in three villages and semi-structured questionnaires were used to interview 20
randomly selected households per village. Focus group discussions were also held in each of
the surveyed villages.
Data obtained in all surveyed villages showed that fuel wood is the main source of
energy for cooking and heating while paraffin and candles are mainly used for lighting. Wood
in these villages is very scarce and communities spend 5 to 6 hours per trip collecting fuel
wood. Women using the load head method of carrying wood and occasionally wheelbarrows
are the main source of labour used in collecting fuel wood.
The paper concluded that there was a need to promote sustainable energy resources
and technologies such as the use of improved wood and charcoal stoves. Furthermore, the
paper recommended the promotion of solar photovoltaic (PV) systems, which have a potential
of being adopted in the area. It is also argued that policies which enhance integrated rural
development and promote sustainable energy utilization in rural communities need to be put
in place and implemented.
In a study in Nsukka by Nnaji, Uzoma, and Chukwu, (2012) on “ factors determining
fuelwood use for cooking by rural households in the area, it was found that notable
socioeconomic factors driving fuelwood consumption were found to be poverty factors such
as low education, high household size and low wealth of farmers. Increase in the income of
poor households as well as access to alternative, affordable renewable energy system and
cleaner fuels would help in reducing fuelwood consumption leading to reduction in pollution,
deforestation and on a more general level, mitigate adverse effects of fuelwood consumption
31
on the environment. The binary logistic regression model was used to analyse the factors
affecting the likelihood of consuming fuelwood in the study area.
A research done by Shittu, Idowu, Otunaiya and Ismail on ‘the demand for energy
among households in Ijebu division, Ogun state, Nigeria’ in 2004 examined the influence of
households’ socio-economic characteristics on household demand for electricity, petrol,
diesel, kerosene, firewood, and domestic gas. Primary data obtained in a cross-section survey
of 90 households selected across six communities in Ijebu-Division of Ogun State, Nigeria
was used in estimating a system of energy demand equations and elasticities. Analytical
framework of the linear logit model of Tyrrel & Mount (1982) was used in running analysis
and results showed that 36% of their average monthly consumption expenditures on the
household energy commodities, and about two-third of this goes to fuel (petrol and diesel) for
the households’ automobiles and generating sets.
The study also revealed that an average household in the sample had about five
members, headed by a 52 year old male that had about nine years of formal education. The
mean monthly household consumption expenditure was N 15,458.63, of which about 25%
was expended on the commodities. While the influence of education and household size on
household energy use were insignificant; income (budget size), household ownership of
electrical/electronic appliances and automobiles, as well as age of household heads exercised
significant influence on the relative shares of some/all of the seven energy commodities in
household budgets in the study area. The income effects were positive for all the energy
commodities, except firewood. Demand for petrol, diesel and domestic gas were income
elastic. Thus, the study concluded that improvement in income would cause increase in
demand for electricity and petroleum products in the study area, but worsening real income
would place greater demand on biomass fuel.
32
Another research by Olatinwo, and Adewumi (2012) on “energy consumption of rural
farming households in kwara state, Nigeria” The study sought to understand the rural energy
consumption of the farming households. The data used for the study were obtained through a
four-stage sampling procedure which resulted in a sample size of 120 households. Logistic
regression procedure was used to determine the energy consumption pattern and the factors
affecting the use. The relationships between the type of energy (modern or traditional)
consumed by the household and educational status, household size, age, total monthly
income, total amount spent on food per month and distance travelled per week to obtain fuel
were established. The regression result showed that age of the household heads and distance
travelled to obtain fuel was significant in explaining the variation in the type of energy
consumed. Observed energy consumption pattern revealed that most of the respondents
consumed more of traditional than the modern energy types.
The study suggested that in order to reduce stress and health hazards associated with
the traditional energy source, modern energy consumption should be encouraged among the
rural households in order that they might reduce the stress and hazards encountered in
obtaining and using the traditional energy and also, to reduce the exploitation of forest
resources for traditional fuel. Farmers should also be educated by agricultural extension
agents on the importance of sustainable farming and the use of sustainable energy.
Research was carried out on “Domestic Energy Consumption Patterns in a Sub
Saharan African City: The Study of Jos-Nigeria” by Anthony, Ogbonna, and Dantong (2011).
The authors reported on an in-depth survey of domestic energy use in a region of Northern
Nigeria. By adopting the US Residential Energy Consumption survey as a reference
framework, the study investigated household energy-using appliances, energy consumption
patterns, and domestic energy fuel mix as well as electricity load profiles. The field study
revealed considerable differences in the patterns of energy consumption among householders
33
of different economic bands living in different house types within the same city ranging from
just above 6,000kWh to over 22,000kWh per annum. Cooking energy consumption accounts
for 42% of the domestic energy demand, while sundry electrical appliances like computers,
phones accounted for the least amount of energy demand (5%). Household cooking and water
heating across all house types was characterised by multiple appliance usage and was often
driven by appliance cost and the need for security of fuel supply for the particular appliance.
A research by Onyekuru and Eboh (2011) on “Determinants of Cooking Energy
Demand in the Rural Households of Enugu State, Nigeria: An Application of the Bivariate
Probit Model” using a Multistage sampling method. The first stage was the division of the
rural areas into different clusters according to the INEC political wards with a list of political
wards obtained from the Enugu State ministry of Local Government Co-Ordinating
Department. The second stage was the selection of two wards randomly from each of the
three agricultural zones in Enugu State, giving a total of twelve wards in all. The last stage
was the random selection of seventeen households from each of the selected wards giving a
total of 102 respondents. The sample frame was made up of all the households in each of the
wards. Data required for the study were collected using questionnaires with the help of trained
enumerators.
The results of the study showed that 23.5% of the respondents were farmers, while
76.5% were paid workers and artisans, 36.8% Bivariate Probit Model was used in the rural
areas since there were only two energy sources (kerosene and fuelwood) identified. and
29.4% attended secondary and primary schools respectively, 8.8% didn't go to school, while
25% attempted tertiary education. Majority of the respondents have income category of N11,
000 and 30,000.00 with the highest percentage of 57.4. The family size range of majority of
the respondents was 5 6. Overall goodness of fit as reflected by Prob > Chi were good (0.044
for fuelwood and < 0.000 for kerosene). In comparison with occupation and income, the
34
probability that a person uses fuel wood was significant and negatively related to occupation
and significant and positively related income respectively at 0.01 < P < 0.05. In the case of
kerosene demand, in comparison with occupation and income, the probabilities that a person
uses kerosene was positively relate to occupation and income at P=0.01 and 0.01<P<0.05
respectively.
Another research was carried out in Gombe state, Nigeria by Nabinta, Yahaya and
Olajide (2007) on “Socio-Economic Implications of Rural Energy Exploitation and Utilisation
on Sustainable Development in Gombe State, Nigeria”. Data for this study was obtained from
a random sample of three hundred female farmers from Kaltungo community in Gombe State,
300 respondents were selected and questionnaire administration followed in their homestead.
The analytical procedure used for achieving the objectives of the study was purely descriptive
statistics involving frequency counts and percentages. Studies revealed that fuel wood is the
primary source of fuel and females are responsible for its collection. Farmers’ average weekly
collection, time spent, number of days, distance covered and amount collected were 53.3
kilograms, 17hours, four days and 11kilometres respectively. Collection strategies developed
include Short Span of Trek (SPOT) with Low Frequency (LF) is 13.3% while Short Span of
Trek with High Frequency is 20 %. Thirty percent belong to Long Span of Trek with Low
Frequency and Long Span of Trek with High Frequency occurred among 36.67%. All farmers
stored fuel wood for consumption, sale and barter traditionally. Constraints to effective and
efficient rural energy supply and use identified were education, labour, capital, time, credit,
decreasing fuel wood availability, and contact with extension.
The study concluded that farmers’ participation in fuel wood production and
utilization is frequent and continuous. However, the depletion of the woodlands combined
with persistent dependency on fuel wood pose a serious problem for household energy
provision and the environment. Woodlots and access to alternate, affordable, renewable,
35
energy system would reduce the pressure on the forests and amount of time and efforts
women devote to obtaining fuel wood. Therefore further research and development of an
integrated energy package using community based participatory mechanisms become
imperative for sustainable rural development in Nigeria.
A research by Adetunji, and Isa, (2006) on “the demand for residential electricity in
Nigeria: a bound testing approach” this paper examined the residential demand for electricity
in Nigeria as a function of real gross domestic product per capita, and the price of electricity,
the price of substitute and population between 1970 and 2006. They made use of the bounds
testing approach to cointegration within an autoregressive distributed framework, suggested
by Pesaran et al. (2001). In the long run, they found that income, the price of substitute and
population emerged as the main determinant of electricity demand in Nigeria, while electricity
price was insignificant. The relationship among variables was more stable and significant.
A study was carried out by Ogunniyi, Adepoju and Olapade (2012) on “Household
consumption pattern in Ogbomosho metropolis, Oyo state, Nigeria”. The study was carried
out to examine the households energy consumption pattern using the Almost Ideal Demand
System (AIDS) model in Ogbomoso Metropolis, Oyo State, Nigeria. Primary data were
collected from 200 heads of household through a multi-stage random sampling technique. The
study revealed that kerosene is the most highly consumed energy source and the reason for
preferring this energy source is its accessibility in the study area. AIDS estimation of demand
functions using primary data indicates that demand for all forms of energy are price inelastic.
Cross price relations indicate that kerosene is a substitute for both electricity and charcoal,
whereas electricity is a substitute for all the two. Charcoal and kerosene are complements. All
the energy sources considered were found to have income elasticity’s less than one owing to
the fact that energy consumption is a necessity. Government should provide electricity to
most areas and there is need for pricing policy for energy such as kerosene.
36
A research on “community survey of the pattern and determinants of household
sources of energy for cooking in rural and urban south western, Nigeria” by Desalu (2012) a
cross sectional study of households in urban (Ado-Ekiti) and rural (Ido-Ekiti) local council
areas from April to July 2010 was carried out. Female respondents in the households were
interviewed by trained interviewers using a semi-structured questionnaire. A total of 670
households participated in the study. Majority of rural dwellers used single source of energy
for cooking (55.6%) and urban dwellers used multiple source of energy (57.8%). Solid fuel
use (SFU) was higher in rural (29.6%) than in urban areas (21.7%). Kerosene was the most
common primary source of energy for cooking in both urban and rural areas (59.0%
vs.66.6%) followed by gas (17.8%) and charcoal (6.6%) in the urban areas, and firewood
(21.6%) and charcoal (7.1%) in the rural areas. The use of solid fuel was strongly associated
with lack of ownership of dwellings and larger household size in urban areas, and lower level
of education and lower level of wealth in the rural areas. Kerosene was associated with higher
level of husband education and modern housing in urban areas and younger age and indoor
cooking in rural areas. Gas was associated with high income and modern housing in the urban
areas and high level of wealth in rural areas. Electricity was associated with high level of
education, availability of electricity and old age in urban and rural areas respectively. The
researcher concluded by stating that the use of solid fuel is high in rural areas, there is a need
to reduce poverty and improve the use of cleaner source of cooking energy particularly in
rural areas and improve long health.
A study on “dynamics of household energy consumption in a traditional African city-
Ibadan” by Ibidun and Afeikhena (2006) was carried out and the result is that prices of
commercial fuels inclusive of kerosene and LPG (cooking gas) have continued to rise beyond
the reach of majority of the Nigerian population. The paper examined the effect of increasing
prices of petroleum-derived energy sources on the pattern of energy use for cooking in low
37
and middle-income households and the environmental implication in Ibadan, the largest truly
indigenous urban centre in sub-Saharan Africa. Results showed that prior to the further
subsidy removal of 1993, majority of households sampled used kerosene for cooking.
Thereafter, a complete or partial switch in the pattern of domestic energy consumption ensued
with more households using fuel wood and other more polluting and less efficient energy
sources for cooking. The paper recommends a transition towards more environmental friendly
energy sources for household use.
“A Comparative Analysis of Household Energy Use in Nigeria: A Case Study of
Ikeja and Oke-Oko Area in Ikorodu Areas of Lagos State”, was carried out by Yaqub,
Olateju, and Aina, (2011) in the research, Primary data were obtained through questionnaires
administered on households in the two different locations. A sample of 100 was selected with
50 from each location. Only 86 questionnaires were found usable with 42 from urban area and
44 from rural area. The questionnaires were analyzed using both descriptive and inferential
statistics. The findings showed that economic factor plays an important factor in the choice of
energy used for cooking, a significant positive correlation between the type of energy use and
dwelling places, education qualification and monthly income of those who live in the urban
area place much emphasis on safety and convenience in their choice of energy use while
majority of the rural dwellers emphasized income in their choice of cooking energy. The use
of kerosene is common in both urban and rural areas but gas is used mainly in the urban area.
Wood and charcoal are used majorly in the rural area while electricity is used majorly in the
urban area.
It was found out that more than half of those who cook with kerosene are not satisfied
with this energy source while all those who cook with gas are satisfied with their choice. The
researchers recommended that In view of the fact that many people prefer to use Gas for
convenience, efficiency and neatness but cannot afford it. Government should make gas
38
available at cheaper rate as this will minimize environmental problems caused by the use of
bio fuel. For the household in the rural area government should provide a modern way of
using this bio fuel so that the environmental and the health hazard of these types of energy
sources. The use of energy efficient stoves should be encouraged as this can greatly improve
the combustion of fuel so that they emit very little smoke. The above would go a long way to
improve the standard of living of women by giving them more time to do other income
generating activities.
2.4 Summary of Literature
From the literatures reviewed, it could be seen that Domestic energy is energy used at
homes for cooking, heating, lightening, cooling, powering electrical appliances and pumping
water. These energies can be sourced from different energy sources ranging from traditional
energy (wood waste, animal dung, crop waste fuelwood, sawdust and charcoal) to the modern
energy source (kerosene, liquefied gas and electricity). At present, one of the most widely
used types of renewable energy that is available to Nigeria is biomass. Biomass includes a
broad spectrum of energy producing products, including fuel wood, saw dust, charcoal and
agricultural residue and municipal waste.
Looking at the two energy models used for this research, it will be seen that electricity
ranks the highest in energy ladder model and energy stack model, yet most households in
Nigeria, approximately 100 million people lack access to it. It was noted that the key factors
in the growth of household electricity consumption are the number of households with access
to electricity supply, penetration rates of electric appliances, and the size and efficiency of
appliances.
However, this research work is to evaluate the fact if Fuel stacking and energy ladder
is not contradictory rather complementary to elucidate fuel switch process and direction.
39
2.5 Gaps in Literature
According to literatures reviewed, most researchers believe that the annual per capita
energy consumption of the urban in the city does not differ significantly from that of the rural,
since the main share of energy consumption in both cases goes to cooking while some others
say that the people living in rural and poor urban areas have energy consumption patterns
entirely different from those of the high-income groups. Instead, the energy consumption
patterns of poor urban areas are very similar to those in rural areas.
Other studies were either based on low sample size while others concentrated in either
rural or urban, hence the need for a comparative study in the state. Besides most studies
concentrated on cooking energy only.
The research wants to investigate if there are differences in energy use patterns of
rural and urban households in Enugu state as well as what could be the possible determinants
of such energy use as against what the households prefer to use as their energy source if
options were made available to them.
40
CHAPTER THREE
METHODOLOGY
3.0 Introduction
This chapter presents the methodology aspect of the research work and it is divided
into eight sub sections. They are: area of study, study design, population of study, sample
size, sampling method, instrument for data collection, sources of data and methods of data
analysis.
3.1 Area of study
The research was carried out in selected two urban and two rural local governments in
Enugu state, Southeast of Nigeria. Enugu State is a mainland state in southeastern Nigeria. Its
capital is Enugu, from which the state - created in 1991 from the old Anambra State - derives
its name. It shares borders with Abia state and Imo to the South, Eboyi state to the North,
Kogi state to the Northwest and Anambra state to the west. The principal cities in the state are
Enugu, Awgu, Ezeagu, Udi, Oji, and Nsukka. In all, Enugu state has a total of 17 Local
Governments with a total population of 1,596,042 males and 1,671,795 females totaling
3,267,837 people and counting, National Population Commission (2006). Its major
occupation is farming, although there is trading (18.8%) and services (12.9%). In the urban
areas trading is the major occupation, followed by services. The seventeen local governments
that makeup the state are Aninri, Awgu, Enugu East, Enugu North, Enugu South, Ezeagu,
Igbo Etiti, Igbo Eze North, Igbo Eze South, Isi Uzo, Nkanu East, Nkanu West, Nsukka, Oji
River, Udenu, Udi, Uzo Uwani (Williams, 2008).
41
3.1.1 Demography of the Rural and Urban communities selected.
The research was conducted in selected rural (the low-income) and urban (the middle-
income) areas of the state. Stratified random sampling technique (were variables are divided
into strata’s and each having equal chances of been selecting) was used to select areas under
Enugu state. The local governments of the state were listed in two strata’s (urban and rural)
and using a proportional sampling of two to randomly pick two communities each from the
stratum, making a total of four research sites. The two rural communities selected are Ezeagu
local government and Isi-Uzo local government, while the two urban communities selected
are Enugu North and Enugu South local governments that made up the 17 local governments
in Enugu state.
Ezeagu local government has a land area of 633 km² and a population of 169, 718 as
at 2006 census (Ezeagu Population Commission). Its geographical coordinates are latitude 6°
25' North, and longitude 7° 15' E (NPC, 2006) as it is bounded by Udi local government, Oji
River local governments and Ebenebe local government of Anambra state. Ezeagu local
government has several communities such as; Mgbagbu owa, Ndiagu umana, Okpugho,
Achala Owa, Olo, Ezeagu Central, Ezeagu South, Umidioha, Obinofia ndi-uno, Ezeagu
North, Iwollo Town, Ezeagu North-East, Awha-Imezi, and Imezi Owa with 20 wards
(Maternity Health Unit, Ezeagu LGA, 2012). The inhabitants of this area are mostly civil
servants (teachers and health workers), farmers and traders (Williams, 2008).
Isi-Uzo local government is a Local Government Area of Enugu State, Nigeria
bordering Benue State and Ebonyi State. Its headquarters are in the town of Ikem. The other
towns are: Eha Amufu (where the Federal College of Education is located), Neke, Mbu, and
Umualor. Although a constituent of Enugu East Senatorial Zone, Isi Uzo is culturally,
linguistically and geographically contiguous with the rest of the Nsukka zone. The natives are
mostly farmers and petty traders (Williams, 2008). There are also claims that the area sits on
42
large deposits of crude oil and gas. It has an area of 877 km² and a population of 179,415 at
the 2006 census (NPC, 2006). The postal code of the area is 412.
Enugu South local government is one of the 17 local governments of Enugu state; it is
bounded by Enugu North and made up of 20 wards. It is located on latitude 6o24’N and
longitude 7o30’E. They are made up of 20 wards and the headquarters are in the town of
Uwani. It has a land area of 67km2 and a population of 94,049 males and 104,674 females
totaling 198,723 as at the 2006 census (NPC, 2006).
Enugu North is bounded by Enugu East local government and Enugu South local
government of Enugu state. The local government has a land area of 106 km² and it is located
on latitude 6°28′N and longitude 7°31′E. Enugu North local government is one of the 17
LGAs in Enugu State and is inhabited mostly by Ibos (one of the major tribes in Nigeria). It is
an urban area with all the infrastructures of urbanization. According to Enugu North
Population Commission (2012), it has a total population of 292, 366. The literacy level in this
region is high, when compared to the National average of 69.5% for males and 53.9% for
females (Williams, 2008). This local government is made up of 20 wards which are Afia
Nine, Amigbo, Asata, Atizan, GRA East, GRA West, Ihewuzu, Independence Layout, Inland
Town, Iva Valley, New Haven East, New Haven West, Ogbete Central, Ogbete East, Ogebte
West, Ogui New Layout, Ogui Township, Onu Asata, Onu Ato, and Umunevo (Enugu North
LGA, 2012).
3.2 Study design
This research adopted a survey research design. The design was chosen due to the
large number of households in Enugu state which could be too cumbersome to investigate.
43
3.3 Study population
The population of the study consists of the total number of households from the selected
rural and urban areas of the state. According to National Bureau of Statistics (2006), an
average person per household in Enugu state is 4.0. It is estimated that in every average 4.0
number of persons there is a household i.e. dividing the total population of the state/local
government area by 4.0 persons to arrive at the expected number of households in any given
local government of Enugu state (NBS, 2006).
Table 1: Population distribution of households
S/N Location Location Total
Population
Total
Number of
Households
Number of HHs
selected
1. Enugu North URBAN
244852 61213 90
2. Enugu South 198723 49681 72
3. Ezeagu RURAL
179718 42429 72
4. Isi-Uzo 194415 44853 66
Total 817708 198176 300
(Source: computed by the researcher, based on National Bureau of Statistics (2006), data.
3.4 Sample size
According to Eboh (2009), using tabulated values of sample size with Yamane (1967)
method, a sample size of 300 households in the four local government areas was used for this
study.
3.5 Sampling method
Stratified random sampling technique was employed to choose the dwelling units where the
questionnaires were administered.
Enugu state was stratified into two strata of rural and urban LGA were two LGA were
picked randomly from each of the stratum. The four LGAs selected for study (from rural and
44
urban communities) was; Ezeagu, Isi-Uzo, Enugu North and Enugu South local government
areas. In each of the communities, (the stratum) was further stratified according to its
residential area density. In all there were three residential densities; high, medium and low
density. Number of questionnaires distributed in each neighborhood was based on the
proportion of the population of the neighborhood in the entire area.
In the urban area; 54% of the questionnaires (total of 162 questionnaires) were
administered in the two selected communities (Enugu North and Enugu South). Enugu North
was administered 30% of the total questionnaires (total of 90 questionnaires) at different
proportions. In distributing the questionnaires the households were stratified into its political
wards, the researcher identified the streets and the numbers of houses starting from the first to
the last number in each stratum and then further divided the questionnaire in a random manner
ensuring that longer streets (high density area) like Ogui Rd. under Ogui township ward
received the highest share of questionnaire in that particular ward. Nkpokiti under New
Layout ward had the smallest questionnaire as it was a low density residential area in that
ward. This process was carried out as such in other wards until the questionnaires were
exhausted in Enugu North. In each of the streets, households that were not accessed either as a
result of the inhabitant’s absence or refusal to fill the question was replaced by the nearest
available household. This method was done in Enugu South in same manner.
In the rural area; Ezeagu local government was administered 22% of the
questionnaires (total of 66 questionnaires). In the distribution, the households were divided
into 20 strata of its political zones and each stratum was further divided into the number of
towns and villages that made it up. In each town; high, medium and low density villages were
also identified by the researcher through the help of local government counselors. The high
density villages were portioned the highest number of questionnaire randomly, e.g. Umuofunu
village at Ogwofia-Owa town received the highest number of questionnaire compared to other
45
villages in the town. In each of the villages, one household was used to represent a kindred
and that household could be replaced if there is need for it. This was also done in same
method at Isi-Uzo local government area.
3.6 Instruments for data collection
The major instrument used for this research was a structured questionnaire. The
questionnaire titled ‘‘Domestic Energy Usage Patterns of Households: a study of selected
urban and rural areas in Enugu state’’ was carefully structured by the researcher into four
sections. The first section dealt with questions on the social and demographic background of
respondents while the second part dealt with the types of energy sources attributable to
different energy uses across the urban and rural areas, the third part dealt with the factors that
influences the choice of energy used by households and the fourth one dealt with the
preferences of household on different energy sources (Appendix 1). The questionnaire was
written in close ended questions of four likert-scale of Strongly Agree (SA), Agree (A),
Disagree (D), Strongly Disagree (SD) which are denoted as 4,3,2 and 1. Where 4, 3 means
Yes and 2, 1 means No in the computations.
3.6.1 Sources of data
Data for this research were sourced from both primary and secondary sources.
Findings from this data were used to differentiate the energy use patterns, determinants and
preferences of households between selected urban and rural communities in Enugu state.
I. Secondary sources
The study used secondary sources of information for population of households, maps
of localities and names of localities. The information was retrieved from the National Bureau
of Statistics, National Population Commission, Textbooks, Journals, Internet and the four
Local Governments of study.
46
II. Primary sources
This represents the information obtained directly from the field in the course of this
study through the help of a structured questionnaire. The characteristics of the samples
population in the urban area of the study sites are high literates, mostly civil servants and
mixed income earners and could fill the questionnaire without assistance while the samples
population at the rural areas are mostly illiterates, farmers and low income earners and this
called for assistance in order to gather information needed in the questionnaire.
3.7 Validation of primary instrument
The questionnaire was validated by professionals in the Institute of Development
studies (IDS). Based on the supervisors suggestion, the instruments was finally modified, for
instance, items that were originally 36 were reduced to 32.
3.8 Data Collection
These questionnaires were distributed and collected between 12th February and 10th
May, 2013 with the help of trained research assistants. The questionnaire titled ‘Domestic
energy use patterns of households in selected rural and urban communities of Enugu state’
with an introductory cover note to create an acquaintance with the respondents were
administered to the selected rural and urban communities at various proportions. Several visits
were made at different times to the various areas and most of the rural population who could
not read nor write were assisted in filling the options they chose. The questionnaire return rate
was 67% as a total of 300 questionnaires were distributed and only 200 was correctly filled
and returned.
3.9 Reliability of Measurement
Cronbach’s Alpha was used to estimate the reliability of the test. This method is
mostly used when internal consistency is present in a test (Cronbach, 1951). Cronbach's alpha
47
indicates the degree to which a set of items measures a single unidimensional latent construct.
The closer to 1 the α is the closer the relationship. Most of the tests fall within the range of
0.75 to 0.83 with at least one claiming a Cronbach’s Alpha above 0.90 (Nunnally 1978, pages
245-246). According to Cronbach (1951) the formula for Cronbach's alpha is as follows:
Where;
n = number of items,
si2 = variance of the ith item, and
sT2 = total score variance
In applying this, a pre-test was carried out in the four study sites, in which 20 copies of
the research questionnaire were pre-tested among some households in Enugu State. The
responses to the questions on the questionnaire were noted and the Cronbach’s Alpha was
used to test the reliability of the research instruments; the result showed a Cronbach Alpha of
0.83 and this affirms its reliability.
3.10 Methods of data analysis
The research involved quantitative method of analysis. Quantitative method in this
study will be simple statistics expressed in frequencies, standard deviations, percentages and
means; and they will be used to give explanations on the demographic and socioeconomic
characteristics of different households as they impinge on household energy use patterns.
The following hypotheses were tested using different analytical tools as shown below;
Hypothesis 1
There are no differences in household energy uses attributable to different energy sources
among the selected rural and urban communities in the state.
48
In testing this hypothesis, analysis of variance (ANOVA) was used as an analytical
tool. This is a statistical method that originated from R. A. Fisher for the analysis of
agricultural experiments, but has been extended to other areas of scientific research (Eboh,
2009). ANOVA is used in this test of hypotheses because there are two categories defined
here by another variable. It helped in the test of difference among the two means (urban and
rural areas), or the test of null hypotheses that the sample means are nulled at P<0.05.
ANOVA breaks the total variation in Energy uses into two separate components, a
component due to X1 (urban settlement of respondents) and another due to X2 (rural
settlement of respondents). The difference between the means of sub-samples is signified by
the value of the variance of the distribution of the means. This can be mathematically
expressed in an equation;
The variables for this hypothesis are the dependent and the independent variables. The
independent variable is locality (urban and rural), while the dependent variables are the
energy types; electricity, gas, kerosene, firewood, charcoal and sawdust.
Hypothesis 2
There are no determinants of household energy use of different energy types in the areas.
In testing this hypothesis, a multiple regression analysis was used. Regression analysis
is used when proving how one variable relates to another, i.e the quantity of change in the
value of another variable which derives from a unit change in the value of another variable. It
establishes a casual or functional relationship between variables.
Multinomial logit regression model was used in analyzing the hypothesis because it
allows the dependent variable to have more than two categories i.e. Energy types = F (price,
home appliances, weather, education………..n) (Tilford, Roberson and Fiser 1995; Archer
49
and Lemeshow, 2006; Fagerland and Hosmer, 2012). Multiple regression analysis is also
denoted with a predictive equation below;
= + 1 x1 + 2 x2 +……….. n xn
Where is the constant:
x1, x2, ………………. n3 is the independent variables which are: income, energy
price, weather, cultural belief and preferences, family size, home appliances, gender, age,
level of education, occupation, access and marital status.
is the dependent variables which are: electricity, gas, petrol, kerosene, firewood, charcoal
and sawdust respectively against each of the energy uses.
1, 2,……………… n are the coefficient reflecting the relative impact on the
criterion variable and can be interpreted as the net change in for each unit in x1, x2, …….xn
holding the other x’s constant. Therefore, when looking for the coefficient 1 that means we
are looking at the net change x2 …………….xn constant and vice versa for x2 ………..xn,
the significance level will be the t-value, t > or equal to 1.95.
The variables for this test are independent variable and dependent variables. The
dependent variables are energy types. While the independent variables are determinant factors
such as energy price, income, home appliances, education, weather, belief, gender,
occupation, etc.
Hypothesis 3
There are no significance differences of the preferences of households for different energy
types in the areas.
The analytical tool for this test was ANOVA analysis given the same reasons and
mathematical expressions in hypotheses one. The variables here include the independent
variables which is locality (rural and urban) while the dependent variables include the modern
energy types (electricity, gas and kerosene), traditional energy types (firewood, charcoal and
sawdust) and both.
50
CHAPTER 4
DATA PRESENTATION
4.0 Introduction
This chapter presents the findings from the survey. They include the socio-
demographic characteristics of respondents, energy types used by households across Enugu;
the factors that influence the use of such energy types and the preferences of households on
energy type uses across the rural and urban areas of the State.
4.1 Questionnaire Distribution and retrieval
The questionnaires were distributed and collected back as follows:
Table 1: Questionnaire return rate
Location Location
Number of
Questionnaires
Administered
Number of
Questionnaires
Retrieved
Enugu North URBAN
90 60
Enugu South 72 48
Ezeagu RURAL
72 49
Isi-Uzo 66 43
Total 300 200
Source: Authors fieldwork, 2013
A total of 300 questionnaires were administered in two selected rural and urban areas
of Enugu State. Out of this only 200 were properly filled and returned. The return rate of the
questionnaire is 67%.
4.2 Socio- Demographic Characteristics of Respondents
Information was obtained on Households demography. These include: relationship of
respondent with the head of household, gender, marital status, religion, ethnicity, educational
51
attainment, occupation, average income, and household size across the two rural and two
urban areas surveyed in the state.
4.2.1 Relationship of Respondent with the Head of household
Information was obtained from the respondents on their relationship with the head of
the household surveyed. Survey result (Fig. 1) shows the relationship of the respondents to the
head of households. In both the rural and urban households’ there were more wives among the
respondents than other members of the family.
Fig 1: Distribution of respondents by relationship to head of household
In the rural households the respondents consisted of 3 husbands, 68 wives, 7children
and 14 other relations. While in the urban households the respondents consisted of 9
husbands, 77 wives, 12 children and 10 other relations.
4.2.2 Distribution of respondents by Gender
Respondents were grouped on the basis of gender (Fig 2). Result show that females in
rural areas were 7.5 times more than males among the respondents while in the urban areas,
females were 4.8 times more than the males.
52
Fig 2: Gender distribution of respondents
4.2.3 Distribution of respondents by Marital status
Information was obtained on the marital status of the respondents (Fig 3). Result show
that married people constituted the highest proportion of the respondents.
Fig 3: Distribution of the respondents by marital status
4.2.4 Distribution of respondents according to Age
The respondents were grouped on the basis of age (Fig 4). Result show most rural
respondents, were between the ages of 41-60 while in the urban areas, majority of them were
between the ages of 20-40years.
53
Fig 4: Distribution of respondents by Age
4.2.5 Distribution of respondents according to Religion
Respondents were grouped on the basis of religion (Fig 5). Result show most of the
respondents in both rural and urban areas are Christians.
Fig 5: Distribution of respondents by religion
4.2.6 Distribution of respondents according to Ethnic groups
Information was obtained from the respondents on their ethnic groupings (Fig 6).
Result shows that in terms of ethnicity, most of the respondents were Igbos.
54
Fig 6: Ethnic distribution of respondents
4.2.7 Distribution of respondents by Educational attainment
Respondents were grouped on the basis of education (Fig 7). Result show there were
more rural respondents with school certificate compared to urban respondents with higher
educational qualifications.
Fig 7: Distribution of respondents by Educational attainment
55
4.2.8 Distribution of respondents by Occupation
Information was obtained from the respondents on their major occupation (Fig 8).
Result show that the predominant occupation among rural respondents is farming while the
urban dwellers were basically traders.
Fig 8: Occupation distribution of respondents
4.2.9 Income distribution of respondents
The respondents were grouped on the basis of income (Fig 9). Result show most rural
respondents, earned less than 20,000 per month while in the urban areas, majority of them
earn between 20,000-40,000 per month.
Fig 9:-Distribution of respondents by Income
56
4.2.10 Distribution of respondents according to Size of households
Households were grouped on the basis of number of persons in the household (Fig 10).
In the rural areas, most of the households’ size was between 9 - 14 persons per household,
while in the urban areas, majority of the households were between 5-8 persons per household.
Fig 10: Distribution of respondents according to Size of households
4.3.1 The relative household energy uses attributable to different energy sources
between urban and rural areas.
Data collected based on the study objective one are presented in tables. In discussing
the data collected, the mean responses will be used in determining the general responses of
the respondents. The scale and decision rule below will be used in discussing the mean
results.
57
SCALE:
Strongly Agree (SA) - 4
Agree (A) - 3
Disagree (D) - 2
Strongly Disagree (SD) - 1
SA and A denoted as - Yes
D and SD denoted as - No
Decision Rule:
If Mean ≥ 2.5, the respondents agree
If Mean < 2.5, the respondents disagree
i. Energy used for cooking
Information was obtained from the respondents on the types of energy used for
cooking (Table 2). Result shows that the most used energy type for cooking in the rural areas
is firewood while in the urban areas the most used is charcoal.
Table 2: Energy types used in cooking
Energy
type
RURAL HHs URBAN HHs TOTAL
% of
Yes % of No Mean % of Yes
% of
No Mean R U
Electricity 1.08 98.9 1.271 4.6 95.3 2.064 92 108
Gas 0 100 1.25 4.5 95.3 1.444 92 108
Kerosene 3.25 96.7 1.5 61 38 2.513 92 108
Firewood 88 11.9 *3.673 10.1 89.7 1.657 92 108
Charcoal 16.2 83.6 1.978 62.9 36 *2.568 92 108
Sawdust 5.43 94.4 1.75 6.4 93.4 1.564 92 108
Source: Field work 2013
58
The table shows that in the rural areas of the state firewood is the most used energy
type for cooking (88%) with a mean of 3.6 showing they strongly agree to its use. In the urban
area, charcoal is the energy type that is most used for cooking ranking (62.9% ) of positive
responses with a mean of 2.5 showing they agree to its use followed by kerosene ( 61% ).
ii. Energy used for ironing
In terms of meeting the ironing needs of respondents, most rural respondents relied on
charcoal as against electricity in the urban areas (Table 3).
Table 3: Energy types used in Ironing
SOURCES RURAL HHs URBAN HHs Total
% of yes % of No Mean % of yes % No Mean R U
Electricity 8.6 91.2 1.5652 58.8 41.1 *2.675 92 102
Petrol 12.9 86.9 1.75 33.5 66.2 2.203 92 104
Charcoal 77 22.8 *3.3695 14.7 85.1 1.953 92 108
Source: Field work 2013
The Table shows that in the rural areas of the state charcoal is the most used energy
type for ironing ranking (77%) of positive responses with a mean of 3.3 showing they
strongly agree to its use. In the urban area, electricity is the energy type that is most used for
ironing ranking (59%) of positive responses with a mean of 2.6 showing they agree to its use.
iii. Energy used for home entertainment
Information was obtained from respondents on the source of energy that is used to
power radios, televisions and other electronic gadgets for entertainment at home (Table 4).
Result show the most used energy source for home entertainment in the rural areas is Battery
while in the urban areas the most used is petrol.
59
Table 4: Energy types used in Home entertainment
SOURCES
RURAL HHs URBAN HHs TOTAL
% of yes % of No Mean % of yes % of No Mean R U
Electricity 17.3 82.6 1.75 27.4 72.2 2.1203 92 108
Petrol 23.8 76 1,739 56.2 43.4 *2.6574 92 108
Battery 59.3 40.1 *2.815 18.5 81.4 1.9907 92 108
Source: Field work 2013
The Table shows that in the rural areas of the state battery is the most used energy type
for home entertainment ranking (59%) of positive responses with a mean of 2.8 showing they
agree to its use. In the urban area, petrol is the energy type that is most used for home
entertainment ranking (56%) of positive responses with a mean of 2.6 showing they agree to
its use.
iv. Energy used for lighting
In terms of lighting in homes, the rural and urban households mostly rely on same
energy types which are kerosene and petrol (table 5).
Table 5: Energy types for lighting
SOURCES
RURAL HHs URBAN HHs TOTAL
% of
Yes
% of
No Mean % of Yes % of No Mean R U
Electricity 24.9 74.9 1.8478 35.1 64.7 2.2222 92 108
Gas 0 99.9 1.3913 0.9 98.8 1.3796 92 108
Kerosene /petrol
65.1 34.7 *2.8478 60.1 39.8 *2.9444 92 108
Candle 6.4 93.4 1.9347 1.8 98 1.4259 92 108
Firewood 3.2 96.7 1.5652 0.9 99 1.4259 92 108
Source: Field work 2013
60
The Table shows that in the rural areas of the state kerosene and petrol is the most used
energy type for lighting ranking (65%) of positive responses with a mean of 2.8 showing they
agree to its use. In the urban area, same kerosene and petrol is the energy type that is most
used for lighting ranking (60%) of positive responses with a mean of 2.9 showing they
strongly agree to its use.
v. Energy used for food preservation
Information was obtained from the respondents on the type of energy used in food
preservation (Table 6). Result shows the most used energy type for food preservation in the
rural areas is firewood while in the urban areas the most used is kerosene.
Table 6: Energy types used in Food preservation
SOURCES
RURAL HHs URBAN HHs TOTAL
% of Yes % of
No Mean % of Yes % of No Mean R U
Electricity 5.4 84.6 1.5760 20.3 79.6 1.8611 92 108
Gas 0 89 1.2717 3.7 96.2 1.5648 92 108
Kerosene 11.8 87.9 1.4456 67.5 32.4 *2.5092 92 108
Firewood 60.9 38.8 *2.7826 24.9 74.9 2.2037 92 108
Charcoal 13.5 85.8 1.6630 12 87.9 1.6111 92 108
Sawdust 5.3 94.5 1.3695 7.3 92.5 1.9722 92 108
Source: Field work 2013
The Table shows that in the rural areas of the state firewood is the most used energy
type for food preservation ranking (60%) of positive responses with a mean of 2.7 showing
they agree to its use. In the urban area, kerosene is the energy type that is most used for food
preservation ranking (68%) of positive responses with a mean of 2.5 showing they agree to its
use.
61
vi. Energy used for cooling
Information was obtained from the respondents on the type of energy used in cooling
(Table 7). Result shows the most used energy type for cooling in the both areas is petrol.
Table 7: Energy types used in cooling
SOURCES RURAL HHs URBAN HHs TOTAL
% of Yes % of No Mean % of Yes % of No Mean R U
Electricity 15.87 84.1 1.445 29.1 70.8 2.12 103 63
Petrol 40.3 35.9 *1.5 67.3 32.6 *2.6 98 62
Source: Field work 2013
The Table shows that in the rural areas of the state petrol is the most used energy type
for cooling ranking (40.3%) of positive responses with a mean of 1.5. In the urban area, petrol
is the energy type that is most used for cooling ranking (67.3%) of positive responses with a
mean of 2.6 showing they agree to its use.
vii. Daily Frequency of energy types used by households
The study sought to find out the amount of energy used daily by the respondents for
various purposes (table 8).The energy use per day by households are distributed as follows;
No response (0), less than 4hours per day (1), 4-6hours per day (2), 8hours per day (3) and
more than 8hours per day (4).
Table 8: Frequency of energy types used by households
Types
Number of hours of use of
energy by Rural HHs
Number of hours of use of
energy by Urban HHs
Total
4 3 2 1 0 4 3 2 1 0 R U
Electricit 2.17 5.43 7.6 28.2 56.5 1.85 17.5 14.8 74 0 92 108
Gas 0 0 0 0 100 - 0.92 2.77 1.85 94.4 92 108
Kerosene 0 8.69 23.9 45.6 21.7 *18.5 41.6 25.9 7.40 6.48 92 108
Firewood *21.7 45.6 10.8 7.6 10.8 9.25 10.1 16.6 2.77 61.1 92 108
Charcoal 18.4 1.08 13.0 44.5 22.8 11.1 12 25 16.6 35.1 92 108
Sawdust 2.17 4.34 1.08 1.08 91.3 1.85 2.77 2.77 0.92 91.6 92 108 Source: Field work 2013
62
From Table 8 it is evident that the most frequently used energy source per day by the
rural households is firewood (21.7%), use for more than eight hours while in the urban is
kerosene rated 18.5% use for more than eight hours.
4.3.2 Test of hypotheses
H01: There are no differences in household energy uses attributable to different energy
types among the rural and urban areas of Enugu state. The significance level of 0.05 will be
used to either accept or reject the null-hypotheses of this test using ANOVA.
i. Difference in types of energy used for cooking between urban and rural
households
Summary of results in Table 9 show that there are significant differences in probability
of energy used for cooking between the rural and urban areas of Enugu. This nulls the
hypothesis that says there are no significant differences in the energy used for cooking in
Enugu.
Table 9: Difference on the type of energy used for cooking
Types
Means (Ms.) Frequenc
y (F.) Prob>F
Bartlett's test for
equal variances:
Prob>chi2 Btw grps Within grps
Electricity 31.24 .2159 144.71 0.0000 0.034
Gas 1.878 .3329 5.64 0.0018 0.000
Kerosene 58.30 .7133 81.73 0.0000 0.000
Firewood 202.01 .6491 311.17 0.0000 0.165
Charcoal 11.22 .9329 12.04 0.0006 0.331
Sawdust 1.703 .4434 3.84 0.0514 0.803
Source: Field work 2013
Responses in Table 9 for electricity show that the probability distribution for the
ANOVA test is 0.000. Based on the decision rule established earlier, the null hypothesis is
63
rejected and the alternative accepted. That is to say that there is significant difference between
electricity use for cooking in rural and urban area. The use of gas energy shows that the
probability distribution for the ANOVA is 0.0018 which is < 0.05, the null hypothesis is
rejected and the alternative accepted. That is to say that there is significant difference between
gas use for cooking in rural and urban area. The use of kerosene energy shows that the
probability distribution is 0.0000 which is < 0.05; the null hypothesis is rejected and the
alternative accepted. That is to say that there is significant difference between kerosene use
for cooking in rural and urban area. The use of firewood energy shows that the probability
distribution is 0.0000 which is < 0.05; the null hypothesis is rejected and the alternative
accepted. That is to say that there is significant difference between firewood use for cooking
in rural and urban area. The use of charcoal energy shows that the probability distribution is
0.0006 which is < 0.05; the null hypothesis is rejected and the alternative accepted. That is to
say that there is significant difference between charcoal use for cooking in rural and urban
area. The use of sawdust energy shows that the probability distribution is 0.05 which is =
0.05; the null hypothesis is accepted and the alternative rejected. That is to say that there is no
significant difference between sawdust use for cooking in rural and urban area.
ii. Difference in types of energy used for ironing between urban and rural
households
Summary of results in Table 10 showed that there are significant differences in
probability of energy used for ironing between the rural and urban areas of Enugu. This nulls
the hypothesis that says there are no significant differences in the energy used for ironing in
Enugu.
64
Table 10: Difference on the type of energy used for ironing
Types
Means (Ms.) Frequency
(F.) Prob>F
Bartlett's test for
equal variances:
Prob>chi2 Btw grps Within grps
Electricity 61.288 1.4760 41.52 0.0000 0.000
Petrol 10.226 .99378 10.29 0.0016 0.349
Charcoal 99.591 .86971 144.51 0.0000 0.156
Source: Field work 2013
Responses in Table 10 for electricity show that the probability distribution for the
ANOVA test is 0.0000. Based on the decision rule established earlier, the null hypothesis is
rejected and the alternative accepted. That is to say that there is significant difference between
electricity use for ironing in rural and urban area. The use of petrol energy shows that the
probability distribution is 0.0016 which is < 0.05; the null hypothesis is rejected and the
alternative accepted. That is to say that there is significant difference between petrol use for
ironing in rural and urban area. The use of charcoal energy shows that the probability
distribution is 0.0000 which is < 0.05; the null hypothesis is rejected and the alternative
accepted. That is to say that there is significant difference between charcoal use for ironing in
rural and urban area.
iii. Difference in types of energy used for home entertainment between urban and
rural households
Summary of results in Table 11 showed that there are differences in probability of
energy used for home entertainment between the rural and urban areas of Enugu. This nulls
the hypothesis that says there are no significant differences in the energy used for home
entertainment in Enugu.
65
Table 11: Difference on the type of energy used for home entertainment
Types
Means (Ms.) Frequency
(F.) Prob>F
Bartlett's test for
equal variances:
Prob>chi2 Btw grps Within grps
Electricity 6.8148 1.0640 6.40 0.0122 0.059
Petrol 41.891 1.5861 26.41 0.0000 0.024
Battery 33.770 1.2063 27.99 0.0000 0.045
Source: Field work 2013
Responses in Table 11 for electricity show that the probability distribution for the
ANOVA test is 0.0122. Based on the decision rule established earlier, the null hypothesis is
rejected and the alternative accepted. That is to say that there is significant difference between
electricity use for home entertainment in rural and urban area. The use of petrol energy shows
that the probability distribution is 0.0000 which is < 0.05; the null hypothesis is rejected and
the alternative accepted. That is to say that there is significant difference between petrol use
for home entertainment in rural and urban area. The use of petrol energy shows that the
probability distribution is 0.0000 which is < 0.05; the null hypothesis is rejected and the
alternative accepted. That is to say that there is significant difference between petrol use for
home entertainment in rural and urban area.
iv. Difference in types of energy used for lighting between urban and rural
households
Summary of results in Table 12 showed that there are no differences in probability of
energy used for lighting between the rural and urban areas of Enugu except for the use of
electricity and candle. This accepts the hypothesis that says there are no significant
differences in the energy used for lighting in Enugu.
66
Table 12: Difference on the type of energy used for lighting
Types Means (Ms.)
Frequency
(F.) Prob>F
Bartlett's test for
equal variances:
Prob>chi2 Btw grps Within grps
Electricity 6.9637 1.4572 4.78 0.0300 0.850
Gas .00677 .26943 0.03 0.8742 0.327
Kerosene/petrol .46376 1.4522 0.32 0.5726 0.002
Firewood .96389 .36876 2.61 0.1075 0.055
Candle 12.863 .38391 33.51 0.0000 0.485
Source: Field work 2013
Responses in Table 12 for electricity show that the probability distribution for the
ANOVA test is 0.0300. Based on the decision rule established earlier, the null hypothesis is
rejected and the alternative accepted. That is to say that there is significant difference between
electricity use for lighting in rural and urban area. The use of gas energy shows that the
probability distribution is 0.8742 which is > 0.05; the null hypothesis is accepted and the
alternative rejected. That is to say that there is no significant difference between gas use for
lighting in rural and urban area. The use of kerosene/petrol energy shows that the probability
distribution is 0.5726 which is > 0.05; the null hypothesis is accepted and the alternative
rejected. That is to say that there is no significant difference between kerosene/petrol use for
lighting in rural and urban area. The use of firewood energy shows that the probability
distribution is 0.1075 which is > 0.05; the null hypothesis is accepted and the alternative
rejected. That is to say that there is no significant difference between firewood use for lighting
in rural and urban area. The use of candle energy shows that the probability distribution is
0.0000 which is < 0.05; the null hypothesis is rejected and the alternative accepted. That is to
say that there is significant difference between gas use for lighting in rural and urban area.
67
v. Difference in types of energy used for cooling between urban and rural
households
Summary of results in Table 13 showed that there is probability of energy used for
cooling between the rural and urban areas of Enugu. This nulls the hypothesis that says there
are no significant differences in the energy used for cooling in Enugu.
Table 13: Difference on the type of energy used for cooling
Types
Means (Ms.) Frequency
(F.) Prob>F
Bartlett's test for
equal variances:
Prob>chi2 Btw grps Within grps
Electricity 23.241 1.4288 16.27 0.0001 0.927
Petrol 60.095 2.1521 27.92 0.0000 0.439
Source: Survey result
Responses in Table 13 for electricity show that the probability distribution for the
ANOVA test is 0.0001. Based on the decision rule established earlier, the null hypothesis is
rejected and the alternative accepted. That is to say that there is significant difference between
electricity use for cooling in rural and urban area. The use of petrol energy shows that the
probability distribution is 0.0000 which is < 0.05; the null hypothesis is rejected and the
alternative accepted. That is to say that there is significant difference between gas use for
cooling in rural and urban area.
vi. Difference in types of energy used for cooking between urban and rural
households
Summary of results in table 14 showed that there are in probability of energy used for
food preservation between the rural and urban areas of Enugu except for electricity and
charcoal. This nulls the hypothesis that says there are no significant differences in the energy
used for food and drink preservation in Enugu.
68
Table 14: Difference on the type of energy used for food preservation
Types
Means (Ms.) Frequency
(F.) Prob>F
Bartlett's test for
equal variances:
Prob>chi2 Btw grps Within grps
Electricity 4.0359 1.0877 3.71 0.0555 0.242
Gas 4.2671 .34723 12.29 0.0006 0.000
Kerosene 56.200 .98847 56.86 0.0000 0.594
Firewood 16.649 1.1776 14.14 0.0002 0.217
Charcoal .88213 .81854 1.08 0.3005 0.323
Sawdust 18.043 .43611 41.37 0.0000 0.074
Source: Field work 2013
Responses in Table 14 for electricity show that the probability distribution for the
ANOVA test is 0.0555. Based on the decision rule established earlier, the null hypothesis is
accepted and the alternative rejected. That is to say that there is no significant difference
between electricity use for food preservation in rural and urban area. The use of gas energy
shows that the probability distribution is 0.0006 which is < 0.05; the null hypothesis is
rejected and the alternative accepted. That is to say that there is significant difference between
gas use for food preservation in rural and urban area. The use of kerosene energy shows that
the probability distribution is 0.0000 which is < 0.05; the null hypothesis is rejected and the
alternative accepted. That is to say that there is significant difference between kerosene use
for food preservation in rural and urban area. The use of firewood energy shows that the
probability distribution is 0.0002 which is < 0.05; the null hypothesis is rejected and the
alternative accepted. That is to say that there is significant difference between firewood use
for food preservation in rural and urban area. The use of charcoal energy shows that the
probability distribution is 0.3005 which is > 0.05; the null hypothesis is accepted and the
alternative rejected. That is to say that there is no significant difference between charcoal use
for food preservation in rural and urban area. The use of sawdust energy shows that the
69
probability distribution is 0.0000 which is < 0.05; the null hypothesis is rejected and the
alternative accepted. That is to say that there is significant difference between sawdust use for
food preservation in rural and urban area.
vii. Difference in types of energy use per day between urban and rural households
Summary of results in table 15 showed that there are differences in probability of
energy use per day between the rural and urban areas of Enugu except for sawdust. This nulls
the hypothesis that says there are no significant differences in the energy use per day in
Enugu.
Table 15: Energy use per day between the rural and urban areas
Types
Means (Ms.) Frequency
(F.) Prob>F
Bartlett's test for
equal variances:
Prob>chi2 Btw grps Within grps
Electricity 24.629 .73497 33.51 0.0000 0.004
Gas .51537 .11050 4.66 0.0320 0.008
Kerosene 95.666 .98347 97.27 0.0000 0.047
Firewood 126.13 1.7842 70.69 0.0000 0.123
Charcoal 26.816 1.6316 16.44 0.0001 0.208
Sawdust .03833 .67634 0.06 0.8121 0.398
Source: Survey result 2013
Responses in Table 15 shows that the probabilities of these energy sources used per
day are less than 0.05. Based on the decision rule established earlier, the null hypothesis is
rejected and the alternative accepted. That is to say that there is significant difference between
energy sources used per in rural and urban area. Except for sawdust that had a probability >
0.05 showing an acceptance of the hypothesis and a rejection of the alternative.
70
In summary of objective one; results and findings show the different households
energy uses attributable to different energy sources and these energy sources and uses differ
by location; rural and urban communities.
4.3.4 The Determinants of Energy Types Used in Households.
This section shows the objective two of the study; factors that influence the choice of
different energy types used in households (Tables presented) using the standard deviation and
the t –values (>or= 1.95). See appendix III.
i. The Determinants of Energy types used for cooking in households
Evidence show that the variables important if and when energy use is basically for
cooking using different energy types are (Table 16); energy price, home appliances, type of
food prepared, income, education, gender, weather, accessibility, location, cultural beliefs and
preferences, size of household, and occupation.
71
Table 16: Determinants of energy types used in cooking
Energy
types
Determinants
Energy
price
Home
app.
Type of
food Income Education Occupation Gender Age Weather Belief HH size Accessibility Location
Electricity -.08559
(-1.03)
.266013*
(2.32)
-.211668*
(-3.69)
.0132029
(0.09)
(all income)
-.46744*
(-2.44)(high) -
.123036
(0.69)
-.295290
(-1.37);
-.45733
(1.86)
-1.644067 (-1.80)
-.0209118
(-0.14)
-.3247786*
(-2.69)(small)
.273177*
(2.78)
.2934047* (2.17)
Gas -189095*
(2.60)
.425668*
(4.06)
-.0589997
(-0.73)
-.255207*
(2.11)(high) - -
-.263458*
(-5.7) (female)
-.0637708
(-0.78)
.1238419
(0.56)
-.4877031*
(4.45)(small)
-.1831947*
(-1.96)
.2490414*
(2.87)
Kerosene .1497076*
(2.25) -
.2939362*
(4.31)
.1816522
(1.90)
(low)
1.571296*
(6.93)
(medium & high)
-1.128927
(-3.21);
-1.493211
(-5.74)
(all levels)
-.3865028
(-1.77)
(all occup.)
-
.7045122
(4.62);
.8828267
(4.97)
(all ages)
- .4165987*
(5.22)
-.3274762* (-2.14)(small);
-.3419255*
(-3.76)(medium)
-.2199593*
(-3.29)
1.109322*
(10.58)
Firewood .524184
(6.87) -
-.557293*
(-3.09)
-.7667703*
(-2.52)(low)
-.7108749* (-3.15)(low); -1.595364*
(-4.79)(none)
-.6466146*
(-3.24) (farmers)
.0824709
(0.56)
(male)
- .549596*
(3.67)
-.262062*
(-2.49)
.3916769* (2.78)(medium);
.7859125*
(2.14)(large)
-.0391967
(-0.51)
-.966508*
(-7.97)
Charcoal .2538723*
(3.35) -
.4595314*
(5.90)
.8617398
(7.92);
1.220093
(6.94)
(all income)
.0563334
(0.34);
.1647311
(0.76)
(all levels)
-.1411535
(-1.21);
-0.829982)
(0.47)
(all occup.)
-.882488
(-5.08);
-.634128
(3.13)
(all ages)
- .1167645
(0.09)
-.5335259
(-4.23);
-.535049
(-2.91)
(all HHs)
-4.327731*
(-4.16)
-5.48222*
(-3.07)
Sawdust .052960
(0.74) - -
-.359089*
(-2.30)(low)
-.9839365
(-6.42);
-.825033
(-3.95)
(all levels.)
- - - .196991*
(3.34)
-.269470*
(-3.55)
-.6565386* (-4.3);
-.6049011*
(-2.79)(large)
-.0789984
(-1.22) -
Source: Field work 2013.
72
Among respondents that use electricity for cooking the determinant factors are; energy
price(2.32), education(-2.44); the highly educated people, accessibility(2.79), HHs(-2.69);
small households, location(2.17); rural households. Among respondents that use gas for
cooking the determinant factors are; energy price(2.60), home appliance(4.06), income(2.11);
the high income earners, gender(-5.7); females, accessibility(-1.96), HHs(4.45); small
households, location(2.87); urban HHs. Among the kerosene using respondents for cooking
the determinants factors are; energy price(2.25), accessibility(-3.29), type of food(4.31),
beliefs(5.22), HHs(4.45, -3.76); small and medium households, and location(11.56); urban
households. Among those that rely on firewood for cooking, the determinant factors are; type
of food(-3.09), income(-2.52); low income earners, education(-3.15, -4.79) low and none
educated people, occupation(-3.24); basically farmers, weather (3.67), free cost of
firewood(6.87), beliefs(-2.49), HHs(2.78, 2.14)); medium and large households, location(-
7.97); rural households. Among the charcoal using respondents for cooking the determinant
factors are; type of food(5.90), energy price(3.35), accessibility(-4.6), location(-3.07); urban
households. Among the sawdust using respondents for cooking the determinants are; income(-
2.30); low income earners, weather(3.34), belief(-3.55), HHs(-4.3, -2.79); large households.
ii. The determinants of Energy types used for Ironing in households
We sought to establish the determinants of energy types used in ironing (Table 17) and
evidence show that the variables important if and when energy use is basically for ironing are;
energy price, home appliances, income, gender, age, weather, accessibility, location, size of
household, and occupation.
73
Table 18: Determinants of energy types used for Ironing
Energy types
Determinants
Energy price
Home applian
ces Income
Occupation
Education Gender
Weather
HHs Age
Accessiblity
Location
Electricity -.076658 (-0.76)
.064858 (0.46)
.042952* (6.12)
(medium); .6498312*
(2.89)(high)
- -1.016866 (-4.74);
-.2757933 (-.1.19);
-.2746619 (0.93)
(all levels)
-1.016866* (-4.74)
(female)
.4206394* (3.88)
- 1.8079354* (-2.18)
(young); -.8047869*
(-2.72) (middle)
.4249492* (3.46)
.698942* (4.28)
Petrol .6278308*
(2.80) .2484785*
(2.27) -1.026866*
(-3.74)(high)
-.8289978 (6.88);
-1.016867 (6.00);
-1.116425 (5.03)
(all occup.)
-.4081092 ((3.49);
-1.021437 (-5.36);
-1.346474 (-5.38)
(all levels)
-
- -.565782*
(-5.42) (small)
- -.0860367 (0.86)
.0916577 (0.80)
Charcoal -.4667716*
(-4.68)
-.0560367 (0.76)
-
.2584785* (2.24)
(farmers)
-
.064858 (0.46);
.066434 (0.74)
(all sex)
.2484785* (2.29)
-.8732809* (-4.91)
(medium); -.9647397*
(-3.21)(large)
.0915577 (0.83)
(all ages)
-
-1.0037* (-9.19)
Source: Field work 2013
74
Result shows for ironing the determinants if electricity is used are; gender(-
4.74); females, accessibility(3.46), income(6.12, 2.89); medium and high income earners,
age(-2.18, -2.72); young and middle aged people and location(4.28) urban. The variables
important for ironing if petrol is used are; energy price(2.80), home appliances(2.27) ,
income(-3.74); high income earners, and HHs(-5.42); small households. The variables
important for ironing if charcoal is used are; energy price(-4.68), weather(2.29),
occupation(2.24); basically farmers, location(-9.19); rural households, HHs(-4.91, -3.21);
medium and large households.
iii. The determinants of Energy types used for cooling in households
Efforts were made to establish the determinants of the type of energy used for cooling
(Table 18), evidence show that the variables important if and when energy use is basically for
cooking are; energy price, home appliances, income, accessibility, location, age and
occupation.
75
Table 18: Determinants of energy types used for cooling
Energy types
Determinants
Energy price
Home applianc
es Income Occupation Weather Age Access
Education HHs
Electricity .263383*
(3.74)
-
.1349676
(1.61)
-.504269* (4.56)
(medium)
-.8065787* (-6.29)
(Civil S.); -.7934744*
(-3.49) (student);
-1.818133* (5.97)(religious worker)
.7751966* (3.03)
-.9800532* (-3.74)
(middle); -1.748648*
(-5.71) (old)
-.1903249* (-2.19)
.002859 (-0.02);
-.1511975 (-0.66)
(all levels)
-1.121155 (-11.93);
-1.353673 (-7.67);
-1.360125 (-5.64)
(all HHs)
Petrol .2334851*
(2.34)
.338096* (4.08)
-.3294004* (-3.09) (high)
-.6410991* (-2.11)
(Civil. S.)
-
-.4674476* (-2.23)
(middle)
.2802207* (3.26)
-.1964245
(-0.86); .1557993
(0.53); -.0023302
(-0.01) (all levels)
-.8212343 (-4.70);
-1.308778 (-5.49);
-.2792006 (-3.00)
(all HHs)
Source: Field work 2013
76
The energy variables important for cooling if electricity is used are; energy
price(3.74), income(-4.56); medium income earners, accessibility(-2.19), occupation(-6.29, -
3.49, -5.97); all occupations excluding farming, age(-3.74, -5.71) middle and old age people
and location(4.23); urban households. The energy variables important for cooling if petrol is
used are; occupation(-2.11); civil servants, income(-3.09); high income earners,
accessibility(3.26), age(-2.23); middle aged people, home appliances(4.08) and energy
price(2.34).
iv. The determinants of Energy types used for lighting in households
Information gathered showed the variables important if and when energy use is
basically for lighting (Table 19) are; accessibility, income, gender, location, energy price, cost
of firewood, education, occupation, age, weather and belief.
77
Table 19: Determinants of energy types used for lighting
determinant Energy types
Energy price
Income Education Occupation Gender Weather Belief Age Accessibilit
y Location
Electricity .1726395
(1.68)
.9175693 (2.71);
.9717211 (2.21) (all)
.0473155 (0.19);
.0814446 (0.26)
(all levels)
-
-.7719886* (3.51)
(female)
.0383886
(0.33) - -.0594578
(-0.21);
-.6188092
(-1.97)
(all age)
.7751966* (3.03)
.0630874 (0.37)
Gas .397286*
(6.92)
.9175693* (2.71);
.9717211* (2.21) (high)
-.8386053* (-4.05)(high)
.34211* (2.35)
(Civil. S)
-.2143955 (-1.37)
- - -.1479872 (-0.77);
-.2978304 (-1.39)
(all age)
.0543398 (0.57)
.1551929 (1.26)
Petrol/Kerosene .440121*
(5.55)
-.738605* (-3.05) (high)
-.7386053 (0.78); .054651 (0.22)
(All level)
- .225127 (1.33)
(female)
.2989501* (4.73)
-
-1.166586* (4.47) (middle)
-.0450122 (-0.60)
.1973098 (1.51)
Firewood .211665*
(2.47)
.402168* (2.55) (low)
-1.549543* (-4.79);
-1.738136* (-3.90)
(low & none)
-.6470824* (-3.13)
(farmer)
-.3847647* (-2.09)
(female)
- .0831657 (0.77) .9278625*
(4.17); 1.044677* (4.07)(old)
-.034202 (-0.40)
-.900893* (-5.87)
Candle .1378084
(1.57)
.7535424 (5.22);
.9896959 (3.59) (All)
-.7693594 (-3.90);
-.7620185 (-3.02)
(all levels)
- -.7477421*
(-4.21) (male)
.1423604 (1.51)
.7639626* (2.74)
.2014969 (0.88);
.2365447 (0.97)
(all ages)
-.0642962 (-0.65)
-.909145* (-4.21)
Source: Field work 2013
78
The energy variables important for lighting if electricity is used are;
accessibility(3.03), gender(3.51); female. The energy variables important for lighting if gas is
used are; occupation (2.35); civil servants and traders, education (-4.05); highly educated
households, income (2.71, 2.21); high income earners, and energy price (6.92). The energy
variables important for lighting if kerosene and/or petrol is used are; weather(4.73), income(-
3.05); high income earners, age(-4.47); middle age people and energy price(5.55). The energy
variables important for lighting if firewood is used are; occupation(-3.13); farmers, gender(-
2.09) females, income(2.55); low income earners, education(-4.79, -3.90); low and non
educated, cost of firewood(2.47), age(4.17, 4.07); aged people, location(-5.87); rural
households. The energy variables important for lighting if candle is used are; belief(2.74),
location(-4.21); urban households, gender(-4.21); males.
v. The determinants of Energy types used for home entertainment in households
Information gathered showed the variables important if and when energy use is
basically for lighting (Table 20) showed the variables important if and when energy use is
basically for home entertainments are; ethnicity, accessibility, age, home appliances, location,
occupation, income and energy price.
79
Table 20: Determinants of energy types used for home entertainment Energy types
Energy price Home app. Occupation Ethnicity Age Access Location Income Education
Electricity .2741927*
(3.05)
.3193238*
(3.02)
-.4726989*
(-2.77)
(Civil S.);
-.8206808*
(-2.06)
(student)
.3841523 (1.10);
-.0476716 (-0.11);
.0638948 (0.11)
(all tribes)
-.478861*
(-1.95);
-.7322933*
(-2.58)
(young)
-.398964*
(2.74)
.4533589*
(2.64)
-.3383678 (-1.72);
-.4473333 (-1.84);
-.246779 (-0.72);
-.4235979 (-0.94)
(all income)
-.0599808
(-0.25);
-.3281741
(-1.07); -.4525295
(-1.15);
-.7872287
(-1.42)
Petrol .1863983*
(2.57)
.2407808*
(2.82)
-.9374649 (-6.81);
-1.714001 (-9.04);
-1.545777 (-4.82)
(all occup.)
.7546556*
(2.68)
(Igbo);
.8474348*
(2.32) (Yoruba)
-.6839251* (-2.98)(middle)
.1265047 (1.37)
-.498964 (-0.55)
-.498964*
(2.54)
(high)
-.0573112
(-0.30);
.0429259
(0.17);
.4391308
(1.39)
Battery .2220457*
(3.23) - -
.3248162*
(2.18)
(Hausa)
-.3818396*
(-2.40)
(middle);
.377861*
(2.57)(old)
-.0133712 (-0.9)
-.3202636*
(-3.53)
-1.490343 (-15.13);
-1.358716 (-8.68);
-1.001867 (-4.27)
(all income)
-.8537475* (-4.53)
(middle); -1.593291*
(-6.47) (low)
Source: Field work 2013
80
The energy variables important for home entertainment if electricity is used are;
location (2.64); urban households, accessibility (2.74), age (-1.95, -2.58); young people, home
appliances (3.02), occupation (-2.77, -2.06); civil servants and students. The energy variables
important for home entertainment if petrol is used are; ethnicity (2.68, 2.32); Igbo and Yoruba
households, age(-2.98); male, home appliances(2.82), energy price(2.57) and income(2.54);
high income earners. The energy variables important for home entertainment if battery is used
are; ethnicity(2.18); Hausa households, location(-3.53), education(-4.53, -6.47); middle and
low educated, age(-2.40, 2.57); the middle and old age and energy price(3.23).
vi. The determinants of Energy types used for food preservation in households
Efforts were made to establish the determinants of the type of energy used for food
preservation, (Table 21) showed the variables important if and when energy use is basically
for food preservation are; beliefs, accessibility, income, location, weather, type of food
preserved, home appliance, energy price, gender, HHs and education.
81
Table 21: Determinants of energy types used for food preservation
Energy
types
Energy
price Home app. Type of food Income Education Occupation
Gender Weather Belief HH size Access Location
Electricity -.31267*
(-2.52)
.3764556*
(2.21)
.8164316*
(7.80)
-.3383678
(-1.72);
-.4473333
(-1.84)
-.3281741
(-1.07);
-.4525295
(-1.15)
-.4726989*
(2.77)(Civil.S);
-.8206808*
(-2.06)
(Student)
- -.5286576*
(-4.46)
-.0209118
(-0.14) -
-.398964*
(-2.74)
.5635311*
(3.55)
Gas -.1517416*
(-2.18)
.2221671*
(2.43)
-.0227259
(-0.28)
-1.037933*
(-3.14) (high)
-.4486227*
(-4.40) (high)
- - - .122861
(0.37)
-.1918823
(-1.75);
-.201648
(-1.15)
.1731596*
(2.25)
.2761796*
(3.05)
Kerosene .1901868*
(2.70)
.0026385
(0.03)
.2121671*
(2.73)
.3139019
(2.90);
.7248481
(4.30)(all)
-
-.6336797
(-5.41);
-.707338
(-4.35)(all)
- .122861
(0.77) -
-1.261256
(-11.24);
-1.180655
(16.61)(all)
.0635221
(0.73)
.4109414*
(4.04)
Firewood .1131405
(1.52) -
.2334748*
(3.21)
-.8129691*
(-9.56)(low)
-.9808909*
(6.02)(middle
-.905999*
(5.40)
(low)
.267082
(2.53);
.445306
(2.98);
.5034418(2.53)all
- -.2335966*
(-3.82)
.3899893*
(4.19) -
.2801618*
(3.93)
.9338196*
(4.28)
Charcoal -.4489334*
(-2.23)
-.0828263
(-0.80)
.4629762*
(2.75)
.0814292
(0.57);
.3587977
(1.76)
-
-.4489334*
(-2.23)
(farmers)
-1.21593*
(-7.34)
(female)
-.4489334*
(-2.23) -
-.0157175
(-0.12);
.0313555
(0.15)
.2052393
(1.98)
-.7107773*
(-6.25)
Sawdust .0529608
(0.74) -
.1257402
(1.80) -
-1.062237
(-11.77);
-.9839365 (96.42)(all)
-.478739*
(2.3)
(farmers)
-.04072
(-0.48)
.010975
(0.11) -
-3.214779*
(-2.42)
(large)
.2653337*
(3.15) -
Source: Field work 2013
82
The energy variables important for food preservation if electricity is used are; type of
food preserved (7.80), occupation(2.77, -2.66); civil servants and students, accessibility(-
2.74), home appliance (2.21) and energy price (-2.52), weather (-4.46) and location (3.55);
urban households. The energy variables important for food preservation if gas is used are;
location (3.05); urban households, accessibility (2.25), education (-4.40); highly educated
people, income(-3.14); high income earners, home appliances(2.43) and energy price(-2.18).
The energy variables important for food preservation if kerosene is used are; type of
food(2.73), energy price(2.70) and location(4.04); urban households. The energy variables
important for food preservation if firewood is used are; accessibility(3.93), income(-9.56, -
6.02); medium and low income earners, type of food(3.21), education(-5.40); low and none
educated people, weather(-3.82), location(4.28) and belief(4.19). Energy variables important
for food preservation if charcoal is used are; energy price(-2.23), occupation(-2.23); farmers,
gender(-7.34); female, weather(-2.23), location(-6.25); urban households and type of
food(2.75). The energy variables important for food preservation if sawdust is used are;
accessibility(3.15), HHs(-2.4); large households, accessibility(3.15) and occupation(-2.3);
farmers.
4.3.4 Test of hypotheses
Ho2: There are no determinants of household energy uses in Enugu. The T-value of
the regression t > or = 1.95 shows that the P>|t| is less than or equal to 0.05 which makes that
variable to be either rejected or accept if t > or = 1.95, the hypotheses that says there are no
significant determinants of energy types used by households in both the rural and urban areas
of Enugu state.
i. The variables important (Table 16) if and when energy type used is basically for
cooking are; energy price, home appliances, type of food prepared, income, education,
83
gender, weather, accessibility, location, cultural beliefs and preferences, size of
household, and occupation. Therefore, this rejects the hypotheses.
ii. The variables important (Table 17) if and when energy type used is basically for
ironing are; energy price, home appliances, income, gender, age, weather,
accessibility, location, size of household, and occupation. Therefore, this rejects the
hypotheses.
iii. The variables important (Table 18) if and when energy type used is basically for
cooling are; energy price, home appliances, income, accessibility, location, age and
occupation. Therefore, this rejects the hypotheses.
iv. The variables important (Table 19) if and when energy type used is basically for
lighting are; accessibility, income, gender, location, energy price, cost of firewood,
education, occupation, age, weather and belief. Therefore, this rejects the hypotheses.
v. The variables important (Table 20) if and when energy type used is basically for home
entertainments are; ethnicity, accessibility, age, home appliances, location, occupation,
income and energy price. Therefore, this rejects the hypotheses.
vi. The variables important (Table 21) if and when energy type used is basically for food
preservation are; beliefs, accessibility, income, location, weather, type of food
preserved, home appliance, energy price, gender, HHs and education. Therefore, this
rejects the hypotheses.
In summary, the key determinants of the energy types used by households in Enugu
state are; energy price, accessibility, age, gender, income, educational attainment, weather,
type of food, area of settlement (location), cultural preferences (belief) and home appliances.
84
4.3.5 The Preferences of Households on different Energy Types.
This section shows the findings and results of objective three of the study; preferences
of both the rural and urban households on different energy types. It explains the percentages
of respondents that strongly agree (4) and agree (3) (denoted as Yes) and the percentage of
respondents that disagree (2) and strongly disagree (1) (denoted as No) to various options
made available to them.
Decision Rule:
If Mean ≥ 2.5, the respondents agree
If Mean < 2.5, the respondents disagree
i. Household preferences for use of modern energy (electricity, gas and Kerosene)
for cooking
Efforts were made to establish the responses of the rural and urban households on their
preferences on the use of electricity, gas and kerosene for cooking if such energy types were
made available, affordable and they earned higher income (Table 22).
Table 22: Household preferences for use of modern energy for cooking
Options
Rural Urban Total
% of
Yes
% of
No Mean % of Yes
% of
No Mean R U
Available 7.5 92.3 1.228261 91.6 9 3.159259 92 108
Affordable 7.6 92.3 1.282609 79.6 20.3 3.351852 92 108
Higher income
17.3 82.5 1.467391 48.1 51.7 2.537037 92 108
Source: Field result 2013
The rural households prefer the use of traditional energy types (firewood, charcoal and
sawdust) for cooking even when the modern energy types were made available (1.2 mean),
affordable (1.2 mean) and they earned higher income (1.4 mean). In the urban households,
the use of modern energy types were preferred for cooking when such energy types were
made available (3.1 mean), affordable (3.3 mean) and they earned higher income (2.5 mean).
85
ii. The use of modern energy (Electricity, Gas and Kerosene) for non-cooking
activities in household.
Efforts were made to establish the responses of rural and urban households on their
preferences on the use of electricity, gas and kerosene for non-cooking if such energy were
made available, affordable and they earned higher income are shown (Table 23).
Table 23: Household preferences for use of modern energy for non-cooking
Options
Rural Urban Total
% of
Yes
% of
No Mean % of Yes % of No Mean R U
Availability 78.2 21.6 3.380435 92 7.3 3.731481 92 108
Affordability 55.3 44.3 2.502174 98.1 1.8 3.916667 92 108
Higher income
80.4 19.5 3.434783 64.8 35.1 3.111111 92 108
Source: Field work 2013
Result show that both the rural and urban households prefer the use of modern energy
types for non-cooking activities like lighting, cooling, food preservation, etc. when such
modern energy types were made available (rural 3.3, urban 3.7 mean), affordable (rural 2.5,
urban 3.9 mean) and they earned higher income (rural 3.4, urban 3.1 mean).
iii. The use of both modern energy and traditional energy types for cooking and non-
cooking activities in household if and when options are made available.
Information gathered showed the responses of rural and urban households on their
choice for the use of both modern and traditional energy sources for cooking and non-cooking
(Table 24).
86
Table 24: Household preferences for use of both modern and traditional energy for
cooking and non-cooking
Options Rural Urban Total
% of Yes % of No Mean % of Yes % of No Mean R U
Both energy types for cooking
15.1 84.7 1.532609 53.6 46 2.675926 92 108
Both energy types for
non-cooking
50.9 48.7 2.58913 19.4 80.5 1.62037 92 108
Source: Field work 2013
Result shows that the combination of modern and traditional energy types for cooking
activities in homes was not acceptable in the rural households (1.5 mean responses), this
shows they preferred a single source for their cooking while its use for non-cooking activities
in rural households was accepted (2.5 mean responses), this shows they accepted both types
for other home uses. In urban households, the use of both modern and traditional energy types
for cooking were accepted (2.6 mean responses), while its use for non-cooking activities was
rejected (1.6 mean responses).
4.3.6 Test of Hypothesis
Ho3: There are no differences in the preferences of households energy use on different
energy sources across the rural and urban areas of Enugu. The significance level of 0.05 will
be used to either accept or reject the null-hypotheses of this test using ANOVA.
i. Difference in the use of modern energy sources (Electricity, Gas, petrol and
Kerosene) for cooking activity by households.
Summary of results in Table 25 showed that there are differences in probability of
energy preferences by households for cooking between the rural and urban areas of Enugu.
87
This nulls the hypothesis that says there are no significant differences in the energy
preferences for cooking in Enugu.
Table 25: The use of modern energy sources for cooking activity by households.
Available
options
Partial SS. Means (Ms.)
Prob>F
Bartlett's
test for equal
variances:
chi2(1) Btw grps Within grps Btw grps
Within grps
Availability 318.247738 71.9472625 318.247738 .363370013 0.0000 0.753
Affordability 212.718196 157.281804 157.281804 .794352543
0.0000 0.000
Higher income 56.8409742 217.754026 56.8409742 1.09976781
0.0000
0.398
Source: Survey result 2013
Responses in Table 25 shows that the probabilities of the preferences of households on
the use of modern energy for cooking activities if such energies were available, affordable and
the users have higher income is less than 0.05. Based on the decision rule established earlier,
the null hypothesis is rejected and the alternative accepted. That is to say that there is
significant difference between the preference of households on the use of modern energy for
cooking in rural and urban area.
ii. Difference in the use of modern energy sources (Electricity, Gas. petrol and
Kerosene) for non-cooking activity by households.
Summary of results in Table 26 showed that there are differences in probability of
energy preferences by households for non-cooking between the rural and urban areas of
Enugu. This nulls the hypothesis that says there are no significant differences in the energy
preferences for cooking in Enugu.
88
Table 26: The use of modern energy sources for non-cooking activity by households.
Available
options
Partial SS. Means (Ms.)
Prob>F
Bartlett's
test for equal
variances:
chi2(1) Btw grps
Within grps
Btw grps Within
grps
Availability 6.12225443 154.897746 6.12225443 .782311846
0.0057
0.000
Affordability 113.950435 190.369565 113.950435 .961462451
0.0000
0.000
Higher income
5.20463768 221.275362 5.20463768 1.11755233 0.0321
0.935
Source: Field work 2013
Responses in Table 26 shows that the probabilities of the preferences of households on
the use of modern energy for non-cooking activities if such energies were available,
affordable and the users have higher income is less than 0.05. Based on the decision rule
established earlier, the null hypothesis is rejected and the alternative accepted. That is to say
that there is significant difference between the preference of households on the use of modern
energy for non-cooking activities in rural and urban area.
iii. Difference in the use of both modern energy sources (Electricity, Gas, petrol and
Kerosene) and traditional energy sources (firewood, charcoal and sawdust) for
cooking and non-cooking activity by households.
Summary of results in Table 27 showed that there are significant differences in
probability of the combination of both modern and traditional energy preferences by
households for cooking and non-cooking between the rural and urban areas of Enugu. This
nulls the hypothesis that says there are no significant differences in preferences of households
in the use of all the energy sources for cooking and non-cooking in Enugu.
89
Table 27: The use of modern and traditional energy types for cooking and non-cooking
Available
options
Partial SS. Means (Ms.)
Prob>F
Bartlett's test
for equal
variances:
chi2(1) Btw grps
Within
grps Btw grps
Within
grps
Cooking 64.9404187 200.559581 64.9404187 1.01292718
0.0000
0.010
Non-cooking
37.4956844 190.424316 37.4956844 .961738968 0.0000
0.185
Source: Field work 2013
Responses in Table 27 shows that the probabilities of the preferences of households on
combining modern and traditional energy for cooking and non-cooking activities if such
energies were available, affordable and the users have higher income is less than 0.05. Based
on the decision rule established earlier, the null hypothesis is rejected and the alternative
accepted. That is to say that there is difference between the preference of households on
combining modern and traditional energy for cooking and non-cooking activities in rural and
urban area.
90
CHAPTER FIVE
DISCUSSION OF RESULTS
This chapter discusses the research findings and results in line with the three
objectives with backup from the literature reviewed.
5.1 The household energy types attributable to different energy uses in Enugu
The most used energy types for cooking in rural and urban areas is firewood and
charcoal respectively. Urban households use kerosene for cooking than rural households.
The energy type used for ironing in the rural area of Enugu state is charcoal and the least is
electricity, while in the urban areas the most used for ironing is electricity while the least is
charcoal. In rural areas of the state, battery is the most used for home entertainment while
the least is electricity. In the urban area, petrol ranked the most used for home
entertainment while the least is battery. In the rural areas of the Enugu, kerosene/petrol is
the most used for lighting while the least is gas. The same goes for the urban area,
kerosene/petrol also ranked the most used for lighting while the least is also gas. In the
rural area, petrol ranked the most used for cooling while the least is electricity. In the urban
and urban areas, petrol ranked the most used for cooling and electricity ranked the least
used. In the rural areas of the Enugu, firewood is the most used for food preservation while
the least is gas. In the urban area, kerosene ranked the most used for food preservation
while the less used is gas. In summary, the most used energy type per day in the urban area
is kerosene, while in the rural area it is firewood.
This agrees with the position of (World Bank, 2005, NBS, 2006), which notes that in
Nigeria traditional energy sources accounts for over 70% Household energy supply. while
rural households rely more on biomass fuels than those in urban areas, a substantial number of
urban poor households’ in Nigeria rely on fuel wood, charcoal, or wood waste to meet their
cooking needs.
91
Findings also show that households rely on multiple sources of energy especially in
the urban areas. The findings agree with those of Akpan, (2007) and Desalu, (2012) that
found that household relies on several energy types and sources. Our findings on the reliance
of rural households on fuel wood also agrees with Afeikhena (2006) and Yaqub, et al ,
(2011), findings show that more rural households use fuel wood and other more polluting and
less efficient energy sources for cooking.
5.2 Factors that influence the types of energy used by households
Accessibility of the different energy types was found to be a strong determinant of its
use especially in rural areas. Majority of the rural dwellers agree that firewood is collected for
free and this influence its high usage in the area, while the urban households resort to the use
of firewood and charcoal owing to its relative cheapness. The type of food prepared and
cultural beliefs is a determinant because respondents agree to the use of traditional energy
source to cooking a particular type of food and some have this belief that food made with
firewood is more natural and it is dominant in the rural. The size of household (large
households), weather, gender, age, and educational level (as respondents with below graduate
level tends to use firewood more for cooking) all these contribute to the type of energy used
for cooking in both sectors.
The energy sources used for ironing in both areas are charcoal for the rural and
electricity for the urban. This is a function of some determinants such as price of such
energies, charcoal is very affordable by the rural since it was made from burnt wood and
wood is believed by most of them to be free and also electricity is scarcer in the rural areas
than in the urban areas of Enugu (accessibility). Income is also a major determinant as high
and medium income earners in the urban use petrol as substitute for ironing when there is no
electricity.
92
The energy sources used for cooling in both areas is petrol and the variables that
determines its use are; energy price, home appliances, income, accessibility, age, weather,
location, and occupation. When it comes to cooling 62.1% of the rural respondents do not
use any energy source for it, this could be as a direct relationship with cultural belief,
weather, energy price and home appliance and it also has relationship with income since
54.3% of them earn below N20, 000 per month. Lack of access to electricity is a major
reason why petrol/kerosene happened to be the most used for cooling and lighting in both
sectors.
The energy sources that is most used for lighting and home entertainment in both
urban and rural areas are; kerosene/petrol and battery respectively. This could be influenced
by occupation, ethnicity, accessibility, age, home appliances, sector, beliefs, income and
energy price. Hausa in Enugu state use battery more to power their radio. Also, occupation
has direct relationship with income, most rural can not afford any other means for their home
entertainment other than battery because it is cheaper than petrol or electricity, moreover
lack of access to electricity hindered its use. Some can not afford a generator set to power
their electronic devices when electric power fails so they go for battery. Lack of access to
electricity hinders its use in the urban for home entertainment, age too is a factor, and if the
home is more of children or younger people they tend to use home electronics more. The
energy type use for home entertainment is also a location factor because what the rural use is
different from what the urban use for same purpose.
The energy sources that is most used for food preservation in both rural and urban
areas are; firewood and kerosene respectively. This has factors that determines the use of
such energy sources such as; cultural beliefs, accessibility, income, sector, type of food
preserved, home appliance and energy price and education. Cultural belief and type of food
preserved has similar explanations to that of cooking. Energy used for food preservation has
93
a location determinant since the energy source for this varies by location. Home appliances
also contribute since some do not have oven, micro wave, refrigerator etc for preserving
foods. Accessibility and its price are also a determinant as well as educational attainments; as
the educated ones use more of modern energy than the less educated. This type of energy use
has similar characteristics to cooking.
This study also agrees with some of the empirical study of this work (Akpan, Wakili
and Akosin 2007; Onyekuru, 2008; Onyeji, 2009; Naji, Uzoma and Chukwu, 2010; Yaqub et
al 2011; Desalu, 2012) and the concept of energy ladder model as used by different
researchers on household energy; (Davis, 1998; Masera, Saatkamp and Kammen, 2000;
Barnett, 2000; Sheilah and Alison, 2002; Arnold, Kohlin and Persson, 2006; Nicolai, 2008)
they found that income, fuel prices, government policies, Intra-household income distribution,
Fuel availability, Distribution network proximity, Cultural preferences, Demographic
distribution, Physical environment (rural or urban) and household characteristics influence
energy consumption levels.
5.3 The Energy preferences of household’s
If modern household energy was made available, affordable and the users earned
higher income in Enugu state; the rural and urban areas responded differently to its use for
cooking and non-cooking in homes for instance, in the rural area of Enugu state, they did not
agree to the use of modern energy for cooking and some percentages of them agrees to its use
for other non-cooking activities like cooling, lighting, etc while in the urban area, a high
percentage agrees to its use for cooking and 92% of them agreed to its use for other non-
cooking activities. The study agrees with Onyekuru (2008) with rising incomes, fuelwood
tends to be replaced by kerosene and kerosene replaced by gas/electricity for cooking and
lighting. This agrees with other non-cooking activities in both areas but disagree with cooking
when it comes to the rural area of Enugu except for the urban, it is as a result of high level
94
illiteracy in the rural. Also a research by Yaqub, Olateju and Aina (2011) agrees with this
study, the researchers found out the fact that many people prefer to use Gas for convenience,
efficiency and neatness but cannot afford it.
This in turn explains the energy theory that is used in the research, the energy ladder
theory and the stack model. The concept of energy ladder hypotheses according to Rajmohan
and Weerahewa (2005) is believed that people with low incomes generally use traditional
fuels as their main energy source and people with higher incomes tend to use modern fuels.
When income increases households not only consumes more of the same good but they also
climb the ladder to more modern goods with higher quality i.e. as a household gains
socioeconomic status, it ascends the ladder to cleaner and more efficient forms of energy.
Further it assumes that cleaner fuels are normal economic goods while traditional fuels are
inferior goods (Rajmohan et al, 2005). in summary when all options are made available to
households like affordable price, higher income and when these energy sources are accessible,
people tends to climb the energy ladder to cleaner energy but this theory does not consider the
cultural beliefs and preferences of people when it comes to energy use. For instance, in the
rural area of Enugu, they prefer the use of traditional energy sources for cooking but not so
for other non-cooking activities.
Looking at the stack model theory, according to Maserea (2000), rural household do
not switch fuels entirely, but more generally follow a multiple fuels or fuels stacking model.
Energy Stack Model is ability of households to combine both traditional and modern fuels to
meet their domestic energy needs. This model rejects the linear simplification of the energy
ladder, suggesting that households do not wholly abandon inefficient fuels in favour of
efficient ones. Rather, modern fuels are integrated slowly into energy-use patterns, resulting
in the contemporaneous use of different cooking fuels (Nicolai, 2008). In the rural area, they
had very poor response to the use of both for cooking. This could be because of their cultural
95
preference of using firewood for cooking and illiteracy as earlier stated but for non-cooking
they responded well to its use. In the urban of Enugu, the respondents were willing to
combine both sources for cooking but not for non-cooking.
96
CHAPTER SIX
SUMMARY OF FINDINGS, CONCLUSION AND RECOMMENDATION
7.0 Introduction
Findings from this study based on the objectives were summarized below. Conclusions,
development implications and recommendations were also reached.
6.1 Summary
The most used energy type by urban households in Enugu for cooking is charcoal
(62.9%) and followed by kerosene (61%), while in the rural, the most used energy is firewood
(88%). In meeting the ironing needs of the state; rural households rely more on charcoal
(77%) while the urban resort to electricity (58.8%). In rural homes, they use more of battery
for home entertainment (59%) while the urban homes use petrol to power their electronic
devices (56%). In terms of lighting in homes; the rural and urban rely mostly on same source
which are kerosene and petrol (65% and 60%) respectively. In food preservations; the rural
households rely mostly on firewood (51%) while the urban households rely on kerosene. The
energy type used mostly for cooling in rural and urban areas is petrol (40% and 70%)
respectively.
In summary, the most energy types frequently used per day by the urban households in
Enugu is kerosene (18.5%), while in the rural area of the state is firewood (22%).
The factors that that influence such choice are found out to be education (less educated
use more of low grade energy types than the educated), age (the middle age tend to spend
more money on cleaner energy), income (high income earners in Enugu use more of modern
energy), households size (larger households use more of low grade energy types), locality
(rural and urban the rural households use more of low grade traditional energy types than the
urban), gender, cultural belief and preferences as will be discussed in the next paragraph,
price of energy (high and expensive energy like electricity, petrol, gas and kerosene are
97
mostly used by the rich and middle class in Enugu state), type of food prepared and
accessibility (most households in Enugu state lack access to steady power supply).
Preferences of households for energy use differed by location; in the rural area of
Enugu state, they did not agree to the use of modern energy for cooking and about 44% of
them agrees to its use for other non-cooking activities like cooling, lighting, etc. while in the
urban area, a high percentage agrees to its use for cooking and 92% of them agreed to its use
for other non-cooking activities. This could be as a result of cultural preferences and belief in
the rural areas and other factors in the urban area.
6.2 Development Implication
The development implication of the research is that if domestic energy is not given
quality attention by the state as it has been seen that most households rely basically on
traditional energy types for their domestic use, will lead to environmental degradation,
erosion and air pollution and above all impair the health of all. When the health of the people
is affected, it will impact on their productivity and economy at large.
6.3 Conclusion
Based on the research findings, households in rural and urban areas of Enugu state
responded differently in their energy usage pattern. The use of solid fuel (firewood; 21.7%)
on daily basis is high in rural areas, while the urban use more of kerosene on daily basis
(18.5%). Their choice of energy use can be related to level of education, age, gender,
occupation, weather, accessibility, location, type of food prepared, income, available home
appliance and energy price. The use of electricity is mostly associated with its availability, gas
is associated with high level of high level of education, cultural belief, high price and high
income, petrol is associated with high income, home appliances and high price. Kerosene is
associated with its availability and high price, firewood is associated with its cheapness,
98
cultural preference and belief, low level of education and location (rural), charcoal is
associated with low energy price and low income, while the use of sawdust is associated with
low energy price and low level of education.
Households in both rural and urban area of Enugu state responded positive to the use of
modern energy for non-cooking activities if such energy was made affordable, available and
they earned higher income, while their response to its use for cooking was different as the
rural preferred solid fuel for cooking as against the urban. Making modern energy available
and affordable as well as sensitizing households on the impact of traditional energy use to
Enugu state environment will help ensure a secured and safe environment.
Based on these findings the study concludes that households in Enugu urban area
tends to climbs the energy ladder from low grade energy types to modern energy when
income increases and such energy made available except for cooking; where they prefer the
use of both energy types because of cultural belief, while the rural still resort to low grade
traditional energy especially for cooking, basically because of their cultural belief.
The high dependence of most households on low grade energy types has
environmental and health implications especially against the backdrop of forest degradation
and deforestation amidst the threats of climate change. This may account to erosions in the
state as they depend more on low grade energy.
6.4 Recommendations
The study recommends that domestic modern energy types be made available,
affordable and accessible to households in Enugu. This is because most urban and many rural
households showed interest in the use. There is also need for sensitization in both areas; the
rural people need to be educated on the negative impact on the environment of the traditional
energy types they use, as well as the urban areas that use charcoal for cooking, aside the
99
energy price, most of them prefer firewood for cooking even when electricity or gas is made
available and affordable to them.
Poverty is also a factor to be addressed as people with low income tend to use more of
traditional energy as against the high income earners. The low income earns showed interest
in the use of modern energy if they had earned higher income, except for cooking in both
areas.
Finally, Energy price reduction is necessary in domestic energy of Enugu state, for
instance, the price of kerosene is far higher than the official price set out for it and this affects
mostly the urban households on its use.
6.5 Suggestions for further study
This study suggests the following for further studies
i. The willingness of rural and urban households to pay more for better energy types in
Enugu state.
ii. Impact of energy types preferred by households on health status.
iii. Socio-economic effects and implications of high energy price in Enugu state.
100
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105
APPENDIX 1
RESEARCH INSTRUMENT (QUESTIONNAIRE)
Letter to Respondents
Institute for Development Studies (IDS) University of Nigeria Enugu Campus Enugu, Enugu State. September 10th, 2012.
Sir/Madam,
My name is Madukwe Chioma Evangeline a student of the above mentioned
institution. I am currently carrying out a research on the energy use pattern of
households in Enugu state.
Your community is among those selected for the research. Your response will
assist in meeting the objectives of the research and is therefore requested.
All responses will be used for academic purposes only and kept in strict
confidence.
Thank you for your understanding and co-operation,
Yours faithfully,
Madukwe Chioma E.
DOMESTIC ENERGY USE PATTERNS IN ENUGU STATE.
106
Section 1: SOCIO-ECONOMIC CHARACTERISTICS OF RESPONDENTS
Please tick the appropriate box provided in each question.
Persons interviewed in rural households (to be filled by the enumerator):
1. The position of the respondent in the household
a. Husband [ ] b. Wife [ ] c. Children [ ]
d. Others. [ ]
2. Gender: a. Male [ ] b. Female [ ]
3. Which of the age categories do you belong to:
a. 20-40 [ ] b. 41-60 [ ] c. 60 & above [ ]
4. Marital status: a. Married [ ] b. Single [ ]
c. Separated [ ] d. Widowed [ ] e. Divorced [ ]
5. Religion: a. Christianity [ ] b. Islam [ ]
c. African Traditional Religion [ ] d. Other [ ]
6. Which ethnic group do you belong. a. Igbo [ ]
b. Hausa [ ] c. Yoruba [ ] d. Efix [ ]
7. What is the highest education attained by the bread winner:
a. Graduate degree [ ] b. SSCE [ ] c. Primary Sch. [ ]
d. No education [ ]
8. What is your occupation? a. Civil service [ ] b. Trader [ ]
c. Farmer [ ] d. Student [ ] e. Religious worker [ ]
9. What is your average income per month (#)? a. 5,000-20,000 [ ]
b. 21,000-40,000 [ ] c. 41,000-60,000 [ ]
d. 60,000 and above [ ]
10. How many persons are in your household? a. less than 5 [ ]
b. 5 - 8 [ ] c. 9 – 14 [ ] d. 15 and above [ ]
107
Section II: The types of energy sources attributable to different energy uses
11. What energy type do you use for cooking in your home
Energy
Sources
Strongly
Agree (4)
Agree
(3)
Disagree
(2)
Strongly Disagree
(1)
Electricity
Gas
Kerosene
Firewood
Charcoal
Sawdust
12. What is the energy type used for ironing in your home
Energy
Sources
Strongly Agree
(4)
Agree
(3)
Disagree
(2)
Strongly Disagree
(1)
Electricity
Petrol
Charcoal
13. What is the energy type used for lighting in your home
Energy
Sources
Strongly
Agree (4)
Agree
(3)
Disagree
(2)
Strongly
Disagree (1)
Electricity
Gas
Kerosene/petrol
Candle
Wood products
108
14. What is the energy type used for cooling in your home
Energy
Sources
Strongly
Agree (4) Agree (3)
Disagree
(2)
Strongly Disagree
(1)
Electricity
petrol
15. The energy type used for food preservation in my home
Energy
Sources
Strongly
Agree (4) Agree (3)
Disagree
(2)
Strongly Disagree
(1)
Electricity
Gas
Kerosene
Firewood
Charcoal
Sawdust
16. How many hours do you make use of each of the following energy sources in your
home per day
Energy sources
Less than 4hrs per
day
(1)
4-6hrs per day
(2)
8hrs per day
(3)
More than 8hrs per
day. (4)
Electricity
Gas
Kerosene
Firewood
Surdust
Charcoal
109
Section III: Factors that influence the choice of energy used by households
a. The increase in price of energy affected my choice of energy use in this
household. a. Strongly Agree [ ] b. Agree [ ]
c. Strongly disagree [ ] d. Disagree [ ]
a. We use more of traditional energy (firewood, sawdust and charcoal) during the
dry season. a. Strongly Agree [ ] b. Agree [ ]
c. Strongly disagree [ ] d. Disagree [ ]
a. The home appliances (like; oven, gas burner, refrigerators, gas lamp) I use at
home affected my type of energy use. a. Strongly Agree [ ]
b. Agree [ ] c. Strongly disagree [ ] d. Disagree [ ]
17. Lack of access to electricity affects its use in my home.
a. Strongly Agree [ ] b. Agree [ ]
c. Strongly disagree [ ] d. Disagree [ ]
18. The type of food prepared at home can determine my choice of energy used
a. Strongly Agree [ ] b. Agree [ ]
c. Strongly disagree [ ] d. Disagree [ ]
19. Fire-woods and Sawdust are collected at very low cost in my environment
a. Strongly Agree [ ] b. Agree [ ]
c. Strongly disagree [ ] d. Disagree [ ]
23. The type of energy used in my home for cooking is affected by my cultural
belief and preferences. a. Strongly Agree [ ] b. Agree [ ]
c. Strongly disagree [ ] d. Disagree [ ]
24. More than 10 percent of my income goes to energy needs in my home
a. Strongly Agree [ ] b. Agree [ ]
c. Strongly disagree [ ] d. Disagree [ ]
110
Section IV: The preferences of household on different energy sources
25. I would spend more on modern energy (Electricity, Gas and Kerosene) for
cooking if I earned higher income. a. Strongly Agree [ ]
b. Agree [ ] c. Strongly disagree [ ] d. Disagree [ ]
26. I would spend more on modern energy (Electricity, Gas and Kerosene) for other
non-cooking activities like ironing, etc. in my home if I earned higher income.
a. Strongly Agree [ ] b. Agree [ ]
c. Strongly disagree [ ] d. Disagree [ ]
27. I would use more of modern energy (Electricity, Gas and Kerosene) for cooking
if the prices were affordable or probably subsidized.
a. Strongly Agree [ ] b. Agree [ ]
c. Strongly disagree [ ] d. Disagree [ ]
28. I would use more of modern energy (Electricity, Gas and Kerosene) for other
non-cooking activities in my home if the prices were affordable or probably
subsidized. a. Strongly Agree [ ] b. Agree [ ]
c. Strongly disagree [ ] d. Disagree [ ]
29. I would use more of modern energy (Electricity, Gas and Kerosene) for cooking
in my household if it was accessible and available. a. Strongly Agree [ ]
b. Agree [ ] c. Strongly disagree [ ] d. Disagree [ ]
30. I would use more of modern energy (Electricity, Gas and Kerosene) for other
non-cooking activities in my household if it was accessible and available.
a. Strongly Agree [ ] b. Agree [ ]
c. Strongly disagree [ ] d. Disagree [ ]
111
31. I would use both modern and traditional energy in my household for cooking if
all were accessible, affordable and I earned higher income
a. Strongly Agree [ ] b. Agree [ ]
c. Strongly disagree [ ] d. Disagree [ ]
32. I would use both modern and traditional energy in my household for non-
cooking activities if they were accessible, affordable and I earned higher
income. a. Strongly Agree [ ] b. Agree [ ]
c. Strongly disagree [ ] d. Disagree [ ]
Thank you for your time.
112
APPENDIX II
RELIABILITY MEASUREMENT USING CRONBACH’S ALPHA
Scale: ALL VARIABLES
Case Processing Summary
N %
Cases Valid 20 100.0
Excluded 0 .0
Total 20 100.0
Reliability Statistics
Cronbach's Alpha
Cronbach's Alpha of Standardized
Items Number of Items
.832 .841 32
Item Statistics
Mean Std. Deviation N
q1b 1.9500 .60481 20
q2b 2.8500 .48936 20
q3b 2.6000 .50262 20
q4b 2.5000 .51299 20
q5b 3.5000 .51299 20
q6b 2.2500 .44426 20
q7b 2.6000 .68056 20
q8b 3.1000 .44721 20
q9b 2.1500 .36635 20
q10b 3.2500 .63867 20
q11b 2.2000 .52315 20
q12b 1.9500 .82558 20
q13b 1.6000 .50262 20
q14b 3.0500 .39403 20
q15b 2.0500 .39403 20
q16b 2.4500 .60481 20
q17b 1.3500 .58714 20
q18b 2.6500 .74516 20
q19b 2.5000 .68825 20
q20b 3.6500 .48936 20
q21b 2.9000 .44721 20
113
q22b 2.7000 .86450 20
q23b 2.9000 .71818 20
q24b 2.1500 .58714 20
q25b 2.2500 .55012 20
q26b 2.0500 .22361 20
q27b 2.1000 .30779 20
q28b 2.7500 .55012 20
q29b 2.5000 .60698 20
q30b 2.7500 .44426 20
q31b 2.3000 .65695 20
q32b 2.1500 .38714 20
APPENDIX II
OBJ I: relative household energy use attributable to different energy
types.(sector 1 = rural, sector 2 = urban HHs).
1. Households that use electricity for cooking in both areas;
sum electricitycook if sector ==2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------
electrici~ok | 108 2.064815 .416329 1 4
sum electricitycook if sector ==1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------
electrici~ok | 92 1.271739 .5157574 1 4
2. Households that use gas for cooking in both areas;
sum gascook if sector ==2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------
gascook | 108 1.444444 .6744099 1 4
sum gascook if sector ==1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------
114
gascook | 92 1.25 .4353854 1 4
3. households that use kerosene for cooking in both areas;
. sum kerosenecook if sector ==1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------
kerosenecook | 92 1.5 .6376468 1 4
. sum kerosenecook if sector ==2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------
kerosenecook | 108 2.583333 .9870659 1 4
4.
ouseholds that use firewood for cooking in both areas;
. sum firewoodcook if sector ==1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------
firewoodcook | 92 3.673913 .742859 1 4
. sum firewoodcook if sector ==2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------
firewoodcook | 108 1.657407 .8555704 1 4
5.
ouseholds that use charcoal for cooking in both areas;
. sum charcoalcook if sector ==1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------
charcoalcook | 92 1.978261 .9136121 1 4
115
. sum charcoalcook if sector ==2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------
charcoalcook | 108 2.518519 1.063247 1 4
6.
ouseholds that use sawdust for cooking in both areas;
. sum sawdustcook if sector ==1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------
sawdustcook | 92 1.75 .6567486 1 4
. sum sawdustcook if sector ==2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------
sawdustcook | 108 1.564815 .6735753 1 4
7.
ouseholds that use electricity for ironing in both areas;
. sum electricityiron if sector ==1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------
electricit~n | 92 1.565217 .8294623 1 4
. sum electricityiron if sector ==2
Variable | Obs Mean Std. Dev. Min Max
-------------+-------------------------------------------------
electricit~n | 108 2.675926 1.465036 0 4
8.
ouseholds that use petrol for ironing in both areas;
. sum petroliron if sector ==1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------
116
keroseneiron | 92 1.75 .9449112 1 4
. sum petroliron if sector ==2
Variable | Obs Mean Std. Dev. Min Max
-------------+-------------------------------------------------
keroseneiron | 108 2.203704 1.039044 0 4
9.
ouseholds that use charcoal for ironing in both areas;
. sum charcoaliron if sector ==1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------
charcoaliron | 92 3.369565 1.002386 1 4
. sum charcoaliron if sector ==2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------
charcoaliron | 108 1.953704 .8688187 1 4
10. households that use electricity for home entertainment in both
areas;
. sum electricityentertainment if sector==1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------
electrici~nt | 92 1.75 .9213583 1 4
. sum electricityentertainment if sector==2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------
electrici~nt | 108 2.12037 1.116717 1 4
11.
ouseholds that use petrol for home entertainment in both areas;
. sum petrolentertainment if sector ==1
117
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------
petrolen~t | 92 1.73913 1.098146 1 4
. sum petrolentertainment if sector ==2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------
petrolen~t | 108 2.657407 1.381872 1 4
12.
ouseholds that use battery for home entertainment in both areas;
. sum batteryentertainment if sector ==1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------
batteryent~t | 92 2.815217 1.212837 1 4
. sum batteryentertainment if sector ==2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------
batteryent~t | 108 1.990741 .9905664 1 4
13.
Use of electricity for lightening in both areas;
. sum electricitylight if sector ==1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
electrici~ht | 92 1.847826 1.194629 1 4
. sum electricitylight if sector ==2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
electrici~ht | 108 2.222222 1.21773 1 4
14.
se of gas for lightening in both areas;
. sum gaslight if sector ==1
118
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
gaslight | 92 1.391304 .4907165 1 2
. sum gaslight if sector ==2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
gaslight | 108 1.37963 .542021 1 4
15.
se of kerosene and petrol for lightening in both areas;
. sum kero/petrollight if sector==1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
keroseneli~t | 92 2.847826 1.390192 1 4
. sum kero/petrollight if sector==2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
keroseneli~t | 108 2.944444 1.021574 1 4
16.
se of candle for lightening in both areas;
. sum candlelight if sector ==1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
candlelight | 92 1.934783 .6428704 1 4
. sum candlelight if sector ==2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
candlelight | 108 1.425926 .5991225 1 4
119
17.
se of firewood for lightening in both areas;
. sum firewoodlight if sector ==1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
firewoodli~t | 92 1.565217 .668019 1 4
. sum firewoodlight if sector ==2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
firewoodli~t | 108 1.425926 .550339 1 4
18.
se of electricity for cooling in both areas;
. sum electricitycooling if sector==1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
electricit~g | 92 1.445652 1.189368 0 4
. sum electricitycooling if sector==2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
electricit~g | 108 2.12963 1.20041 0 4
19.
se of petrol for cooling in both areas;
. sum petrolcooling if sector==1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
keroseneco~g | 92 1.576087 1.528007 0 4
. sum petrolcooling if sector==2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
petrolcoolig | 108 2.675926 1.413081 0 4
120
20.
se of electricity for food and drink preservation in both areas;
. sum electricityfoodanddrink if sector ==1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
electrici~nk | 92 1.576087 .9747775 1 4
. sum electricityfoodanddrink if sector ==2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
electrici~nk | 108 1.861111 1.097647 1 4
21.
se of gas for food and drink preservation in both areas;
. sum gasfoodanddrink if sector ==1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
gasfoodand~k | 92 1.271739 .4472937 1 2
. sum gasfoodanddrink if sector ==2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
gasfoodand~k | 108 1.564815 .6873102 1 4
22.
se of kerosene for food and drink preservation in both areas;
. sum kerosenefoodanddrink if sector ==1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
kerosenefo~k | 92 1.445652 .9649248 1 4
. sum kerosenefoodanddrink if sector ==2
Variable | Obs Mean Std. Dev. Min Max
121
-------------+--------------------------------------------------------
kerosenefo~k | 108 2.509259 1.018478 1 4
23.
se of firewood for food and drink preservation in both areas;
. sum firewoodfoodanddrink if sector ==1
Variable | Obs Mean Std. Dev. Min Max
-------------+-------------------------------------------------------
-
firewoodfo~k | 92 2.782609 1.156217 1 4
. sum firewoodfoodanddrink if sector ==2
Variable | Obs Mean Std. Dev. Min Max
-------------+-------------------------------------------------------
-
firewoodfo~k | 108 2.203704 1.020896 1 4
24.
se of charcoal for food and drink preservation in both areas;
. sum charcoalfoodanddrink if sector ==1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
charcoalfo~k | 92 1.663043 .9524656 1 4
. sum charcoalfoodanddrink if sector ==2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
charcoalfo~k | 108 1.611111 .90516 1 4
25.
se of sawdust for food and ddrink preservation in both areas;
. sum sawdustfoodanddrink if sector ==1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
122
sawdustfoo~k | 92 1.369565 .7219843 1 4
. sum sawdustfoodanddrink if sector ==2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
sawdustfoo~k | 108 1.972222 .6030814 1 4
26. Distribution of energy types by its use per day in HHs
(Less than 4hrs per day (1), 4-6hrs per day (2), 8hrs per day (3), More
than 8hrs per day (4))
tab electricityuseperday sector
Electricit | sector
yuseperday | 1 2 | Total
-----------+----------------------+----------
0 | 52 0 | 52
1 | 26 80 | 106
2 | 7 16 | 23
3 | 5 10 | 15
4 | 2 2 | 4
-----------+----------------------+----------
Total | 92 108 | 200
. tab keroseneuseperday sector
keroseneus | sector
eperday | 1 2 | Total
-----------+----------------------+----------
0 | 20 7 | 27
1 | 42 8 | 50
2 | 22 28 | 50
3 | 8 45 | 53
4 | 0 20 | 20
-----------+----------------------+----------
123
Total | 92 108 | 200
. tab gasuseperday sector
Gasuseperd | sector
ay | 1 2 | Total
-----------+----------------------+----------
0 | 92 102 | 194
1 | 0 2 | 2
2 | 0 3 | 3
3 | 0 1 | 1
-----------+----------------------+----------
Total | 92 108 | 200
. tab firewooduseperday sector
firewoodus | sector
eperday | 1 2 | Total
-----------+----------------------+----------
0 | 10 66 | 76
1 | 7 3 | 10
2 | 10 18 | 28
3 | 45 11 | 56
4 | 20 10 | 30
-----------+----------------------+----------
Total | 92 108 | 200
. tab charcoaluseperday sector
charcoalus | sector
eperday | 1 2 | Total
-----------+----------------------+----------
0 | 21 61 | 82
1 | 41 8 | 49
2 | 12 7 | 19
3 | 1 16 | 17
124
4 | 17 16 | 33
-----------+----------------------+----------
Total | 92 108 | 200
. tab sawdustuseperday sector
Sawdustuse | sector
perday | 1 2 | Total
-----------+----------------------+----------
0 | 84 99 | 183
1 | 1 1 | 2
2 | 1 3 | 4
3 | 4 3 | 7
4 | 2 2 | 4
-----------+----------------------+----------
Total | 92 108 | 200
27. Free collection of firewood in both sectors
. tab freefirewood sector
freefirewo | sector
od | 1 2 | Total
-----------+----------------------+----------
0 | 0 35 | 35
1 | 5 30 | 35
2 | 8 15 | 23
3 | 6 20 | 26
4 | 73 8 | 81
-----------+----------------------+----------
Total | 92 108 | 200
. sum freefirewood if sector == 1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
125
freefirewood | 92 3.597826 .8651975 1 4
. sum freefirewood if sector == 2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
freefirewood | 108 1.407407 1.311609 0 4
ANOVA TEST FOR HYPOTHESES I
Hypotheses 1: there are no significant differences in the energy use of
households attributable to different energy uses.
Energy used for cooking
oneway electricitycook sector
Analysis of Variance
Source SS df MS F Prob >
F
---------------------------------------------------------------------
--
Between groups 31.247182 1 31.247182 144.71
0.0000
Within groups 42.752818 198 .215923323
---------------------------------------------------------------------
--
Total 74 199 .371859296
Bartlett's test for equal variances: chi2(1) = 4.5057 Prob>chi2 =
0.034
. oneway gascook sector
Analysis of Variance
Source SS df MS F Prob >
F
---------------------------------------------------------------------
--
Between groups 1.87833333 1 1.87833333 5.64
0.0185
Within groups 65.9166667 198 .332912458
126
---------------------------------------------------------------------
--
Total 67.795 199 .340678392
Bartlett's test for equal variances: chi2(1) = 17.7697 Prob>chi2 =
0.000
. oneway kerosenecook sector
Analysis of Variance
Source SS df MS F Prob >
F
---------------------------------------------------------------------
--
Between groups 58.305 1 58.305 81.73
0.0000
Within groups 141.25 198 .713383838
---------------------------------------------------------------------
--
Total 199.555 199 1.00278894
Bartlett's test for equal variances: chi2(1) = 17.7187 Prob>chi2 =
0.000
. oneway firewoodcook sector
Analysis of Variance
Source SS df MS F Prob >
F
---------------------------------------------------------------------
--
Between groups 202.013535 1 202.013535 311.17
0.0000
Within groups 128.541465 198 .64919932
---------------------------------------------------------------------
--
Total 330.555 199 1.6610804
Bartlett's test for equal variances: chi2(1) = 1.9316 Prob>chi2 =
0.165
. oneway charcoalcook sector
Analysis of Variance
127
Source SS df MS F Prob >
F
---------------------------------------------------------------------
--
Between groups 11.2299597 1 11.2299597 12.04
0.0006
Within groups 184.72504 198 .932954749
---------------------------------------------------------------------
--
Total 195.955 199 .984698492
Bartlett's test for equal variances: chi2(1) = 0.9438 Prob>chi2 =
0.331
. oneway sawdustcook sector
Analysis of Variance
Source SS df MS F Prob >
F
---------------------------------------------------------------------
--
Between groups 1.7037037 1 1.7037037 3.84
0.0514
Within groups 87.7962963 198 .443415638
---------------------------------------------------------------------
--
Total 89.5 199 .449748744
Bartlett's test for equal variances: chi2(1) = 0.0625 Prob>chi2 =
0.803
2. Energy typed for ironing
. oneway electricityiron sector
Analysis of Variance
Source SS df MS F Prob >
F
---------------------------------------------------------------------
--
Between groups 61.2888969 1 61.2888969 41.52
0.0000
128
Within groups 292.266103 198 1.47609143
---------------------------------------------------------------------
--
Total 353.555 199 1.77665829
Bartlett's test for equal variances: chi2(1) = 29.2593 Prob>chi2 =
0.000
. oneway keroseneiron sector
Analysis of Variance
Source SS df MS F Prob >
F
---------------------------------------------------------------------
--
Between groups 10.2264815 1 10.2264815 10.29
0.0016
Within groups 196.768519 198 .993780397
---------------------------------------------------------------------
--
Total 206.995 199 1.04017588
Bartlett's test for equal variances: chi2(1) = 0.8767 Prob>chi2 =
0.349
. oneway charcoaliron sector
Analysis of Variance
Source SS df MS F Prob >
F
---------------------------------------------------------------------
--
Between groups 99.5916989 1 99.5916989 114.51
0.0000
Within groups 172.203301 198 .869713642
---------------------------------------------------------------------
--
Total 271.795 199 1.36580402
Bartlett's test for equal variances: chi2(1) = 2.0098 Prob>chi2 =
0.156
3. Energy type is home entertainment
129
oneway electricityentertainment sector
Analysis of Variance
Source SS df MS F Prob >
F
---------------------------------------------------------------------
--
Between groups 6.81481481 1 6.81481481 6.40
0.0122
Within groups 210.685185 198 1.06406659
---------------------------------------------------------------------
--
Total 217.5 199 1.09296482
Bartlett's test for equal variances: chi2(1) = 3.5600 Prob>chi2 =
0.059
. oneway keroseneentertainment sector
Analysis of Variance
Source SS df MS F Prob >
F
---------------------------------------------------------------------
--
Between groups 41.8917955 1 41.8917955 26.41
0.0000
Within groups 314.063205 198 1.5861778
---------------------------------------------------------------------
--
Total 355.955 199 1.78871859
Bartlett's test for equal variances: chi2(1) = 5.0616 Prob>chi2 =
0.024
. oneway batteryentertainment sector
Analysis of Variance
Source SS df MS F Prob >
F
---------------------------------------------------------------------
--
Between groups 33.7705636 1 33.7705636 27.99
0.0000
130
Within groups 238.849436 198 1.20631028
---------------------------------------------------------------------
--
Total 272.62 199 1.36994975
Bartlett's test for equal variances: chi2(1) = 4.0268 Prob>chi2 =
0.045
4. Energy type for lightening
. oneway electricitylight sector
Analysis of Variance
Source SS df MS F Prob >
F
---------------------------------------------------------------------
--
Between groups 6.96376812 1 6.96376812 4.78
0.0300
Within groups 288.536232 198 1.4572537
---------------------------------------------------------------------
--
Total 295.5 199 1.48492462
Bartlett's test for equal variances: chi2(1) = 0.0359 Prob>chi2 =
0.850
. oneway gaslight sector
Analysis of Variance
Source SS df MS F Prob >
F
---------------------------------------------------------------------
--
Between groups .006771337 1 .006771337 0.03
0.8742
Within groups 53.3482287 198 .269435498
---------------------------------------------------------------------
--
Total 53.355 199 .268115578
Bartlett's test for equal variances: chi2(1) = 0.9609 Prob>chi2 =
0.327
131
. oneway kerosenelight sector
Analysis of Variance
Source SS df MS F Prob >
F
---------------------------------------------------------------------
--
Between groups .463768116 1 .463768116 0.32
0.5726
Within groups 287.536232 198 1.45220319
---------------------------------------------------------------------
--
Total 288 199 1.44723618
Bartlett's test for equal variances: chi2(1) = 9.2967 Prob>chi2 =
0.002
. oneway candlelight sector
Analysis of Variance
Source SS df MS F Prob >
F
---------------------------------------------------------------------
--
Between groups 12.8638969 1 12.8638969 33.51
0.0000
Within groups 76.0161031 198 .383919712
---------------------------------------------------------------------
--
Total 88.88 199 .446633166
Bartlett's test for equal variances: chi2(1) = 0.4875 Prob>chi2 =
0.485
. oneway firewoodlight sector
Analysis of Variance
Source SS df MS F Prob >
F
---------------------------------------------------------------------
--
Between groups .96389694 1 .96389694 2.61
0.1075
132
Within groups 73.0161031 198 .368768197
---------------------------------------------------------------------
--
Total 73.98 199 .371758794
Bartlett's test for equal variances: chi2(1) = 3.6901 Prob>chi2 =
0.055
5. Energy used for cooling
oneway electricitycooling sector
Analysis of Variance
Source SS df MS F Prob >
F
---------------------------------------------------------------------
--
Between groups 23.2415539 1 23.2415539 16.27
0.0001
Within groups 282.913446 198 1.42885579
---------------------------------------------------------------------
-
Total 306.155 199 1.53846734
Bartlett's test for equal variances: chi2(1) = 0.0084 Prob>chi2 =
0.927
. oneway kerosenecooling sector
Analysis of Variance
Source SS df MS F Prob >
F
---------------------------------------------------------------------
--
Between groups 60.0952013 1 60.0952013 27.92
0.0000
Within groups 426.124799 198 2.15214545
---------------------------------------------------------------------
--
Total 486.22 199 2.44331658
133
Bartlett's test for equal variances: chi2(1) = 0.6002 Prob>chi2 =
0.439
6. Energy typed for food and drink preservation
. oneway electricityfoodanddrink sector
Analysis of Variance
Source SS df MS F Prob >
F
---------------------------------------------------------------------
--
Between groups 4.03594203 1 4.03594203 3.71
0.0555
Within groups 215.384058 198 1.08779827
---------------------------------------------------------------------
--
Total 219.42 199 1.10261307
Bartlett's test for equal variances: chi2(1) = 1.3672 Prob>chi2 =
0.242
. oneway gasfoodanddrink sector
Analysis of Variance
Source SS df MS F Prob >
F
---------------------------------------------------------------------
--
Between groups 4.26718196 1 4.26718196 12.29
0.0006
Within groups 68.752818 198 .347236455
---------------------------------------------------------------------
--
Total 73.02 199 .366934673
Bartlett's test for equal variances: chi2(1) = 17.1480 Prob>chi2 =
0.000
. oneway kerosenefoodanddrink sector
Analysis of Variance
Source SS df MS F Prob >
F
134
---------------------------------------------------------------------
--
Between groups 56.2009984 1 56.2009984 56.86
0.0000
Within groups 195.719002 198 .988479806
---------------------------------------------------------------------
--
Total 251.92 199 1.26592965
Bartlett's test for equal variances: chi2(1) = 0.2845 Prob>chi2 =
0.594
. oneway firewoodfoodanddrink sector
Analysis of Variance
Source SS df MS F Prob >
F
---------------------------------------------------------------------
--
Between groups 16.6493076 1 16.6493076 14.14
0.0002
Within groups 233.170692 198 1.17762976
---------------------------------------------------------------------
--
Total 249.82 199 1.25537688
Bartlett's test for equal variances: chi2(1) = 1.5224 Prob>chi2 =
0.217
. oneway charcoalfoodanddrink sector
Analysis of Variance
Source SS df MS F Prob >
F
---------------------------------------------------------------------
---
Between groups .882133655 1 .882133655 1.08
0.3005
Within groups 162.072866 198 .81854983
---------------------------------------------------------------------
--
Total 162.955 199 .818869347
135
Bartlett's test for equal variances: chi2(1) = 0.9767 Prob>chi2 =
0.323
. oneway sawdustfoodanddrink sector
Analysis of Variance
Source SS df MS F Prob >
F
---------------------------------------------------------------------
--
Between groups 18.0435507 1 18.0435507 41.37
0.0000
Within groups 86.3514493 198 .436118431
---------------------------------------------------------------------
--
Total 104.395 199 .52459799
Bartlett's test for equal variances: chi2(1) = 3.1825 Prob>chi2 =
0.074
7. Energy used per day by HHs
oneway electricityuseperday sector
Analysis of Variance
Source SS df MS F Prob >
F
---------------------------------------------------------------------
--
Between groups 24.6296377 1 24.6296377 33.51
0.0000
Within groups 145.525362 198 .734976577
---------------------------------------------------------------------
--
Total 170.155 199 .855050251
Bartlett's test for equal variances: chi2(1) = 8.3289 Prob>chi2 =
0.004
. oneway gasuseperday sector
Analysis of Variance
Source SS df MS F Prob >
F
136
---------------------------------------------------------------------
--
Between groups .51537037 1 .51537037 4.66
0.0320
Within groups 21.8796296 198 .11050318
---------------------------------------------------------------------
--
Total 22.395 199 .112537688
. oneway keroseneuseperday sector
Analysis of Variance
Source SS df MS F Prob >
F
---------------------------------------------------------------------
--
Between groups 95.6667391 1 95.6667391 97.27
0.0000
Within groups 194.728261 198 .983476065
---------------------------------------------------------------------
--
Total 290.395 199 1.45927136
Bartlett's test for equal variances: chi2(1) = 3.9434 Prob>chi2 =
0.047
. oneway firewooduseperday sector
Analysis of Variance
Source SS df MS F Prob >
F
---------------------------------------------------------------------
--
Between groups 126.133366 1 126.133366 70.69
0.0000
Within groups 353.286634 198 1.78427593
---------------------------------------------------------------------
--
Total 479.42 199 2.40914573
Bartlett's test for equal variances: chi2(1) = 2.3725 Prob>chi2 =
0.123
137
oneway charcoaluseperday sector
Analysis of Variance
Source SS df MS F Prob >
F
---------------------------------------------------------------------
--
Between groups 26.8166264 1 26.8166264 16.44
0.0001
Within groups 323.058374 198 1.63160795
---------------------------------------------------------------------
--
Total 349.875 199 1.75816583
Bartlett's test for equal variances: chi2(1) = 1.5849 Prob>chi2 =
0.208
. oneway sawdustuseperday sector
Analysis of Variance
Source SS df MS F Prob >
F
---------------------------------------------------------------------
--
Between groups .038333333 1 .038333333 0.06
0.8121
Within groups 133.916667 198 .676346801
---------------------------------------------------------------------
--
Total 133.955 199 .673140704
Bartlett's test for equal variances: chi2(1) = 0.7151 Prob>chi2 =
0.398
APPENDIX IV
OBJECTIVE 11: Determinants OF HH’s Energy use on different energy sources,
1. Determinant for electricity used for cooking
xi:reg electricitycook priceenergyuse weatherenergy homeappliances
accessibilty typeoffood i.averageincome i.age i.gender i.education sector
i.numberofpersonsperhhs i.averageincome belief
138
_Iaveragein_1-5 (naturally coded; _Iaveragein_1 omitted)
i.age _Iage_1-3 (naturally coded; _Iage_1 omitted)
i.gender _Igender_1-2 (naturally coded; _Igender_1 omitted)
i.education _Ieducation_1-4 (naturally coded; _Ieducation_1 omitted)
i.numberofper~s _Inumberofp_1-4 (naturally coded; _Inumberofp_1 omitted)
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 23, 176) = 24.33
Model | 56.296333 23 2.44766665 Prob > F = 0.0000
Residual | 17.703667 176 .100589017 R-squared = 0.7608
-------------+------------------------------ Adj R-squared = 0.7295
Total | 74 199 .371859296 Root MSE = .31716
---------------------------------------------------------------------------
--
electrici~ok | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+-------------------------------------------------------------
--
priceenerg~e | -.0855919 .0831616 -1.03 0.305 -.2497142 .0785304
weatherene~y | -.1644067 .0912232 -1.80 0.073 -.3444389 .0156255
homeapplia~s | .266013 .1148714 2.32 0.022 .0393103 .4927157
accessibilty | .273177 .0991838 2.78 0.037 -.1184249 .2730603
typeoffood | -.211668 .0891366 -3.69 0.003 -.237081 .1147474
_Iaveragei~2 | .0132029 .1414382 0.09 0.926 -.2659302 .292336
_Iaveragei~3 | .0109597 .2190652 0.05 0.960 -.4213731 .4432924
_Iaveragei~4 | .2759979 .2712213 1.02 0.310 -.2592666 .8112625
_Iaveragei~5 | .3647582 .3486149 1.05 0.297 -.3232453 1.052762
_Iage_2 | -.2952908 .2211343 -1.34 0.183 -.7317069 .1411252
_Iage_3 | -.4573361 .2456488 -1.86 0.064 -.9421324 .0274601
_Igender_2 | .123036 .1789568 0.69 0.493 -.2301413 .4762134
_Ieducatio~2 | -.4674465 .1915148 -2.44 0.016 -.8454074 -.0894855
_Ieducatio~3 | -.4400881 .2451002 -1.80 0.074 -.9238017 .0436256
_Ieducatio~4 | -.5561507 .2931691 -1.80 0.079 -1.13473 .0224286
_Ieducatio~5 | .026706 .4131444 -1.49 0.064 -1.842061 -.2113513
sector | .2934047 .135143 2.17 0.031 .0266954 .560114
139
_Inumberof~2 | -.3247786 .1206456 -2.69 0.008 -.5628769 -.0866804
_Inumberof~3 | -.4743192 .1959044 -1.81 0.078 -.7609432 .0123049
_Inumberof~4 | -.3399088 .2706654 -1.26 0.211 -.8740763 .1942587
beliefs | -.0209118 .1464868 -0.14 0.887 -.3098908 .2680672
_cons | 2.045381 .5368128 3.81 0.000 .9859622 3.104799
------------------------------------------------------------------------------
. ovtest
Ramsey RESET test using powers of the fitted values of
electricitycook
Ho: model has no omitted variables
F(3, 186) = 8.89
Prob > F = 0.0000
The hypotheses will be rejected because the prob > F is less than
0.05.
2. Determinant for electricity used for ironing
xi:reg electricityiron priceenergyuse weatherenergy homeappliances accessibilty
i.numberofpersonsperhhs i.averageincome i.age i.gender i.education sector
i.numberofper~s _Inumberofp_1-4 (naturally coded; _Inumberofp_1 omitted)
i.averageincome _Iaveragein_1-5 (naturally coded; _Iaveragein_1 omitted)
i.age _Iage_1-3 (naturally coded; _Iage_1 omitted)
i.gender _Igender_1-2 (naturally coded; _Igender_1 omitted)
i.education _Ieducation_1-5 (naturally coded; _Ieducation_1 omitted)
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 22, 177) = 101.56
Model | 327.603042 22 14.8910474 Prob > F = 0.0000
Residual | 25.9519582 177 .146621233 R-squared = 0.9266
-------------+------------------------------ Adj R-squared = 0.9175
Total | 353.555 199 1.77665829 Root MSE = .38291
------------------------------------------------------------------------------
electricit~n | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
priceenerg~e | -.076658 .1002592 -0.76 0.446 -.2745154 .1211993
140
weatherene~y | .4206394 .1083853 3.88 0.000 .2067457 .6345332
homeapplia~s | .0648586 .1363501 0.48 0.635 -.2042224 .3339396
accessibilty | .4249492 .1197377 3.46 0.000 -.061348 .4112464
_Inumberof~2 | .2839335 .1155427 2.46 0.015 .055915 .5119521
_Inumberof~3 | -.272478 .2243979 -1.21 0.226 -.7153177 .1703617
_Inumberof~4 | -.1539453 .315731 -0.49 0.626 -.7770269 .4691362
_Iaveragei~2 | 1.042952 .1702802 6.12 0.000 .7069112 1.378993
_Iaveragei~3 | .6498312 .263715 2.89 0.040 -.0221182 1.018742
_Iaveragei~4 | .542016 .3273506 2.00 0.067 .0081892 1.300214
_Iaveragei~5 | -.0927264 .4207961 -0.22 0.826 -.9231494 .7376967
_Iage_2 | -.8079354 .2869657 -2.18 0.008 -.5747807 .4789099
_Iage_3 | -.2647869 .2257897 -1.72 0.227 -.8388515 -.1210585
_Igender_2 | -1.016866 .2146165 -4.74 0.000 -1.440403 -.5933298
_Ieducatio~2 | -.2757933 .2312189 -1.19 0.235 -.7320939 .1805072
_Ieducatio~3 | -.2746619 .2955672 -0.93 0.354 -.8579511 .3086274
_Ieducatio~4 | .4285486 .3533777 1.21 0.227 -.2688272 1.125924
_Ieducatio~5 | .084634 .4987014 1.17 0.131 .1004682 2.0688
sector | .6989423 .1631582 4.28 0.000 .3769564 1.020928
_cons | .4416944 .608982 0.73 0.469 -.7601055 1.643494
. ovtest
Ramsey RESET test using powers of the fitted values of electricityiron
Ho: model has no omitted variables
F(3, 183) = 103.8
Prob > F = 0.0000
3. Determinant for electricity used for lightening
xi:reg electricitylight priceenergyuse weatherenergy homeappliances accessibilty
i.averageincome i.age i.gender i.education sector
i.averageincome _Iaveragein_1-5 (naturally coded; _Iaveragein_1 omitted)
i.age _Iage_1-3 (naturally coded; _Iage_1 omitted)
i.gender _Igender_1-2 (naturally coded; _Igender_1 omitted)
i.education _Ieducation_1-5 (naturally coded; _Ieducation_1 omitted)
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 19, 180) = 81.08
141
Model | 264.586351 19 13.9255974 Prob > F = 0.0000
Residual | 30.9136494 180 .171742496 R-squared = 0.8954
-------------+------------------------------ Adj R-squared = 0.8843
Total | 295.5 199 1.48492462 Root MSE = .41442
------------------------------------------------------------------------------
electrici~ht | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
priceenerg~e | .1726395 .1028515 1.68 0.095 -.0303103 .3755892
weatherene~y | .0383886 .1158421 0.33 0.741 -.1901945 .2669717
homeapplia~s | .0365685 .13955 5.28 0.070 .4612041 1.011933
accessibilty | .7751966 .1230736 3.02 0.000 -.1176561 .3680492
_Iaveragei~2 | -.0254073 .1790033 -0.14 0.887 -.3786221 .3278075
_Iaveragei~3 | .3481936 .276209 1.26 0.209 -.1968306 .8932178
_Iaveragei~4 | .9175693 .3384526 2.71 0.007 .2497242 1.585414
_Iaveragei~5 | .9717211 .4388301 2.21 0.028 .1058081 1.837634
_Iage_2 | -.0594578 .2860888 -0.21 0.836 -.623977 .5050615
_Iage_3 | -.6188092 .31353 -1.97 0.050 -1.237476 -.0001422
_Igender_2 | -.7719886 .2197838 -3.51 0.001 -1.205673 -.3383045
_Ieducatio~2 | .0473155 .2477837 0.19 0.849 -.4416188 .5362499
_Ieducatio~3 | .0814446 .3173938 0.26 0.798 -.5448466 .7077358
_Ieducatio~4 | .3749355 .378437 0.99 0.323 -.3718081 1.121679
_Ieducatio~5 | -.0611104 .5308326 -0.12 0.908 -1.108566 .9863448
sector | .0630874 .1703035 0.37 0.711 -.2729607 .3991354
_cons | -.4015753 .6368196 -0.63 0.529 -1.658167 .8550168
. ovtest
Ramsey RESET test using powers of the fitted values of electricitylight
Ho: model has no omitted variables
F(3, 183) = 31.21
Prob > F = 0.0000
4. Electricity for cooling
. xi:reg electricitycool priceenergyuse homeappliances accessibilty
i.averageincome i.age i.sector i.education i.occupation i.numberofpersonsperhhs
142
i.averageincome _Iaveragein_1-5 (naturally coded; _Iaveragein_1 omitted)
i.age _Iage_1-3 (naturally coded; _Iage_1 omitted)
i.sector _Isector_1-2 (naturally coded; _Isector_1 omitted)
i.education _Ieducation_1-5 (naturally coded; _Ieducation_1 omitted)
i.occupation _Ioccupatio_1-5 (naturally coded; _Ioccupatio_1 omitted)
i.numberofper~s _Inumberofp_1-4 (naturally coded; _Inumberofp_1 omitted)
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 24, 175) = 147.87
Model | 291.767446 24 12.1569769 Prob > F = 0.0000
Residual | 14.3875536 175 .082214592 R-squared = 0.9530
-------------+------------------------------ Adj R-squared = 0.9466
Total | 306.155 199 1.53846734 Root MSE = .28673
------------------------------------------------------------------------------
electricit~g | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
priceenerg~e | .263382 .070932 3.71 0.000 .1233897 .4033744
homeapplia~s | -.1349676 .0836679 -1.61 0.109 -.3000956 .0301604
accessibilty | -.1903249 .0867887 -2.19 0.030 -.361612 -.0190377
_Iaveragei~2 | -.5042269 .1105751 -4.56 0.000 -.7224592 -.2859945
_Iaveragei~3 | -.0011729 .180881 -0.01 0.995 -.3581619 .3558162
_Iaveragei~4 | -.0362735 .2498601 -0.15 0.885 -.5294005 .4568535
_Iaveragei~5 | .174227 .3221776 0.54 0.589 -.4616267 .8100807
_Iage_2 | -.7982895 .2845692 -2.62 0.008 -.0625576 .2359786
_Iage_3 | -.6399294 .2116982 -3.02 0.003 -1.05774 -.2221191
_Isector_2 | .5371453 .1269189 4.23 0.000 .2866566 .787634
_Ieducatio~2 | .002859 .1750494 0.02 0.987 -.3426208 .3483388
_Ieducatio~3 | -.1511975 .2307423 -0.66 0.513 -.6065935 .3041984
_Ieducatio~4 | -.3788274 .2950498 -1.28 0.201 -.9611414 .2034866
_Ieducatio~5 | -.164358 .3997324 -0.41 0.681 -.9532749 .6245588
_Ioccupati~2 | -.8065787 .1282198 -6.29 0.000 -1.059635 -.5535226
_Ioccupati~3 | -.3402717 .1802535 -1.89 0.061 -.6960222 .0154789
_Ioccupati~4 | -.7934744 .2270932 -3.49 0.001 -1.241668 -.3452803
143
_Ioccupati~5 | -1.818133 .3047834 -5.97 0.000 -2.419657 -1.216609
_Inumberof~2 | -1.121155 .0939652 -11.93 0.000 -1.306606 -.9357044
_Inumberof~3 | -1.353673 .1763878 -7.67 0.000 -1.701794 -1.005552
_Inumberof~4 | -1.360125 .2409543 -5.64 0.000 -1.835675 -.8845744
_cons | 3.844783 .4517293 8.51 0.000 2.953244 4.736321
. ovtest
Ramsey RESET test using powers of the fitted values of electricitycooling
Ho: model has no omitted variables
F(3, 183) = 15.79
Prob > F = 0.0000
5. Type of energy for electricity on food preservation
. xi: reg electricityfood i.averageincome priceenergyuse weatherenergy
homeappliances accessibilty typeoffood freefirewood beliefs sector
i.averageincome _Iaveragein_1-5 (naturally coded; _Iaveragein_1 omitted)
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 12, 187) = 61.70
Model | 175.178998 12 14.5982498 Prob > F = 0.0000
Residual | 44.2410019 187 .236582898 R-squared = 0.7984
-------------+------------------------------ Adj R-squared = 0.7854
Total | 219.42 199 1.10261307 Root MSE = .4864
------------------------------------------------------------------------------
electrici~nk | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_Iaveragei~2 | -.5910091 .122653 -4.82 0.000 -.8329705 -.3490477
_Iaveragei~3 | -1.674732 .2774937 -6.04 0.000 -2.222153 -1.127312
_Iaveragei~4 | -1.219624 .3556091 -3.43 0.001 -1.921145 -.5181033
_Iaveragei~5 | -.9121369 .4047295 -2.25 0.025 -1.710559 -.1137144
priceenerg~e | -.3126714 .1239894 -2.52 0.013 -.5572691 -.0680738
weatherene~y | -.5682576 .1274276 -4.46 0.000 -.8196379 -.3168773
homeapplia~s | .3764556 .1702482 2.21 0.028 .0406017 .7123094
accessibilty | -.1074513 .1180049 -0.91 0.364 -.3402433 .1253406
typeoffood | .8164316 .1046671 7.80 0.000 .6099515 1.022912
144
freefirewood | .276314 .1154918 2.39 0.018 .0484798 .5041481
beliefs | -.0209118 .1464868 -0.14 0.887 -.3098908 .2680672
sector | .5635311 .1586293 3.55 0.000 .2505981 .8764641
_cons | .7127865 .534231 1.33 0.184 -.3411076 1.766681
. ovtest
Ramsey RESET test using powers of the fitted values of
electricityfoodanddrink
Ho: model has no omitted variables
F(3, 187) = 81.85
Prob > F = 0.0000
6. Energy for electricity on home entertainment
. xi:reg electricityentertainment priceenergyuse i.ethnicity homeappliances
accessibilty i.averageincome weather i.age i.education i.occupation sector
i.ethnicity _Iethnicity_1-4 (naturally coded; _Iethnicity_1 omitted)
i.averageincome _Iaveragein_1-5 (naturally coded; _Iaveragein_1 omitted)
i.age _Iage_1-3 (naturally coded; _Iage_1 omitted)
i.education _Ieducation_1-5 (naturally coded; _Ieducation_1 omitted)
i.occupation _Ioccupatio_1-5 (naturally coded; _Ioccupatio_1 omitted)
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 24, 175) = 53.37
Model | 191.357678 24 7.97323658 Prob > F = 0.0000
Residual | 26.142322 175 .149384697 R-squared = 0.8798
-------------+------------------------------ Adj R-squared = 0.8633
Total | 217.5 199 1.09296482 Root MSE = .3865
------------------------------------------------------------------------------
electrici~nt | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
priceenerg~e | .2741927 .0897718 3.05 0.003 .0970179 .4513675
_Iethnicit~2 | .3841523 .3485676 1.10 0.272 -.3037851 1.07209
_Iethnicit~3 | -.0476716 .4525308 -0.11 0.916 -.940792 .8454488
_Iethnicit~4 | .0638948 .5713584 0.11 0.911 -1.063745 1.191535
homeapplia~s | .3193238 .1057897 3.02 0.003 .110536 .5281117
145
accessibilty | -.398964 .1145921 -2.74 0.024 -.4251244 .0271965
_Iaveragei~2 | -.3383678 .1466677 -1.72 0.000 -1.127833 -.5489025
_Iaveragei~3 | -.4473333 .2431846 -1.84 0.068 -.9272854 .0326188
_Iaveragei~4 | -.246779 .3405589 -0.72 0.470 -.9189102 .4253522
_Iaveragei~5 | -.4235979 .449102 -0.94 0.347 -1.309951 .4627554
_Iweather | -.5286576 .1708097 -4.46 0.006 -.809811 -.1355868
_Iage_2 | -.7322933 .2843384 -2.58 0.011 -1.293467 -.1711195
_Iage_3 | -.478861 .2449964 -1.95 0.052 -.9623889 .0046669
_Ieducatio~2 | -.0599808 .2362166 -0.25 0.800 -.5261808 .4062191
_Ieducatio~3 | -.3281741 .3056482 -1.07 0.284 -.9314053 .275057
_Ieducatio~4 | -.4525295 .3926801 -1.15 0.251 -1.227528 .3224688
_Ieducatio~5 | -.7872287 .5558433 -1.42 0.158 -1.884248 .3097905
_Ioccupati~2 | -.4726989 .1708097 -2.77 0.006 -.809811 -.1355868
_Ioccupati~3 | -.0994945 .2350076 -1.83 0.064 -1.363308 -.4356806
_Ioccupati~4 | -.8206808 .3978773 -2.06 0.041 -1.605936 -.0354253
_Ioccupati~5 | -.6688427 .5338421 -1.25 0.212 -1.72244 .3847548
sector | .4533589 .1717092 2.64 0.009 .1144714 .7922463
_cons | 1.628412 .6649874 2.45 0.015 .3159845 2.940839
. ovtest
Ramsey RESET test using powers of the fitted values of
electricityentertainment
Ho: model has no omitted variables
F(3, 180) = 7.43
Prob > F = 0.0001
ENERGY USED OF GAS
7. Energy use of Gas in cooking
. xi: reg gascook i.gender i.averageincome priceenergyuse weatherenergy
homeappliances accessibilty sector i.numberofpersonsperhhs typeoffood belief
i.gender _Igender_1-2 (naturally coded; _Igender_1 omitted)
i.averageincome _Iaveragein_1-5 (naturally coded; _Iaveragein_1 omitted)
i.numberofper~s _Inumberofp_1-4 (naturally coded; _Inumberofp_1 omitted)
Source | SS df MS Number of obs = 200
146
-------------+------------------------------ F( 14, 185) = 40.93
Model | 51.2506632 14 3.66076166 Prob > F = 0.0000
Residual | 16.5443368 185 .089428848 R-squared = 0.7560
-------------+------------------------------ Adj R-squared = 0.7375
Total | 67.795 199 .340678392 Root MSE = .29905
------------------------------------------------------------------------------
gascook | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_Igender_2 | -.2634585 .1025482 -2.57 0.011 -.4657727 -.0611444
_Iaveragei~2 | .0103429 .0897222 0.12 0.908 -.1666673 .1873532
_Iaveragei~3 | .1610546 .1813151 0.89 0.376 -.1966564 .5187657
_Iaveragei~4 | .1238419 .2202414 0.56 0.575 -.3106657 .5583495
_Iaveragei~5 | -.255207 .2289445 2.11 0.026 -.196476 .7068795
priceenerg~e | .1890956 .0726834 2.60 0.010 .0457006 .3324906
weatherene~y | -.0637708 .0817848 -0.78 0.437 -.2251216 .0975801
homeapplia~s | .4256685 .1047343 4.06 0.000 .2190414 .6322956
accessibilty | -.1831947 .0737904 -1.96 0.057 -.2887736 .0023842
sector | .2490414 .0869209 2.87 0.005 .0775578 .4205249
_Inumberof~2 | -.4877031 .1095689 -4.45 0.000 -.7038683 -.2715378
_Inumberof~3 | -.1054355 .1694038 -0.62 0.534 -.4396471 .2287761
_Inumberof~4 | -.2522748 .2203064 -1.15 0.254 -.6869107 .1823612
typeoffood | -.0589997 .0805099 -0.73 0.465 -.2178353 .0998358
_Ibelief | .1238419 .2202414 0.56 0.575 -.3106657 .5583495
_cons | .4721154 .3452532 1.37 0.173 -.2090241 1.153255
. ovtest
Ramsey RESET test using powers of the fitted values of gascook
Ho: model has no omitted variables
F(3, 188) = 23.94
Prob > F = 0.0000
1. Energy use of gas for lighting
. xi: reg gaslight i.averageincome i.age i.occupation accessibilty i.education
i.morethan10incomeonenergy priceenergyuse sector i.gender
i.averageincome _Iaveragein_1-5 (naturally coded; _Iaveragein_1 omitted)
147
i.age _Iage_1-3 (naturally coded; _Iage_1 omitted)
i.occupation _Ioccupatio_1-5 (naturally coded; _Ioccupatio_1 omitted)
i.education _Ieducation_1-5 (naturally coded; _Ieducation_1 omitted)
i.morethan10i~y _Imorethan1_1-4 (naturally coded; _Imorethan1_1 omitted)
i.gender _Igender_1-2 (naturally coded; _Igender_1 omitted)
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 24, 175) = 25.72
Model | 41.5697515 24 1.73207298 Prob > F = 0.0000
Residual | 11.7852485 175 .067344277 R-squared = 0.7791
-------------+------------------------------ Adj R-squared = 0.7488
Total | 53.355 199 .268115578 Root MSE = .25951
------------------------------------------------------------------------------
gaslight | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_Iaveragei~2 | -.0504401 .1368225 -0.37 0.713 -.3204747 .2195945
_Iaveragei~3 | .1089058 .1774938 0.61 0.540 -.2413981 .4592098
_Iaveragei~4 | .552974 .228577 2.42 0.017 .1018515 1.004096
_Iaveragei~5 | .208006 .3033263 0.69 0.494 -.3906425 .8066545
_Iage_2 | -.1479872 .1922261 -0.77 0.442 -.5273671 .2313927
_Iage_3 | -.2978304 .2144139 -1.39 0.167 -.7210003 .1253396
_Ioccupati~2 | .34211 .1455047 2.35 0.020 .0549401 .6292798
_Ioccupati~3 | .2538131 .1623465 1.56 0.120 -.0665961 .5742222
_Ioccupati~4 | .3494924 .2003042 1.74 0.083 -.0458304 .7448153
_Ioccupati~5 | .3308243 .2587926 1.28 0.203 -.179932 .8415806
accessibilty | .0543398 .0946613 0.57 0.567 -.132485 .2411646
_Ieducatio~2 | -.8386053 .1917555 -4.05 0.001 -.3278479 .3106373
_Ieducatio~3 | -.2399762 .2176629 -1.10 0.272 -.6695584 .189606
_Ieducatio~4 | -.1007057 .2676213 -0.38 0.707 -.6288865 .4274751
_Ieducatio~5 | -.1372582 .3630541 -0.38 0.706 -.8537862 .5792699
_Imorethan~2 | -.4422034 .1579968 -2.80 0.006 -.7540278 -.130379
_Imorethan~3 | -.8372045 .1923927 -4.35 0.000 -1.216913 -.4574958
_Imorethan~4 | -.4176449 .2643858 -1.58 0.116 -.93944 .1041503
148
priceenerg~e | .3972866 .057395 6.92 0.000 .2840111 .5105622
sector | .1551929 .1228329 1.26 0.208 -.0872316 .3976175
_Igender_2 | -.2143955 .1562823 -1.37 0.172 -.5228362 .0940452
_cons | .4645514 .4511012 1.03 0.305 -.4257475 1.35485
. ovtest
Ramsey RESET test using powers of the fitted values of gaslight
Ho: model has no omitted variables
F(3, 180) = 47.56
Prob > F = 0.0000
2. energy use of gas on food preservation
. xi: reg gasfood i.averageincome priceenergyuse homeappliances accessibilty
sector i.education i.ethnicity i.numberofpersonsperhhs typeoffood belief
i.averageincome _Iaveragein_1-5 (naturally coded; _Iaveragein_1 omitted)
i.education _Ieducation_1-5 (naturally coded; _Ieducation_1 omitted)
i.ethnicity _Iethnicity_1-4 (naturally coded; _Iethnicity_1 omitted)
i.numberofper~s _Inumberofp_1-4 (naturally coded; _Inumberofp_1 omitted)
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 20, 179) = 31.98
Model | 57.0540317 20 2.85270158 Prob > F = 0.0000
Residual | 15.9659683 179 .089195354 R-squared = 0.7813
-------------+------------------------------ Adj R-squared = 0.7569
Total | 73.02 199 .366934673 Root MSE = .29866
------------------------------------------------------------------------------
gasfoodand~k | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_Iaveragei~2 | .0064881 .0871768 0.07 0.941 -.1655383 .1785145
_Iaveragei~3 | -.6027434 .1701697 -3.54 0.001 -.9385401 -.2669467
_Iaveragei~4 | -.8556179 .2563711 -3.34 0.001 -1.361516 -.3497193
_Iaveragei~5 | -1.037933 .3307228 -3.14 0.002 -1.69055 -.3853155
priceenerg~e | -.1517416 .0697548 -2.18 0.031 -.2893891 -.0140941
homeapplia~s | .2985113 .0822243 3.63 0.000 .1362577 .4607649
accessibilty | .1731596 .0771081 2.25 0.026 .0210018 .3253174
sector | .2761796 .0904546 3.05 0.003 .0976851 .4546741
149
_Ieducatio~2 | -.4486227 .1019785 -4.40 0.000 -.6498574 -.2473879
_Ieducatio~3 | -.2460609 .1766417 -1.39 0.165 -.594629 .1025071
_Ieducatio~4 | .3042781 .2312788 1.32 0.190 -.1521055 .7606618
_Ieducatio~5 | .4819298 .3328428 1.45 0.149 -.1748707 1.13873
_Iethnicit~2 | .3240251 .1703079 1.90 0.059 -.0120444 .6600946
_Iethnicit~3 | .3003116 .2934386 1.02 0.307 -.2787324 .8793556
_Iethnicit~4 | .122861 .3354805 0.37 0.715 -.5391445 .7848666
_Inumberof~2 | -.1918823 .109509 -1.75 0.081 -.4079771 .0242125
_Inumberof~3 | -.201648 .1750427 -1.15 0.251 -.5470607 .1437647
_Inumberof~4 | -.1258585 .2964559 -0.42 0.672 -.7108564 .4591395
typeoffood | -.0227259 .0811 -0.28 0.780 -.1827609 .1373092
_Ibelief | .122861 .3354805 0.37 0.715 -.5391445 .7848666
_cons | .827872 .418482 1.98 0.049 .0020792 1.653665
. ovtest
Ramsey RESET test using powers of the fitted values of gasfoodpreservation
Ho: model has no omitted variables
F(3, 183) = 10.91
Prob > F = 0.0000
Energy use of Kerosene and Petrol
1. energy use on kerosene for cooking
. xi: reg kerosenecook i.numberofpersonsperhhs i.age i.occupation i.averageincome
i.education priceenergyuse beliefs sector typeoffood accessibilty
i.numberofper~s _Inumberofp_1-4 (naturally coded; _Inumberofp_1 omitted)
i.age _Iage_1-3 (naturally coded; _Iage_1 omitted)
i.occupation _Ioccupatio_1-5 (naturally coded; _Ioccupatio_1 omitted)
i.averageincome _Iaveragein_1-5 (naturally coded; _Iaveragein_1 omitted)
i.education _Ieducation_1-5 (naturally coded; _Ieducation_1 omitted)
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 25, 174) = 128.05
Model | 189.267276 25 7.57069104 Prob > F = 0.0000
Residual | 10.287724 174 .05912485 R-squared = 0.9484
-------------+------------------------------ Adj R-squared = 0.9410
Total | 199.555 199 1.00278894 Root MSE = .24316
150
------------------------------------------------------------------------------
kerosenecook | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_Inumberof~2 | -.3274762 .206617 -2.14 0.004 -.5658883 -.1290642
_Inumberof~3 | -.3419255 .116169 -3.76 0.002 -.439826 .1959751
_Inumberof~4 | -.1246406 .1138666 -1.39 0.069 -.8467474 -.0025339
_Iage_2 | .7045122 .1523666 4.62 0.000 .4037876 1.005237
_Iage_3 | .8828267 .1774765 4.97 0.000 .5325428 1.233111
_Ioccupati~2 | -.3865028 .1004953 -1.77 0.078 -.5887968 -.1842088
_Ioccupati~3 | -.1194145 .1546211 -0.77 0.441 -.4245889 .1857599
_Ioccupati~4 | -.1616824 .1853761 -0.87 0.384 -.5275577 .2041928
_Ioccupati~5 | -.3855463 .2527179 -1.53 0.129 -.8843335 .1132409
_Iaveragei~2 | .1816522 .0953604 1.90 0.058 -.0065598 .3698642
_Iaveragei~3 | .5913439 .1540711 3.84 0.000 .2872551 .8954327
_Iaveragei~4 | 1.571296 .2267456 6.93 0.000 1.12377 2.018822
_Iaveragei~5 | 1.039566 .2881912 3.61 0.000 .4707656 1.608367
_Ieducatio~2 | -.9805083 .1473407 -6.65 0.000 -1.271313 -.6897032
_Ieducatio~3 | -1.766323 .1903638 -9.28 0.000 -2.142042 -1.390603
_Ieducatio~4 | -1.493211 .260177 -5.74 0.000 -2.006721 -.9797021
_Ieducatio~5 | -1.128927 .3521039 -3.21 0.002 -1.823871 -.4339824
priceenerg~e | .1497076 .066452 2.25 0.026 .0185519 .2808634
beliefs | .4165987 .0797426 5.22 0.000 .2592114 .5739859
sector | 1.109322 .1048936 10.58 0.000 .9022946 1.31635
typeoffood | .2939362 .0682498 4.31 0.000 .1592321 .4286402
accessibilty | -.2199593 .0692099 -3.29 0.003 -.1565584 .1166397
_cons | -1.489946 .4858386 -3.07 0.003 -2.448842 -.531051
. ovtest
Ramsey RESET test using powers of the fitted values of kerosenecook
Ho: model has no omitted variables
F(3, 179) = 49.31
Prob > F = 0.0000
2. energy use of petrol on cooling
151
. xi:reg petrolcool priceenergyuse homeappliances accessibilty i.averageincome
i.age sector i.education i.numberofpersonsperhhs i.occupation
i.averageincome _Iaveragein_1-5 (naturally coded; _Iaveragein_1 omitted)
i.age _Iage_1-3 (naturally coded; _Iage_1 omitted)
i.education _Ieducation_1-5 (naturally coded; _Ieducation_1 omitted)
i.numberofper~s _Inumberofp_1-4 (naturally coded; _Inumberofp_1 omitted)
i.occupation _Ioccupatio_1-5 (naturally coded; _Ioccupatio_1 omitted)
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 24, 175) = 244.12
Model | 472.118001 24 19.6715834 Prob > F = 0.0000
Residual | 14.1019993 175 .080582853 R-squared = 0.9710
-------------+------------------------------ Adj R-squared = 0.9670
Total | 486.22 199 2.44331658 Root MSE = .28387
------------------------------------------------------------------------------
petrolcooling | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
priceenerg~e | .2334851 .1702246 2.34 0.008 -.0051111 .2720812
homeapplia~s | .3380968 .0828335 4.08 0.000 .1746157 .501578
accessibilty | .2802207 .0859231 3.26 0.001 .1106418 .4497996
_Iaveragei~2 | .1355834 .1094723 1.24 0.217 -.0804724 .3516392
_Iaveragei~3 | .1846802 .179077 1.03 0.304 -.1687484 .5381089
_Iaveragei~4 | .2721936 .2473681 1.10 0.273 -.2160153 .7604024
_Iaveragei~5 | -.3294004 .3789644 -3.09 0.004 -.6589124 .6001117
_Iage_2 | -.4674476 .2095869 -2.23 0.027 -.8810908 -.0538043
_Iage_3 | -.0231967 .1827284 -0.13 0.899 -.3838318 .3374384
sector | .0240833 .1256531 0.19 0.848 -.2239072 .2720737
_Ieducatio~2 | .0180624 .1733036 0.10 0.917 -.3239717 .3600966
_Ieducatio~3 | -.1964245 .2284411 -0.86 0.391 -.6472786 .2544297
_Ieducatio~4 | .1557993 .2921072 0.53 0.594 -.4207071 .7323057
_Ieducatio~5 | -.0023302 .3957457 -0.01 0.995 -.7833789 .7787185
_Inumberof~2 | -.2792006 .0930281 -3.00 0.003 -.462802 -.0955992
_Inumberof~3 | -.8212343 .1746286 -4.70 0.000 -1.165883 -.4765851
152
_Inumberof~4 | -1.308778 .2385512 -5.49 0.000 -1.779586 -.8379709
_Ioccupati~2 | -.6410991 .226941 -2.11 0.021 -.3916314 .1094333
_Ioccupati~3 | -.0688696 .1284558 -1.87 0.143 -1.221072 -.5166671
_Ioccupati~4 | -.0135383 .2248283 -0.06 0.952 -.4572623 .4301857
_Ioccupati~5 | -.0339523 .3017437 -0.11 0.911 -.6294774 .5615728
_cons | .9506438 .5067046 1.88 0.062 -.0493947 1.950682
. ovtest
Ramsey RESET test using powers of the fitted values of petrolcooling
Ho: model has no omitted variables
F(3, 183) = 27.33
Prob > F = 0.0000
3. energy use of petrol on ironing
. xi: reg petrolironing i.education homeappliances i.occupation i.averageincome
priceenergyuse i.numberofpersonsperhhs morethan10incomeonenergy sector
accessibilty
i.education _Ieducation_1-5 (naturally coded; _Ieducation_1 omitted)
i.occupation _Ioccupatio_1-5 (naturally coded; _Ioccupatio_1 omitted)
i.averageincome _Iaveragein_1-5 (naturally coded; _Iaveragein_1 omitted)
i.numberofper~s _Inumberofp_1-4 (naturally coded; _Inumberofp_1 omitted)
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 21, 178) = 86.04
Model | 188.430794 21 8.97289496 Prob > F = 0.0000
Residual | 18.5642058 178 .104293291 R-squared = 0.9103
-------------+------------------------------ Adj R-squared = 0.8997
Total | 206.995 199 1.04017588 Root MSE = .32294
------------------------------------------------------------------------------
petrolironing | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_Ieducatio~2 | -.4081092 .1169412 -3.49 0.001 -.6388787 -.1773398
_Ieducatio~3 | -1.021437 .1903897 -5.36 0.000 -1.397149 -.6457255
_Ieducatio~4 | -1.346474 .2501177 -5.38 0.000 -1.840051 -.852896
_Ieducatio~5 | -1.370758 .3580024 -3.83 0.000 -2.077233 -.6642827
homeapplia~s | .2486785 .084672 2.27 0.309 -.0807163 .2534639
153
_Ioccupati~2 | -.8289978 .1205674 -6.88 0.000 -1.066923 -.5910724
_Ioccupati~3 | -1.016867 .169468 -6.00 0.000 -1.351292 -.6824422
_Ioccupati~4 | -1.116425 .2220376 -5.03 0.000 -1.554589 -.6782598
_Ioccupati~5 | -1.857503 .3088461 -6.01 0.000 -2.466974 -1.248032
_Iaveragei~2 | -.6353107 .1044574 -6.08 0.000 -.8414448 -.4291765
_Iaveragei~3 | -.1274547 .1936166 -0.66 0.511 -.5095339 .2546246
_Iaveragei~4 | -.2873773 .2725485 -1.05 0.293 -.8252193 .2504646
_Iaveragei~5 | -.1576317 .3608812 -0.44 0.663 -.8697879 .5545244
priceenerg~e | .6278308 .1822067 2.80 0.044 -.0143943 .310056
_Inumberof~2 | -.565782 .1043451 -5.42 0.000 -.7716945 -.3598694
_Inumberof~3 | -.351824 .1836748 -1.92 0.057 -.7142844 .0106363
_Inumberof~4 | -.4998057 .2650985 -1.89 0.061 -1.022946 .0233347
morethan10~y | -.1626202 .0882671 -1.84 0.067 -.3368047 .0115644
sector | .0916577 .1138795 0.80 0.422 -.13307 .3163853
accessibilty | -.0860367 .0998299 -0.86 0.390 -.283039 .1109656
_cons | 3.897408 .4833849 8.06 0.000 2.943505 4.851311
. ovtest
Ramsey RESET test using powers of the fitted values of petroliron
Ho: model has no omitted variables
F(3, 183) = 16.39
Prob > F = 0.0000
4. energy use of kerosene/petrol for lightening
. xi:reg petro/kerollighting priceenergyuse weatherenergy accessibilty
i.averageincome i.age i.gender sector i.education
i.averageincome _Iaveragein_1-5 (naturally coded; _Iaveragein_1 omitted)
i.age _Iage_1-3 (naturally coded; _Iage_1 omitted)
i.gender _Igender_1-2 (naturally coded; _Igender_1 omitted)
i.education _Ieducation_1-5 (naturally coded; _Ieducation_1 omitted)
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 18, 181) = 146.70
Model | 269.525722 18 14.9736512 Prob > F = 0.0000
Residual | 18.4742778 181 .102067833 R-squared = 0.9359
154
-------------+------------------------------ Adj R-squared = 0.9295
Total | 288 199 1.44723618 Root MSE = .31948
------------------------------------------------------------------------------
petrollighting | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
priceenerg~e | .4401219 .0792595 5.55 0.000 .2837304 .5965134
weatherene~y | .2989501 .0632089 4.73 0.000 .1742291 .4236712
accessibilty | -.0450122 .0745856 -0.60 0.547 -.1921813 .1021569
_Iaveragei~2 | -.2130853 .1379724 -1.54 0.124 -.4853265 .059156
_Iaveragei~3 | .0837126 .2086168 0.40 0.689 -.3279212 .4953463
_Iaveragei~4 | .2810368 .1600781 2.70 0.047 -.3321381 .6942118
_Iaveragei~5 | -.497009 .3368171 -1.48 0.142 -1.161602 .1675841
_Iage_2 | -1.930936 .2781418 -6.94 0.000 -2.479753 -1.382119
_Iage_3 | -.166869 .2204181 -0.76 0.450 -.6017885 .2680504
_Igender_2 | .225127 .1689297 1.33 0.184 -.1081979 .5584518
sector | .1973098 .1310887 1.51 0.134 -.0613488 .4559683
_Ieducatio~2 | . -.7386053 .1909423 0.78 0.439 -.2287767 .5247414
_Ieducatio~3 | .054651 .244681 0.22 0.824 -.4281429 .537445
_Ieducatio~4 | .497534 .2891637 1.72 0.087 -.0730314 1.068099
_Ieducatio~5 | .12956 .1053493 1.20 0.092 .4957821 2.095418
_cons | .8784686 .4871838 1.80 0.073 -.0828215 1.839759
. ovtest
Ramsey RESET test using powers of the fitted values of electricitylight
Ho: model has no omitted variables
F(3, 184) = 26.96
Prob > F = 0.0000
5. energy use of kerosene for food preservation
. xi: reg kerosenefoodanddrink i.averageincome priceenergyuse accessibilty
i.numberofpersonsperhhs i.age i.occupation homeappliances typeoffood
morethan10incomeonenergy sector weather
i.averageincome _Iaveragein_1-5 (naturally coded; _Iaveragein_1 omitted)
i.numberofper~s _Inumberofp_1-4 (naturally coded; _Inumberofp_1 omitted)
155
i.age _Iage_1-3 (naturally coded; _Iage_1 omitted)
i.occupation _Ioccupatio_1-5 (naturally coded; _Ioccupatio_1 omitted)
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 21, 178) = 142.54
Model | 237.779995 21 11.3228569 Prob > F = 0.0000
Residual | 14.1400051 178 .079438231 R-squared = 0.9439
-------------+------------------------------ Adj R-squared = 0.9372
Total | 251.92 199 1.26592965 Root MSE = .28185
------------------------------------------------------------------------------
kerosenefo~k | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_Iaveragei~2 | .3139019 .1082468 2.90 0.004 .1002898 .527514
_Iaveragei~3 | .7248481 .168714 4.30 0.000 .3919112 1.057785
_Iaveragei~4 | .7774678 .2218063 3.51 0.001 .3397596 1.215176
_Iaveragei~5 | .515399 .2489684 2.07 0.040 .0240895 1.006708
priceenerg~e | .1901868 .0704328 2.70 0.008 .0511961 .3291775
accessibilty | .0635221 .0871506 0.73 0.467 -.1084591 .2355034
_Inumberof~2 | -1.261256 .1122155 -11.24 0.000 -1.4827 -1.039812
_Inumberof~3 | -1.180655 .1785896 -6.61 0.000 -1.533081 -.8282299
_Inumberof~4 | -1.545521 .2309968 -6.69 0.000 -2.001366 -1.089677
_Iage_2 | -.5144014 .1110807 -4.63 0.000 -.7336059 -.2951969
_Iage_3 | -.4484142 .149591 -3.00 0.003 -.7436142 -.1532142
_Ioccupati~2 | -.6336797 .1171046 -5.41 0.000 -.8647718 -.4025877
_Ioccupati~3 | -.707338 .1627384 -4.35 0.000 -1.028483 -.3861932
_Ioccupati~4 | -.5034782 .2029433 -2.48 0.014 -.9039627 -.1029938
_Ioccupati~5 | -.4935142 .2863565 -2.72 0.087 -1.058605 .0715762
homeapplia~s | .0026385 .0815231 0.03 0.974 -.1582377 .1635147
typeoffood | .2121671 .0778083 2.73 0.007 .0586218 .3657124
morethan10~y | -.2348146 .0796952 -2.95 0.004 -.3920835 -.0775456
sector | .4109414 .1018413 4.04 0.000 .2099697 .6119132
_Iweather | .122861 .3354805 0.77 0.715 -.5391445 .7848666
_cons | 2.419435 .4737852 5.11 0.000 1.484476 3.354393
156
. ovtest
Ramsey RESET test using powers of the fitted values of kerosenefood
Ho: model has no omitted variables
F(3, 186) = 52.56
Prob > F = 0.0000
6. energy use of petrol for entertainment
. xi:reg petrolentertainment priceenergyuse i.ethnicity homeappliances accessibilty
i.averageincome sector i.occupation i.education i.age
i.ethnicity _Iethnicity_1-4 (naturally coded; _Iethnicity_1 omitted)
i.averageincome _Iaveragein_1-5 (naturally coded; _Iaveragein_1 omitted)
i.occupation _Ioccupatio_1-5 (naturally coded; _Ioccupatio_1 omitted)
i.education _Ieducation_1-5 (naturally coded; _Ieducation_1 omitted)
i.age _Iage_1-3 (naturally coded; _Iage_1 omitted)
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 24, 175) = 145.36
Model | 338.952304 24 14.1230126 Prob > F = 0.0000
Residual | 17.0026965 175 .097158265 R-squared = 0.9522
-------------+------------------------------ Adj R-squared = 0.9457
Total | 355.955 199 1.78871859 Root MSE = .3117
----------------------------------------------------------------------------
petrolentert | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
priceenerg~e | .1863983 .0723981 2.57 0.011 .0435126 .329284
_Iethnicit~1 | .7546556 .2811084 2.68 0.008 .1998565 1.309455
_Iethnicit~3 | .8474348 .3649513 2.32 0.021 .1271623 1.567707
_Iethnicit~4 | .2453673 .160782 1.05 0.442 .0359623 1.854772
homeapplia~s | .2407808 .0853159 2.82 0.005 .0724002 .4091614
accessibilty | .1265047 .0924148 1.37 0.173 -.0558863 .3088957
_Iaveragei~2 | .0277309 .1182828 0.23 0.815 -.2057134 .2611752
_Iaveragei~3 | -.1149469 .1961204 -0.59 0.559 -.5020126 .2721189
_Iaveragei~4 | -.1057485 .2746496 -0.39 0.701 -.6478004 .4363034
_Iaveragei~5 | -.498964 .3621861 -1.07 2.54 -1.102771 .3268587
sector | -.0764149 .1384779 -0.55 0.582 -.3497166 .1968868
157
_Ioccupati~2 | -.9374649 .1377524 -6.81 0.000 -1.209335 -.665595
_Ioccupati~3 | -1.714001 .189526 -9.04 0.000 -2.088051 -1.33995
_Ioccupati~4 | -1.545777 .320875 -4.82 0.000 -2.17906 -.9124936
_Ioccupati~5 | -1.614779 .4305263 -3.75 0.000 -2.464471 -.7650871
_Ieducatio~2 | -.0573112 .190501 -0.30 0.764 -.4332862 .3186639
_Ieducatio~3 | .0429259 .2464953 0.17 0.862 -.4435603 .5294122
_Ieducatio~4 | .4391308 .3166837 1.39 0.167 -.1858801 1.064142
_Ieducatio~5 | .1453331 .4482695 0.32 0.746 -.7393771 1.030043
_Iage_2 | -.6839251 .2293097 -2.98 0.003 -1.136494 -.2313567
_Iage_3 | .098113 .1975816 0.50 0.620 -.2918365 .4880625
_cons | 1.918991 .5362906 3.58 0.000 .8605613 2.977421
. ovtest
Ramsey RESET test using powers of the fitted values of petolentertainment
Ho: model has no omitted variables
F(3, 180) = 38.25
Prob > F = 0.0000
Energy use of Firewood
1. energy use of firewood on cooking
. xi: reg firewoodcook energyprice i.education i.averageincome
i.numberofpersonsperhhs weatherenergy accessibilt
> y i.gender i.occupation typeoffood beliefs sector
i.education _Ieducation_1-5 (naturally coded; _Ieducation_1 omitted)
i.averageincome _Iaveragein_1-5 (naturally coded; _Iaveragein_1 omitted)
i.numberofper~s _Inumberofp_1-4 (naturally coded; _Inumberofp_1 omitted)
i.gender _Igender_1-2 (naturally coded; _Igender_1 omitted)
i.occupation _Ioccupatio_1-5 (naturally coded; _Ioccupatio_1 omitted)
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 23, 176) = 161.71
Model | 315.619967 23 13.7226072 Prob > F = 0.0000
Residual | 14.9350333 176 .084858144 R-squared = 0.9548
-------------+------------------------------ Adj R-squared = 0.9489
Total | 330.555 199 1.6610804 Root MSE = .2913
------------------------------------------------------------------------------
158
firewoodcook | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
energyprice | .5241845 .0763477 6.87 0.000 .3735097 .6748594
_Ieducatio~2 | -.2016257 .1069223 -1.89 0.059 -.936323 -.4669285
_Ieducatio~3 | -.2750039 .1054131 -1.85 0.214 -1.021188 -.3288202
_Ieducatio~4 | -1.074977 .2396751 -4.49 0.000 -1.547984 -.6019702
_Ieducatio~5 | -1.595364 .3330648 -4.79 0.000 -2.252679 -.9380495
_Iaveragei~2 | -.7667703 .2393748 -2.52 0.002 -.3826252 .0490845
_Iaveragei~3 | -.1398538 .1289218 -0.97 0.675 -1.173227 -.5064808
_Iaveragei~4 | -.2148408 .1641603 -1.46 0.156 -1.43617 -.3935114
_Inumberof~2 | .2177252 .1356483 1.61 0.110 -.0499815 .4854318
_Inumberof~3 | .3916769 .1854862 2.11 0.036 .0256136 .7577402
_Inumberof~4 | .7859125 .2231626 2.14 0.002 -1.106801 .282505
weatherene~y | .5495962 .3739743 3.67 0.000 -.9963946 .1955869
accessibilty | -.0391967 .0767645 -0.51 0.610 -.190694 .1123006
_Igender_2 | .0824709 .1485751 0.56 0.580 -.2107472 .3756889
_Ioccupati~2 | .0942439 .138878 0.68 0.498 -.1798365 .3683244
_Ioccupati~3 | -.6466146 .2436534 -3.24 0.000 -.4287962 .335567
_Ioccupati~4 | .070684 .2187718 0.32 0.747 -.3610697 .5024377
_Ioccupati~5 | .5649674 .289306 1.90 0.058 -.005988 1.135923
typeoffood | -.5572938 .240833 -3.09 0.003 -.1711564 .1565687
beliefs | -.2620625 .1051917 -2.49 0.014 -.469662 -.054463
sector | -.9665087 .1212754 -7.97 0.000 -1.20585 -.7271675
_cons | 4.324026 .5368583 8.05 0.000 3.264517 5.383534
Ovtest
Ramsey RESET test using powers of the fitted values of firewoodcook
Ho: model has no omitted variables
F(3, 181) = 25.59
Prob > F = 0.0000
Energy use of firewood on lighting
. xi: reg firewoodlight i.education i.averageincome priceenergy i.age
i.numberofpersonsperhhs access
> ibilty sector freefirewood beliefs i.ethnicity i.occupation i.gender
i.education _Ieducation_1-5 (naturally coded; _Ieducation_1 omitted)
159
i.averageincome _Iaveragein_1-5 (naturally coded; _Iaveragein_1 omitted)
i.age _Iage_1-3 (naturally coded; _Iage_1 omitted)
i.numberofper~s _Inumberofp_1-4 (naturally coded; _Inumberofp_1 omitted)
i.ethnicity _Iethnicity_1-4 (naturally coded; _Iethnicity_1 omitted)
i.occupation _Ioccupatio_1-5 (naturally coded; _Ioccupatio_1 omitted)
i.gender _Igender_1-2 (naturally coded; _Igender_1 omitted)
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 29, 170) = 22.88
Model | 58.8939083 29 2.03082442 Prob > F = 0.0000
Residual | 15.0860917 170 .088741716 R-squared = 0.7961
-------------+------------------------------ Adj R-squared = 0.7613
Total | 73.98 199 .371758794 Root MSE = .2979
------------------------------------------------------------------------------
firewoodli~t | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_Ieducatio~2 | -.1096776 .1376963 -1.38 0.000 -1.567291 -.8262599
_Ieducatio~3 | -.0813268 .2366205 -0.66 0.000 -2.280361 -1.346175
_Ieducatio~4 | -1.549543 .3235695 -4.79 0.000 -2.188275 -.9108118
_Ieducatio~5 | -1.738136 .4451516 -3.90 0.000 -2.616873 -.8593994
_Iaveragei~2 | .402168 .1578686 2.55 0.012 .0905327 .7138033
_Iaveragei~3 | .002506 .1322513 0.02 0.985 -.2585603 .2635724
_Iaveragei~4 | .2640391 .310248 0.85 0.396 -.3483955 .8764738
_Iaveragei~5 | .0023819 .1415244 0.02 0.003 .3904237 1.857214
freefirewood | .2116653 .0857394 2.47 0.015 .0424142 .3809163
_Iage_2 | .2775371 .2881196 0.96 0.337 -.2912159 .8462901
_Iage_3 | .9278627 .222769 4.17 0.000 .488113 1.367612
_Inumberof~2 | .002506 .1322513 0.02 0.985 -.2585603 .2635724
_Inumberof~3 | .2696624 .1993888 1.35 0.178 -.1239345 .6632593
_Inumberof~4 | .2640391 .310248 0.85 0.396 -.3483955 .8764738
accessibilty | -.0342023 .0854429 -0.40 0.689 -.202868 .1344634
sector | -.9008936 .1535231 -5.87 0.000 -1.203951 -.5978365
beliefs | .0831657 .1075811 0.77 0.441 -.1292011 .2955325
160
_Iethnicit~2 | .0980504 .2795158 0.35 0.726 -.4537185 .6498193
_Iethnicit~3 | .6763387 .3910333 1.73 0.086 -.0955676 1.448245
_Iethnicit~4 | .7392029 .4772776 1.55 0.123 -.202951 1.681357
_Ioccupati~2 | -.5655263 .4361485 -1.42 0.125 -1.2342857 -.1967668
_Ioccupati~3 | -.6470824 .2070625 -3.13 0.002 -1.055827 -.2383375
_Ioccupati~4 | -.6002861 .3115496 -1.93 0.056 -1.21529 .0147181
_Ioccupati~5 | -.5837353 .4112739 -1.42 0.158 -1.395597 .2281263
_Igender_2 | -.3847647 .1837216 -2.09 0.038 -.7474343 -.0220952
_cons | 2.717468 .5999904 4.53 0.000 1.533077 3.901859
. ovtest
Ramsey RESET test using powers of the fitted values of firewoodlight
Ho: model has no omitted variables
F(3, 180) = 29.24
Prob > F = 0.0000
energy use of firewood on food preservation
. xi: reg firewoodfoodanddrink sector i.education i.occupation i.averageincome
weatherenergy accessibilty typeoffood freefirewood beliefs
i.education _Ieducation_1-5 (naturally coded; _Ieducation_1 omitted)
i.occupation _Ioccupatio_1-5 (naturally coded; _Ioccupatio_1 omitted)
i.averageincome _Iaveragein_1-5 (naturally coded; _Iaveragein_1 omitted)
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 19, 180) = 148.09
Model | 234.798944 19 12.3578392 Prob > F = 0.0000
Residual | 15.021056 180 .083450311 R-squared = 0.9399
-------------+------------------------------ Adj R-squared = 0.9335
Total | 249.82 199 1.25537688 Root MSE = .28888
------------------------------------------------------------------------------
firewoodfo~k | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
sector | .9338196 .1690236 4.28 0.000 -.2010414 .2686805
_Ieducatio~2 | -.0950882 .1101422 -0.86 0.389 -.3124241 .1222477
_Ieducatio~3 | -.905999 .1678015 -5.40 0.000 -1.23711 -.574888
_Ieducatio~4 | -.8457279 .2133518 -3.96 0.000 -1.26672 -.4247355
161
_Ieducatio~5 | -.8323338 .3036093 -2.74 0.007 -1.431425 -.2332426
_Ioccupati~2 | .267082 .1056775 2.53 0.012 .058556 .475608
_Ioccupati~3 | .445306 .149482 2.98 0.003 .1503435 .7402685
_Ioccupati~4 | .5034418 .198753 2.53 0.012 .1112562 .8956274
_Ioccupati~5 | .6044313 .2685286 2.25 0.026 .0745624 1.1343
_Iaveragei~2 | -.8129691 .085068 -9.56 0.000 -.9808278 -.6451104
_Iaveragei~3 | -.9808909 .1630653 -6.02 0.000 -1.302656 -.6591256
_Iaveragei~4 | -.3766211 .2074573 -1.43 0.116 -1.045179 -.108063
_Iaveragei~5 | -.3354719 .3070832 -1.09 0.276 -.9414179 .270474
weatherene~y | -.2335966 .061208 -3.82 0.000 -.3543741 -.112819
accessibilty | .2801618 .0712801 3.93 0.000 .1395098 .4208139
typeoffood | .2334748 .0728271 3.21 0.002 .0897701 .3771794
freefirewood | .1131405 .0744407 1.52 0.130 -.0337482 .2600293
beliefs | .3899893 .0930568 4.19 0.000 .2063669 .5736118
_cons | .8364212 .4496498 1.86 0.064 -.0508417 1.723684
.ovtest
Ramsey RESET test using powers of the fitted values of firewoodfood
Ho: model has no omitted variables
F(3, 184) = 34.75
Prob > F = 0.0000
Energy use of Charcoal
1. energy use of charcoal on cooking
. xi: reg charcoalcook i.numberofpersonsperhhs i.age i.occupation i.averageincome
i.education sector beliefs typeoffood accessibilty priceenergyuse
i.numberofper~s _Inumberofp_1-4 (naturally coded; _Inumberofp_1 omitted)
i.age _Iage_1-3 (naturally coded; _Iage_1 omitted)
i.occupation _Ioccupatio_1-5 (naturally coded; _Ioccupatio_1 omitted)
i.averageincome _Iaveragein_1-5 (naturally coded; _Iaveragein_1 omitted)
i.education _Ieducation_1-5 (naturally coded; _Ieducation_1 omitted)
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 25, 174) = 94.93
Model | 182.569841 25 7.30279365 Prob > F = 0.0000
Residual | 13.3851587 174 .0769262 R-squared = 0.9317
162
-------------+------------------------------ Adj R-squared = 0.9219
Total | 195.955 199 .984698492 Root MSE = .27736
------------------------------------------------------------------------------
charcoalcook | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_Inumberof~2 | -.5335259 .1262262 -4.23 0.000 -.7826574 -.2843944
_Inumberof~3 | -.535049 .1837233 -2.91 0.004 -.897662 -.1724359
_Inumberof~4 | -.4892898 .2439468 -2.01 0.046 -.9707655 -.007814
_Iage_2 | -.8824889 .1737968 -5.08 0.000 -1.22551 -.5394676
_Iage_3 | -.6341288 .2024385 -3.13 0.002 -1.03368 -.2345777
_Ioccupati~2 | -.1411535 .1169111 -1.21 0.229 -.3719 .089593
_Ioccupati~3 | -.0829982 .1763685 -0.47 0.639 -.4310952 .2650987
_Ioccupati~4 | -.2194958 .2114491 -1.04 0.301 -.6368311 .1978396
_Ioccupati~5 | -.4191564 .2882625 -1.45 0.148 -.9880976 .1497849
_Iaveragei~2 | .8617398 .1087727 7.92 0.000 .6470559 1.076424
_Iaveragei~3 | 1.220093 .1757411 6.94 0.000 .8732338 1.566951
_Iaveragei~4 | 1.332315 .2586372 5.15 0.000 .821845 1.842785
_Iaveragei~5 | 1.422735 .3287251 4.33 0.000 .7739331 2.071537
_Ieducatio~2 | .0563334 .168064 0.34 0.738 -.2753731 .38804
_Ieducatio~3 | .1647311 .2171383 0.76 0.449 -.2638329 .5932952
_Ieducatio~4 | .2200109 .2967708 0.74 0.459 -.365723 .8057448
_Ieducatio~5 | .4531542 .401627 1.13 0.261 -.3395337 1.245842
sector | -.5482221 .0896468 -3.07 0.001 -.344368 .5279238
beliefs | .1167645 .0909583 1.28 0.201 -.0627592 .2962881
typeoffood | .4595314 .0778491 5.90 0.000 .3058813 .6131814
accessibilty | -.4327731 .0789443 -4.16 0.002 -.1685847 .1430385
priceenerg~e | .2538723 .0757984 3.35 0.001 .1042696 .403475
_cons | .6823087 .5541714 1.23 0.220 -.4114547 1.776072
. ovtest
Ramsey RESET test using powers of the fitted values of charcoalcook
Ho: model has no omitted variables
F(3, 179) = 22.16
163
Prob > F = 0.0000
2. Energy use of charcoal on food preservation
. xi: reg charcoalfoodanddrink i.averageincome priceenergyuse accessibilty
i.numberofpersonsperhhs i.occupation i.gender belief typeoffood
morethan10incomeonenergy homeappliances sector
i.averageincome _Iaveragein_1-5 (naturally coded; _Iaveragein_1 omitted)
i.numberofper~s _Inumberofp_1-4 (naturally coded; _Inumberofp_1 omitted)
i.occupation _Ioccupatio_1-5 (naturally coded; _Ioccupatio_1 omitted)
i.gender _Igender_1-2 (naturally coded; _Igender_1 omitted)
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 18, 181) = 63.61
Model | 140.709741 18 7.81720781 Prob > F = 0.0000
Residual | 22.2452594 181 .122901986 R-squared = 0.8635
-------------+------------------------------ Adj R-squared = 0.8499
Total | 162.955 199 .818869347 Root MSE = .35057
------------------------------------------------------------------------------
charcoalfo~k | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_Iaveragei~2 | .0814292 .1429696 0.57 0.570 -.2006722 .3635307
_Iaveragei~3 | .3587977 .204412 1.76 0.081 -.0445393 .7621348
_Iaveragei~4 | .3144605 .2807439 1.12 0.264 -.2394912 .8684122
_Iaveragei~5 | .3358316 .308111 1.09 0.277 -.2721198 .943783
_priceenergy | -.4489334 .201717 -2.23 0.027 -.8469527 -.0509141
accessibilty | .2052393 .1037848 1.98 0.049 .0004556 .4100229
_Inumberof~2 | -.0157175 .1353209 -0.12 0.908 -.2827269 .2512919
_Inumberof~3 | .0313555 .2128935 0.15 0.883 -.3887168 .4514277
_Inumberof~4 | .4428433 .2770136 1.60 0.112 -.103748 .9894347
_Ioccupati~2 | -.295814 .1548018 -1.91 0.058 -.6012623 .0096343
_Ioccupati~3 | -.4489334 .201717 -2.23 0.027 -.8469527 -.0509141
_Ioccupati~4 | -.1926491 .2473113 -0.78 0.437 -.6806332 .2953349
_Ioccupati~5 | .0512184 .3339083 0.15 0.878 -.607635 .7100719
_Igender_2 | -1.215935 .165769 -7.34 0.000 -1.543023 -.8888464
164
_Ibelief | -.4489334 .201717 -2.23 0.027 -.8469527 -.0509141
typeoffood | .4629762 .2031267 2.75 0.002 -.7807775 .0467299
morethan10~y | .4082707 .0960914 4.25 0.000 .2186672 .5978741
homeapplia~s | -.0828263 .1005965 -0.82 0.411 -.281319 .1156664
sector | -.7107773 .1137579 -6.25 0.000 -.9352394 -.4863152
_cons | 2.203877 .460876 4.78 0.000 1.294496 3.113258
. ovtest
Ramsey RESET test using powers of the fitted values of charcoalfoodanddrink
Ho: model has no omitted variables
F(3, 185) = 28.48
Prob > F = 0.0000
Energy use of sawdust
1. Sawdust for cooking
. xi: reg sawdustcook i.numberofpersonsperhhs weather i.averageincome i.education
priceenergyuse sector beliefs typeoffood accessibilty
i.numberofper~s _Inumberofp_1-4 (naturally coded; _Inumberofp_1 omitted)
i.averageincome _Iaveragein_1-5 (naturally coded; _Iaveragein_1 omitted)
i.education _Ieducation_1-5 (naturally coded; _Ieducation_1 omitted)
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 18, 181) = 61.23
Model | 76.8755346 18 4.27086303 Prob > F = 0.0000
Residual | 12.6244654 181 .069748428 R-squared = 0.8589
-------------+------------------------------ Adj R-squared = 0.8449
Total | 89.5 199 .449748744 Root MSE = .2641
------------------------------------------------------------------------------
sawdustcook | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_Inumberof~2 | -.0833616 .1051992 -0.79 0.429 -.290936 .1242129
_Inumberof~3 | -.6565386 .1520747 -4.32 0.000 -.9566059 -.3564713
_Inumberof~4 | -.6049011 .2171562 -2.79 0.006 -1.033384 -.1764177
weatherene~y | .1969916 .0589847 3.34 0.001 .0806056 .3133776
_Iaveragei~2 | -.359089 .1563732 -2.30 0.023 -.6676379 -.0505401
_Iaveragei~3 | .0604415 .0778471 0.78 0.439 -.0931631 .2140461
165
_Iaveragei~4 | -.0977226 .2175975 -0.45 0.654 -.5270766 .3316315
_Iaveragei~5 | -.3619035 .2787213 -1.30 0.196 -.9118643 .1880573
_Ieducatio~2 | -1.062237 .0902788 -11.77 0.000 -1.240371 -.8841028
_Ieducatio~3 | -.9839365 .153191 -6.42 0.000 -1.286206 -.6816665
_Ieducatio~4 | -.825033 .2089128 -3.95 0.000 -1.237251 -.4128152
_Ieducatio~5 | -1.120846 .2962778 -3.78 0.000 -1.705449 -.5362438
priceenerg~e | .0529608 .0719871 0.74 0.463 -.089081 .1950027
sector | -.3396357 .0838408 -4.05 0.000 -.5050668 -.1742046
beliefs | -.2694701 .0758282 -3.55 0.000 -.419091 -.1198492
typeoffood | .1257402 .0700299 1.80 0.074 -.0124398 .2639202
accessibilty | -.0789984 .064841 -1.22 0.225 -.2069398 .0489431
_cons | 3.485466 .4515166 7.72 0.000 2.594553 4.376379
. ovtest
Ramsey RE SET test using powers of the fitted values of sawdustcook
Ho: model has no omitted variables
F(3, 183) = 24.58
Prob > F = 0.0000
2. Energy use of sawdust on food preservation
. xi: reg sawdustfood i.averageincome priceenergyuse accessibilty
i.numberofpersonsperhhs i.age typeoffood beliefs sector i.occupation weather
i.averageincome _Iaveragein_1-5 (naturally coded; _Iaveragein_1 omitted)
i.numberofper~s _Inumberofp_1-4 (naturally coded; _Inumberofp_1 omitted)
i.age _Iage_1-3 (naturally coded; _Iage_1 omitted)
i.occupation _Ioccupatio_1-5 (naturally coded; _Ioccupatio_1 omitted)
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 20, 179) = 34.77
Model | 83.0220448 20 4.15110224 Prob > F = 0.0000
Residual | 21.3729552 179 .119401984 R-squared = 0.7953
-------------+------------------------------ Adj R-squared = 0.7724
Total | 104.395 199 .52459799 Root MSE = .34555
------------------------------------------------------------------------------
sawdustfoo~k | Coef. Std. Err. t P>|t| [95% Conf. Interval]
166
-------------+----------------------------------------------------------------
_Iaveragei~2 | .1030016 .1279372 0.81 0.422 -.1494575 .3554608
_Iaveragei~3 | .1943406 .2065283 0.94 0.348 -.2132028 .601884
_Iaveragei~4 | .3087685 .2979899 1.04 0.302 -.2792565 .8967935
_Iaveragei~5 | .3510821 .3248115 1.08 0.281 -.2898701 .9920344
priceenerg~e | -.0409515 .0897843 -0.46 0.649 -.2181233 .1362204
accessibilty | .2653337 .0843646 3.15 0.002 .0988567 .4318108
_Inumberof~2 | -.131846 .1502824 -0.88 0.381 -.428399 .164707
_Inumberof~3 | -.1116855 .2256874 -0.49 0.621 -.5570357 .3336648
_Inumberof~4 | -.3214779 .0867721 -2.42 0.002 -.6873668 .444411
_Iage_2 | -1.137077 .1284485 -8.85 0.000 -1.390545 -.8836086
_Iage_3 | -1.195166 .1804263 -6.62 0.000 -1.551202 -.8391296
typeoffood | -.0407288 .0918978 -0.44 0.658 -.2220712 .1406136
beliefs | .010975 .1004876 0.11 0.913 -.1873177 .2092678
sector | .0728471 .1208193 0.60 0.547 -.1655664 .3112605
_Ioccupati~2 | .0982134 .1297392 0.76 0.450 -.1578016 .3542284
_Ioccupati~3 | -.4784739 .0184702 -2.34 0.003 -.6898493 .1329014
_Ioccupati~4 | -.3816767 .242548 -1.57 0.117 -.860298 .0969445
_Ioccupati~5 | -.4143128 .3431511 -1.21 0.229 -1.091455 .2628291
Weather | -.0407288 .0918978 -0.48 0.658 -.2220712 .1406136
_cons | 2.154631 .5870083 3.67 0.000 .9962843 3.312978
. ovtest
Ramsey RESET test using powers of the fitted values of sawdustfoodanddrink
Ho: model has no omitted variables
F(3, 183) = 8.86
Prob > F = 0.0000
Energy use of Battery
1. Energy use of Battery on entertainment
. xi:reg batteryentertainment priceenergyuse i.ethnicity belief accessibilty
i.averageincome i.age i.education i.numberofpersonsperhhs sector
i.ethnicity _Iethnicity_1-4 (naturally coded; _Iethnicity_1 omitted)
i.averageincome _Iaveragein_1-5 (naturally coded; _Iaveragein_1 omitted)
i.age _Iage_1-3 (naturally coded; _Iage_1 omitted)
167
i.education _Ieducation_1-5 (naturally coded; _Ieducation_1 omitted)
i.numberofper~s _Inumberofp_1-4 (naturally coded; _Inumberofp_1 omitted)
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 23, 176) = 176.50
Model | 261.291698 23 11.3605086 Prob > F = 0.0000
Residual | 11.3283016 176 .06436535 R-squared = 0.9584
-------------+------------------------------ Adj R-squared = 0.9530
Total | 272.62 199 1.36994975 Root MSE = .2537
------------------------------------------------------------------------------
batteryent~t | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
priceenerg~e | .2220457 .0687718 3.23 0.001 .0863223 .3577692
_Iethnicit~2 | .3248162 .0413042 2.18 0.001 -.0540525 .3036849
_Iethnicit~3 | -.2020312 .2743417 -0.74 0.462 -.7434541 .3393916
_Iethnicit~4 | .0350998 .3111813 0.11 0.910 -.5790272 .6492268
beliefs | .0320757 .0740112 0.43 0.665 -.1139879 .1781394
accessibilty | -.0133712 .0690463 -0.19 0.847 -.1496365 .1228941
_Iaveragei~2 | -1.490343 .0984857 -15.13 0.000 -1.684708 -1.295978
_Iaveragei~3 | -1.358716 .1565233 -8.68 0.000 -1.66762 -1.049812
_Iaveragei~4 | -1.001867 .2345706 -4.27 0.000 -1.4648 -.5389338
_Iaveragei~5 | -.836332 .2978338 -2.81 0.006 -1.424117 -.2485468
_Iage_2 | -.3818396 .1588676 -2.40 0.017 -.6953703 -.0683088
_Iage_3 | .377861 .2587377 2.57 0.008 -.3627668 .6584888
_Ieducatio~2 | -.2401977 .1467013 -1.64 0.103 -.5297178 .0493224
_Ieducatio~3 | -.8537475 .1884408 -4.53 0.000 -1.225642 -.481853
_Ieducatio~4 | -1.593291 .2460926 -6.47 0.000 -2.078963 -1.107618
_Ieducatio~5 | -1.888658 .347724 -5.43 0.000 -2.574904 -1.202413
_Inumberof~2 | .0429002 .0850255 0.50 0.615 -.1249005 .2107009
_Inumberof~3 | .0665132 .1499726 0.44 0.658 -.2294628 .3624893
_Inumberof~4 | .0705433 .2524124 0.28 0.780 -.4276012 .5686878
sector | -.3202636 .0906801 -3.53 0.001 -.4992239 -.1413033
_cons | 3.59746 .4580313 7.85 0.000 2.69352 4.501401
168
. ovtest
Ramsey RESET test using powers of the fitted values of batteryentertainment
Ho: model has no omitted variables
F(3, 180) = 40.22
Prob > F = 0.0000
Energy use of candle on lightening
. xi:reg candlelight priceenergyuse weatherenergy homeappliances belief
accessibilty i.averageincome i.sector i.gender i.age i.education
i.averageincome _Iaveragein_1-5 (naturally coded; _Iaveragein_1 omitted)
i.sector _Isector_1-2 (naturally coded; _Isector_1 omitted)
i.gender _Igender_1-2 (naturally coded; _Igender_1 omitted)
i.age _Iage_1-3 (naturally coded; _Iage_1 omitted)
i.education _Ieducation_1-5 (naturally coded; _Ieducation_1 omitted)
Source | SS df MS Number of obs = 200
-------------+------------------------------ F( 20, 179) = 32.35
Model | 69.6170637 20 3.48085319 Prob > F = 0.0000
Residual | 19.2629363 179 .107614169 R-squared = 0.7833
-------------+------------------------------ Adj R-squared = 0.7591
Total | 88.88 199 .446633166 Root MSE = .32805
------------------------------------------------------------------------------
candlelight | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
priceenerg~e | .1378084 .0876044 1.57 0.117 -.0350619 .3106786
weatherene~y | .1423604 .09406 1.51 0.132 -.0432487 .3279694
homeapplia~s | .0897596 .111828 0.80 0.423 -.1309111 .3104304
beliefs | .7639626 .1869815 2.74 0.003 -.476785 .2356036
accessibilty | -.0642962 .0994671 -0.65 0.519 -.2605752 .1319828
_Iaveragei~2 | .7535424 .1444862 5.22 0.000 .468427 1.038658
_Iaveragei~3 | .8041911 .2186424 3.68 0.000 .3727429 1.235639
_Iaveragei~4 | .9896959 .2756213 3.59 0.000 .445811 1.533581
_Iaveragei~5 | 1.70304 .3559027 4.79 0.000 1.000735 2.405344
_Isector_2 | -.9091458 .1370887 -6.63 0.000 -1.179664 -.638628
169
_Igender_2 | -.7477421 .1777466 -4.21 0.000 -1.09849 -.3969937
_Iage_2 | .2014969 .2283035 0.88 0.379 -.2490156 .6520094
_Iage_3 | .2365447 .2520101 0.94 0.349 -.2607482 .7338377
_Ieducatio~2 | -.7693594 .1970278 -3.90 0.000 -1.158155 -.3805633
_Ieducatio~3 | -.7620185 .2519729 -3.02 0.003 -1.259238 -.264799
_Ieducatio~4 | -1.243196 .3104976 -4.00 0.000 -1.855902 -.6304891
_Ieducatio~5 | -1.858084 .4418549 -4.21 0.000 -2.729998 -.9861691
_cons | 1.89098 .4772506 3.96 0.000 .9492193 2.832742
. ovtest
Ramsey RESET test using powers of the fitted values of candlelight
Ho: model has no omitted variables
F(3, 182) = 22.53
Prob > F = 0.0000
APPENDIX V
Objective III: PREFERENCES OF HOUSEHOLDS ENERGY USE ON
DIFFERENT ENERGY TYPES
Using Achievement test
1. I would use more of modern energy for cooking if available
. sum availabilityforcooking if sector == 1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
availabili~g | 92 1.228261 .6131035 1 4
. sum availabilityforcooking if sector == 2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
availabili~g | 108 3.159259 .5939 2 4
2. I would use more of modern energy for non-cooking activities if
available
. sum availabiltyfornoncooking if sector == 1
170
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
availabilt~g | 92 3.380435 1.097874 1 4
sum availabiltyfornoncooking if sector == 2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
availabilt~g | 108 3.731481 .6500393 1 4
3. I would use more of modern energy for cooking if affordable probably
subsidized
sum pricecostoncooking if sector == 1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
pricecosto~g | 92 1.282609 .5990118 1 3
. sum pricecostoncooking if sector == 2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
pricecosto~g | 108 3.351852 1.079242 1 4
4. I would use more of modern energy for non-cooking if affordable
probably subsidised
sum pricecostfornoncooking if sector == 1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
pricecostf~g | 92 2.402174 1.399056 1 4
. sum pricecostfornoncooking if sector == 2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
pricecostf~g | 108 3.916667 .3383578 2 4
5. I would use more of modern energy for cooking if I earned higher
income
171
. sum incomeforcooking if sector == 1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
incomeforc~g | 92 1.467391 .9994623 1 4
. sum incomeforcooking if sector == 2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
incomeforc~g | 108 2.537037 1.088821 1 4
6. I would use more of modern energy for n0n-cooking activity if I
earned higher income
. sum incomefornoncooking if sector == 1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
incomeforn~g | 92 3.434783 1.06187 1 4
. sum incomefornoncooking if sector == 2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
incomeforn~g | 108 3.111111 1.053107 1 4
7. I would use both traditional and modern for cooking if all options
were made
. sum bothforcooking if sector == 1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
bothforcoo~g | 92 1.532609 .8574315 1 4
sum bothforcooking if sector == 2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
bothforcoo~g | 108 2.675926 1.117647 1 4
8. I would use both traditional and modern for non-cooking activities if
all options were made
172
sum bothfornoncooking if sector == 1
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
bothfornon~g | 92 2.48913 .9077755 1 4
sum bothfornoncooking if sector == 2
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
bothfornon~g | 108 1.62037 1.038669 1 4
TEST OF HYPOTHESIS 111
Ho3: There are no significant differences in the preferences of households
energy use on different energy sources across urban and rural areas of
Enugu state.
. oneway availabilityforcooking sector
Analysis of Variance
Source SS df MS F Prob > F
------------------------------------------------------------------------
Between groups 318.247738 1 318.247738 875.82 0.0000
Within groups 71.9472625 198 .363370013
------------------------------------------------------------------------
Total 390.195 199 1.96077889
Bartlett's test for equal variances: chi2(1) = 0.0992 Prob>chi2 = 0.753
. oneway availabiltyfornoncooking sector
Analysis of Variance
Source SS df MS F Prob > F
------------------------------------------------------------------------
Between groups 6.12225443 1 6.12225443 7.83 0.0057
Within groups 154.897746 198 .782311846
------------------------------------------------------------------------
Total 161.02 199 .809145729
Bartlett's test for equal variances: chi2(1) = 26.4362 Prob>chi2 = 0.000
173
. oneway pricecostoncooking sector
Analysis of Variance
Source SS df MS F Prob > F
------------------------------------------------------------------------
Between groups 212.718196 1 212.718196 267.79 0.0000
Within groups 157.281804 198 .794352543
------------------------------------------------------------------------
Total 370 199 1.85929648
Bartlett's test for equal variances: chi2(1) = 31.2068 Prob>chi2 = 0.000
. oneway pricecostfornoncooking sector
Analysis of Variance
Source SS df MS F Prob > F
------------------------------------------------------------------------
Between groups 113.950435 1 113.950435 118.52 0.0000
Within groups 190.369565 198 .961462451
------------------------------------------------------------------------
Total 304.32 199 1.52924623
Bartlett's test for equal variances: chi2(1) = 162.1786 Prob>chi2 = 0.000
. oneway incomeforcooking sector
Analysis of Variance
Source SS df MS F Prob > F
------------------------------------------------------------------------
Between groups 56.8409742 1 56.8409742 51.68 0.0000
Within groups 217.754026 198 1.09976781
------------------------------------------------------------------------
Total 274.595 199 1.37987437
Bartlett's test for equal variances: chi2(1) = 0.7134 Prob>chi2 = 0.398
. oneway incomefornoncooking sector
Analysis of Variance
Source SS df MS F Prob > F
174
------------------------------------------------------------------------
Between groups 5.20463768 1 5.20463768 4.66 0.0321
Within groups 221.275362 198 1.11755233
------------------------------------------------------------------------
Total 226.48 199 1.13809045
Bartlett's test for equal variances: chi2(1) = 0.0067 Prob>chi2 = 0.935
. oneway bothforcooking sector
Analysis of Variance
Source SS df MS F Prob > F
------------------------------------------------------------------------
Between groups 64.9404187 1 64.9404187 64.11 0.0000
Within groups 200.559581 198 1.01292718
------------------------------------------------------------------------
Total 265.5 199 1.33417085
Bartlett's test for equal variances: chi2(1) = 6.7009 Prob>chi2 = 0.010
. oneway bothfornoncooking sector
Analysis of Variance
Source SS df MS F Prob > F
------------------------------------------------------------------------
Between groups 37.4956844 1 37.4956844 38.99 0.0000
Within groups 190.424316 198 .961738968
------------------------------------------------------------------------
Total 227.92 199 1.14532663
Bartlett's test for equal variances: chi2(1) = 1.7574 Prob>chi2 = 0.185
175