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Proceedings of the Australian Academy of Business and Social Sciences Conference 2014
(in partnership with The Journal of Developing Areas)
ISBN 978-0-9925622-0-5
AN EMPIRICAL ANALYSIS OF ENGEL CURVE ON ENERGY FOR HOUSEHOLDS
IN SABAH AND SARAWAK BASED ON LOCATION AND INCOME GROUP.
Vivin Vincent Chandran
Caroline Geetha
Kwang Jing Yii
Universiti Malaysia Sabah, Malaysia
Amran Ahmed
Universiti Malaysia Perlis, Malaysia
ABSTRACT
The study was focused in forming an empirical model of Engel curve on energy for households in Sabah and Sarawak. Open-
ended questionnaire was used as an instrument of study involving 1,002 respondents in Sabah and Sarawak. The dependent
variable was budget share of energy expenditure meanwhile the independent variables were total household expenditure, age,
household size, educational level, gender and regional variables. All the independent variables were converted to logarithm form
except for gender and all regional variables as the dummy variables. The Ordinary Least Square estimation was carried out on the
cross sectional data. The coefficients were used to estimate the Engel curve expenditure elasticities. The findings revealed that
total household expenditure (income) and household size were the important determinant in explaining the energy expenditure.
Meanwhile the estimation of Engel curve expenditure elasticities showed that energy was a necessity good for all income groups
in both urban and rural areas. Low income group was found to be more sensitive on energy expenditure to the changes in
household income.
JEL Classifications: C13, D12, Q49, R22.
Keywords: Engel Curve, Energy, Expenditure Elasticity, Households, Location, Income Group.
Corresponding Author’s Email Address: [email protected], [email protected], [email protected],
INTRODUCTION
Malaysia is a country that has a total area of 329,847km2 with an estimated population of 29 million in 2012 (CIA,
2011). Malaysia is rich with natural resources in areas like minerals, agriculture and forestry. In the mineral area, tin
used to be the main contributor to Malaysia’s economy in 1980s. After the collapse of tin market, petroleum and
natural gas replaced tin as the most important pillar for Malaysia’s economy. However, by tracking on the current
production rates, the government estimates that the country will be able to produce oil for another 18 years and
natural gas up to 35 years only (Chua and Oh, 2010). Unfortunately, this is due to the oil resources depleting around
2030 if there is no new oil field found and eventually affect the energy sectors such as industrial, transport and
residential and commercial.
In Malaysia, energy was consumed by industrial sector, transport sector, residential and commercial sector,
non-energy sector, and agriculture and forestry sector. According to Ninth Malaysia Plan, the largest energy
consumer in Malaysia was the transport sector which occupied 41.1 percent of the total energy demand in 2010. This
was followed by industrial sector with 38.8 percent. Besides, residential and commercial sector appeared as the third
largest sector for energy demand which was amounted to 12.8 percent. For non-energy together with agriculture and
forestry were only amounted to 6.5 percent and 0.8 percent of total energy demand.
However, this study concentrated towards the residential sector because of various reasons. Firstly, based on
the classical energy ladder model hypothesis and the multiple fuel model hypothesis, changes in consumer behavior
towards the consumption of energy can take place due to changes in household income. This enables the consumers
to switch to different sources of energy and the effect of income can be easily measured. Secondly, the energy
demand on the residential users is mainly influenced by its welfare factors. The welfare factors can be measured
based on age, gender and education level of the head of household, household size, income status and location (urban
and rural). Thirdly, the study concentrates on residents because it is in line with the aim of the government to make
Malaysia a high income nation. Therefore, measures need to be taken to increase the purchasing power of consumers
especially the residents because they are the main contributors of the GDP reflected by the component of consumer
expenditure used in calculating GDP with expenditure approach.
Proceedings of the Australian Academy of Business and Social Sciences Conference 2014
(in partnership with The Journal of Developing Areas)
ISBN 978-0-9925622-0-5
On the other hand, since Malaysia consists of a nation with two major geographical locations, Peninsular
Malaysia and East Malaysia., this study focused only on East Malaysia which consists of Sabah and Sarawak. Sabah
and Sarawak are located in the island of Borneo. Peninsular Malaysia is different from Sabah and Sarawak not only
in terms of its geographical location but also in terms of its economic activities, affordability (purchasing power),
demographic structure and infrastructure.
No doubt, energy expenditure aimed to provide positive impact to the economy but many researchers found
that expenditure on energy as one of the principal cause of unsustainable development in Malaysia. Malaysian
government had a great deal of leeway in giving subsidy for the consumption of energy. However, Malaysia still
experienced difficulties in balancing its budget due to subsidy given for energy expenditure. The budget deficit was
mostly managed from substantial oil revenue. But petroleum was a depleting resource and was highly volatile.
Besides, the extensive subsidies system incurred excess consumption and shortages. Since the energy consumption
was closely related to subsidy, a discipline fiscal management in controlling energy expenditure was essential
towards deciding the amount of energy expenditure made by household. Therefore, the consumers’ behavior need to
be accessed through their expenditure. This can be created by forming an empirical model of the Engel curve for
energy expenditure.
In Malaysia, researchers like Tey et al. (2008a, 2008b, 2009) used Engel curve to investigate the demand
for food like rice, meat and vegetable but yet to be done for energy demand. Therefore, this study examined the
energy expenditure using Engle curve. The objectives of this study were specified as follow:
i. To identify the relationship between budget share of energy expenditure with total household
expenditure (income) and demographic variables in Sabah and Sarawak based on location and income
group.
ii. To estimate the expenditure elasticity of energy in Sabah and Sarawak based on location and income
group.
LITERATURE REVIEW
Gundimeda and Kohlin (2008) used Engel curve to form a linear relationship between the budget share of energy and
the logarithm of total expenditure at the first stage of the study. Demographic variables were added in the model
since the parameters were not constant across all households. Statistical Analysis Software (SAS) was used to
estimate the model. The cross sectional data that consists of 10,000 samples from National Sample Survey
Organisation (NSSO) were used. The findings claimed that energy consumption was not only influenced by the
growth of population and income, but also other factors such as geographical area, forest cover, and occupation.
From the coefficient of expenditure with the negative sign indicated that the domestic energy was a necessity good
where the share of energy expenditure decreased when income increased. It was only insignificant among low
income group in urban area. Besides, household size was also found to influence the energy demand with positive
relation. The regional dummy variables were found to be significant except for high income group in urban area. The
Engel expenditure elasticities for energy among all income groups were estimated between 0 to 1 which supported
that energy was necessary type.
Besides, Chambwera and Folmer (2007) analyzed urban energy demand particularly firewood in energy
mix context among 500 households in Harare in 2003. At the first stage of the study, an Engel function that
represented the share of energy expenditure in total household expenditure in logarithm form was estimated. The
function was extended to include other household characteristics such as household size, number of rooms used by
household, assets, education level of household head and occupancy. The findings revealed that the share of energy
decreased when total expenditure increased. This indicated that energy was a necessity good for both electrified and
non-electrified households. The coefficients for both groups were statistically significant but the electrified
households were found to be more sensitive to changes in income. This was due to the basic energy needs for non-
electrified households may hardly to be identified. Besides, household size had a positive impact on the energy
consumption for both groups but only electrified households was found to be significant. Meanwhile the educational
level was found to be not significant for both households which indicated that psychogenic needs did not exist at the
first stage of budgeting process.
Olivia and Gibson (2006) adapted 1999 SUSENAS survey on 28,964 households in Java, Indonesia to
examine their monthly expenditure for around 300 different products. The quantity purchased of food, fuels and
electricity were requested in order to obtain the unit values where the actual market prices was not being collected.
Based on Deaton (1990), the unit values helped to correct biases and acquire the price responses more precisely than
Proceedings of the Australian Academy of Business and Social Sciences Conference 2014
(in partnership with The Journal of Developing Areas)
ISBN 978-0-9925622-0-5
the actual market prices. The study used the cluster sampling which select sixteen households within districts and
regions. The findings in the first stage estimation indicated that kerosene and lubricant oil occupied 47 percent and
27 percent in budget shares which were the highest and lowest percentage respectively. Lubricant oil, LPG and
gasoline had the high expenditure elasticity of quantity which account to 2.80, 2.74 and 2.69 respectively. For the
unit value equation, only lubricant oil significantly responded to the household expenditures which the quality
elasticity was 0.07.
In addition, Filippini and Pachauri (2004) discussed the price elasticities and income elasticities of
electricity demand in urban India households. The study used the disaggregated level survey data from around
30,000 households. The model established by the electricity demand was a function of total household expenditure,
monetary expenditure on electricity and physical quantity of electricity consumed, average price of electricity, and
geographic and socio-economic variables. Due to the different weather in India, monthly data was used to estimate
the three different seasons which were monsoon, summer and winter. The findings revealed that the estimates of
income, own and cross price elasticities of electricity demand in urban India was fairly stable over the three seasons.
The income elasticities of electricity demand was found to be elastic while the price elasticities was inelastic. The
electricity demand during the summer months was more price inelastic than others seasons of the year due to the
higher temperature and the usage of air conditioners increased. The model revealed that income was the most
important economic variable.
Reddy (1995) examined the relationship between energy consumption with household income, household
size and energy prices in Bangalore, India by using multiple regression models. The total of 1000 households’
sample was used in the survey. The findings indicated that income was found to be significant in influencing the
energy consumption. When household income increased, energy consumption increased as well. The own price
elasticities of energy was found to have the expected negative signs and significant at the 5 percent level. However,
household size was found to have positive relationship with energy consumptions. This did not support most studies
where household size had a negative impact on per capita fuel consumption especially for modern fuels. This was
due to the large households who used energy more efficiently compared to the smaller households.
Furthermore, Hughes (1985) used a cross sectional survey to examine the relationship between energy
consumption and income on urban household in Nairobi, Kenya. The study explained the energy consumption
patterns in the Nairobi household sector. The survey consists of four types of data which are social and basic
necessities, household income, fuel consumption, price and end use, and appliance ownership and usage. The single
regression model examined the relationship between energy consumption as dependent variables and income as
independent variables. Meanwhile the multiple regression model was established by the logarithms of energy
consumption as dependent variable while the independent variables were logarithms of household income, household
size, energy prices and number of energy appliances. The findings indicated that energy consumption had a positive
relation with income and household size. Meanwhile price was not used as a determinant of energy consumption in
the study. It was also seen that energy consumption was highly consumed by high income group compared to low
income group.
METHODOLOGY
The research instrument used was a questionnaire with open ended questions. The questionnaire enquires the
demographic information of the respondents. This includes gender, level of education, household size, household
income and the location (rural or urban) to determine the welfare effect or consequences of the subsidy given to the
residents in Sabah and Sarawak. Then, it looks into the monthly expenditure of the household for energy (petrol,
diesel, electricity, LPG and kerosene) and non-energy use (medical, education, loan installment, food, utility,
entertainment, investment, clothing and public transport).
In this study, the two-stage cluster sampling method was used to choose the respondents. The state of Sabah
was divided into 5 clusters which represented by Kota Kinabalu, Tawau, Sandakan, Kudat and Beufort. Meanwhile
Sarawak was represented by Kuching, Sibu, Sarikei, Mukah and Miri. The respondents were further distinguished
into urban and rural areas. Then, the respondents were also categorized into high, middle and low income groups
respectively based on total monthly household income. Referring to the formula developed by Cochran (1963), the
total sample size of 1,002 households were chosen from Sabah and Sarawak. A structured face to face interview was
done individually which completed within 10 to 15 minutes per session.
Model Formation of Engel Curve
Proceedings of the Australian Academy of Business and Social Sciences Conference 2014
(in partnership with The Journal of Developing Areas)
ISBN 978-0-9925622-0-5
Williams (1977) defined that Engel curves are the diagrammatic representation of how expenditure on a commodity
(or quantity consumed) varies with income or total expenditure. Total expenditure is used as a better proxy than
income as a measurement of households’ resources when estimating Engel curves. It is also found to be more stable
than income in interpreting as encompassing aspects of permanent income. Besides, the estimates of income in
household budget studies often suffer significant measurement bias because households don not reveal their actual
income earned. The dependent variable in an Engel curve can be expressed in either quantity or expenditure terms.
However, the income elasticity of demand for a commodity may itself vary with income where Engel curve will
exhibit different geometric properties at different income levels.
The preferred shape of Engel curves was influenced by the formal introduction of income into demand
theory by Hicks and Allen. Allen and Bowley (1935) assumed that marginal utilities were linear in commodities to
derive and estimate linear Engel curves (constant marginal propensities to consume) from British household
expenditure surveys. Meanwhile linear curves may be valid over some income ranges, they have the undesirable
property that all income elasticities converge to unity as income increases. Prais and Houthakker (1955) used a range
of functional forms in order to find the best fit of the model including the double logarithmic form where the
coefficient on income provides a direct measure of the income elasticity of demand.
However, using broad commodity classes in the study would exhaust total expenditure. This requires Engel
curve estimates to satisfy a number of global properties such as predicted budget shares lying between zero and one,
and summing to one (adding-up property). The double logarithmic form does not satisfy the adding-up property
unless all income elasticities are unity. The alternative to solve this problem was using semi-logarithmic equation
where budget share as the dependent variable (Working, 1943).
Therefore, the linear relationship in Engel curve in this study is established between the budget share of
energy expenditure ( ) and the logarithm of total household expenditure ( ).
(1)
where = total energy expenditure / total household expenditure.
Since the preference parameters are unlikely to be constant across all households, demographic variables are
allowed in the model. Thus, the following Engel form is estimated as
∑ (2)
where ∑ , and is the demographic variables like the age, educational level and gender of
household head, household size and regional dummy variables. The parameters , , and are the
characteristics of the household preference and estimated by weighted least squares. Finally, the completed Engel
equation for each income group in both urban and rural areas for Sabah is formed as
(3)
For Sarawak, it is established as
(4)
where is age of household head, is household size, is educational level in terms of schooling years of
household head, is dummy variable where male equal to 1 and otherwise zero, the regional dummy
variable refers to the selected region equal to 1 and otherwise zero and so on.
The expenditure elasticity of the energy demand, for an average household is calculated using formula
as below:-
(5)
Proceedings of the Australian Academy of Business and Social Sciences Conference 2014
(in partnership with The Journal of Developing Areas)
ISBN 978-0-9925622-0-5
If is greater than 0, the commodity is a luxury since the budget share increases with income. Meanwhile
if is less than 0, the commodity is a necessity due to the budget share falls as income increases. When is found
to be negative sign and is less than in absolute value, the commodity is claimed as an inferior type. These
coefficients allow the impact of household characteristics to be in detail based on budget share.
FINDINGS
Engel Curve Estimation
Low Income Households in Rural Sabah
Table 1 showed the estimation of Engel curve for low income households in rural area in Sabah. The ratio of
monthly total energy expenditure over total household expenditure was regressed against the total household
expenditure, the age of the household head, size of household, level of education for the household head, gender of
the household head and regions in Sabah such as Tawau, Sandakan, Kudat and Beufort. Kota Kinabalu was excluded
because it was used as the reference category due to its large sample size compared to other regions. The
independent variables of total household expenditure (sig.=0.0118), age (sig.=0.0153), household size (sig.=0.0004)
and the region of Tawau (sig.=0.0000) were found to be significant at 5 percent significance level. All other variables
like educational level (sig.=0.8068) and gender (sig.=0.9452) of the household head as well as the regions like
Sandakan (sig.=0.1664), Kudat (sig.=0.5618) and Beufort (sig.=0.6944) were found to be insignificant at 5 percent
significance level. The value of R-squared showed that 39.62 percent changes in the budget share of total energy
expenditure over total household expenditure could be explained by total household expenditure and the
demographic variables such as the head of household’s age, educational level, gender, household size and regions.
Meanwhile around 60.38 percent changes in the budget share of energy expenditure were explained by other factors.
The negative sign of the coefficient for total household expenditure (-0.050248) indicated that energy was a
necessity commodity for low income group in rural area in Sabah. It also showed that one unit increase in total
household expenditure would reduce the budget share of energy expenditure by 5.03 percent. Besides, one unit
increase in the age of household head would increase the budget share of energy expenditure by 9 percent.
Meanwhile household size was found to influence the budget share of energy expenditure by 6.82 percent when the
number of the household members was increased by one unit. The only significant Tawau region dummy variable
had positive coefficient which indicated that low income households in rural area in Tawau experienced the growth
in budget share of energy expenditure compared to Kota Kinabalu which was used as the reference category.
TABLE 1: ESTIMATION OF ENGEL CURVE FOR LOW INCOME HOUSEHOLDS IN RURAL AREA IN
SABAH
Variable Coefficient Std. Error t-Statistic Prob.
C 0.088719 0.194133 0.457004 0.6486 LOG(TEXP) -0.050248 0.019623 -2.560654 0.0118
LOG(AGE) 0.090096 0.036559 2.464377 0.0153
LOG(HS) 0.068236 0.018545 3.679547 0.0004 LOG(EDU) -0.002984 0.012171 -0.245174 0.8068
GENDER 0.001232 0.017875 0.068906 0.9452
TWU 0.095494 0.018297 5.219143 0.0000 SDKN 0.024119 0.017312 1.393172 0.1664
KUDAT -0.017929 0.030813 -0.581878 0.5618
BEUF 0.012041 0.030572 0.393849 0.6944 R-squared 0.396195
Middle Income Households in Rural Sabah
Table 2 showed the estimation of Engel curve for middle income households in rural area in Sabah. The budget share
of monthly total energy expenditure was regressed against the total household expenditure, the age of the household
head, size of household, level of education for the household head, gender of the household head and regions in
Sabah such as Kota Kinabalu, Sandakan, Kudat and Beufort. Now, Tawau was excluded and used as the reference
category because it was the region with the largest sample size among the middle income group in rural area in
Sabah.
The independent variables, total household expenditure (sig.=0.0007) and household size (sig.=0.0034)
were found to be significant in explaining the changes in budget share of energy expenditure at 5 percent
Proceedings of the Australian Academy of Business and Social Sciences Conference 2014
(in partnership with The Journal of Developing Areas)
ISBN 978-0-9925622-0-5
significance level. In contrast, other variables like age (sig.=0.3471), educational level (sig.=0.0764) and gender
(sig.=0.1039) of the household head as well as all the regions like Kota Kinabalu (sig.=0.2227), Sandakan
(sig.=0.4216), Kudat (sig.=0.0846) and Beufort (sig.=0.1029) were found to be insignificant at 5 percent significance
level. However, the household head’s educational level and region variable of Kudat were found to be significant at
10 percent significance level. This indicated that these two variables also influenced the budget share of energy
expenditure but with less significant effect compared to total household expenditure and household size. The value of
R-squared indicated that 29.54 percent changes in the budget share of energy expenditure could be explained by total
household expenditure, the head of household’s age, educational level, gender, household size and regions. The
remaining value of R-squared ascertained that another 70.46 percent changes in the budget share of energy
expenditure were explained by other factors.
Energy was also found to be a necessity commodity for middle income among rural households in Sabah
due to the negative sign of coefficient for total household expenditure (-0.062429). The coefficient also ascertained
that one unit increased in total household expenditure would reduce the budget share of energy expenditure by 6.24
percent. In addition, one unit increased in household size would significantly raise the budget share of energy
expenditure by 4.06 percent. On the other hand, educational level would increase the budget share of energy
expenditure by 2.85 percent when one unit increases in schooling year of household head was used. For the regional
dummy variable, Kudat was found to have less budget share of energy expenditure by 3.74 percent compared to
Tawau which was used as the reference category.
TABLE 2: ESTIMATION OF ENGEL CURVE FOR MIDDLE INCOME HOUSEHOLDS IN RURAL
AREA IN SABAH
Variable Coefficient Std. Error t-Statistic Prob.
C 0.402062 0.167272 2.403639 0.0185 LOG(TEXP) -0.062429 0.017783 -3.510566 0.0007
LOG(AGE) 0.032582 0.034449 0.945815 0.3471
LOG(HS) 0.040569 0.013450 3.016307 0.0034 LOG(EDU) 0.028529 0.015894 1.794898 0.0764
GENDER 0.023284 0.014158 1.644586 0.1039
KK -0.014756 0.012008 -1.228880 0.2227 SDKN -0.011269 0.013950 -0.807801 0.4216
KUDAT -0.037447 0.021443 -1.746295 0.0846
BEUF -0.041585 0.025206 -1.649813 0.1029
R-squared 0.295426
High Income Households in Rural Sabah
Table 3 showed the estimation of Engel curve for high income households in rural area in Sabah. The budget share of
monthly total energy expenditure was regressed against the total household expenditure, the age of the household
head, size of household, level of education for the household head, gender of the household head and regions in
Sabah such as Kota Kinabalu, Sandakan, Kudat and Beufort. Tawau was excluded because it was used as the
reference category due to its large sample size. All the independent variables were found to be insignificant in
explaining the changes in budget share of energy expenditure at 5 percent significance level. The value of R-squared
indicated that 23.34 percent changes in the budget share of energy expenditure could be explained by total household
expenditure, the head of household’s age, educational level, gender, household size and regions. The remaining R-
squared showed that 76.66 percent changes in the budget share of energy expenditure were explained by other
factors. Energy was also a necessity commodity for high income households in rural area in Sabah due to the
negative sign of coefficient for total household expenditure.
TABLE 3: ESTIMATION OF ENGEL CURVE FOR HIGH INCOME HOUSEHOLDS IN RURAL AREA
IN SABAH
Variable Coefficient Std. Error t-Statistic Prob.
C 1.020528 0.685738 1.488218 0.1605
LOG(TEXP) -0.093811 0.077745 -1.206651 0.2491
LOG(AGE) -0.034833 0.102059 -0.341300 0.7383 LOG(HS) 0.072753 0.058156 1.250981 0.2330
Proceedings of the Australian Academy of Business and Social Sciences Conference 2014
(in partnership with The Journal of Developing Areas)
ISBN 978-0-9925622-0-5
LOG(EDU) -0.028623 0.088415 -0.323731 0.7513
GENDER -0.000934 0.041448 -0.022528 0.9824 KK 0.015008 0.042712 0.351379 0.7309
SDKN -0.014161 0.041781 -0.338934 0.7401
KUDAT -0.014148 0.054564 -0.259298 0.7995 BEUF -0.012052 0.085854 -0.140377 0.8905
R-squared 0.233432
Low Income Households in Urban Sabah
Table 4 indicated the estimation of Engel curve for low income households in urban area in Sabah. The ratio of
monthly total energy expenditure over total household expenditure was regressed against the total household
expenditure, the age of the household head, size of household, level of education for the household head, gender of
the household head and regions in Sabah such as Tawau, Sandakan, Kudat and Beufort. Kota Kinabalu was excluded
because it was used as the reference category due to its large sample size.
The independent variables such as total household expenditure (sig.=0.0106) and age (sig.=0.0264) were
found to be significant in explaining the changes in the budget share of energy expenditure at 5 percent significance
level. All other variables like household size (sig.=0.3182), educational level (sig.=0.0658) and gender (sig.=0.5794)
of the household head as well as all the regions like Tawau (sig.=0.9688), Sandakan (sig.=0.9958), Kudat
(sig.=0.8418) and Beufort (sig.=0.6404) were found to be insignificant at 5 percent significance level. However,
educational level was found to be significant at 10 percent significance level which had less effect on the budget
share of energy expenditure. The value of R-squared ascertained that 21.33 percent changes in the budget share of
energy expenditure could be explained by total household expenditure, the head of household’s age, educational
level, gender, household size and regions. Another 78.67 percent changes in the energy expenditure budget share
were explained by other factors.
The negative sign of coefficient for total household expenditure (-0.049219) showed that energy was a
necessity commodity for low income among urban households in Sabah. The coefficient also explained that there
was 4.92 percent decreased in the budget share of energy expenditure when the total household expenditure was
increased by one unit. Besides, the age of household head was found to raise the budget share of energy expenditure
by 6.82 percent when one unit increased in it. Meanwhile one unit of schooling year increase in educational level
would slightly increase the energy expenditure budget share by 2.65 percent.
TABLE 4: ESTIMATION OF ENGEL CURVE FOR LOW INCOME HOUSEHOLDS IN URBAN AREA IN
SABAH
Variable Coefficient Std. Error t-Statistic Prob.
C 0.205144 0.139780 1.467613 0.1470
LOG(TEXP) -0.049219 0.018713 -2.630267 0.0106
LOG(AGE) 0.068272 0.030050 2.271973 0.0264 LOG(HS) 0.016104 0.016010 1.005870 0.3182
LOG(EDU) 0.026518 0.014176 1.870671 0.0658
GENDER 0.007832 0.014059 0.557057 0.5794 TWU 0.000740 0.018849 0.039259 0.9688
SDKN 9.50E-05 0.017916 0.005301 0.9958
KUDAT 0.005573 0.027817 0.200352 0.8418 BEUF -0.016840 0.035887 -0.469267 0.6404
R-squared 0.213339
Middle Income Households in Urban Sabah
Table 5 showed the estimation of Engel curve for middle income households in urban area in Sabah. The budget
share of monthly total energy expenditure was regressed against the total household expenditure, the age of the
household head, size of household, level of education for the household head, gender of the household head and
regions in Sabah such as Tawau, Sandakan, Kudat and Beufort. Kota Kinabalu was excluded and used as the
reference category because it was the region with the largest sample size.
The independent variables such as total household expenditure (sig.=0.0312) and household size
(sig.=0.0007) were found to be significant in explaining the changes in budget share of energy expenditure at 5
Proceedings of the Australian Academy of Business and Social Sciences Conference 2014
(in partnership with The Journal of Developing Areas)
ISBN 978-0-9925622-0-5
percent significance level. On the contrary, other variables like age (sig.=0.7272), educational level (sig.=0.0606)
and gender (sig.=0.3061) of the household head as well as all the regions like Tawau (sig.=0.6217), Sandakan
(sig.=0.0536), Kudat (sig.=0.3849) and Beufort (sig.=0.6246) were found to be insignificant at 5 percent significance
level. Nevertheless, it could be seen that educational level and region variable of Sandakan influences the budget
share at 10 percent significance level. The value of R-squared indicated that 23.73 percent changes in the budget
share of energy expenditure could be explained by total household expenditure, the age, educational level and gender
of household head, household size and regions. The remaining of 76.27 percent changes in the budget share were
explained by other factors.
Energy was also found to be a necessity commodity for middle income among urban households in Sabah
due to the negative sign of coefficient for total household expenditure (-0.03411). The coefficient also explained that
one unit increase in total household expenditure would reduce the budget share of energy expenditure by 3.41
percent. Moreover, one unit increase in household size would significantly raise the budget share of energy
expenditure by 3.61 percent. On the other hand, educational level influenced the budget share by reducing it with
3.14 percent when there was one unit increase in schooling year. Meanwhile households in Sandakan were
significantly found to have less energy expenditure with 1.84 percent of budget share compared to Kota Kinabalu.
TABLE 5: ESTIMATION OF ENGEL CURVE FOR MIDDLE INCOME HOUSEHOLDS IN URBAN
AREA IN SABAH
Variable Coefficient Std. Error t-Statistic Prob.
C 0.429872 0.132521 3.243806 0.0015
LOG(TEXP) -0.034110 0.015638 -2.181212 0.0312
LOG(AGE) 0.007408 0.021188 0.349663 0.7272 LOG(HS) 0.036134 0.010346 3.492566 0.0007
LOG(EDU) -0.031449 0.016600 -1.894589 0.0606
GENDER -0.008676 0.008440 -1.027981 0.3061 TWU 0.004447 0.008987 0.494808 0.6217
SDKN -0.018370 0.009423 -1.949430 0.0536
KUDAT -0.012550 0.014390 -0.872162 0.3849 BEUF 0.011778 0.024009 0.490580 0.6246
R-squared 0.237287
High Income Households in Urban Sabah
Table 6 showed the estimation of Engel curve for high income among the urban households in Sabah. The ratio of
monthly total energy expenditure over total household expenditure was regressed against the total household
expenditure, the age of the household head, size of household, level of education for the household head, gender of
the household head and regions in Sabah such as Kota Kinabalu, Sandakan, Kudat and Beufort. Tawau was excluded
because it was used as the reference category due to its large sample size.
The only independent variable that influenced the budget share of energy expenditure significantly at 5
percent significance level was total household expenditure (sig.=0.0008). All other variables like the age
(sig.=0.3850), educational level (sig.=0.1365) and gender (sig.=0.4845) of the household head and household size
(sig.=0.1295) as well as all the regions variables like Kota Kinabalu (sig.=0.7256), Sandakan (sig.=0.5872), Kudat
(sig.=0.4854) and Beufort (sig.=0.1178) were found to be insignificant at 5 percent significance level. The value of
R-squared indicated that 34.35 percent changes in the budget share of total energy expenditure over total household
expenditure could be explained by total household expenditure and the demographic variables such as the age,
educational level and gender of household head, household size and regions. The remaining of 65.65 percent changes
in the energy expenditure budget share were explained by other factors.
The negative sign of coefficient for total household expenditure (-0.070223) indicated that energy was also
a necessity commodity for high income group in urban area in Sabah. The coefficient also explained that one unit
increased in total household expenditure would reduce the budget share of energy expenditure by 7.02 percent.
TABLE 6: ESTIMATION OF ENGEL CURVE FOR HIGH INCOME HOUSEHOLDS IN URBAN AREA
IN SABAH
Variable Coefficient Std. Error t-Statistic Prob.
C 0.699550 0.182654 3.829926 0.0003
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LOG(TEXP) -0.070223 0.019768 -3.552272 0.0008
LOG(AGE) 0.025493 0.029100 0.876061 0.3850 LOG(HS) 0.028318 0.018388 1.540091 0.1295
LOG(EDU) -0.026841 0.017754 -1.511795 0.1365
GENDER -0.009643 0.013698 -0.703959 0.4845 KK -0.005323 0.015083 -0.352894 0.7256
SDKN -0.007652 0.014011 -0.546180 0.5872
KUDAT -0.021103 0.030036 -0.702595 0.4854 BEUF -0.027504 0.017297 -1.590053 0.1178
R-squared 0.343526
Low Income Households in Rural Sarawak
Table 7 showed the estimation of Engel curve for low income households in rural area in Sarawak. The ratio of
monthly total energy expenditure over total household expenditure was regressed against the total household
expenditure, the age of the household head, size of household, level of education for the household head, gender of
the household head and regions in Sarawak such as Sibu, Sarikei, Mukah and Miri. Kuching was excluded and used
as the reference category due to it was the region with the largest sample size.
The independent variables of total household expenditure (sig.=0.0000) and the region of Mukah
(sig.=0.0017) were found to be significant in explaining the changes in the budget share of energy expenditure at 5
percent significance level. All other variables like age (sig.=0.4996), educational level (sig.=0.4069) and gender
(sig.=0.9761) of the household head and household size (sig.=0.0955) as well as the regions like Sibu (sig.=0.1108),
Sarikei (sig.=0.4163) and Miri (sig.=0.7266) were found to be insignificant at 5 percent significance level.
Nonetheless, household size was claimed to be significant at 10 percent significance level which might influence the
budget share with less effect compared to total household expenditure and region variable of Mukah. The value of R-
squared showed that 32.35 percent changes in the budget share of total energy expenditure over total household
expenditure could be explained by total household expenditure and the demographic variables such as the household
head’s age, educational level and gender, household size and regions. The remaining of 67.65 percent changes in the
budget share were explained by other factors.
Energy was claimed as a necessity commodity for low income group in rural area in Sarawak due to the
negative sign of coefficient for total household expenditure (-0.059325). The coefficient also ascertained that 5.93
percent decreased in the budget share of energy expenditure when one unit increased in total household expenditure.
Besides, one unit change in the region of Mukah would raise the budget share of energy expenditure by 11.36
percent. For household size, one unit increase in the number of household member would lead to 1.94 percent
increase in the budget share.
TABLE 7: ESTIMATION OF ENGEL CURVE FOR LOW INCOME HOUSEHOLDS IN RURAL AREA IN
SARAWAK
Variable Coefficient Std. Error t-Statistic Prob.
C 0.546191 0.095265 5.733392 0.0000
LOG(TEXP) -0.059325 0.010614 -5.589319 0.0000 LOG(AGE) 0.012655 0.018699 0.676745 0.4996
LOG(HS) 0.019386 0.011555 1.677677 0.0955
LOG(EDU) 0.005693 0.006844 0.831810 0.4069 GENDER 0.000275 0.009171 0.030002 0.9761
SIBU -0.017630 0.010990 -1.604219 0.1108
SARIKEI -0.015048 0.018460 -0.815158 0.4163 MUKAH 0.113635 0.035544 3.197029 0.0017
MIRI -0.003921 0.011195 -0.350276 0.7266
R-squared 0.323487
Middle Income Households in Rural Sarawak
Table 8 showed the estimation of Engel curve for middle income households in rural area in Sarawak. The budget
share of monthly total energy expenditure was regressed against the total household expenditure, the age of the
household head, size of household, level of education for the household head, gender of the household head and
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ISBN 978-0-9925622-0-5
regions in Sarawak such as Sibu, Sarikei, Mukah and Miri. Kuching was excluded because it was used as the
reference category due to its large sample size.
The independent variables such as total household expenditure (sig.=0.0058) and household size
(sig.=0.0008) were found to be significant in explaining the changes in budget share of energy expenditure at 5
percent significance level. In contrast, other variables like age (sig.=0.6226), educational level (sig.=0.6504) and
gender (sig.=0.2071) of the household head as well as all the regions like Sibu (sig.=0.3555), Sarikei (sig.=0.5714),
Mukah (sig.=0.9503) and Miri (sig.=0.0597) were found to be insignificant at 5 percent significance level. However,
it could be seen that the region of Miri was significant at 10 percent significance level. The value of R-squared
indicated that 28.89 percent changes in the budget share of energy expenditure could be explained by total household
expenditure, the age, educational level and gender of household head, household size and regions. Another 71.11
percent changes in the budget share were explained by other factors.
The negative sign of coefficient for total household expenditure (-0.063972) indicated that energy was a
necessity commodity for middle income among rural households in Sarawak. The coefficient also explained that one
unit increased in total household expenditure would reduce the budget share of energy expenditure by 6.40 percent.
In addition, one unit increase in household size would significantly raise the budget share of energy expenditure by
8.75 percent. For the regional dummy variable, middle income households in rural areas in Miri were found to
experience the reduction in budget share of energy expenditure compared to Kuching which was the reference
category.
TABLE 8: ESTIMATION OF ENGEL CURVE FOR MIDDLE INCOME HOUSEHOLDS IN RURAL
AREA IN SARAWAK
Variable Coefficient Std. Error t-Statistic Prob.
C 0.466235 0.194966 2.391364 0.0196
LOG(TEXP) -0.063972 0.022439 -2.850855 0.0058 LOG(AGE) 0.014241 0.028801 0.494447 0.6226
LOG(HS) 0.087517 0.024937 3.509562 0.0008
LOG(EDU) 0.006769 0.014868 0.455277 0.6504 GENDER 0.016231 0.012742 1.273884 0.2071
SIBU 0.016367 0.017591 0.930405 0.3555
SARIKEI -0.019080 0.033548 -0.568751 0.5714 MUKAH -0.001538 0.024581 -0.062569 0.9503
MIRI -0.029170 0.015228 -1.915523 0.0597
R-squared 0.288915
High Income Households in Rural Sarawak
Table 9 showed the estimation of Engel curve for high income households in rural area in Sarawak. The budget share
of monthly total energy expenditure was regressed against the total household expenditure, the age of the household
head, size of household, level of education for the household head, gender of the household head and regions in
Sarawak such as Sibu and Miri. Sarikei and Mukah were not included due to zero sample size found in the study.
Meanwhile Kuching was also excluded because it was used as reference category due to its largest sample size.
The only independent variable that significantly influence the budget share of energy expenditure was
household size (sig.=0.0266). All other variables like total household expenditure (sig.=0.8867), the age
(sig.=0.1571), educational level (sig.=0.7884) and gender (sig.=0.2093) of household head as well as the regions like
Sibu (sig.=0.4176) and Miri (sig.=0.0761) were found to be insignificant at 5 percent significance level.
Nevertheless, it could be seen that the region of Miri was significant at 10 percent significance level. The value of R-
squared indicated that 87.68 percent changes in the budget share of energy expenditure could be explained by total
household expenditure, the household head’s age, educational level, gender, household size and regions. The
remaining of 12.32 percent changes in the budget share were explained by other factors.
Energy was also claimed as a necessity commodity for high income households in rural area in Sarawak due
to the sign of total household expenditure coefficient (-0.022989) was negative. On the other hand, the coefficient of
household size (0.098369) explained that one unit increase in household size would raise the budget share of energy
expenditure by 9.84 percent. Moreover, households in Miri were found to reduce the budget share of energy
expenditure by 6.21 percent compared to Kuching households.
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TABLE 9: ESTIMATION OF ENGEL CURVE FOR HIGH INCOME HOUSEHOLDS IN RURAL AREA
IN SARAWAK
Variable Coefficient Std. Error t-Statistic Prob.
C 0.649481 1.356456 0.478807 0.6523
LOG(TEXP) -0.022989 0.153349 -0.149912 0.8867
LOG(AGE) -0.124012 0.074550 -1.663458 0.1571 LOG(HS) 0.098369 0.031635 3.109504 0.0266
LOG(EDU) -0.013053 0.046096 -0.283172 0.7884
GENDER 0.066041 0.045842 1.440607 0.2093 SIBU -0.037343 0.042285 -0.883120 0.4176
MIRI -0.062129 0.027858 -2.230225 0.0761
R-squared 0.876754
Low Income Households in Urban Sarawak
Table 10 showed the estimation of Engel curve for low income among the urban households in Sarawak. The ratio of
monthly total energy expenditure over total household expenditure was regressed against the total household
expenditure, the age of the household head, size of household, level of education for the household head, gender of
the household head and regions in Sarawak such as Sibu, Sarikei, Mukah and Miri. Kuching was excluded and used
as the reference category due to it was the region with the largest sample size.
The independent variables like the household head’s gender (sig.=0.0404) and the region of Miri
(sig.=0.0005) were found to be significant in explaining the changes in the budget share of energy expenditure at 5
percent significance level. All other variables like total household expenditure (sig.=0.0760), age (sig.=4196) and
educational level (sig.=0.8468) of the household head and household size (sig.=0.6401) as well as all the included
regions like Sibu (sig.=0.3303), Sarikei (sig.=0.7964) and Mukah (sig.=0.0909) were found to be insignificant at 5
percent significance level. However, total household expenditure and region variable of Mukah were claimed to
significant at 10 percent significance level. The value of R-squared showed that 30.10 percent changes in the budget
share of total energy expenditure over total household expenditure could be explained by total household expenditure
and the demographic variables such as the household head’s age, educational level and gender, household size and
regions. Another 69.90 percent changes in the budget share were explained by other factors.
Energy was known as a necessity commodity for low income group in urban area in Sarawak due to the
negative sign of coefficient for total household expenditure (-0.030284). It also ascertained that there was 3.03
percent decrease in budget share when one unit increase in total household expenditure. The coefficient of gender
indicated that the male household head would raise the budget share of energy expenditure compared to female
household head. Besides, the low income households in urban Miri would have less budget share of energy
expenditure by 5.20 percent compared to Kuching which was the reference category. On the other hand, households
in Mukah would experience the increase in budget share by 8.77 percent compared to Kuching.
TABLE 10: ESTIMATION OF ENGEL CURVE FOR LOW INCOME HOUSEHOLDS IN URBAN AREA
IN SARAWAK
Variable Coefficient Std. Error t-Statistic Prob.
C 0.329020 0.138006 2.384100 0.0201 LOG(TEXP) -0.030284 0.016791 -1.803628 0.0760
LOG(AGE) 0.017182 0.021152 0.812302 0.4196
LOG(HS) 0.006074 0.012928 0.469841 0.6401 LOG(EDU) 0.002200 0.011336 0.194048 0.8468
GENDER 0.029219 0.013963 2.092572 0.0404
SIBU -0.030466 0.031060 -0.980876 0.3303
SARIKEI 0.009455 0.036488 0.259136 0.7964
MUKAH 0.087669 0.051074 1.716492 0.0909
MIRI -0.052046 0.014297 -3.640339 0.0005 R-squared 0.300954
Middle Income Households in Urban Sarawak
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Table 11 showed the estimation of Engel curve for middle income households in urban area in Sarawak. The budget
share of monthly total energy expenditure was regressed against the total household expenditure, the age of the
household head, size of household, level of education for the household head, gender of the household head and
regions in Sarawak such as Sibu, Sarikei, Mukah and Miri. Kuching was excluded because it was used as the
reference category due to its large sample size.
The independent variables like the total household expenditure (sig.=0.0000), household size (sig.=0.0000)
and the region of Miri (sig.=0.0006) were found to be significant in explaining the changes in budget share of energy
expenditure at 5 percent significance level. On the contrary, other variables like age (sig.=0.4681), educational level
(sig.=0.7346) and gender (sig.=0.8178) of the household head as well as the regions like Sibu (sig.=0.6040), Sarikei
(sig.=0.5891) and Mukah (sig.=0.8222) were found to be insignificant at 5 percent significance level. The value of
R-squared indicated that 35.32 percent changes in the budget share of energy expenditure could be explained by total
household expenditure, the age, educational level and gender of household head, household size and regions.
Another 64.68 percent changes in the budget share were explained by other factors.
The negative sign of the coefficient for total household expenditure (-0.096374) indicated that energy was a
necessity commodity for middle income among urban households in Sarawak. The coefficient also explained that
one unit increased in total household expenditure would reduce the budget share of energy expenditure by 9.64
percent. Besides, one unit increased in household size would significantly raise the budget share of energy
expenditure by 5.41 percent. Moreover, the middle income households in urban Miri possessed the fall in the budget
share of energy expenditure with 4.25 percent compared to Kuching which was the reference category.
TABLE 11: ESTIMATION OF ENGEL CURVE FOR MIDDLE INCOME HOUSEHOLDS IN URBAN
AREA IN SARAWAK
Variable Coefficient Std. Error t-Statistic Prob.
C 0.816439 0.161263 5.062781 0.0000 LOG(TEXP) -0.096374 0.021086 -4.570525 0.0000
LOG(AGE) 0.016656 0.022885 0.727797 0.4681
LOG(HS) 0.054132 0.012209 4.433815 0.0000 LOG(EDU) -0.004767 0.014032 -0.339728 0.7346
GENDER -0.002111 0.009148 -0.230813 0.8178
SIBU -0.005629 0.010827 -0.519923 0.6040 SARIKEI -0.009893 0.018267 -0.541555 0.5891
MUKAH -0.004516 0.020049 -0.225233 0.8222
MIRI -0.042524 0.012152 -3.499343 0.0006
R-squared 0.353158
High Income Households in Urban Sarawak
Table 12 showed the estimation of Engel curve for high income households in urban area in Sarawak. The budget
share of monthly total energy expenditure was regressed against the total household expenditure, the age of the
household head, size of household, level of education for the household head, gender of the household head and
regions in Sarawak such as Sibu, Sarikei and Miri. Kuching was excluded and used as the reference category due to
it was the region with the largest sample size. Meanwhile Mukah was also excluded due to zero sample size found in
the study.
All the independent variables were found to be insignificant in explaining the changes in the budget share of
energy expenditure at 5 percent significance level. However, the variable of age (sig.=0.0913) was found to be
significant at 10 percent significance level. The value of R-squared indicated that 27.28 percent changes in the
budget share of energy expenditure could be explained by total household expenditure, the household head’s age,
educational level, gender, household size and regions. The remaining of 72.72 percent in the budget share were
explained by other factors. Energy was claimed as a necessity commodity for high income households in urban area
in Sarawak due to the sign of coefficient for total household expenditure (-0.041519) was negative. Moreover, one
unit increase in the age of household head, there was 13.02 percent decrease in the budget share of energy
expenditure.
TABLE 12: ESTIMATION OF ENGEL CURVE FOR HIGH INCOME HOUSEHOLDS IN URBAN AREA
IN SARAWAK
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Variable Coefficient Std. Error t-Statistic Prob.
C 1.017325 0.521887 1.949320 0.0595 LOG(TEXP) -0.041519 0.055095 -0.753582 0.4563
LOG(AGE) -0.130209 0.074919 -1.738006 0.0913
LOG(HS) 0.077516 0.052410 1.479049 0.1483 LOG(EDU) -0.055288 0.045310 -1.220214 0.2308
GENDER 0.006435 0.028740 0.223904 0.8242
SIBU -0.004775 0.033671 -0.141817 0.8881 SARIKEI 0.050866 0.083806 0.606949 0.5479
MIRI -0.042543 0.028792 -1.477594 0.1487
R-squared 0.272773
Expenditure Elasticity
Table 13 showed the estimation of expenditure elasticity of Engel curve based on location and income group in
Sabah. It could be seen that all the value of expenditure elasticity for energy were in positive sign and less than one.
This indicated that energy was a necessity commodity for all income groups in both urban and rural area in Sabah. In
the urban area, middle income group possessed the highest expenditure elasticity which was amounted to 0.770. This
was followed by low income group with 0.724 and high income group with 0.536 in the estimation of expenditure
elasticity for energy. In contrast, the situation in rural area showed a difference where low income group occupied
the highest ranking of expenditure elasticity with 0.764. This was followed by middle income group with
expenditure elasticity of 0.624. High income group was still found to have lowest responsiveness of demand for
energy towards the changes in income with only 0.351 expenditure elasticity.
TABLE 13: EXPENDITURE ELASTICITY OF ENGEL CURVE BASED ON LOCATION AND INCOME
GROUP IN SABAH
Income Group
Sabah
Urban Rural
High 0.536 0.351
Middle 0.770 0.624
Low 0.724 0.764
Table 14 showed the estimation of expenditure elasticity of Engel curve based on location and income
group in Sarawak. Similar to Sabah, all the value of expenditure elasticity for energy were in positive sign and less
than one. This indicated that energy was a necessity commodity for all income groups in both urban and rural area in
Sarawak. In urban area, low income group was found to have the highest responsiveness of demand for energy
towards the changes in income. The expenditure elasticity was estimated as high as 0.840 for the urban low income
group. The expenditure elasticity of energy demand among high income group in urban Sarawak was estimated as
0.725 which was larger than middle income group with only 0.382. On the other hand, high income group in rural
area responded significantly on energy demand towards the changes in income compared to low and middle income
groups where the expenditure elasticity were estimated as 0.862, 0.730 and 0.661 respectively.
TABLE 14: EXPENDITURE ELASTICITY OF ENGEL CURVE BASED ON LOCATION AND INCOME
GROUP IN SARAWAK
Income Group
Sarawak
Urban Rural
High 0.725 0.862
Middle 0.382 0.661
Low 0.840 0.730
DISCUSSION AND CONCLUSION
From the estimation of Engel curve, it could be seen that the variable of total household expenditure was significant
in influencing the budget share of energy expenditure in both urban and rural areas in Sabah and Sarawak. It was
only applicable for low and middle income groups. This meant that income was a very important factor for
households to make decision in consuming energy. However, the energy expenditure among high income groups was
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not affected by their income except for urban households in Sabah. All the coefficients of total household
expenditure were in negative sign which indicated that energy was a necessity commodity to households in Sabah
and Sarawak. This was in line with the study conducted by Gundimeda and Kohlin (2008) and Chambwera and
Folmer (2007).
Another important factor in influencing the energy expenditure was household size. In Sabah, the variable
was found to be significant among middle income groups in both urban and rural areas as well as low income group
in rural area. All the income groups in rural area in Sarawak were influenced by household size in consuming energy
meanwhile it was only for middle income group in urban area. The respective positive sign for the coefficient of
household size indicated that the budget share of energy expenditure raised as the number of household member
increased. This was in line with the findings by Deaton and Muellbauer (1980) who claimed that the larger
households spent a larger budget share on necessities goods compared to smaller households for the same level of
total expenditure. Reddy (1995) argued that this might be due to inefficient consuming had occurred when energy
expenditure raised as household size increased. Meanwhile Chambwera and Folmer (2007) asserted that the
economies of scale found was inverted U-type pattern where initially positive impact on budget share with household
size but it turned to negative impact when the household size increased.
The variable of educational level of household head only influenced the energy expenditure among
households in Sabah but with less significant effect. This indicated that psychogenic needs existed in the budgeting
process. Low income group in urban area and middle income group in rural area comprise of positive relationship
which indicated that higher schooling years obtained by household head raised the energy expenditure. In contrast,
middle income group in urban Sabah possessed negative relationship between educational level and energy
expenditure. The findings were in contrast with the study by Chambwera and Folmer (2007) who found the
educational level to be insignificant in explaining the energy expenditure.
The age of household head was found to be positively influenced the budget share of energy expenditure
among low income group in both urban and rural areas in Sabah. However, the situation was different in Sarawak
where the households head’s age only affected the energy expenditure among high income group with negative and
smaller effect. Moreover, the variable of gender of household head only significantly influenced the energy
expenditure among low income group in urban area in Sarawak. It showed that the male household head led to the
increase in budget share of demand for energy compared to the female. This may due to female household head had
higher consciousness on saving.
As for regional dummy variable in Sabah, low income household in rural area in Tawau was found to have
higher budget share of energy expenditure compared to Kota Kinabalu. For middle income group in Sabah, the urban
household in Sandakan and rural household in Kudat were claimed to consume less energy compared to Kota
Kinabalu and Tawau respectively but with less significant effect. In Sarawak, it was found that Miri households
experienced reduction in budget share of energy demand compared to Kuching among low and middle income
groups. On the contrary, low income households in Mukah possessed higher energy expenditure compared to
Kuching in both urban and rural areas.
Finally, all the Engel expenditure elasticities for energy were estimated in the value between 0 to 1. This
supported that energy was a necessity good to all the income groups in both urban and rural areas in Sabah and
Sarawak. But low income group was found to be more sensitive on budget share of energy expenditure to the
changes in household income. The findings were in line with the study of Gundimeda and Kohlin (2008). However,
it was different to the study conducted by Olivia and Gibson (2006) and Filippini and Pachauri (2004) who estimated
the expenditure or income elasticities as greater than 1 that indicated that energy was luxury good.
REFERENCES
Allen, R.G.D and Bowley, A.L., “Family Expenditure. A Study of its Variation”, London: P.S. King and
Son, 1935.
Chambwera, M. and Folmer, H., “Fuel switching in Harare: An almost ideal demand system approach”,
Energy Policy, 2007, Vol. 35, No. 4, pp. 2538-2548.
Proceedings of the Australian Academy of Business and Social Sciences Conference 2014
(in partnership with The Journal of Developing Areas)
ISBN 978-0-9925622-0-5
Chua, S.C. and Oh, T.H., “Review on Malaysia national energy developments: Key policies, agencies,
programmes and international involvements”, Renewable and Sustainable Energy Reviews, 2010, Vol. 14, pp. 2916-
2925.
CIA (Central Intelligence Agency), “The World Factbook: Malaysia”,
https://www.cia.gov/library/publications/the-world-factbook/goes/my.html, Retrieved 20 July 2011.
Cochran, W.G., “Sampling Techniques”, 2nd
Ed., New York: John Wiley and Sons, Inc., 1963.
Deaton, A., “Price elasticities from survey data: Extensions and Indonesian results”, Journal of
Economietrics, 1990, Vol. 44, No. 3, pp. 281-309.
Deaton, A. and MuellBauer, J., “Economics and Consumer Behaviour”, Cambridge University Press,
Cambridge, 1980.
Filippini, M. and Pachauri, S., “Elasticities of electricity demand in urban Indian households”, Energy
Policy, 2004, Vol. 32, No. 3, pp. 429-436.
Gundimeda, H. and Kohlin, G., “Fuel demand elasticities for energy and environmental policies: Indian
sample survey evidence”, Energy Economics, 2008, Vol. 30, No. 2, pp. 517-546.
Hicks, J., “A Revision of Demand Theory”, Clarendon Press, 1986.
Hughes, C.E.L., “Nairobi households and their energy use: An economic analysis of consumption patterns”,
Energy Economics, 1985, Vol. 7, No. 4, pp. 265-278.
Olivia, S. and Gibson, J., “Household Energy Demand and the Equity and Efficiency Aspects of Subsidy
Reform in Indonesia”, Paper presented at the International Association of Agricultural Economists Conference, Gold
Coast, Australia, 2006, August 12-18.
Prais, J.S. and Houthakker, H.S., “The Analysis of Family Budgets”, Cambridge: Cambridge University
Press, 1955.
Reddy, B.S., “Econometric analysis of energy use in urban households”, Energy Source, 1995, Vol. 35, pp.
359-371.
Tey, Y.S., Fatimah, M.A., Mad Nasir, S., Zainalabidin, M. Alias, R., “Demand for Meat Quantity and
Quality in Malaysia: Implications to Australia”, Munich Personal RePEc Archive (MPRA) Paper No.15032, 2008a.
Tey, Y.S., Mad Nasir, S., Zainalabidin, M., Amin, M. A., and Alias, R., “Demand Analyses of Rice in
Malaysia”, Munich Personal RePEc Archive (MPRA) Paper No. 15062, 2008b.
Tey, Y.S., Mad Nasir, S., Zainalabidin, M., Jinap, S., and Abdul Gariff, R., “Demand for quality vegetables
in Malaysia”, International Food Research Journal, 2009, Vol. 16, pp. 313-327.
Williams, R.A., “Engel Curve and Demand Systems: Demographic Effects on Consumption Patterns in
Australia”, Preliminary Working Paper No. SP-07 Melbourne January 1977.
Working, H., “Statistical laws of family expenditure”, Journal of the American Statistical Association,
1943, Vol. 33, pp. 43-56.